is qualitative research descriptive in nature

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

Chapter 5: Qualitative descriptive research

Darshini Ayton

Learning outcomes

Upon completion of this chapter, you should be able to:

• Identify the key terms and concepts used in qualitative descriptive research.
• Discuss the advantages and disadvantages of qualitative descriptive research.

What is a qualitative descriptive study?

The key concept of the qualitative descriptive study is description.

Qualitative descriptive studies (also known as ‘exploratory studies’ and ‘qualitative description approaches’) are relatively new in the qualitative research landscape. They emerged predominantly in the field of nursing and midwifery over the past two decades. 1 The design of qualitative descriptive studies evolved as a means to define aspects of qualitative research that did not resemble qualitative research designs to date, despite including elements of those other study designs. 2

Qualitative descriptive studies  describe  phenomena rather than explain them. Phenomenological studies, ethnographic studies and those using grounded theory seek to explain a phenomenon. Qualitative descriptive studies aim to provide a comprehensive summary of events. The approach to this study design is journalistic, with the aim being to answer the questions who, what, where and how. 3

A qualitative descriptive study is an important and appropriate design for research questions that are focused on gaining insights about a poorly understood research area, rather than on a specific phenomenon. Since qualitative descriptive study design seeks to describe rather than explain, explanatory frameworks and theories are not required to explain or ‘ground’ a study and its results. 4 The researcher may decide that a framework or theory adds value to their interpretations, and in that case, it is perfectly acceptable to use them. However, the hallmark of genuine curiosity (naturalistic enquiry) is that the researcher does not know in advance what they will be observing or describing. 4 Because a phenomenon is being described, the qualitative descriptive analysis is more categorical and less conceptual than other methods. Qualitative content analysis is usually the main approach to data analysis in qualitative descriptive studies. 4 This has led to criticism of descriptive research being less sophisticated because less interpretation is required than with other qualitative study designs in which interpretation and explanation are key characteristics (e.g. phenomenology, grounded theory, case studies).

Diverse approaches to data collection can be utilised in qualitative description studies. However, most qualitative descriptive studies use semi-structured interviews (see Chapter 13) because they provide a reliable way to collect data. 3 The technique applied to data analysis is generally categorical and less conceptual when compared to other qualitative research designs (see Section 4). 2,3 Hence, this study design is well suited to research by practitioners, student researchers and policymakers. Its straightforward approach enables these studies to be conducted in shorter timeframes than other study designs. 3 Descriptive studies are common as the qualitative component in mixed-methods research ( see Chapter 11 ) and evaluations ( see Chapter 12 ), 1 because qualitative descriptive studies can provide information to help develop and refine questionnaires or interventions.

For example, in our research to develop a patient-reported outcome measure for people who had undergone a percutaneous coronary intervention (PCI), which is a common cardiac procedure to treat heart disease, we started by conducting a qualitative descriptive study. 5 This project was a large, mixed-methods study funded by a private health insurer. The entire research process needed to be straightforward and achievable within a year, as we had engaged an undergraduate student to undertake the research tasks. The aim of the qualitative component of the mixed-methods study was to identify and explore patients’ perceptions following PCI. We used inductive approaches to collect and analyse the data. The study was guided by the following domains for the development of patient-reported outcomes, according to US Food and Drug Administration (FDA) guidelines, which included:

• Feeling: How the patient feels physically and psychologically after medical intervention
• Function: The patient’s mobility and ability to maintain their regular routine
• Evaluation: The patient’s overall perception of the success or failure of their procedure and their perception of what contributed to it. 5(p458)

We conducted focus groups and interviews, and asked participants three questions related to the FDA outcome domains:

• From your perspective, what would be considered a successful outcome of the procedure?

Probing questions: Did the procedure meet your expectations? How do you define whether the procedure was successful?

• How did you feel after the procedure?

Probing question: How did you feel one week after and how does that compare with how you feel now?

• After your procedure, tell me about your ability to do your daily activities?

Prompt for activities including gardening, housework, personal care, work-related and family-related tasks.

Probing questions: Did you attend cardiac rehabilitation? Can you tell us about your experience of cardiac rehabilitation? What impact has medication had on your recovery?

• What, if any, lifestyle changes have you made since your procedure? 5(p459)

Data collection was conducted with 32 participants. The themes were mapped to the FDA patient-reported outcome domains, with the results confirming previous research and also highlighting new areas for exploration in the development of a new patient-reported outcome measure. For example, participants reported a lack of confidence following PCI and the importance of patient and doctor communication. Women, in particular, reported that they wanted doctors to recognise how their experiences of cardiac symptoms were different to those of men.

The study described phenomena and resulted in the development of a patient-reported outcome measure that was tested and refined using a discrete-choice experiment survey, 6 a pilot of the measure in the Victorian Cardiac Outcomes Registry and a Rasch analysis to validate the measurement’s properties. 7

Advantages and disadvantages of qualitative descriptive studies

A qualitative descriptive study is an effective design for research by practitioners, policymakers and students, due to their relatively short timeframes and low costs. The researchers can remain close to the data and the events described, and this can enable the process of analysis to be relatively simple. Qualitative descriptive studies are also useful in mixed-methods research studies. Some of the advantages of qualitative descriptive studies have led to criticism of the design approach, due to a lack of engagement with theory and the lack of interpretation and explanation of the data. 2

Table 5.1. Examples of qualitative descriptive studies

Qualitative descriptive studies are gaining popularity in health and social care due to their utility, from a resource and time perspective, for research by practitioners, policymakers and researchers. Descriptive studies can be conducted as stand-alone studies or as part of larger, mixed-methods studies.

• Bradshaw C, Atkinson S, Doody O. Employing a qualitative description approach in health care research. Glob Qual Nurs Res. 2017;4. doi:10.1177/2333393617742282
• Lambert VA, Lambert CE. Qualitative descriptive research: an acceptable design. Pac Rim Int J Nurs Res Thail. 2012;16(4):255-256. Accessed June 6, 2023. https://he02.tci-thaijo.org/index.php/PRIJNR/article/download/5805/5064
• Doyle L et al. An overview of the qualitative descriptive design within nursing research. J Res Nurs. 2020;25(5):443-455. doi:10.1177/174498711988023
• Kim H, Sefcik JS, Bradway C. Characteristics of qualitative descriptive studies: a systematic review. Res Nurs Health. 2017;40(1):23-42. doi:10.1002/nur.21768
• Ayton DR et al. Exploring patient-reported outcomes following percutaneous coronary intervention: a qualitative study. Health Expect. 2018;21(2):457-465. doi:10.1111/hex.1263
• Barker AL et al. Symptoms and feelings valued by patients after a percutaneous coronary intervention: a discrete-choice experiment to inform development of a new patient-reported outcome. BMJ Open. 2018;8:e023141. doi:10.1136/bmjopen-2018-023141
• Soh SE et al. What matters most to patients following percutaneous coronary interventions? a new patient-reported outcome measure developed using Rasch analysis. PLoS One. 2019;14(9):e0222185. doi:10.1371/journal.pone.0222185
• Hiller RM et al. Coping and support-seeking in out-of-home care: a qualitative study of the views of young people in care in England. BMJ Open. 2021;11:e038461. doi:10.1136/bmjopen-2020-038461
• Backman C, Cho-Young D. Engaging patients and informal caregivers to improve safety and facilitate person- and family-centered care during transitions from hospital to home – a qualitative descriptive study. Patient Prefer Adherence. 2019;13:617-626. doi:10.2147/PPA.S201054

Qualitative Research – a practical guide for health and social care researchers and practitioners Copyright © 2023 by Darshini Ayton is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

Share This Book

Educational resources and simple solutions for your research journey

What is Descriptive Research? Definition, Methods, Types and Examples

Descriptive research is a methodological approach that seeks to depict the characteristics of a phenomenon or subject under investigation. In scientific inquiry, it serves as a foundational tool for researchers aiming to observe, record, and analyze the intricate details of a particular topic. This method provides a rich and detailed account that aids in understanding, categorizing, and interpreting the subject matter.

Descriptive research design is widely employed across diverse fields, and its primary objective is to systematically observe and document all variables and conditions influencing the phenomenon.

After this descriptive research definition, let’s look at this example. Consider a researcher working on climate change adaptation, who wants to understand water management trends in an arid village in a specific study area. She must conduct a demographic survey of the region, gather population data, and then conduct descriptive research on this demographic segment. The study will then uncover details on “what are the water management practices and trends in village X.” Note, however, that it will not cover any investigative information about “why” the patterns exist.

Table of Contents

What is descriptive research?

If you’ve been wondering “What is descriptive research,” we’ve got you covered in this post! In a nutshell, descriptive research is an exploratory research method that helps a researcher describe a population, circumstance, or phenomenon. It can help answer what , where , when and how questions, but not why questions. In other words, it does not involve changing the study variables and does not seek to establish cause-and-effect relationships.

Importance of descriptive research

Now, let’s delve into the importance of descriptive research. This research method acts as the cornerstone for various academic and applied disciplines. Its primary significance lies in its ability to provide a comprehensive overview of a phenomenon, enabling researchers to gain a nuanced understanding of the variables at play. This method aids in forming hypotheses, generating insights, and laying the groundwork for further in-depth investigations. The following points further illustrate its importance:

Provides insights into a population or phenomenon: Descriptive research furnishes a comprehensive overview of the characteristics and behaviors of a specific population or phenomenon, thereby guiding and shaping the research project.

Offers baseline data: The data acquired through this type of research acts as a reference for subsequent investigations, laying the groundwork for further studies.

Allows validation of sampling methods: Descriptive research validates sampling methods, aiding in the selection of the most effective approach for the study.

Helps reduce time and costs: It is cost-effective and time-efficient, making this an economical means of gathering information about a specific population or phenomenon.

Ensures replicability: Descriptive research is easily replicable, ensuring a reliable way to collect and compare information from various sources.

When to use descriptive research design?

Determining when to use descriptive research depends on the nature of the research question. Before diving into the reasons behind an occurrence, understanding the how, when, and where aspects is essential. Descriptive research design is a suitable option when the research objective is to discern characteristics, frequencies, trends, and categories without manipulating variables. It is therefore often employed in the initial stages of a study before progressing to more complex research designs. To put it in another way, descriptive research precedes the hypotheses of explanatory research. It is particularly valuable when there is limited existing knowledge about the subject.

Some examples are as follows, highlighting that these questions would arise before a clear outline of the research plan is established:

• In the last two decades, what changes have occurred in patterns of urban gardening in Mumbai?
• What are the differences in climate change perceptions of farmers in coastal versus inland villages in the Philippines?

Characteristics of descriptive research

Coming to the characteristics of descriptive research, this approach is characterized by its focus on observing and documenting the features of a subject. Specific characteristics are as below.

• Quantitative nature: Some descriptive research types involve quantitative research methods to gather quantifiable information for statistical analysis of the population sample.
• Qualitative nature: Some descriptive research examples include those using the qualitative research method to describe or explain the research problem.
• Observational nature: This approach is non-invasive and observational because the study variables remain untouched. Researchers merely observe and report, without introducing interventions that could impact the subject(s).
• Cross-sectional nature: In descriptive research, different sections belonging to the same group are studied, providing a “snapshot” of sorts.
• Springboard for further research: The data collected are further studied and analyzed using different research techniques. This approach helps guide the suitable research methods to be employed.

Types of descriptive research

There are various descriptive research types, each suited to different research objectives. Take a look at the different types below.

• Surveys: This involves collecting data through questionnaires or interviews to gather qualitative and quantitative data.
• Observational studies: This involves observing and collecting data on a particular population or phenomenon without influencing the study variables or manipulating the conditions. These may be further divided into cohort studies, case studies, and cross-sectional studies:
• Cohort studies: Also known as longitudinal studies, these studies involve the collection of data over an extended period, allowing researchers to track changes and trends.
• Case studies: These deal with a single individual, group, or event, which might be rare or unusual.
• Cross-sectional studies : A researcher collects data at a single point in time, in order to obtain a snapshot of a specific moment.
• Focus groups: In this approach, a small group of people are brought together to discuss a topic. The researcher moderates and records the group discussion. This can also be considered a “participatory” observational method.
• Descriptive classification: Relevant to the biological sciences, this type of approach may be used to classify living organisms.

Descriptive research methods

Several descriptive research methods can be employed, and these are more or less similar to the types of approaches mentioned above.

• Surveys: This method involves the collection of data through questionnaires or interviews. Surveys may be done online or offline, and the target subjects might be hyper-local, regional, or global.
• Observational studies: These entail the direct observation of subjects in their natural environment. These include case studies, dealing with a single case or individual, as well as cross-sectional and longitudinal studies, for a glimpse into a population or changes in trends over time, respectively. Participatory observational studies such as focus group discussions may also fall under this method.

Researchers must carefully consider descriptive research methods, types, and examples to harness their full potential in contributing to scientific knowledge.

Examples of descriptive research

Now, let’s consider some descriptive research examples.

• In social sciences, an example could be a study analyzing the demographics of a specific community to understand its socio-economic characteristics.
• In business, a market research survey aiming to describe consumer preferences would be a descriptive study.
• In ecology, a researcher might undertake a survey of all the types of monocots naturally occurring in a region and classify them up to species level.

These examples showcase the versatility of descriptive research across diverse fields.

Advantages of descriptive research

There are several advantages to this approach, which every researcher must be aware of. These are as follows:

• Owing to the numerous descriptive research methods and types, primary data can be obtained in diverse ways and be used for developing a research hypothesis .
• It is a versatile research method and allows flexibility.
• Detailed and comprehensive information can be obtained because the data collected can be qualitative or quantitative.
• It is carried out in the natural environment, which greatly minimizes certain types of bias and ethical concerns.
• It is an inexpensive and efficient approach, even with large sample sizes

Disadvantages of descriptive research

On the other hand, this design has some drawbacks as well:

• It is limited in its scope as it does not determine cause-and-effect relationships.
• The approach does not generate new information and simply depends on existing data.
• Study variables are not manipulated or controlled, and this limits the conclusions to be drawn.
• Descriptive research findings may not be generalizable to other populations.
• Finally, it offers a preliminary understanding rather than an in-depth understanding.

To reiterate, the advantages of descriptive research lie in its ability to provide a comprehensive overview, aid hypothesis generation, and serve as a preliminary step in the research process. However, its limitations include a potential lack of depth, inability to establish cause-and-effect relationships, and susceptibility to bias.

Frequently asked questions

When should researchers conduct descriptive research.

Descriptive research is most appropriate when researchers aim to portray and understand the characteristics of a phenomenon without manipulating variables. It is particularly valuable in the early stages of a study.

What is the difference between descriptive and exploratory research?

Descriptive research focuses on providing a detailed depiction of a phenomenon, while exploratory research aims to explore and generate insights into an issue where little is known.

What is the difference between descriptive and experimental research?

Descriptive research observes and documents without manipulating variables, whereas experimental research involves intentional interventions to establish cause-and-effect relationships.

Is descriptive research only for social sciences?

No, various descriptive research types may be applicable to all fields of study, including social science, humanities, physical science, and biological science.

How important is descriptive research?

The importance of descriptive research lies in its ability to provide a glimpse of the current state of a phenomenon, offering valuable insights and establishing a basic understanding. Further, the advantages of descriptive research include its capacity to offer a straightforward depiction of a situation or phenomenon, facilitate the identification of patterns or trends, and serve as a useful starting point for more in-depth investigations. Additionally, descriptive research can contribute to the development of hypotheses and guide the formulation of research questions for subsequent studies.

Editage All Access is a subscription-based platform that unifies the best AI tools and services designed to speed up, simplify, and streamline every step of a researcher’s journey. The Editage All Access Pack is a one-of-a-kind subscription that unlocks full access to an AI writing assistant, literature recommender, journal finder, scientific illustration tool, and exclusive discounts on professional publication services from Editage.  

Based on 22+ years of experience in academia, Editage All Access empowers researchers to put their best research forward and move closer to success. Explore our top AI Tools pack, AI Tools + Publication Services pack, or Build Your Own Plan. Find everything a researcher needs to succeed, all in one place –  Get All Access now starting at just $14 a month !    

Related Posts

Back to School – Lock-in All Access Pack for a Year at the Best Price

Journal Turnaround Time: Researcher.Life and Scholarly Intelligence Join Hands to Empower Researchers with Publication Time Insights 

Qualitative Research: Characteristics, Design, Methods & Examples

Lauren McCall

MSc Health Psychology Graduate

MSc, Health Psychology, University of Nottingham

Lauren obtained an MSc in Health Psychology from The University of Nottingham with a distinction classification.

Learn about our Editorial Process

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Qualitative research is a type of research methodology that focuses on gathering and analyzing non-numerical data to gain a deeper understanding of human behavior, experiences, and perspectives.

It aims to explore the “why” and “how” of a phenomenon rather than the “what,” “where,” and “when” typically addressed by quantitative research.

Unlike quantitative research, which focuses on gathering and analyzing numerical data for statistical analysis, qualitative research involves researchers interpreting data to identify themes, patterns, and meanings.

Qualitative research can be used to:

• Gain deep contextual understandings of the subjective social reality of individuals
• To answer questions about experience and meaning from the participant’s perspective
• To design hypotheses, theory must be researched using qualitative methods to determine what is important before research can begin. 

Examples of qualitative research questions include: 

• How does stress influence young adults’ behavior?
• What factors influence students’ school attendance rates in developed countries?
• How do adults interpret binge drinking in the UK?
• What are the psychological impacts of cervical cancer screening in women?
• How can mental health lessons be integrated into the school curriculum? 

Characteristics 

Naturalistic setting.

Individuals are studied in their natural setting to gain a deeper understanding of how people experience the world. This enables the researcher to understand a phenomenon close to how participants experience it. 

Naturalistic settings provide valuable contextual information to help researchers better understand and interpret the data they collect.

The environment, social interactions, and cultural factors can all influence behavior and experiences, and these elements are more easily observed in real-world settings.

Reality is socially constructed

Qualitative research aims to understand how participants make meaning of their experiences – individually or in social contexts. It assumes there is no objective reality and that the social world is interpreted (Yilmaz, 2013). 

The primacy of subject matter 

The primary aim of qualitative research is to understand the perspectives, experiences, and beliefs of individuals who have experienced the phenomenon selected for research rather than the average experiences of groups of people (Minichiello, 1990).

An in-depth understanding is attained since qualitative techniques allow participants to freely disclose their experiences, thoughts, and feelings without constraint (Tenny et al., 2022). 

Variables are complex, interwoven, and difficult to measure

Factors such as experiences, behaviors, and attitudes are complex and interwoven, so they cannot be reduced to isolated variables , making them difficult to measure quantitatively.

However, a qualitative approach enables participants to describe what, why, or how they were thinking/ feeling during a phenomenon being studied (Yilmaz, 2013). 

Emic (insider’s point of view)

The phenomenon being studied is centered on the participants’ point of view (Minichiello, 1990).

Emic is used to describe how participants interact, communicate, and behave in the research setting (Scarduzio, 2017).

Interpretive analysis

In qualitative research, interpretive analysis is crucial in making sense of the collected data.

This process involves examining the raw data, such as interview transcripts, field notes, or documents, and identifying the underlying themes, patterns, and meanings that emerge from the participants’ experiences and perspectives.

Collecting Qualitative Data

There are four main research design methods used to collect qualitative data: observations, interviews,  focus groups, and ethnography.

Observations

This method involves watching and recording phenomena as they occur in nature. Observation can be divided into two types: participant and non-participant observation.

In participant observation, the researcher actively participates in the situation/events being observed.

In non-participant observation, the researcher is not an active part of the observation and tries not to influence the behaviors they are observing (Busetto et al., 2020). 

Observations can be covert (participants are unaware that a researcher is observing them) or overt (participants are aware of the researcher’s presence and know they are being observed).

However, awareness of an observer’s presence may influence participants’ behavior. 

Interviews give researchers a window into the world of a participant by seeking their account of an event, situation, or phenomenon. They are usually conducted on a one-to-one basis and can be distinguished according to the level at which they are structured (Punch, 2013). 

Structured interviews involve predetermined questions and sequences to ensure replicability and comparability. However, they are unable to explore emerging issues.

Informal interviews consist of spontaneous, casual conversations which are closer to the truth of a phenomenon. However, information is gathered using quick notes made by the researcher and is therefore subject to recall bias. 

Semi-structured interviews have a flexible structure, phrasing, and placement so emerging issues can be explored (Denny & Weckesser, 2022).

The use of probing questions and clarification can lead to a detailed understanding, but semi-structured interviews can be time-consuming and subject to interviewer bias. 

Focus groups 

Similar to interviews, focus groups elicit a rich and detailed account of an experience. However, focus groups are more dynamic since participants with shared characteristics construct this account together (Denny & Weckesser, 2022).

A shared narrative is built between participants to capture a group experience shaped by a shared context. 

The researcher takes on the role of a moderator, who will establish ground rules and guide the discussion by following a topic guide to focus the group discussions.

Typically, focus groups have 4-10 participants as a discussion can be difficult to facilitate with more than this, and this number allows everyone the time to speak.

Ethnography

Ethnography is a methodology used to study a group of people’s behaviors and social interactions in their environment (Reeves et al., 2008).

Data are collected using methods such as observations, field notes, or structured/ unstructured interviews.

The aim of ethnography is to provide detailed, holistic insights into people’s behavior and perspectives within their natural setting. In order to achieve this, researchers immerse themselves in a community or organization. 

Due to the flexibility and real-world focus of ethnography, researchers are able to gather an in-depth, nuanced understanding of people’s experiences, knowledge and perspectives that are influenced by culture and society.

In order to develop a representative picture of a particular culture/ context, researchers must conduct extensive field work. 

This can be time-consuming as researchers may need to immerse themselves into a community/ culture for a few days, or possibly a few years.

Qualitative Data Analysis Methods

Different methods can be used for analyzing qualitative data. The researcher chooses based on the objectives of their study. 

The researcher plays a key role in the interpretation of data, making decisions about the coding, theming, decontextualizing, and recontextualizing of data (Starks & Trinidad, 2007). 

Grounded theory

Grounded theory is a qualitative method specifically designed to inductively generate theory from data. It was developed by Glaser and Strauss in 1967 (Glaser & Strauss, 2017).

This methodology aims to develop theories (rather than test hypotheses) that explain a social process, action, or interaction (Petty et al., 2012). To inform the developing theory, data collection and analysis run simultaneously. 

There are three key types of coding used in grounded theory: initial (open), intermediate (axial), and advanced (selective) coding. 

Throughout the analysis, memos should be created to document methodological and theoretical ideas about the data. Data should be collected and analyzed until data saturation is reached and a theory is developed. 

Content analysis

Content analysis was first used in the early twentieth century to analyze textual materials such as newspapers and political speeches.

Content analysis is a research method used to identify and analyze the presence and patterns of themes, concepts, or words in data (Vaismoradi et al., 2013). 

This research method can be used to analyze data in different formats, which can be written, oral, or visual. 

The goal of content analysis is to develop themes that capture the underlying meanings of data (Schreier, 2012). 

Qualitative content analysis can be used to validate existing theories, support the development of new models and theories, and provide in-depth descriptions of particular settings or experiences.

The following six steps provide a guideline for how to conduct qualitative content analysis.

• Define a Research Question : To start content analysis, a clear research question should be developed.
• Identify and Collect Data : Establish the inclusion criteria for your data. Find the relevant sources to analyze.
• Define the Unit or Theme of Analysis : Categorize the content into themes. Themes can be a word, phrase, or sentence.
• Develop Rules for Coding your Data : Define a set of coding rules to ensure that all data are coded consistently.
• Code the Data : Follow the coding rules to categorize data into themes.
• Analyze the Results and Draw Conclusions : Examine the data to identify patterns and draw conclusions in relation to your research question.

Discourse analysis

Discourse analysis is a research method used to study written/ spoken language in relation to its social context (Wood & Kroger, 2000).

In discourse analysis, the researcher interprets details of language materials and the context in which it is situated.

Discourse analysis aims to understand the functions of language (how language is used in real life) and how meaning is conveyed by language in different contexts. Researchers use discourse analysis to investigate social groups and how language is used to achieve specific communication goals.

Different methods of discourse analysis can be used depending on the aims and objectives of a study. However, the following steps provide a guideline on how to conduct discourse analysis.

• Define the Research Question : Develop a relevant research question to frame the analysis.
• Gather Data and Establish the Context : Collect research materials (e.g., interview transcripts, documents). Gather factual details and review the literature to construct a theory about the social and historical context of your study.
• Analyze the Content : Closely examine various components of the text, such as the vocabulary, sentences, paragraphs, and structure of the text. Identify patterns relevant to the research question to create codes, then group these into themes.
• Review the Results : Reflect on the findings to examine the function of the language, and the meaning and context of the discourse. 

Thematic analysis

Thematic analysis is a method used to identify, interpret, and report patterns in data, such as commonalities or contrasts. 

Although the origin of thematic analysis can be traced back to the early twentieth century, understanding and clarity of thematic analysis is attributed to Braun and Clarke (2006).

Thematic analysis aims to develop themes (patterns of meaning) across a dataset to address a research question. 

In thematic analysis, qualitative data is gathered using techniques such as interviews, focus groups, and questionnaires. Audio recordings are transcribed. The dataset is then explored and interpreted by a researcher to identify patterns. 

This occurs through the rigorous process of data familiarisation, coding, theme development, and revision. These identified patterns provide a summary of the dataset and can be used to address a research question.

Themes are developed by exploring the implicit and explicit meanings within the data. Two different approaches are used to generate themes: inductive and deductive. 

An inductive approach allows themes to emerge from the data. In contrast, a deductive approach uses existing theories or knowledge to apply preconceived ideas to the data.

Phases of Thematic Analysis

Braun and Clarke (2006) provide a guide of the six phases of thematic analysis. These phases can be applied flexibly to fit research questions and data. 

Template analysis

Template analysis refers to a specific method of thematic analysis which uses hierarchical coding (Brooks et al., 2014).

Template analysis is used to analyze textual data, for example, interview transcripts or open-ended responses on a written questionnaire.

To conduct template analysis, a coding template must be developed (usually from a subset of the data) and subsequently revised and refined. This template represents the themes identified by researchers as important in the dataset. 

Codes are ordered hierarchically within the template, with the highest-level codes demonstrating overarching themes in the data and lower-level codes representing constituent themes with a narrower focus.

A guideline for the main procedural steps for conducting template analysis is outlined below.

• Familiarization with the Data : Read (and reread) the dataset in full. Engage, reflect, and take notes on data that may be relevant to the research question.
• Preliminary Coding : Identify initial codes using guidance from the a priori codes, identified before the analysis as likely to be beneficial and relevant to the analysis.
• Organize Themes : Organize themes into meaningful clusters. Consider the relationships between the themes both within and between clusters.
• Produce an Initial Template : Develop an initial template. This may be based on a subset of the data.
• Apply and Develop the Template : Apply the initial template to further data and make any necessary modifications. Refinements of the template may include adding themes, removing themes, or changing the scope/title of themes. 
• Finalize Template : Finalize the template, then apply it to the entire dataset. 

Frame analysis

Frame analysis is a comparative form of thematic analysis which systematically analyzes data using a matrix output.

Ritchie and Spencer (1994) developed this set of techniques to analyze qualitative data in applied policy research. Frame analysis aims to generate theory from data.

Frame analysis encourages researchers to organize and manage their data using summarization.

This results in a flexible and unique matrix output, in which individual participants (or cases) are represented by rows and themes are represented by columns. 

Each intersecting cell is used to summarize findings relating to the corresponding participant and theme.

Frame analysis has five distinct phases which are interrelated, forming a methodical and rigorous framework.

• Familiarization with the Data : Familiarize yourself with all the transcripts. Immerse yourself in the details of each transcript and start to note recurring themes.
• Develop a Theoretical Framework : Identify recurrent/ important themes and add them to a chart. Provide a framework/ structure for the analysis.
• Indexing : Apply the framework systematically to the entire study data.
• Summarize Data in Analytical Framework : Reduce the data into brief summaries of participants’ accounts.
• Mapping and Interpretation : Compare themes and subthemes and check against the original transcripts. Group the data into categories and provide an explanation for them.

Preventing Bias in Qualitative Research

To evaluate qualitative studies, the CASP (Critical Appraisal Skills Programme) checklist for qualitative studies can be used to ensure all aspects of a study have been considered (CASP, 2018).

The quality of research can be enhanced and assessed using criteria such as checklists, reflexivity, co-coding, and member-checking. 

Co-coding 

Relying on only one researcher to interpret rich and complex data may risk key insights and alternative viewpoints being missed. Therefore, coding is often performed by multiple researchers.

A common strategy must be defined at the beginning of the coding process  (Busetto et al., 2020). This includes establishing a useful coding list and finding a common definition of individual codes.

Transcripts are initially coded independently by researchers and then compared and consolidated to minimize error or bias and to bring confirmation of findings. 

Member checking

Member checking (or respondent validation) involves checking back with participants to see if the research resonates with their experiences (Russell & Gregory, 2003).

Data can be returned to participants after data collection or when results are first available. For example, participants may be provided with their interview transcript and asked to verify whether this is a complete and accurate representation of their views.

Participants may then clarify or elaborate on their responses to ensure they align with their views (Shenton, 2004).

This feedback becomes part of data collection and ensures accurate descriptions/ interpretations of phenomena (Mays & Pope, 2000). 

Reflexivity in qualitative research

Reflexivity typically involves examining your own judgments, practices, and belief systems during data collection and analysis. It aims to identify any personal beliefs which may affect the research. 

Reflexivity is essential in qualitative research to ensure methodological transparency and complete reporting. This enables readers to understand how the interaction between the researcher and participant shapes the data.

Depending on the research question and population being researched, factors that need to be considered include the experience of the researcher, how the contact was established and maintained, age, gender, and ethnicity.

These details are important because, in qualitative research, the researcher is a dynamic part of the research process and actively influences the outcome of the research (Boeije, 2014). 

Reflexivity Example

Who you are and your characteristics influence how you collect and analyze data. Here is an example of a reflexivity statement for research on smoking. I am a 30-year-old white female from a middle-class background. I live in the southwest of England and have been educated to master’s level. I have been involved in two research projects on oral health. I have never smoked, but I have witnessed how smoking can cause ill health from my volunteering in a smoking cessation clinic. My research aspirations are to help to develop interventions to help smokers quit.

Establishing Trustworthiness in Qualitative Research

Trustworthiness is a concept used to assess the quality and rigor of qualitative research. Four criteria are used to assess a study’s trustworthiness: credibility, transferability, dependability, and confirmability.

1. Credibility in Qualitative Research

Credibility refers to how accurately the results represent the reality and viewpoints of the participants.

To establish credibility in research, participants’ views and the researcher’s representation of their views need to align (Tobin & Begley, 2004).

To increase the credibility of findings, researchers may use data source triangulation, investigator triangulation, peer debriefing, or member checking (Lincoln & Guba, 1985). 

2. Transferability in Qualitative Research

Transferability refers to how generalizable the findings are: whether the findings may be applied to another context, setting, or group (Tobin & Begley, 2004).

Transferability can be enhanced by giving thorough and in-depth descriptions of the research setting, sample, and methods (Nowell et al., 2017). 

3. Dependability in Qualitative Research

Dependability is the extent to which the study could be replicated under similar conditions and the findings would be consistent.

Researchers can establish dependability using methods such as audit trails so readers can see the research process is logical and traceable (Koch, 1994).

4. Confirmability in Qualitative Research

Confirmability is concerned with establishing that there is a clear link between the researcher’s interpretations/ findings and the data.

Researchers can achieve confirmability by demonstrating how conclusions and interpretations were arrived at (Nowell et al., 2017).

This enables readers to understand the reasoning behind the decisions made. 

Audit Trails in Qualitative Research

An audit trail provides evidence of the decisions made by the researcher regarding theory, research design, and data collection, as well as the steps they have chosen to manage, analyze, and report data. 

The researcher must provide a clear rationale to demonstrate how conclusions were reached in their study.

A clear description of the research path must be provided to enable readers to trace through the researcher’s logic (Halpren, 1983).

Researchers should maintain records of the raw data, field notes, transcripts, and a reflective journal in order to provide a clear audit trail. 

Discovery of unexpected data

Open-ended questions in qualitative research mean the researcher can probe an interview topic and enable the participant to elaborate on responses in an unrestricted manner.

This allows unexpected data to emerge, which can lead to further research into that topic. 

The exploratory nature of qualitative research helps generate hypotheses that can be tested quantitatively (Busetto et al., 2020).

Flexibility

Data collection and analysis can be modified and adapted to take the research in a different direction if new ideas or patterns emerge in the data.

This enables researchers to investigate new opportunities while firmly maintaining their research goals. 

Naturalistic settings

The behaviors of participants are recorded in real-world settings. Studies that use real-world settings have high ecological validity since participants behave more authentically. 

Limitations

Time-consuming .

Qualitative research results in large amounts of data which often need to be transcribed and analyzed manually.

Even when software is used, transcription can be inaccurate, and using software for analysis can result in many codes which need to be condensed into themes. 

Subjectivity 

The researcher has an integral role in collecting and interpreting qualitative data. Therefore, the conclusions reached are from their perspective and experience.

Consequently, interpretations of data from another researcher may vary greatly. 

Limited generalizability

The aim of qualitative research is to provide a detailed, contextualized understanding of an aspect of the human experience from a relatively small sample size.

Despite rigorous analysis procedures, conclusions drawn cannot be generalized to the wider population since data may be biased or unrepresentative.

Therefore, results are only applicable to a small group of the population. 

While individual qualitative studies are often limited in their generalizability due to factors such as sample size and context, metasynthesis enables researchers to synthesize findings from multiple studies, potentially leading to more generalizable conclusions.

By integrating findings from studies conducted in diverse settings and with different populations, metasynthesis can provide broader insights into the phenomenon of interest.

Extraneous variables

Qualitative research is often conducted in real-world settings. This may cause results to be unreliable since extraneous variables may affect the data, for example:

• Situational variables : different environmental conditions may influence participants’ behavior in a study. The random variation in factors (such as noise or lighting) may be difficult to control in real-world settings.
• Participant characteristics : this includes any characteristics that may influence how a participant answers/ behaves in a study. This may include a participant’s mood, gender, age, ethnicity, sexual identity, IQ, etc.
• Experimenter effect : experimenter effect refers to how a researcher’s unintentional influence can change the outcome of a study. This occurs when (i) their interactions with participants unintentionally change participants’ behaviors or (ii) due to errors in observation, interpretation, or analysis. 

What sample size should qualitative research be?

The sample size for qualitative studies has been recommended to include a minimum of 12 participants to reach data saturation (Braun, 2013).

Are surveys qualitative or quantitative?

Surveys can be used to gather information from a sample qualitatively or quantitatively. Qualitative surveys use open-ended questions to gather detailed information from a large sample using free text responses.

The use of open-ended questions allows for unrestricted responses where participants use their own words, enabling the collection of more in-depth information than closed-ended questions.

In contrast, quantitative surveys consist of closed-ended questions with multiple-choice answer options. Quantitative surveys are ideal to gather a statistical representation of a population.

What are the ethical considerations of qualitative research?

Before conducting a study, you must think about any risks that could occur and take steps to prevent them. Participant Protection : Researchers must protect participants from physical and mental harm. This means you must not embarrass, frighten, offend, or harm participants. Transparency : Researchers are obligated to clearly communicate how they will collect, store, analyze, use, and share the data. Confidentiality : You need to consider how to maintain the confidentiality and anonymity of participants’ data.

What is triangulation in qualitative research?

Triangulation refers to the use of several approaches in a study to comprehensively understand phenomena. This method helps to increase the validity and credibility of research findings. 

Types of triangulation include method triangulation (using multiple methods to gather data); investigator triangulation (multiple researchers for collecting/ analyzing data), theory triangulation (comparing several theoretical perspectives to explain a phenomenon), and data source triangulation (using data from various times, locations, and people; Carter et al., 2014).

Why is qualitative research important?

Qualitative research allows researchers to describe and explain the social world. The exploratory nature of qualitative research helps to generate hypotheses that can then be tested quantitatively.

In qualitative research, participants are able to express their thoughts, experiences, and feelings without constraint.

Additionally, researchers are able to follow up on participants’ answers in real-time, generating valuable discussion around a topic. This enables researchers to gain a nuanced understanding of phenomena which is difficult to attain using quantitative methods.

What is coding data in qualitative research?

Coding data is a qualitative data analysis strategy in which a section of text is assigned with a label that describes its content.

These labels may be words or phrases which represent important (and recurring) patterns in the data.

This process enables researchers to identify related content across the dataset. Codes can then be used to group similar types of data to generate themes.

What is the difference between qualitative and quantitative research?

Qualitative research involves the collection and analysis of non-numerical data in order to understand experiences and meanings from the participant’s perspective.

This can provide rich, in-depth insights on complicated phenomena. Qualitative data may be collected using interviews, focus groups, or observations.

In contrast, quantitative research involves the collection and analysis of numerical data to measure the frequency, magnitude, or relationships of variables. This can provide objective and reliable evidence that can be generalized to the wider population.

Quantitative data may be collected using closed-ended questionnaires or experiments.

What is trustworthiness in qualitative research?

Trustworthiness is a concept used to assess the quality and rigor of qualitative research. Four criteria are used to assess a study’s trustworthiness: credibility, transferability, dependability, and confirmability. 

Credibility refers to how accurately the results represent the reality and viewpoints of the participants. Transferability refers to whether the findings may be applied to another context, setting, or group.

Dependability is the extent to which the findings are consistent and reliable. Confirmability refers to the objectivity of findings (not influenced by the bias or assumptions of researchers).

What is data saturation in qualitative research?

Data saturation is a methodological principle used to guide the sample size of a qualitative research study.

Data saturation is proposed as a necessary methodological component in qualitative research (Saunders et al., 2018) as it is a vital criterion for discontinuing data collection and/or analysis. 

The intention of data saturation is to find “no new data, no new themes, no new coding, and ability to replicate the study” (Guest et al., 2006). Therefore, enough data has been gathered to make conclusions.

Why is sampling in qualitative research important?

In quantitative research, large sample sizes are used to provide statistically significant quantitative estimates.

This is because quantitative research aims to provide generalizable conclusions that represent populations.

However, the aim of sampling in qualitative research is to gather data that will help the researcher understand the depth, complexity, variation, or context of a phenomenon. The small sample sizes in qualitative studies support the depth of case-oriented analysis.

What is narrative analysis?

Narrative analysis is a qualitative research method used to understand how individuals create stories from their personal experiences.

There is an emphasis on understanding the context in which a narrative is constructed, recognizing the influence of historical, cultural, and social factors on storytelling.

Researchers can use different methods together to explore a research question.

Some narrative researchers focus on the content of what is said, using thematic narrative analysis, while others focus on the structure, such as holistic-form or categorical-form structural narrative analysis. Others focus on how the narrative is produced and performed.

Boeije, H. (2014). Analysis in qualitative research. Sage.

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative research in psychology , 3 (2), 77-101. https://doi.org/10.1191/1478088706qp063oa

Brooks, J., McCluskey, S., Turley, E., & King, N. (2014). The utility of template analysis in qualitative psychology research. Qualitative Research in Psychology , 12 (2), 202–222. https://doi.org/10.1080/14780887.2014.955224

Busetto, L., Wick, W., & Gumbinger, C. (2020). How to use and assess qualitative research methods. Neurological research and practice , 2 (1), 14-14. https://doi.org/10.1186/s42466-020-00059-z 

Carter, N., Bryant-Lukosius, D., DiCenso, A., Blythe, J., & Neville, A. J. (2014). The use of triangulation in qualitative research. Oncology nursing forum , 41 (5), 545–547. https://doi.org/10.1188/14.ONF.545-547

Critical Appraisal Skills Programme. (2018). CASP Checklist: 10 questions to help you make sense of a Qualitative research. https://casp-uk.net/images/checklist/documents/CASP-Qualitative-Studies-Checklist/CASP-Qualitative-Checklist-2018_fillable_form.pdf Accessed: March 15 2023

Clarke, V., & Braun, V. (2013). Successful qualitative research: A practical guide for beginners. Successful Qualitative Research , 1-400.

Denny, E., & Weckesser, A. (2022). How to do qualitative research?: Qualitative research methods. BJOG : an international journal of obstetrics and gynaecology , 129 (7), 1166-1167. https://doi.org/10.1111/1471-0528.17150 

Glaser, B. G., & Strauss, A. L. (2017). The discovery of grounded theory. The Discovery of Grounded Theory , 1–18. https://doi.org/10.4324/9780203793206-1

Guest, G., Bunce, A., & Johnson, L. (2006). How many interviews are enough? An experiment with data saturation and variability. Field Methods, 18 (1), 59-82. doi:10.1177/1525822X05279903

Halpren, E. S. (1983). Auditing naturalistic inquiries: The development and application of a model (Unpublished doctoral dissertation). Indiana University, Bloomington.

Hammarberg, K., Kirkman, M., & de Lacey, S. (2016). Qualitative research methods: When to use them and how to judge them. Human Reproduction , 31 (3), 498–501. https://doi.org/10.1093/humrep/dev334

Koch, T. (1994). Establishing rigour in qualitative research: The decision trail. Journal of Advanced Nursing, 19, 976–986. doi:10.1111/ j.1365-2648.1994.tb01177.x

Lincoln, Y., & Guba, E. G. (1985). Naturalistic inquiry. Newbury Park, CA: Sage.

Mays, N., & Pope, C. (2000). Assessing quality in qualitative research. BMJ, 320(7226), 50–52.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Nowell, L. S., Norris, J. M., White, D. E., & Moules, N. J. (2017). Thematic Analysis: Striving to Meet the Trustworthiness Criteria. International Journal of Qualitative Methods, 16 (1). https://doi.org/10.1177/1609406917733847

Petty, N. J., Thomson, O. P., & Stew, G. (2012). Ready for a paradigm shift? part 2: Introducing qualitative research methodologies and methods. Manual Therapy , 17 (5), 378–384. https://doi.org/10.1016/j.math.2012.03.004

Punch, K. F. (2013). Introduction to social research: Quantitative and qualitative approaches. London: Sage

Reeves, S., Kuper, A., & Hodges, B. D. (2008). Qualitative research methodologies: Ethnography. BMJ , 337 (aug07 3). https://doi.org/10.1136/bmj.a1020

Russell, C. K., & Gregory, D. M. (2003). Evaluation of qualitative research studies. Evidence Based Nursing, 6 (2), 36–40.

Saunders, B., Sim, J., Kingstone, T., Baker, S., Waterfield, J., Bartlam, B., Burroughs, H., & Jinks, C. (2018). Saturation in qualitative research: exploring its conceptualization and operationalization. Quality & quantity , 52 (4), 1893–1907. https://doi.org/10.1007/s11135-017-0574-8

Scarduzio, J. A. (2017). Emic approach to qualitative research. The International Encyclopedia of Communication Research Methods, 1–2 . https://doi.org/10.1002/9781118901731.iecrm0082

Schreier, M. (2012). Qualitative content analysis in practice / Margrit Schreier.

Shenton, A. K. (2004). Strategies for ensuring trustworthiness in qualitative research projects. Education for Information, 22 , 63–75.

Starks, H., & Trinidad, S. B. (2007). Choose your method: a comparison of phenomenology, discourse analysis, and grounded theory. Qualitative health research , 17 (10), 1372–1380. https://doi.org/10.1177/1049732307307031

Tenny, S., Brannan, J. M., & Brannan, G. D. (2022). Qualitative Study. In StatPearls. StatPearls Publishing.

Tobin, G. A., & Begley, C. M. (2004). Methodological rigour within a qualitative framework. Journal of Advanced Nursing, 48, 388–396. doi:10.1111/j.1365-2648.2004.03207.x

Vaismoradi, M., Turunen, H., & Bondas, T. (2013). Content analysis and thematic analysis: Implications for conducting a qualitative descriptive study. Nursing & health sciences , 15 (3), 398-405. https://doi.org/10.1111/nhs.12048

Wood L. A., Kroger R. O. (2000). Doing discourse analysis: Methods for studying action in talk and text. Sage.

Yilmaz, K. (2013). Comparison of Quantitative and Qualitative Research Traditions: epistemological, theoretical, and methodological differences. European journal of education , 48 (2), 311-325. https://doi.org/10.1111/ejed.12014

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• Descriptive Research | Definition, Types, Methods & Examples

Descriptive Research | Definition, Types, Methods & Examples

Published on May 15, 2019 by Shona McCombes . Revised on June 22, 2023.

Descriptive research aims to accurately and systematically describe a population, situation or phenomenon. It can answer what , where , when and how   questions , but not why questions.

A descriptive research design can use a wide variety of research methods  to investigate one or more variables . Unlike in experimental research , the researcher does not control or manipulate any of the variables, but only observes and measures them.

Table of contents

When to use a descriptive research design, descriptive research methods, other interesting articles.

Descriptive research is an appropriate choice when the research aim is to identify characteristics, frequencies, trends, and categories.

It is useful when not much is known yet about the topic or problem. Before you can research why something happens, you need to understand how, when and where it happens.

Descriptive research question examples

• How has the Amsterdam housing market changed over the past 20 years?
• Do customers of company X prefer product X or product Y?
• What are the main genetic, behavioural and morphological differences between European wildcats and domestic cats?
• What are the most popular online news sources among under-18s?
• How prevalent is disease A in population B?

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
• Vague sentences
• Style consistency

See an example

Descriptive research is usually defined as a type of quantitative research , though qualitative research can also be used for descriptive purposes. The research design should be carefully developed to ensure that the results are valid and reliable .

Survey research allows you to gather large volumes of data that can be analyzed for frequencies, averages and patterns. Common uses of surveys include:

• Describing the demographics of a country or region
• Gauging public opinion on political and social topics
• Evaluating satisfaction with a company’s products or an organization’s services

Observations

Observations allow you to gather data on behaviours and phenomena without having to rely on the honesty and accuracy of respondents. This method is often used by psychological, social and market researchers to understand how people act in real-life situations.

Observation of physical entities and phenomena is also an important part of research in the natural sciences. Before you can develop testable hypotheses , models or theories, it’s necessary to observe and systematically describe the subject under investigation.

Case studies

A case study can be used to describe the characteristics of a specific subject (such as a person, group, event or organization). Instead of gathering a large volume of data to identify patterns across time or location, case studies gather detailed data to identify the characteristics of a narrowly defined subject.

Rather than aiming to describe generalizable facts, case studies often focus on unusual or interesting cases that challenge assumptions, add complexity, or reveal something new about a research problem .

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

McCombes, S. (2023, June 22). Descriptive Research | Definition, Types, Methods & Examples. Scribbr. Retrieved September 3, 2024, from https://www.scribbr.com/methodology/descriptive-research/

Is this article helpful?

Shona McCombes

Other students also liked, what is quantitative research | definition, uses & methods, correlational research | when & how to use, descriptive statistics | definitions, types, examples, what is your plagiarism score.

• Privacy Policy

Home » Qualitative Research – Methods, Analysis Types and Guide

Qualitative Research – Methods, Analysis Types and Guide

Table of Contents

Qualitative Research

Qualitative research is a type of research methodology that focuses on exploring and understanding people’s beliefs, attitudes, behaviors, and experiences through the collection and analysis of non-numerical data. It seeks to answer research questions through the examination of subjective data, such as interviews, focus groups, observations, and textual analysis.

Qualitative research aims to uncover the meaning and significance of social phenomena, and it typically involves a more flexible and iterative approach to data collection and analysis compared to quantitative research. Qualitative research is often used in fields such as sociology, anthropology, psychology, and education.

Qualitative Research Methods

Qualitative Research Methods are as follows:

One-to-One Interview

This method involves conducting an interview with a single participant to gain a detailed understanding of their experiences, attitudes, and beliefs. One-to-one interviews can be conducted in-person, over the phone, or through video conferencing. The interviewer typically uses open-ended questions to encourage the participant to share their thoughts and feelings. One-to-one interviews are useful for gaining detailed insights into individual experiences.

