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Case Study – Methods, Examples and Guide

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A case study is a research method that involves an in-depth examination and analysis of a particular phenomenon or case, such as an individual, organization, community, event, or situation.

It is a qualitative research approach that aims to provide a detailed and comprehensive understanding of the case being studied. Case studies typically involve multiple sources of data, including interviews, observations, documents, and artifacts, which are analyzed using various techniques, such as content analysis, thematic analysis, and grounded theory. The findings of a case study are often used to develop theories, inform policy or practice, or generate new research questions.

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

A single-case study is an in-depth analysis of a single case. This type of case study is useful when the researcher wants to understand a specific phenomenon in detail.

For Example , A researcher might conduct a single-case study on a particular individual to understand their experiences with a particular health condition or a specific organization to explore their management practices. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a single-case study are often used to generate new research questions, develop theories, or inform policy or practice.

Multiple-Case Study

A multiple-case study involves the analysis of several cases that are similar in nature. This type of case study is useful when the researcher wants to identify similarities and differences between the cases.

For Example, a researcher might conduct a multiple-case study on several companies to explore the factors that contribute to their success or failure. The researcher collects data from each case, compares and contrasts the findings, and uses various techniques to analyze the data, such as comparative analysis or pattern-matching. The findings of a multiple-case study can be used to develop theories, inform policy or practice, or generate new research questions.

Exploratory Case Study

An exploratory case study is used to explore a new or understudied phenomenon. This type of case study is useful when the researcher wants to generate hypotheses or theories about the phenomenon.

For Example, a researcher might conduct an exploratory case study on a new technology to understand its potential impact on society. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as grounded theory or content analysis. The findings of an exploratory case study can be used to generate new research questions, develop theories, or inform policy or practice.

Descriptive Case Study

A descriptive case study is used to describe a particular phenomenon in detail. This type of case study is useful when the researcher wants to provide a comprehensive account of the phenomenon.

For Example, a researcher might conduct a descriptive case study on a particular community to understand its social and economic characteristics. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a descriptive case study can be used to inform policy or practice or generate new research questions.

Instrumental Case Study

An instrumental case study is used to understand a particular phenomenon that is instrumental in achieving a particular goal. This type of case study is useful when the researcher wants to understand the role of the phenomenon in achieving the goal.

For Example, a researcher might conduct an instrumental case study on a particular policy to understand its impact on achieving a particular goal, such as reducing poverty. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of an instrumental case study can be used to inform policy or practice or generate new research questions.

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

Interviews involve asking questions to individuals who have knowledge or experience relevant to the case study. Interviews can be structured (where the same questions are asked to all participants) or unstructured (where the interviewer follows up on the responses with further questions). Interviews can be conducted in person, over the phone, or through video conferencing.

Observations

Observations involve watching and recording the behavior and activities of individuals or groups relevant to the case study. Observations can be participant (where the researcher actively participates in the activities) or non-participant (where the researcher observes from a distance). Observations can be recorded using notes, audio or video recordings, or photographs.

Documents can be used as a source of information for case studies. Documents can include reports, memos, emails, letters, and other written materials related to the case study. Documents can be collected from the case study participants or from public sources.

Surveys involve asking a set of questions to a sample of individuals relevant to the case study. Surveys can be administered in person, over the phone, through mail or email, or online. Surveys can be used to gather information on attitudes, opinions, or behaviors related to the case study.

Artifacts are physical objects relevant to the case study. Artifacts can include tools, equipment, products, or other objects that provide insights into the case study phenomenon.

How to conduct Case Study Research

Conducting a case study research involves several steps that need to be followed to ensure the quality and rigor of the study. Here are the steps to conduct case study research:

Define the research questions: The first step in conducting a case study research is to define the research questions. The research questions should be specific, measurable, and relevant to the case study phenomenon under investigation.
Select the case: The next step is to select the case or cases to be studied. The case should be relevant to the research questions and should provide rich and diverse data that can be used to answer the research questions.
Collect data: Data can be collected using various methods, such as interviews, observations, documents, surveys, and artifacts. The data collection method should be selected based on the research questions and the nature of the case study phenomenon.
Analyze the data: The data collected from the case study should be analyzed using various techniques, such as content analysis, thematic analysis, or grounded theory. The analysis should be guided by the research questions and should aim to provide insights and conclusions relevant to the research questions.
Draw conclusions: The conclusions drawn from the case study should be based on the data analysis and should be relevant to the research questions. The conclusions should be supported by evidence and should be clearly stated.
Validate the findings: The findings of the case study should be validated by reviewing the data and the analysis with participants or other experts in the field. This helps to ensure the validity and reliability of the findings.
Write the report: The final step is to write the report of the case study research. The report should provide a clear description of the case study phenomenon, the research questions, the data collection methods, the data analysis, the findings, and the conclusions. The report should be written in a clear and concise manner and should follow the guidelines for academic writing.

Examples of Case Study

Here are some examples of case study research:

The Hawthorne Studies : Conducted between 1924 and 1932, the Hawthorne Studies were a series of case studies conducted by Elton Mayo and his colleagues to examine the impact of work environment on employee productivity. The studies were conducted at the Hawthorne Works plant of the Western Electric Company in Chicago and included interviews, observations, and experiments.
The Stanford Prison Experiment: Conducted in 1971, the Stanford Prison Experiment was a case study conducted by Philip Zimbardo to examine the psychological effects of power and authority. The study involved simulating a prison environment and assigning participants to the role of guards or prisoners. The study was controversial due to the ethical issues it raised.
The Challenger Disaster: The Challenger Disaster was a case study conducted to examine the causes of the Space Shuttle Challenger explosion in 1986. The study included interviews, observations, and analysis of data to identify the technical, organizational, and cultural factors that contributed to the disaster.
The Enron Scandal: The Enron Scandal was a case study conducted to examine the causes of the Enron Corporation’s bankruptcy in 2001. The study included interviews, analysis of financial data, and review of documents to identify the accounting practices, corporate culture, and ethical issues that led to the company’s downfall.
The Fukushima Nuclear Disaster : The Fukushima Nuclear Disaster was a case study conducted to examine the causes of the nuclear accident that occurred at the Fukushima Daiichi Nuclear Power Plant in Japan in 2011. The study included interviews, analysis of data, and review of documents to identify the technical, organizational, and cultural factors that contributed to the disaster.

Application of Case Study

Case studies have a wide range of applications across various fields and industries. Here are some examples:

Business and Management

Case studies are widely used in business and management to examine real-life situations and develop problem-solving skills. Case studies can help students and professionals to develop a deep understanding of business concepts, theories, and best practices.

Case studies are used in healthcare to examine patient care, treatment options, and outcomes. Case studies can help healthcare professionals to develop critical thinking skills, diagnose complex medical conditions, and develop effective treatment plans.

Case studies are used in education to examine teaching and learning practices. Case studies can help educators to develop effective teaching strategies, evaluate student progress, and identify areas for improvement.

Social Sciences

Case studies are widely used in social sciences to examine human behavior, social phenomena, and cultural practices. Case studies can help researchers to develop theories, test hypotheses, and gain insights into complex social issues.

Law and Ethics

Case studies are used in law and ethics to examine legal and ethical dilemmas. Case studies can help lawyers, policymakers, and ethical professionals to develop critical thinking skills, analyze complex cases, and make informed decisions.

Purpose of Case Study

The purpose of a case study is to provide a detailed analysis of a specific phenomenon, issue, or problem in its real-life context. A case study is a qualitative research method that involves the in-depth exploration and analysis of a particular case, which can be an individual, group, organization, event, or community.

The primary purpose of a case study is to generate a comprehensive and nuanced understanding of the case, including its history, context, and dynamics. Case studies can help researchers to identify and examine the underlying factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and detailed understanding of the case, which can inform future research, practice, or policy.

Case studies can also serve other purposes, including:

Illustrating a theory or concept: Case studies can be used to illustrate and explain theoretical concepts and frameworks, providing concrete examples of how they can be applied in real-life situations.
Developing hypotheses: Case studies can help to generate hypotheses about the causal relationships between different factors and outcomes, which can be tested through further research.
Providing insight into complex issues: Case studies can provide insights into complex and multifaceted issues, which may be difficult to understand through other research methods.
Informing practice or policy: Case studies can be used to inform practice or policy by identifying best practices, lessons learned, or areas for improvement.