Focus Groups

This method involves bringing together a group of people to discuss a specific topic in a structured setting. The focus group is led by a moderator who guides the discussion and encourages participants to share their thoughts and opinions. Focus groups are useful for generating ideas and insights, exploring social norms and attitudes, and understanding group dynamics.

Ethnographic Studies

This method involves immersing oneself in a culture or community to gain a deep understanding of its norms, beliefs, and practices. Ethnographic studies typically involve long-term fieldwork and observation, as well as interviews and document analysis. Ethnographic studies are useful for understanding the cultural context of social phenomena and for gaining a holistic understanding of complex social processes.

Text Analysis

This method involves analyzing written or spoken language to identify patterns and themes. Text analysis can be quantitative or qualitative. Qualitative text analysis involves close reading and interpretation of texts to identify recurring themes, concepts, and patterns. Text analysis is useful for understanding media messages, public discourse, and cultural trends.

This method involves an in-depth examination of a single person, group, or event to gain an understanding of complex phenomena. Case studies typically involve a combination of data collection methods, such as interviews, observations, and document analysis, to provide a comprehensive understanding of the case. Case studies are useful for exploring unique or rare cases, and for generating hypotheses for further research.

Process of Observation

This method involves systematically observing and recording behaviors and interactions in natural settings. The observer may take notes, use audio or video recordings, or use other methods to document what they see. Process of observation is useful for understanding social interactions, cultural practices, and the context in which behaviors occur.

Record Keeping

This method involves keeping detailed records of observations, interviews, and other data collected during the research process. Record keeping is essential for ensuring the accuracy and reliability of the data, and for providing a basis for analysis and interpretation.

This method involves collecting data from a large sample of participants through a structured questionnaire. Surveys can be conducted in person, over the phone, through mail, or online. Surveys are useful for collecting data on attitudes, beliefs, and behaviors, and for identifying patterns and trends in a population.

Qualitative data analysis is a process of turning unstructured data into meaningful insights. It involves extracting and organizing information from sources like interviews, focus groups, and surveys. The goal is to understand people’s attitudes, behaviors, and motivations

Qualitative Research Analysis Methods

Qualitative Research analysis methods involve a systematic approach to interpreting and making sense of the data collected in qualitative research. Here are some common qualitative data analysis methods:

Thematic Analysis

This method involves identifying patterns or themes in the data that are relevant to the research question. The researcher reviews the data, identifies keywords or phrases, and groups them into categories or themes. Thematic analysis is useful for identifying patterns across multiple data sources and for generating new insights into the research topic.

Content Analysis

This method involves analyzing the content of written or spoken language to identify key themes or concepts. Content analysis can be quantitative or qualitative. Qualitative content analysis involves close reading and interpretation of texts to identify recurring themes, concepts, and patterns. Content analysis is useful for identifying patterns in media messages, public discourse, and cultural trends.

Discourse Analysis

This method involves analyzing language to understand how it constructs meaning and shapes social interactions. Discourse analysis can involve a variety of methods, such as conversation analysis, critical discourse analysis, and narrative analysis. Discourse analysis is useful for understanding how language shapes social interactions, cultural norms, and power relationships.

Grounded Theory Analysis

This method involves developing a theory or explanation based on the data collected. Grounded theory analysis starts with the data and uses an iterative process of coding and analysis to identify patterns and themes in the data. The theory or explanation that emerges is grounded in the data, rather than preconceived hypotheses. Grounded theory analysis is useful for understanding complex social phenomena and for generating new theoretical insights.

Narrative Analysis

This method involves analyzing the stories or narratives that participants share to gain insights into their experiences, attitudes, and beliefs. Narrative analysis can involve a variety of methods, such as structural analysis, thematic analysis, and discourse analysis. Narrative analysis is useful for understanding how individuals construct their identities, make sense of their experiences, and communicate their values and beliefs.

Phenomenological Analysis

This method involves analyzing how individuals make sense of their experiences and the meanings they attach to them. Phenomenological analysis typically involves in-depth interviews with participants to explore their experiences in detail. Phenomenological analysis is useful for understanding subjective experiences and for developing a rich understanding of human consciousness.

Comparative Analysis

This method involves comparing and contrasting data across different cases or groups to identify similarities and differences. Comparative analysis can be used to identify patterns or themes that are common across multiple cases, as well as to identify unique or distinctive features of individual cases. Comparative analysis is useful for understanding how social phenomena vary across different contexts and groups.

Applications of Qualitative Research

Qualitative research has many applications across different fields and industries. Here are some examples of how qualitative research is used:

• Market Research: Qualitative research is often used in market research to understand consumer attitudes, behaviors, and preferences. Researchers conduct focus groups and one-on-one interviews with consumers to gather insights into their experiences and perceptions of products and services.
• Health Care: Qualitative research is used in health care to explore patient experiences and perspectives on health and illness. Researchers conduct in-depth interviews with patients and their families to gather information on their experiences with different health care providers and treatments.
• Education: Qualitative research is used in education to understand student experiences and to develop effective teaching strategies. Researchers conduct classroom observations and interviews with students and teachers to gather insights into classroom dynamics and instructional practices.
• Social Work : Qualitative research is used in social work to explore social problems and to develop interventions to address them. Researchers conduct in-depth interviews with individuals and families to understand their experiences with poverty, discrimination, and other social problems.
• Anthropology : Qualitative research is used in anthropology to understand different cultures and societies. Researchers conduct ethnographic studies and observe and interview members of different cultural groups to gain insights into their beliefs, practices, and social structures.
• Psychology : Qualitative research is used in psychology to understand human behavior and mental processes. Researchers conduct in-depth interviews with individuals to explore their thoughts, feelings, and experiences.
• Public Policy : Qualitative research is used in public policy to explore public attitudes and to inform policy decisions. Researchers conduct focus groups and one-on-one interviews with members of the public to gather insights into their perspectives on different policy issues.

How to Conduct Qualitative Research

Here are some general steps for conducting qualitative research:

• Identify your research question: Qualitative research starts with a research question or set of questions that you want to explore. This question should be focused and specific, but also broad enough to allow for exploration and discovery.
• Select your research design: There are different types of qualitative research designs, including ethnography, case study, grounded theory, and phenomenology. You should select a design that aligns with your research question and that will allow you to gather the data you need to answer your research question.
• Recruit participants: Once you have your research question and design, you need to recruit participants. The number of participants you need will depend on your research design and the scope of your research. You can recruit participants through advertisements, social media, or through personal networks.
• Collect data: There are different methods for collecting qualitative data, including interviews, focus groups, observation, and document analysis. You should select the method or methods that align with your research design and that will allow you to gather the data you need to answer your research question.
• Analyze data: Once you have collected your data, you need to analyze it. This involves reviewing your data, identifying patterns and themes, and developing codes to organize your data. You can use different software programs to help you analyze your data, or you can do it manually.
• Interpret data: Once you have analyzed your data, you need to interpret it. This involves making sense of the patterns and themes you have identified, and developing insights and conclusions that answer your research question. You should be guided by your research question and use your data to support your conclusions.
• Communicate results: Once you have interpreted your data, you need to communicate your results. This can be done through academic papers, presentations, or reports. You should be clear and concise in your communication, and use examples and quotes from your data to support your findings.

Examples of Qualitative Research

Here are some real-time examples of qualitative research:

• Customer Feedback: A company may conduct qualitative research to understand the feedback and experiences of its customers. This may involve conducting focus groups or one-on-one interviews with customers to gather insights into their attitudes, behaviors, and preferences.
• Healthcare : A healthcare provider may conduct qualitative research to explore patient experiences and perspectives on health and illness. This may involve conducting in-depth interviews with patients and their families to gather information on their experiences with different health care providers and treatments.
• Education : An educational institution may conduct qualitative research to understand student experiences and to develop effective teaching strategies. This may involve conducting classroom observations and interviews with students and teachers to gather insights into classroom dynamics and instructional practices.
• Social Work: A social worker may conduct qualitative research to explore social problems and to develop interventions to address them. This may involve conducting in-depth interviews with individuals and families to understand their experiences with poverty, discrimination, and other social problems.
• Anthropology : An anthropologist may conduct qualitative research to understand different cultures and societies. This may involve conducting ethnographic studies and observing and interviewing members of different cultural groups to gain insights into their beliefs, practices, and social structures.
• Psychology : A psychologist may conduct qualitative research to understand human behavior and mental processes. This may involve conducting in-depth interviews with individuals to explore their thoughts, feelings, and experiences.
• Public Policy: A government agency or non-profit organization may conduct qualitative research to explore public attitudes and to inform policy decisions. This may involve conducting focus groups and one-on-one interviews with members of the public to gather insights into their perspectives on different policy issues.

Purpose of Qualitative Research

The purpose of qualitative research is to explore and understand the subjective experiences, behaviors, and perspectives of individuals or groups in a particular context. Unlike quantitative research, which focuses on numerical data and statistical analysis, qualitative research aims to provide in-depth, descriptive information that can help researchers develop insights and theories about complex social phenomena.

Qualitative research can serve multiple purposes, including:

• Exploring new or emerging phenomena : Qualitative research can be useful for exploring new or emerging phenomena, such as new technologies or social trends. This type of research can help researchers develop a deeper understanding of these phenomena and identify potential areas for further study.
• Understanding complex social phenomena : Qualitative research can be useful for exploring complex social phenomena, such as cultural beliefs, social norms, or political processes. This type of research can help researchers develop a more nuanced understanding of these phenomena and identify factors that may influence them.
• Generating new theories or hypotheses: Qualitative research can be useful for generating new theories or hypotheses about social phenomena. By gathering rich, detailed data about individuals’ experiences and perspectives, researchers can develop insights that may challenge existing theories or lead to new lines of inquiry.
• Providing context for quantitative data: Qualitative research can be useful for providing context for quantitative data. By gathering qualitative data alongside quantitative data, researchers can develop a more complete understanding of complex social phenomena and identify potential explanations for quantitative findings.

When to use Qualitative Research

Here are some situations where qualitative research may be appropriate:

• Exploring a new area: If little is known about a particular topic, qualitative research can help to identify key issues, generate hypotheses, and develop new theories.
• Understanding complex phenomena: Qualitative research can be used to investigate complex social, cultural, or organizational phenomena that are difficult to measure quantitatively.
• Investigating subjective experiences: Qualitative research is particularly useful for investigating the subjective experiences of individuals or groups, such as their attitudes, beliefs, values, or emotions.
• Conducting formative research: Qualitative research can be used in the early stages of a research project to develop research questions, identify potential research participants, and refine research methods.
• Evaluating interventions or programs: Qualitative research can be used to evaluate the effectiveness of interventions or programs by collecting data on participants’ experiences, attitudes, and behaviors.

Characteristics of Qualitative Research

Qualitative research is characterized by several key features, including:

• Focus on subjective experience: Qualitative research is concerned with understanding the subjective experiences, beliefs, and perspectives of individuals or groups in a particular context. Researchers aim to explore the meanings that people attach to their experiences and to understand the social and cultural factors that shape these meanings.
• Use of open-ended questions: Qualitative research relies on open-ended questions that allow participants to provide detailed, in-depth responses. Researchers seek to elicit rich, descriptive data that can provide insights into participants’ experiences and perspectives.
• Sampling-based on purpose and diversity: Qualitative research often involves purposive sampling, in which participants are selected based on specific criteria related to the research question. Researchers may also seek to include participants with diverse experiences and perspectives to capture a range of viewpoints.
• Data collection through multiple methods: Qualitative research typically involves the use of multiple data collection methods, such as in-depth interviews, focus groups, and observation. This allows researchers to gather rich, detailed data from multiple sources, which can provide a more complete picture of participants’ experiences and perspectives.
• Inductive data analysis: Qualitative research relies on inductive data analysis, in which researchers develop theories and insights based on the data rather than testing pre-existing hypotheses. Researchers use coding and thematic analysis to identify patterns and themes in the data and to develop theories and explanations based on these patterns.
• Emphasis on researcher reflexivity: Qualitative research recognizes the importance of the researcher’s role in shaping the research process and outcomes. Researchers are encouraged to reflect on their own biases and assumptions and to be transparent about their role in the research process.

Advantages of Qualitative Research

Qualitative research offers several advantages over other research methods, including:

• Depth and detail: Qualitative research allows researchers to gather rich, detailed data that provides a deeper understanding of complex social phenomena. Through in-depth interviews, focus groups, and observation, researchers can gather detailed information about participants’ experiences and perspectives that may be missed by other research methods.
• Flexibility : Qualitative research is a flexible approach that allows researchers to adapt their methods to the research question and context. Researchers can adjust their research methods in real-time to gather more information or explore unexpected findings.
• Contextual understanding: Qualitative research is well-suited to exploring the social and cultural context in which individuals or groups are situated. Researchers can gather information about cultural norms, social structures, and historical events that may influence participants’ experiences and perspectives.
• Participant perspective : Qualitative research prioritizes the perspective of participants, allowing researchers to explore subjective experiences and understand the meanings that participants attach to their experiences.
• Theory development: Qualitative research can contribute to the development of new theories and insights about complex social phenomena. By gathering rich, detailed data and using inductive data analysis, researchers can develop new theories and explanations that may challenge existing understandings.
• Validity : Qualitative research can offer high validity by using multiple data collection methods, purposive and diverse sampling, and researcher reflexivity. This can help ensure that findings are credible and trustworthy.

Limitations of Qualitative Research

Qualitative research also has some limitations, including:

• Subjectivity : Qualitative research relies on the subjective interpretation of researchers, which can introduce bias into the research process. The researcher’s perspective, beliefs, and experiences can influence the way data is collected, analyzed, and interpreted.
• Limited generalizability: Qualitative research typically involves small, purposive samples that may not be representative of larger populations. This limits the generalizability of findings to other contexts or populations.
• Time-consuming: Qualitative research can be a time-consuming process, requiring significant resources for data collection, analysis, and interpretation.
• Resource-intensive: Qualitative research may require more resources than other research methods, including specialized training for researchers, specialized software for data analysis, and transcription services.
• Limited reliability: Qualitative research may be less reliable than quantitative research, as it relies on the subjective interpretation of researchers. This can make it difficult to replicate findings or compare results across different studies.
• Ethics and confidentiality: Qualitative research involves collecting sensitive information from participants, which raises ethical concerns about confidentiality and informed consent. Researchers must take care to protect the privacy and confidentiality of participants and obtain informed consent.

Also see Research Methods

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Mixed Methods Research – Types & Analysis

Textual Analysis – Types, Examples and Guide

Descriptive Research Design – Types, Methods and...

Basic Research – Types, Methods and Examples

Survey Research – Types, Methods, Examples

Observational Research – Methods and Guide

Pardon Our Interruption

As you were browsing something about your browser made us think you were a bot. There are a few reasons this might happen:

• You've disabled JavaScript in your web browser.
• You're a power user moving through this website with super-human speed.
• You've disabled cookies in your web browser.
• A third-party browser plugin, such as Ghostery or NoScript, is preventing JavaScript from running. Additional information is available in this support article .

To regain access, please make sure that cookies and JavaScript are enabled before reloading the page.

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here .

Loading metrics

Open Access

Peer-reviewed

Research Article

A qualitative exploration of barriers to efficient and effective structured medication reviews in primary care: Findings from the DynAIRx study

Roles Data curation, Formal analysis, Methodology, Validation, Writing – original draft, Writing – review & editing

Affiliations Academic Unit for Ageing & Stroke Research, Bradford Teaching Hospitals NHS Foundation Trust, University of Leeds, Bradford, United Kingdom, Faculty of Medicine and Health, School of Medicine, University of Leeds, Leeds, United Kingdom

Roles Data curation, Formal analysis, Validation, Writing – original draft, Writing – review & editing

Affiliation Institute of Population Health, University of Liverpool, Liverpool, United Kingdom

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Supervision, Validation, Writing – original draft, Writing – review & editing

Affiliations Institute of Population Health, University of Liverpool, Liverpool, United Kingdom, Directorate of Mental Health and Learning Disabilities, Powys Teaching Health Board, Bronllys, United Kingdom

Roles Conceptualization, Formal analysis, Investigation, Methodology, Supervision, Validation, Writing – review & editing

Affiliation General Practice and Primary Care, School of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom

Roles Conceptualization, Investigation, Methodology, Supervision, Writing – review & editing

Roles Conceptualization, Supervision, Writing – review & editing

Roles Conceptualization, Formal analysis, Investigation, Methodology, Validation, Writing – review & editing

Roles Writing – review & editing

Affiliations Faculty of Medicine and Health, School of Medicine, University of Leeds, Leeds, United Kingdom, Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom

Roles Conceptualization, Writing – review & editing

Affiliation Department of Computer Science, University of Liverpool, Liverpool, United Kingdom

Affiliation Division of Informatics, Imaging & Data Science, University of Manchester, Manchester, United Kingdom

Affiliation NIHR Applied Research Collaboration North West Coast, United Kingdom

Affiliations Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom, School of Computing, University of Leeds, Leeds, United Kingdom

Roles Project administration, Resources

Affiliation Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, United Kingdom

Affiliation Merseycare NHS Foundation Trust, Liverpool, United Kingdom

Roles Conceptualization, Funding acquisition, Investigation, Methodology, Resources, Supervision, Writing – review & editing

•  [ ... ],

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations Centre for Experimental Therapeutics, University of Liverpool, Liverpool, United Kingdom, Liverpool University Hospitals NHS Foundation Trust, Liverpool, United Kingdom

• [ view all ]
• [ view less ]
• Aseel S. Abuzour, 
• Samantha A. Wilson, 
• Alan A. Woodall, 
• Frances S. Mair, 
• Andrew Clegg, 
• Eduard Shantsila, 
• Mark Gabbay, 
• Michael Abaho, 
• Asra Aslam, 

• Published: August 30, 2024
• https://doi.org/10.1371/journal.pone.0299770
• Reader Comments

Introduction

Structured medication reviews (SMRs), introduced in the United Kingdom (UK) in 2020, aim to enhance shared decision-making in medication optimisation, particularly for patients with multimorbidity and polypharmacy. Despite its potential, there is limited empirical evidence on the implementation of SMRs, and the challenges faced in the process. This study is part of a larger DynAIRx (Artificial Intelligence for dynamic prescribing optimisation and care integration in multimorbidity) project which aims to introduce Artificial Intelligence (AI) to SMRs and develop machine learning models and visualisation tools for patients with multimorbidity. Here, we explore how SMRs are currently undertaken and what barriers are experienced by those involved in them.

Qualitative focus groups and semi-structured interviews took place between 2022–2023. Six focus groups were conducted with doctors, pharmacists and clinical pharmacologists (n = 21), and three patient focus groups with patients with multimorbidity (n = 13). Five semi-structured interviews were held with 2 pharmacists, 1 trainee doctor, 1 policy-maker and 1 psychiatrist. Transcripts were analysed using thematic analysis.

Two key themes limiting the effectiveness of SMRs in clinical practice were identified: ‘Medication Reviews in Practice’ and ‘Medication-related Challenges’. Participants noted limitations to the efficient and effectiveness of SMRs in practice including the scarcity of digital tools for identifying and prioritising patients for SMRs; organisational and patient-related challenges in inviting patients for SMRs and ensuring they attend; the time-intensive nature of SMRs, the need for multiple appointments and shared decision-making; the impact of the healthcare context on SMR delivery; poor communication and data sharing issues between primary and secondary care; difficulties in managing mental health medications and specific challenges associated with anticholinergic medication.

SMRs are complex, time consuming and medication optimisation may require multiple follow-up appointments to enable a comprehensive review. There is a need for a prescribing support system to identify, prioritise and reduce the time needed to understand the patient journey when dealing with large volumes of disparate clinical information in electronic health records. However, monitoring the effects of medication optimisation changes with a feedback loop can be challenging to establish and maintain using current electronic health record systems.

Citation: Abuzour AS, Wilson SA, Woodall AA, Mair FS, Clegg A, Shantsila E, et al. (2024) A qualitative exploration of barriers to efficient and effective structured medication reviews in primary care: Findings from the DynAIRx study. PLoS ONE 19(8): e0299770. https://doi.org/10.1371/journal.pone.0299770

Editor: Kathleen Bennett, Royal College of Surgeons in Ireland, IRELAND

Received: February 16, 2024; Accepted: June 24, 2024; Published: August 30, 2024

Copyright: © 2024 Abuzour et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: DynAIRx has been funded by the National Institute for Health and Care Research (NIHR) Artificial Intelligence for Multiple Long-Term Conditions (AIM) call (NIHR 203986). MG is partly funded by the NIHR Applied Research Collaboration North West Coast (ARC NWC). AW is partly funded by a Health and Care Research Wales Research Time Award (NHS-RTA-21-02). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This research is supported by the NIHR ARC NWC. The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.

Competing interests: No competing interests

Structured medication reviews (SMRs) were introduced in the United Kingdom (UK) in October 2020 and incorporated into the NHS England Directed Enhanced Service (DES) contract for 2021 [ 1 ]. SMRs represent a National Institute for Health and Care Excellence (NICE)-approved clinical intervention facilitating shared-decision making between clinicians and patients, to inform treatment decisions. The objective is to reduce medication-related harm in patients with complex or problematic polypharmacy [ 1 , 2 ]. While General Practitioners (GPs), pharmacists and advanced nurse practitioners (ANPs) who meet training criteria can conduct SMRs, the prevailing expectation is for clinical pharmacists within Primary Care Networks (PCNs) to assume primary responsibility as a commissioned service [ 3 ]. The varied methods employed by PCNs to proactively identify patients suitable for SMRs, and conduct these reviews, is contingent on available resources and capacity. Anecdotal evidence suggests that PCNs currently use limited digital tools, such as searching electronic health records (EHR) based on the total number of drugs prescribed or disease codes, to identify patients at risk of medication-related harm.

There is sparse empirical evidence reporting on the implementation of SMRs, their impact on patient outcomes, and the challenges faced by healthcare professionals (HCPs) and patients during SMRs [ 4 , 5 ]. This scarcity of evidence is unsurprising given that SMRs were introduced in 2020 amidst the COVID-19 pandemic [ 1 ]. Nonetheless, estimates suggest a percentage reduction in per-patient medicines following an SMR ranging from 2.7% to 9.9%, with up to 19.5% reduction in use for the highest-risk group in care homes [ 6 ].

Patients with complex multimorbidity and polypharmacy, whose medicines have not been optimised are at risk of adverse outcomes and medication-related harm [ 7 ]. The definition of complex multimorbidity is contentious [ 8 ] but here we are referring to patients living with four or more conditions, which is strongly associated with polypharmacy [ 8 , 9 ]. The use of data from EHRs to develop evidence-based digital health tools can be a promising resource to assist HCPs in conducting targeted, efficient and effective SMRs.

The NIHR-funded DynAIRx study (Artificial Intelligence for dynamic prescribing optimisation and care integration in multimorbidity) aims to develop AI-driven tools that integrate information from electronic health and social care records, clinical guidelines and risk-prediction models in order to support the delivery of SMRs [ 10 ]. The DynAIRx project will produce machine learning models, dashboards, and different tools including Causal Inferencing to provide clinicians and patients with evidence-based information to prioritise patients at most risk of harm and/or patients most likely to benefit from SMRs. Aligned with the NICE multimorbidity guidelines, [ 7 , 11 ] DynAIRx will focus on three patient groups at high-risk of rapidly worsening health from multimorbidity: (a) individuals with mental and physical health co-morbidity, [ 12 – 14 ] in whom the prescribing for mental health improvement can lead to adverse physical health consequences; (b) those with complex multimorbidity (four or more long-term health conditions taking ten or more drugs); [ 9 , 15 ] and (c) older people with frailty who are at high risk of adverse outcomes [ 16 ].

The initial step towards introducing AI-driven prescribing support tools into clinical practice involves understanding the current scope of work, how SMRs are presently undertaken and by whom, the time required in real-world clinical practice to undertake them, and crucially, investigate what determinants act as potential barriers to efficient and effective SMR implementation. The aim of this study was to explore how SMRs are undertaken and what barriers those undertaking them (and receiving them) experience.

Participants and recruitment

This study sought to recruit health care or management professionals working in health care settings (primary care in the community or secondary care in hospital services) across the UK where review of prescription medications is a regular part of the clinical workload. This included those working in General Practice, secondary care hospital services (geriatric medicine, clinical pharmacology, falls clinics, mental health practitioners), clinical commissioning of services or management of clinical services (practice managers), and pharmacists, including PCN pharmacists (those involved in conducting SMRs across several neighbouring GP practices). Patient participants included those with (a) multiple and physical co-morbidities; (b) complex multimorbidity; (c) older people with frailty. Patient and carer representatives of the three key multimorbidity groups outlined above were also invited. This included recruiting adult individuals (over the age of 18) with/or caring for someone with multiple (4 or more) long-term health conditions, co-existing mental and physical health problems, prescribed ≥10 regular medications, frailty.

Purposive sampling identified potential HCP participants that were known to be involved in medicines optimisation services through the researchers own clinical and professional networks. Snowballing (wherein research participants were asked to assist the recruitment by attempting to identify other potential participants) was employed to identify contacts through existing service providers along with advertisement in GP forums and at national events for individuals participating in clinical polypharmacy research [ 17 ]. Purposive sampling of potential patient representatives were identified through advertisement across the NIHR Applied Research Collaboration public advisor networks and through research databases at the researchers host institutions. Potential participants were provided with study information and an invitation to participate. Participants received comprehensive briefings from researchers about the study, and written consent was obtained prior to the focus group or interview participation. Withdrawal of consent was permitted at any stage, even after the focus group or interview.

Ethical approval

The Newcastle North Tyneside Research Ethics Committee (REC reference:22/NE/0088) granted ethical approval for the DynAIRx study.

Data collection

Data collection occurred from November 2022 to November 2023. Focus groups and semi-structured interviews were conducted to gather participant views. Focus groups were utilised for patient participants in order to stimulate discussion of common and shared experiences. Individual interviews supplemented the HCP focus groups in order to ensure as many HCPs could be involved as possible owing to time constraints for some HCPs. Focus group topic guides and interview schedules were developed and refined by the clinical members of the research team (LW, AA, AW, FM, AG) and tailored to HCP and patient groups. The topic guides (see S1 Appendix ) included questions exploring the experience of conducting or receiving SMRs, barriers to undertaking them and opinions on key medication challenges in multimorbidity groups from both the clinician and patient perspective. Sessions occurred in person and online (via Microsoft Teams), lasting from 49 to 109 minutes. Audio recordings underwent verbatim transcription and anonymisation to remove any potentially identifiable information. Each participant was assigned a code, and recordings were subsequently deleted. Data collection and analysis occurred concurrently. The Consolidated Criteria for Reporting Qualitative Research checklist was used to ensure comprehensive reporting of our qualitative study (see S1 Appendix ). No participant withdrew consent for their data to be used in the study.

Data analysis

Transcripts were imported into QSR NVivo 12® and analysed using thematic analysis [ 18 ]. Transcripts were read to familiarise researchers with the data. Inductive reasoning guided the initial coding by AA and SW, who collated and examined codes to identify themes. The multidisciplinary coding team (AA, SW, LW, AW, FM) consisted of clinicians and researchers, and engaged in regular reflexive practices to ensure a rigorous and transparent qualitative study. Recognising the diverse expertise and perspectives within the team, we held regular coding clinics where codes and initial themes were reviewed and discussed. By openly sharing our perspectives and challenging each other’s viewpoints, we aimed to balance our interpretations and avoid overemphasis on any single disciplinary lens. This allowed us to critically examine how our professional backgrounds and assumptions might shape the interpretation of findings. These reflexive sessions were instrumental in identifying and mitigating biases, ensuring that our analysis remained grounded in the participants’ narratives rather than our preconceptions. Themes were defined and supported by quotes. Detailed notes of discussions and developments were kept to ensure analytical rigour and plausibility [ 19 ]. The remaining dataset underwent hybrid inductive and deductive thematic analysis using the inductively defined themes, with codes and themes iteratively revised. Once all team members agreed no new codes or meaning to influence thematic analysis were emerging, we assumed saturation was achieved [ 20 ]. At this point, further recruitment was stopped.

Six focus groups with HCPs (n = 21) and 3 patient focus groups (n = 13) were conducted. A further five semi-structured interviews with HCPs took place (see Table 1 for details).

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0299770.t001

Two overarching themes developed from analysis of the HCP and patient interviews and focus groups, within which a number of sub-themes emerged:

• Limited availability of digital tools to assist in identifying and prioritising patients for a SMR
• Organisational challenges and patient factors affecting patient engagement for a SMR
• Time consuming “detective work”
• SMRs require multiple appointments
• Influence of healthcare context on delivering SMR
• Factors influencing deprescribing discussions
• Poor communication and data sharing between primary and secondary care
• Difficulties managing mental health medication for prescriber and patient
• Challenges around anticholinergic medication optimisation for prescriber

Fig 1 displays each key theme from this study and a detailed list of the barriers to each stage of the SMR process. Supplementary quotes for each theme can be found in S2 Appendix .

https://doi.org/10.1371/journal.pone.0299770.g001

• a. Limited availability of digital tools to assist in identifying and prioritising patients for a SMR

PCN pharmacists reported proactively identifying and prioritising patients to conduct SMRs. Patient identification was based on the criteria outlined by the DESGP contract, which includes patients in care homes (both nursing and residential facilities), individuals with complex multimorbidity and polypharmacy, urgently referred patients, older individuals encountering medication-related harms, and patients recently discharged from hospital. HCPs also referenced using available healthcare management automated search tools within the EHR, including ‘Ardens search’ [ 21 ] and ‘ProActive Register Management (PARM) diabetes’ [ 22 ], to identify pre-determined conditions, prescribing alerts and other variables that form part of the practice workload. They also used targets from the Investment and Impact Fund (IIF) for patient identification (IIF is an incentive scheme where PCNs can improve health and the quality of care for patients with multimorbidity), which participants described as beneficial but a waste of resources in the context of SMRs which should focus on patients with complex multimorbidity and polypharmacy [ 23 ]. Despite this, HCPs noted a limited number of digital tools to assist in identifying and prioritising patients for a SMR.

“I’ll be honest that we’ve not really had any tools that have been developed specific for supporting SMR.” (Pharmacist 2, Interview)

Since the introduction of EHRs in the NHS, HCPs are expected to assign ‘SNOMED codes’ to document patients with specific diagnostic, symptom or treatment codes in a logical hierarchical manner to specify clinical information [ 24 ]. These codes facilitate searches for specific medical conditions, symptoms and treatments within the GP EHR, facilitating the identification of individuals requiring an SMR. Pharmacists noted that EHR searches do not adequately consider the complexity of patients, making it challenging to stratify those that would benefit most from an SMR. Additionally, participants highlighted a lack of sensitivity and specificity in the current search mechanisms (meaning the searches either do not identify all the intended patients or identify too many).

“ The actual indicator that my team has been focusing on is supposed to be the ones where patients are prone to medication errors … but when I actually look at the patients, I haven’t got a clue why the actual computer system has decided that most of the time.” (Participant 1, Pharmacist FG1)

HCPs were concerned that the identification of patients who would benefit from an SMR could exceed the clinical capacity of the staff available to meet the need. They felt that any digital tool used to prioritise patients’ needs should match the clinical capacity of the practice.

“ The tools have to be a bit cleverer … We can generate a list of patients today … PCNs at the moment essentially do that, but what you have to do is almost the list that’s generated to the capacity … People would not switch it on if they felt that it could generate lots of patients you would not then see.” (Policy-maker, Interview)

• b. Organisational challenges and patient factors affecting patient engagement for a SMR

GPs or secondary care clinicians (excluding clinical pharmacologists) often conducted opportunistic medication reviews, compared to the proactive SMRs conducted by PCN pharmacists. In alignment with the DES requirements, HCPs described how the task of conducting an SMR was contingent upon organisational contracts, practice size, and staff availability. The presence of a PCN pharmacist for SMRs facilitated streamlined tasks, enabling GPs to focus on patients with more complex medical profiles. GPs voiced concerns about burnout in areas where demand for SMR exceeded the clinical capacity to undertake them. This challenge was particularly pronounced in regions of lower socioeconomic status where patients often presented with complex multimorbidity and polypharmacy at a younger age, especially those with co-morbid mental and physical health problems. Moreover, respondents felt that patients residing in deprived areas were less likely to attend scheduled SMRs, compelling GPs to resort to opportunistic reviews. This highlights potential inequity in access to SMRs and overall health surveillance.

“In the poorer area of the practice there’s no clinical pharmacist, that’s all done opportunistically, if done at all, by the GP partners there. I think that there’s a couple that are approaching burn out, if not complete burn out and the practice is almost run by locums. So, when I’m going in there, it’s quite tough and I will often see medications that are inappropriately prescribed, polypharmacy, several of the same drugs, and I will opportunistically undertake a structured medical review.” (Participant 3, GP FG1)

• c. Time consuming “detective work”

Whether HCPs identified patients proactively or opportunistically, the preparation time for a medication review ranged from 10 minutes to 1 hour. Several factors influenced this preparation time, including the availability of information, case complexity, barriers to accessing information, information density, and time constraints. The challenge in finding and collating information within the patient’s clinical records constituted a significant portion of the preparation time. For instance, discharge letters from hospitals are often located as attachments within the patient record, requiring HCPs to locate and read the letter. These necessary preparatory activities take away from the face-to-face time available with the patient.

“ Probably double the amount of prep time than it was actually with the patient. I mean, granted we did spend a while with the patient because we both like to talk, and the patient certainly did, but I think, and that’s the problem, isn’t it? You get the best information out of your patient when you let them talk and you let them tell you lots of things that you wouldn’t normally ask, but you haven’t got the time to do that so it’s tricky isn’t it to find the balance. But the biggest thing with the prep time was getting the information.” (Participant 3, Pharmacist FG1)

HCPs also conveyed frustration regarding the substantial time required to determine the original indication for a particular prescription and the ongoing necessity for it, even during major transition periods such as a patient’s admission to a care home.

“ We don’t get enough actual structured reviews, so they’ll be getting put on medication, people in care homes, and then left on those medicines. There’s no recognition of the changes. As you move in a care home, you’re generally more frailer, your renal function, haematic function might not be as great and, you know, you’re not moving as much, so your need for some medicines might not be as great as it was when the medicine was first started. ” (Policy-maker, Interview) “So along with what you said about deprescribing Selective Serotonin Reuptake Inhibitors (SSRIs) , especially , you know they’ve been on them for 4–5 years and they are adamant they don’t want to reduce them or stop them or have any sort of conversation about it , yeah , they’re quite challenging I think . Also , I think sometimes you can’t quite work out what medications people have been on . I mean if we talk about SSRIs they may have tried multiple different ones in the past and sometimes it’s difficult to work out what they’ve been on without having to go through the long , long list searching all the different medications that are SSRIs that they’ve tried . It would be so helpful if you know it could just bring up yeah been used before , and then know which one you could try…knowing that you want to try a different one . ” (Participant 6, GP FG1)

Although it is possible within the EHR system to link the prescription of an individual drug to its clinical indication, anecdotal evidence suggests this is time consuming and therefore may not be done in clinical practice.. As such, indications for prescribed medicines are recorded in the free text for the consultation which can easily become obscured over time within the extensive information contained in the clinical record. Examining the clinical free text for this information was emphasised as a challenge in efficiently conducting SMRs.

“ Although in my letters I would clearly state to the GP why I am prescribing the second line antipsychotic just so that people know, but over time that tends to get lost, the rationale for that prescribing tends to get lost and before you know you leave post, somebody else comes and begins to increase that second antipsychotic you know, so that becomes a problem. ” (Participant 1, Psychiatrist FG)

Moreover, existing EHRs are not adept at presenting patient histories in a manner conducive to HCPs pinpointing areas for potential deprescribing. This deficiency in the system leads to a cumulative high pill burden for patients, as illustrated in the quote below.

“ At the age of [ 18 – 20 ], I was diagnosed with bipolar. I am now [71–74] and I have lived for that period of time on medication, a lot of medication actually … I counted the number of tablets and my boxes on my bedside the other day and there was 13 different tablets, so that is what I am being prescribed by my GP. ” (Patient 1, Mental and physical co-morbidities FG)

Patients also expressed uncertainty about the initial reasons for starting medications. Patients reported receiving medications for many years and being unsure whether the medication was still necessary.

“ She is also on a daily injection of adult growth hormone which another consultant put her on at the time and she has been having them for probably 10 to 15 years, and no-one seems to know now who initially prescribed it and who is in charge of that. I am concerned, does she really need them? She is having them every day … Initially it was an asset to go with the immunodeficiency but now I don’t really know. ” (Patient 5, Mental and physical co-morbidities FG)

• d. SMRs require multiple appointments

SMRs typically lasted a minimum of 30 minutes, often extending beyond this duration. The variability in duration was contingent upon the patient’s complexity and the focused nature of the review. Allowing adequate time to address broader health concerns was deemed crucial, enabling the identification of potential issues requiring deeper exploration by the clinician.

HCPs acknowledged that SMRs were not a singular event, and patients might necessitate multiple appointments for a comprehensive review. Consequently, EHR systems were recognised as needing functionality to alert HCPs to schedule additional appointments after the initial SMR, emphasising the iterative and ongoing nature of medication reviews.

“ The first time I see patients, you want almost a bit of a holistic conversation, but actually when you start making interventions you go with what matters most to the patient or where the biggest risk is and you then table the others … You can imagine that being 2 or 3 hours in 4 different appointments before you get to the bottom of where you want to be … I think we had to contact on average about 2 to 3 times per patient, but there were more complex patients as well … I don’t think you can stop medicines or optimise medicines without seeing that patient again as least once. ” (Policy-maker interview)

Patients expressed a desire to be involved in the decision-making during reviews and valued the opportunity to discuss issues such as how medications fit into their routines and other resources that may be available to them.

"I’ve got a series of chronic things, take a load of pills and they’re each for separate things, and I have been concerned for years whether there’s any interaction with them, between them. And also they make me feel tired all the time and perhaps there are some of them where I could actually get rid of them." (Patient 1, Older people with frailty FG)

• e. Influence of healthcare context on delivering SMR

The duration of conducting a SMR was also contingent on the specific healthcare setting in which it took place. One pharmacist highlighted that SMRs conducted in care homes lacked a strict time limit and were oriented towards achieving specific outcomes, such as the number of medicines deprescribed in particular patient groups. This reflected the contextual variability in the conduct and objectives of SMRs, emphasising the need for flexibility in the approach based on the healthcare environment and patient population.

“ We were just told take whatever time you need but because we were not measured on the quantity, we were measured on the quality, and we were recorded the number of medicines basically stopped and in particular groups actually so, and then that would have gone on the report because that was the way of showing what we were doing and the basic value for money I guess .” (Participant 1, Pharmacist FG1)

The emphasis placed on a medication list varied depending on the reviewer and the healthcare setting. A pharmacist working in secondary care articulated a tendency to allocate less attention to certain medicines in a hospital setting, prioritising focus on medications more likely to cause harm. This perspective highlights the nuanced approach that different HCPs may adopt based on their expertise and the specific context in which they operate.

“ At the moment, the bisphosphonate would be something that I’m less concerned about it a very high acuity environment, that’s the thing that I’m probably going to, maybe if I get a chance, write in the discharge summary for the GP to check up on whether or not that’s still appropriate. Whereas I’m chasing those big harms .” (Participant 2, Pharmacist FG1)

• f. Factors influencing deprescribing discussions

Discussions around deprescribing between HCPs and patients were reported to be influenced by several factors. These included the specific type of medicine to be deprescribed, the patient’s willingness to discontinue the medication, sociodemographic location, availability of additional health services in the area, whether the medication was initially prescribed in primary or secondary care and the existence of pre-established stopping criteria for certain medications (e.g., bisphosphonates for more than 5 years). Additionally, HCPs and patients acknowledged a degree of reluctance to engage in deprescribing due to perceived potential risks associated with the cessation of certain medications. These multifaceted factors contribute to the complexity and individualised nature of deprescribing discussions within the healthcare context.

“I find it really, really difficult because all of the guidelines will say, oh, you should have this patient on statins, etc., and you think I really probably shouldn’t they’re 95, but having stopped them in the past then a patient unfortunately ends up with a stroke, they go to hospital, the hospital tells them it’s because their GP stopped their statin and puts them back on .” (Participant 2, GP FG2) “ The antidepressant one is interesting . So , I did work for 9 years in a really deprived area . . . SSRIs for a long time and they were pretty reluctant to come off it but usually willing to accept if it didn’t work out just put them back on it . It just meant in a much more middle-class area there’s the opposite where they’re all desperate to come off it and probably coming off it far too soon . So , I don’t think it needs to be doctor-led , it seems to be more about their external pressures and there’s been a lot of areas done in deprived communities with link workers and social prescribers and I think if you’re going to look at polypharmacy in these sort of groups then that’s probably the way to do it stopping all their meds .” (Participant 4, GP FG1)

In one case, a pharmacist highlighted the challenges associated with decision-making when optimising medicines for complex, younger patients, emphasising the impact of side-effects on their quality of life. Equally, the importance of considering quality of life in frail older adults with polypharmacy was acknowledged, although perceived as less complicated than in younger adults.

"I know well that’s it isn’t it, it’s not necessarily that they’re a complex medicine it’s that there is evidence to say that this can prolong your life but it’s causing them that much upset, so it’s, for me it’s not necessarily the, I can’t think of any particular group of drug its more the younger you get, you know 50 is very young and that you have got a lot of life left to live so that’s when it becomes more of a clinical decision for me that as a pharmacist I don’t feel like I would be able to make" (Participant 1, Pharmacist FG1)

• 2. Medication-related Challenges

Potential for medication-related harm identified by our key stakeholder groups included issues with specific medicines, conditions, and risky medication combinations; mental health medications; prescriptions from specialists; anticholinergic medicines; difficulties in determining prescription timelines to assist in decision-making; challenges with younger complex patients; and siloed care.

• a. Poor communication & data sharing between primary and secondary care

HCPs identified the challenge of extracting information from hospital discharge letters as a key source of frustration. Patients, in particular, assumed that EHRs seamlessly connected primary and secondary care, and in some cases, their community pharmacy. This assumption left patients bewildered and, in certain cases, reliant on the HCPs knowing the complete narrative behind their health records. The disjointed communication and misconceptions surrounding record integration emphasised the need for improved interoperability to enhance the continuum of care.

“ We will be waiting a week for an outpatient letter to come through and it’s really confusing, stop this, change that, increase the dose here, and you’re kind of stuck in the middle. So sometimes the patient will have left that meeting there and it will be ‘like go and see your GP and they will do this bit’, well [that’s] not happening until I’ve got that letter. There is that real mismatch of communication. ” (Participant 1, GP FG2) “ When the repeat prescription came through , despite the fact that the surgery had received the discharge letter , everything was all wrong , and this is just one of those things that happens . So , you get a review and it is whether that data from that review and the story behind it and who it goes back to and whether it is acted on , I think that is important .” (Patient 3, Complex multimorbidity FG)

Communication gaps between GPs, specialist clinicians, and patients were evident due to varying expectations. GPs expressed challenges in managing specialist medications with patients, including concerns about patients’ ability to self-manage their medicines. These challenges highlighted aspects of fragmented care between primary care and specialist clinicians. HCPs also cited difficulties and reluctance in communicating and potentially engaging in conflict with specialist doctors. Participants described specialist doctors as lacking a holistic view when prescribing for patients, favouring certain medicines, and having limited knowledge in drug interactions.

“I think multidisciplinary is key, however when I notice that I liaise with specialists, depending on who I speak to, their drug is the most important and trying to get a consensus about what is best for the patient is obviously one of the challenges like that’s how we got in this situation to begin with you know, so I do find that’s one of the challenges. so, I think you need someone who is you know, who is a specialist but has also got a sort of holistic view of patient care as well which doesn’t always happen in secondary care, but sometimes does.” (Participant 3, Polypharmacy FG)

Participants noted that central nervous system medicines had complicated medication regimens and hence required more coordinated care and responsibility between the specialist prescriber and GP. Addressing these challenges calls for enhanced collaboration, knowledge exchange, and a holistic approach to patient care between primary care and specialist clinicians.

“ I find it, with the pain management clinic, they stop medication, give you a list of all these other tablets you need to start to see how things go and then sort of leave you to it, discharge the patient in your hands and expect you to sort of manage it all. And the same thing is with migraine and headaches from neurology. That’s just a minefield … I think when you’re in specialty, you feel that you can give any sort of long protracted complicated regime and the patient is just going to manage it because that’s the only medication that you think that they’re on. So yeah, they can be quite difficult .” (Participant 5, GP FG1) “ He [GP] says that we can’t actually change any medication to do with your bipolar , that has got to be done by your psychiatrists … I don’t think they would change anything to do with psychiatry .” (Patient 1, Mental and physical co-morbidities FG)

• b. Difficulties managing mental health medication

Mental health medication and management emerged as a consistent sub-theme across key HCP stakeholder groups, irrespective of their professional background. Both doctors and pharmacists described difficulties in monitoring and adjusting psychiatric medicines, including uncertainties about how to address specific issues related to psychiatric medicines. Participants expressed a sense of being ‘out of their depth’, particularly concerning antipsychotic medicines. They conveyed a lack of confidence in assessing the risks and benefits of antipsychotic prescribing, feeling deskilled in this specific area of medication management, and finding it challenging to safely challenge prescribers. This sense of unease prompted participants to seek ways of contacting the mental health team, only to encounter additional hurdles, such as difficulties in locating relevant information within patient records to facilitate multidisciplinary coordinated patient care.

“ But the other one is someone with very complex psychiatric problems, still maybe under the mental health team, and I haven’t got really access to the details apart from maybe I’ve got, you know, some of the other diagnoses. But if I think maybe one of those drugs is potentially causing more harm than could then I’m not clear how then to action that and who to speak to and who were they actually seeing ” (Participant 1, Polypharmacy FG)

For example, a clinical pharmacologist explained that evaluating the success of managing antipsychotics is not as straightforward as assessing physical health conditions. This complexity may contribute to the observed lack of confidence among HCPs when it comes to deprescribing psychiatric medicines. The nuanced nature of mental health outcomes, compared to more tangible markers of success in physical health, adds an additional layer of intricacy to the decision-making process in psychiatry. This includes the complexity of managing mental health medication.

“ The biggest challenge group that I think we face in a deprived area is the patients who are on long term opioid medication, long term neuropathic meds, they’ve probably got a diagnosis of fibromyalgia, they’ve probably got personality disorder plus / minus mental health problems. And the issues that we have is that they’ve almost been sequentially added medication on because GPs don’t really often know what to do with them unless you have a special interest in that field like I do. And when they go and see pharmacists, they are very challenging to pharmacists and pharmacists don’t have the clinical knowledge to be able to sift through what can often be quite dramatic presentations. ” (Participant 1, GP FG1)

• c. Challenges around anticholinergic medicines

Anticholinergic medicines, which inhibit the neurotransmitter acetylcholine involved in numerous physiological functions, has been associated with adverse outcomes such as cognitive decline and falls, particularly when multiple anticholinergic medicines are used concurrently (termed anticholinergic burden) [ 25 , 26 ]. GPs, clinical pharmacologists, and pharmacists described the importance of reviewing and deprescribing anticholinergic medicines where possible. However, the process of calculating anticholinergic burden (ACB) in frail, older adults is time consuming, primarily due to the absence of automated calculators embedded within the EHR system.

Doctors and pharmacists expressed frustrations around the re-prescribing of anticholinergics after deprescribing them. They attributed the persistence of high ACB to limitations in prescribing guidelines and a scarcity of alternative options to replace anticholinergic drugs. These challenges highlighted the need for tools within EHR systems to facilitate efficient assessment of ACB, alongside a broader exploration of prescribing guidelines and alternatives to enhance deprescribing practices.

“ One of the things that I often see in general practice is that there’s lots of anticholinergics, usually amitriptyline because it’s kind of given out for other reasons for what it’s licensed for. So, sleep is probably the most common thing that I see it used for, or avoiding long term opioids in chronic arthritic pain, and often that’s because we have other options for them but we’re not allowed to prescribe them. So, melatonin is probably the most common thing that we could put them on which has a lot better safety profile, but we are just completely discouraged from prescribing it. And likewise access to other interventions that would help arthritic pain rather than putting them on NSAIDS which obviously carry risk or opioids which aren’t overly effective outside the acute pain window. It’s often the lack of other stuff that raises all of the anticholinergic burden. ” (Participant 1, GP FG1)

Participants welcomed any digital tool that could streamline routine work processes, including information retrieval, automated dose calculations, and assessing the risk of developing diseases to optimise medicines during a SMR in a patient-centred manner, with the goal of enhancing efficiency in the medication optimisation process.

Medication reviews by HCPs can take significant preparation, and are time consuming, primarily due to the need to gather and understand patient information and to develop an understanding of a patient’s medical history and social circumstances. In addition, currently, there is no easy way to identify from the EHR which patients are at greatest risk of medication-related harm and those most likely to benefit from an SMR. The EHR systems used in primary care contain enormous volumes of information which becomes particularly challenging and time-consuming to navigate for complex individuals living with multiple long-term conditions and taking many medications. The way that information is organised in the system leads to a large proportion of time spent linking medications to their original indication and examining the patient journey. This time could be better spent discussing shared decisions with the patient. The EHR has not evolved in line with increasing patient complexity. The findings of this report emphasise the need for enhanced functionalities in EHRs to support effective medication management in the context of deprescribing discussions where a nuanced understanding of a patient’s medication history is crucial.

Our study has highlighted the challenges facing those undertaking SMRs in more socioeconomically disadvantaged areas, where people experience multimorbidity (and co-existent polypharmacy) 10–15 years earlier than their affluent peers [ 27 , 28 ]. These populations have complex healthcare needs at a younger age, the care of which falls to the already over-stretched GPs. Areas with greater socioeconomic disadvantage often have lower health literacy, resulting from a combination of lower educational attainment, economic barriers like the need to prioritise food and heating over health seeking, and psychosocial stressors affecting decision-making relating to health [ 29 , 30 ]. Health literacy applies not only to the patient but to the clinician who may also be unaware of the psychosocioeconomic situation of the patient, leading to a communication gap when discussing the risk and benefit of medicines to reach a shared-decision [ 31 – 33 ]. Accordingly, complex conversations involving numeracy calculations of risk may take longer and require repetition, but may also be of less priority for the patient and/or carer than other more immediate life concerns.

As preparation time is repeatedly cited as a barrier to effective SMR, a potential solution that would support SMRs in those with lower health literacy should include any digital intervention that saves on preparation time. This would enable more time for the clinician to engage with the patient and discuss complexities around risk and benefit, which would go some way to addressing the existing health disparity that affects those experiencing socioeconomic disadvantage. For HCPs working in areas of socioeconomic deprivation, lack of HCP capacity alongside patients declining SMR invitations were cited as barriers to undertaking SMRs. Moreover, HCPs described the usefulness of a system to identify availability of different health services in surrounding areas (e.g. weight management service) [ 27 , 28 ]. A recent study co-produced SMR resources to empower patients in their healthcare and support them in making the most out of their SMR. This included producing resources in a number of different languages including audio recorded resources for patients with visual impairment [ 34 , 35 ]. Embedded links to resources for HCPs to provide to patients before and/or after an SMR can be one potential way to utilise digital health and empower patients to reduce inequity in access to healthcare.

Our study also highlighted medication-related challenges such as difficulties managing mental health, specialist and anticholinergic medications. HCPs reported that a lack of alternatives to medication for symptom management hampered their ability to optimise some of the more potentially harmful medication classes such as opioids, anti-depressants, anticholinergics and gabapentinoids. Non-pharmacological alternatives, where appropriate, such as counselling need to be readily and equitably accessible for this approach to be considered a reliable option [ 36 ]. Mental health medication management stood out as a consistent challenge. HCPs in our study, regardless of their professional background, expressed difficulty in monitoring and adjusting psychiatric drugs. There was also a general lack of confidence and skill when it came to monitoring and adjusting antipsychotic medications, with the measurement of success in managing these medications being ambiguous. This is consistent with previous studies that note GPs lack of confidence in managing patients with serious mental health illness [ 12 , 37 ]. One recent study reported that less than half of GP trainees in England and Wales have trained in a mental health setting between 2013 and 2015 [ 38 ]. In addition to the need for HCPs in primary care to become trained to address issues related to psychiatric medicines, EHRs must include basic information about the indication for the prescribed psychiatric medicine and the appropriate mental health team contact details for GPs to be able to address these issues. This would have the potential to enable multidisciplinary coordination of care with mental health patients.

Another challenging group of medicines was those with anticholinergic effects. This drug class was also a recurring issue among GPs, clinical pharmacologists, and pharmacists. HCPs found it time consuming to calculate the ACB in frail, older adults. Although there are several ACB scales available that have been developed and validated, participants stated that automated calculators to calculate ACB are not easily accessible or embedded into EHRs. In addition, there is considerable variability between anticholinergic scales making it difficult to ascertain which scale to use to calculate ACB [ 39 ]. As such, taking the time out to include every medicine a patient is taking to calculate their ACB is time consuming, reducing opportunities for potential deprescribing discussions with patients.

Limitations

This study was conducted in the UK, which provides universal access to healthcare. However, findings from our study may also be applicable internationally to other health systems which operate a universal social insurance model where there is a primary care and secondary care gatekeeping model, including the need for HCP coordination [ 40 , 41 ] or deprescribing challenges [ 42 , 43 ]. This study is part of a larger qualitative study examining both barriers to SMRs and potential digital solutions, including AI-assisted approaches. As such, the HCP participants likely included a number of clinicians with a particular interest in digital-driven solutions in healthcare. We sought to include a wide variety of HCPs from different practice backgrounds in order to mitigate this. In addition, some of the focus groups contained unexpected small numbers of clinicians at one time, owing to the competing demands on clinician time. However, the data collected were rich and contributed significantly to achieving thematic saturation. The insights gained from these discussions were consistent with those from larger groups, reinforcing the validity of our findings.

Conclusions

There are few useful digital tools that can identify patients that would benefit most from an SMR or monitor the effects of medication optimisation when medicines are altered. Our findings showed that significant time is needed to prepare and conduct a SMR, with complex patients sometimes needing multiple appointments to enable a comprehensive review. The DynAIRx project will use findings from this study to address the barriers of conducting an SMR by producing dashboards and visualisations to summarise the patient’s medical journey; develop digital tools to prioritise patients that would benefit most from an SMR; and identify optimal interventions for specific multimorbidity and polypharmacy patient groups.

Supporting information

S1 appendix. topic guides for interviews and focus groups and coreq checklist..

https://doi.org/10.1371/journal.pone.0299770.s001

S2 Appendix. Table containing additional explanatory quotes relating to subthemes.

https://doi.org/10.1371/journal.pone.0299770.s002

• 1. NHS. Network Contract Directed Enhanced Service. Structured medication reviews and medicines optimisation: guidance. In: NHS, editor. 2020.
• 2. NHS England. Network contract DES guidance for 2024/25: Part A ‐ Clinical and support services (Section 8). In: NHS England, editor. 2024.
• 3. NHS. Network Contract Directed Enhanced Service. Contract specification 2021/22 ‐ PCN Requirements and Entitlements. 2021.
• View Article
• PubMed/NCBI
• Google Scholar
• 7. NICE. Multimorbidity and polypharmacy 2017 [Available from: https://www.nice.org.uk/advice/ktt18 ].
• 11. NICE. Multimorbidity: clinical assessment and management: NICE,; 2016 [Available from: https://www.nice.org.uk/guidance/ng56 ].
• 20. Pope C, Mays N. Qualitative research in health care. Fourth edition ed. Hoboken: Wiley-Blackwell; 2020.
• 21. Ardens Healthcare Informatics. The leaders in providing EMIS Web & SystmOne templates & resources [Available from: https://www.ardens.org.uk/ ].
• 22. PARM Diabetes. A health management tool for people with diabetes co-created by Lilly and NHS Devon CCG [Available from: https://parmdiabetes.co.uk/ ].
• 23. NHS. Network Contract Directed Enhanced Service. Investment and Impact Fund 2020/21: guidance. 2020.
• 24. NHS Digital. SNOMED CT 2023 [Available from: https://digital.nhs.uk/services/terminology-and-classifications/snomed-ct ].
• 32. Citizens Advice. A very general practice. How much time do GPs spend on issues other than health?: Citizens Advice,; 2015 [Available from: https://www.citizensadvice.org.uk/Global/CitizensAdvice/Public%20services%20publications/CitizensAdvice_AVeryGeneralPractice_May2015.pdf ].
• 35. Health Innovation Network. Resources to support patients having a Structured Medication Review [Available from: https://thehealthinnovationnetwork.co.uk/programmes/medicines/polypharmacy/patient-information/ ].
• 38. MIND. Better equipped, better care. Improving mental health training for GPs and practice nurses 2016 [cited 2023 December 11]. Available from: https://www.mind.org.uk/media-a/4501/find-the-words-report-better-equipped-better-care.pdf .

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List

What is Qualitative in Qualitative Research

Patrik aspers.

1 Department of Sociology, Uppsala University, Uppsala, Sweden

2 Seminar for Sociology, Universität St. Gallen, St. Gallen, Switzerland

3 Department of Media and Social Sciences, University of Stavanger, Stavanger, Norway

What is qualitative research? If we look for a precise definition of qualitative research, and specifically for one that addresses its distinctive feature of being “qualitative,” the literature is meager. In this article we systematically search, identify and analyze a sample of 89 sources using or attempting to define the term “qualitative.” Then, drawing on ideas we find scattered across existing work, and based on Becker’s classic study of marijuana consumption, we formulate and illustrate a definition that tries to capture its core elements. We define qualitative research as an iterative process in which improved understanding to the scientific community is achieved by making new significant distinctions resulting from getting closer to the phenomenon studied. This formulation is developed as a tool to help improve research designs while stressing that a qualitative dimension is present in quantitative work as well. Additionally, it can facilitate teaching, communication between researchers, diminish the gap between qualitative and quantitative researchers, help to address critiques of qualitative methods, and be used as a standard of evaluation of qualitative research.

If we assume that there is something called qualitative research, what exactly is this qualitative feature? And how could we evaluate qualitative research as good or not? Is it fundamentally different from quantitative research? In practice, most active qualitative researchers working with empirical material intuitively know what is involved in doing qualitative research, yet perhaps surprisingly, a clear definition addressing its key feature is still missing.

To address the question of what is qualitative we turn to the accounts of “qualitative research” in textbooks and also in empirical work. In his classic, explorative, interview study of deviance Howard Becker ( 1963 ) asks ‘How does one become a marijuana user?’ In contrast to pre-dispositional and psychological-individualistic theories of deviant behavior, Becker’s inherently social explanation contends that becoming a user of this substance is the result of a three-phase sequential learning process. First, potential users need to learn how to smoke it properly to produce the “correct” effects. If not, they are likely to stop experimenting with it. Second, they need to discover the effects associated with it; in other words, to get “high,” individuals not only have to experience what the drug does, but also to become aware that those sensations are related to using it. Third, they require learning to savor the feelings related to its consumption – to develop an acquired taste. Becker, who played music himself, gets close to the phenomenon by observing, taking part, and by talking to people consuming the drug: “half of the fifty interviews were conducted with musicians, the other half covered a wide range of people, including laborers, machinists, and people in the professions” (Becker 1963 :56).

Another central aspect derived through the common-to-all-research interplay between induction and deduction (Becker 2017 ), is that during the course of his research Becker adds scientifically meaningful new distinctions in the form of three phases—distinctions, or findings if you will, that strongly affect the course of his research: its focus, the material that he collects, and which eventually impact his findings. Each phase typically unfolds through social interaction, and often with input from experienced users in “a sequence of social experiences during which the person acquires a conception of the meaning of the behavior, and perceptions and judgments of objects and situations, all of which make the activity possible and desirable” (Becker 1963 :235). In this study the increased understanding of smoking dope is a result of a combination of the meaning of the actors, and the conceptual distinctions that Becker introduces based on the views expressed by his respondents. Understanding is the result of research and is due to an iterative process in which data, concepts and evidence are connected with one another (Becker 2017 ).

Indeed, there are many definitions of qualitative research, but if we look for a definition that addresses its distinctive feature of being “qualitative,” the literature across the broad field of social science is meager. The main reason behind this article lies in the paradox, which, to put it bluntly, is that researchers act as if they know what it is, but they cannot formulate a coherent definition. Sociologists and others will of course continue to conduct good studies that show the relevance and value of qualitative research addressing scientific and practical problems in society. However, our paper is grounded in the idea that providing a clear definition will help us improve the work that we do. Among researchers who practice qualitative research there is clearly much knowledge. We suggest that a definition makes this knowledge more explicit. If the first rationale for writing this paper refers to the “internal” aim of improving qualitative research, the second refers to the increased “external” pressure that especially many qualitative researchers feel; pressure that comes both from society as well as from other scientific approaches. There is a strong core in qualitative research, and leading researchers tend to agree on what it is and how it is done. Our critique is not directed at the practice of qualitative research, but we do claim that the type of systematic work we do has not yet been done, and that it is useful to improve the field and its status in relation to quantitative research.

The literature on the “internal” aim of improving, or at least clarifying qualitative research is large, and we do not claim to be the first to notice the vagueness of the term “qualitative” (Strauss and Corbin 1998 ). Also, others have noted that there is no single definition of it (Long and Godfrey 2004 :182), that there are many different views on qualitative research (Denzin and Lincoln 2003 :11; Jovanović 2011 :3), and that more generally, we need to define its meaning (Best 2004 :54). Strauss and Corbin ( 1998 ), for example, as well as Nelson et al. (1992:2 cited in Denzin and Lincoln 2003 :11), and Flick ( 2007 :ix–x), have recognized that the term is problematic: “Actually, the term ‘qualitative research’ is confusing because it can mean different things to different people” (Strauss and Corbin 1998 :10–11). Hammersley has discussed the possibility of addressing the problem, but states that “the task of providing an account of the distinctive features of qualitative research is far from straightforward” ( 2013 :2). This confusion, as he has recently further argued (Hammersley 2018 ), is also salient in relation to ethnography where different philosophical and methodological approaches lead to a lack of agreement about what it means.

Others (e.g. Hammersley 2018 ; Fine and Hancock 2017 ) have also identified the treat to qualitative research that comes from external forces, seen from the point of view of “qualitative research.” This threat can be further divided into that which comes from inside academia, such as the critique voiced by “quantitative research” and outside of academia, including, for example, New Public Management. Hammersley ( 2018 ), zooming in on one type of qualitative research, ethnography, has argued that it is under treat. Similarly to Fine ( 2003 ), and before him Gans ( 1999 ), he writes that ethnography’ has acquired a range of meanings, and comes in many different versions, these often reflecting sharply divergent epistemological orientations. And already more than twenty years ago while reviewing Denzin and Lincoln’ s Handbook of Qualitative Methods Fine argued:

While this increasing centrality [of qualitative research] might lead one to believe that consensual standards have developed, this belief would be misleading. As the methodology becomes more widely accepted, querulous challengers have raised fundamental questions that collectively have undercut the traditional models of how qualitative research is to be fashioned and presented (1995:417).

According to Hammersley, there are today “serious treats to the practice of ethnographic work, on almost any definition” ( 2018 :1). He lists five external treats: (1) that social research must be accountable and able to show its impact on society; (2) the current emphasis on “big data” and the emphasis on quantitative data and evidence; (3) the labor market pressure in academia that leaves less time for fieldwork (see also Fine and Hancock 2017 ); (4) problems of access to fields; and (5) the increased ethical scrutiny of projects, to which ethnography is particularly exposed. Hammersley discusses some more or less insufficient existing definitions of ethnography.

The current situation, as Hammersley and others note—and in relation not only to ethnography but also qualitative research in general, and as our empirical study shows—is not just unsatisfactory, it may even be harmful for the entire field of qualitative research, and does not help social science at large. We suggest that the lack of clarity of qualitative research is a real problem that must be addressed.

Towards a Definition of Qualitative Research

Seen in an historical light, what is today called qualitative, or sometimes ethnographic, interpretative research – or a number of other terms – has more or less always existed. At the time the founders of sociology – Simmel, Weber, Durkheim and, before them, Marx – were writing, and during the era of the Methodenstreit (“dispute about methods”) in which the German historical school emphasized scientific methods (cf. Swedberg 1990 ), we can at least speak of qualitative forerunners.

Perhaps the most extended discussion of what later became known as qualitative methods in a classic work is Bronisław Malinowski’s ( 1922 ) Argonauts in the Western Pacific , although even this study does not explicitly address the meaning of “qualitative.” In Weber’s ([1921–-22] 1978) work we find a tension between scientific explanations that are based on observation and quantification and interpretative research (see also Lazarsfeld and Barton 1982 ).

If we look through major sociology journals like the American Sociological Review , American Journal of Sociology , or Social Forces we will not find the term qualitative sociology before the 1970s. And certainly before then much of what we consider qualitative classics in sociology, like Becker’ study ( 1963 ), had already been produced. Indeed, the Chicago School often combined qualitative and quantitative data within the same study (Fine 1995 ). Our point being that before a disciplinary self-awareness the term quantitative preceded qualitative, and the articulation of the former was a political move to claim scientific status (Denzin and Lincoln 2005 ). In the US the World War II seem to have sparked a critique of sociological work, including “qualitative work,” that did not follow the scientific canon (Rawls 2018 ), which was underpinned by a scientifically oriented and value free philosophy of science. As a result the attempts and practice of integrating qualitative and quantitative sociology at Chicago lost ground to sociology that was more oriented to surveys and quantitative work at Columbia under Merton-Lazarsfeld. The quantitative tradition was also able to present textbooks (Lundberg 1951 ) that facilitated the use this approach and its “methods.” The practices of the qualitative tradition, by and large, remained tacit or was part of the mentoring transferred from the renowned masters to their students.

This glimpse into history leads us back to the lack of a coherent account condensed in a definition of qualitative research. Many of the attempts to define the term do not meet the requirements of a proper definition: A definition should be clear, avoid tautology, demarcate its domain in relation to the environment, and ideally only use words in its definiens that themselves are not in need of definition (Hempel 1966 ). A definition can enhance precision and thus clarity by identifying the core of the phenomenon. Preferably, a definition should be short. The typical definition we have found, however, is an ostensive definition, which indicates what qualitative research is about without informing us about what it actually is :

Qualitative research is multimethod in focus, involving an interpretative, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Qualitative research involves the studied use and collection of a variety of empirical materials – case study, personal experience, introspective, life story, interview, observational, historical, interactional, and visual texts – that describe routine and problematic moments and meanings in individuals’ lives. (Denzin and Lincoln 2005 :2)

Flick claims that the label “qualitative research” is indeed used as an umbrella for a number of approaches ( 2007 :2–4; 2002 :6), and it is not difficult to identify research fitting this designation. Moreover, whatever it is, it has grown dramatically over the past five decades. In addition, courses have been developed, methods have flourished, arguments about its future have been advanced (for example, Denzin and Lincoln 1994) and criticized (for example, Snow and Morrill 1995 ), and dedicated journals and books have mushroomed. Most social scientists have a clear idea of research and how it differs from journalism, politics and other activities. But the question of what is qualitative in qualitative research is either eluded or eschewed.

We maintain that this lacuna hinders systematic knowledge production based on qualitative research. Paul Lazarsfeld noted the lack of “codification” as early as 1955 when he reviewed 100 qualitative studies in order to offer a codification of the practices (Lazarsfeld and Barton 1982 :239). Since then many texts on “qualitative research” and its methods have been published, including recent attempts (Goertz and Mahoney 2012 ) similar to Lazarsfeld’s. These studies have tried to extract what is qualitative by looking at the large number of empirical “qualitative” studies. Our novel strategy complements these endeavors by taking another approach and looking at the attempts to codify these practices in the form of a definition, as well as to a minor extent take Becker’s study as an exemplar of what qualitative researchers actually do, and what the characteristic of being ‘qualitative’ denotes and implies. We claim that qualitative researchers, if there is such a thing as “qualitative research,” should be able to codify their practices in a condensed, yet general way expressed in language.

Lingering problems of “generalizability” and “how many cases do I need” (Small 2009 ) are blocking advancement – in this line of work qualitative approaches are said to differ considerably from quantitative ones, while some of the former unsuccessfully mimic principles related to the latter (Small 2009 ). Additionally, quantitative researchers sometimes unfairly criticize the first based on their own quality criteria. Scholars like Goertz and Mahoney ( 2012 ) have successfully focused on the different norms and practices beyond what they argue are essentially two different cultures: those working with either qualitative or quantitative methods. Instead, similarly to Becker ( 2017 ) who has recently questioned the usefulness of the distinction between qualitative and quantitative research, we focus on similarities.

The current situation also impedes both students and researchers in focusing their studies and understanding each other’s work (Lazarsfeld and Barton 1982 :239). A third consequence is providing an opening for critiques by scholars operating within different traditions (Valsiner 2000 :101). A fourth issue is that the “implicit use of methods in qualitative research makes the field far less standardized than the quantitative paradigm” (Goertz and Mahoney 2012 :9). Relatedly, the National Science Foundation in the US organized two workshops in 2004 and 2005 to address the scientific foundations of qualitative research involving strategies to improve it and to develop standards of evaluation in qualitative research. However, a specific focus on its distinguishing feature of being “qualitative” while being implicitly acknowledged, was discussed only briefly (for example, Best 2004 ).

In 2014 a theme issue was published in this journal on “Methods, Materials, and Meanings: Designing Cultural Analysis,” discussing central issues in (cultural) qualitative research (Berezin 2014 ; Biernacki 2014 ; Glaeser 2014 ; Lamont and Swidler 2014 ; Spillman 2014). We agree with many of the arguments put forward, such as the risk of methodological tribalism, and that we should not waste energy on debating methods separated from research questions. Nonetheless, a clarification of the relation to what is called “quantitative research” is of outmost importance to avoid misunderstandings and misguided debates between “qualitative” and “quantitative” researchers. Our strategy means that researchers, “qualitative” or “quantitative” they may be, in their actual practice may combine qualitative work and quantitative work.

In this article we accomplish three tasks. First, we systematically survey the literature for meanings of qualitative research by looking at how researchers have defined it. Drawing upon existing knowledge we find that the different meanings and ideas of qualitative research are not yet coherently integrated into one satisfactory definition. Next, we advance our contribution by offering a definition of qualitative research and illustrate its meaning and use partially by expanding on the brief example introduced earlier related to Becker’s work ( 1963 ). We offer a systematic analysis of central themes of what researchers consider to be the core of “qualitative,” regardless of style of work. These themes – which we summarize in terms of four keywords: distinction, process, closeness, improved understanding – constitute part of our literature review, in which each one appears, sometimes with others, but never all in the same definition. They serve as the foundation of our contribution. Our categories are overlapping. Their use is primarily to organize the large amount of definitions we have identified and analyzed, and not necessarily to draw a clear distinction between them. Finally, we continue the elaboration discussed above on the advantages of a clear definition of qualitative research.

In a hermeneutic fashion we propose that there is something meaningful that deserves to be labelled “qualitative research” (Gadamer 1990 ). To approach the question “What is qualitative in qualitative research?” we have surveyed the literature. In conducting our survey we first traced the word’s etymology in dictionaries, encyclopedias, handbooks of the social sciences and of methods and textbooks, mainly in English, which is common to methodology courses. It should be noted that we have zoomed in on sociology and its literature. This discipline has been the site of the largest debate and development of methods that can be called “qualitative,” which suggests that this field should be examined in great detail.

In an ideal situation we should expect that one good definition, or at least some common ideas, would have emerged over the years. This common core of qualitative research should be so accepted that it would appear in at least some textbooks. Since this is not what we found, we decided to pursue an inductive approach to capture maximal variation in the field of qualitative research; we searched in a selection of handbooks, textbooks, book chapters, and books, to which we added the analysis of journal articles. Our sample comprises a total of 89 references.

In practice we focused on the discipline that has had a clear discussion of methods, namely sociology. We also conducted a broad search in the JSTOR database to identify scholarly sociology articles published between 1998 and 2017 in English with a focus on defining or explaining qualitative research. We specifically zoom in on this time frame because we would have expect that this more mature period would have produced clear discussions on the meaning of qualitative research. To find these articles we combined a number of keywords to search the content and/or the title: qualitative (which was always included), definition, empirical, research, methodology, studies, fieldwork, interview and observation .

As a second phase of our research we searched within nine major sociological journals ( American Journal of Sociology , Sociological Theory , American Sociological Review , Contemporary Sociology , Sociological Forum , Sociological Theory , Qualitative Research , Qualitative Sociology and Qualitative Sociology Review ) for articles also published during the past 19 years (1998–2017) that had the term “qualitative” in the title and attempted to define qualitative research.

Lastly we picked two additional journals, Qualitative Research and Qualitative Sociology , in which we could expect to find texts addressing the notion of “qualitative.” From Qualitative Research we chose Volume 14, Issue 6, December 2014, and from Qualitative Sociology we chose Volume 36, Issue 2, June 2017. Within each of these we selected the first article; then we picked the second article of three prior issues. Again we went back another three issues and investigated article number three. Finally we went back another three issues and perused article number four. This selection criteria was used to get a manageable sample for the analysis.

The coding process of the 89 references we gathered in our selected review began soon after the first round of material was gathered, and we reduced the complexity created by our maximum variation sampling (Snow and Anderson 1993 :22) to four different categories within which questions on the nature and properties of qualitative research were discussed. We call them: Qualitative and Quantitative Research, Qualitative Research, Fieldwork, and Grounded Theory. This – which may appear as an illogical grouping – merely reflects the “context” in which the matter of “qualitative” is discussed. If the selection process of the material – books and articles – was informed by pre-knowledge, we used an inductive strategy to code the material. When studying our material, we identified four central notions related to “qualitative” that appear in various combinations in the literature which indicate what is the core of qualitative research. We have labeled them: “distinctions”, “process,” “closeness,” and “improved understanding.” During the research process the categories and notions were improved, refined, changed, and reordered. The coding ended when a sense of saturation in the material arose. In the presentation below all quotations and references come from our empirical material of texts on qualitative research.

Analysis – What is Qualitative Research?

In this section we describe the four categories we identified in the coding, how they differently discuss qualitative research, as well as their overall content. Some salient quotations are selected to represent the type of text sorted under each of the four categories. What we present are examples from the literature.

Qualitative and Quantitative

This analytic category comprises quotations comparing qualitative and quantitative research, a distinction that is frequently used (Brown 2010 :231); in effect this is a conceptual pair that structures the discussion and that may be associated with opposing interests. While the general goal of quantitative and qualitative research is the same – to understand the world better – their methodologies and focus in certain respects differ substantially (Becker 1966 :55). Quantity refers to that property of something that can be determined by measurement. In a dictionary of Statistics and Methodology we find that “(a) When referring to *variables, ‘qualitative’ is another term for *categorical or *nominal. (b) When speaking of kinds of research, ‘qualitative’ refers to studies of subjects that are hard to quantify, such as art history. Qualitative research tends to be a residual category for almost any kind of non-quantitative research” (Stiles 1998:183). But it should be obvious that one could employ a quantitative approach when studying, for example, art history.

The same dictionary states that quantitative is “said of variables or research that can be handled numerically, usually (too sharply) contrasted with *qualitative variables and research” (Stiles 1998:184). From a qualitative perspective “quantitative research” is about numbers and counting, and from a quantitative perspective qualitative research is everything that is not about numbers. But this does not say much about what is “qualitative.” If we turn to encyclopedias we find that in the 1932 edition of the Encyclopedia of the Social Sciences there is no mention of “qualitative.” In the Encyclopedia from 1968 we can read:

Qualitative Analysis. For methods of obtaining, analyzing, and describing data, see [the various entries:] CONTENT ANALYSIS; COUNTED DATA; EVALUATION RESEARCH, FIELD WORK; GRAPHIC PRESENTATION; HISTORIOGRAPHY, especially the article on THE RHETORIC OF HISTORY; INTERVIEWING; OBSERVATION; PERSONALITY MEASUREMENT; PROJECTIVE METHODS; PSYCHOANALYSIS, article on EXPERIMENTAL METHODS; SURVEY ANALYSIS, TABULAR PRESENTATION; TYPOLOGIES. (Vol. 13:225)

Some, like Alford, divide researchers into methodologists or, in his words, “quantitative and qualitative specialists” (Alford 1998 :12). Qualitative research uses a variety of methods, such as intensive interviews or in-depth analysis of historical materials, and it is concerned with a comprehensive account of some event or unit (King et al. 1994 :4). Like quantitative research it can be utilized to study a variety of issues, but it tends to focus on meanings and motivations that underlie cultural symbols, personal experiences, phenomena and detailed understanding of processes in the social world. In short, qualitative research centers on understanding processes, experiences, and the meanings people assign to things (Kalof et al. 2008 :79).

Others simply say that qualitative methods are inherently unscientific (Jovanović 2011 :19). Hood, for instance, argues that words are intrinsically less precise than numbers, and that they are therefore more prone to subjective analysis, leading to biased results (Hood 2006 :219). Qualitative methodologies have raised concerns over the limitations of quantitative templates (Brady et al. 2004 :4). Scholars such as King et al. ( 1994 ), for instance, argue that non-statistical research can produce more reliable results if researchers pay attention to the rules of scientific inference commonly stated in quantitative research. Also, researchers such as Becker ( 1966 :59; 1970 :42–43) have asserted that, if conducted properly, qualitative research and in particular ethnographic field methods, can lead to more accurate results than quantitative studies, in particular, survey research and laboratory experiments.

Some researchers, such as Kalof, Dan, and Dietz ( 2008 :79) claim that the boundaries between the two approaches are becoming blurred, and Small ( 2009 ) argues that currently much qualitative research (especially in North America) tries unsuccessfully and unnecessarily to emulate quantitative standards. For others, qualitative research tends to be more humanistic and discursive (King et al. 1994 :4). Ragin ( 1994 ), and similarly also Becker, ( 1996 :53), Marchel and Owens ( 2007 :303) think that the main distinction between the two styles is overstated and does not rest on the simple dichotomy of “numbers versus words” (Ragin 1994 :xii). Some claim that quantitative data can be utilized to discover associations, but in order to unveil cause and effect a complex research design involving the use of qualitative approaches needs to be devised (Gilbert 2009 :35). Consequently, qualitative data are useful for understanding the nuances lying beyond those processes as they unfold (Gilbert 2009 :35). Others contend that qualitative research is particularly well suited both to identify causality and to uncover fine descriptive distinctions (Fine and Hallett 2014 ; Lichterman and Isaac Reed 2014 ; Katz 2015 ).

There are other ways to separate these two traditions, including normative statements about what qualitative research should be (that is, better or worse than quantitative approaches, concerned with scientific approaches to societal change or vice versa; Snow and Morrill 1995 ; Denzin and Lincoln 2005 ), or whether it should develop falsifiable statements; Best 2004 ).

We propose that quantitative research is largely concerned with pre-determined variables (Small 2008 ); the analysis concerns the relations between variables. These categories are primarily not questioned in the study, only their frequency or degree, or the correlations between them (cf. Franzosi 2016 ). If a researcher studies wage differences between women and men, he or she works with given categories: x number of men are compared with y number of women, with a certain wage attributed to each person. The idea is not to move beyond the given categories of wage, men and women; they are the starting point as well as the end point, and undergo no “qualitative change.” Qualitative research, in contrast, investigates relations between categories that are themselves subject to change in the research process. Returning to Becker’s study ( 1963 ), we see that he questioned pre-dispositional theories of deviant behavior working with pre-determined variables such as an individual’s combination of personal qualities or emotional problems. His take, in contrast, was to understand marijuana consumption by developing “variables” as part of the investigation. Thereby he presented new variables, or as we would say today, theoretical concepts, but which are grounded in the empirical material.

Qualitative Research

This category contains quotations that refer to descriptions of qualitative research without making comparisons with quantitative research. Researchers such as Denzin and Lincoln, who have written a series of influential handbooks on qualitative methods (1994; Denzin and Lincoln 2003 ; 2005 ), citing Nelson et al. (1992:4), argue that because qualitative research is “interdisciplinary, transdisciplinary, and sometimes counterdisciplinary” it is difficult to derive one single definition of it (Jovanović 2011 :3). According to them, in fact, “the field” is “many things at the same time,” involving contradictions, tensions over its focus, methods, and how to derive interpretations and findings ( 2003 : 11). Similarly, others, such as Flick ( 2007 :ix–x) contend that agreeing on an accepted definition has increasingly become problematic, and that qualitative research has possibly matured different identities. However, Best holds that “the proliferation of many sorts of activities under the label of qualitative sociology threatens to confuse our discussions” ( 2004 :54). Atkinson’s position is more definite: “the current state of qualitative research and research methods is confused” ( 2005 :3–4).

Qualitative research is about interpretation (Blumer 1969 ; Strauss and Corbin 1998 ; Denzin and Lincoln 2003 ), or Verstehen [understanding] (Frankfort-Nachmias and Nachmias 1996 ). It is “multi-method,” involving the collection and use of a variety of empirical materials (Denzin and Lincoln 1998; Silverman 2013 ) and approaches (Silverman 2005 ; Flick 2007 ). It focuses not only on the objective nature of behavior but also on its subjective meanings: individuals’ own accounts of their attitudes, motivations, behavior (McIntyre 2005 :127; Creswell 2009 ), events and situations (Bryman 1989) – what people say and do in specific places and institutions (Goodwin and Horowitz 2002 :35–36) in social and temporal contexts (Morrill and Fine 1997). For this reason, following Weber ([1921-22] 1978), it can be described as an interpretative science (McIntyre 2005 :127). But could quantitative research also be concerned with these questions? Also, as pointed out below, does all qualitative research focus on subjective meaning, as some scholars suggest?

Others also distinguish qualitative research by claiming that it collects data using a naturalistic approach (Denzin and Lincoln 2005 :2; Creswell 2009 ), focusing on the meaning actors ascribe to their actions. But again, does all qualitative research need to be collected in situ? And does qualitative research have to be inherently concerned with meaning? Flick ( 2007 ), referring to Denzin and Lincoln ( 2005 ), mentions conversation analysis as an example of qualitative research that is not concerned with the meanings people bring to a situation, but rather with the formal organization of talk. Still others, such as Ragin ( 1994 :85), note that qualitative research is often (especially early on in the project, we would add) less structured than other kinds of social research – a characteristic connected to its flexibility and that can lead both to potentially better, but also worse results. But is this not a feature of this type of research, rather than a defining description of its essence? Wouldn’t this comment also apply, albeit to varying degrees, to quantitative research?

In addition, Strauss ( 2003 ), along with others, such as Alvesson and Kärreman ( 2011 :10–76), argue that qualitative researchers struggle to capture and represent complex phenomena partially because they tend to collect a large amount of data. While his analysis is correct at some points – “It is necessary to do detailed, intensive, microscopic examination of the data in order to bring out the amazing complexity of what lies in, behind, and beyond those data” (Strauss 2003 :10) – much of his analysis concerns the supposed focus of qualitative research and its challenges, rather than exactly what it is about. But even in this instance we would make a weak case arguing that these are strictly the defining features of qualitative research. Some researchers seem to focus on the approach or the methods used, or even on the way material is analyzed. Several researchers stress the naturalistic assumption of investigating the world, suggesting that meaning and interpretation appear to be a core matter of qualitative research.

We can also see that in this category there is no consensus about specific qualitative methods nor about qualitative data. Many emphasize interpretation, but quantitative research, too, involves interpretation; the results of a regression analysis, for example, certainly have to be interpreted, and the form of meta-analysis that factor analysis provides indeed requires interpretation However, there is no interpretation of quantitative raw data, i.e., numbers in tables. One common thread is that qualitative researchers have to get to grips with their data in order to understand what is being studied in great detail, irrespective of the type of empirical material that is being analyzed. This observation is connected to the fact that qualitative researchers routinely make several adjustments of focus and research design as their studies progress, in many cases until the very end of the project (Kalof et al. 2008 ). If you, like Becker, do not start out with a detailed theory, adjustments such as the emergence and refinement of research questions will occur during the research process. We have thus found a number of useful reflections about qualitative research scattered across different sources, but none of them effectively describe the defining characteristics of this approach.

Although qualitative research does not appear to be defined in terms of a specific method, it is certainly common that fieldwork, i.e., research that entails that the researcher spends considerable time in the field that is studied and use the knowledge gained as data, is seen as emblematic of or even identical to qualitative research. But because we understand that fieldwork tends to focus primarily on the collection and analysis of qualitative data, we expected to find within it discussions on the meaning of “qualitative.” But, again, this was not the case.

Instead, we found material on the history of this approach (for example, Frankfort-Nachmias and Nachmias 1996 ; Atkinson et al. 2001), including how it has changed; for example, by adopting a more self-reflexive practice (Heyl 2001), as well as the different nomenclature that has been adopted, such as fieldwork, ethnography, qualitative research, naturalistic research, participant observation and so on (for example, Lofland et al. 2006 ; Gans 1999 ).

We retrieved definitions of ethnography, such as “the study of people acting in the natural courses of their daily lives,” involving a “resocialization of the researcher” (Emerson 1988 :1) through intense immersion in others’ social worlds (see also examples in Hammersley 2018 ). This may be accomplished by direct observation and also participation (Neuman 2007 :276), although others, such as Denzin ( 1970 :185), have long recognized other types of observation, including non-participant (“fly on the wall”). In this category we have also isolated claims and opposing views, arguing that this type of research is distinguished primarily by where it is conducted (natural settings) (Hughes 1971:496), and how it is carried out (a variety of methods are applied) or, for some most importantly, by involving an active, empathetic immersion in those being studied (Emerson 1988 :2). We also retrieved descriptions of the goals it attends in relation to how it is taught (understanding subjective meanings of the people studied, primarily develop theory, or contribute to social change) (see for example, Corte and Irwin 2017 ; Frankfort-Nachmias and Nachmias 1996 :281; Trier-Bieniek 2012 :639) by collecting the richest possible data (Lofland et al. 2006 ) to derive “thick descriptions” (Geertz 1973 ), and/or to aim at theoretical statements of general scope and applicability (for example, Emerson 1988 ; Fine 2003 ). We have identified guidelines on how to evaluate it (for example Becker 1996 ; Lamont 2004 ) and have retrieved instructions on how it should be conducted (for example, Lofland et al. 2006 ). For instance, analysis should take place while the data gathering unfolds (Emerson 1988 ; Hammersley and Atkinson 2007 ; Lofland et al. 2006 ), observations should be of long duration (Becker 1970 :54; Goffman 1989 ), and data should be of high quantity (Becker 1970 :52–53), as well as other questionable distinctions between fieldwork and other methods:

Field studies differ from other methods of research in that the researcher performs the task of selecting topics, decides what questions to ask, and forges interest in the course of the research itself . This is in sharp contrast to many ‘theory-driven’ and ‘hypothesis-testing’ methods. (Lofland and Lofland 1995 :5)

But could not, for example, a strictly interview-based study be carried out with the same amount of flexibility, such as sequential interviewing (for example, Small 2009 )? Once again, are quantitative approaches really as inflexible as some qualitative researchers think? Moreover, this category stresses the role of the actors’ meaning, which requires knowledge and close interaction with people, their practices and their lifeworld.

It is clear that field studies – which are seen by some as the “gold standard” of qualitative research – are nonetheless only one way of doing qualitative research. There are other methods, but it is not clear why some are more qualitative than others, or why they are better or worse. Fieldwork is characterized by interaction with the field (the material) and understanding of the phenomenon that is being studied. In Becker’s case, he had general experience from fields in which marihuana was used, based on which he did interviews with actual users in several fields.

Grounded Theory

Another major category we identified in our sample is Grounded Theory. We found descriptions of it most clearly in Glaser and Strauss’ ([1967] 2010 ) original articulation, Strauss and Corbin ( 1998 ) and Charmaz ( 2006 ), as well as many other accounts of what it is for: generating and testing theory (Strauss 2003 :xi). We identified explanations of how this task can be accomplished – such as through two main procedures: constant comparison and theoretical sampling (Emerson 1998:96), and how using it has helped researchers to “think differently” (for example, Strauss and Corbin 1998 :1). We also read descriptions of its main traits, what it entails and fosters – for instance, an exceptional flexibility, an inductive approach (Strauss and Corbin 1998 :31–33; 1990; Esterberg 2002 :7), an ability to step back and critically analyze situations, recognize tendencies towards bias, think abstractly and be open to criticism, enhance sensitivity towards the words and actions of respondents, and develop a sense of absorption and devotion to the research process (Strauss and Corbin 1998 :5–6). Accordingly, we identified discussions of the value of triangulating different methods (both using and not using grounded theory), including quantitative ones, and theories to achieve theoretical development (most comprehensively in Denzin 1970 ; Strauss and Corbin 1998 ; Timmermans and Tavory 2012 ). We have also located arguments about how its practice helps to systematize data collection, analysis and presentation of results (Glaser and Strauss [1967] 2010 :16).

Grounded theory offers a systematic approach which requires researchers to get close to the field; closeness is a requirement of identifying questions and developing new concepts or making further distinctions with regard to old concepts. In contrast to other qualitative approaches, grounded theory emphasizes the detailed coding process, and the numerous fine-tuned distinctions that the researcher makes during the process. Within this category, too, we could not find a satisfying discussion of the meaning of qualitative research.

Defining Qualitative Research

In sum, our analysis shows that some notions reappear in the discussion of qualitative research, such as understanding, interpretation, “getting close” and making distinctions. These notions capture aspects of what we think is “qualitative.” However, a comprehensive definition that is useful and that can further develop the field is lacking, and not even a clear picture of its essential elements appears. In other words no definition emerges from our data, and in our research process we have moved back and forth between our empirical data and the attempt to present a definition. Our concrete strategy, as stated above, is to relate qualitative and quantitative research, or more specifically, qualitative and quantitative work. We use an ideal-typical notion of quantitative research which relies on taken for granted and numbered variables. This means that the data consists of variables on different scales, such as ordinal, but frequently ratio and absolute scales, and the representation of the numbers to the variables, i.e. the justification of the assignment of numbers to object or phenomenon, are not questioned, though the validity may be questioned. In this section we return to the notion of quality and try to clarify it while presenting our contribution.

Broadly, research refers to the activity performed by people trained to obtain knowledge through systematic procedures. Notions such as “objectivity” and “reflexivity,” “systematic,” “theory,” “evidence” and “openness” are here taken for granted in any type of research. Next, building on our empirical analysis we explain the four notions that we have identified as central to qualitative work: distinctions, process, closeness, and improved understanding. In discussing them, ultimately in relation to one another, we make their meaning even more precise. Our idea, in short, is that only when these ideas that we present separately for analytic purposes are brought together can we speak of qualitative research.

Distinctions

We believe that the possibility of making new distinctions is one the defining characteristics of qualitative research. It clearly sets it apart from quantitative analysis which works with taken-for-granted variables, albeit as mentioned, meta-analyses, for example, factor analysis may result in new variables. “Quality” refers essentially to distinctions, as already pointed out by Aristotle. He discusses the term “qualitative” commenting: “By a quality I mean that in virtue of which things are said to be qualified somehow” (Aristotle 1984:14). Quality is about what something is or has, which means that the distinction from its environment is crucial. We see qualitative research as a process in which significant new distinctions are made to the scholarly community; to make distinctions is a key aspect of obtaining new knowledge; a point, as we will see, that also has implications for “quantitative research.” The notion of being “significant” is paramount. New distinctions by themselves are not enough; just adding concepts only increases complexity without furthering our knowledge. The significance of new distinctions is judged against the communal knowledge of the research community. To enable this discussion and judgements central elements of rational discussion are required (cf. Habermas [1981] 1987 ; Davidsson [ 1988 ] 2001) to identify what is new and relevant scientific knowledge. Relatedly, Ragin alludes to the idea of new and useful knowledge at a more concrete level: “Qualitative methods are appropriate for in-depth examination of cases because they aid the identification of key features of cases. Most qualitative methods enhance data” (1994:79). When Becker ( 1963 ) studied deviant behavior and investigated how people became marihuana smokers, he made distinctions between the ways in which people learned how to smoke. This is a classic example of how the strategy of “getting close” to the material, for example the text, people or pictures that are subject to analysis, may enable researchers to obtain deeper insight and new knowledge by making distinctions – in this instance on the initial notion of learning how to smoke. Others have stressed the making of distinctions in relation to coding or theorizing. Emerson et al. ( 1995 ), for example, hold that “qualitative coding is a way of opening up avenues of inquiry,” meaning that the researcher identifies and develops concepts and analytic insights through close examination of and reflection on data (Emerson et al. 1995 :151). Goodwin and Horowitz highlight making distinctions in relation to theory-building writing: “Close engagement with their cases typically requires qualitative researchers to adapt existing theories or to make new conceptual distinctions or theoretical arguments to accommodate new data” ( 2002 : 37). In the ideal-typical quantitative research only existing and so to speak, given, variables would be used. If this is the case no new distinction are made. But, would not also many “quantitative” researchers make new distinctions?

Process does not merely suggest that research takes time. It mainly implies that qualitative new knowledge results from a process that involves several phases, and above all iteration. Qualitative research is about oscillation between theory and evidence, analysis and generating material, between first- and second -order constructs (Schütz 1962 :59), between getting in contact with something, finding sources, becoming deeply familiar with a topic, and then distilling and communicating some of its essential features. The main point is that the categories that the researcher uses, and perhaps takes for granted at the beginning of the research process, usually undergo qualitative changes resulting from what is found. Becker describes how he tested hypotheses and let the jargon of the users develop into theoretical concepts. This happens over time while the study is being conducted, exemplifying what we mean by process.

In the research process, a pilot-study may be used to get a first glance of, for example, the field, how to approach it, and what methods can be used, after which the method and theory are chosen or refined before the main study begins. Thus, the empirical material is often central from the start of the project and frequently leads to adjustments by the researcher. Likewise, during the main study categories are not fixed; the empirical material is seen in light of the theory used, but it is also given the opportunity to kick back, thereby resisting attempts to apply theoretical straightjackets (Becker 1970 :43). In this process, coding and analysis are interwoven, and thus are often important steps for getting closer to the phenomenon and deciding what to focus on next. Becker began his research by interviewing musicians close to him, then asking them to refer him to other musicians, and later on doubling his original sample of about 25 to include individuals in other professions (Becker 1973:46). Additionally, he made use of some participant observation, documents, and interviews with opiate users made available to him by colleagues. As his inductive theory of deviance evolved, Becker expanded his sample in order to fine tune it, and test the accuracy and generality of his hypotheses. In addition, he introduced a negative case and discussed the null hypothesis ( 1963 :44). His phasic career model is thus based on a research design that embraces processual work. Typically, process means to move between “theory” and “material” but also to deal with negative cases, and Becker ( 1998 ) describes how discovering these negative cases impacted his research design and ultimately its findings.

Obviously, all research is process-oriented to some degree. The point is that the ideal-typical quantitative process does not imply change of the data, and iteration between data, evidence, hypotheses, empirical work, and theory. The data, quantified variables, are, in most cases fixed. Merging of data, which of course can be done in a quantitative research process, does not mean new data. New hypotheses are frequently tested, but the “raw data is often the “the same.” Obviously, over time new datasets are made available and put into use.