Advantages of Case Study Research

There are several advantages of case study research, including:

In-depth exploration: Case study research allows for a detailed exploration and analysis of a specific phenomenon, issue, or problem in its real-life context. This can provide a comprehensive understanding of the case and its dynamics, which may not be possible through other research methods.
Rich data: Case study research can generate rich and detailed data, including qualitative data such as interviews, observations, and documents. This can provide a nuanced understanding of the case and its complexity.
Holistic perspective: Case study research allows for a holistic perspective of the case, taking into account the various factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and comprehensive understanding of the case.
Theory development: Case study research can help to develop and refine theories and concepts by providing empirical evidence and concrete examples of how they can be applied in real-life situations.
Practical application: Case study research can inform practice or policy by identifying best practices, lessons learned, or areas for improvement.
Contextualization: Case study research takes into account the specific context in which the case is situated, which can help to understand how the case is influenced by the social, cultural, and historical factors of its environment.

Limitations of Case Study Research

There are several limitations of case study research, including:

Limited generalizability : Case studies are typically focused on a single case or a small number of cases, which limits the generalizability of the findings. The unique characteristics of the case may not be applicable to other contexts or populations, which may limit the external validity of the research.
Biased sampling: Case studies may rely on purposive or convenience sampling, which can introduce bias into the sample selection process. This may limit the representativeness of the sample and the generalizability of the findings.
Subjectivity: Case studies rely on the interpretation of the researcher, which can introduce subjectivity into the analysis. The researcher’s own biases, assumptions, and perspectives may influence the findings, which may limit the objectivity of the research.
Limited control: Case studies are typically conducted in naturalistic settings, which limits the control that the researcher has over the environment and the variables being studied. This may limit the ability to establish causal relationships between variables.
Time-consuming: Case studies can be time-consuming to conduct, as they typically involve a detailed exploration and analysis of a specific case. This may limit the feasibility of conducting multiple case studies or conducting case studies in a timely manner.
Resource-intensive: Case studies may require significant resources, including time, funding, and expertise. This may limit the ability of researchers to conduct case studies in resource-constrained settings.

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What Is a Case Study? | Definition, Examples & Methods

What Is a Case Study? | Definition, Examples & Methods

Published on May 8, 2019 by Shona McCombes . Revised on November 20, 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organization, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating and understanding different aspects of a research problem .

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyze the case, other interesting articles.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

Prevent plagiarism. Run a free check.

Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

Provide new or unexpected insights into the subject
Challenge or complicate existing assumptions and theories
Propose practical courses of action to resolve a problem
Open up new directions for future research

TipIf your research is more practical in nature and aims to simultaneously investigate an issue as you solve it, consider conducting action research instead.

Unlike quantitative or experimental research , a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

Example of an outlying case studyIn the 1960s the town of Roseto, Pennsylvania was discovered to have extremely low rates of heart disease compared to the US average. It became an important case study for understanding previously neglected causes of heart disease.

However, you can also choose a more common or representative case to exemplify a particular category, experience or phenomenon.

Example of a representative case studyIn the 1920s, two sociologists used Muncie, Indiana as a case study of a typical American city that supposedly exemplified the changing culture of the US at the time.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

Exemplify a theory by showing how it explains the case under investigation
Expand on a theory by uncovering new concepts and ideas that need to be incorporated
Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews , observations , and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data.

Example of a mixed methods case studyFor a case study of a wind farm development in a rural area, you could collect quantitative data on employment rates and business revenue, collect qualitative data on local people’s perceptions and experiences, and analyze local and national media coverage of the development.

The aim is to gain as thorough an understanding as possible of the case and its context.

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In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis , with separate sections or chapters for the methods , results and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyze its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

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

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McCombes, S. (2023, November 20). What Is a Case Study? | Definition, Examples & Methods. Scribbr. Retrieved April 9, 2024, from https://www.scribbr.com/methodology/case-study/

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Case Study | Definition, Examples & Methods

Case Study | Definition, Examples & Methods

Published on 5 May 2022 by Shona McCombes . Revised on 30 January 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organisation, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating, and understanding different aspects of a research problem .

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyse the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

Prevent plagiarism, run a free check.

Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

Provide new or unexpected insights into the subject
Challenge or complicate existing assumptions and theories
Propose practical courses of action to resolve a problem
Open up new directions for future research

Unlike quantitative or experimental research, a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

If you find yourself aiming to simultaneously investigate and solve an issue, consider conducting action research . As its name suggests, action research conducts research and takes action at the same time, and is highly iterative and flexible.

However, you can also choose a more common or representative case to exemplify a particular category, experience, or phenomenon.

Exemplify a theory by showing how it explains the case under investigation
Expand on a theory by uncovering new concepts and ideas that need to be incorporated
Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews, observations, and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data .

The aim is to gain as thorough an understanding as possible of the case and its context.

In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis, with separate sections or chapters for the methods , results , and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyse its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

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 Reference Generator.

McCombes, S. (2023, January 30). Case Study | Definition, Examples & Methods. Scribbr. Retrieved 9 April 2024, from https://www.scribbr.co.uk/research-methods/case-studies/

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Shona McCombes

Other students also liked, correlational research | guide, design & examples, a quick guide to experimental design | 5 steps & examples, descriptive research design | definition, methods & examples.

Research Design in Business and Management pp 141–170 Cite as

Single Case Research Design

Stefan Hunziker 3 &
Michael Blankenagel 3
First Online: 04 January 2024

333 Accesses

This chapter addresses single-case research designs’ peculiarities, characteristics, and significant fallacies. A single case research design is a collective term for an in-depth analysis of a small non-random sample. The focus of this design is in-depth. This characteristic distinguishes the case study research from other research designs that understand the individual case as a relatively insignificant and interchangeable aspect of a population or sample. Also, researchers find relevant information on writing a single case research design paper and learn about typical methods used for this research design. The chapter closes by referring to overlapping and adjacent research designs.

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11 Case research

Case research—also called case study—is a method of intensively studying a phenomenon over time within its natural setting in one or a few sites. Multiple methods of data collection, such as interviews, observations, pre-recorded documents, and secondary data, may be employed and inferences about the phenomenon of interest tend to be rich, detailed, and contextualised. Case research can be employed in a positivist manner for the purpose of theory testing or in an interpretive manner for theory building. This method is more popular in business research than in other social science disciplines.

Case research has several unique strengths over competing research methods such as experiments and survey research. First, case research can be used for either theory building or theory testing, while positivist methods can be used for theory testing only. In interpretive case research, the constructs of interest need not be known in advance, but may emerge from the data as the research progresses. Second, the research questions can be modified during the research process if the original questions are found to be less relevant or salient. This is not possible in any positivist method after the data is collected. Third, case research can help derive richer, more contextualised, and more authentic interpretation of the phenomenon of interest than most other research methods by virtue of its ability to capture a rich array of contextual data. Fourth, the phenomenon of interest can be studied from the perspectives of multiple participants and using multiple levels of analysis (e.g., individual and organisational).

At the same time, case research also has some inherent weaknesses. Because it involves no experimental control, internal validity of inferences remain weak. Of course, this is a common problem for all research methods except experiments. However, as described later, the problem of controls may be addressed in case research using ‘natural controls’. Second, the quality of inferences derived from case research depends heavily on the integrative powers of the researcher. An experienced researcher may see concepts and patterns in case data that a novice researcher may miss. Hence, the findings are sometimes criticised as being subjective. Finally, because the inferences are heavily contextualised, it may be difficult to generalise inferences from case research to other contexts or other organisations.

It is important to recognise that case research is different from case descriptions such as Harvard case studies discussed in business classes. While case descriptions typically describe an organisational problem in rich detail with the goal of stimulating classroom discussion and critical thinking among students, or analysing how well an organisation handled a specific problem, case research is a formal research technique that involves a scientific method to derive explanations of organisational phenomena.

Case research is a difficult research method that requires advanced research skills on the part of the researcher, and is therefore often prone to error. Benbasat, Goldstein and Mead (1987) [1] describe five problems frequently encountered in case research studies. First, many case research studies start without specific research questions, and therefore end up without having any specific answers or insightful inferences. Second, case sites are often chosen based on access and convenience, rather than based on the fit with the research questions, and are therefore cannot adequately address the research questions of interest. Third, researchers often do not validate or triangulate data collected using multiple means, which may lead to biased interpretation based on responses from biased interviewees. Fourth, many studies provide very little details on how data was collected (e.g., what interview questions were used, which documents were examined, the organisational positions of each interviewee, etc.) or analysed, which may raise doubts about the reliability of the inferences. Finally, despite its strength as a longitudinal research method, many case research studies do not follow through a phenomenon in a longitudinal manner, and hence present only a cross-sectional and limited view of organisational processes and phenomena that are temporal in nature.