Another characteristic that is emphasized in our sample is that qualitative researchers – and in particular ethnographers – can, or as Goffman put it, ought to ( 1989 ), get closer to the phenomenon being studied and their data than quantitative researchers (for example, Silverman 2009 :85). Put differently, essentially because of their methods qualitative researchers get into direct close contact with those being investigated and/or the material, such as texts, being analyzed. Becker started out his interview study, as we noted, by talking to those he knew in the field of music to get closer to the phenomenon he was studying. By conducting interviews he got even closer. Had he done more observations, he would undoubtedly have got even closer to the field.

Additionally, ethnographers’ design enables researchers to follow the field over time, and the research they do is almost by definition longitudinal, though the time in the field is studied obviously differs between studies. The general characteristic of closeness over time maximizes the chances of unexpected events, new data (related, for example, to archival research as additional sources, and for ethnography for situations not necessarily previously thought of as instrumental – what Mannay and Morgan ( 2015 ) term the “waiting field”), serendipity (Merton and Barber 2004 ; Åkerström 2013 ), and possibly reactivity, as well as the opportunity to observe disrupted patterns that translate into exemplars of negative cases. Two classic examples of this are Becker’s finding of what medical students call “crocks” (Becker et al. 1961 :317), and Geertz’s ( 1973 ) study of “deep play” in Balinese society.

By getting and staying so close to their data – be it pictures, text or humans interacting (Becker was himself a musician) – for a long time, as the research progressively focuses, qualitative researchers are prompted to continually test their hunches, presuppositions and hypotheses. They test them against a reality that often (but certainly not always), and practically, as well as metaphorically, talks back, whether by validating them, or disqualifying their premises – correctly, as well as incorrectly (Fine 2003 ; Becker 1970 ). This testing nonetheless often leads to new directions for the research. Becker, for example, says that he was initially reading psychological theories, but when facing the data he develops a theory that looks at, you may say, everything but psychological dispositions to explain the use of marihuana. Especially researchers involved with ethnographic methods have a fairly unique opportunity to dig up and then test (in a circular, continuous and temporal way) new research questions and findings as the research progresses, and thereby to derive previously unimagined and uncharted distinctions by getting closer to the phenomenon under study.

Let us stress that getting close is by no means restricted to ethnography. The notion of hermeneutic circle and hermeneutics as a general way of understanding implies that we must get close to the details in order to get the big picture. This also means that qualitative researchers can literally also make use of details of pictures as evidence (cf. Harper 2002). Thus, researchers may get closer both when generating the material or when analyzing it.

Quantitative research, we maintain, in the ideal-typical representation cannot get closer to the data. The data is essentially numbers in tables making up the variables (Franzosi 2016 :138). The data may originally have been “qualitative,” but once reduced to numbers there can only be a type of “hermeneutics” about what the number may stand for. The numbers themselves, however, are non-ambiguous. Thus, in quantitative research, interpretation, if done, is not about the data itself—the numbers—but what the numbers stand for. It follows that the interpretation is essentially done in a more “speculative” mode without direct empirical evidence (cf. Becker 2017 ).

Improved Understanding

While distinction, process and getting closer refer to the qualitative work of the researcher, improved understanding refers to its conditions and outcome of this work. Understanding cuts deeper than explanation, which to some may mean a causally verified correlation between variables. The notion of explanation presupposes the notion of understanding since explanation does not include an idea of how knowledge is gained (Manicas 2006 : 15). Understanding, we argue, is the core concept of what we call the outcome of the process when research has made use of all the other elements that were integrated in the research. Understanding, then, has a special status in qualitative research since it refers both to the conditions of knowledge and the outcome of the process. Understanding can to some extent be seen as the condition of explanation and occurs in a process of interpretation, which naturally refers to meaning (Gadamer 1990 ). It is fundamentally connected to knowing, and to the knowing of how to do things (Heidegger [1927] 2001 ). Conceptually the term hermeneutics is used to account for this process. Heidegger ties hermeneutics to human being and not possible to separate from the understanding of being ( 1988 ). Here we use it in a broader sense, and more connected to method in general (cf. Seiffert 1992 ). The abovementioned aspects – for example, “objectivity” and “reflexivity” – of the approach are conditions of scientific understanding. Understanding is the result of a circular process and means that the parts are understood in light of the whole, and vice versa. Understanding presupposes pre-understanding, or in other words, some knowledge of the phenomenon studied. The pre-understanding, even in the form of prejudices, are in qualitative research process, which we see as iterative, questioned, which gradually or suddenly change due to the iteration of data, evidence and concepts. However, qualitative research generates understanding in the iterative process when the researcher gets closer to the data, e.g., by going back and forth between field and analysis in a process that generates new data that changes the evidence, and, ultimately, the findings. Questioning, to ask questions, and put what one assumes—prejudices and presumption—in question, is central to understand something (Heidegger [1927] 2001 ; Gadamer 1990 :368–384). We propose that this iterative process in which the process of understanding occurs is characteristic of qualitative research.

Improved understanding means that we obtain scientific knowledge of something that we as a scholarly community did not know before, or that we get to know something better. It means that we understand more about how parts are related to one another, and to other things we already understand (see also Fine and Hallett 2014 ). Understanding is an important condition for qualitative research. It is not enough to identify correlations, make distinctions, and work in a process in which one gets close to the field or phenomena. Understanding is accomplished when the elements are integrated in an iterative process.

It is, moreover, possible to understand many things, and researchers, just like children, may come to understand new things every day as they engage with the world. This subjective condition of understanding – namely, that a person gains a better understanding of something –is easily met. To be qualified as “scientific,” the understanding must be general and useful to many; it must be public. But even this generally accessible understanding is not enough in order to speak of “scientific understanding.” Though we as a collective can increase understanding of everything in virtually all potential directions as a result also of qualitative work, we refrain from this “objective” way of understanding, which has no means of discriminating between what we gain in understanding. Scientific understanding means that it is deemed relevant from the scientific horizon (compare Schütz 1962 : 35–38, 46, 63), and that it rests on the pre-understanding that the scientists have and must have in order to understand. In other words, the understanding gained must be deemed useful by other researchers, so that they can build on it. We thus see understanding from a pragmatic, rather than a subjective or objective perspective. Improved understanding is related to the question(s) at hand. Understanding, in order to represent an improvement, must be an improvement in relation to the existing body of knowledge of the scientific community (James [ 1907 ] 1955). Scientific understanding is, by definition, collective, as expressed in Weber’s famous note on objectivity, namely that scientific work aims at truths “which … can claim, even for a Chinese, the validity appropriate to an empirical analysis” ([1904] 1949 :59). By qualifying “improved understanding” we argue that it is a general defining characteristic of qualitative research. Becker‘s ( 1966 ) study and other research of deviant behavior increased our understanding of the social learning processes of how individuals start a behavior. And it also added new knowledge about the labeling of deviant behavior as a social process. Few studies, of course, make the same large contribution as Becker’s, but are nonetheless qualitative research.

Understanding in the phenomenological sense, which is a hallmark of qualitative research, we argue, requires meaning and this meaning is derived from the context, and above all the data being analyzed. The ideal-typical quantitative research operates with given variables with different numbers. This type of material is not enough to establish meaning at the level that truly justifies understanding. In other words, many social science explanations offer ideas about correlations or even causal relations, but this does not mean that the meaning at the level of the data analyzed, is understood. This leads us to say that there are indeed many explanations that meet the criteria of understanding, for example the explanation of how one becomes a marihuana smoker presented by Becker. However, we may also understand a phenomenon without explaining it, and we may have potential explanations, or better correlations, that are not really understood.

We may speak more generally of quantitative research and its data to clarify what we see as an important distinction. The “raw data” that quantitative research—as an idealtypical activity, refers to is not available for further analysis; the numbers, once created, are not to be questioned (Franzosi 2016 : 138). If the researcher is to do “more” or “change” something, this will be done by conjectures based on theoretical knowledge or based on the researcher’s lifeworld. Both qualitative and quantitative research is based on the lifeworld, and all researchers use prejudices and pre-understanding in the research process. This idea is present in the works of Heidegger ( 2001 ) and Heisenberg (cited in Franzosi 2010 :619). Qualitative research, as we argued, involves the interaction and questioning of concepts (theory), data, and evidence.

Ragin ( 2004 :22) points out that “a good definition of qualitative research should be inclusive and should emphasize its key strengths and features, not what it lacks (for example, the use of sophisticated quantitative techniques).” We define qualitative research as an iterative process in which improved understanding to the scientific community is achieved by making new significant distinctions resulting from getting closer to the phenomenon studied. Qualitative research, as defined here, is consequently a combination of two criteria: (i) how to do things –namely, generating and analyzing empirical material, in an iterative process in which one gets closer by making distinctions, and (ii) the outcome –improved understanding novel to the scholarly community. Is our definition applicable to our own study? In this study we have closely read the empirical material that we generated, and the novel distinction of the notion “qualitative research” is the outcome of an iterative process in which both deduction and induction were involved, in which we identified the categories that we analyzed. We thus claim to meet the first criteria, “how to do things.” The second criteria cannot be judged but in a partial way by us, namely that the “outcome” —in concrete form the definition-improves our understanding to others in the scientific community.

We have defined qualitative research, or qualitative scientific work, in relation to quantitative scientific work. Given this definition, qualitative research is about questioning the pre-given (taken for granted) variables, but it is thus also about making new distinctions of any type of phenomenon, for example, by coining new concepts, including the identification of new variables. This process, as we have discussed, is carried out in relation to empirical material, previous research, and thus in relation to theory. Theory and previous research cannot be escaped or bracketed. According to hermeneutic principles all scientific work is grounded in the lifeworld, and as social scientists we can thus never fully bracket our pre-understanding.

We have proposed that quantitative research, as an idealtype, is concerned with pre-determined variables (Small 2008 ). Variables are epistemically fixed, but can vary in terms of dimensions, such as frequency or number. Age is an example; as a variable it can take on different numbers. In relation to quantitative research, qualitative research does not reduce its material to number and variables. If this is done the process of comes to a halt, the researcher gets more distanced from her data, and it makes it no longer possible to make new distinctions that increase our understanding. We have above discussed the components of our definition in relation to quantitative research. Our conclusion is that in the research that is called quantitative there are frequent and necessary qualitative elements.

Further, comparative empirical research on researchers primarily working with ”quantitative” approaches and those working with ”qualitative” approaches, we propose, would perhaps show that there are many similarities in practices of these two approaches. This is not to deny dissimilarities, or the different epistemic and ontic presuppositions that may be more or less strongly associated with the two different strands (see Goertz and Mahoney 2012 ). Our point is nonetheless that prejudices and preconceptions about researchers are unproductive, and that as other researchers have argued, differences may be exaggerated (e.g., Becker 1996 : 53, 2017 ; Marchel and Owens 2007 :303; Ragin 1994 ), and that a qualitative dimension is present in both kinds of work.

Several things follow from our findings. The most important result is the relation to quantitative research. In our analysis we have separated qualitative research from quantitative research. The point is not to label individual researchers, methods, projects, or works as either “quantitative” or “qualitative.” By analyzing, i.e., taking apart, the notions of quantitative and qualitative, we hope to have shown the elements of qualitative research. Our definition captures the elements, and how they, when combined in practice, generate understanding. As many of the quotations we have used suggest, one conclusion of our study holds that qualitative approaches are not inherently connected with a specific method. Put differently, none of the methods that are frequently labelled “qualitative,” such as interviews or participant observation, are inherently “qualitative.” What matters, given our definition, is whether one works qualitatively or quantitatively in the research process, until the results are produced. Consequently, our analysis also suggests that those researchers working with what in the literature and in jargon is often called “quantitative research” are almost bound to make use of what we have identified as qualitative elements in any research project. Our findings also suggest that many” quantitative” researchers, at least to some extent, are engaged with qualitative work, such as when research questions are developed, variables are constructed and combined, and hypotheses are formulated. Furthermore, a research project may hover between “qualitative” and “quantitative” or start out as “qualitative” and later move into a “quantitative” (a distinct strategy that is not similar to “mixed methods” or just simply combining induction and deduction). More generally speaking, the categories of “qualitative” and “quantitative,” unfortunately, often cover up practices, and it may lead to “camps” of researchers opposing one another. For example, regardless of the researcher is primarily oriented to “quantitative” or “qualitative” research, the role of theory is neglected (cf. Swedberg 2017 ). Our results open up for an interaction not characterized by differences, but by different emphasis, and similarities.

Let us take two examples to briefly indicate how qualitative elements can fruitfully be combined with quantitative. Franzosi ( 2010 ) has discussed the relations between quantitative and qualitative approaches, and more specifically the relation between words and numbers. He analyzes texts and argues that scientific meaning cannot be reduced to numbers. Put differently, the meaning of the numbers is to be understood by what is taken for granted, and what is part of the lifeworld (Schütz 1962 ). Franzosi shows how one can go about using qualitative and quantitative methods and data to address scientific questions analyzing violence in Italy at the time when fascism was rising (1919–1922). Aspers ( 2006 ) studied the meaning of fashion photographers. He uses an empirical phenomenological approach, and establishes meaning at the level of actors. In a second step this meaning, and the different ideal-typical photographers constructed as a result of participant observation and interviews, are tested using quantitative data from a database; in the first phase to verify the different ideal-types, in the second phase to use these types to establish new knowledge about the types. In both of these cases—and more examples can be found—authors move from qualitative data and try to keep the meaning established when using the quantitative data.

A second main result of our study is that a definition, and we provided one, offers a way for research to clarify, and even evaluate, what is done. Hence, our definition can guide researchers and students, informing them on how to think about concrete research problems they face, and to show what it means to get closer in a process in which new distinctions are made. The definition can also be used to evaluate the results, given that it is a standard of evaluation (cf. Hammersley 2007 ), to see whether new distinctions are made and whether this improves our understanding of what is researched, in addition to the evaluation of how the research was conducted. By making what is qualitative research explicit it becomes easier to communicate findings, and it is thereby much harder to fly under the radar with substandard research since there are standards of evaluation which make it easier to separate “good” from “not so good” qualitative research.

To conclude, our analysis, which ends with a definition of qualitative research can thus both address the “internal” issues of what is qualitative research, and the “external” critiques that make it harder to do qualitative research, to which both pressure from quantitative methods and general changes in society contribute.

Acknowledgements

Financial Support for this research is given by the European Research Council, CEV (263699). The authors are grateful to Susann Krieglsteiner for assistance in collecting the data. The paper has benefitted from the many useful comments by the three reviewers and the editor, comments by members of the Uppsala Laboratory of Economic Sociology, as well as Jukka Gronow, Sebastian Kohl, Marcin Serafin, Richard Swedberg, Anders Vassenden and Turid Rødne.

Biographies

is professor of sociology at the Department of Sociology, Uppsala University and Universität St. Gallen. His main focus is economic sociology, and in particular, markets. He has published numerous articles and books, including Orderly Fashion (Princeton University Press 2010), Markets (Polity Press 2011) and Re-Imagining Economic Sociology (edited with N. Dodd, Oxford University Press 2015). His book Ethnographic Methods (in Swedish) has already gone through several editions.

is associate professor of sociology at the Department of Media and Social Sciences, University of Stavanger. His research has been published in journals such as Social Psychology Quarterly, Sociological Theory, Teaching Sociology, and Music and Arts in Action. As an ethnographer he is working on a book on he social world of big-wave surfing.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Patrik Aspers, Email: [email protected] .

Ugo Corte, Email: [email protected] .

• Åkerström M. Curiosity and serendipity in qualitative research. Qualitative Sociology Review. 2013; 9 (2):10–18. [ Google Scholar ]
• Alford, Robert R. 1998. The craft of inquiry. Theories, methods, evidence . Oxford: Oxford University Press.
• Alvesson M, Kärreman D. Qualitative research and theory development . Mystery as method . London: SAGE Publications; 2011. [ Google Scholar ]
• Aspers, Patrik. 2006. Markets in Fashion, A Phenomenological Approach. London Routledge.
• Atkinson P. Qualitative research. Unity and diversity. Forum: Qualitative Social Research. 2005; 6 (3):1–15. [ Google Scholar ]
• Becker HS. Outsiders. Studies in the sociology of deviance . New York: The Free Press; 1963. [ Google Scholar ]
• Becker HS. Whose side are we on? Social Problems. 1966; 14 (3):239–247. [ Google Scholar ]
• Becker HS. Sociological work. Method and substance. New Brunswick: Transaction Books; 1970. [ Google Scholar ]
• Becker HS. The epistemology of qualitative research. In: Richard J, Anne C, Shweder RA, editors. Ethnography and human development. Context and meaning in social inquiry. Chicago: University of Chicago Press; 1996. pp. 53–71. [ Google Scholar ]
• Becker HS. Tricks of the trade. How to think about your research while you're doing it. Chicago: University of Chicago Press; 1998. [ Google Scholar ]
• Becker, Howard S. 2017. Evidence . Chigaco: University of Chicago Press.
• Becker H, Geer B, Hughes E, Strauss A. Boys in White, student culture in medical school. New Brunswick: Transaction Publishers; 1961. [ Google Scholar ]
• Berezin M. How do we know what we mean? Epistemological dilemmas in cultural sociology. Qualitative Sociology. 2014; 37 (2):141–151. [ Google Scholar ]
• Best, Joel. 2004. Defining qualitative research. In Workshop on Scientific Foundations of Qualitative Research , eds . Charles, Ragin, Joanne, Nagel, and Patricia White, 53-54. http://www.nsf.gov/pubs/2004/nsf04219/nsf04219.pdf .
• Biernacki R. Humanist interpretation versus coding text samples. Qualitative Sociology. 2014; 37 (2):173–188. [ Google Scholar ]
• Blumer H. Symbolic interactionism: Perspective and method. Berkeley: University of California Press; 1969. [ Google Scholar ]
• Brady H, Collier D, Seawright J. Refocusing the discussion of methodology. In: Henry B, David C, editors. Rethinking social inquiry. Diverse tools, shared standards. Lanham: Rowman and Littlefield; 2004. pp. 3–22. [ Google Scholar ]
• Brown AP. Qualitative method and compromise in applied social research. Qualitative Research. 2010; 10 (2):229–248. [ Google Scholar ]
• Charmaz K. Constructing grounded theory. London: Sage; 2006. [ Google Scholar ]
• Corte, Ugo, and Katherine Irwin. 2017. “The Form and Flow of Teaching Ethnographic Knowledge: Hands-on Approaches for Learning Epistemology” Teaching Sociology 45(3): 209-219.
• Creswell JW. Research design. Qualitative, quantitative, and mixed method approaches. 3. Thousand Oaks: SAGE Publications; 2009. [ Google Scholar ]
• Davidsson D. The myth of the subjective. In: Davidsson D, editor. Subjective, intersubjective, objective. Oxford: Oxford University Press; 1988. pp. 39–52. [ Google Scholar ]
• Denzin NK. The research act: A theoretical introduction to Ssociological methods. Chicago: Aldine Publishing Company Publishers; 1970. [ Google Scholar ]
• Denzin NK, Lincoln YS. Introduction. The discipline and practice of qualitative research. In: Denzin NK, Lincoln YS, editors. Collecting and interpreting qualitative materials. Thousand Oaks: SAGE Publications; 2003. pp. 1–45. [ Google Scholar ]
• Denzin NK, Lincoln YS. Introduction. The discipline and practice of qualitative research. In: Denzin NK, Lincoln YS, editors. The Sage handbook of qualitative research. Thousand Oaks: SAGE Publications; 2005. pp. 1–32. [ Google Scholar ]
• Emerson RM, editor. Contemporary field research. A collection of readings. Prospect Heights: Waveland Press; 1988. [ Google Scholar ]
• Emerson RM, Fretz RI, Shaw LL. Writing ethnographic fieldnotes. Chicago: University of Chicago Press; 1995. [ Google Scholar ]
• Esterberg KG. Qualitative methods in social research. Boston: McGraw-Hill; 2002. [ Google Scholar ]
• Fine, Gary Alan. 1995. Review of “handbook of qualitative research.” Contemporary Sociology 24 (3): 416–418.
• Fine, Gary Alan. 2003. “ Toward a Peopled Ethnography: Developing Theory from Group Life.” Ethnography . 4(1):41-60.
• Fine GA, Hancock BH. The new ethnographer at work. Qualitative Research. 2017; 17 (2):260–268. [ Google Scholar ]
• Fine GA, Hallett T. Stranger and stranger: Creating theory through ethnographic distance and authority. Journal of Organizational Ethnography. 2014; 3 (2):188–203. [ Google Scholar ]
• Flick U. Qualitative research. State of the art. Social Science Information. 2002; 41 (1):5–24. [ Google Scholar ]
• Flick U. Designing qualitative research. London: SAGE Publications; 2007. [ Google Scholar ]
• Frankfort-Nachmias C, Nachmias D. Research methods in the social sciences. 5. London: Edward Arnold; 1996. [ Google Scholar ]
• Franzosi R. Sociology, narrative, and the quality versus quantity debate (Goethe versus Newton): Can computer-assisted story grammars help us understand the rise of Italian fascism (1919- 1922)? Theory and Society. 2010; 39 (6):593–629. [ Google Scholar ]
• Franzosi R. From method and measurement to narrative and number. International journal of social research methodology. 2016; 19 (1):137–141. [ Google Scholar ]
• Gadamer, Hans-Georg. 1990. Wahrheit und Methode, Grundzüge einer philosophischen Hermeneutik . Band 1, Hermeneutik. Tübingen: J.C.B. Mohr.
• Gans H. Participant Observation in an Age of “Ethnography” Journal of Contemporary Ethnography. 1999; 28 (5):540–548. [ Google Scholar ]
• Geertz C. The interpretation of cultures. New York: Basic Books; 1973. [ Google Scholar ]
• Gilbert N. Researching social life. 3. London: SAGE Publications; 2009. [ Google Scholar ]
• Glaeser A. Hermeneutic institutionalism: Towards a new synthesis. Qualitative Sociology. 2014; 37 :207–241. [ Google Scholar ]
• Glaser, Barney G., and Anselm L. Strauss. [1967] 2010. The discovery of grounded theory. Strategies for qualitative research. Hawthorne: Aldine.
• Goertz G, Mahoney J. A tale of two cultures: Qualitative and quantitative research in the social sciences. Princeton: Princeton University Press; 2012. [ Google Scholar ]
• Goffman E. On fieldwork. Journal of Contemporary Ethnography. 1989; 18 (2):123–132. [ Google Scholar ]
• Goodwin J, Horowitz R. Introduction. The methodological strengths and dilemmas of qualitative sociology. Qualitative Sociology. 2002; 25 (1):33–47. [ Google Scholar ]
• Habermas, Jürgen. [1981] 1987. The theory of communicative action . Oxford: Polity Press.
• Hammersley M. The issue of quality in qualitative research. International Journal of Research & Method in Education. 2007; 30 (3):287–305. [ Google Scholar ]
• Hammersley, Martyn. 2013. What is qualitative research? Bloomsbury Publishing.
• Hammersley M. What is ethnography? Can it survive should it? Ethnography and Education. 2018; 13 (1):1–17. [ Google Scholar ]
• Hammersley M, Atkinson P. Ethnography . Principles in practice . London: Tavistock Publications; 2007. [ Google Scholar ]
• Heidegger M. Sein und Zeit. Tübingen: Max Niemeyer Verlag; 2001. [ Google Scholar ]
• Heidegger, Martin. 1988. 1923. Ontologie. Hermeneutik der Faktizität, Gesamtausgabe II. Abteilung: Vorlesungen 1919-1944, Band 63, Frankfurt am Main: Vittorio Klostermann.
• Hempel CG. Philosophy of the natural sciences. Upper Saddle River: Prentice Hall; 1966. [ Google Scholar ]
• Hood JC. Teaching against the text. The case of qualitative methods. Teaching Sociology. 2006; 34 (3):207–223. [ Google Scholar ]
• James W. Pragmatism. New York: Meredian Books; 1907. [ Google Scholar ]
• Jovanović G. Toward a social history of qualitative research. History of the Human Sciences. 2011; 24 (2):1–27. [ Google Scholar ]
• Kalof L, Dan A, Dietz T. Essentials of social research. London: Open University Press; 2008. [ Google Scholar ]
• Katz J. Situational evidence: Strategies for causal reasoning from observational field notes. Sociological Methods & Research. 2015; 44 (1):108–144. [ Google Scholar ]
• King G, Keohane RO, Sidney S, Verba S. Scientific inference in qualitative research. Princeton: Princeton University Press; 1994. Designing social inquiry. [ Google Scholar ]
• Lamont M. Evaluating qualitative research: Some empirical findings and an agenda. In: Lamont M, White P, editors. Report from workshop on interdisciplinary standards for systematic qualitative research. Washington, DC: National Science Foundation; 2004. pp. 91–95. [ Google Scholar ]
• Lamont M, Swidler A. Methodological pluralism and the possibilities and limits of interviewing. Qualitative Sociology. 2014; 37 (2):153–171. [ Google Scholar ]
• Lazarsfeld P, Barton A. Some functions of qualitative analysis in social research. In: Kendall P, editor. The varied sociology of Paul Lazarsfeld. New York: Columbia University Press; 1982. pp. 239–285. [ Google Scholar ]
• Lichterman, Paul, and Isaac Reed I (2014), Theory and Contrastive Explanation in Ethnography. Sociological methods and research. Prepublished 27 October 2014; 10.1177/0049124114554458.
• Lofland J, Lofland L. Analyzing social settings. A guide to qualitative observation and analysis. 3. Belmont: Wadsworth; 1995. [ Google Scholar ]
• Lofland J, Snow DA, Anderson L, Lofland LH. Analyzing social settings. A guide to qualitative observation and analysis. 4. Belmont: Wadsworth/Thomson Learning; 2006. [ Google Scholar ]
• Long AF, Godfrey M. An evaluation tool to assess the quality of qualitative research studies. International Journal of Social Research Methodology. 2004; 7 (2):181–196. [ Google Scholar ]
• Lundberg G. Social research: A study in methods of gathering data. New York: Longmans, Green and Co.; 1951. [ Google Scholar ]
• Malinowski B. Argonauts of the Western Pacific: An account of native Enterprise and adventure in the archipelagoes of Melanesian New Guinea. London: Routledge; 1922. [ Google Scholar ]
• Manicas P. A realist philosophy of science: Explanation and understanding. Cambridge: Cambridge University Press; 2006. [ Google Scholar ]
• Marchel C, Owens S. Qualitative research in psychology. Could William James get a job? History of Psychology. 2007; 10 (4):301–324. [ PubMed ] [ Google Scholar ]
• McIntyre LJ. Need to know. Social science research methods. Boston: McGraw-Hill; 2005. [ Google Scholar ]
• Merton RK, Barber E. The travels and adventures of serendipity . A Study in Sociological Semantics and the Sociology of Science. Princeton: Princeton University Press; 2004. [ Google Scholar ]
• Mannay D, Morgan M. Doing ethnography or applying a qualitative technique? Reflections from the ‘waiting field‘ Qualitative Research. 2015; 15 (2):166–182. [ Google Scholar ]
• Neuman LW. Basics of social research. Qualitative and quantitative approaches. 2. Boston: Pearson Education; 2007. [ Google Scholar ]
• Ragin CC. Constructing social research. The unity and diversity of method. Thousand Oaks: Pine Forge Press; 1994. [ Google Scholar ]
• Ragin, Charles C. 2004. Introduction to session 1: Defining qualitative research. In Workshop on Scientific Foundations of Qualitative Research , 22, ed. Charles C. Ragin, Joane Nagel, Patricia White. http://www.nsf.gov/pubs/2004/nsf04219/nsf04219.pdf
• Rawls, Anne. 2018. The Wartime narrative in US sociology, 1940–7: Stigmatizing qualitative sociology in the name of ‘science,’ European Journal of Social Theory (Online first).
• Schütz A. Collected papers I: The problem of social reality. The Hague: Nijhoff; 1962. [ Google Scholar ]
• Seiffert H. Einführung in die Hermeneutik. Tübingen: Franke; 1992. [ Google Scholar ]
• Silverman D. Doing qualitative research. A practical handbook. 2. London: SAGE Publications; 2005. [ Google Scholar ]
• Silverman D. A very short, fairly interesting and reasonably cheap book about qualitative research. London: SAGE Publications; 2009. [ Google Scholar ]
• Silverman D. What counts as qualitative research? Some cautionary comments. Qualitative Sociology Review. 2013; 9 (2):48–55. [ Google Scholar ]
• Small ML. “How many cases do I need?” on science and the logic of case selection in field-based research. Ethnography. 2009; 10 (1):5–38. [ Google Scholar ]
• Small, Mario L 2008. Lost in translation: How not to make qualitative research more scientific. In Workshop on interdisciplinary standards for systematic qualitative research, ed in Michelle Lamont, and Patricia White, 165–171. Washington, DC: National Science Foundation.
• Snow DA, Anderson L. Down on their luck: A study of homeless street people. Berkeley: University of California Press; 1993. [ Google Scholar ]
• Snow DA, Morrill C. New ethnographies: Review symposium: A revolutionary handbook or a handbook for revolution? Journal of Contemporary Ethnography. 1995; 24 (3):341–349. [ Google Scholar ]
• Strauss AL. Qualitative analysis for social scientists. 14. Chicago: Cambridge University Press; 2003. [ Google Scholar ]
• Strauss AL, Corbin JM. Basics of qualitative research. Techniques and procedures for developing grounded theory. 2. Thousand Oaks: Sage Publications; 1998. [ Google Scholar ]
• Swedberg, Richard. 2017. Theorizing in sociological research: A new perspective, a new departure? Annual Review of Sociology 43: 189–206.
• Swedberg R. The new 'Battle of Methods'. Challenge January–February. 1990; 3 (1):33–38. [ Google Scholar ]
• Timmermans S, Tavory I. Theory construction in qualitative research: From grounded theory to abductive analysis. Sociological Theory. 2012; 30 (3):167–186. [ Google Scholar ]
• Trier-Bieniek A. Framing the telephone interview as a participant-centred tool for qualitative research. A methodological discussion. Qualitative Research. 2012; 12 (6):630–644. [ Google Scholar ]
• Valsiner J. Data as representations. Contextualizing qualitative and quantitative research strategies. Social Science Information. 2000; 39 (1):99–113. [ Google Scholar ]
• Weber, Max. 1904. 1949. Objectivity’ in social Science and social policy. Ed. Edward A. Shils and Henry A. Finch, 49–112. New York: The Free Press.

What is Qualitative in Qualitative Research

• Open access
• Published: 27 February 2019
• Volume 42 , pages 139–160, ( 2019 )

Cite this article

You have full access to this open access article

• Patrik Aspers 1 , 2 &
• Ugo Corte 3  

627k Accesses

342 Citations

25 Altmetric

Explore all metrics

What is qualitative research? If we look for a precise definition of qualitative research, and specifically for one that addresses its distinctive feature of being “qualitative,” the literature is meager. In this article we systematically search, identify and analyze a sample of 89 sources using or attempting to define the term “qualitative.” Then, drawing on ideas we find scattered across existing work, and based on Becker’s classic study of marijuana consumption, we formulate and illustrate a definition that tries to capture its core elements. We define qualitative research as an iterative process in which improved understanding to the scientific community is achieved by making new significant distinctions resulting from getting closer to the phenomenon studied. This formulation is developed as a tool to help improve research designs while stressing that a qualitative dimension is present in quantitative work as well. Additionally, it can facilitate teaching, communication between researchers, diminish the gap between qualitative and quantitative researchers, help to address critiques of qualitative methods, and be used as a standard of evaluation of qualitative research.

Similar content being viewed by others

What is Qualitative in Research

Unsettling definitions of qualitative research, what is “qualitative” in qualitative research why the answer does not matter but the question is important, explore related subjects.

• Artificial Intelligence

Avoid common mistakes on your manuscript.

If we assume that there is something called qualitative research, what exactly is this qualitative feature? And how could we evaluate qualitative research as good or not? Is it fundamentally different from quantitative research? In practice, most active qualitative researchers working with empirical material intuitively know what is involved in doing qualitative research, yet perhaps surprisingly, a clear definition addressing its key feature is still missing.

To address the question of what is qualitative we turn to the accounts of “qualitative research” in textbooks and also in empirical work. In his classic, explorative, interview study of deviance Howard Becker ( 1963 ) asks ‘How does one become a marijuana user?’ In contrast to pre-dispositional and psychological-individualistic theories of deviant behavior, Becker’s inherently social explanation contends that becoming a user of this substance is the result of a three-phase sequential learning process. First, potential users need to learn how to smoke it properly to produce the “correct” effects. If not, they are likely to stop experimenting with it. Second, they need to discover the effects associated with it; in other words, to get “high,” individuals not only have to experience what the drug does, but also to become aware that those sensations are related to using it. Third, they require learning to savor the feelings related to its consumption – to develop an acquired taste. Becker, who played music himself, gets close to the phenomenon by observing, taking part, and by talking to people consuming the drug: “half of the fifty interviews were conducted with musicians, the other half covered a wide range of people, including laborers, machinists, and people in the professions” (Becker 1963 :56).

Another central aspect derived through the common-to-all-research interplay between induction and deduction (Becker 2017 ), is that during the course of his research Becker adds scientifically meaningful new distinctions in the form of three phases—distinctions, or findings if you will, that strongly affect the course of his research: its focus, the material that he collects, and which eventually impact his findings. Each phase typically unfolds through social interaction, and often with input from experienced users in “a sequence of social experiences during which the person acquires a conception of the meaning of the behavior, and perceptions and judgments of objects and situations, all of which make the activity possible and desirable” (Becker 1963 :235). In this study the increased understanding of smoking dope is a result of a combination of the meaning of the actors, and the conceptual distinctions that Becker introduces based on the views expressed by his respondents. Understanding is the result of research and is due to an iterative process in which data, concepts and evidence are connected with one another (Becker 2017 ).

Indeed, there are many definitions of qualitative research, but if we look for a definition that addresses its distinctive feature of being “qualitative,” the literature across the broad field of social science is meager. The main reason behind this article lies in the paradox, which, to put it bluntly, is that researchers act as if they know what it is, but they cannot formulate a coherent definition. Sociologists and others will of course continue to conduct good studies that show the relevance and value of qualitative research addressing scientific and practical problems in society. However, our paper is grounded in the idea that providing a clear definition will help us improve the work that we do. Among researchers who practice qualitative research there is clearly much knowledge. We suggest that a definition makes this knowledge more explicit. If the first rationale for writing this paper refers to the “internal” aim of improving qualitative research, the second refers to the increased “external” pressure that especially many qualitative researchers feel; pressure that comes both from society as well as from other scientific approaches. There is a strong core in qualitative research, and leading researchers tend to agree on what it is and how it is done. Our critique is not directed at the practice of qualitative research, but we do claim that the type of systematic work we do has not yet been done, and that it is useful to improve the field and its status in relation to quantitative research.

The literature on the “internal” aim of improving, or at least clarifying qualitative research is large, and we do not claim to be the first to notice the vagueness of the term “qualitative” (Strauss and Corbin 1998 ). Also, others have noted that there is no single definition of it (Long and Godfrey 2004 :182), that there are many different views on qualitative research (Denzin and Lincoln 2003 :11; Jovanović 2011 :3), and that more generally, we need to define its meaning (Best 2004 :54). Strauss and Corbin ( 1998 ), for example, as well as Nelson et al. (1992:2 cited in Denzin and Lincoln 2003 :11), and Flick ( 2007 :ix–x), have recognized that the term is problematic: “Actually, the term ‘qualitative research’ is confusing because it can mean different things to different people” (Strauss and Corbin 1998 :10–11). Hammersley has discussed the possibility of addressing the problem, but states that “the task of providing an account of the distinctive features of qualitative research is far from straightforward” ( 2013 :2). This confusion, as he has recently further argued (Hammersley 2018 ), is also salient in relation to ethnography where different philosophical and methodological approaches lead to a lack of agreement about what it means.

Others (e.g. Hammersley 2018 ; Fine and Hancock 2017 ) have also identified the treat to qualitative research that comes from external forces, seen from the point of view of “qualitative research.” This threat can be further divided into that which comes from inside academia, such as the critique voiced by “quantitative research” and outside of academia, including, for example, New Public Management. Hammersley ( 2018 ), zooming in on one type of qualitative research, ethnography, has argued that it is under treat. Similarly to Fine ( 2003 ), and before him Gans ( 1999 ), he writes that ethnography’ has acquired a range of meanings, and comes in many different versions, these often reflecting sharply divergent epistemological orientations. And already more than twenty years ago while reviewing Denzin and Lincoln’ s Handbook of Qualitative Methods Fine argued:

While this increasing centrality [of qualitative research] might lead one to believe that consensual standards have developed, this belief would be misleading. As the methodology becomes more widely accepted, querulous challengers have raised fundamental questions that collectively have undercut the traditional models of how qualitative research is to be fashioned and presented (1995:417).

According to Hammersley, there are today “serious treats to the practice of ethnographic work, on almost any definition” ( 2018 :1). He lists five external treats: (1) that social research must be accountable and able to show its impact on society; (2) the current emphasis on “big data” and the emphasis on quantitative data and evidence; (3) the labor market pressure in academia that leaves less time for fieldwork (see also Fine and Hancock 2017 ); (4) problems of access to fields; and (5) the increased ethical scrutiny of projects, to which ethnography is particularly exposed. Hammersley discusses some more or less insufficient existing definitions of ethnography.

The current situation, as Hammersley and others note—and in relation not only to ethnography but also qualitative research in general, and as our empirical study shows—is not just unsatisfactory, it may even be harmful for the entire field of qualitative research, and does not help social science at large. We suggest that the lack of clarity of qualitative research is a real problem that must be addressed.

Towards a Definition of Qualitative Research

Seen in an historical light, what is today called qualitative, or sometimes ethnographic, interpretative research – or a number of other terms – has more or less always existed. At the time the founders of sociology – Simmel, Weber, Durkheim and, before them, Marx – were writing, and during the era of the Methodenstreit (“dispute about methods”) in which the German historical school emphasized scientific methods (cf. Swedberg 1990 ), we can at least speak of qualitative forerunners.

Perhaps the most extended discussion of what later became known as qualitative methods in a classic work is Bronisław Malinowski’s ( 1922 ) Argonauts in the Western Pacific , although even this study does not explicitly address the meaning of “qualitative.” In Weber’s ([1921–-22] 1978) work we find a tension between scientific explanations that are based on observation and quantification and interpretative research (see also Lazarsfeld and Barton 1982 ).

If we look through major sociology journals like the American Sociological Review , American Journal of Sociology , or Social Forces we will not find the term qualitative sociology before the 1970s. And certainly before then much of what we consider qualitative classics in sociology, like Becker’ study ( 1963 ), had already been produced. Indeed, the Chicago School often combined qualitative and quantitative data within the same study (Fine 1995 ). Our point being that before a disciplinary self-awareness the term quantitative preceded qualitative, and the articulation of the former was a political move to claim scientific status (Denzin and Lincoln 2005 ). In the US the World War II seem to have sparked a critique of sociological work, including “qualitative work,” that did not follow the scientific canon (Rawls 2018 ), which was underpinned by a scientifically oriented and value free philosophy of science. As a result the attempts and practice of integrating qualitative and quantitative sociology at Chicago lost ground to sociology that was more oriented to surveys and quantitative work at Columbia under Merton-Lazarsfeld. The quantitative tradition was also able to present textbooks (Lundberg 1951 ) that facilitated the use this approach and its “methods.” The practices of the qualitative tradition, by and large, remained tacit or was part of the mentoring transferred from the renowned masters to their students.

This glimpse into history leads us back to the lack of a coherent account condensed in a definition of qualitative research. Many of the attempts to define the term do not meet the requirements of a proper definition: A definition should be clear, avoid tautology, demarcate its domain in relation to the environment, and ideally only use words in its definiens that themselves are not in need of definition (Hempel 1966 ). A definition can enhance precision and thus clarity by identifying the core of the phenomenon. Preferably, a definition should be short. The typical definition we have found, however, is an ostensive definition, which indicates what qualitative research is about without informing us about what it actually is :

Qualitative research is multimethod in focus, involving an interpretative, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Qualitative research involves the studied use and collection of a variety of empirical materials – case study, personal experience, introspective, life story, interview, observational, historical, interactional, and visual texts – that describe routine and problematic moments and meanings in individuals’ lives. (Denzin and Lincoln 2005 :2)

Flick claims that the label “qualitative research” is indeed used as an umbrella for a number of approaches ( 2007 :2–4; 2002 :6), and it is not difficult to identify research fitting this designation. Moreover, whatever it is, it has grown dramatically over the past five decades. In addition, courses have been developed, methods have flourished, arguments about its future have been advanced (for example, Denzin and Lincoln 1994) and criticized (for example, Snow and Morrill 1995 ), and dedicated journals and books have mushroomed. Most social scientists have a clear idea of research and how it differs from journalism, politics and other activities. But the question of what is qualitative in qualitative research is either eluded or eschewed.

We maintain that this lacuna hinders systematic knowledge production based on qualitative research. Paul Lazarsfeld noted the lack of “codification” as early as 1955 when he reviewed 100 qualitative studies in order to offer a codification of the practices (Lazarsfeld and Barton 1982 :239). Since then many texts on “qualitative research” and its methods have been published, including recent attempts (Goertz and Mahoney 2012 ) similar to Lazarsfeld’s. These studies have tried to extract what is qualitative by looking at the large number of empirical “qualitative” studies. Our novel strategy complements these endeavors by taking another approach and looking at the attempts to codify these practices in the form of a definition, as well as to a minor extent take Becker’s study as an exemplar of what qualitative researchers actually do, and what the characteristic of being ‘qualitative’ denotes and implies. We claim that qualitative researchers, if there is such a thing as “qualitative research,” should be able to codify their practices in a condensed, yet general way expressed in language.

Lingering problems of “generalizability” and “how many cases do I need” (Small 2009 ) are blocking advancement – in this line of work qualitative approaches are said to differ considerably from quantitative ones, while some of the former unsuccessfully mimic principles related to the latter (Small 2009 ). Additionally, quantitative researchers sometimes unfairly criticize the first based on their own quality criteria. Scholars like Goertz and Mahoney ( 2012 ) have successfully focused on the different norms and practices beyond what they argue are essentially two different cultures: those working with either qualitative or quantitative methods. Instead, similarly to Becker ( 2017 ) who has recently questioned the usefulness of the distinction between qualitative and quantitative research, we focus on similarities.

The current situation also impedes both students and researchers in focusing their studies and understanding each other’s work (Lazarsfeld and Barton 1982 :239). A third consequence is providing an opening for critiques by scholars operating within different traditions (Valsiner 2000 :101). A fourth issue is that the “implicit use of methods in qualitative research makes the field far less standardized than the quantitative paradigm” (Goertz and Mahoney 2012 :9). Relatedly, the National Science Foundation in the US organized two workshops in 2004 and 2005 to address the scientific foundations of qualitative research involving strategies to improve it and to develop standards of evaluation in qualitative research. However, a specific focus on its distinguishing feature of being “qualitative” while being implicitly acknowledged, was discussed only briefly (for example, Best 2004 ).

In 2014 a theme issue was published in this journal on “Methods, Materials, and Meanings: Designing Cultural Analysis,” discussing central issues in (cultural) qualitative research (Berezin 2014 ; Biernacki 2014 ; Glaeser 2014 ; Lamont and Swidler 2014 ; Spillman 2014). We agree with many of the arguments put forward, such as the risk of methodological tribalism, and that we should not waste energy on debating methods separated from research questions. Nonetheless, a clarification of the relation to what is called “quantitative research” is of outmost importance to avoid misunderstandings and misguided debates between “qualitative” and “quantitative” researchers. Our strategy means that researchers, “qualitative” or “quantitative” they may be, in their actual practice may combine qualitative work and quantitative work.

In this article we accomplish three tasks. First, we systematically survey the literature for meanings of qualitative research by looking at how researchers have defined it. Drawing upon existing knowledge we find that the different meanings and ideas of qualitative research are not yet coherently integrated into one satisfactory definition. Next, we advance our contribution by offering a definition of qualitative research and illustrate its meaning and use partially by expanding on the brief example introduced earlier related to Becker’s work ( 1963 ). We offer a systematic analysis of central themes of what researchers consider to be the core of “qualitative,” regardless of style of work. These themes – which we summarize in terms of four keywords: distinction, process, closeness, improved understanding – constitute part of our literature review, in which each one appears, sometimes with others, but never all in the same definition. They serve as the foundation of our contribution. Our categories are overlapping. Their use is primarily to organize the large amount of definitions we have identified and analyzed, and not necessarily to draw a clear distinction between them. Finally, we continue the elaboration discussed above on the advantages of a clear definition of qualitative research.

In a hermeneutic fashion we propose that there is something meaningful that deserves to be labelled “qualitative research” (Gadamer 1990 ). To approach the question “What is qualitative in qualitative research?” we have surveyed the literature. In conducting our survey we first traced the word’s etymology in dictionaries, encyclopedias, handbooks of the social sciences and of methods and textbooks, mainly in English, which is common to methodology courses. It should be noted that we have zoomed in on sociology and its literature. This discipline has been the site of the largest debate and development of methods that can be called “qualitative,” which suggests that this field should be examined in great detail.

In an ideal situation we should expect that one good definition, or at least some common ideas, would have emerged over the years. This common core of qualitative research should be so accepted that it would appear in at least some textbooks. Since this is not what we found, we decided to pursue an inductive approach to capture maximal variation in the field of qualitative research; we searched in a selection of handbooks, textbooks, book chapters, and books, to which we added the analysis of journal articles. Our sample comprises a total of 89 references.

In practice we focused on the discipline that has had a clear discussion of methods, namely sociology. We also conducted a broad search in the JSTOR database to identify scholarly sociology articles published between 1998 and 2017 in English with a focus on defining or explaining qualitative research. We specifically zoom in on this time frame because we would have expect that this more mature period would have produced clear discussions on the meaning of qualitative research. To find these articles we combined a number of keywords to search the content and/or the title: qualitative (which was always included), definition, empirical, research, methodology, studies, fieldwork, interview and observation .

As a second phase of our research we searched within nine major sociological journals ( American Journal of Sociology , Sociological Theory , American Sociological Review , Contemporary Sociology , Sociological Forum , Sociological Theory , Qualitative Research , Qualitative Sociology and Qualitative Sociology Review ) for articles also published during the past 19 years (1998–2017) that had the term “qualitative” in the title and attempted to define qualitative research.

Lastly we picked two additional journals, Qualitative Research and Qualitative Sociology , in which we could expect to find texts addressing the notion of “qualitative.” From Qualitative Research we chose Volume 14, Issue 6, December 2014, and from Qualitative Sociology we chose Volume 36, Issue 2, June 2017. Within each of these we selected the first article; then we picked the second article of three prior issues. Again we went back another three issues and investigated article number three. Finally we went back another three issues and perused article number four. This selection criteria was used to get a manageable sample for the analysis.

The coding process of the 89 references we gathered in our selected review began soon after the first round of material was gathered, and we reduced the complexity created by our maximum variation sampling (Snow and Anderson 1993 :22) to four different categories within which questions on the nature and properties of qualitative research were discussed. We call them: Qualitative and Quantitative Research, Qualitative Research, Fieldwork, and Grounded Theory. This – which may appear as an illogical grouping – merely reflects the “context” in which the matter of “qualitative” is discussed. If the selection process of the material – books and articles – was informed by pre-knowledge, we used an inductive strategy to code the material. When studying our material, we identified four central notions related to “qualitative” that appear in various combinations in the literature which indicate what is the core of qualitative research. We have labeled them: “distinctions”, “process,” “closeness,” and “improved understanding.” During the research process the categories and notions were improved, refined, changed, and reordered. The coding ended when a sense of saturation in the material arose. In the presentation below all quotations and references come from our empirical material of texts on qualitative research.

Analysis – What is Qualitative Research?

In this section we describe the four categories we identified in the coding, how they differently discuss qualitative research, as well as their overall content. Some salient quotations are selected to represent the type of text sorted under each of the four categories. What we present are examples from the literature.

Qualitative and Quantitative

This analytic category comprises quotations comparing qualitative and quantitative research, a distinction that is frequently used (Brown 2010 :231); in effect this is a conceptual pair that structures the discussion and that may be associated with opposing interests. While the general goal of quantitative and qualitative research is the same – to understand the world better – their methodologies and focus in certain respects differ substantially (Becker 1966 :55). Quantity refers to that property of something that can be determined by measurement. In a dictionary of Statistics and Methodology we find that “(a) When referring to *variables, ‘qualitative’ is another term for *categorical or *nominal. (b) When speaking of kinds of research, ‘qualitative’ refers to studies of subjects that are hard to quantify, such as art history. Qualitative research tends to be a residual category for almost any kind of non-quantitative research” (Stiles 1998:183). But it should be obvious that one could employ a quantitative approach when studying, for example, art history.

The same dictionary states that quantitative is “said of variables or research that can be handled numerically, usually (too sharply) contrasted with *qualitative variables and research” (Stiles 1998:184). From a qualitative perspective “quantitative research” is about numbers and counting, and from a quantitative perspective qualitative research is everything that is not about numbers. But this does not say much about what is “qualitative.” If we turn to encyclopedias we find that in the 1932 edition of the Encyclopedia of the Social Sciences there is no mention of “qualitative.” In the Encyclopedia from 1968 we can read:

Qualitative Analysis. For methods of obtaining, analyzing, and describing data, see [the various entries:] CONTENT ANALYSIS; COUNTED DATA; EVALUATION RESEARCH, FIELD WORK; GRAPHIC PRESENTATION; HISTORIOGRAPHY, especially the article on THE RHETORIC OF HISTORY; INTERVIEWING; OBSERVATION; PERSONALITY MEASUREMENT; PROJECTIVE METHODS; PSYCHOANALYSIS, article on EXPERIMENTAL METHODS; SURVEY ANALYSIS, TABULAR PRESENTATION; TYPOLOGIES. (Vol. 13:225)

Some, like Alford, divide researchers into methodologists or, in his words, “quantitative and qualitative specialists” (Alford 1998 :12). Qualitative research uses a variety of methods, such as intensive interviews or in-depth analysis of historical materials, and it is concerned with a comprehensive account of some event or unit (King et al. 1994 :4). Like quantitative research it can be utilized to study a variety of issues, but it tends to focus on meanings and motivations that underlie cultural symbols, personal experiences, phenomena and detailed understanding of processes in the social world. In short, qualitative research centers on understanding processes, experiences, and the meanings people assign to things (Kalof et al. 2008 :79).

Others simply say that qualitative methods are inherently unscientific (Jovanović 2011 :19). Hood, for instance, argues that words are intrinsically less precise than numbers, and that they are therefore more prone to subjective analysis, leading to biased results (Hood 2006 :219). Qualitative methodologies have raised concerns over the limitations of quantitative templates (Brady et al. 2004 :4). Scholars such as King et al. ( 1994 ), for instance, argue that non-statistical research can produce more reliable results if researchers pay attention to the rules of scientific inference commonly stated in quantitative research. Also, researchers such as Becker ( 1966 :59; 1970 :42–43) have asserted that, if conducted properly, qualitative research and in particular ethnographic field methods, can lead to more accurate results than quantitative studies, in particular, survey research and laboratory experiments.

Some researchers, such as Kalof, Dan, and Dietz ( 2008 :79) claim that the boundaries between the two approaches are becoming blurred, and Small ( 2009 ) argues that currently much qualitative research (especially in North America) tries unsuccessfully and unnecessarily to emulate quantitative standards. For others, qualitative research tends to be more humanistic and discursive (King et al. 1994 :4). Ragin ( 1994 ), and similarly also Becker, ( 1996 :53), Marchel and Owens ( 2007 :303) think that the main distinction between the two styles is overstated and does not rest on the simple dichotomy of “numbers versus words” (Ragin 1994 :xii). Some claim that quantitative data can be utilized to discover associations, but in order to unveil cause and effect a complex research design involving the use of qualitative approaches needs to be devised (Gilbert 2009 :35). Consequently, qualitative data are useful for understanding the nuances lying beyond those processes as they unfold (Gilbert 2009 :35). Others contend that qualitative research is particularly well suited both to identify causality and to uncover fine descriptive distinctions (Fine and Hallett 2014 ; Lichterman and Isaac Reed 2014 ; Katz 2015 ).

There are other ways to separate these two traditions, including normative statements about what qualitative research should be (that is, better or worse than quantitative approaches, concerned with scientific approaches to societal change or vice versa; Snow and Morrill 1995 ; Denzin and Lincoln 2005 ), or whether it should develop falsifiable statements; Best 2004 ).

We propose that quantitative research is largely concerned with pre-determined variables (Small 2008 ); the analysis concerns the relations between variables. These categories are primarily not questioned in the study, only their frequency or degree, or the correlations between them (cf. Franzosi 2016 ). If a researcher studies wage differences between women and men, he or she works with given categories: x number of men are compared with y number of women, with a certain wage attributed to each person. The idea is not to move beyond the given categories of wage, men and women; they are the starting point as well as the end point, and undergo no “qualitative change.” Qualitative research, in contrast, investigates relations between categories that are themselves subject to change in the research process. Returning to Becker’s study ( 1963 ), we see that he questioned pre-dispositional theories of deviant behavior working with pre-determined variables such as an individual’s combination of personal qualities or emotional problems. His take, in contrast, was to understand marijuana consumption by developing “variables” as part of the investigation. Thereby he presented new variables, or as we would say today, theoretical concepts, but which are grounded in the empirical material.

Qualitative Research

This category contains quotations that refer to descriptions of qualitative research without making comparisons with quantitative research. Researchers such as Denzin and Lincoln, who have written a series of influential handbooks on qualitative methods (1994; Denzin and Lincoln 2003 ; 2005 ), citing Nelson et al. (1992:4), argue that because qualitative research is “interdisciplinary, transdisciplinary, and sometimes counterdisciplinary” it is difficult to derive one single definition of it (Jovanović 2011 :3). According to them, in fact, “the field” is “many things at the same time,” involving contradictions, tensions over its focus, methods, and how to derive interpretations and findings ( 2003 : 11). Similarly, others, such as Flick ( 2007 :ix–x) contend that agreeing on an accepted definition has increasingly become problematic, and that qualitative research has possibly matured different identities. However, Best holds that “the proliferation of many sorts of activities under the label of qualitative sociology threatens to confuse our discussions” ( 2004 :54). Atkinson’s position is more definite: “the current state of qualitative research and research methods is confused” ( 2005 :3–4).

Qualitative research is about interpretation (Blumer 1969 ; Strauss and Corbin 1998 ; Denzin and Lincoln 2003 ), or Verstehen [understanding] (Frankfort-Nachmias and Nachmias 1996 ). It is “multi-method,” involving the collection and use of a variety of empirical materials (Denzin and Lincoln 1998; Silverman 2013 ) and approaches (Silverman 2005 ; Flick 2007 ). It focuses not only on the objective nature of behavior but also on its subjective meanings: individuals’ own accounts of their attitudes, motivations, behavior (McIntyre 2005 :127; Creswell 2009 ), events and situations (Bryman 1989) – what people say and do in specific places and institutions (Goodwin and Horowitz 2002 :35–36) in social and temporal contexts (Morrill and Fine 1997). For this reason, following Weber ([1921-22] 1978), it can be described as an interpretative science (McIntyre 2005 :127). But could quantitative research also be concerned with these questions? Also, as pointed out below, does all qualitative research focus on subjective meaning, as some scholars suggest?

Others also distinguish qualitative research by claiming that it collects data using a naturalistic approach (Denzin and Lincoln 2005 :2; Creswell 2009 ), focusing on the meaning actors ascribe to their actions. But again, does all qualitative research need to be collected in situ? And does qualitative research have to be inherently concerned with meaning? Flick ( 2007 ), referring to Denzin and Lincoln ( 2005 ), mentions conversation analysis as an example of qualitative research that is not concerned with the meanings people bring to a situation, but rather with the formal organization of talk. Still others, such as Ragin ( 1994 :85), note that qualitative research is often (especially early on in the project, we would add) less structured than other kinds of social research – a characteristic connected to its flexibility and that can lead both to potentially better, but also worse results. But is this not a feature of this type of research, rather than a defining description of its essence? Wouldn’t this comment also apply, albeit to varying degrees, to quantitative research?

In addition, Strauss ( 2003 ), along with others, such as Alvesson and Kärreman ( 2011 :10–76), argue that qualitative researchers struggle to capture and represent complex phenomena partially because they tend to collect a large amount of data. While his analysis is correct at some points – “It is necessary to do detailed, intensive, microscopic examination of the data in order to bring out the amazing complexity of what lies in, behind, and beyond those data” (Strauss 2003 :10) – much of his analysis concerns the supposed focus of qualitative research and its challenges, rather than exactly what it is about. But even in this instance we would make a weak case arguing that these are strictly the defining features of qualitative research. Some researchers seem to focus on the approach or the methods used, or even on the way material is analyzed. Several researchers stress the naturalistic assumption of investigating the world, suggesting that meaning and interpretation appear to be a core matter of qualitative research.

We can also see that in this category there is no consensus about specific qualitative methods nor about qualitative data. Many emphasize interpretation, but quantitative research, too, involves interpretation; the results of a regression analysis, for example, certainly have to be interpreted, and the form of meta-analysis that factor analysis provides indeed requires interpretation However, there is no interpretation of quantitative raw data, i.e., numbers in tables. One common thread is that qualitative researchers have to get to grips with their data in order to understand what is being studied in great detail, irrespective of the type of empirical material that is being analyzed. This observation is connected to the fact that qualitative researchers routinely make several adjustments of focus and research design as their studies progress, in many cases until the very end of the project (Kalof et al. 2008 ). If you, like Becker, do not start out with a detailed theory, adjustments such as the emergence and refinement of research questions will occur during the research process. We have thus found a number of useful reflections about qualitative research scattered across different sources, but none of them effectively describe the defining characteristics of this approach.

Although qualitative research does not appear to be defined in terms of a specific method, it is certainly common that fieldwork, i.e., research that entails that the researcher spends considerable time in the field that is studied and use the knowledge gained as data, is seen as emblematic of or even identical to qualitative research. But because we understand that fieldwork tends to focus primarily on the collection and analysis of qualitative data, we expected to find within it discussions on the meaning of “qualitative.” But, again, this was not the case.

Instead, we found material on the history of this approach (for example, Frankfort-Nachmias and Nachmias 1996 ; Atkinson et al. 2001), including how it has changed; for example, by adopting a more self-reflexive practice (Heyl 2001), as well as the different nomenclature that has been adopted, such as fieldwork, ethnography, qualitative research, naturalistic research, participant observation and so on (for example, Lofland et al. 2006 ; Gans 1999 ).

We retrieved definitions of ethnography, such as “the study of people acting in the natural courses of their daily lives,” involving a “resocialization of the researcher” (Emerson 1988 :1) through intense immersion in others’ social worlds (see also examples in Hammersley 2018 ). This may be accomplished by direct observation and also participation (Neuman 2007 :276), although others, such as Denzin ( 1970 :185), have long recognized other types of observation, including non-participant (“fly on the wall”). In this category we have also isolated claims and opposing views, arguing that this type of research is distinguished primarily by where it is conducted (natural settings) (Hughes 1971:496), and how it is carried out (a variety of methods are applied) or, for some most importantly, by involving an active, empathetic immersion in those being studied (Emerson 1988 :2). We also retrieved descriptions of the goals it attends in relation to how it is taught (understanding subjective meanings of the people studied, primarily develop theory, or contribute to social change) (see for example, Corte and Irwin 2017 ; Frankfort-Nachmias and Nachmias 1996 :281; Trier-Bieniek 2012 :639) by collecting the richest possible data (Lofland et al. 2006 ) to derive “thick descriptions” (Geertz 1973 ), and/or to aim at theoretical statements of general scope and applicability (for example, Emerson 1988 ; Fine 2003 ). We have identified guidelines on how to evaluate it (for example Becker 1996 ; Lamont 2004 ) and have retrieved instructions on how it should be conducted (for example, Lofland et al. 2006 ). For instance, analysis should take place while the data gathering unfolds (Emerson 1988 ; Hammersley and Atkinson 2007 ; Lofland et al. 2006 ), observations should be of long duration (Becker 1970 :54; Goffman 1989 ), and data should be of high quantity (Becker 1970 :52–53), as well as other questionable distinctions between fieldwork and other methods:

Field studies differ from other methods of research in that the researcher performs the task of selecting topics, decides what questions to ask, and forges interest in the course of the research itself . This is in sharp contrast to many ‘theory-driven’ and ‘hypothesis-testing’ methods. (Lofland and Lofland 1995 :5)

But could not, for example, a strictly interview-based study be carried out with the same amount of flexibility, such as sequential interviewing (for example, Small 2009 )? Once again, are quantitative approaches really as inflexible as some qualitative researchers think? Moreover, this category stresses the role of the actors’ meaning, which requires knowledge and close interaction with people, their practices and their lifeworld.

It is clear that field studies – which are seen by some as the “gold standard” of qualitative research – are nonetheless only one way of doing qualitative research. There are other methods, but it is not clear why some are more qualitative than others, or why they are better or worse. Fieldwork is characterized by interaction with the field (the material) and understanding of the phenomenon that is being studied. In Becker’s case, he had general experience from fields in which marihuana was used, based on which he did interviews with actual users in several fields.

Grounded Theory

Another major category we identified in our sample is Grounded Theory. We found descriptions of it most clearly in Glaser and Strauss’ ([1967] 2010 ) original articulation, Strauss and Corbin ( 1998 ) and Charmaz ( 2006 ), as well as many other accounts of what it is for: generating and testing theory (Strauss 2003 :xi). We identified explanations of how this task can be accomplished – such as through two main procedures: constant comparison and theoretical sampling (Emerson 1998:96), and how using it has helped researchers to “think differently” (for example, Strauss and Corbin 1998 :1). We also read descriptions of its main traits, what it entails and fosters – for instance, an exceptional flexibility, an inductive approach (Strauss and Corbin 1998 :31–33; 1990; Esterberg 2002 :7), an ability to step back and critically analyze situations, recognize tendencies towards bias, think abstractly and be open to criticism, enhance sensitivity towards the words and actions of respondents, and develop a sense of absorption and devotion to the research process (Strauss and Corbin 1998 :5–6). Accordingly, we identified discussions of the value of triangulating different methods (both using and not using grounded theory), including quantitative ones, and theories to achieve theoretical development (most comprehensively in Denzin 1970 ; Strauss and Corbin 1998 ; Timmermans and Tavory 2012 ). We have also located arguments about how its practice helps to systematize data collection, analysis and presentation of results (Glaser and Strauss [1967] 2010 :16).

Grounded theory offers a systematic approach which requires researchers to get close to the field; closeness is a requirement of identifying questions and developing new concepts or making further distinctions with regard to old concepts. In contrast to other qualitative approaches, grounded theory emphasizes the detailed coding process, and the numerous fine-tuned distinctions that the researcher makes during the process. Within this category, too, we could not find a satisfying discussion of the meaning of qualitative research.

Defining Qualitative Research

In sum, our analysis shows that some notions reappear in the discussion of qualitative research, such as understanding, interpretation, “getting close” and making distinctions. These notions capture aspects of what we think is “qualitative.” However, a comprehensive definition that is useful and that can further develop the field is lacking, and not even a clear picture of its essential elements appears. In other words no definition emerges from our data, and in our research process we have moved back and forth between our empirical data and the attempt to present a definition. Our concrete strategy, as stated above, is to relate qualitative and quantitative research, or more specifically, qualitative and quantitative work. We use an ideal-typical notion of quantitative research which relies on taken for granted and numbered variables. This means that the data consists of variables on different scales, such as ordinal, but frequently ratio and absolute scales, and the representation of the numbers to the variables, i.e. the justification of the assignment of numbers to object or phenomenon, are not questioned, though the validity may be questioned. In this section we return to the notion of quality and try to clarify it while presenting our contribution.

Broadly, research refers to the activity performed by people trained to obtain knowledge through systematic procedures. Notions such as “objectivity” and “reflexivity,” “systematic,” “theory,” “evidence” and “openness” are here taken for granted in any type of research. Next, building on our empirical analysis we explain the four notions that we have identified as central to qualitative work: distinctions, process, closeness, and improved understanding. In discussing them, ultimately in relation to one another, we make their meaning even more precise. Our idea, in short, is that only when these ideas that we present separately for analytic purposes are brought together can we speak of qualitative research.

Distinctions

We believe that the possibility of making new distinctions is one the defining characteristics of qualitative research. It clearly sets it apart from quantitative analysis which works with taken-for-granted variables, albeit as mentioned, meta-analyses, for example, factor analysis may result in new variables. “Quality” refers essentially to distinctions, as already pointed out by Aristotle. He discusses the term “qualitative” commenting: “By a quality I mean that in virtue of which things are said to be qualified somehow” (Aristotle 1984:14). Quality is about what something is or has, which means that the distinction from its environment is crucial. We see qualitative research as a process in which significant new distinctions are made to the scholarly community; to make distinctions is a key aspect of obtaining new knowledge; a point, as we will see, that also has implications for “quantitative research.” The notion of being “significant” is paramount. New distinctions by themselves are not enough; just adding concepts only increases complexity without furthering our knowledge. The significance of new distinctions is judged against the communal knowledge of the research community. To enable this discussion and judgements central elements of rational discussion are required (cf. Habermas [1981] 1987 ; Davidsson [ 1988 ] 2001) to identify what is new and relevant scientific knowledge. Relatedly, Ragin alludes to the idea of new and useful knowledge at a more concrete level: “Qualitative methods are appropriate for in-depth examination of cases because they aid the identification of key features of cases. Most qualitative methods enhance data” (1994:79). When Becker ( 1963 ) studied deviant behavior and investigated how people became marihuana smokers, he made distinctions between the ways in which people learned how to smoke. This is a classic example of how the strategy of “getting close” to the material, for example the text, people or pictures that are subject to analysis, may enable researchers to obtain deeper insight and new knowledge by making distinctions – in this instance on the initial notion of learning how to smoke. Others have stressed the making of distinctions in relation to coding or theorizing. Emerson et al. ( 1995 ), for example, hold that “qualitative coding is a way of opening up avenues of inquiry,” meaning that the researcher identifies and develops concepts and analytic insights through close examination of and reflection on data (Emerson et al. 1995 :151). Goodwin and Horowitz highlight making distinctions in relation to theory-building writing: “Close engagement with their cases typically requires qualitative researchers to adapt existing theories or to make new conceptual distinctions or theoretical arguments to accommodate new data” ( 2002 : 37). In the ideal-typical quantitative research only existing and so to speak, given, variables would be used. If this is the case no new distinction are made. But, would not also many “quantitative” researchers make new distinctions?

Process does not merely suggest that research takes time. It mainly implies that qualitative new knowledge results from a process that involves several phases, and above all iteration. Qualitative research is about oscillation between theory and evidence, analysis and generating material, between first- and second -order constructs (Schütz 1962 :59), between getting in contact with something, finding sources, becoming deeply familiar with a topic, and then distilling and communicating some of its essential features. The main point is that the categories that the researcher uses, and perhaps takes for granted at the beginning of the research process, usually undergo qualitative changes resulting from what is found. Becker describes how he tested hypotheses and let the jargon of the users develop into theoretical concepts. This happens over time while the study is being conducted, exemplifying what we mean by process.

In the research process, a pilot-study may be used to get a first glance of, for example, the field, how to approach it, and what methods can be used, after which the method and theory are chosen or refined before the main study begins. Thus, the empirical material is often central from the start of the project and frequently leads to adjustments by the researcher. Likewise, during the main study categories are not fixed; the empirical material is seen in light of the theory used, but it is also given the opportunity to kick back, thereby resisting attempts to apply theoretical straightjackets (Becker 1970 :43). In this process, coding and analysis are interwoven, and thus are often important steps for getting closer to the phenomenon and deciding what to focus on next. Becker began his research by interviewing musicians close to him, then asking them to refer him to other musicians, and later on doubling his original sample of about 25 to include individuals in other professions (Becker 1973:46). Additionally, he made use of some participant observation, documents, and interviews with opiate users made available to him by colleagues. As his inductive theory of deviance evolved, Becker expanded his sample in order to fine tune it, and test the accuracy and generality of his hypotheses. In addition, he introduced a negative case and discussed the null hypothesis ( 1963 :44). His phasic career model is thus based on a research design that embraces processual work. Typically, process means to move between “theory” and “material” but also to deal with negative cases, and Becker ( 1998 ) describes how discovering these negative cases impacted his research design and ultimately its findings.

Obviously, all research is process-oriented to some degree. The point is that the ideal-typical quantitative process does not imply change of the data, and iteration between data, evidence, hypotheses, empirical work, and theory. The data, quantified variables, are, in most cases fixed. Merging of data, which of course can be done in a quantitative research process, does not mean new data. New hypotheses are frequently tested, but the “raw data is often the “the same.” Obviously, over time new datasets are made available and put into use.

Another characteristic that is emphasized in our sample is that qualitative researchers – and in particular ethnographers – can, or as Goffman put it, ought to ( 1989 ), get closer to the phenomenon being studied and their data than quantitative researchers (for example, Silverman 2009 :85). Put differently, essentially because of their methods qualitative researchers get into direct close contact with those being investigated and/or the material, such as texts, being analyzed. Becker started out his interview study, as we noted, by talking to those he knew in the field of music to get closer to the phenomenon he was studying. By conducting interviews he got even closer. Had he done more observations, he would undoubtedly have got even closer to the field.

Additionally, ethnographers’ design enables researchers to follow the field over time, and the research they do is almost by definition longitudinal, though the time in the field is studied obviously differs between studies. The general characteristic of closeness over time maximizes the chances of unexpected events, new data (related, for example, to archival research as additional sources, and for ethnography for situations not necessarily previously thought of as instrumental – what Mannay and Morgan ( 2015 ) term the “waiting field”), serendipity (Merton and Barber 2004 ; Åkerström 2013 ), and possibly reactivity, as well as the opportunity to observe disrupted patterns that translate into exemplars of negative cases. Two classic examples of this are Becker’s finding of what medical students call “crocks” (Becker et al. 1961 :317), and Geertz’s ( 1973 ) study of “deep play” in Balinese society.

By getting and staying so close to their data – be it pictures, text or humans interacting (Becker was himself a musician) – for a long time, as the research progressively focuses, qualitative researchers are prompted to continually test their hunches, presuppositions and hypotheses. They test them against a reality that often (but certainly not always), and practically, as well as metaphorically, talks back, whether by validating them, or disqualifying their premises – correctly, as well as incorrectly (Fine 2003 ; Becker 1970 ). This testing nonetheless often leads to new directions for the research. Becker, for example, says that he was initially reading psychological theories, but when facing the data he develops a theory that looks at, you may say, everything but psychological dispositions to explain the use of marihuana. Especially researchers involved with ethnographic methods have a fairly unique opportunity to dig up and then test (in a circular, continuous and temporal way) new research questions and findings as the research progresses, and thereby to derive previously unimagined and uncharted distinctions by getting closer to the phenomenon under study.

Let us stress that getting close is by no means restricted to ethnography. The notion of hermeneutic circle and hermeneutics as a general way of understanding implies that we must get close to the details in order to get the big picture. This also means that qualitative researchers can literally also make use of details of pictures as evidence (cf. Harper 2002). Thus, researchers may get closer both when generating the material or when analyzing it.

Quantitative research, we maintain, in the ideal-typical representation cannot get closer to the data. The data is essentially numbers in tables making up the variables (Franzosi 2016 :138). The data may originally have been “qualitative,” but once reduced to numbers there can only be a type of “hermeneutics” about what the number may stand for. The numbers themselves, however, are non-ambiguous. Thus, in quantitative research, interpretation, if done, is not about the data itself—the numbers—but what the numbers stand for. It follows that the interpretation is essentially done in a more “speculative” mode without direct empirical evidence (cf. Becker 2017 ).

Improved Understanding

While distinction, process and getting closer refer to the qualitative work of the researcher, improved understanding refers to its conditions and outcome of this work. Understanding cuts deeper than explanation, which to some may mean a causally verified correlation between variables. The notion of explanation presupposes the notion of understanding since explanation does not include an idea of how knowledge is gained (Manicas 2006 : 15). Understanding, we argue, is the core concept of what we call the outcome of the process when research has made use of all the other elements that were integrated in the research. Understanding, then, has a special status in qualitative research since it refers both to the conditions of knowledge and the outcome of the process. Understanding can to some extent be seen as the condition of explanation and occurs in a process of interpretation, which naturally refers to meaning (Gadamer 1990 ). It is fundamentally connected to knowing, and to the knowing of how to do things (Heidegger [1927] 2001 ). Conceptually the term hermeneutics is used to account for this process. Heidegger ties hermeneutics to human being and not possible to separate from the understanding of being ( 1988 ). Here we use it in a broader sense, and more connected to method in general (cf. Seiffert 1992 ). The abovementioned aspects – for example, “objectivity” and “reflexivity” – of the approach are conditions of scientific understanding. Understanding is the result of a circular process and means that the parts are understood in light of the whole, and vice versa. Understanding presupposes pre-understanding, or in other words, some knowledge of the phenomenon studied. The pre-understanding, even in the form of prejudices, are in qualitative research process, which we see as iterative, questioned, which gradually or suddenly change due to the iteration of data, evidence and concepts. However, qualitative research generates understanding in the iterative process when the researcher gets closer to the data, e.g., by going back and forth between field and analysis in a process that generates new data that changes the evidence, and, ultimately, the findings. Questioning, to ask questions, and put what one assumes—prejudices and presumption—in question, is central to understand something (Heidegger [1927] 2001 ; Gadamer 1990 :368–384). We propose that this iterative process in which the process of understanding occurs is characteristic of qualitative research.

Improved understanding means that we obtain scientific knowledge of something that we as a scholarly community did not know before, or that we get to know something better. It means that we understand more about how parts are related to one another, and to other things we already understand (see also Fine and Hallett 2014 ). Understanding is an important condition for qualitative research. It is not enough to identify correlations, make distinctions, and work in a process in which one gets close to the field or phenomena. Understanding is accomplished when the elements are integrated in an iterative process.

It is, moreover, possible to understand many things, and researchers, just like children, may come to understand new things every day as they engage with the world. This subjective condition of understanding – namely, that a person gains a better understanding of something –is easily met. To be qualified as “scientific,” the understanding must be general and useful to many; it must be public. But even this generally accessible understanding is not enough in order to speak of “scientific understanding.” Though we as a collective can increase understanding of everything in virtually all potential directions as a result also of qualitative work, we refrain from this “objective” way of understanding, which has no means of discriminating between what we gain in understanding. Scientific understanding means that it is deemed relevant from the scientific horizon (compare Schütz 1962 : 35–38, 46, 63), and that it rests on the pre-understanding that the scientists have and must have in order to understand. In other words, the understanding gained must be deemed useful by other researchers, so that they can build on it. We thus see understanding from a pragmatic, rather than a subjective or objective perspective. Improved understanding is related to the question(s) at hand. Understanding, in order to represent an improvement, must be an improvement in relation to the existing body of knowledge of the scientific community (James [ 1907 ] 1955). Scientific understanding is, by definition, collective, as expressed in Weber’s famous note on objectivity, namely that scientific work aims at truths “which … can claim, even for a Chinese, the validity appropriate to an empirical analysis” ([1904] 1949 :59). By qualifying “improved understanding” we argue that it is a general defining characteristic of qualitative research. Becker‘s ( 1966 ) study and other research of deviant behavior increased our understanding of the social learning processes of how individuals start a behavior. And it also added new knowledge about the labeling of deviant behavior as a social process. Few studies, of course, make the same large contribution as Becker’s, but are nonetheless qualitative research.

Understanding in the phenomenological sense, which is a hallmark of qualitative research, we argue, requires meaning and this meaning is derived from the context, and above all the data being analyzed. The ideal-typical quantitative research operates with given variables with different numbers. This type of material is not enough to establish meaning at the level that truly justifies understanding. In other words, many social science explanations offer ideas about correlations or even causal relations, but this does not mean that the meaning at the level of the data analyzed, is understood. This leads us to say that there are indeed many explanations that meet the criteria of understanding, for example the explanation of how one becomes a marihuana smoker presented by Becker. However, we may also understand a phenomenon without explaining it, and we may have potential explanations, or better correlations, that are not really understood.

We may speak more generally of quantitative research and its data to clarify what we see as an important distinction. The “raw data” that quantitative research—as an idealtypical activity, refers to is not available for further analysis; the numbers, once created, are not to be questioned (Franzosi 2016 : 138). If the researcher is to do “more” or “change” something, this will be done by conjectures based on theoretical knowledge or based on the researcher’s lifeworld. Both qualitative and quantitative research is based on the lifeworld, and all researchers use prejudices and pre-understanding in the research process. This idea is present in the works of Heidegger ( 2001 ) and Heisenberg (cited in Franzosi 2010 :619). Qualitative research, as we argued, involves the interaction and questioning of concepts (theory), data, and evidence.

Ragin ( 2004 :22) points out that “a good definition of qualitative research should be inclusive and should emphasize its key strengths and features, not what it lacks (for example, the use of sophisticated quantitative techniques).” We define qualitative research as an iterative process in which improved understanding to the scientific community is achieved by making new significant distinctions resulting from getting closer to the phenomenon studied. Qualitative research, as defined here, is consequently a combination of two criteria: (i) how to do things –namely, generating and analyzing empirical material, in an iterative process in which one gets closer by making distinctions, and (ii) the outcome –improved understanding novel to the scholarly community. Is our definition applicable to our own study? In this study we have closely read the empirical material that we generated, and the novel distinction of the notion “qualitative research” is the outcome of an iterative process in which both deduction and induction were involved, in which we identified the categories that we analyzed. We thus claim to meet the first criteria, “how to do things.” The second criteria cannot be judged but in a partial way by us, namely that the “outcome” —in concrete form the definition-improves our understanding to others in the scientific community.

We have defined qualitative research, or qualitative scientific work, in relation to quantitative scientific work. Given this definition, qualitative research is about questioning the pre-given (taken for granted) variables, but it is thus also about making new distinctions of any type of phenomenon, for example, by coining new concepts, including the identification of new variables. This process, as we have discussed, is carried out in relation to empirical material, previous research, and thus in relation to theory. Theory and previous research cannot be escaped or bracketed. According to hermeneutic principles all scientific work is grounded in the lifeworld, and as social scientists we can thus never fully bracket our pre-understanding.

We have proposed that quantitative research, as an idealtype, is concerned with pre-determined variables (Small 2008 ). Variables are epistemically fixed, but can vary in terms of dimensions, such as frequency or number. Age is an example; as a variable it can take on different numbers. In relation to quantitative research, qualitative research does not reduce its material to number and variables. If this is done the process of comes to a halt, the researcher gets more distanced from her data, and it makes it no longer possible to make new distinctions that increase our understanding. We have above discussed the components of our definition in relation to quantitative research. Our conclusion is that in the research that is called quantitative there are frequent and necessary qualitative elements.

Further, comparative empirical research on researchers primarily working with ”quantitative” approaches and those working with ”qualitative” approaches, we propose, would perhaps show that there are many similarities in practices of these two approaches. This is not to deny dissimilarities, or the different epistemic and ontic presuppositions that may be more or less strongly associated with the two different strands (see Goertz and Mahoney 2012 ). Our point is nonetheless that prejudices and preconceptions about researchers are unproductive, and that as other researchers have argued, differences may be exaggerated (e.g., Becker 1996 : 53, 2017 ; Marchel and Owens 2007 :303; Ragin 1994 ), and that a qualitative dimension is present in both kinds of work.

Several things follow from our findings. The most important result is the relation to quantitative research. In our analysis we have separated qualitative research from quantitative research. The point is not to label individual researchers, methods, projects, or works as either “quantitative” or “qualitative.” By analyzing, i.e., taking apart, the notions of quantitative and qualitative, we hope to have shown the elements of qualitative research. Our definition captures the elements, and how they, when combined in practice, generate understanding. As many of the quotations we have used suggest, one conclusion of our study holds that qualitative approaches are not inherently connected with a specific method. Put differently, none of the methods that are frequently labelled “qualitative,” such as interviews or participant observation, are inherently “qualitative.” What matters, given our definition, is whether one works qualitatively or quantitatively in the research process, until the results are produced. Consequently, our analysis also suggests that those researchers working with what in the literature and in jargon is often called “quantitative research” are almost bound to make use of what we have identified as qualitative elements in any research project. Our findings also suggest that many” quantitative” researchers, at least to some extent, are engaged with qualitative work, such as when research questions are developed, variables are constructed and combined, and hypotheses are formulated. Furthermore, a research project may hover between “qualitative” and “quantitative” or start out as “qualitative” and later move into a “quantitative” (a distinct strategy that is not similar to “mixed methods” or just simply combining induction and deduction). More generally speaking, the categories of “qualitative” and “quantitative,” unfortunately, often cover up practices, and it may lead to “camps” of researchers opposing one another. For example, regardless of the researcher is primarily oriented to “quantitative” or “qualitative” research, the role of theory is neglected (cf. Swedberg 2017 ). Our results open up for an interaction not characterized by differences, but by different emphasis, and similarities.

Let us take two examples to briefly indicate how qualitative elements can fruitfully be combined with quantitative. Franzosi ( 2010 ) has discussed the relations between quantitative and qualitative approaches, and more specifically the relation between words and numbers. He analyzes texts and argues that scientific meaning cannot be reduced to numbers. Put differently, the meaning of the numbers is to be understood by what is taken for granted, and what is part of the lifeworld (Schütz 1962 ). Franzosi shows how one can go about using qualitative and quantitative methods and data to address scientific questions analyzing violence in Italy at the time when fascism was rising (1919–1922). Aspers ( 2006 ) studied the meaning of fashion photographers. He uses an empirical phenomenological approach, and establishes meaning at the level of actors. In a second step this meaning, and the different ideal-typical photographers constructed as a result of participant observation and interviews, are tested using quantitative data from a database; in the first phase to verify the different ideal-types, in the second phase to use these types to establish new knowledge about the types. In both of these cases—and more examples can be found—authors move from qualitative data and try to keep the meaning established when using the quantitative data.

A second main result of our study is that a definition, and we provided one, offers a way for research to clarify, and even evaluate, what is done. Hence, our definition can guide researchers and students, informing them on how to think about concrete research problems they face, and to show what it means to get closer in a process in which new distinctions are made. The definition can also be used to evaluate the results, given that it is a standard of evaluation (cf. Hammersley 2007 ), to see whether new distinctions are made and whether this improves our understanding of what is researched, in addition to the evaluation of how the research was conducted. By making what is qualitative research explicit it becomes easier to communicate findings, and it is thereby much harder to fly under the radar with substandard research since there are standards of evaluation which make it easier to separate “good” from “not so good” qualitative research.

To conclude, our analysis, which ends with a definition of qualitative research can thus both address the “internal” issues of what is qualitative research, and the “external” critiques that make it harder to do qualitative research, to which both pressure from quantitative methods and general changes in society contribute.

Åkerström, Malin. 2013. Curiosity and serendipity in qualitative research. Qualitative Sociology Review 9 (2): 10–18.

Google Scholar  

Alford, Robert R. 1998. The craft of inquiry. Theories, methods, evidence . Oxford: Oxford University Press.

Alvesson, Mats, and Dan Kärreman. 2011. Qualitative research and theory development. Mystery as method . London: SAGE Publications.

Book   Google Scholar  

Aspers, Patrik. 2006. Markets in Fashion, A Phenomenological Approach. London Routledge.

Atkinson, Paul. 2005. Qualitative research. Unity and diversity. Forum: Qualitative Social Research 6 (3): 1–15.

Becker, Howard S. 1963. Outsiders. Studies in the sociology of deviance . New York: The Free Press.

Becker, Howard S. 1966. Whose side are we on? Social Problems 14 (3): 239–247.

Article   Google Scholar  

Becker, Howard S. 1970. Sociological work. Method and substance . New Brunswick: Transaction Books.

Becker, Howard S. 1996. The epistemology of qualitative research. In Ethnography and human development. Context and meaning in social inquiry , ed. Jessor Richard, Colby Anne, and Richard A. Shweder, 53–71. Chicago: University of Chicago Press.

Becker, Howard S. 1998. Tricks of the trade. How to think about your research while you're doing it . Chicago: University of Chicago Press.

Becker, Howard S. 2017. Evidence . Chigaco: University of Chicago Press.

Becker, Howard, Blanche Geer, Everett Hughes, and Anselm Strauss. 1961. Boys in White, student culture in medical school . New Brunswick: Transaction Publishers.

Berezin, Mabel. 2014. How do we know what we mean? Epistemological dilemmas in cultural sociology. Qualitative Sociology 37 (2): 141–151.

Best, Joel. 2004. Defining qualitative research. In Workshop on Scientific Foundations of Qualitative Research , eds . Charles, Ragin, Joanne, Nagel, and Patricia White, 53-54. http://www.nsf.gov/pubs/2004/nsf04219/nsf04219.pdf .

Biernacki, Richard. 2014. Humanist interpretation versus coding text samples. Qualitative Sociology 37 (2): 173–188.

Blumer, Herbert. 1969. Symbolic interactionism: Perspective and method . Berkeley: University of California Press.

Brady, Henry, David Collier, and Jason Seawright. 2004. Refocusing the discussion of methodology. In Rethinking social inquiry. Diverse tools, shared standards , ed. Brady Henry and Collier David, 3–22. Lanham: Rowman and Littlefield.

Brown, Allison P. 2010. Qualitative method and compromise in applied social research. Qualitative Research 10 (2): 229–248.

Charmaz, Kathy. 2006. Constructing grounded theory . London: Sage.

Corte, Ugo, and Katherine Irwin. 2017. “The Form and Flow of Teaching Ethnographic Knowledge: Hands-on Approaches for Learning Epistemology” Teaching Sociology 45(3): 209-219.

Creswell, John W. 2009. Research design. Qualitative, quantitative, and mixed method approaches . 3rd ed. Thousand Oaks: SAGE Publications.

Davidsson, David. 1988. 2001. The myth of the subjective. In Subjective, intersubjective, objective , ed. David Davidsson, 39–52. Oxford: Oxford University Press.

Denzin, Norman K. 1970. The research act: A theoretical introduction to Ssociological methods . Chicago: Aldine Publishing Company Publishers.

Denzin, Norman K., and Yvonna S. Lincoln. 2003. Introduction. The discipline and practice of qualitative research. In Collecting and interpreting qualitative materials , ed. Norman K. Denzin and Yvonna S. Lincoln, 1–45. Thousand Oaks: SAGE Publications.

Denzin, Norman K., and Yvonna S. Lincoln. 2005. Introduction. The discipline and practice of qualitative research. In The Sage handbook of qualitative research , ed. Norman K. Denzin and Yvonna S. Lincoln, 1–32. Thousand Oaks: SAGE Publications.

Emerson, Robert M., ed. 1988. Contemporary field research. A collection of readings . Prospect Heights: Waveland Press.

Emerson, Robert M., Rachel I. Fretz, and Linda L. Shaw. 1995. Writing ethnographic fieldnotes . Chicago: University of Chicago Press.

Esterberg, Kristin G. 2002. Qualitative methods in social research . Boston: McGraw-Hill.

Fine, Gary Alan. 1995. Review of “handbook of qualitative research.” Contemporary Sociology 24 (3): 416–418.

Fine, Gary Alan. 2003. “ Toward a Peopled Ethnography: Developing Theory from Group Life.” Ethnography . 4(1):41-60.

Fine, Gary Alan, and Black Hawk Hancock. 2017. The new ethnographer at work. Qualitative Research 17 (2): 260–268.

Fine, Gary Alan, and Timothy Hallett. 2014. Stranger and stranger: Creating theory through ethnographic distance and authority. Journal of Organizational Ethnography 3 (2): 188–203.

Flick, Uwe. 2002. Qualitative research. State of the art. Social Science Information 41 (1): 5–24.

Flick, Uwe. 2007. Designing qualitative research . London: SAGE Publications.

Frankfort-Nachmias, Chava, and David Nachmias. 1996. Research methods in the social sciences . 5th ed. London: Edward Arnold.

Franzosi, Roberto. 2010. Sociology, narrative, and the quality versus quantity debate (Goethe versus Newton): Can computer-assisted story grammars help us understand the rise of Italian fascism (1919- 1922)? Theory and Society 39 (6): 593–629.

Franzosi, Roberto. 2016. From method and measurement to narrative and number. International journal of social research methodology 19 (1): 137–141.

Gadamer, Hans-Georg. 1990. Wahrheit und Methode, Grundzüge einer philosophischen Hermeneutik . Band 1, Hermeneutik. Tübingen: J.C.B. Mohr.

Gans, Herbert. 1999. Participant Observation in an Age of “Ethnography”. Journal of Contemporary Ethnography 28 (5): 540–548.

Geertz, Clifford. 1973. The interpretation of cultures . New York: Basic Books.

Gilbert, Nigel. 2009. Researching social life . 3rd ed. London: SAGE Publications.

Glaeser, Andreas. 2014. Hermeneutic institutionalism: Towards a new synthesis. Qualitative Sociology 37: 207–241.

Glaser, Barney G., and Anselm L. Strauss. [1967] 2010. The discovery of grounded theory. Strategies for qualitative research. Hawthorne: Aldine.

Goertz, Gary, and James Mahoney. 2012. A tale of two cultures: Qualitative and quantitative research in the social sciences . Princeton: Princeton University Press.

Goffman, Erving. 1989. On fieldwork. Journal of Contemporary Ethnography 18 (2): 123–132.

Goodwin, Jeff, and Ruth Horowitz. 2002. Introduction. The methodological strengths and dilemmas of qualitative sociology. Qualitative Sociology 25 (1): 33–47.

Habermas, Jürgen. [1981] 1987. The theory of communicative action . Oxford: Polity Press.

Hammersley, Martyn. 2007. The issue of quality in qualitative research. International Journal of Research & Method in Education 30 (3): 287–305.

Hammersley, Martyn. 2013. What is qualitative research? Bloomsbury Publishing.

Hammersley, Martyn. 2018. What is ethnography? Can it survive should it? Ethnography and Education 13 (1): 1–17.

Hammersley, Martyn, and Paul Atkinson. 2007. Ethnography. Principles in practice . London: Tavistock Publications.

Heidegger, Martin. [1927] 2001. Sein und Zeit . Tübingen: Max Niemeyer Verlag.

Heidegger, Martin. 1988. 1923. Ontologie. Hermeneutik der Faktizität, Gesamtausgabe II. Abteilung: Vorlesungen 1919-1944, Band 63, Frankfurt am Main: Vittorio Klostermann.

Hempel, Carl G. 1966. Philosophy of the natural sciences . Upper Saddle River: Prentice Hall.

Hood, Jane C. 2006. Teaching against the text. The case of qualitative methods. Teaching Sociology 34 (3): 207–223.

James, William. 1907. 1955. Pragmatism . New York: Meredian Books.

Jovanović, Gordana. 2011. Toward a social history of qualitative research. History of the Human Sciences 24 (2): 1–27.

Kalof, Linda, Amy Dan, and Thomas Dietz. 2008. Essentials of social research . London: Open University Press.

Katz, Jack. 2015. Situational evidence: Strategies for causal reasoning from observational field notes. Sociological Methods & Research 44 (1): 108–144.

King, Gary, Robert O. Keohane, S. Sidney, and S. Verba. 1994. Designing social inquiry. In Scientific inference in qualitative research . Princeton: Princeton University Press.

Chapter   Google Scholar  

Lamont, Michelle. 2004. Evaluating qualitative research: Some empirical findings and an agenda. In Report from workshop on interdisciplinary standards for systematic qualitative research , ed. M. Lamont and P. White, 91–95. Washington, DC: National Science Foundation.

Lamont, Michèle, and Ann Swidler. 2014. Methodological pluralism and the possibilities and limits of interviewing. Qualitative Sociology 37 (2): 153–171.

Lazarsfeld, Paul, and Alan Barton. 1982. Some functions of qualitative analysis in social research. In The varied sociology of Paul Lazarsfeld , ed. Patricia Kendall, 239–285. New York: Columbia University Press.

Lichterman, Paul, and Isaac Reed I (2014), Theory and Contrastive Explanation in Ethnography. Sociological methods and research. Prepublished 27 October 2014; https://doi.org/10.1177/0049124114554458 .

Lofland, John, and Lyn Lofland. 1995. Analyzing social settings. A guide to qualitative observation and analysis . 3rd ed. Belmont: Wadsworth.

Lofland, John, David A. Snow, Leon Anderson, and Lyn H. Lofland. 2006. Analyzing social settings. A guide to qualitative observation and analysis . 4th ed. Belmont: Wadsworth/Thomson Learning.

Long, Adrew F., and Mary Godfrey. 2004. An evaluation tool to assess the quality of qualitative research studies. International Journal of Social Research Methodology 7 (2): 181–196.

Lundberg, George. 1951. Social research: A study in methods of gathering data . New York: Longmans, Green and Co..

Malinowski, Bronislaw. 1922. Argonauts of the Western Pacific: An account of native Enterprise and adventure in the archipelagoes of Melanesian New Guinea . London: Routledge.

Manicas, Peter. 2006. A realist philosophy of science: Explanation and understanding . Cambridge: Cambridge University Press.

Marchel, Carol, and Stephanie Owens. 2007. Qualitative research in psychology. Could William James get a job? History of Psychology 10 (4): 301–324.

McIntyre, Lisa J. 2005. Need to know. Social science research methods . Boston: McGraw-Hill.

Merton, Robert K., and Elinor Barber. 2004. The travels and adventures of serendipity. A Study in Sociological Semantics and the Sociology of Science . Princeton: Princeton University Press.

Mannay, Dawn, and Melanie Morgan. 2015. Doing ethnography or applying a qualitative technique? Reflections from the ‘waiting field‘. Qualitative Research 15 (2): 166–182.

Neuman, Lawrence W. 2007. Basics of social research. Qualitative and quantitative approaches . 2nd ed. Boston: Pearson Education.

Ragin, Charles C. 1994. Constructing social research. The unity and diversity of method . Thousand Oaks: Pine Forge Press.

Ragin, Charles C. 2004. Introduction to session 1: Defining qualitative research. In Workshop on Scientific Foundations of Qualitative Research , 22, ed. Charles C. Ragin, Joane Nagel, Patricia White. http://www.nsf.gov/pubs/2004/nsf04219/nsf04219.pdf

Rawls, Anne. 2018. The Wartime narrative in US sociology, 1940–7: Stigmatizing qualitative sociology in the name of ‘science,’ European Journal of Social Theory (Online first).

Schütz, Alfred. 1962. Collected papers I: The problem of social reality . The Hague: Nijhoff.

Seiffert, Helmut. 1992. Einführung in die Hermeneutik . Tübingen: Franke.

Silverman, David. 2005. Doing qualitative research. A practical handbook . 2nd ed. London: SAGE Publications.

Silverman, David. 2009. A very short, fairly interesting and reasonably cheap book about qualitative research . London: SAGE Publications.

Silverman, David. 2013. What counts as qualitative research? Some cautionary comments. Qualitative Sociology Review 9 (2): 48–55.

Small, Mario L. 2009. “How many cases do I need?” on science and the logic of case selection in field-based research. Ethnography 10 (1): 5–38.

Small, Mario L 2008. Lost in translation: How not to make qualitative research more scientific. In Workshop on interdisciplinary standards for systematic qualitative research, ed in Michelle Lamont, and Patricia White, 165–171. Washington, DC: National Science Foundation.