Key decisions in case research

Several key decisions must be made by a researcher when considering a case research method. First, is this the right method for the research questions being studied? The case research method is particularly appropriate for exploratory studies, for discovering relevant constructs in areas where theory building is in the formative stages, for studies where the experiences of participants and context of actions are critical, and for studies aimed at understanding complex, temporal processes (why and how) rather than factors or causes (what). This method is well-suited for studying complex organisational processes that involve multiple participants and interacting sequences of events, such as organisational change and large-scale technology implementation projects.

Second, what is the appropriate unit of analysis for a case research study? Since case research can simultaneously examine multiple units of analyses, the researcher must decide whether she wishes to study a phenomenon at the individual, group, or organisational level or at multiple levels. For instance, a study of group decision-making or group work may combine individual-level constructs such as individual participation in group activities with group-level constructs, such as group cohesion and group leadership, to derive richer understanding than can be achieved from a single level of analysis.

Third, should the researcher employ a single-case or multiple-case design? The single-case design is more appropriate at the outset of theory generation, if the situation is unique or extreme, if it is revelatory (i.e., the situation was previously inaccessible for scientific investigation), or if it represents a critical or contrary case for testing a well-formulated theory. The multiple-case design is more appropriate for theory testing, for establishing generalisability of inferences, and for developing richer and more nuanced interpretations of a phenomenon. Yin (1984) [2] recommends the use of multiple case sites with replication logic, viewing each case site as similar to one experimental study, and following rules of scientific rigor similar to that used in positivist research.

Fourth, what sites should be chosen for case research? Given the contextualised nature of inferences derived from case research, site selection is a particularly critical issue because selecting the wrong site may lead to the wrong inferences. If the goal of the research is to test theories or examine generalisability of inferences, then dissimilar case sites should be selected to increase variance in observations. For instance, if the goal of the research is to understand the process of technology implementation in firms, a mix of large, mid-sized, and small firms should be selected to examine whether the technology implementation process differs with firm size. Site selection should not be opportunistic or based on convenience, but rather based on the fit with research questions though a process called ‘theoretical sampling’.

Fifth, what techniques of data collection should be used in case research? Although interview (either open-ended/unstructured or focused/structured) is by far the most popular data collection technique for case research, interview data can be supplemented or corroborated with other techniques such as direct observation (e.g., attending executive meetings, briefings, and planning sessions), documentation (e.g., internal reports, presentations, and memoranda, as well as external accounts such as newspaper reports), archival records (e.g., organisational charts, financial records, etc.), and physical artefacts (e.g., devices, outputs, tools). Furthermore, the researcher should triangulate or validate observed data by comparing responses between interviewees.

Conducting case research

Most case research studies tend to be interpretive in nature. Interpretive case research is an inductive technique where evidence collected from one or more case sites is systematically analysed and synthesised to allow concepts and patterns to emerge for the purpose of building new theories or expanding existing ones. Eisenhardt (1989) [3] proposed a ‘roadmap’ for building theories from case research—a slightly modified version of which is described below. For positivist case research, some of the following stages may need to be rearranged or modified, however sampling, data collection, and data analytic techniques should generally remain the same.

Define research questions. Like any other scientific research, case research must also start with defining research questions that are theoretically and practically interesting, and identifying some intuitive expectations about possible answers to those research questions or preliminary constructs to guide initial case design. In positivist case research, the preliminary constructs are based on theory, while no such theories or hypotheses should be considered ex ante in interpretive research. These research questions and constructs may be changed in interpretive case research later on, if needed, but not in positivist case research.

Select case sites. The researcher should use a process of ‘theoretical sampling’—not random sampling—to identify case sites. In this approach, case sites are chosen based on theoretical rather than statistical considerations—for instance, to replicate previous cases, to extend preliminary theories, or to fill theoretical categories or polar types. Care should be taken to ensure that the selected sites fit the nature of research questions, minimise extraneous variance or noise due to firm size, industry effects, and so forth, and maximise variance in the dependent variables of interest. For instance, if the goal of the research is to examine how some firms innovate better than others, the researcher should select firms of similar size within the same industry to reduce industry or size effects, and select some more innovative and some less innovative firms to increase variation in firm innovation. Instead of cold-calling or writing to a potential site, it is better to contact someone at executive level inside each firm who has the authority to approve the project, or someone who can identify a person of authority. During initial conversations, the researcher should describe the nature and purpose of the project, any potential benefits to the case site, how the collected data will be used, the people involved in data collection (other researchers, research assistants, etc.), desired interviewees, and the amount of time, effort, and expense required of the sponsoring organisation. The researcher must also assure confidentiality, privacy, and anonymity of both the firm and the individual respondents.

Create instruments and protocols. Since the primary mode of data collection in case research is interviews, an interview protocol should be designed to guide the interview process. This is essentially a list of questions to be asked. Questions may be open-ended (unstructured) or closed-ended (structured) or a combination of both. The interview protocol must be strictly followed, and the interviewer must not change the order of questions or skip any question during the interview process, although some deviations are allowed to probe further into a respondent’s comments if they are ambiguous or interesting. The interviewer must maintain a neutral tone, and not lead respondents in any specific direction—for example, by agreeing or disagreeing with any response. More detailed interviewing techniques are discussed in the chapter on surveys. In addition, additional sources of data—such as internal documents and memorandums, annual reports, financial statements, newspaper articles, and direct observations—should be sought to supplement and validate interview data.

Select respondents. Select interview respondents at different organisational levels, departments, and positions to obtain divergent perspectives on the phenomenon of interest. A random sampling of interviewees is most preferable, however a snowball sample is acceptable, as long as a diversity of perspectives is represented in the sample. Interviewees must be selected based on their personal involvement with the phenomenon under investigation and their ability and willingness to answer the researcher’s questions accurately and adequately, and not based on convenience or access.

Start data collection . It is usually a good idea to electronically record interviews for future reference. However, such recording must only be done with the interviewee’s consent. Even when interviews are being recorded, the interviewer should take notes to capture important comments or critical observations, behavioural responses (e.g., the respondent’s body language), and the researcher’s personal impressions about the respondent and his/her comments. After each interview is completed, the entire interview should be transcribed verbatim into a text document for analysis.

Conduct within-case data analysis. Data analysis may follow or overlap with data collection. Overlapping data collection and analysis has the advantage of adjusting the data collection process based on themes emerging from data analysis, or to further probe into these themes. Data analysis is done in two stages. In the first stage (within-case analysis), the researcher should examine emergent concepts separately at each case site and patterns between these concepts to generate an initial theory of the problem of interest. The researcher can use interview data subjectively to ‘make sense’ of the research problem in conjunction with using his/her personal observations or experience at the case site. Alternatively, a coding strategy such as Glaser and Strauss’ (1967) [4] grounded theory approach, using techniques such as open coding, axial coding, and selective coding, may be used to derive a chain of evidence and inferences. These techniques are discussed in detail in a later chapter. Homegrown techniques, such as graphical representation of data (e.g., network diagram) or sequence analysis (for longitudinal data) may also be used. Note that there is no predefined way of analysing the various types of case data, and the data analytic techniques can be modified to fit the nature of the research project.

Conduct cross-case analysis. Multi-site case research requires cross-case analysis as the second stage of data analysis. In such analysis, the researcher should look for similar concepts and patterns between different case sites, ignoring contextual differences that may lead to idiosyncratic conclusions. Such patterns may be used for validating the initial theory, or for refining it—by adding or dropping concepts and relationships—to develop a more inclusive and generalisable theory. This analysis may take several forms. For instance, the researcher may select categories (e.g., firm size, industry, etc.) and look for within-group similarities and between-group differences (e.g., high versus low performers, innovators versus laggards). Alternatively, they can compare firms in a pairwise manner listing similarities and differences across pairs of firms.

Build and test hypotheses. Tenative hypotheses are constructed based on emergent concepts and themes that are generalisable across case sites. These hypotheses should be compared iteratively with observed evidence to see if they fit the observed data, and if not, the constructs or relationships should be refined. Also the researcher should compare the emergent constructs and hypotheses with those reported in the prior literature to make a case for their internal validity and generalisability. Conflicting findings must not be rejected, but rather reconciled using creative thinking to generate greater insight into the emergent theory. When further iterations between theory and data yield no new insights or changes in the existing theory, ‘theoretical saturation’ is reached and the theory building process is complete.