Snow, David A., and Leon Anderson. 1993. Down on their luck: A study of homeless street people . Berkeley: University of California Press.

Snow, David A., and Calvin Morrill. 1995. New ethnographies: Review symposium: A revolutionary handbook or a handbook for revolution? Journal of Contemporary Ethnography 24 (3): 341–349.

Strauss, Anselm L. 2003. Qualitative analysis for social scientists . 14th ed. Chicago: Cambridge University Press.

Strauss, Anselm L., and Juliette M. Corbin. 1998. Basics of qualitative research. Techniques and procedures for developing grounded theory . 2nd ed. Thousand Oaks: Sage Publications.

Swedberg, Richard. 2017. Theorizing in sociological research: A new perspective, a new departure? Annual Review of Sociology 43: 189–206.

Swedberg, Richard. 1990. The new 'Battle of Methods'. Challenge January–February 3 (1): 33–38.

Timmermans, Stefan, and Iddo Tavory. 2012. Theory construction in qualitative research: From grounded theory to abductive analysis. Sociological Theory 30 (3): 167–186.

Trier-Bieniek, Adrienne. 2012. Framing the telephone interview as a participant-centred tool for qualitative research. A methodological discussion. Qualitative Research 12 (6): 630–644.

Valsiner, Jaan. 2000. Data as representations. Contextualizing qualitative and quantitative research strategies. Social Science Information 39 (1): 99–113.

Weber, Max. 1904. 1949. Objectivity’ in social Science and social policy. Ed. Edward A. Shils and Henry A. Finch, 49–112. New York: The Free Press.

Download references

Acknowledgements

Financial Support for this research is given by the European Research Council, CEV (263699). The authors are grateful to Susann Krieglsteiner for assistance in collecting the data. The paper has benefitted from the many useful comments by the three reviewers and the editor, comments by members of the Uppsala Laboratory of Economic Sociology, as well as Jukka Gronow, Sebastian Kohl, Marcin Serafin, Richard Swedberg, Anders Vassenden and Turid Rødne.

Author information

Authors and affiliations.

Department of Sociology, Uppsala University, Uppsala, Sweden

Patrik Aspers

Seminar for Sociology, Universität St. Gallen, St. Gallen, Switzerland

Department of Media and Social Sciences, University of Stavanger, Stavanger, Norway

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Patrik Aspers .

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Reprints and permissions

About this article

Aspers, P., Corte, U. What is Qualitative in Qualitative Research. Qual Sociol 42 , 139–160 (2019). https://doi.org/10.1007/s11133-019-9413-7

Download citation

Published : 27 February 2019

Issue Date : 01 June 2019

DOI : https://doi.org/10.1007/s11133-019-9413-7

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Qualitative research
• Epistemology
• Philosophy of science
• Phenomenology
• Find a journal
• Publish with us
• Track your research

• Open access
• Published: 31 August 2024

Motivation, responsibilities, and experiences of HIV/AIDS counselors in the Volta Region of Ghana: a descriptive phenomenological study

• Festus Dwomoh   ORCID: orcid.org/0009-0007-4561-5016 1 ,
• Mbuyiselo Douglas   ORCID: orcid.org/0000-0003-2777-8384 2 ,
• Veronica O. Charles-Unadike   ORCID: orcid.org/0009-0009-9061-4965 3 ,
• Joyce Komesuor   ORCID: orcid.org/0000-0003-4016-6948 3 &
• Emmanuel Manu   ORCID: orcid.org/0000-0002-2724-3649 3  

AIDS Research and Therapy volume  21 , Article number:  60 ( 2024 ) Cite this article

Metrics details

The provision of professional counseling services for persons living with human immunodeficiency virus (PLHIV) is crucial in the prevention and treatment continuum of the disease. However, for counselors of people infected with the human immunodeficiency virus (HIV) leading to acquired immune deficiency syndrome (AIDS) to give their best, their motivations to become counselors and the challenges they face in their line of duty need to be contextually understood and addressed. We ascertained the roles, motivations, and experiences of HIV/AIDS counselors in the Volta Region of Ghana to inform HIV/AIDS counseling decision-making in the region and the country.

A phenomenological study conducted among sixteen (16) HIV/AIDS counselors from five HIV/AIDS sentinel sites in the Volta region of Ghana, recruited through a purposive sampling approach and interviewed to ascertain their HIV/AIDS counseling-related experiences. The data were thematically analyzed using the Atlas. ti software, and sub-themes supported with verbatim quotes.

Five motives for becoming an HIV/AIDS counselor were found. These include being randomly assigned to the unit, developing interest in the job, because of the status of a relative, witnessing bad attitudes of healthcare providers, and seeing HIV- clients lacking knowledge of the condition. The study found that these counselors performed six core roles: providing nutritional counseling, educating clients on HIV, treatment, and medication provision, conducting testing and comprehensive counseling of clients, providing social support to clients, and offering financial support to clients. The experiences these counselors had were boosting clients’ health status, counseling clients back to a normal mental state, cooperation from clients, participants gaining knowledge on HIV through counseling, counseling clients to accept their status, and when a client delivered an HIV-negative baby. Their negative experiences included clients denying their HIV status, clients defaulting on their treatment, uncooperative clients, death of clients due to fear of breach of confidentiality, self-stigmatization among clients, and the myths some people hold towards HIV/AIDS.

By organizing capacity-building training programs for HIV/AIDS counselors in the Volta region and addressing the negative experiences they encounter, they could be empowered to provide effective counseling, curative, and social services to people living with HIV in the region, leading to improved health outcomes.

Introduction

Counseling plays a crucial role in the prevention and management of the human immunodeficiency virus (HIV) and acquired immune deficiency syndrome (AIDS) [ 1 ]. When individuals learn about their HIV status, they often experience significant psychosocial and psychological distress associated with the fear of rejection, social stigma, disease progression, and the uncertainties about future HIV management [ 2 ]. HIV/AIDS counselors are responsible for providing the necessary time, attention, and respect to help these individuals live peacefully and resourcefully with the virus [ 3 ].

HIV/AIDS counseling seeks to address the biopsychosocial needs of persons living with HIV (PLHIV) for timely management [ 4 ]. Counselors assist in preventing HIV transmission and support those affected by the virus to improve their health outcomes [ 5 ]. They also promote behavioral changes in affected individuals and collaborate with their families to encourage treatment adherence and sustain behavior changes [ 6 ]. Furthermore, HIV/AIDS counselors provide support and education to PLHIV, which is crucial for preventing the spread of the disease [ 7 ]. They help patients and families understand their diagnosis, manage their treatment plans, and cope with the social and emotional impacts of living with a chronic illness [ 8 ]. Additionally, counselors collaborate with other healthcare professionals to coordinate care for their clients [ 9 ].

The demanding role of HIV/AIDS counselors underscores the necessity for motivated counselors, as the quality of the counseling services depends on their performance. It is crucial to have experienced, dedicated, and motivated HIV/AIDS counselors providing counseling services to PLHIV [ 10 ]. A lack of motivation among HIV/AIDS counselors can affect the care provided to PLHIV and may even lead some to leave their jobs [ 11 , 12 ].

Despite the vital role of HIV/AIDS counselors, they face many challenges, many of which are underreported. For example, often face stigma and discrimination due to the nature of their work [ 13 ]. Some people believe HIV is contracted through witchcraft and promiscuity, which undermines the efforts of HIV/AIDS counselors in combating the disease [ 14 ]. These challenges are context-specific to the African continent. In South Africa, for instance, challenges such as confidentiality, stigma, emotional responses when informing clients of their HIV status, cultural and contextual factors, situational stressors related to the work environment, and inadequate support from their work infrastructure are some of the issues HIV/AIDS counselors encounter [ 15 ]. In Nigeria, poor governance, weak institutions, poverty, and cultural norms and practices have been found to negatively affect HIV/AIDS counseling in general [ 16 ].

In Kenya, training needs, lack of in-depth understanding of the root causes of risky sexual behaviors among PLHIV, especially among men that sleep with men, and perceived intractability of clients’ issues have been reported as some of the challenges facing HIV/AIDS [ 17 ].

In Ghana, HIV/AIDs remain a significant public health issue, with an estimated prevalence of 1.8% according to the Ghana AIDS Commission [ 18 ]. As of the end of 2022, there were a total number of 354,927 people living with HIV in the country, with an estimated 16,574 new cases in 2023 [ 19 ]. The Volta region ranks seventh out of the sixteen regions in terms of HIV burden, with 16,996 HIV/AIDS cases. To reduce the transmission of the virus and improve the health outcomes of those already affected in the region, it is essential to motivate HIV/AIDs counselors and address context-specific challenges they face.

Despite the important contributions of HIV/AIDS counselors in Ghana, the literature on HIV/AIDs in the country has often overlooked their crucial role. While some studies focus solely on the experiences of HIV/AIDs counselors [ 20 , 21 , 22 , 23 ], most literature emphasizes HIV counseling and testing [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ]. In the Volta region, research has primarily focused on the epidemiology of the disease, its risk factors, and transmission patterns [ 36 , 37 , 38 , 39 , 40 ]. Therefore, the specific roles, motivations, and experiences of HIV/AIDS counselors have not been comprehensively documented in the country to inform policy on HIV/AIDS counseling. This study aims to contribute to the existing literature on HIV/AIDS by exploring the core roles, motivations, and experiences of HIV/AIDS counselors in the Volta region of Ghana, following the standards for reporting qualitative research (SRQR) as outlined by O’Brien et al. [ 41 ].

Study site description

The study was conducted at five HIV/AIDS sentinel sites in the Volta Region of Ghana. The region has a total of 749 health facilities, including 29 hospitals, 156 health centers, 44 clinics, 4 polyclinics, 14 maternity homes, 452 CHPS compounds, and 50 private health facilities [ 42 ]. Among these, the Volta Regional Hospital, the Ho Teaching Hospital, the Bator Catholic Hospital, the Adidome Hospital and the Torgorme Hospital serve as the HIV/AIDS sentinel sites, with certified HIV/AIDS counselors. These five facilities were specifically selected for the study. According to the 2021 population and housing census figures, the region has an estimated population of 1,659,040 [ 42 , 43 ].

Map of Ghana indicating the Volta Region

Study design

A qualitative approach, using a descriptive phenomenological design, was employed to explore the motivation, responsibilities, and experiences of HIV/AIDS counselors in the Volta Region of Ghana. Descriptive phenomenology suggests that a phenomenon should be described rather than explained or investigated for its causal relationship [ 44 ]. It examines and aims to comprehend the subjective human lived experience concerning a phenomenon as it appears in a natural context [ 45 ]. This approach enabled the participants to share a narrative about their lived experiences. Since there is a paucity of research data on the motivation, responsibilities, and experiences of HIV/AIDS counselors in the Volta Region of Ghana, descriptive phenomenology facilitated the elucidation of poorly understood aspects of this human lived experience.

Researchers’ characteristics and reflexivity

The research team consisted of an Associate Professor experienced in qualitative research (MD), three Senior Lecturers (EM, JK & VC) and a public health degree holder (FD), all with qualitative research experience. To ensure objectivity, we bracketed our biases by identifying and acknowledging our preconceptions and assumptions and setting them aside during the interview and data analysis processes in order not to misrepresent participants’ meanings and experiences, as explained by Chan and colleagues [ 46 ].

Study population

The study was conducted among HIV/AIDS counselors at the five (5) HIV/AIDS sentinel sites in the Volta region of Ghana. According to the records of the Volta Regional Health Directorate, there were a total of twenty-five (25) certified HIV/AIDS counselors across these sites at the time of the study. Of these twenty-five (25) certified counselors, eighteen (18) met the inclusion criteria of having at least three (3) years of experience working as HIV/AIDS counselors. However, two of these eligible counselors were on leave during data collection and were thus excluded from the study.

Sample size determination

The sample for the study consisted of sixteen (16) purposely selected certified HIV/AIDS counselors from the five (5) sentinel sites. Although data saturation could have been reached by the 13th interview [ 47 ], we decided to interview all the eligible participants (16) since the total available sample was small enough (16 people) to include everyone.

Sampling procedure

At each sentinel site, all potential participants who met the inclusion criteria and were on duty were purposely recruited and interviewed. Therefore, a census approach was followed in recruiting eligible participants at the various sentinel sites for the study.

Data collection tool

A semi-structured interview guide was used to moderate the interviews and gather data for this study. This approach was preferred because it provided structure, focusing on core issues while allowing flexibility for participants to express themselves and for the interviewer to probe for clarity when necessary. The guide was designed to explore four thematic areas: the socio-demographic characteristics of the participants, their motivation to become HIV counselors, their responsibilities, and their experiences as HIV/AIDS counselors.

The guide was pre-tested among two (2) HIV/AIDS counselors from the Oti region, which has similar demographic characteristics to the Volta Region. This pre-testing aimed to identify and clarify any areas of the guide that needed adjustment before the actual data collection.

Data collection procedure

In-depth individual interviews (IDIs) were conducted with participants from June 10, 2023 to July 17, 2023. IDIs were preferred because participants were scattered across the various sentinel sites in the region, making it financially burdensome to converge them at one place for focus group discussion. Additionally, participants may have felt more comfortable expressing themselves in one-on-one interviews compared to group settings, influencing the choice of IDIs [ 48 ].

The interviews were moderated by the Principal Investigator (FD), a Bachelor of Public Health Student at the time, along with an experienced team member under the supervision of the team lead (EM). Each interview lasted an average of 45 min and was conducted in English, as all participants had tertiary-level education. To ensure privacy, interviews were held in secluded rooms away from the public. Hand-written notes (with pens and notebooks) and call recorders were used to document the interviews. The notes were taken by a team member (either JK or VC) while the interviews were recorded by the Principal Investigator (FD). The use of both call recorders and handwritten notes was to ensure that interviews were not halted as a result of equipment breakdown during the interview process and also to fall back on the notes as a backup in case of data loss.The handwritten notes also helped to capture nonverbal cues of the participants.

Data analysis

Data collection and analysis were conducted concurrently. Each interview was transcribed and compiled into a Microsoft Word file, from which codes, sub-themes and themes were developed using the Atlas ti. v7.5 software. To reduce subjectivity in the analysis process, the data were analyzed by two research team members [FD and EM]. The data analysis followed the descriptive phenomenological method proposed by Colaizzi [ 49 ]. The steps employed included: (1) repeated reading of the transcripts to obtain a general sense of the whole content, (2) extracting and recording significant statements related to the phenomenon under study for each transcript, (3) formulating meanings from these significant statements, (4) sorting the formulated meanings into categories, clusters of themes, and overarching themes, (5) integrating the findings into an exhaustive description of the phenomenon under study, (6) describing the fundamental structure of the phenomenon, and (7) conducting member checking with seven of the participants to validate the descriptive results.

The authors met regularly to discuss and verify the accuracy of the emerging themes and their meanings. In cases of disagreement, an independent coder served as an arbitrator to resolve the issues. A theme was not accepted until an agreement was reached between the two authors. Member checking was utilized to ensure the rigor of the analysis process. The themes that emerged capturing the motivations, responsibilities, and experiences of HIV/AIDS counselors in the Volta Region of Ghana were emailed to all the participants for verification and confirmation of the researchers’ interpretation of their experiences. All seven participants confirmed that the results accurately reflected what they said during the interview process. Verbatim quotes from participants were used to support the study findings.

Following Lincoln and Guba’s criteria [ 50 ], the credibility of the study findings was ensured through several measures. First, rapport was developed between the research team and HIV/AIDS counselors before the study commenced, encouraging participants to share sensitive but vital information openly. Additionally, peers with expertise in qualitative research were consulted to assist in the study design. Dependability of the study findings was guaranteed through the involvement of professional qualitative researchers on the research team and the evaluation of processes, transcripts, and findings by peers with qualitative expertise to minimize bias. Transferability of the findings was ensured by providing a detailed description of the methods, ensuring that they could be replicated in similar settings if needed. Finally, confirmability was ensured by having seven participants review the transcripts and the results for approval, thus validating the accuracy of the researcher’s interpretations and ensuring the findings were grounded in the participants’ experiences.

Socio-demographic characteristics of participants

The total number of participants in the study was sixteen (16). The majority, 10 (62.5%), were females. Additionally, half, 8 (50%), were aged between 30 and 40 years. most participants, 11 (68.8%) were Ewe. All participants, 16 (100%), were Christians and the majority, 9 (56.2%), were single. All participants had a tertiary educational background, and the majority, 9 (56.3%), had working experience between 3 and 5 years (Table  1 ).

Thematic findings

From the data, four (4) main themes, along with their accompanying sub-themes, were generated. These themes are: motivation to become HIV/AIDS counselor (random assignment, personal interest, HIV status of a relative, bad attitude of healthcare workers), responsibilities as an HIV/AIDS counselor (nutritional counseling, education, counseling on medication, comprehensive counseling and testing, provision of social support), positive experiences as an HIV/AIDS counselor (boosting client’s health status, counseling client back to a normal psychological state, client’s acceptance of their HIV/AIDS status, gaining knowledge on HIV/AIDS, and client delivering an HIV negative child) and the negative experiences as an HIV/AIDS counselor (denial of HIV status, treatment default, uncooperative clients, self-stigmatization, death of a client, myths surrounding HIV/AIDS). The themes and their corresponding sub-themes are presented in Table  2 .

Motivation to become an HIV/AIDS counsellor

About what motivated participants to become HIV/AIDS counselors, five sub-themes emerged. These include being randomly assigned to the unit, developing an interest in the job, having a relative with HIV status, witnessing the bad attitudes of healthcare providers, and seeing HIV- positive clients lacking knowledge of their condition.

Random assignment as an HIV/AIDS counselor

Some of the respondents indicated that they had no specific motivation to become HIV/AIDS counselors. Rather, they were simply assigned to the antiretroviral therapy (ART) unit and began working there as counselors before receiving formal training. This sentiment is exemplified by the following story:

“I am a practitioner in the health system who was assigned to the unit based on my profession as a nurse to work with the HIV unit or the department. The HIV/AIDS unit is part of the healthcare system. So , when I was sent here , I knew it was just a service to humanity and also to facilitate and speed up the work at the department.“- (R3, Male, 26 years).

Developed an interest in the job

Some respondents mentioned that they simply developed an interest in the job. They were intrigued by the HIV/AIDS services and wanted to learn about the disease. One of them explained:

“There is no particular thing that motivated me , I will say. I just developed an interest in the job and volunteered. I can’t point out something in particular that motivated me.”- (R14, Female, 30 years).

HIV status of a relative

Participants said they decided to become HIV/AIDS counselors as a result of a relative contracting the disease. One of them explained:

“What motivated me is that I have a family member who has been exposed to HIV and through the interactions we had , I felt that the information they passed on to the family member was not enough , and that this nearly contributed to a lot of things that nearly led to them making a certain decision which was out of context.” - (R4, Male, 42 years).

As a Result of the poor attitudes of healthcare providers towards HIV/AIDS

One of the participants said they were motivated due to the poor attitudes of healthcare providers towards HIV/AIDS clients. He had experienced a situation where a friend was mistreated by healthcare providers and decided to become a counselor himself. The quotation below summarizes what the participant had to say:

“I have a friend who has been in the hospital and was tested. So , during that time , anytime I went to the hospital , the way the healthcare providers handled the patient or talked to the patient was not nice at all , so I was touched to become a counselor who would sympathize with the client and calm them down”.-(R1 , Male , 36 years).

Primary responsibilities of an HIV/AIDS Counselor

We also identified the primary responsibilities of HIV/AIDS counselors in the Volta region of Ghana. Some of the responses that emerged include nutritional counseling, educating clients about HIV, providing treatment and medication to clients, conducting testing and comprehensive counseling, offering social support to clients, and providing financial support to clients.

Counseling clients on their nutritional needs

Most participants emphasized that their primary responsibility as an HIV/AIDS counselor was to provide nutritional counseling to clients. This is crucial because the medication the clients take increases their calorie demands, necessitating a balanced diet for effective treatment. This sentiment was expressed in narratives such as:

“When the patients arrive , you talk to them about their medication , the type of food they are supposed to eat , and what they are supposed to do and what they are not supposed to do.” -(R1 , Male , 36 years).

Educating clients about the disease (HIV/AIDS)

Some participants indicated that their primary responsibility is to educate clients about HIV/AIDS. They emphasized the importance of ensuring that clients have comprehensive information about the disease to guide them in managing their health effectively. One participant explained:

“My primary responsibilities as an HIV/AIDS counselor are to pass on relevant information about the disease condition to anybody who has come for counseling and testing. Also , I must ensure the person receives adequate information so that , regardless of the testing outcome , they can accept the result and move forward.” -(R4 , Male , 42 years).

Providing counseling to clients on medication and adherence

Participants mentioned that their primary responsibility as an HIV/AIDS counselor was to provide counseling related to clients’ treatment and medication. They emphasized the importance of guiding clients through their treatment plans and ensuring adherence to medications. This is elaborated below:

“Yes , I do play a role in their treatments and medications. We call it adherence counseling for those who are receiving treatment. So , we give them information on how the medication works , how they should take it , and the duration that they should take it”. (R4 , Male , 42 years).

Comprehensive HIV counseling and testing of clients

Participants highlighted that their primary responsibility as HIV/AIDS counselors was to provide comprehensive counseling and testing for clients. They stressed the need for conducting thorough counseling sessions to ensure clients understand their HIV status, the implications of the disease, and the necessary steps for managing their health. This is illustrated by the explanation provided by a participant:

“Okay , the first one is counseling and testing , and the other ones are educating the client on viral loads , EID (Early Infant Diagnosis) and other services , but providing enough and adequate information to the client is the major responsibility. So , for the counseling , it depends on the client you have with you”.-(R10 , Female , 29 years).

Providing social support to clients

Some participants mentioned that they offer social support to clients, particularly those who face discrimination from their families. They aim to provide a sense of community and understanding to help clients cope with these challenges. The quotation below summarizes what a participant said:

“We also provide them with social support. It is done in a way like caring for them as if they were our family members , attending to their day-to-day needs.” -(R3 , Male , 26 years).

Provision of financial support for some clients

Some participants mentioned that they provide financial support to clients. Although it is not supposed to be one of their core roles, the dire financial situation of some clients often compels counselors to help them financially. One of them explained:

“For financial support , some of them come and will be like ‘I do not have money to buy food to take the drug’ and we do our best to assist them. We also advise them to eat well because the drug that they are on needs a whole lot of balanced diet”. -(R3 , Male , 26 years).

Positive experiences of HIV/AIDS counselors

The study explored the experiences of HIV/AIDS counselors in the Volta region of Ghana. Responses that emerged under this theme include: boosting clients’ health status, counseling clients back to a normal psychological state, cooperating with clients, gaining knowledge on the disease through counseling, counseling clients to accept their HIV/AIDS status, and clients delivering HIV-negative babies.

Improving clients’ health status

When participants were asked to share their experiences as an HIV/AIDS counselor, some mentioned that they cherished the aspect of being able to restore a client’s health to normalcy through counseling. One of them mentioned:

“The experience is that I have counseled people who are and not close to me , and the outcome is that they are doing very well. Initially , when some of them come , it looks like there is no hope but with time , as they listen to us , they improve greatly.” -(R4 , Male 42 years).

Counsel clients to return to a normal psychological state

Some participants mentioned that they found fulfillment in counseling a client back to their normal psychological state after receiving the shocking results. The quote below sums up their views.

“Okay , so when it comes to the experiences , the people who are living with HIV at first , when you break the news to them , they break down and are in denial. Some even want to hurt themselves , but if you can counsel them to get back to their normal state , it is such an experience.” -(R1 , Male , 36 years).

Cooperation from clients

Few of the participants mentioned that cooperation from clients was a significant experience they often encountered, as many clients are unruly and in denial after receiving HIV- positive results. Therefore, when a client cooperates and accepts their status immediately after testing, it becomes a notable experience for them. They recounted:

“Some clients are a bit enlightened on the condition because there is some literature among them. The disease is not only for the illiterate. Some have heard of the condition , and they accept it in good faith that it has come and there is nothing they can do , and they stick to treatment , and they see improvement.” -(R14 , Female , 30 years).

Gaining knowledge of the disease through counseling

Some participants reported that their experience as an HIV/AIDS counselor involved gaining knowledge about the disease. They explained that prior to becoming counselors, they had limited knowledge about HIV/AIDS. However, their involvement in counseling and testing exposed them to a wealth of information about the disease, thereby enhancing their understanding over time. They explained:

“When I came here first , I had a different perception of HIV and how to deal with the clients , but after some time , I got used to the system and was very comfortable. I can boldly say that I have gained much knowledge of the disease.” -(R10 , Female , 29 years).

Client giving birth to an HIV-negative baby

One participant expressed that witnessing a client deliver an HIV-negative baby brings him great joy, knowing that his counseling efforts contributed to this positive outcome. He narrated:

“The experiences as an HIV/AIDS counselor are rewarding when you witness mothers who are HIV-positive , provide care for them , and by the end of the day , their babies test negative and are doing well and healthy is a significant motivation. " -(R7 , Male , 37 years).

Negative experiences of HIV/AIDS counselors

Another theme that emerged from the study was the negative experiences of HIV/AIDS counselors in the Volta region of Ghana. Again, six (6) sub-themes were identified under this theme. These include clients denying their HIV status, clients defaulting on their treatment, clients being uncooperative, the death of some clients due to fear of breach of confidentiality, self-stigmatization, and the myths surrounding HIV/AIDS.

Denial of HIV- status by clients

Three participants mentioned that one of their negative experiences as HIV/AIDS counselors was clients’ denial of their status after testing positive for HIV. This hindered effective counseling and treatment, as the clients did not accept the reality that they were infected with the virus. One of them explained:

“One of the negative experiences as an HIV/AIDS counselor is when I had a mother who initially came here saying she was coming to fight me. She was tested , took medication once , and then never returned. So , I used to call her intermittently , but one day I called her , and she got crazy on the phone saying that she does not have HIV”. -(R15 , Female , 40 years).

Client defaulting on their treatment

Additionally, a few participants mentioned that clients defaulting on treatment and medication was a negative experience they encountered as HIV/AIDS counselors. This is particularly challenging because the counselors put in a lot of effort to enroll the clients on ART to control their viral load, only for them to default on treatment and disrupt the entire treatment course. They shared:

“Clients discontinuing treatment after all the counseling and reassurance given to them is both frustrating and sad at the same time. It poses a big challenge to us because it looks like we are not doing our work very well.” -(R3 , Male. 26 years) .

Uncooperative clients

Some participants complained that uncooperative clients pose a significant challenge to HIV/AIDS counselors. Despite the efforts they put in, some clients still see them as the problem and refuse to listen, making their efforts seem in vain. A male participant summarized this, stating:

“The negative experience was when a pregnant woman who had been diagnosed with HIV. Despite the counseling and empowering I had provided , she did not cooperate or adhere to the treatment. After giving birth , the child became infected , which deeply saddened me.” - (R7 , Male , 37 years).

Self-stigmatization by clients

A few participants explained that clients sometimes stigmatize themselves (self-stigmatization), feeling worthless and becoming shy or afraid to engage with healthcare providers. One of them explained:

“There is still some form of stigmatization. The clients themselves have self –stigma , so society also plays a certain part. That’s why they still have that self-stigma because they see themselves as worthless as they are infected with some sort of extraordinary or dangerous disease , which I think is not true.” -(R14 , Female , 30 years).

Client committing suicide due to breach of confidentiality

A participant recounted that a client committed suicide due to a breach of confidentiality, which was a negative experience he faced as an HIV/AIDS counselor. He narrated that one of his clients took her own life when a nurse in the community disclosed her status to the community members, leaving a lasting impression on his mind. He explained:

“One of the negative experiences I have encountered as an HIV/AIDS counselor is that after counseling someone I know who had tested positive , later on , she passed on information to another health staff working here that because of me , she is not comfortable with it , so she wants to transfer to another facility.” - (R4 , Male , 42 years).

Myths surrounding the transmission of HIV/AIDS

Furthermore, another participant was concerned about the myths and beliefs people hold towards HIV/AIDS. For example, some clients still believe that HIV is a curse and only affects those who live promiscuous lives. Therefore, these beliefs make the counseling process difficult as people do not readily accept their status or commence treatment. She explained:

“Some people still feel that HIV/AIDS is a curse done on them , so they prefer visiting herbalists , prayer camps , and the like. So , talking to them , you get to know what they believe in some myths and superstitions , as they remain in denial.” - (R9, Female, 28 years).

This study aimed to understand the motivations, responsibilities, and experiences of HIV/AIDS counselors in the Volta Region of Ghana to guide HIV/AIDS counseling policy in the area. Four (4) main themes were generated. These themes are: motivation to become HIV/AIDS counselor, responsibilities as an HIV/AIDS counselor, positive experiences as an HIV/AIDS counselor, and the negative experiences as an HIV/AIDS counselor.

The motivation of participants to become HIV/AIDS counselors was influenced by many factors. Some were randomly assigned to the HIV counseling unit, others developed an interest in the job, some were motivated by a relative’s status with HIV, while others were driven by witnessing poor attitudes of healthcare providers towards HIV clients. Additionally, some participants were inspired to become counselors after encountering HIV clients who lacked knowledge about HIV/AIDS and wanted to help educate and support them.

The practice of randomly assigning healthcare workers as HIV/AIDS counselors is prevalent in some African countries like Eswatini [ 11 ]. The reason for such random assignment in these settings is to fill the gap left by the lack of professionally trained HIV/AIDS counselors in providing counseling services to clients [ 12 ]. In the long run, nevertheless, this could be counterproductive since the personnel were not motivated to work as HIV/AIDS counselors, which could compromise commitment and the quality of services delivered to clients [ 51 ].

Conversely, some participants were self-motivated to become HIV/AIDS counselors by developing an interest in the job. This could have been due to curiosity to get a better understanding of the nature of the disease or to make a potential impact on the lives of people living with HIV [ 52 ]. On the African continent, the interest in helping others and a desire to make a difference in the lives of HIV clients are common motivations for becoming an HIV/AIDS counselor [ 53 ]. This is because the concept of “Ubuntu” transcends the African continent [ 54 ]. Recruiting individuals with the passion and motivation to become HIV/AIDS counselors could thus help improve the quality of care given to HIV/AIDS clients in the Volta region and Ghana in general, helping curb the transmission, morbidity, and mortality associated with HIV/AIDS.

Another reason why some participants chose to become HIV/AIDS counselors was due to a family member being diagnosed with the disease. Having a close relative or friend who is living with HIV/AIDS can inspire one to pursue a career in counseling to make a difference in the lives of others affected by HIV/AIDS and gain a better understanding of the challenges they face [ 55 ]. Similar reasons have been cited by some HIV/AIDS counselors in South Africa, where the HIV status of a family member or friend motivated them to become involved in HIV/AIDS counseling [ 56 , 57 ]. In some African communities, supporting and embracing the HIV status of family members often inspires individuals to become HIV/AIDS counselors to provide quality HIV/AIDS counseling services to family members suffering from the disease [ 58 ].

Witnessing the bad attitude of some healthcare providers towards people living with HIV/AIDS also served as motivation for some participants to become HIV/AIDS counselors. Bad attitudes of healthcare providers can have a significant impact on the quality of care that patients receive [ 59 ]. In Ghana, the narrative has often been that health workers are harsh and frequently treat patients without respect, ordering them around or yelling at them [ 60 ]. While this could be the case for some healthcare workers, others who detest such behavior see it as motivation to get involved and offer quality and humane healthcare services, as found in the present study.

As HIV/AIDS counselors, participants had responsibilities that included counseling in six thematic areas: nutritional counseling, educating clients about HIV/AIDS, providing treatment and medication, testing and offering comprehensive counseling, as well as providing social and financial support to clients.

HIV/AIDS treatment goes hand in hand with nutrition. Research has shown that malnutrition is a major complication of HIV infection that accelerates disease progression, increases morbidity, and reduces survival outcomes [ 61 ]. This is often exacerbated by the fact that antiretroviral drugs place much more energy demands on patients and thus require them to eat nutritious meals [ 62 ]. Providing nutritional counseling to HIV/AIDS clients to eat healthy food in the appropriate amounts regularly can help them better tolerate HIV drugs, maintain a healthy weight, and improve their health outcomes. This is a primary focus of HIV/AIDS counseling in Ghana [ 63 ].

The provision of comprehensive knowledge on HIV/IDS is another important role of HIV/AIDS counselors in the Volta region. This aligns with the World Health Organization’s recommendation that HIV/AIDS counselors provide a range of services, including educating clients on the prevention and modes of transmission of the disease [ 64 ]. In some jurisdictions, HIV/AIDS counselors are encouraged to patiently educate clients on the disease by communicating at the client’s level of understanding to ensure they gain substantial insight and knowledge about the condition [ 1 ]. Providing accurate information about the disease, its transmission, and treatment options can help dispel myths and misconceptions, thereby reducing discrimination and promoting acceptance within communities [ 65 ].

Participants also mentioned the provision of medication/treatment, counseling, testing, and educating persons living with HIV/AIDS about the disease as their responsibilities. The practice of HIV/AIDS counselors also acting as treatment providers is common in HIV/AIDS care across Africa. In Botswana for instance, HIV counselors are responsible for treatment and medication services for persons living with HIV [ 66 ]. As previously explained, the diverse roles fulfilled by HIV/AIDS counselors on the continent stem from the insufficient number of healthcare providers. Thus, HIV/AIDS counselors in the Volta region of Ghana are responsible for monitoring persons living with HIV/IDS regarding treatment adherence and managing medication side effects [ 67 ]. Comprehensive education of clients on HIV/AIDS through counseling is essential to raising awareness about the disease, improving treatment accessibility, and reducing stigma among people living with HIV [ 68 ]. This ensures that clients receive thorough and holistic guidance and support pre- and post-testing [ 69 ]. To offer comprehensive counseling services, HIV/AIDS counselors must be empowered. In this context, empowerment involves promoting the emotional well-being of clients and educating them on managing HIV/AIDS [ 70 ].

Moreover, providing social and financial support to clients was another responsibility undertaken by the HIV/AIDS counselors in the Volta region of Ghana, according to findings. Social support involves emotional, esteem, companionship, and informational support that HIV/AIDS counselors give to clients, while financial support involves providing monetary assistance or resources to persons living with HIV/AIDS [ 71 ]. Effective HIV/AIDS care encompasses not only medication management but also addressing the psychological, social, and economic aspects of an individual’s life [ 11 ]. Similar roles have been reported among South African HIV/AIDS counselors [ 72 ]. Although it is not their mandate to provide financial support, HIV/AIDS counselors, out of empathy, share their own money with some clients as they struggle with medication costs, transportation, and access to nutritious foods. This finding was also reported as a negative experience of HIV/AIDS counselors, where PLHIV lacked financial and social support, hampering their treatment process and outcomes. Therefore, stakeholders in the fight against HIV/AIDS could consider channeling funding through HIV/AIDS counselors who readily identify clients in financial need of assistance to improve treatment adherence and outcomes. Additionally, efforts should be made to strengthen support networks for PLHIV in the Volta region of Ghana.

Participants’ experiences as HIV/AIDS counselors included both positive and negative aspects in working with persons living with HIV. Positive experiences revealed by the study include the improvement in clients’ health status, counseling clients back to a normal psychological state, cooperation from clients, gaining knowledge on HIV/AIDS, and clients delivering HIV-negative babies. One of the most significant achievements for HIV/AIDS counselors is experiencing both losses and successes, with the restoration of clients’ health to normalcy [ 14 ]. This was identified as one of the cherished experiences among participants. According to Okal et al., counselors experience both losses and successes, with the restoration of clients’ health being a major source of happiness for them [ 73 ]. The joy stems from the fact that their counseling and educational efforts resulted in positive outcomes [ 74 ].

Another positive experience mentioned by participants was gaining knowledge on the disease and being able to calm a client down after testing positive for the first time. They highlighted the importance of gaining knowledge and experience in HIV counseling, which assists counselors in calming difficult clients. It is reported that it takes at least three years of working experience for counselors to gain the expertise needed to handle clients who test positive for HIV for the first time [ 75 ]. Given that all the study participants had practiced as HIV/AIDS counselors for no less than three years, this finding is well-supported. Thus, as a form of incentive, HIV/AIDS counselors in the Volta region should be provided with ongoing capacity-building training programs to optimize their operations [ 76 ].

With regard to the negative experiences, participants recounted clients denying their HIV status, treatment default, lack of cooperation from some clients, self-stigmatization, clients committing suicide due to a breach of confidentiality, and the myths that some clients hold about HIV/AIDS. Clients defaulting on their treatment was a significant concern for most participants, as it leads to negative health outcomes. Treatment default among HIV/AIDS clients is a major global challenge. For instance, in Nigeria, clients defaulting on treatment and medication have been one of the greatest challenges faced by HIV/AIDS counselors [ 77 ]. Unfortunately, some PLHIV often stop taking medication under the mistaken belief that they have been healed or remain in denial about the disease [ 78 , 79 ]. Even long-term HIV clients on ART often misinterpret undetectable test results, leading to noncompliance with the therapeutic regimen [ 53 ]. Therefore, client education programs could be established as an effective intervention strategy to improve treatment adherence and minimize treatment default [ 80 ].

Participants also raised concerns about self-stigma among people living with HIV. They mentioned that one of their biggest challenges was counseling clients out of self-stigma. Upon receiving an HIV- positive diagnosis, clients often experience self-pity, low self-esteem and social isolation. This hinders their treatment and recovery, as they may default on treatment or not receive the support they need. This challenge is also prominent among HIV/AIDS counselors in Nigeria [ 81 ]. Persons living with HIV often face rejection and stigmatization upon disclosure of their status [ 82 ], leading them to isolate themselves from society, loved ones, and healthcare providers.

Additionally, a client committing suicide due to a breach of confidentiality was identified as a negative experience. Confidentiality in HIV/AIDS treatment is crucial for the progress and success of treatment [ 83 ]. When clients do not trust healthcare providers to keep their HIV status private, they may change health facilities, opting for ones further from where they live, which can lead to financial strain and health complications [ 84 ]. To address this, healthcare providers should assure clients that their status will not be disclosed to a third party to maintain their trust and sustain them on treatment.

Finally, participants were concerned about the myths and misconceptions that some clients hold about the disease. Some clients believe that HIV is contracted through a charm or only affects those who lead promiscuous lives. According to Agyeman and colleagues, this is a long-held perception in Ghana [ 85 ]. Similar misconceptions are found in South Africa, where some believe that HIV can be transmitted through mosquito bites [ 86 ]. These may be attributed to the lack of education on the disease in Sub-Saharan African countries [ 87 ]. HIV/AIDS-related myths and misconceptions, if not addressed, could hamper HIV prevention and treatment efforts in the Volta region and across the country. Extensive education on the disease is essential to dispel these misconceptions and promote effective HIV/AIDS care.

Strengths and limitations of the study

The robustness of the methods, such as allowing both the participants and independent qualitative research experts to thoroughly review the transcripts and results before and after data analysis, coupled with the detailed description of the methods, strengthens the scientific and credible nature of the findings. However, the study sample was female-dominated, which could have skewed the responses obtained. Therefore, interpretation of the findings should be done with caution.

In this study, we ascertained the varied motives, responsibilities, and experiences of HIV/AIDS counselors in the Volta region of Ghana. Based on our findings, we conclude that by organizing capacity-building training programs for HIV/AIDS counselors in the Volta region and addressing the negative experiences they encounter, they could be empowered to provide effective counseling, curative, and social services to PLHIV in the region, leading to improved health outcomes for PLHIV.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

Acquired Immune Deficiency Syndrome

Community-Based Health Planning Services

Human Immunodeficiency Virus

In-depth Interviews

Persons Living with HIV

Standards for Reporting Qualitative Research

Mwisongo A, Mehlomakhulu V, Mohlabane N, Peltzer K, Mthembu J, Van Rooyen H. Evaluation of the HIV lay counseling and testing profession in South Africa. BMC Health Serv Res. 2015;15:1–7.

Article   Google Scholar  

Dlamini-Simelane T, Moyer E. Task shifting or shifting care practices? The impact of task shifting on patients’ experiences and health care arrangements in Swaziland. BMC Health Serv Res. 2017;17(1):1–2.

Piamonte SB, Behavioral. Normative, and Control Beliefs of Filipino Men Who Have Sex with men on repeat HIV Testing and Counseling. Acta Med Philippina. 2021;55(7).

Bemelmans M, Baert S, Negussie E, Bygrave H, Biot M, Jamet C, Ellman T, Banda A, van den Akker T, Ford N. Sustaining the future of HIV counseling to reach 90-90‐90: a regional country analysis. J Int AIDS Soc. 2016;19(1):20751.

Article   PubMed   PubMed Central   Google Scholar  

Chippindale S, French L. HIV counseling and the psychosocial management of patients with HIV or AIDS. BMJ. 2001;322(7301):1533–5.

Article   PubMed   PubMed Central   CAS   Google Scholar  

Bor R, Miller R, Goldman E. Theory and practice of HIV counseling: a systemic approach. Routledge; 2013 Oct. p. 28.

Prinz R, Feufel A, Gigerenzer M, Wegwarth G. What counselors tell low-risk clients about HIV test performance. Curr HIV Res. 2015;13(5):369–80.

Article   PubMed   CAS   Google Scholar  

World Health Organization. Consolidated guidelines on HIV prevention, diagnosis, treatment, and care for key populations. World Health Organization; 2016.

Remien RH, Stirratt MJ, Nguyen N, Robbins RN, Pala AN, Mellins CA. Mental health and HIV/AIDS: the need for an integrated response. AIDS. 2019;33(9):1411.

Article   PubMed   Google Scholar  

Lefeuvre D, Dieng M, Lamara F, Raguin G, Michon C. Community health workers in HIV/AIDS care. Sante Publique. France). 2014;26(6):879–88. (Vandoeuvre-les-Nancy.

Google Scholar  

Mbilinyi D, Daniel ML, Lie GT. Health worker motivation in the context of HIV care and treatment challenges in Mbeya Region, Tanzania: a qualitative study. BMC Health Serv Res. 2011;11:1–8.

Rajaraman D, Palmer N. Changing roles and responses of healthcare workers in HIV treatment and care. Tropical Med Int Health. 2008;13(11):1357–63.

Taegtmeyer M, Davies A, Mwangome M, van der Elst EM, Graham SM, Price MA, Sanders EJ. Challenges in providing counseling to MSM in highly stigmatized contexts: results of a qualitative study from Kenya. PLoS ONE. 2013;8(6):e64527.

Isaacs PO. The psychosocial challenges faced by HIV/AIDS lay counselors at health facilities in the Eastern Cape (Doctoral dissertation, Stellenbosch: Stellenbosch University).

Nulty M, Edwards D. The experiences and needs of HIV/AIDS counselors at a South African hospital. J Psychol Afr. 2005;15(1):1–0.

Gesinde AM. Challenges in meeting HIV/AIDS counselling needs in Nigeria. Int J Advancement Counselling. 2012;34:297–306.

Taegtmeyer M, Davies A, Mwangome M, van der Elst EM, Graham SM, Price MA, Sanders EJ. Challenges in providing counselling to MSM in highly stigmatized contexts: results of a qualitative study from Kenya. PLoS ONE. 2013;8(6):e64527.

Ghana AIDS, Commission. (2022). https://www.ghanaids.gov.gh/

World health organization. (2023). Strengthening the fight against HIV in Ghana. https://www.afro.who.int/countries/ghana/news/strengthening-fight-against-hiv-ghana

Awusabo-Asare K. HIV/AIDS education and counseling: experiences from Ghana. Health Transition Rev. 1995;5:229–36.

Ego ML, Moran M. HIV/AIDS counseling program: a rural Ghana experience. Health Transition Rev 1993 Jan 1:85–92.

Van der Geest S. Wisdom and counseling: a note on advising people with HIV/AIDS in Ghana. Afr J AIDS Res. 2015;14(3):255–64.

Puplampu GL. Understanding Hope in HIV care: A narrative inquiry into the experiences of nurses in Ghana.

Wyss S, Ablordeppey J, Okrah J, Kyei A. Reaching disenfranchised youth and mobile populations in Ghana through voluntary counseling and testing services for HIV. Afr J AIDS Res. 2007;6(2):121–8.

Baiden F, Akanlu G, Hodgson A, Akweongo P, Debpuur C, Binka F. Using lay counselors to promote community-based voluntary counseling and HIV testing in rural northern Ghana: a baseline survey on community acceptance and stigma. J Biosoc Sci. 2007;39(5):721–33.

Oppong Asante K. HIV/AIDS knowledge and uptake of HIV counseling and testing among undergraduate private university students in Accra, Ghana. Reproductive Health. 2013;10(1):1–8.

Apanga PA, Akparibo R, Awoonor-Williams JK. Factors influencing uptake of voluntary counseling and testing services for HIV/AIDS in the Lower Manya Krobo Municipality (LMKM) in the Eastern Region of Ghana: a cross-sectional household survey. J Health Popul Nutr. 2015;33:1–7.

Gadegbeku C, Legon AG, Saka R, Mensah B. Attitude of the youth towards voluntary counseling and testing (VCT) of HIV/AIDS in Accra, Ghana. J Biology Agric Healthc. 2013;3(11).

Ayisi Addo S, Abdulai M, Yawson A, Baddoo AN, Zhao J, Workneh N, Okae I, Wiah E. Availability of HIV services along the continuum of HIV testing, care, and treatment in Ghana. BMC Health Serv Res. 2018;18:1–0.

Ofori KN. Application of the health belief model to HIV testing and counseling among youth living in selected rural communities in Ghana. Educ Behav Sci. 2019;5:11–8.

Agamlor E, Pencille L, Lutala P, Akoku DA, Tarkang E. Uptake of HIV testing and counseling among tertiary institution students in the Hohoe Municipality, Ghana. J Public Health Afr. 2019;10(2).

Andoh-Robertson T. HIV testing and counseling among the youth of Ghana: the case of Tarkwa-Nsuaem and FanteAkwa District. Adv Social Sci Res J. 2018;5(6).

Abu-Ba’are GR, Aidoo-Frimpong G, Stockton M, Zigah EY, Amuah S, Amu-Adu P, Amoh-Otoo RP, Nyblade L, Torpey K, Nelson LE. I told myself, be bold and go and test: motivators and barriers to HIV testing among gay, bisexual, and other cis-gender men who have sex with men in Ghana–West Africa. PLOS Global Public Health. 2024;4(1):e0002231.

Abana CZ, Kushitor DK, Asigbee TW, Parbie PK, Ishikawa K, Kiyono H, Mizutani T, Siaw S, Ofori SB, Addo-Tetebo G, Ansong MR. Implementing a community-based HIV screening that serves basic science research and contributes to achieving public health goals.

Anaba EA, Buabeng Z, Okai GA, Voluntary HIV. Counseling and testing services: knowledge, attitudes, and correlates of Utilisation among Young people in the Tema Metropolis. Ghana Youth. 2022;2(4):469–78.

Tepe-Mensah M, Osarfo J, Agbeno EK, Ampofo GD. Willingness to test for human immunodeficiency virus (HIV) infection among first-year students of a Public University in the Volta Region of Ghana. Venereology. 2022;1(2):212–22.

Tsorhe B. Factors associated with the decline in HIV and AIDS prevalence rate in Volta Region of Ghana (Doctoral dissertation, University of Cape Coast).

Dzamboe CE, Manu E, Tarkang EE. Clients’ satisfaction with prevention of mother-to-child transmission of HIV services received at health facilities in the Volta Region of Ghana. Int J Health Promotion Educ 2022 May 25:1–4.

Alhassan RK. Predictors of HIV status disclosure among PLHIV in the Volta region of Ghana: the disclosure conundrum and its policy implications.

Atugba TA, Aninagyei E, Binka FN, Duedu KO. Factors influencing HIV Status Disclosure to partners among antiretroviral therapy clients in the Upper East Region, Ghana. AIDS Behav. 2022;26(8):2653–63.