Write case research report. In writing the report, the researcher should describe very clearly the detailed process used for sampling, data collection, data analysis, and hypotheses development, so that readers can independently assess the reasonableness, strength, and consistency of the reported inferences. A high level of clarity in research methods is needed to ensure that the findings are not biased by the researcher’s preconceptions.

Interpretive case research exemplar

Perhaps the best way to learn about interpretive case research is to examine an illustrative example. One such example is Eisenhardt’s (1989) [5] study of how executives make decisions in high-velocity environments (HVE). Readers are advised to read the original paper published in Academy of Management Journal before reading the synopsis in this chapter. In this study, Eisenhardt examined how executive teams in some HVE firms make fast decisions, while those in other firms cannot, and whether faster decisions improve or worsen firm performance in such environments. HVE was defined as one where demand, competition, and technology changes so rapidly and discontinuously that the information available is often inaccurate, unavailable or obsolete. The implicit assumptions were thatit is hard to make fast decisions with inadequate information in HVE, and fast decisions may not be efficient and may result in poor firm performance.

Reviewing the prior literature on executive decision-making, Eisenhardt found several patterns, although none of these patterns were specific to high-velocity environments. The literature suggested that in the interest of expediency, firms that make faster decisions obtain input from fewer sources, consider fewer alternatives, make limited analysis, restrict user participation in decision-making, centralise decision-making authority, and have limited internal conflicts. However, Eisenhardt contended that these views may not necessarily explain how decision makers make decisions in high-velocity environments, where decisions must be made quickly and with incomplete information, while maintaining high decision quality.

To examine this phenomenon, Eisenhardt conducted an inductive study of eight firms in the personal computing industry. The personal computing industry was undergoing dramatic changes in technology with the introduction of the UNIX operating system, RISC architecture, and 64KB random access memory in the 1980s, increased competition with the entry of IBM into the personal computing business, and growing customer demand with double-digit demand growth, and therefore fit the profile of the high-velocity environment. This was a multiple case design with replication logic, where each case was expected to confirm or disconfirm inferences from other cases. Case sites were selected based on their access and proximity to the researcher, however, all of these firms operated in the high-velocity personal computing industry in California’s Silicon Valley area. The collocation of firms in the same industry and the same area ruled out any ‘noise’ or variance in dependent variables (decision speed or performance) attributable to industry or geographic differences.

The study employed an embedded design with multiple levels of analysis: decision (comparing multiple strategic decisions within each firm), executive teams (comparing different teams responsible for strategic decisions), and the firm (overall firm performance). Data was collected from five sources:

Initial interviews with Chief Executive Officers . CEOs were asked questions about their firm’s competitive strategy, distinctive competencies, major competitors, performance, and recent/ongoing major strategic decisions. Based on these interviews, several strategic decisions were selected in each firm for further investigation. Four criteria were used to select decisions: the decisions must involve the firm’s strategic positioning, the decisions must have high stakes, the decisions must involve multiple functions, and the decisions must be representative of strategic decision-making process in that firm.

Interviews with divisional heads . Each divisional head was asked sixteen open-ended questions, ranging from their firm’s competitive strategy, functional strategy, top management team members, frequency and nature of interaction with team, typical decision-making processes, how each of the decisions were made, and how long it took them to make those decisions. Interviews lasted between one and a half and two hours, and sometimes extended to four hours. To focus on facts and actual events rather than respondents’ perceptions or interpretations, a ‘courtroom’ style questioning was employed, such as ‘When did this happen?’, ‘What did you do?’, etc. Interviews were conducted by two people, and the data was validated by cross-checking facts and impressions made by the interviewer and notetaker. All interview data was recorded, however notes were also taken during each interview, which ended with the interviewer’s overall impressions. Using a ‘24-hour rule’, detailed field notes were completed within 24 hours of the interview, so that some data or impressions were not lost to recall.

Questionnaires . Executive team members at each firm were asked tocomplete a survey questionnaire that captured quantitative data on the extent of conflict and power distribution in their firm.

Secondary data . Industry reports and internal documents such as demographics of the executive teams responsible for strategic decisions, financial performance of firms, and so forth, were examined.

Personal observation . Lastly, the researcher attended a one-day strategy session and a weekly executive meeting at two firms in her sample.

Data analysis involved a combination of quantitative and qualitative techniques. Quantitative data on conflict and power were analysed for patterns across firms/decisions. Qualitative interview data was combined into decision climate profiles, using profile traits (e.g., impatience) mentioned by more than one executive. For within-case analysis, decision stories were created for each strategic decision by combining executive accounts of the key decision events into a timeline. For cross-case analysis, pairs of firms were compared for similarities and differences, categorised along variables of interest such as decision speed and firm performance. Based on these analyses, tentative constructs and propositions were derived inductively from each decision story within firm categories. Each decision case was revisited to confirm the proposed relationships. The inferred propositions were compared with findings from the existing literature to examine differences, and to generate new insights from the case findings. Finally, the validated propositions were synthesised into an inductive theory of strategic decision-making by firms in high-velocity environments.

Inferences derived from this multiple case research contradicted several decision-making patterns expected from the existing literature. First, fast decision-makers in high-velocity environments used more information, and not less information as suggested by the previous literature. However, these decision-makers used more real-time information—an insight not available from prior research—which helped them identify and respond to problems, opportunities, and changing circumstances faster. Second, fast decision-makers examined more—not fewer—alternatives. However, they considered these multiple alternatives in a simultaneous manner, while slower decision-makers examined fewer alternatives in a sequential manner. Third, fast decision-makers did not centralise decision-making or restrict inputs from others as the literature suggested. Rather, these firms used a two-tiered decision process in which experienced counsellors were asked for inputs in the first stage, followed by a rapid comparison and decision selection in the second stage. Fourth, fast decision-makers did not have less conflict—as expected from the literature—but employed better conflict resolution techniques to reduce conflict and improve decision-making speed. Finally, fast decision-makers exhibited superior firm performance by virtue of their built-in cognitive, emotional, and political processes that led to rapid closure of major decisions.

Positivist case research exemplar

Case research can also be used in a positivist manner to test theories or hypotheses. Such studies are rare, but Markus (1983) [6] provides an exemplary illustration in her study of technology implementation at the pseudonymous Golden Triangle Company (GTC). The goal of this study was to understand why a newly implemented financial information system (FIS)—intended to improve the productivity and performance of accountants at GTC—was supported by accountants at GTC’s corporate headquarters, but resisted by divisional accountants at GTC branches. Given the uniqueness of the phenomenon of interest, this was a single-case research study.

To explore the reasons behind user resistance of FIS, Markus posited three alternative explanations:

System-determined theory : The resistance was caused by factors related to an inadequate system, such as its technical deficiencies, poor ergonomic design, or lack of user friendliness.

People-determined theory : The resistance was caused by factors internal to users, such as the accountants’ cognitive styles or personality traits that were incompatible with using the system.

Interaction theory : The resistance was not caused not by factors intrinsic to the system or the people, but by the interaction between the two set of factors. Specifically, interaction theory suggested that the FIS engendered a redistribution of intra-organisational power, and accountants who lost organisational status, relevance, or power as a result of FIS implementation resisted the system while those gaining power favoured it.

In order to test the three theories, Markus predicted alternative outcomes expected from each theoretical explanation and analysed the extent to which those predictions matched with her observations at GTC. For instance, the system-determined theory suggested that since user resistance was caused by an inadequate system, fixing the technical problems of the system would eliminate resistance. The computer running the FIS system was subsequently upgraded with a more powerful operating system, online processing (from initial batch processing, which delayed immediate processing of accounting information), and a simplified software for new account creation by managers. One year after these changes were made, the resistant users were still resisting the system and felt that it should be replaced. Hence, the system-determined theory was rejected.

The people-determined theory predicted that replacing individual resistors or co-opting them with less resistant users would reduce their resistance toward the FIS. Subsequently, GTC started a job rotation and mobility policy, moving accountants in and out of the resistant divisions, but resistance not only persisted, but in some cases increased. In one instance, an accountant who was one of the system’s designers and advocates when he worked for corporate accounting started resisting the system after he was moved to the divisional controller’s office. Failure to realise the predictions of the people-determined theory led to the rejection of this theory.

Finally, the interaction theory predicted that neither changing the system nor the people (i.e., user education or job rotation policies) would reduce resistance until the power imbalance and redistribution from the pre-implementation phase was addressed. Before FIS implementation, divisional accountants at GTC felt that they owned all accounting data related to their divisional operations. They maintained this data in thick, manual ledger books, controlled others’ access to the data, and could reconcile unusual accounting events before releasing those reports. Corporate accountants relied heavily on divisional accountants for access to the divisional data for corporate reporting and consolidation. Because the FIS system automatically collected all data at the source and consolidated it into a single corporate database, it obviated the need for divisional accountants, loosened their control and autonomy over their division’s accounting data, and making their job somewhat irrelevant. Corporate accountants could now query the database and access divisional data directly without going through the divisional accountants, analyse and compare the performance of individual divisions, and report unusual patterns and activities to the executive committee, resulting in further erosion of the divisions’ power. Though Markus did not empirically test this theory, her observations about the redistribution of organisational power, coupled with the rejection of the two alternative theories, led to the justification of interaction theory.

Comparisons with traditional research

Positivist case research, aimed at hypotheses testing, is often criticised by natural science researchers as lacking in controlled observations, controlled deductions, replicability, and generalisability of findings—the traditional principles of positivist research. However, these criticisms can be overcome through appropriate case research designs. For instance, the problem of controlled observations refers to the difficulty of obtaining experimental or statistical control in case research. However, case researchers can compensate for such lack of controls by employing ’natural controls’. This natural control in Markus’ (1983) study was the corporate accountant who was one of the system advocates initially, but started resisting it once he moved to the controlling division. In this instance, the change in his behaviour may be attributed to his new divisional position. However, such natural controls cannot be anticipated in advance, and case researchers may overlook them unless they are proactively looking for such controls. Incidentally, natural controls are also used in natural science disciplines such as astronomy, geology, and human biology—for example, waiting for comets to pass close enough to the earth in order to make inferences about comets and their composition.

Third, the problem of replicability refers to the difficulty of observing the same phenomenon considering the uniqueness and idiosyncrasy of a given case site. However, using Markus’ three theories as an illustration, a different researcher can test the same theories at a different case site, where three different predictions may emerge based on the idiosyncratic nature of the new case site, and the three resulting predictions may be tested accordingly. In other words, it is possible to replicate the inferences of case research, even if the case research site or context may not be replicable.

Fourth, case research tends to examine unique and non-replicable phenomena that may not be generalised to other settings. Generalisability in natural sciences is established through additional studies. Likewise, additional case studies conducted in different contexts with different predictions can establish generalisability of findings if such findings are observed to be consistent across studies.

Lastly, British philosopher Karl Popper described four requirements of scientific theories: theories should be falsifiable, they should be logically consistent, they should have adequate predictive ability, and they should provide better explanation than rival theories. In case research, the first three requirements can be improved by increasing the degrees of freedom of observed findings—for example, by increasing the number of case sites, the number of alternative predictions, and the number of levels of analysis examined. This was accomplished in Markus’ study by examining the behaviour of multiple groups (divisional accountants and corporate accountants) and providing multiple (three) rival explanations. Popper’s fourth condition was accomplished in this study when one hypothesis was found to match observed evidence better than the two rival hypotheses.

Benbasat, I., Goldstein, D. K., & Mead, M. (1987). The case research strategy in studies of information systems. MIS Quarterly , 11(3), 369–386. ↵
Yin, R. (1984). Case study research: Design and methods . London: Sage Publications. ↵
Eisenhardt, K. M. (1989). Building theories from case research. Academy of Management Review , 14(4), 532–550 ↵
Glaser, B., & Strauss, A. (1967). The discovery of grounded theory: Strategies for qualitative research . New York: Aldine Pub Co. ↵
Eisenhardt, K. M. (1989). Making fast strategic decisions in high-velocity environments. Academy of Management Journal , 32(3), 543–576. ↵
Markus, M. L. (1983). Power, politics and MIS implementations. Communications of the ACM , 26(6), 430–444. ↵

Social Science Research: Principles, Methods and Practices (Revised edition) Copyright © 2019 by Anol Bhattacherjee is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Case study research: opening up research opportunities

RAUSP Management Journal

ISSN : 2531-0488

Article publication date: 30 December 2019

Issue publication date: 3 March 2020

The case study approach has been widely used in management studies and the social sciences more generally. However, there are still doubts about when and how case studies should be used. This paper aims to discuss this approach, its various uses and applications, in light of epistemological principles, as well as the criteria for rigor and validity.

Design/methodology/approach

This paper discusses the various concepts of case and case studies in the methods literature and addresses the different uses of cases in relation to epistemological principles and criteria for rigor and validity.

The use of this research approach can be based on several epistemologies, provided the researcher attends to the internal coherence between method and epistemology, or what the authors call “alignment.”

Originality/value

This study offers a number of implications for the practice of management research, as it shows how the case study approach does not commit the researcher to particular data collection or interpretation methods. Furthermore, the use of cases can be justified according to multiple epistemological orientations.

Epistemology

Takahashi, A.R.W. and Araujo, L. (2020), "Case study research: opening up research opportunities", RAUSP Management Journal , Vol. 55 No. 1, pp. 100-111. https://doi.org/10.1108/RAUSP-05-2019-0109

Emerald Publishing Limited

Published in RAUSP Management Journal . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

The case study as a research method or strategy brings us to question the very term “case”: after all, what is a case? A case-based approach places accords the case a central role in the research process ( Ragin, 1992 ). However, doubts still remain about the status of cases according to different epistemologies and types of research designs.

Despite these doubts, the case study is ever present in the management literature and represents the main method of management research in Brazil ( Coraiola, Sander, Maccali, & Bulgacov, 2013 ). Between 2001 and 2010, 2,407 articles (83.14 per cent of qualitative research) were published in conferences and management journals as case studies (Takahashi & Semprebom, 2013 ). A search on Spell.org.br for the term “case study” under title, abstract or keywords, for the period ranging from January 2010 to July 2019, yielded 3,040 articles published in the management field. Doing research using case studies, allows the researcher to immerse him/herself in the context and gain intensive knowledge of a phenomenon, which in turn demands suitable methodological principles ( Freitas et al. , 2017 ).

Our objective in this paper is to discuss notions of what constitutes a case and its various applications, considering epistemological positions as well as criteria for rigor and validity. The alignment between these dimensions is put forward as a principle advocating coherence among all phases of the research process.

This article makes two contributions. First, we suggest that there are several epistemological justifications for using case studies. Second, we show that the quality and rigor of academic research with case studies are directly related to the alignment between epistemology and research design rather than to choices of specific forms of data collection or analysis. The article is structured as follows: the following four sections discuss concepts of what is a case, its uses, epistemological grounding as well as rigor and quality criteria. The brief conclusions summarize the debate and invite the reader to delve into the literature on the case study method as a way of furthering our understanding of contemporary management phenomena.

2. What is a case study?

The debate over what constitutes a case in social science is a long-standing one. In 1988, Howard Becker and Charles Ragin organized a workshop to discuss the status of the case as a social science method. As the discussion was inconclusive, they posed the question “What is a case?” to a select group of eight social scientists in 1989, and later to participants in a symposium on the subject. Participants were unable to come up with a consensual answer. Since then, we have witnessed that further debates and different answers have emerged. The original question led to an even broader issue: “How do we, as social scientists, produce results and seem to know what we know?” ( Ragin, 1992 , p. 16).

An important step that may help us start a reflection on what is a case is to consider the phenomena we are looking at. To do that, we must know something about what we want to understand and how we might study it. The answer may be a causal explanation, a description of what was observed or a narrative of what has been experienced. In any case, there will always be a story to be told, as the choice of the case study method demands an answer to what the case is about.

A case may be defined ex ante , prior to the start of the research process, as in Yin’s (2015) classical definition. But, there is no compelling reason as to why cases must be defined ex ante . Ragin (1992 , p. 217) proposed the notion of “casing,” to indicate that what the case is emerges from the research process:

Rather than attempt to delineate the many different meanings of the term “case” in a formal taxonomy, in this essay I offer instead a view of cases that follows from the idea implicit in many of the contributions – that concocting cases is a varied but routine social scientific activity. […] The approach of this essay is that this activity, which I call “casing”, should be viewed in practical terms as a research tactic. It is selectively invoked at many different junctures in the research process, usually to resolve difficult issues in linking ideas and evidence.

In other words, “casing” is tied to the researcher’s practice, to the way he/she delimits or declares a case as a significant outcome of a process. In 2013, Ragin revisited the 1992 concept of “casing” and explored its multiple possibilities of use, paying particular attention to “negative cases.”

According to Ragin (1992) , a case can be centered on a phenomenon or a population. In the first scenario, cases are representative of a phenomenon, and are selected based on what can be empirically observed. The process highlights different aspects of cases and obscures others according to the research design, and allows for the complexity, specificity and context of the phenomenon to be explored. In the alternative, population-focused scenario, the selection of cases precedes the research. Both positive and negative cases are considered in exploring a phenomenon, with the definition of the set of cases dependent on theory and the central objective to build generalizations. As a passing note, it is worth mentioning here that a study of multiple cases requires a definition of the unit of analysis a priori . Otherwise, it will not be possible to make cross-case comparisons.

These two approaches entail differences that go beyond the mere opposition of quantitative and qualitative data, as a case often includes both types of data. Thus, the confusion about how to conceive cases is associated with Ragin’s (1992) notion of “small vs large N,” or McKeown’s (1999) “statistical worldview” – the notion that relevant findings are only those that can be made about a population based on the analysis of representative samples. In the same vein, Byrne (2013) argues that we cannot generate nomothetic laws that apply in all circumstances, periods and locations, and that no social science method can claim to generate invariant laws. According to the same author, case studies can help us understand that there is more than one ideographic variety and help make social science useful. Generalizations still matter, but they should be understood as part of defining the research scope, and that scope points to the limitations of knowledge produced and consumed in concrete time and space.

Thus, what defines the orientation and the use of cases is not the mere choice of type of data, whether quantitative or qualitative, but the orientation of the study. A statistical worldview sees cases as data units ( Byrne, 2013 ). Put differently, there is a clear distinction between statistical and qualitative worldviews; the use of quantitative data does not by itself means that the research is (quasi) statistical, or uses a deductive logic:

Case-based methods are useful, and represent, among other things, a way of moving beyond a useless and destructive tradition in the social sciences that have set quantitative and qualitative modes of exploration, interpretation, and explanation against each other ( Byrne, 2013 , p. 9).

Other authors advocate different understandings of what a case study is. To some, it is a research method, to others it is a research strategy ( Creswell, 1998 ). Sharan Merrian and Robert Yin, among others, began to write about case study research as a methodology in the 1980s (Merrian, 2009), while authors such as Eisenhardt (1989) called it a research strategy. Stake (2003) sees the case study not as a method, but as a choice of what to be studied, the unit of study. Regardless of their differences, these authors agree that case studies should be restricted to a particular context as they aim to provide an in-depth knowledge of a given phenomenon: “A case study is an in-depth description and analysis of a bounded system” (Merrian, 2009, p. 40). According to Merrian, a qualitative case study can be defined by the process through which the research is carried out, by the unit of analysis or the final product, as the choice ultimately depends on what the researcher wants to know. As a product of research, it involves the analysis of a given entity, phenomenon or social unit.

Thus, whether it is an organization, an individual, a context or a phenomenon, single or multiple, one must delimit it, and also choose between possible types and configurations (Merrian, 2009; Yin, 2015 ). A case study may be descriptive, exploratory, explanatory, single or multiple ( Yin, 2015 ); intrinsic, instrumental or collective ( Stake, 2003 ); and confirm or build theory ( Eisenhardt, 1989 ).

both went through the same process of implementing computer labs intended for the use of information and communication technologies in 2007;

both took part in the same regional program (Paraná Digital); and

they shared similar characteristics regarding location (operation in the same neighborhood of a city), number of students, number of teachers and technicians and laboratory sizes.

However, the two institutions differed in the number of hours of program use, with one of them displaying a significant number of hours/use while the other showed a modest number, according to secondary data for the period 2007-2013. Despite the context being similar and the procedures for implementing the technology being the same, the mechanisms of social integration – an idiosyncratic factor of each institution – were different in each case. This explained differences in their use of resource, processes of organizational learning and capacity to absorb new knowledge.

On the other hand, multiple case studies seek evidence in different contexts and do not necessarily require direct comparisons ( Stake, 2003 ). Rather, there is a search for patterns of convergence and divergence that permeate all the cases, as the same issues are explored in every case. Cases can be added progressively until theoretical saturation is achieved. An example is of a study that investigated how entrepreneurial opportunity and management skills were developed through entrepreneurial learning ( Zampier & Takahashi, 2014 ). The authors conducted nine case studies, based on primary and secondary data, with each one analyzed separately, so a search for patterns could be undertaken. The convergence aspects found were: the predominant way of transforming experience into knowledge was exploitation; managerial skills were developed through by taking advantages of opportunities; and career orientation encompassed more than one style. As for divergence patterns: the experience of success and failure influenced entrepreneurs differently; the prevailing rationality logic of influence was different; and the combination of styles in career orientation was diverse.

A full discussion of choice of case study design is outside the scope of this article. For the sake of illustration, we make a brief mention to other selection criteria such as the purpose of the research, the state of the art of the research theme, the time and resources involved and the preferred epistemological position of the researcher. In the next section, we look at the possibilities of carrying out case studies in line with various epistemological traditions, as the answers to the “what is a case?” question reveal varied methodological commitments as well as diverse epistemological and ontological positions ( Ragin, 2013 ).

3. Epistemological positioning of case study research

Ontology and epistemology are like skin, not a garment to be occasionally worn ( Marsh & Furlong, 2002 ). According to these authors, ontology and epistemology guide the choice of theory and method because they cannot or should not be worn as a garment. Hence, one must practice philosophical “self-knowledge” to recognize one’s vision of what the world is and of how knowledge of that world is accessed and validated. Ontological and epistemological positions are relevant in that they involve the positioning of the researcher in social science and the phenomena he or she chooses to study. These positions do not tend to vary from one project to another although they can certainly change over time for a single researcher.

Ontology is the starting point from which the epistemological and methodological positions of the research arise ( Grix, 2002 ). Ontology expresses a view of the world, what constitutes reality, nature and the image one has of social reality; it is a theory of being ( Marsh & Furlong, 2002 ). The central question is the nature of the world out there regardless of our ability to access it. An essentialist or foundationalist ontology acknowledges that there are differences that persist over time and these differences are what underpin the construction of social life. An opposing, anti-foundationalist position presumes that the differences found are socially constructed and may vary – i.e. they are not essential but specific to a given culture at a given time ( Marsh & Furlong, 2002 ).

Epistemology is centered around a theory of knowledge, focusing on the process of acquiring and validating knowledge ( Grix, 2002 ). Positivists look at social phenomena as a world of causal relations where there is a single truth to be accessed and confirmed. In this tradition, case studies test hypotheses and rely on deductive approaches and quantitative data collection and analysis techniques. Scholars in the field of anthropology and observation-based qualitative studies proposed alternative epistemologies based on notions of the social world as a set of manifold and ever-changing processes. In management studies since the 1970s, the gradual acceptance of qualitative research has generated a diverse range of research methods and conceptions of the individual and society ( Godoy, 1995 ).

The interpretative tradition, in direct opposition to positivism, argues that there is no single objective truth to be discovered about the social world. The social world and our knowledge of it are the product of social constructions. Thus, the social world is constituted by interactions, and our knowledge is hermeneutic as the world does not exist independent of our knowledge ( Marsh & Furlong, 2002 ). The implication is that it is not possible to access social phenomena through objective, detached methods. Instead, the interaction mechanisms and relationships that make up social constructions have to be studied. Deductive approaches, hypothesis testing and quantitative methods are not relevant here. Hermeneutics, on the other hand, is highly relevant as it allows the analysis of the individual’s interpretation, of sayings, texts and actions, even though interpretation is always the “truth” of a subject. Methods such as ethnographic case studies, interviews and observations as data collection techniques should feed research designs according to interpretivism. It is worth pointing out that we are to a large extent, caricaturing polar opposites rather characterizing a range of epistemological alternatives, such as realism, conventionalism and symbolic interactionism.

If diverse ontologies and epistemologies serve as a guide to research approaches, including data collection and analysis methods, and if they should be regarded as skin rather than clothing, how does one make choices regarding case studies? What are case studies, what type of knowledge they provide and so on? The views of case study authors are not always explicit on this point, so we must delve into their texts to glean what their positions might be.

Two of the cited authors in case study research are Robert Yin and Kathleen Eisenhardt. Eisenhardt (1989) argues that a case study can serve to provide a description, test or generate a theory, the latter being the most relevant in contributing to the advancement of knowledge in a given area. She uses terms such as populations and samples, control variables, hypotheses and generalization of findings and even suggests an ideal number of case studies to allow for theory construction through replication. Although Eisenhardt includes observation and interview among her recommended data collection techniques, the approach is firmly anchored in a positivist epistemology:

Third, particularly in comparison with Strauss (1987) and Van Maanen (1988), the process described here adopts a positivist view of research. That is, the process is directed toward the development of testable hypotheses and theory which are generalizable across settings. In contrast, authors like Strauss and Van Maanen are more concerned that a rich, complex description of the specific cases under study evolve and they appear less concerned with development of generalizable theory ( Eisenhardt, 1989 , p. 546).

This position attracted a fair amount of criticism. Dyer & Wilkins (1991) in a critique of Eisenhardt’s (1989) article focused on the following aspects: there is no relevant justification for the number of cases recommended; it is the depth and not the number of cases that provides an actual contribution to theory; and the researcher’s purpose should be to get closer to the setting and interpret it. According to the same authors, discrepancies from prior expectations are also important as they lead researchers to reflect on existing theories. Eisenhardt & Graebner (2007 , p. 25) revisit the argument for the construction of a theory from multiple cases:

A major reason for the popularity and relevance of theory building from case studies is that it is one of the best (if not the best) of the bridges from rich qualitative evidence to mainstream deductive research.

Although they recognize the importance of single-case research to explore phenomena under unique or rare circumstances, they reaffirm the strength of multiple case designs as it is through them that better accuracy and generalization can be reached.

Likewise, Robert Yin emphasizes the importance of variables, triangulation in the search for “truth” and generalizable theoretical propositions. Yin (2015 , p. 18) suggests that the case study method may be appropriate for different epistemological orientations, although much of his work seems to invoke a realist epistemology. Authors such as Merrian (2009) and Stake (2003) suggest an interpretative version of case studies. Stake (2003) looks at cases as a qualitative option, where the most relevant criterion of case selection should be the opportunity to learn and understand a phenomenon. A case is not just a research method or strategy; it is a researcher’s choice about what will be studied:

Even if my definition of case study was agreed upon, and it is not, the term case and study defy full specification (Kemmis, 1980). A case study is both a process of inquiry about the case and the product of that inquiry ( Stake, 2003 , p. 136).

Later, Stake (2003 , p. 156) argues that:

[…] the purpose of a case report is not to represent the world, but to represent the case. […] The utility of case research to practitioners and policy makers is in its extension of experience.

Still according to Stake (2003 , pp. 140-141), to do justice to complex views of social phenomena, it is necessary to analyze the context and relate it to the case, to look for what is peculiar rather than common in cases to delimit their boundaries, to plan the data collection looking for what is common and unusual about facts, what could be valuable whether it is unique or common:

Reflecting upon the pertinent literature, I find case study methodology written largely by people who presume that the research should contribute to scientific generalization. The bulk of case study work, however, is done by individuals who have intrinsic interest in the case and little interest in the advance of science. Their designs aim the inquiry toward understanding of what is important about that case within its own world, which is seldom the same as the worlds of researchers and theorists. Those designs develop what is perceived to be the case’s own issues, contexts, and interpretations, its thick descriptions . In contrast, the methods of instrumental case study draw the researcher toward illustrating how the concerns of researchers and theorists are manifest in the case. Because the critical issues are more likely to be know in advance and following disciplinary expectations, such a design can take greater advantage of already developed instruments and preconceived coding schemes.

The aforementioned authors were listed to illustrate differences and sometimes opposing positions on case research. These differences are not restricted to a choice between positivism and interpretivism. It is worth noting that Ragin’s (2013 , p. 523) approach to “casing” is compatible with the realistic research perspective:

In essence, to posit cases is to engage in ontological speculation regarding what is obdurately real but only partially and indirectly accessible through social science. Bringing a realist perspective to the case question deepens and enriches the dialogue, clarifying some key issues while sweeping others aside.

cases are actual entities, reflecting their operations of real causal mechanism and process patterns;

case studies are interactive processes and are open to revisions and refinements; and

social phenomena are complex, contingent and context-specific.

Ragin (2013 , p. 532) concludes:

Lurking behind my discussion of negative case, populations, and possibility analysis is the implication that treating cases as members of given (and fixed) populations and seeking to infer the properties of populations may be a largely illusory exercise. While demographers have made good use of the concept of population, and continue to do so, it is not clear how much the utility of the concept extends beyond their domain. In case-oriented work, the notion of fixed populations of cases (observations) has much less analytic utility than simply “the set of relevant cases,” a grouping that must be specified or constructed by the researcher. The demarcation of this set, as the work of case-oriented researchers illustrates, is always tentative, fluid, and open to debate. It is only by casing social phenomena that social scientists perceive the homogeneity that allows analysis to proceed.

In summary, case studies are relevant and potentially compatible with a range of different epistemologies. Researchers’ ontological and epistemological positions will guide their choice of theory, methodologies and research techniques, as well as their research practices. The same applies to the choice of authors describing the research method and this choice should be coherent. We call this research alignment , an attribute that must be judged on the internal coherence of the author of a study, and not necessarily its evaluator. The following figure illustrates the interrelationship between the elements of a study necessary for an alignment ( Figure 1 ).

In addition to this broader aspect of the research as a whole, other factors should be part of the researcher’s concern, such as the rigor and quality of case studies. We will look into these in the next section taking into account their relevance to the different epistemologies.

4. Rigor and quality in case studies

Traditionally, at least in positivist studies, validity and reliability are the relevant quality criteria to judge research. Validity can be understood as external, internal and construct. External validity means identifying whether the findings of a study are generalizable to other studies using the logic of replication in multiple case studies. Internal validity may be established through the theoretical underpinning of existing relationships and it involves the use of protocols for the development and execution of case studies. Construct validity implies defining the operational measurement criteria to establish a chain of evidence, such as the use of multiple sources of evidence ( Eisenhardt, 1989 ; Yin, 2015 ). Reliability implies conducting other case studies, instead of just replicating results, to minimize the errors and bias of a study through case study protocols and the development of a case database ( Yin, 2015 ).

Several criticisms have been directed toward case studies, such as lack of rigor, lack of generalization potential, external validity and researcher bias. Case studies are often deemed to be unreliable because of a lack of rigor ( Seuring, 2008 ). Flyvbjerg (2006 , p. 219) addresses five misunderstandings about case-study research, and concludes that:

[…] a scientific discipline without a large number of thoroughly executed case studies is a discipline without systematic production of exemplars, and a discipline without exemplars is an ineffective one.

theoretical knowledge is more valuable than concrete, practical knowledge;

the case study cannot contribute to scientific development because it is not possible to generalize on the basis of an individual case;

the case study is more useful for generating rather than testing hypotheses;

the case study contains a tendency to confirm the researcher’s preconceived notions; and

it is difficult to summarize and develop general propositions and theories based on case studies.

These criticisms question the validity of the case study as a scientific method and should be corrected.

The critique of case studies is often framed from the standpoint of what Ragin (2000) labeled large-N research. The logic of small-N research, to which case studies belong, is different. Cases benefit from depth rather than breadth as they: provide theoretical and empirical knowledge; contribute to theory through propositions; serve not only to confirm knowledge, but also to challenge and overturn preconceived notions; and the difficulty in summarizing their conclusions is because of the complexity of the phenomena studies and not an intrinsic limitation of the method.

Thus, case studies do not seek large-scale generalizations as that is not their purpose. And yet, this is a limitation from a positivist perspective as there is an external reality to be “apprehended” and valid conclusions to be extracted for an entire population. If positivism is the epistemology of choice, the rigor of a case study can be demonstrated by detailing the criteria used for internal and external validity, construct validity and reliability ( Gibbert & Ruigrok, 2010 ; Gibbert, Ruigrok, & Wicki, 2008 ). An example can be seen in case studies in the area of information systems, where there is a predominant orientation of positivist approaches to this method ( Pozzebon & Freitas, 1998 ). In this area, rigor also involves the definition of a unit of analysis, type of research, number of cases, selection of sites, definition of data collection and analysis procedures, definition of the research protocol and writing a final report. Creswell (1998) presents a checklist for researchers to assess whether the study was well written, if it has reliability and validity and if it followed methodological protocols.

In case studies with a non-positivist orientation, rigor can be achieved through careful alignment (coherence among ontology, epistemology, theory and method). Moreover, the concepts of validity can be understood as concern and care in formulating research, research development and research results ( Ollaik & Ziller, 2012 ), and to achieve internal coherence ( Gibbert et al. , 2008 ). The consistency between data collection and interpretation, and the observed reality also help these studies meet coherence and rigor criteria. Siggelkow (2007) argues that a case study should be persuasive and that even a single case study may be a powerful example to contest a widely held view. To him, the value of a single case study or studies with few cases can be attained by their potential to provide conceptual insights and coherence to the internal logic of conceptual arguments: “[…] a paper should allow a reader to see the world, and not just the literature, in a new way” ( Siggelkow, 2007 , p. 23).

Interpretative studies should not be justified by criteria derived from positivism as they are based on a different ontology and epistemology ( Sandberg, 2005 ). The rejection of an interpretive epistemology leads to the rejection of an objective reality: “As Bengtsson points out, the life-world is the subjects’ experience of reality, at the same time as it is objective in the sense that it is an intersubjective world” ( Sandberg, 2005 , p. 47). In this event, how can one demonstrate what positivists call validity and reliability? What would be the criteria to justify knowledge as truth, produced by research in this epistemology? Sandberg (2005 , p. 62) suggests an answer based on phenomenology:

This was demonstrated first by explicating life-world and intentionality as the basic assumptions underlying the interpretative research tradition. Second, based on those assumptions, truth as intentional fulfillment, consisting of perceived fulfillment, fulfillment in practice, and indeterminate fulfillment, was proposed. Third, based on the proposed truth constellation, communicative, pragmatic, and transgressive validity and reliability as interpretative awareness were presented as the most appropriate criteria for justifying knowledge produced within interpretative approach. Finally, the phenomenological epoché was suggested as a strategy for achieving these criteria.

From this standpoint, the research site must be chosen according to its uniqueness so that one can obtain relevant insights that no other site could provide ( Siggelkow, 2007 ). Furthermore, the view of what is being studied is at the center of the researcher’s attention to understand its “truth,” inserted in a given context.

The case researcher is someone who can reduce the probability of misinterpretations by analyzing multiple perceptions, searches for data triangulation to check for the reliability of interpretations ( Stake, 2003 ). It is worth pointing out that this is not an option for studies that specifically seek the individual’s experience in relation to organizational phenomena.

In short, there are different ways of seeking rigor and quality in case studies, depending on the researcher’s worldview. These different forms pervade everything from the research design, the choice of research questions, the theory or theories to look at a phenomenon, research methods, the data collection and analysis techniques, to the type and style of research report produced. Validity can also take on different forms. While positivism is concerned with validity of the research question and results, interpretivism emphasizes research processes without neglecting the importance of the articulation of pertinent research questions and the sound interpretation of results ( Ollaik & Ziller, 2012 ). The means to achieve this can be diverse, such as triangulation (of multiple theories, multiple methods, multiple data sources or multiple investigators), pre-tests of data collection instrument, pilot case, study protocol, detailed description of procedures such as field diary in observations, researcher positioning (reflexivity), theoretical-empirical consistency, thick description and transferability.

5. Conclusions

The central objective of this article was to discuss concepts of case study research, their potential and various uses, taking into account different epistemologies as well as criteria of rigor and validity. Although the literature on methodology in general and on case studies in particular, is voluminous, it is not easy to relate this approach to epistemology. In addition, method manuals often focus on the details of various case study approaches which confuse things further.

Faced with this scenario, we have tried to address some central points in this debate and present various ways of using case studies according to the preferred epistemology of the researcher. We emphasize that this understanding depends on how a case is defined and the particular epistemological orientation that underpins that conceptualization. We have argued that whatever the epistemological orientation is, it is possible to meet appropriate criteria of research rigor and quality provided there is an alignment among the different elements of the research process. Furthermore, multiple data collection techniques can be used in in single or multiple case study designs. Data collection techniques or the type of data collected do not define the method or whether cases should be used for theory-building or theory-testing.

Finally, we encourage researchers to consider case study research as one way to foster immersion in phenomena and their contexts, stressing that the approach does not imply a commitment to a particular epistemology or type of research, such as qualitative or quantitative. Case study research allows for numerous possibilities, and should be celebrated for that diversity rather than pigeon-holed as a monolithic research method.

The interrelationship between the building blocks of research

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Acknowledgements

Author contributions: Both authors contributed equally.

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Int J Qual Stud Health Well-being

Methodology or method? A critical review of qualitative case study reports

Despite on-going debate about credibility, and reported limitations in comparison to other approaches, case study is an increasingly popular approach among qualitative researchers. We critically analysed the methodological descriptions of published case studies. Three high-impact qualitative methods journals were searched to locate case studies published in the past 5 years; 34 were selected for analysis. Articles were categorized as health and health services ( n= 12), social sciences and anthropology ( n= 7), or methods ( n= 15) case studies. The articles were reviewed using an adapted version of established criteria to determine whether adequate methodological justification was present, and if study aims, methods, and reported findings were consistent with a qualitative case study approach. Findings were grouped into five themes outlining key methodological issues: case study methodology or method, case of something particular and case selection, contextually bound case study, researcher and case interactions and triangulation, and study design inconsistent with methodology reported. Improved reporting of case studies by qualitative researchers will advance the methodology for the benefit of researchers and practitioners.

Case study research is an increasingly popular approach among qualitative researchers (Thomas, 2011 ). Several prominent authors have contributed to methodological developments, which has increased the popularity of case study approaches across disciplines (Creswell, 2013b ; Denzin & Lincoln, 2011b ; Merriam, 2009 ; Ragin & Becker, 1992 ; Stake, 1995 ; Yin, 2009 ). Current qualitative case study approaches are shaped by paradigm, study design, and selection of methods, and, as a result, case studies in the published literature vary. Differences between published case studies can make it difficult for researchers to define and understand case study as a methodology.

Experienced qualitative researchers have identified case study research as a stand-alone qualitative approach (Denzin & Lincoln, 2011b ). Case study research has a level of flexibility that is not readily offered by other qualitative approaches such as grounded theory or phenomenology. Case studies are designed to suit the case and research question and published case studies demonstrate wide diversity in study design. There are two popular case study approaches in qualitative research. The first, proposed by Stake ( 1995 ) and Merriam ( 2009 ), is situated in a social constructivist paradigm, whereas the second, by Yin ( 2012 ), Flyvbjerg ( 2011 ), and Eisenhardt ( 1989 ), approaches case study from a post-positivist viewpoint. Scholarship from both schools of inquiry has contributed to the popularity of case study and development of theoretical frameworks and principles that characterize the methodology.

The diversity of case studies reported in the published literature, and on-going debates about credibility and the use of case study in qualitative research practice, suggests that differences in perspectives on case study methodology may prevent researchers from developing a mutual understanding of practice and rigour. In addition, discussion about case study limitations has led some authors to query whether case study is indeed a methodology (Luck, Jackson, & Usher, 2006 ; Meyer, 2001 ; Thomas, 2010 ; Tight, 2010 ). Methodological discussion of qualitative case study research is timely, and a review is required to analyse and understand how this methodology is applied in the qualitative research literature. The aims of this study were to review methodological descriptions of published qualitative case studies, to review how the case study methodological approach was applied, and to identify issues that need to be addressed by researchers, editors, and reviewers. An outline of the current definitions of case study and an overview of the issues proposed in the qualitative methodological literature are provided to set the scene for the review.

Definitions of qualitative case study research

Case study research is an investigation and analysis of a single or collective case, intended to capture the complexity of the object of study (Stake, 1995 ). Qualitative case study research, as described by Stake ( 1995 ), draws together “naturalistic, holistic, ethnographic, phenomenological, and biographic research methods” in a bricoleur design, or in his words, “a palette of methods” (Stake, 1995 , pp. xi–xii). Case study methodology maintains deep connections to core values and intentions and is “particularistic, descriptive and heuristic” (Merriam, 2009 , p. 46).

As a study design, case study is defined by interest in individual cases rather than the methods of inquiry used. The selection of methods is informed by researcher and case intuition and makes use of naturally occurring sources of knowledge, such as people or observations of interactions that occur in the physical space (Stake, 1998 ). Thomas ( 2011 ) suggested that “analytical eclecticism” is a defining factor (p. 512). Multiple data collection and analysis methods are adopted to further develop and understand the case, shaped by context and emergent data (Stake, 1995 ). This qualitative approach “explores a real-life, contemporary bounded system (a case ) or multiple bounded systems (cases) over time, through detailed, in-depth data collection involving multiple sources of information … and reports a case description and case themes ” (Creswell, 2013b , p. 97). Case study research has