O’Brien BC, Harris IB, Beckman TJ, Reed DA, Cook DA. Standards for reporting qualitative research: a synthesis of recommendations. Acad Med. 2014;89(9):1245–51.

Tarkang EE, Manu E, Aku FY, Anaman-Torgbor J, Khuzwayo N. Sociodemographic and health systems determinants of antiretroviral therapy adherence among human immunodeficiency virus patients in the Volta Region of Ghana: a multi-center study. SAGE Open Med. 2024;12:20503121241229056.

Ghana Statistical Service. Ghana 2021 Population and Housing Census General Report Volume 3A. Retrieved from population of regions and drsitricts report (statsghana.gov.gh).

Nxumalo CT, Tokwe L, Ngcobo SJ, Gam NP, Mchunu GG, Makhado L. Exploring the perceptions and lived experiences of family members living with people diagnosed with COVID-19 in South Africa: a descriptive phenomenological study. Int J Qualitative Stud Health Well-being. 2023;18(1):2247622.

Haffejee F, Maharajh R, Sibiya MN. Exploring the lived experiences of vulnerable females from a low-resource setting during the COVID-19 pandemic. Int J Environ Res Public Health. 2023;20(22):7040.

Chan ZC, Fung YL, Chien WT. Bracketing in phenomenology: only undertaken in the data collection and analysis process. Qualitative Rep. 2013;18(30):1–9.

Mwita K. Factors influencing data saturation in qualitative studies. Int J Res Bus Social Sci. 2022;11(4):2147–4478.

Johnstone ML. Depth Interviews and Focus Groups. Formative Research in Social Marketing: Innovative Methods to gain consumer insights. 2017:67–87.

Colaizzi PF. Psychological Research as the phenomenologist views it. 1978.

Lincoln YS, Guba EG. Establishing Dependability and Confirmability in naturalistic inquiry through an Audit. (1982).

Dong K, Thabethe Z, Hurtado R, Sibaya T, Dlwati H, Walker B, Wilson D. Challenges to the success of HIV and Tuberculosis care and treatment in the public health sector in South Africa. J Infect Dis. 2007;196(Supplement3):S491–6.

Barz G. Singing for life: HIV/AIDS and music in Uganda. Routledge; 2014. Jun 17.

Nannozi V, Wobudeya E, Matsiko N, Gahagan J. Motivators of couple HIV counseling and testing (CHCT) uptake in a rural setting in Uganda. BMC Public Health. 2017;17(1):1–6.

Swanson DM, Ubuntu. An African contribution to (re) search for/with a ‘humble togetherness’. J Contemp Issues Educ. 2007;2(2):53–67.

Ngonzi J, Godfrey M, Kivunike M, Julius M, Salongo W. Predictors and patterns of HIV status disclosure among HIV pregnant women at Mbarara regional referral hospital, South-Western Uganda. Obstet Gynecol Int J. 2017;6(6):00230.

van Loggerenberg F, Gray D, Gengiah S, Kunene P, Gengiah TN, Naidoo K, Grant AD, CAPRISA 058 Study Team. A qualitative study of patient motivation to adhere to combination antiretroviral therapy in South Africa. AIDS Patient Care STDs. 2015;29(5):299–306.

Kanekar AS. HIV/AIDS counseling skills and strategies: can testing and counseling curb the epidemic? Int J Prev Med. 2011;2(1):10.

PubMed   PubMed Central   Google Scholar  

Campbell JA. Counselors’ knowledge about HIV transmission and prevention. 2020.

Larsson IE, Sahlsten MJ, Segesten K, Plos KA. Patients’ perceptions of nurses’ behavior that influence patient participation in nursing care: a critical incident study. Nursing research and practice. 2011;2011.

Dapaah JM, Senah KA. HIV/AIDS clients, privacy, and confidentiality; the case of two health centers in the Ashanti Region of Ghana. BMC Med Ethics. 2016;17:1–0.

Birhane M, Loha E, Alemayehu FR. Nutritional status and associated factors among adult HIV/AIDS patients receiving ART in Dilla University referral hospital, Dilla, Southern Ethiopia. J Med Physiol Biophys. 2021;70:8–15.

Friis H, Olsen MF, Filteau S. (2017). Nutrition and HIV. In The International Encyclopedia of Public Health .

Nanewortor BM, Saah FI, Appiah PK, Amu H, Kissah-Korsah K. Nutritional status and associated factors among people living with HIV/AIDS in Ghana: a cross-sectional study of highly active antiretroviral therapy clients. BMC Nutr. 2021;7(1):1–8.

World Health Organization. HIV and adolescents: guidance for HIV testing and counseling and care for adolescents living with HIV: recommendations for a public health approach and considerations for policymakers and managers. World Health Organization; 2013.

Enwereji EE, Ezeama MC, Onyemachi PE. Basic principles of nutrition, HIV and AIDS: making improvements in diet to enhance health. Nutrition and HIV/AIDS-Implication for Treatment, Prevention and Cure. Apr. 2019;19:1–6.

Ledikwe JH, Kejelepula M, Maupo K, Sebetso S, Thekiso M, Smith M, Mbayi B, Houghton N, Thankane K, O’Malley G, Semo BW. Evaluation of a well-established task-shifting initiative: the lay counselor cadre in Botswana. PLoS ONE. 2013;8(4):e61601.

Rasmussen LM. Counseling clients to follow ‘the rules’ of safe sex and ARV treatment. Cult Health Sex. 2013;15(sup4):S537–52.

Mosende AG, Lacambra CB, De los Santos JA. Knowledge, attitudes, and behaviors toward HIV/AIDS among Health Care Workers in Urban Cities in Leyte Philippines. Malaysian J Nurs (MJN). 2023;14(3):185–93.

Visser M, Mabota P. The emotional well-being of lay HIV counseling and testing counselors. Afr J AIDS Res. 2015;14(2):169–77.

World Health Organization. Consolidated guidelines on HIV testing services: 5Cs: consent, confidentiality, counseling, correct results, and connection 2015.

Nguyen LH, Tran BX, Nguyen NP, Phan HT, Bui TT, Latkin CA. Mobilization for HIV voluntary counseling and testing services in Vietnam: clients’ risk behaviors, attitudes, and willingness to pay. AIDS Behav. 2016;20:848–58.

Jackson D, Naik R, Tabana H, Pillay M, Madurai S, Zembe W, Doherty T. Quality of home-based rapid HIV testing by community lay counselors in a rural district of South Africa. J Int AIDS Soc. 2013;16(1):18744.

Okal JO, Sarna A, Lango D, Matheka J, Owuor D, Kinywa EA, Kalibala S. Client experiences in a mobile-phone counseling intervention for Enhancing Access to Prevention of Mother To-Child transmission (PMTCT) services in Kenya. Front Global Women’s Health. 2022;3:785194.

Peltzer K, Matseke G, Louw J. Secondary trauma and job burnout and associated factors among HIV lay counselors in Nkangala district, South Africa. Br J Guidance Couns. 2014;42(4):410–22.

Mkhabele C, Peu D. The experiences of lay counselors regarding HIV and Aids care and support in tshwane clinics. Social Work Practitioner-Researcher. 2016;28(3):316–29.

Joseph EB, Bhatti RS. Psychosocial problems and coping patterns of HIV sero wives of men with HIV/AIDS. In Social Work Visions from around the Globe 2013 Jul 4 (pp. 29–47). Routledge.

Ilesanmi OS, Afolabi AA. Causes of defaulting from antiretroviral therapy and their prevention: a qualitative study of adherent clients and defaulters in Nigeria. HIV AIDS Rev Int J HIV-Related Probl. 2021;20(2):136–43.

Maeri I, El Ayadi A, Getahun M, Charlebois E, Akatukwasa C, Tumwebaze D, Itiakorit H, Owino L, Kwarisiima D, Ssemmondo E, Sang N. How can I tell? Consequences of HIV status disclosure among couples in eastern African communities in the context of an ongoing HIV test-and-treat trial. AIDS Care. 2016;28(sup3):59–66.

Abdulrahman SA, Ganasegeran K, Rampal L, Martins OF. HIV Treatment Adherence-A Shared Burden for patients, Healthcare Providers, and other stakeholders. AIDS Rev. 2019;21(1).

Lifson AR, Demissie W, Tadesse A, Ketema K, May R, Yakob B, Metekia M, Slater L, Shenie T. Barriers to retention in care as perceived by persons living with HIV in rural Ethiopia: focus group results and recommended strategies. J Int Association Providers AIDS Care (JIAPAC). 2013;12(1):32–8.

Odimegwu C, Adedini SA, Ononokpono DN. HIV/AIDS stigma and utilization of voluntary counseling and testing in Nigeria. BMC Public Health. 2013;13(1):1–4.

Gilbert L, Walker L. ‘My biggest fear was that people would reject me once they knew my status… Stigma as experienced by patients in an HIV/AIDS clinic in Johannesburg,South Africa. Health & Social Care in the Community. 2010;18(2):139 – 46.

Salvadori M, Hahn GV. Medical confidentiality in the care of patients with HIV/AIDS. Revista Bioética. 2019;27:153–63.

Terzian AS, Younes N, Greenberg AE, Opoku J, Hubbard J, Happ LP, Kumar P, Jones RR, Castel AD, DC Cohort Executive Committee. Identifying spatial variation along the HIV care continuum: the role of distance to care on retention and viral suppression. AIDS Behav. 2018;22:3009–23.

Agyemang S, Buor D, Tagoe-Darko E. The extent of knowledge about HIV/AIDS among young people in the Ejura-Sekyedumase district of Ghana.

Rauf W, Hitchcock S, Rauf A, Becker PJ. Knowledge of and misconceptions about the spread and prevention of HIV infection among older urban women attending the Tshwane District Hospital, South Africa. South Afr Family Pract. 2010;52(2):142–8.

Amuche NJ, Emmanuel EI, Innocent NE. HIV/AIDS in sub-Saharan Africa: current status, challenges, and prospects.

Download references

Acknowledgements

We acknowledge the contributions of our study participants for the realization of this study and its publication thereof.

Author information

Authors and affiliations.

Department of Epidemiology and Biostatistics, Fred N. Binka School of Public Health, University of Health and Allied Sciences, Ho, Ghana

Festus Dwomoh

Department of Public Health, Faculty of Health Sciences, Walter Sisulu University, Nelson Mandela Drive, Mthatha, South Africa

Mbuyiselo Douglas

Department of Population and Behavioral Sciences, Fred N. Binka School of Public Health, University of Health and Allied Sciences, Ho, Ghana

Veronica O. Charles-Unadike, Joyce Komesuor & Emmanuel Manu

You can also search for this author in PubMed   Google Scholar

Contributions

F.D., E.M., and M.D conceptualized the study. F.D., V.C., and J.K. collected the data. F.D., and E.M. analysed the data and drafted the manuscript. All authors reviewed and approved the manuscript.

Corresponding author

Correspondence to Emmanuel Manu .

Ethics declarations

Ethics approval and consent to participate.

Ethical approval for this study was sought from the University of Health and Allied Sciences Research Ethics Committee (UHAS-REC A.10[041]22–23). Permission was also sought from all the health facilities where the study was conducted with a letter of introduction from the University of Health and Allied Sciences before the commencement of the study. Signed informed consent was obtained from the participants. All participants were informed of their right to withdraw from the study at any time. Participants’ anonymity and confidentiality were ensured by assigning them alpha-numeric codes.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

Dwomoh, F., Douglas, M., Charles-Unadike, V.O. et al. Motivation, responsibilities, and experiences of HIV/AIDS counselors in the Volta Region of Ghana: a descriptive phenomenological study. AIDS Res Ther 21 , 60 (2024). https://doi.org/10.1186/s12981-024-00648-x

Download citation

Received : 06 February 2024

Accepted : 22 August 2024

Published : 31 August 2024

DOI : https://doi.org/10.1186/s12981-024-00648-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• HIV counseling
• HIV prevention and treatment
• Ghana AIDS Commission
• Volta Region

AIDS Research and Therapy

ISSN: 1742-6405

• Submission enquiries: [email protected]

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 31 August 2024

Knowledge mapping and evolution of research on older adults’ technology acceptance: a bibliometric study from 2013 to 2023

• Xianru Shang   ORCID: orcid.org/0009-0000-8906-3216 1 ,
• Zijian Liu 1 ,
• Chen Gong 1 ,
• Zhigang Hu 1 ,
• Yuexuan Wu 1 &
• Chengliang Wang   ORCID: orcid.org/0000-0003-2208-3508 2  

Humanities and Social Sciences Communications volume  11 , Article number:  1115 ( 2024 ) Cite this article

Metrics details

• Science, technology and society

The rapid expansion of information technology and the intensification of population aging are two prominent features of contemporary societal development. Investigating older adults’ acceptance and use of technology is key to facilitating their integration into an information-driven society. Given this context, the technology acceptance of older adults has emerged as a prioritized research topic, attracting widespread attention in the academic community. However, existing research remains fragmented and lacks a systematic framework. To address this gap, we employed bibliometric methods, utilizing the Web of Science Core Collection to conduct a comprehensive review of literature on older adults’ technology acceptance from 2013 to 2023. Utilizing VOSviewer and CiteSpace for data assessment and visualization, we created knowledge mappings of research on older adults’ technology acceptance. Our study employed multidimensional methods such as co-occurrence analysis, clustering, and burst analysis to: (1) reveal research dynamics, key journals, and domains in this field; (2) identify leading countries, their collaborative networks, and core research institutions and authors; (3) recognize the foundational knowledge system centered on theoretical model deepening, emerging technology applications, and research methods and evaluation, uncovering seminal literature and observing a shift from early theoretical and influential factor analyses to empirical studies focusing on individual factors and emerging technologies; (4) moreover, current research hotspots are primarily in the areas of factors influencing technology adoption, human-robot interaction experiences, mobile health management, and aging-in-place technology, highlighting the evolutionary context and quality distribution of research themes. Finally, we recommend that future research should deeply explore improvements in theoretical models, long-term usage, and user experience evaluation. Overall, this study presents a clear framework of existing research in the field of older adults’ technology acceptance, providing an important reference for future theoretical exploration and innovative applications.

Similar content being viewed by others

Research progress and intellectual structure of design for digital equity (DDE): A bibliometric analysis based on citespace

Exploring the role of interaction in older-adult service innovation: insights from the testing stage

Smart device interest, perceived usefulness, and preferences in rural Alabama seniors

Introduction.

In contemporary society, the rapid development of information technology has been intricately intertwined with the intensifying trend of population aging. According to the latest United Nations forecast, by 2050, the global population aged 65 and above is expected to reach 1.6 billion, representing about 16% of the total global population (UN 2023 ). Given the significant challenges of global aging, there is increasing evidence that emerging technologies have significant potential to maintain health and independence for older adults in their home and healthcare environments (Barnard et al. 2013 ; Soar 2010 ; Vancea and Solé-Casals 2016 ). This includes, but is not limited to, enhancing residential safety with smart home technologies (Touqeer et al. 2021 ; Wang et al. 2022 ), improving living independence through wearable technologies (Perez et al. 2023 ), and increasing medical accessibility via telehealth services (Kruse et al. 2020 ). Technological innovations are redefining the lifestyles of older adults, encouraging a shift from passive to active participation (González et al. 2012 ; Mostaghel 2016 ). Nevertheless, the effective application and dissemination of technology still depends on user acceptance and usage intentions (Naseri et al. 2023 ; Wang et al. 2023a ; Xia et al. 2024 ; Yu et al. 2023 ). Particularly, older adults face numerous challenges in accepting and using new technologies. These challenges include not only physical and cognitive limitations but also a lack of technological experience, along with the influences of social and economic factors (Valk et al. 2018 ; Wilson et al. 2021 ).

User acceptance of technology is a significant focus within information systems (IS) research (Dai et al. 2024 ), with several models developed to explain and predict user behavior towards technology usage, including the Technology Acceptance Model (TAM) (Davis 1989 ), TAM2, TAM3, and the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al. 2003 ). Older adults, as a group with unique needs, exhibit different behavioral patterns during technology acceptance than other user groups, and these uniquenesses include changes in cognitive abilities, as well as motivations, attitudes, and perceptions of the use of new technologies (Chen and Chan 2011 ). The continual expansion of technology introduces considerable challenges for older adults, rendering the understanding of their technology acceptance a research priority. Thus, conducting in-depth research into older adults’ acceptance of technology is critically important for enhancing their integration into the information society and improving their quality of life through technological advancements.

Reviewing relevant literature to identify research gaps helps further solidify the theoretical foundation of the research topic. However, many existing literature reviews primarily focus on the factors influencing older adults’ acceptance or intentions to use technology. For instance, Ma et al. ( 2021 ) conducted a comprehensive analysis of the determinants of older adults’ behavioral intentions to use technology; Liu et al. ( 2022 ) categorized key variables in studies of older adults’ technology acceptance, noting a shift in focus towards social and emotional factors; Yap et al. ( 2022 ) identified seven categories of antecedents affecting older adults’ use of technology from an analysis of 26 articles, including technological, psychological, social, personal, cost, behavioral, and environmental factors; Schroeder et al. ( 2023 ) extracted 119 influencing factors from 59 articles and further categorized these into six themes covering demographics, health status, and emotional awareness. Additionally, some studies focus on the application of specific technologies, such as Ferguson et al. ( 2021 ), who explored barriers and facilitators to older adults using wearable devices for heart monitoring, and He et al. ( 2022 ) and Baer et al. ( 2022 ), who each conducted in-depth investigations into the acceptance of social assistive robots and mobile nutrition and fitness apps, respectively. In summary, current literature reviews on older adults’ technology acceptance exhibit certain limitations. Due to the interdisciplinary nature and complex knowledge structure of this field, traditional literature reviews often rely on qualitative analysis, based on literature analysis and periodic summaries, which lack sufficient objectivity and comprehensiveness. Additionally, systematic research is relatively limited, lacking a macroscopic description of the research trajectory from a holistic perspective. Over the past decade, research on older adults’ technology acceptance has experienced rapid growth, with a significant increase in literature, necessitating the adoption of new methods to review and examine the developmental trends in this field (Chen 2006 ; Van Eck and Waltman 2010 ). Bibliometric analysis, as an effective quantitative research method, analyzes published literature through visualization, offering a viable approach to extracting patterns and insights from a large volume of papers, and has been widely applied in numerous scientific research fields (Achuthan et al. 2023 ; Liu and Duffy 2023 ). Therefore, this study will employ bibliometric methods to systematically analyze research articles related to older adults’ technology acceptance published in the Web of Science Core Collection from 2013 to 2023, aiming to understand the core issues and evolutionary trends in the field, and to provide valuable references for future related research. Specifically, this study aims to explore and answer the following questions:

RQ1: What are the research dynamics in the field of older adults’ technology acceptance over the past decade? What are the main academic journals and fields that publish studies related to older adults’ technology acceptance?

RQ2: How is the productivity in older adults’ technology acceptance research distributed among countries, institutions, and authors?

RQ3: What are the knowledge base and seminal literature in older adults’ technology acceptance research? How has the research theme progressed?

RQ4: What are the current hot topics and their evolutionary trajectories in older adults’ technology acceptance research? How is the quality of research distributed?

Methodology and materials

Research method.

In recent years, bibliometrics has become one of the crucial methods for analyzing literature reviews and is widely used in disciplinary and industrial intelligence analysis (Jing et al. 2023 ; Lin and Yu 2024a ; Wang et al. 2024a ; Xu et al. 2021 ). Bibliometric software facilitates the visualization analysis of extensive literature data, intuitively displaying the network relationships and evolutionary processes between knowledge units, and revealing the underlying knowledge structure and potential information (Chen et al. 2024 ; López-Robles et al. 2018 ; Wang et al. 2024c ). This method provides new insights into the current status and trends of specific research areas, along with quantitative evidence, thereby enhancing the objectivity and scientific validity of the research conclusions (Chen et al. 2023 ; Geng et al. 2024 ). VOSviewer and CiteSpace are two widely used bibliometric software tools in academia (Pan et al. 2018 ), recognized for their robust functionalities based on the JAVA platform. Although each has its unique features, combining these two software tools effectively constructs mapping relationships between literature knowledge units and clearly displays the macrostructure of the knowledge domains. Particularly, VOSviewer, with its excellent graphical representation capabilities, serves as an ideal tool for handling large datasets and precisely identifying the focal points and hotspots of research topics. Therefore, this study utilizes VOSviewer (version 1.6.19) and CiteSpace (version 6.1.R6), combined with in-depth literature analysis, to comprehensively examine and interpret the research theme of older adults’ technology acceptance through an integrated application of quantitative and qualitative methods.

Data source

Web of Science is a comprehensively recognized database in academia, featuring literature that has undergone rigorous peer review and editorial scrutiny (Lin and Yu 2024b ; Mongeon and Paul-Hus 2016 ; Pranckutė 2021 ). This study utilizes the Web of Science Core Collection as its data source, specifically including three major citation indices: Science Citation Index Expanded (SCIE), Social Sciences Citation Index (SSCI), and Arts & Humanities Citation Index (A&HCI). These indices encompass high-quality research literature in the fields of science, social sciences, and arts and humanities, ensuring the comprehensiveness and reliability of the data. We combined “older adults” with “technology acceptance” through thematic search, with the specific search strategy being: TS = (elder OR elderly OR aging OR ageing OR senile OR senior OR old people OR “older adult*”) AND TS = (“technology acceptance” OR “user acceptance” OR “consumer acceptance”). The time span of literature search is from 2013 to 2023, with the types limited to “Article” and “Review” and the language to “English”. Additionally, the search was completed by October 27, 2023, to avoid data discrepancies caused by database updates. The initial search yielded 764 journal articles. Given that searches often retrieve articles that are superficially relevant but actually non-compliant, manual screening post-search was essential to ensure the relevance of the literature (Chen et al. 2024 ). Through manual screening, articles significantly deviating from the research theme were eliminated and rigorously reviewed. Ultimately, this study obtained 500 valid sample articles from the Web of Science Core Collection. The complete PRISMA screening process is illustrated in Fig. 1 .

Presentation of the data culling process in detail.

Data standardization

Raw data exported from databases often contain multiple expressions of the same terminology (Nguyen and Hallinger 2020 ). To ensure the accuracy and consistency of data, it is necessary to standardize the raw data (Strotmann and Zhao 2012 ). This study follows the data standardization process proposed by Taskin and Al ( 2019 ), mainly executing the following operations:

(1) Standardization of author and institution names is conducted to address different name expressions for the same author. For instance, “Chan, Alan Hoi Shou” and “Chan, Alan H. S.” are considered the same author, and distinct authors with the same name are differentiated by adding identifiers. Diverse forms of institutional names are unified to address variations caused by name changes or abbreviations, such as standardizing “FRANKFURT UNIV APPL SCI” and “Frankfurt University of Applied Sciences,” as well as “Chinese University of Hong Kong” and “University of Hong Kong” to consistent names.

(2) Different expressions of journal names are unified. For example, “International Journal of Human-Computer Interaction” and “Int J Hum Comput Interact” are standardized to a single name. This ensures consistency in journal names and prevents misclassification of literature due to differing journal names. Additionally, it involves checking if the journals have undergone name changes in the past decade to prevent any impact on the analysis due to such changes.

(3) Keywords data are cleansed by removing words that do not directly pertain to specific research content (e.g., people, review), merging synonyms (e.g., “UX” and “User Experience,” “aging-in-place” and “aging in place”), and standardizing plural forms of keywords (e.g., “assistive technologies” and “assistive technology,” “social robots” and “social robot”). This reduces redundant information in knowledge mapping.

Bibliometric results and analysis

Distribution power (rq1), literature descriptive statistical analysis.

Table 1 presents a detailed descriptive statistical overview of the literature in the field of older adults’ technology acceptance. After deduplication using the CiteSpace software, this study confirmed a valid sample size of 500 articles. Authored by 1839 researchers, the documents encompass 792 research institutions across 54 countries and are published in 217 different academic journals. As of the search cutoff date, these articles have accumulated 13,829 citations, with an annual average of 1156 citations, and an average of 27.66 citations per article. The h-index, a composite metric of quantity and quality of scientific output (Kamrani et al. 2021 ), reached 60 in this study.

Trends in publications and disciplinary distribution

The number of publications and citations are significant indicators of the research field’s development, reflecting its continuity, attention, and impact (Ale Ebrahim et al. 2014 ). The ranking of annual publications and citations in the field of older adults’ technology acceptance studies is presented chronologically in Fig. 2A . The figure shows a clear upward trend in the amount of literature in this field. Between 2013 and 2017, the number of publications increased slowly and decreased in 2018. However, in 2019, the number of publications increased rapidly to 52 and reached a peak of 108 in 2022, which is 6.75 times higher than in 2013. In 2022, the frequency of document citations reached its highest point with 3466 citations, reflecting the widespread recognition and citation of research in this field. Moreover, the curve of the annual number of publications fits a quadratic function, with a goodness-of-fit R 2 of 0.9661, indicating that the number of future publications is expected to increase even more rapidly.

A Trends in trends in annual publications and citations (2013–2023). B Overlay analysis of the distribution of discipline fields.

Figure 2B shows that research on older adults’ technology acceptance involves the integration of multidisciplinary knowledge. According to Web of Science Categories, these 500 articles are distributed across 85 different disciplines. We have tabulated the top ten disciplines by publication volume (Table 2 ), which include Medical Informatics (75 articles, 15.00%), Health Care Sciences & Services (71 articles, 14.20%), Gerontology (61 articles, 12.20%), Public Environmental & Occupational Health (57 articles, 11.40%), and Geriatrics & Gerontology (52 articles, 10.40%), among others. The high output in these disciplines reflects the concentrated global academic interest in this comprehensive research topic. Additionally, interdisciplinary research approaches provide diverse perspectives and a solid theoretical foundation for studies on older adults’ technology acceptance, also paving the way for new research directions.

Knowledge flow analysis

A dual-map overlay is a CiteSpace map superimposed on top of a base map, which shows the interrelationships between journals in different domains, representing the publication and citation activities in each domain (Chen and Leydesdorff 2014 ). The overlay map reveals the link between the citing domain (on the left side) and the cited domain (on the right side), reflecting the knowledge flow of the discipline at the journal level (Leydesdorff and Rafols 2012 ). We utilize the in-built Z-score algorithm of the software to cluster the graph, as shown in Fig. 3 .

The left side shows the citing journal, and the right side shows the cited journal.

Figure 3 shows the distribution of citing journals clusters for older adults’ technology acceptance on the left side, while the right side refers to the main cited journals clusters. Two knowledge flow citation trajectories were obtained; they are presented by the color of the cited regions, and the thickness of these trajectories is proportional to the Z-score scaled frequency of citations (Chen et al. 2014 ). Within the cited regions, the most popular fields with the most records covered are “HEALTH, NURSING, MEDICINE” and “PSYCHOLOGY, EDUCATION, SOCIAL”, and the elliptical aspect ratio of these two fields stands out. Fields have prominent elliptical aspect ratios, highlighting their significant influence on older adults’ technology acceptance research. Additionally, the major citation trajectories originate in these two areas and progress to the frontier research area of “PSYCHOLOGY, EDUCATION, HEALTH”. It is worth noting that the citation trajectory from “PSYCHOLOGY, EDUCATION, SOCIAL” has a significant Z-value (z = 6.81), emphasizing the significance and impact of this development path. In the future, “MATHEMATICS, SYSTEMS, MATHEMATICAL”, “MOLECULAR, BIOLOGY, IMMUNOLOGY”, and “NEUROLOGY, SPORTS, OPHTHALMOLOGY” may become emerging fields. The fields of “MEDICINE, MEDICAL, CLINICAL” may be emerging areas of cutting-edge research.

Main research journals analysis

Table 3 provides statistics for the top ten journals by publication volume in the field of older adults’ technology acceptance. Together, these journals have published 137 articles, accounting for 27.40% of the total publications, indicating that there is no highly concentrated core group of journals in this field, with publications being relatively dispersed. Notably, Computers in Human Behavior , Journal of Medical Internet Research , and International Journal of Human-Computer Interaction each lead with 15 publications. In terms of citation metrics, International Journal of Medical Informatics and Computers in Human Behavior stand out significantly, with the former accumulating a total of 1,904 citations, averaging 211.56 citations per article, and the latter totaling 1,449 citations, with an average of 96.60 citations per article. These figures emphasize the academic authority and widespread impact of these journals within the research field.

Research power (RQ2)

Countries and collaborations analysis.

The analysis revealed the global research pattern for country distribution and collaboration (Chen et al. 2019 ). Figure 4A shows the network of national collaborations on older adults’ technology acceptance research. The size of the bubbles represents the amount of publications in each country, while the thickness of the connecting lines expresses the closeness of the collaboration among countries. Generally, this research subject has received extensive international attention, with China and the USA publishing far more than any other countries. China has established notable research collaborations with the USA, UK and Malaysia in this field, while other countries have collaborations, but the closeness is relatively low and scattered. Figure 4B shows the annual publication volume dynamics of the top ten countries in terms of total publications. Since 2017, China has consistently increased its annual publications, while the USA has remained relatively stable. In 2019, the volume of publications in each country increased significantly, this was largely due to the global outbreak of the COVID-19 pandemic, which has led to increased reliance on information technology among the elderly for medical consultations, online socialization, and health management (Sinha et al. 2021 ). This phenomenon has led to research advances in technology acceptance among older adults in various countries. Table 4 shows that the top ten countries account for 93.20% of the total cumulative number of publications, with each country having published more than 20 papers. Among these ten countries, all of them except China are developed countries, indicating that the research field of older adults’ technology acceptance has received general attention from developed countries. Currently, China and the USA were the leading countries in terms of publications with 111 and 104 respectively, accounting for 22.20% and 20.80%. The UK, Germany, Italy, and the Netherlands also made significant contributions. The USA and China ranked first and second in terms of the number of citations, while the Netherlands had the highest average citations, indicating the high impact and quality of its research. The UK has shown outstanding performance in international cooperation, while the USA highlights its significant academic influence in this field with the highest h-index value.

A National collaboration network. B Annual volume of publications in the top 10 countries.

Institutions and authors analysis

Analyzing the number of publications and citations can reveal an institution’s or author’s research strength and influence in a particular research area (Kwiek 2021 ). Tables 5 and 6 show the statistics of the institutions and authors whose publication counts are in the top ten, respectively. As shown in Table 5 , higher education institutions hold the main position in this research field. Among the top ten institutions, City University of Hong Kong and The University of Hong Kong from China lead with 14 and 9 publications, respectively. City University of Hong Kong has the highest h-index, highlighting its significant influence in the field. It is worth noting that Tilburg University in the Netherlands is not among the top five in terms of publications, but the high average citation count (130.14) of its literature demonstrates the high quality of its research.

After analyzing the authors’ output using Price’s Law (Redner 1998 ), the highest number of publications among the authors counted ( n  = 10) defines a publication threshold of 3 for core authors in this research area. As a result of quantitative screening, a total of 63 core authors were identified. Table 6 shows that Chen from Zhejiang University, China, Ziefle from RWTH Aachen University, Germany, and Rogers from Macquarie University, Australia, were the top three authors in terms of the number of publications, with 10, 9, and 8 articles, respectively. In terms of average citation rate, Peek and Wouters, both scholars from the Netherlands, have significantly higher rates than other scholars, with 183.2 and 152.67 respectively. This suggests that their research is of high quality and widely recognized. Additionally, Chen and Rogers have high h-indices in this field.

Knowledge base and theme progress (RQ3)

Research knowledge base.

Co-citation relationships occur when two documents are cited together (Zhang and Zhu 2022 ). Co-citation mapping uses references as nodes to represent the knowledge base of a subject area (Min et al. 2021). Figure 5A illustrates co-occurrence mapping in older adults’ technology acceptance research, where larger nodes signify higher co-citation frequencies. Co-citation cluster analysis can be used to explore knowledge structure and research boundaries (Hota et al. 2020 ; Shiau et al. 2023 ). The co-citation clustering mapping of older adults’ technology acceptance research literature (Fig. 5B ) shows that the Q value of the clustering result is 0.8129 (>0.3), and the average value of the weight S is 0.9391 (>0.7), indicating that the clusters are uniformly distributed with a significant and credible structure. This further proves that the boundaries of the research field are clear and there is significant differentiation in the field. The figure features 18 cluster labels, each associated with thematic color blocks corresponding to different time slices. Highlighted emerging research themes include #2 Smart Home Technology, #7 Social Live, and #10 Customer Service. Furthermore, the clustering labels extracted are primarily classified into three categories: theoretical model deepening, emerging technology applications, research methods and evaluation, as detailed in Table 7 .

A Co-citation analysis of references. B Clustering network analysis of references.

Seminal literature analysis

The top ten nodes in terms of co-citation frequency were selected for further analysis. Table 8 displays the corresponding node information. Studies were categorized into four main groups based on content analysis. (1) Research focusing on specific technology usage by older adults includes studies by Peek et al. ( 2014 ), Ma et al. ( 2016 ), Hoque and Sorwar ( 2017 ), and Li et al. ( 2019 ), who investigated the factors influencing the use of e-technology, smartphones, mHealth, and smart wearables, respectively. (2) Concerning the development of theoretical models of technology acceptance, Chen and Chan ( 2014 ) introduced the Senior Technology Acceptance Model (STAM), and Macedo ( 2017 ) analyzed the predictive power of UTAUT2 in explaining older adults’ intentional behaviors and information technology usage. (3) In exploring older adults’ information technology adoption and behavior, Lee and Coughlin ( 2015 ) emphasized that the adoption of technology by older adults is a multifactorial process that includes performance, price, value, usability, affordability, accessibility, technical support, social support, emotion, independence, experience, and confidence. Yusif et al. ( 2016 ) conducted a literature review examining the key barriers affecting older adults’ adoption of assistive technology, including factors such as privacy, trust, functionality/added value, cost, and stigma. (4) From the perspective of research into older adults’ technology acceptance, Mitzner et al. ( 2019 ) assessed the long-term usage of computer systems designed for the elderly, whereas Guner and Acarturk ( 2020 ) compared information technology usage and acceptance between older and younger adults. The breadth and prevalence of this literature make it a vital reference for researchers in the field, also providing new perspectives and inspiration for future research directions.

Research thematic progress

Burst citation is a node of literature that guides the sudden change in dosage, which usually represents a prominent development or major change in a particular field, with innovative and forward-looking qualities. By analyzing the emergent literature, it is often easy to understand the dynamics of the subject area, mapping the emerging thematic change (Chen et al. 2022 ). Figure 6 shows the burst citation mapping in the field of older adults’ technology acceptance research, with burst citations represented by red nodes (Fig. 6A ). For the ten papers with the highest burst intensity (Fig. 6B ), this study will conduct further analysis in conjunction with literature review.

A Burst detection of co-citation. B The top 10 references with the strongest citation bursts.

As shown in Fig. 6 , Mitzner et al. ( 2010 ) broke the stereotype that older adults are fearful of technology, found that they actually have positive attitudes toward technology, and emphasized the centrality of ease of use and usefulness in the process of technology acceptance. This finding provides an important foundation for subsequent research. During the same period, Wagner et al. ( 2010 ) conducted theory-deepening and applied research on technology acceptance among older adults. The research focused on older adults’ interactions with computers from the perspective of Social Cognitive Theory (SCT). This expanded the understanding of technology acceptance, particularly regarding the relationship between behavior, environment, and other SCT elements. In addition, Pan and Jordan-Marsh ( 2010 ) extended the TAM to examine the interactions among predictors of perceived usefulness, perceived ease of use, subjective norm, and convenience conditions when older adults use the Internet, taking into account the moderating roles of gender and age. Heerink et al. ( 2010 ) adapted and extended the UTAUT, constructed a technology acceptance model specifically designed for older users’ acceptance of assistive social agents, and validated it using controlled experiments and longitudinal data, explaining intention to use by combining functional assessment and social interaction variables.

Then the research theme shifted to an in-depth analysis of the factors influencing technology acceptance among older adults. Two papers with high burst strengths emerged during this period: Peek et al. ( 2014 ) (Strength = 12.04), Chen and Chan ( 2014 ) (Strength = 9.81). Through a systematic literature review and empirical study, Peek STM and Chen K, among others, identified multidimensional factors that influence older adults’ technology acceptance. Peek et al. ( 2014 ) analyzed literature on the acceptance of in-home care technology among older adults and identified six factors that influence their acceptance: concerns about technology, expected benefits, technology needs, technology alternatives, social influences, and older adult characteristics, with a focus on differences between pre- and post-implementation factors. Chen and Chan ( 2014 ) constructed the STAM by administering a questionnaire to 1012 older adults and adding eight important factors, including technology anxiety, self-efficacy, cognitive ability, and physical function, based on the TAM. This enriches the theoretical foundation of the field. In addition, Braun ( 2013 ) highlighted the role of perceived usefulness, trust in social networks, and frequency of Internet use in older adults’ use of social networks, while ease of use and social pressure were not significant influences. These findings contribute to the study of older adults’ technology acceptance within specific technology application domains.

Recent research has focused on empirical studies of personal factors and emerging technologies. Ma et al. ( 2016 ) identified key personal factors affecting smartphone acceptance among older adults through structured questionnaires and face-to-face interviews with 120 participants. The study found that cost, self-satisfaction, and convenience were important factors influencing perceived usefulness and ease of use. This study offers empirical evidence to comprehend the main factors that drive smartphone acceptance among Chinese older adults. Additionally, Yusif et al. ( 2016 ) presented an overview of the obstacles that hinder older adults’ acceptance of assistive technologies, focusing on privacy, trust, and functionality.

In summary, research on older adults’ technology acceptance has shifted from early theoretical deepening and analysis of influencing factors to empirical studies in the areas of personal factors and emerging technologies, which have greatly enriched the theoretical basis of older adults’ technology acceptance and provided practical guidance for the design of emerging technology products.

Research hotspots, evolutionary trends, and quality distribution (RQ4)

Core keywords analysis.

Keywords concise the main idea and core of the literature, and are a refined summary of the research content (Huang et al. 2021 ). In CiteSpace, nodes with a centrality value greater than 0.1 are considered to be critical nodes. Analyzing keywords with high frequency and centrality helps to visualize the hot topics in the research field (Park et al. 2018 ). The merged keywords were imported into CiteSpace, and the top 10 keywords were counted and sorted by frequency and centrality respectively, as shown in Table 9 . The results show that the keyword “TAM” has the highest frequency (92), followed by “UTAUT” (24), which reflects that the in-depth study of the existing technology acceptance model and its theoretical expansion occupy a central position in research related to older adults’ technology acceptance. Furthermore, the terms ‘assistive technology’ and ‘virtual reality’ are both high-frequency and high-centrality terms (frequency = 17, centrality = 0.10), indicating that the research on assistive technology and virtual reality for older adults is the focus of current academic attention.

Research hotspots analysis

Using VOSviewer for keyword co-occurrence analysis organizes keywords into groups or clusters based on their intrinsic connections and frequencies, clearly highlighting the research field’s hot topics. The connectivity among keywords reveals correlations between different topics. To ensure accuracy, the analysis only considered the authors’ keywords. Subsequently, the keywords were filtered by setting the keyword frequency to 5 to obtain the keyword clustering map of the research on older adults’ technology acceptance research keyword clustering mapping (Fig. 7 ), combined with the keyword co-occurrence clustering network (Fig. 7A ) and the corresponding density situation (Fig. 7B ) to make a detailed analysis of the following four groups of clustered themes.

A Co-occurrence clustering network. B Keyword density.

Cluster #1—Research on the factors influencing technology adoption among older adults is a prominent topic, covering age, gender, self-efficacy, attitude, and and intention to use (Berkowsky et al. 2017 ; Wang et al. 2017 ). It also examined older adults’ attitudes towards and acceptance of digital health technologies (Ahmad and Mozelius, 2022 ). Moreover, the COVID-19 pandemic, significantly impacting older adults’ technology attitudes and usage, has underscored the study’s importance and urgency. Therefore, it is crucial to conduct in-depth studies on how older adults accept, adopt, and effectively use new technologies, to address their needs and help them overcome the digital divide within digital inclusion. This will improve their quality of life and healthcare experiences.

Cluster #2—Research focuses on how older adults interact with assistive technologies, especially assistive robots and health monitoring devices, emphasizing trust, usability, and user experience as crucial factors (Halim et al. 2022 ). Moreover, health monitoring technologies effectively track and manage health issues common in older adults, like dementia and mild cognitive impairment (Lussier et al. 2018 ; Piau et al. 2019 ). Interactive exercise games and virtual reality have been deployed to encourage more physical and cognitive engagement among older adults (Campo-Prieto et al. 2021 ). Personalized and innovative technology significantly enhances older adults’ participation, improving their health and well-being.

Cluster #3—Optimizing health management for older adults using mobile technology. With the development of mobile health (mHealth) and health information technology, mobile applications, smartphones, and smart wearable devices have become effective tools to help older users better manage chronic conditions, conduct real-time health monitoring, and even receive telehealth services (Dupuis and Tsotsos 2018 ; Olmedo-Aguirre et al. 2022 ; Kim et al. 2014 ). Additionally, these technologies can mitigate the problem of healthcare resource inequality, especially in developing countries. Older adults’ acceptance and use of these technologies are significantly influenced by their behavioral intentions, motivational factors, and self-management skills. These internal motivational factors, along with external factors, jointly affect older adults’ performance in health management and quality of life.

Cluster #4—Research on technology-assisted home care for older adults is gaining popularity. Environmentally assisted living enhances older adults’ independence and comfort at home, offering essential support and security. This has a crucial impact on promoting healthy aging (Friesen et al. 2016 ; Wahlroos et al. 2023 ). The smart home is a core application in this field, providing a range of solutions that facilitate independent living for the elderly in a highly integrated and user-friendly manner. This fulfills different dimensions of living and health needs (Majumder et al. 2017 ). Moreover, eHealth offers accurate and personalized health management and healthcare services for older adults (Delmastro et al. 2018 ), ensuring their needs are met at home. Research in this field often employs qualitative methods and structural equation modeling to fully understand older adults’ needs and experiences at home and analyze factors influencing technology adoption.

Evolutionary trends analysis

To gain a deeper understanding of the evolutionary trends in research hotspots within the field of older adults’ technology acceptance, we conducted a statistical analysis of the average appearance times of keywords, using CiteSpace to generate the time-zone evolution mapping (Fig. 8 ) and burst keywords. The time-zone mapping visually displays the evolution of keywords over time, intuitively reflecting the frequency and initial appearance of keywords in research, commonly used to identify trends in research topics (Jing et al. 2024a ; Kumar et al. 2021 ). Table 10 lists the top 15 keywords by burst strength, with the red sections indicating high-frequency citations and their burst strength in specific years. These burst keywords reveal the focus and trends of research themes over different periods (Kleinberg 2002 ). Combining insights from the time-zone mapping and burst keywords provides more objective and accurate research insights (Wang et al. 2023b ).

Reflecting the frequency and time of first appearance of keywords in the study.

An integrated analysis of Fig. 8 and Table 10 shows that early research on older adults’ technology acceptance primarily focused on factors such as perceived usefulness, ease of use, and attitudes towards information technology, including their use of computers and the internet (Pan and Jordan-Marsh 2010 ), as well as differences in technology use between older adults and other age groups (Guner and Acarturk 2020 ). Subsequently, the research focus expanded to improving the quality of life for older adults, exploring how technology can optimize health management and enhance the possibility of independent living, emphasizing the significant role of technology in improving the quality of life for the elderly. With ongoing technological advancements, recent research has shifted towards areas such as “virtual reality,” “telehealth,” and “human-robot interaction,” with a focus on the user experience of older adults (Halim et al. 2022 ). The appearance of keywords such as “physical activity” and “exercise” highlights the value of technology in promoting physical activity and health among older adults. This phase of research tends to make cutting-edge technology genuinely serve the practical needs of older adults, achieving its widespread application in daily life. Additionally, research has focused on expanding and quantifying theoretical models of older adults’ technology acceptance, involving keywords such as “perceived risk”, “validation” and “UTAUT”.

In summary, from 2013 to 2023, the field of older adults’ technology acceptance has evolved from initial explorations of influencing factors, to comprehensive enhancements in quality of life and health management, and further to the application and deepening of theoretical models and cutting-edge technologies. This research not only reflects the diversity and complexity of the field but also demonstrates a comprehensive and in-depth understanding of older adults’ interactions with technology across various life scenarios and needs.

Research quality distribution

To reveal the distribution of research quality in the field of older adults’ technology acceptance, a strategic diagram analysis is employed to calculate and illustrate the internal development and interrelationships among various research themes (Xie et al. 2020 ). The strategic diagram uses Centrality as the X-axis and Density as the Y-axis to divide into four quadrants, where the X-axis represents the strength of the connection between thematic clusters and other themes, with higher values indicating a central position in the research field; the Y-axis indicates the level of development within the thematic clusters, with higher values denoting a more mature and widely recognized field (Li and Zhou 2020 ).

Through cluster analysis and manual verification, this study categorized 61 core keywords (Frequency ≥5) into 11 thematic clusters. Subsequently, based on the keywords covered by each thematic cluster, the research themes and their directions for each cluster were summarized (Table 11 ), and the centrality and density coordinates for each cluster were precisely calculated (Table 12 ). Finally, a strategic diagram of the older adults’ technology acceptance research field was constructed (Fig. 9 ). Based on the distribution of thematic clusters across the quadrants in the strategic diagram, the structure and developmental trends of the field were interpreted.

Classification and visualization of theme clusters based on density and centrality.

As illustrated in Fig. 9 , (1) the theme clusters of #3 Usage Experience and #4 Assisted Living Technology are in the first quadrant, characterized by high centrality and density. Their internal cohesion and close links with other themes indicate their mature development, systematic research content or directions have been formed, and they have a significant influence on other themes. These themes play a central role in the field of older adults’ technology acceptance and have promising prospects. (2) The theme clusters of #6 Smart Devices, #9 Theoretical Models, and #10 Mobile Health Applications are in the second quadrant, with higher density but lower centrality. These themes have strong internal connections but weaker external links, indicating that these three themes have received widespread attention from researchers and have been the subject of related research, but more as self-contained systems and exhibit independence. Therefore, future research should further explore in-depth cooperation and cross-application with other themes. (3) The theme clusters of #7 Human-Robot Interaction, #8 Characteristics of the Elderly, and #11 Research Methods are in the third quadrant, with lower centrality and density. These themes are loosely connected internally and have weak links with others, indicating their developmental immaturity. Compared to other topics, they belong to the lower attention edge and niche themes, and there is a need for further investigation. (4) The theme clusters of #1 Digital Healthcare Technology, #2 Psychological Factors, and #5 Socio-Cultural Factors are located in the fourth quadrant, with high centrality but low density. Although closely associated with other research themes, the internal cohesion within these clusters is relatively weak. This suggests that while these themes are closely linked to other research areas, their own development remains underdeveloped, indicating a core immaturity. Nevertheless, these themes are crucial within the research domain of elderly technology acceptance and possess significant potential for future exploration.

Discussion on distribution power (RQ1)

Over the past decade, academic interest and influence in the area of older adults’ technology acceptance have significantly increased. This trend is evidenced by a quantitative analysis of publication and citation volumes, particularly noticeable in 2019 and 2022, where there was a substantial rise in both metrics. The rise is closely linked to the widespread adoption of emerging technologies such as smart homes, wearable devices, and telemedicine among older adults. While these technologies have enhanced their quality of life, they also pose numerous challenges, sparking extensive research into their acceptance, usage behaviors, and influencing factors among the older adults (Pirzada et al. 2022 ; Garcia Reyes et al. 2023 ). Furthermore, the COVID-19 pandemic led to a surge in technology demand among older adults, especially in areas like medical consultation, online socialization, and health management, further highlighting the importance and challenges of technology. Health risks and social isolation have compelled older adults to rely on technology for daily activities, accelerating its adoption and application within this demographic. This phenomenon has made technology acceptance a critical issue, driving societal and academic focus on the study of technology acceptance among older adults.

The flow of knowledge at the level of high-output disciplines and journals, along with the primary publishing outlets, indicates the highly interdisciplinary nature of research into older adults’ technology acceptance. This reflects the complexity and breadth of issues related to older adults’ technology acceptance, necessitating the integration of multidisciplinary knowledge and approaches. Currently, research is primarily focused on medical health and human-computer interaction, demonstrating academic interest in improving health and quality of life for older adults and addressing the urgent needs related to their interactions with technology. In the field of medical health, research aims to provide advanced and innovative healthcare technologies and services to meet the challenges of an aging population while improving the quality of life for older adults (Abdi et al. 2020 ; Wilson et al. 2021 ). In the field of human-computer interaction, research is focused on developing smarter and more user-friendly interaction models to meet the needs of older adults in the digital age, enabling them to actively participate in social activities and enjoy a higher quality of life (Sayago, 2019 ). These studies are crucial for addressing the challenges faced by aging societies, providing increased support and opportunities for the health, welfare, and social participation of older adults.

Discussion on research power (RQ2)

This study analyzes leading countries and collaboration networks, core institutions and authors, revealing the global research landscape and distribution of research strength in the field of older adults’ technology acceptance, and presents quantitative data on global research trends. From the analysis of country distribution and collaborations, China and the USA hold dominant positions in this field, with developed countries like the UK, Germany, Italy, and the Netherlands also excelling in international cooperation and research influence. The significant investment in technological research and the focus on the technological needs of older adults by many developed countries reflect their rapidly aging societies, policy support, and resource allocation.

China is the only developing country that has become a major contributor in this field, indicating its growing research capabilities and high priority given to aging societies and technological innovation. Additionally, China has close collaborations with countries such as USA, the UK, and Malaysia, driven not only by technological research needs but also by shared challenges and complementarities in aging issues among these nations. For instance, the UK has extensive experience in social welfare and aging research, providing valuable theoretical guidance and practical experience. International collaborations, aimed at addressing the challenges of aging, integrate the strengths of various countries, advancing in-depth and widespread development in the research of technology acceptance among older adults.

At the institutional and author level, City University of Hong Kong leads in publication volume, with research teams led by Chan and Chen demonstrating significant academic activity and contributions. Their research primarily focuses on older adults’ acceptance and usage behaviors of various technologies, including smartphones, smart wearables, and social robots (Chen et al. 2015 ; Li et al. 2019 ; Ma et al. 2016 ). These studies, targeting specific needs and product characteristics of older adults, have developed new models of technology acceptance based on existing frameworks, enhancing the integration of these technologies into their daily lives and laying a foundation for further advancements in the field. Although Tilburg University has a smaller publication output, it holds significant influence in the field of older adults’ technology acceptance. Particularly, the high citation rate of Peek’s studies highlights their excellence in research. Peek extensively explored older adults’ acceptance and usage of home care technologies, revealing the complexity and dynamics of their technology use behaviors. His research spans from identifying systemic influencing factors (Peek et al. 2014 ; Peek et al. 2016 ), emphasizing familial impacts (Luijkx et al. 2015 ), to constructing comprehensive models (Peek et al. 2017 ), and examining the dynamics of long-term usage (Peek et al. 2019 ), fully reflecting the evolving technology landscape and the changing needs of older adults. Additionally, the ongoing contributions of researchers like Ziefle, Rogers, and Wouters in the field of older adults’ technology acceptance demonstrate their research influence and leadership. These researchers have significantly enriched the knowledge base in this area with their diverse perspectives. For instance, Ziefle has uncovered the complex attitudes of older adults towards technology usage, especially the trade-offs between privacy and security, and how different types of activities affect their privacy needs (Maidhof et al. 2023 ; Mujirishvili et al. 2023 ; Schomakers and Ziefle 2023 ; Wilkowska et al. 2022 ), reflecting a deep exploration and ongoing innovation in the field of older adults’ technology acceptance.

Discussion on knowledge base and thematic progress (RQ3)

Through co-citation analysis and systematic review of seminal literature, this study reveals the knowledge foundation and thematic progress in the field of older adults’ technology acceptance. Co-citation networks and cluster analyses illustrate the structural themes of the research, delineating the differentiation and boundaries within this field. Additionally, burst detection analysis offers a valuable perspective for understanding the thematic evolution in the field of technology acceptance among older adults. The development and innovation of theoretical models are foundational to this research. Researchers enhance the explanatory power of constructed models by deepening and expanding existing technology acceptance theories to address theoretical limitations. For instance, Heerink et al. ( 2010 ) modified and expanded the UTAUT model by integrating functional assessment and social interaction variables to create the almere model. This model significantly enhances the ability to explain the intentions of older users in utilizing assistive social agents and improves the explanation of actual usage behaviors. Additionally, Chen and Chan ( 2014 ) extended the TAM to include age-related health and capability features of older adults, creating the STAM, which substantially improves predictions of older adults’ technology usage behaviors. Personal attributes, health and capability features, and facilitating conditions have a direct impact on technology acceptance. These factors more effectively predict older adults’ technology usage behaviors than traditional attitudinal factors.

With the advancement of technology and the application of emerging technologies, new research topics have emerged, increasingly focusing on older adults’ acceptance and use of these technologies. Prior to this, the study by Mitzner et al. ( 2010 ) challenged the stereotype of older adults’ conservative attitudes towards technology, highlighting the central roles of usability and usefulness in the technology acceptance process. This discovery laid an important foundation for subsequent research. Research fields such as “smart home technology,” “social life,” and “customer service” are emerging, indicating a shift in focus towards the practical and social applications of technology in older adults’ lives. Research not only focuses on the technology itself but also on how these technologies integrate into older adults’ daily lives and how they can improve the quality of life through technology. For instance, studies such as those by Ma et al. ( 2016 ), Hoque and Sorwar ( 2017 ), and Li et al. ( 2019 ) have explored factors influencing older adults’ use of smartphones, mHealth, and smart wearable devices.

Furthermore, the diversification of research methodologies and innovation in evaluation techniques, such as the use of mixed methods, structural equation modeling (SEM), and neural network (NN) approaches, have enhanced the rigor and reliability of the findings, enabling more precise identification of the factors and mechanisms influencing technology acceptance. Talukder et al. ( 2020 ) employed an effective multimethodological strategy by integrating SEM and NN to leverage the complementary strengths of both approaches, thus overcoming their individual limitations and more accurately analyzing and predicting older adults’ acceptance of wearable health technologies (WHT). SEM is utilized to assess the determinants’ impact on the adoption of WHT, while neural network models validate SEM outcomes and predict the significance of key determinants. This combined approach not only boosts the models’ reliability and explanatory power but also provides a nuanced understanding of the motivations and barriers behind older adults’ acceptance of WHT, offering deep research insights.

Overall, co-citation analysis of the literature in the field of older adults’ technology acceptance has uncovered deeper theoretical modeling and empirical studies on emerging technologies, while emphasizing the importance of research methodological and evaluation innovations in understanding complex social science issues. These findings are crucial for guiding the design and marketing strategies of future technology products, especially in the rapidly growing market of older adults.

Discussion on research hotspots and evolutionary trends (RQ4)

By analyzing core keywords, we can gain deep insights into the hot topics, evolutionary trends, and quality distribution of research in the field of older adults’ technology acceptance. The frequent occurrence of the keywords “TAM” and “UTAUT” indicates that the applicability and theoretical extension of existing technology acceptance models among older adults remain a focal point in academia. This phenomenon underscores the enduring influence of the studies by Davis ( 1989 ) and Venkatesh et al. ( 2003 ), whose models provide a robust theoretical framework for explaining and predicting older adults’ acceptance and usage of emerging technologies. With the widespread application of artificial intelligence (AI) and big data technologies, these theoretical models have incorporated new variables such as perceived risk, trust, and privacy issues (Amin et al. 2024 ; Chen et al. 2024 ; Jing et al. 2024b ; Seibert et al. 2021 ; Wang et al. 2024b ), advancing the theoretical depth and empirical research in this field.

Keyword co-occurrence cluster analysis has revealed multiple research hotspots in the field, including factors influencing technology adoption, interactive experiences between older adults and assistive technologies, the application of mobile health technology in health management, and technology-assisted home care. These studies primarily focus on enhancing the quality of life and health management of older adults through emerging technologies, particularly in the areas of ambient assisted living, smart health monitoring, and intelligent medical care. In these domains, the role of AI technology is increasingly significant (Qian et al. 2021 ; Ho 2020 ). With the evolution of next-generation information technologies, AI is increasingly integrated into elder care systems, offering intelligent, efficient, and personalized service solutions by analyzing the lifestyles and health conditions of older adults. This integration aims to enhance older adults’ quality of life in aspects such as health monitoring and alerts, rehabilitation assistance, daily health management, and emotional support (Lee et al. 2023 ). A survey indicates that 83% of older adults prefer AI-driven solutions when selecting smart products, demonstrating the increasing acceptance of AI in elder care (Zhao and Li 2024 ). Integrating AI into elder care presents both opportunities and challenges, particularly in terms of user acceptance, trust, and long-term usage effects, which warrant further exploration (Mhlanga 2023 ). These studies will help better understand the profound impact of AI technology on the lifestyles of older adults and provide critical references for optimizing AI-driven elder care services.

The Time-zone evolution mapping and burst keyword analysis further reveal the evolutionary trends of research hotspots. Early studies focused on basic technology acceptance models and user perceptions, later expanding to include quality of life and health management. In recent years, research has increasingly focused on cutting-edge technologies such as virtual reality, telehealth, and human-robot interaction, with a concurrent emphasis on the user experience of older adults. This evolutionary process demonstrates a deepening shift from theoretical models to practical applications, underscoring the significant role of technology in enhancing the quality of life for older adults. Furthermore, the strategic coordinate mapping analysis clearly demonstrates the development and mutual influence of different research themes. High centrality and density in the themes of Usage Experience and Assisted Living Technology indicate their mature research status and significant impact on other themes. The themes of Smart Devices, Theoretical Models, and Mobile Health Applications demonstrate self-contained research trends. The themes of Human-Robot Interaction, Characteristics of the Elderly, and Research Methods are not yet mature, but they hold potential for development. Themes of Digital Healthcare Technology, Psychological Factors, and Socio-Cultural Factors are closely related to other themes, displaying core immaturity but significant potential.

In summary, the research hotspots in the field of older adults’ technology acceptance are diverse and dynamic, demonstrating the academic community’s profound understanding of how older adults interact with technology across various life contexts and needs. Under the influence of AI and big data, research should continue to focus on the application of emerging technologies among older adults, exploring in depth how they adapt to and effectively use these technologies. This not only enhances the quality of life and healthcare experiences for older adults but also drives ongoing innovation and development in this field.

Research agenda

Based on the above research findings, to further understand and promote technology acceptance and usage among older adults, we recommend future studies focus on refining theoretical models, exploring long-term usage, and assessing user experience in the following detailed aspects:

Refinement and validation of specific technology acceptance models for older adults: Future research should focus on developing and validating technology acceptance models based on individual characteristics, particularly considering variations in technology acceptance among older adults across different educational levels and cultural backgrounds. This includes factors such as age, gender, educational background, and cultural differences. Additionally, research should examine how well specific technologies, such as wearable devices and mobile health applications, meet the needs of older adults. Building on existing theoretical models, this research should integrate insights from multiple disciplines such as psychology, sociology, design, and engineering through interdisciplinary collaboration to create more accurate and comprehensive models, which should then be validated in relevant contexts.

Deepening the exploration of the relationship between long-term technology use and quality of life among older adults: The acceptance and use of technology by users is a complex and dynamic process (Seuwou et al. 2016 ). Existing research predominantly focuses on older adults’ initial acceptance or short-term use of new technologies; however, the impact of long-term use on their quality of life and health is more significant. Future research should focus on the evolution of older adults’ experiences and needs during long-term technology usage, and the enduring effects of technology on their social interactions, mental health, and life satisfaction. Through longitudinal studies and qualitative analysis, this research reveals the specific needs and challenges of older adults in long-term technology use, providing a basis for developing technologies and strategies that better meet their requirements. This understanding aids in comprehensively assessing the impact of technology on older adults’ quality of life and guiding the optimization and improvement of technological products.

Evaluating the Importance of User Experience in Research on Older Adults’ Technology Acceptance: Understanding the mechanisms of information technology acceptance and use is central to human-computer interaction research. Although technology acceptance models and user experience models differ in objectives, they share many potential intersections. Technology acceptance research focuses on structured prediction and assessment, while user experience research concentrates on interpreting design impacts and new frameworks. Integrating user experience to assess older adults’ acceptance of technology products and systems is crucial (Codfrey et al. 2022 ; Wang et al. 2019 ), particularly for older users, where specific product designs should emphasize practicality and usability (Fisk et al. 2020 ). Researchers need to explore innovative age-appropriate design methods to enhance older adults’ usage experience. This includes studying older users’ actual usage preferences and behaviors, optimizing user interfaces, and interaction designs. Integrating feedback from older adults to tailor products to their needs can further promote their acceptance and continued use of technology products.

Conclusions

This study conducted a systematic review of the literature on older adults’ technology acceptance over the past decade through bibliometric analysis, focusing on the distribution power, research power, knowledge base and theme progress, research hotspots, evolutionary trends, and quality distribution. Using a combination of quantitative and qualitative methods, this study has reached the following conclusions:

Technology acceptance among older adults has become a hot topic in the international academic community, involving the integration of knowledge across multiple disciplines, including Medical Informatics, Health Care Sciences Services, and Ergonomics. In terms of journals, “PSYCHOLOGY, EDUCATION, HEALTH” represents a leading field, with key publications including Computers in Human Behavior , Journal of Medical Internet Research , and International Journal of Human-Computer Interaction . These journals possess significant academic authority and extensive influence in the field.

Research on technology acceptance among older adults is particularly active in developed countries, with China and USA publishing significantly more than other nations. The Netherlands leads in high average citation rates, indicating the depth and impact of its research. Meanwhile, the UK stands out in terms of international collaboration. At the institutional level, City University of Hong Kong and The University of Hong Kong in China are in leading positions. Tilburg University in the Netherlands demonstrates exceptional research quality through its high average citation count. At the author level, Chen from China has the highest number of publications, while Peek from the Netherlands has the highest average citation count.

Co-citation analysis of references indicates that the knowledge base in this field is divided into three main categories: theoretical model deepening, emerging technology applications, and research methods and evaluation. Seminal literature focuses on four areas: specific technology use by older adults, expansion of theoretical models of technology acceptance, information technology adoption behavior, and research perspectives. Research themes have evolved from initial theoretical deepening and analysis of influencing factors to empirical studies on individual factors and emerging technologies.

Keyword analysis indicates that TAM and UTAUT are the most frequently occurring terms, while “assistive technology” and “virtual reality” are focal points with high frequency and centrality. Keyword clustering analysis reveals that research hotspots are concentrated on the influencing factors of technology adoption, human-robot interaction experiences, mobile health management, and technology for aging in place. Time-zone evolution mapping and burst keyword analysis have revealed the research evolution from preliminary exploration of influencing factors, to enhancements in quality of life and health management, and onto advanced technology applications and deepening of theoretical models. Furthermore, analysis of research quality distribution indicates that Usage Experience and Assisted Living Technology have become core topics, while Smart Devices, Theoretical Models, and Mobile Health Applications point towards future research directions.

Through this study, we have systematically reviewed the dynamics, core issues, and evolutionary trends in the field of older adults’ technology acceptance, constructing a comprehensive Knowledge Mapping of the domain and presenting a clear framework of existing research. This not only lays the foundation for subsequent theoretical discussions and innovative applications in the field but also provides an important reference for relevant scholars.

Limitations

To our knowledge, this is the first bibliometric analysis concerning technology acceptance among older adults, and we adhered strictly to bibliometric standards throughout our research. However, this study relies on the Web of Science Core Collection, and while its authority and breadth are widely recognized, this choice may have missed relevant literature published in other significant databases such as PubMed, Scopus, and Google Scholar, potentially overlooking some critical academic contributions. Moreover, given that our analysis was confined to literature in English, it may not reflect studies published in other languages, somewhat limiting the global representativeness of our data sample.

It is noteworthy that with the rapid development of AI technology, its increasingly widespread application in elderly care services is significantly transforming traditional care models. AI is profoundly altering the lifestyles of the elderly, from health monitoring and smart diagnostics to intelligent home systems and personalized care, significantly enhancing their quality of life and health care standards. The potential for AI technology within the elderly population is immense, and research in this area is rapidly expanding. However, due to the restrictive nature of the search terms used in this study, it did not fully cover research in this critical area, particularly in addressing key issues such as trust, privacy, and ethics.

Consequently, future research should not only expand data sources, incorporating multilingual and multidatabase literature, but also particularly focus on exploring older adults’ acceptance of AI technology and its applications, in order to construct a more comprehensive academic landscape of older adults’ technology acceptance, thereby enriching and extending the knowledge system and academic trends in this field.

Data availability

The datasets analyzed during the current study are available in the Dataverse repository: https://doi.org/10.7910/DVN/6K0GJH .

Abdi S, de Witte L, Hawley M (2020) Emerging technologies with potential care and support applications for older people: review of gray literature. JMIR Aging 3(2):e17286. https://doi.org/10.2196/17286

Article   PubMed   PubMed Central   Google Scholar  

Achuthan K, Nair VK, Kowalski R, Ramanathan S, Raman R (2023) Cyberbullying research—Alignment to sustainable development and impact of COVID-19: Bibliometrics and science mapping analysis. Comput Human Behav 140:107566. https://doi.org/10.1016/j.chb.2022.107566

Article   Google Scholar  

Ahmad A, Mozelius P (2022) Human-Computer Interaction for Older Adults: a Literature Review on Technology Acceptance of eHealth Systems. J Eng Res Sci 1(4):119–126. https://doi.org/10.55708/js0104014

Ale Ebrahim N, Salehi H, Embi MA, Habibi F, Gholizadeh H, Motahar SM (2014) Visibility and citation impact. Int Educ Stud 7(4):120–125. https://doi.org/10.5539/ies.v7n4p120

Amin MS, Johnson VL, Prybutok V, Koh CE (2024) An investigation into factors affecting the willingness to disclose personal health information when using AI-enabled caregiver robots. Ind Manag Data Syst 124(4):1677–1699. https://doi.org/10.1108/IMDS-09-2023-0608

Baer NR, Vietzke J, Schenk L (2022) Middle-aged and older adults’ acceptance of mobile nutrition and fitness apps: a systematic mixed studies review. PLoS One 17(12):e0278879. https://doi.org/10.1371/journal.pone.0278879

Barnard Y, Bradley MD, Hodgson F, Lloyd AD (2013) Learning to use new technologies by older adults: Perceived difficulties, experimentation behaviour and usability. Comput Human Behav 29(4):1715–1724. https://doi.org/10.1016/j.chb.2013.02.006

Berkowsky RW, Sharit J, Czaja SJ (2017) Factors predicting decisions about technology adoption among older adults. Innov Aging 3(1):igy002. https://doi.org/10.1093/geroni/igy002

Braun MT (2013) Obstacles to social networking website use among older adults. Comput Human Behav 29(3):673–680. https://doi.org/10.1016/j.chb.2012.12.004

Article   MathSciNet   Google Scholar  

Campo-Prieto P, Rodríguez-Fuentes G, Cancela-Carral JM (2021) Immersive virtual reality exergame promotes the practice of physical activity in older people: An opportunity during COVID-19. Multimodal Technol Interact 5(9):52. https://doi.org/10.3390/mti5090052

Chen C (2006) CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. J Am Soc Inf Sci Technol 57(3):359–377. https://doi.org/10.1002/asi.20317

Chen C, Dubin R, Kim MC (2014) Emerging trends and new developments in regenerative medicine: a scientometric update (2000–2014). Expert Opin Biol Ther 14(9):1295–1317. https://doi.org/10.1517/14712598.2014.920813

Article   PubMed   Google Scholar  

Chen C, Leydesdorff L (2014) Patterns of connections and movements in dual‐map overlays: A new method of publication portfolio analysis. J Assoc Inf Sci Technol 65(2):334–351. https://doi.org/10.1002/asi.22968

Chen J, Wang C, Tang Y (2022) Knowledge mapping of volunteer motivation: A bibliometric analysis and cross-cultural comparative study. Front Psychol 13:883150. https://doi.org/10.3389/fpsyg.2022.883150

Chen JY, Liu YD, Dai J, Wang CL (2023) Development and status of moral education research: Visual analysis based on knowledge graph. Front Psychol 13:1079955. https://doi.org/10.3389/fpsyg.2022.1079955

Chen K, Chan AH (2011) A review of technology acceptance by older adults. Gerontechnology 10(1):1–12. https://doi.org/10.4017/gt.2011.10.01.006.00

Chen K, Chan AH (2014) Gerontechnology acceptance by elderly Hong Kong Chinese: a senior technology acceptance model (STAM). Ergonomics 57(5):635–652. https://doi.org/10.1080/00140139.2014.895855

Chen K, Zhang Y, Fu X (2019) International research collaboration: An emerging domain of innovation studies? Res Policy 48(1):149–168. https://doi.org/10.1016/j.respol.2018.08.005

Chen X, Hu Z, Wang C (2024) Empowering education development through AIGC: A systematic literature review. Educ Inf Technol 1–53. https://doi.org/10.1007/s10639-024-12549-7

Chen Y, Chen CM, Liu ZY, Hu ZG, Wang XW (2015) The methodology function of CiteSpace mapping knowledge domains. Stud Sci Sci 33(2):242–253. https://doi.org/10.16192/j.cnki.1003-2053.2015.02.009

Codfrey GS, Baharum A, Zain NHM, Omar M, Deris FD (2022) User Experience in Product Design and Development: Perspectives and Strategies. Math Stat Eng Appl 71(2):257–262. https://doi.org/10.17762/msea.v71i2.83

Dai J, Zhang X, Wang CL (2024) A meta-analysis of learners’ continuance intention toward online education platforms. Educ Inf Technol 1–36. https://doi.org/10.1007/s10639-024-12654-7

Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13(3):319–340. https://doi.org/10.2307/249008

Delmastro F, Dolciotti C, Palumbo F, Magrini M, Di Martino F, La Rosa D, Barcaro U (2018) Long-term care: how to improve the quality of life with mobile and e-health services. In 2018 14th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), pp. 12–19. IEEE. https://doi.org/10.1109/WiMOB.2018.8589157

Dupuis K, Tsotsos LE (2018) Technology for remote health monitoring in an older population: a role for mobile devices. Multimodal Technol Interact 2(3):43. https://doi.org/10.3390/mti2030043

Ferguson C, Hickman LD, Turkmani S, Breen P, Gargiulo G, Inglis SC (2021) Wearables only work on patients that wear them”: Barriers and facilitators to the adoption of wearable cardiac monitoring technologies. Cardiovasc Digit Health J 2(2):137–147. https://doi.org/10.1016/j.cvdhj.2021.02.001

Fisk AD, Czaja SJ, Rogers WA, Charness N, Sharit J (2020) Designing for older adults: Principles and creative human factors approaches. CRC Press. https://doi.org/10.1201/9781420080681

Friesen S, Brémault-Phillips S, Rudrum L, Rogers LG (2016) Environmental design that supports healthy aging: Evaluating a new supportive living facility. J Hous Elderly 30(1):18–34. https://doi.org/10.1080/02763893.2015.1129380

Garcia Reyes EP, Kelly R, Buchanan G, Waycott J (2023) Understanding Older Adults’ Experiences With Technologies for Health Self-management: Interview Study. JMIR Aging 6:e43197. https://doi.org/10.2196/43197

Geng Z, Wang J, Liu J, Miao J (2024) Bibliometric analysis of the development, current status, and trends in adult degenerative scoliosis research: A systematic review from 1998 to 2023. J Pain Res 17:153–169. https://doi.org/10.2147/JPR.S437575

González A, Ramírez MP, Viadel V (2012) Attitudes of the elderly toward information and communications technologies. Educ Gerontol 38(9):585–594. https://doi.org/10.1080/03601277.2011.595314

Guner H, Acarturk C (2020) The use and acceptance of ICT by senior citizens: a comparison of technology acceptance model (TAM) for elderly and young adults. Univ Access Inf Soc 19(2):311–330. https://doi.org/10.1007/s10209-018-0642-4

Halim I, Saptari A, Perumal PA, Abdullah Z, Abdullah S, Muhammad MN (2022) A Review on Usability and User Experience of Assistive Social Robots for Older Persons. Int J Integr Eng 14(6):102–124. https://penerbit.uthm.edu.my/ojs/index.php/ijie/article/view/8566

He Y, He Q, Liu Q (2022) Technology acceptance in socially assistive robots: Scoping review of models, measurement, and influencing factors. J Healthc Eng 2022(1):6334732. https://doi.org/10.1155/2022/6334732

Heerink M, Kröse B, Evers V, Wielinga B (2010) Assessing acceptance of assistive social agent technology by older adults: the almere model. Int J Soc Robot 2:361–375. https://doi.org/10.1007/s12369-010-0068-5

Ho A (2020) Are we ready for artificial intelligence health monitoring in elder care? BMC Geriatr 20(1):358. https://doi.org/10.1186/s12877-020-01764-9

Hoque R, Sorwar G (2017) Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model. Int J Med Inform 101:75–84. https://doi.org/10.1016/j.ijmedinf.2017.02.002

Hota PK, Subramanian B, Narayanamurthy G (2020) Mapping the intellectual structure of social entrepreneurship research: A citation/co-citation analysis. J Bus Ethics 166(1):89–114. https://doi.org/10.1007/s10551-019-04129-4

Huang R, Yan P, Yang X (2021) Knowledge map visualization of technology hotspots and development trends in China’s textile manufacturing industry. IET Collab Intell Manuf 3(3):243–251. https://doi.org/10.1049/cim2.12024

Article   ADS   Google Scholar  

Jing Y, Wang C, Chen Y, Wang H, Yu T, Shadiev R (2023) Bibliometric mapping techniques in educational technology research: A systematic literature review. Educ Inf Technol 1–29. https://doi.org/10.1007/s10639-023-12178-6

Jing YH, Wang CL, Chen ZY, Shen SS, Shadiev R (2024a) A Bibliometric Analysis of Studies on Technology-Supported Learning Environments: Hotopics and Frontier Evolution. J Comput Assist Learn 1–16. https://doi.org/10.1111/jcal.12934

Jing YH, Wang HM, Chen XJ, Wang CL (2024b) What factors will affect the effectiveness of using ChatGPT to solve programming problems? A quasi-experimental study. Humanit Soc Sci Commun 11:319. https://doi.org/10.1057/s41599-024-02751-w

Kamrani P, Dorsch I, Stock WG (2021) Do researchers know what the h-index is? And how do they estimate its importance? Scientometrics 126(7):5489–5508. https://doi.org/10.1007/s11192-021-03968-1

Kim HS, Lee KH, Kim H, Kim JH (2014) Using mobile phones in healthcare management for the elderly. Maturitas 79(4):381–388. https://doi.org/10.1016/j.maturitas.2014.08.013

Article   MathSciNet   PubMed   Google Scholar  

Kleinberg J (2002) Bursty and hierarchical structure in streams. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 91–101. https://doi.org/10.1145/775047.775061

Kruse C, Fohn J, Wilson N, Patlan EN, Zipp S, Mileski M (2020) Utilization barriers and medical outcomes commensurate with the use of telehealth among older adults: systematic review. JMIR Med Inform 8(8):e20359. https://doi.org/10.2196/20359

Kumar S, Lim WM, Pandey N, Christopher Westland J (2021) 20 years of electronic commerce research. Electron Commer Res 21:1–40. https://doi.org/10.1007/s10660-021-09464-1

Kwiek M (2021) What large-scale publication and citation data tell us about international research collaboration in Europe: Changing national patterns in global contexts. Stud High Educ 46(12):2629–2649. https://doi.org/10.1080/03075079.2020.1749254

Lee C, Coughlin JF (2015) PERSPECTIVE: Older adults’ adoption of technology: an integrated approach to identifying determinants and barriers. J Prod Innov Manag 32(5):747–759. https://doi.org/10.1111/jpim.12176

Lee CH, Wang C, Fan X, Li F, Chen CH (2023) Artificial intelligence-enabled digital transformation in elderly healthcare field: scoping review. Adv Eng Inform 55:101874. https://doi.org/10.1016/j.aei.2023.101874

Leydesdorff L, Rafols I (2012) Interactive overlays: A new method for generating global journal maps from Web-of-Science data. J Informetr 6(2):318–332. https://doi.org/10.1016/j.joi.2011.11.003

Li J, Ma Q, Chan AH, Man S (2019) Health monitoring through wearable technologies for older adults: Smart wearables acceptance model. Appl Ergon 75:162–169. https://doi.org/10.1016/j.apergo.2018.10.006

Article   ADS   PubMed   Google Scholar  

Li X, Zhou D (2020) Product design requirement information visualization approach for intelligent manufacturing services. China Mech Eng 31(07):871, http://www.cmemo.org.cn/EN/Y2020/V31/I07/871

Google Scholar  

Lin Y, Yu Z (2024a) An integrated bibliometric analysis and systematic review modelling students’ technostress in higher education. Behav Inf Technol 1–25. https://doi.org/10.1080/0144929X.2024.2332458

Lin Y, Yu Z (2024b) A bibliometric analysis of artificial intelligence chatbots in educational contexts. Interact Technol Smart Educ 21(2):189–213. https://doi.org/10.1108/ITSE-12-2022-0165

Liu L, Duffy VG (2023) Exploring the future development of Artificial Intelligence (AI) applications in chatbots: a bibliometric analysis. Int J Soc Robot 15(5):703–716. https://doi.org/10.1007/s12369-022-00956-0

Liu R, Li X, Chu J (2022) Evolution of applied variables in the research on technology acceptance of the elderly. In: International Conference on Human-Computer Interaction, Cham: Springer International Publishing, pp 500–520. https://doi.org/10.1007/978-3-031-05581-23_5

Luijkx K, Peek S, Wouters E (2015) “Grandma, you should do it—It’s cool” Older Adults and the Role of Family Members in Their Acceptance of Technology. Int J Environ Res Public Health 12(12):15470–15485. https://doi.org/10.3390/ijerph121214999

Lussier M, Lavoie M, Giroux S, Consel C, Guay M, Macoir J, Bier N (2018) Early detection of mild cognitive impairment with in-home monitoring sensor technologies using functional measures: a systematic review. IEEE J Biomed Health Inform 23(2):838–847. https://doi.org/10.1109/JBHI.2018.2834317

López-Robles JR, Otegi-Olaso JR, Porto Gomez I, Gamboa-Rosales NK, Gamboa-Rosales H, Robles-Berumen H (2018) Bibliometric network analysis to identify the intellectual structure and evolution of the big data research field. In: International Conference on Intelligent Data Engineering and Automated Learning, Cham: Springer International Publishing, pp 113–120. https://doi.org/10.1007/978-3-030-03496-2_13

Ma Q, Chan AH, Chen K (2016) Personal and other factors affecting acceptance of smartphone technology by older Chinese adults. Appl Ergon 54:62–71. https://doi.org/10.1016/j.apergo.2015.11.015

Ma Q, Chan AHS, Teh PL (2021) Insights into Older Adults’ Technology Acceptance through Meta-Analysis. Int J Hum-Comput Interact 37(11):1049–1062. https://doi.org/10.1080/10447318.2020.1865005

Macedo IM (2017) Predicting the acceptance and use of information and communication technology by older adults: An empirical examination of the revised UTAUT2. Comput Human Behav 75:935–948. https://doi.org/10.1016/j.chb.2017.06.013

Maidhof C, Offermann J, Ziefle M (2023) Eyes on privacy: acceptance of video-based AAL impacted by activities being filmed. Front Public Health 11:1186944. https://doi.org/10.3389/fpubh.2023.1186944

Majumder S, Aghayi E, Noferesti M, Memarzadeh-Tehran H, Mondal T, Pang Z, Deen MJ (2017) Smart homes for elderly healthcare—Recent advances and research challenges. Sensors 17(11):2496. https://doi.org/10.3390/s17112496

Article   ADS   PubMed   PubMed Central   Google Scholar  

Mhlanga D (2023) Artificial Intelligence in elderly care: Navigating ethical and responsible AI adoption for seniors. Available at SSRN 4675564. 4675564 min) Identifying citation patterns of scientific breakthroughs: A perspective of dynamic citation process. Inf Process Manag 58(1):102428. https://doi.org/10.1016/j.ipm.2020.102428

Mitzner TL, Boron JB, Fausset CB, Adams AE, Charness N, Czaja SJ, Sharit J (2010) Older adults talk technology: Technology usage and attitudes. Comput Human Behav 26(6):1710–1721. https://doi.org/10.1016/j.chb.2010.06.020

Mitzner TL, Savla J, Boot WR, Sharit J, Charness N, Czaja SJ, Rogers WA (2019) Technology adoption by older adults: Findings from the PRISM trial. Gerontologist 59(1):34–44. https://doi.org/10.1093/geront/gny113

Mongeon P, Paul-Hus A (2016) The journal coverage of Web of Science and Scopus: a comparative analysis. Scientometrics 106:213–228. https://doi.org/10.1007/s11192-015-1765-5

Mostaghel R (2016) Innovation and technology for the elderly: Systematic literature review. J Bus Res 69(11):4896–4900. https://doi.org/10.1016/j.jbusres.2016.04.049

Mujirishvili T, Maidhof C, Florez-Revuelta F, Ziefle M, Richart-Martinez M, Cabrero-García J (2023) Acceptance and privacy perceptions toward video-based active and assisted living technologies: Scoping review. J Med Internet Res 25:e45297. https://doi.org/10.2196/45297

Naseri RNN, Azis SN, Abas N (2023) A Review of Technology Acceptance and Adoption Models in Consumer Study. FIRM J Manage Stud 8(2):188–199. https://doi.org/10.33021/firm.v8i2.4536

Nguyen UP, Hallinger P (2020) Assessing the distinctive contributions of Simulation & Gaming to the literature, 1970–2019: A bibliometric review. Simul Gaming 51(6):744–769. https://doi.org/10.1177/1046878120941569

Olmedo-Aguirre JO, Reyes-Campos J, Alor-Hernández G, Machorro-Cano I, Rodríguez-Mazahua L, Sánchez-Cervantes JL (2022) Remote healthcare for elderly people using wearables: A review. Biosensors 12(2):73. https://doi.org/10.3390/bios12020073

Pan S, Jordan-Marsh M (2010) Internet use intention and adoption among Chinese older adults: From the expanded technology acceptance model perspective. Comput Human Behav 26(5):1111–1119. https://doi.org/10.1016/j.chb.2010.03.015

Pan X, Yan E, Cui M, Hua W (2018) Examining the usage, citation, and diffusion patterns of bibliometric map software: A comparative study of three tools. J Informetr 12(2):481–493. https://doi.org/10.1016/j.joi.2018.03.005

Park JS, Kim NR, Han EJ (2018) Analysis of trends in science and technology using keyword network analysis. J Korea Ind Inf Syst Res 23(2):63–73. https://doi.org/10.9723/jksiis.2018.23.2.063

Peek ST, Luijkx KG, Rijnaard MD, Nieboer ME, Van Der Voort CS, Aarts S, Wouters EJ (2016) Older adults’ reasons for using technology while aging in place. Gerontology 62(2):226–237. https://doi.org/10.1159/000430949

Peek ST, Luijkx KG, Vrijhoef HJ, Nieboer ME, Aarts S, van der Voort CS, Wouters EJ (2017) Origins and consequences of technology acquirement by independent-living seniors: Towards an integrative model. BMC Geriatr 17:1–18. https://doi.org/10.1186/s12877-017-0582-5

Peek ST, Wouters EJ, Van Hoof J, Luijkx KG, Boeije HR, Vrijhoef HJ (2014) Factors influencing acceptance of technology for aging in place: a systematic review. Int J Med Inform 83(4):235–248. https://doi.org/10.1016/j.ijmedinf.2014.01.004

Peek STM, Luijkx KG, Vrijhoef HJM, Nieboer ME, Aarts S, Van Der Voort CS, Wouters EJM (2019) Understanding changes and stability in the long-term use of technologies by seniors who are aging in place: a dynamical framework. BMC Geriatr 19:1–13. https://doi.org/10.1186/s12877-019-1241-9

Perez AJ, Siddiqui F, Zeadally S, Lane D (2023) A review of IoT systems to enable independence for the elderly and disabled individuals. Internet Things 21:100653. https://doi.org/10.1016/j.iot.2022.100653

Piau A, Wild K, Mattek N, Kaye J (2019) Current state of digital biomarker technologies for real-life, home-based monitoring of cognitive function for mild cognitive impairment to mild Alzheimer disease and implications for clinical care: systematic review. J Med Internet Res 21(8):e12785. https://doi.org/10.2196/12785

Pirzada P, Wilde A, Doherty GH, Harris-Birtill D (2022) Ethics and acceptance of smart homes for older adults. Inform Health Soc Care 47(1):10–37. https://doi.org/10.1080/17538157.2021.1923500

Pranckutė R (2021) Web of Science (WoS) and Scopus: The titans of bibliographic information in today’s academic world. Publications 9(1):12. https://doi.org/10.3390/publications9010012

Qian K, Zhang Z, Yamamoto Y, Schuller BW (2021) Artificial intelligence internet of things for the elderly: From assisted living to health-care monitoring. IEEE Signal Process Mag 38(4):78–88. https://doi.org/10.1109/MSP.2021.3057298

Redner S (1998) How popular is your paper? An empirical study of the citation distribution. Eur Phys J B-Condens Matter Complex Syst 4(2):131–134. https://doi.org/10.1007/s100510050359

Sayago S (ed.) (2019) Perspectives on human-computer interaction research with older people. Switzerland: Springer International Publishing. https://doi.org/10.1007/978-3-030-06076-3

Schomakers EM, Ziefle M (2023) Privacy vs. security: trade-offs in the acceptance of smart technologies for aging-in-place. Int J Hum Comput Interact 39(5):1043–1058. https://doi.org/10.1080/10447318.2022.2078463

Schroeder T, Dodds L, Georgiou A, Gewald H, Siette J (2023) Older adults and new technology: Mapping review of the factors associated with older adults’ intention to adopt digital technologies. JMIR Aging 6(1):e44564. https://doi.org/10.2196/44564

Seibert K, Domhoff D, Bruch D, Schulte-Althoff M, Fürstenau D, Biessmann F, Wolf-Ostermann K (2021) Application scenarios for artificial intelligence in nursing care: rapid review. J Med Internet Res 23(11):e26522. https://doi.org/10.2196/26522

Seuwou P, Banissi E, Ubakanma G (2016) User acceptance of information technology: A critical review of technology acceptance models and the decision to invest in Information Security. In: Global Security, Safety and Sustainability-The Security Challenges of the Connected World: 11th International Conference, ICGS3 2017, London, UK, January 18-20, 2017, Proceedings 11:230-251. Springer International Publishing. https://doi.org/10.1007/978-3-319-51064-4_19

Shiau WL, Wang X, Zheng F (2023) What are the trend and core knowledge of information security? A citation and co-citation analysis. Inf Manag 60(3):103774. https://doi.org/10.1016/j.im.2023.103774

Sinha S, Verma A, Tiwari P (2021) Technology: Saving and enriching life during COVID-19. Front Psychol 12:647681. https://doi.org/10.3389/fpsyg.2021.647681

Soar J (2010) The potential of information and communication technologies to support ageing and independent living. Ann Telecommun 65:479–483. https://doi.org/10.1007/s12243-010-0167-1

Strotmann A, Zhao D (2012) Author name disambiguation: What difference does it make in author‐based citation analysis? J Am Soc Inf Sci Technol 63(9):1820–1833. https://doi.org/10.1002/asi.22695

Talukder MS, Sorwar G, Bao Y, Ahmed JU, Palash MAS (2020) Predicting antecedents of wearable healthcare technology acceptance by elderly: A combined SEM-Neural Network approach. Technol Forecast Soc Change 150:119793. https://doi.org/10.1016/j.techfore.2019.119793

Taskin Z, Al U (2019) Natural language processing applications in library and information science. Online Inf Rev 43(4):676–690. https://doi.org/10.1108/oir-07-2018-0217

Touqeer H, Zaman S, Amin R, Hussain M, Al-Turjman F, Bilal M (2021) Smart home security: challenges, issues and solutions at different IoT layers. J Supercomput 77(12):14053–14089. https://doi.org/10.1007/s11227-021-03825-1

United Nations Department of Economic and Social Affairs (2023) World population ageing 2023: Highlights. https://www.un.org/zh/193220

Valk CAL, Lu Y, Randriambelonoro M, Jessen J (2018) Designing for technology acceptance of wearable and mobile technologies for senior citizen users. In: 21st DMI: Academic Design Management Conference (ADMC 2018), Design Management Institute, pp 1361–1373. https://www.dmi.org/page/ADMC2018

Van Eck N, Waltman L (2010) Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84(2):523–538. https://doi.org/10.1007/s11192-009-0146-3

Vancea M, Solé-Casals J (2016) Population aging in the European Information Societies: towards a comprehensive research agenda in eHealth innovations for elderly. Aging Dis 7(4):526. https://doi.org/10.14336/AD.2015.1214

Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: Toward a unified view. MIS Q 27(3):425–478. https://doi.org/10.2307/30036540

Wagner N, Hassanein K, Head M (2010) Computer use by older adults: A multi-disciplinary review. Comput Human Behav 26(5):870–882. https://doi.org/10.1016/j.chb.2010.03.029

Wahlroos N, Narsakka N, Stolt M, Suhonen R (2023) Physical environment maintaining independence and self-management of older people in long-term care settings—An integrative literature review. J Aging Environ 37(3):295–313. https://doi.org/10.1080/26892618.2022.2092927

Wang CL, Chen XJ, Yu T, Liu YD, Jing YH (2024a) Education reform and change driven by digital technology: a bibliometric study from a global perspective. Humanit Soc Sci Commun 11(1):1–17. https://doi.org/10.1057/s41599-024-02717-y

Wang CL, Dai J, Zhu KK, Yu T, Gu XQ (2023a) Understanding the Continuance Intention of College Students Toward New E-learning Spaces Based on an Integrated Model of the TAM and TTF. Int J Hum-comput Int 1–14. https://doi.org/10.1080/10447318.2023.2291609

Wang CL, Wang HM, Li YY, Dai J, Gu XQ, Yu T (2024b) Factors Influencing University Students’ Behavioral Intention to Use Generative Artificial Intelligence: Integrating the Theory of Planned Behavior and AI Literacy. Int J Hum-comput Int 1–23. https://doi.org/10.1080/10447318.2024.2383033

Wang J, Zhao W, Zhang Z, Liu X, Xie T, Wang L, Zhang Y (2024c) A journey of challenges and victories: a bibliometric worldview of nanomedicine since the 21st century. Adv Mater 36(15):2308915. https://doi.org/10.1002/adma.202308915

Wang J, Chen Y, Huo S, Mai L, Jia F (2023b) Research hotspots and trends of social robot interaction design: A bibliometric analysis. Sensors 23(23):9369. https://doi.org/10.3390/s23239369

Wang KH, Chen G, Chen HG (2017) A model of technology adoption by older adults. Soc Behav Personal 45(4):563–572. https://doi.org/10.2224/sbp.5778

Wang S, Bolling K, Mao W, Reichstadt J, Jeste D, Kim HC, Nebeker C (2019) Technology to Support Aging in Place: Older Adults’ Perspectives. Healthcare 7(2):60. https://doi.org/10.3390/healthcare7020060

Wang Z, Liu D, Sun Y, Pang X, Sun P, Lin F, Ren K (2022) A survey on IoT-enabled home automation systems: Attacks and defenses. IEEE Commun Surv Tutor 24(4):2292–2328. https://doi.org/10.1109/COMST.2022.3201557

Wilkowska W, Offermann J, Spinsante S, Poli A, Ziefle M (2022) Analyzing technology acceptance and perception of privacy in ambient assisted living for using sensor-based technologies. PloS One 17(7):e0269642. https://doi.org/10.1371/journal.pone.0269642

Wilson J, Heinsch M, Betts D, Booth D, Kay-Lambkin F (2021) Barriers and facilitators to the use of e-health by older adults: a scoping review. BMC Public Health 21:1–12. https://doi.org/10.1186/s12889-021-11623-w

Xia YQ, Deng YL, Tao XY, Zhang SN, Wang CL (2024) Digital art exhibitions and psychological well-being in Chinese Generation Z: An analysis based on the S-O-R framework. Humanit Soc Sci Commun 11:266. https://doi.org/10.1057/s41599-024-02718-x

Xie H, Zhang Y, Duan K (2020) Evolutionary overview of urban expansion based on bibliometric analysis in Web of Science from 1990 to 2019. Habitat Int 95:102100. https://doi.org/10.1016/j.habitatint.2019.10210

Xu Z, Ge Z, Wang X, Skare M (2021) Bibliometric analysis of technology adoption literature published from 1997 to 2020. Technol Forecast Soc Change 170:120896. https://doi.org/10.1016/j.techfore.2021.120896

Yap YY, Tan SH, Choon SW (2022) Elderly’s intention to use technologies: a systematic literature review. Heliyon 8(1). https://doi.org/10.1016/j.heliyon.2022.e08765

Yu T, Dai J, Wang CL (2023) Adoption of blended learning: Chinese university students’ perspectives. Humanit Soc Sci Commun 10:390. https://doi.org/10.1057/s41599-023-01904-7

Yusif S, Soar J, Hafeez-Baig A (2016) Older people, assistive technologies, and the barriers to adoption: A systematic review. Int J Med Inform 94:112–116. https://doi.org/10.1016/j.ijmedinf.2016.07.004

Zhang J, Zhu L (2022) Citation recommendation using semantic representation of cited papers’ relations and content. Expert Syst Appl 187:115826. https://doi.org/10.1016/j.eswa.2021.115826

Zhao Y, Li J (2024) Opportunities and challenges of integrating artificial intelligence in China’s elderly care services. Sci Rep 14(1):9254. https://doi.org/10.1038/s41598-024-60067-w

Article   ADS   MathSciNet   PubMed   PubMed Central   Google Scholar  

Download references

Acknowledgements

This research was supported by the Social Science Foundation of Shaanxi Province in China (Grant No. 2023J014).

Author information

Authors and affiliations.

School of Art and Design, Shaanxi University of Science and Technology, Xi’an, China

Xianru Shang, Zijian Liu, Chen Gong, Zhigang Hu & Yuexuan Wu

Department of Education Information Technology, Faculty of Education, East China Normal University, Shanghai, China

Chengliang Wang

You can also search for this author in PubMed   Google Scholar

Contributions

Conceptualization, XS, YW, CW; methodology, XS, ZL, CG, CW; software, XS, CG, YW; writing-original draft preparation, XS, CW; writing-review and editing, XS, CG, ZH, CW; supervision, ZL, ZH, CW; project administration, ZL, ZH, CW; funding acquisition, XS, CG. All authors read and approved the final manuscript. All authors have read and approved the re-submission of the manuscript.

Corresponding author

Correspondence to Chengliang Wang .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Ethical approval

Ethical approval was not required as the study did not involve human participants.

Informed consent

Informed consent was not required as the study did not involve human participants.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

Shang, X., Liu, Z., Gong, C. et al. Knowledge mapping and evolution of research on older adults’ technology acceptance: a bibliometric study from 2013 to 2023. Humanit Soc Sci Commun 11 , 1115 (2024). https://doi.org/10.1057/s41599-024-03658-2

Download citation

Received : 20 June 2024

Accepted : 21 August 2024

Published : 31 August 2024

DOI : https://doi.org/10.1057/s41599-024-03658-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies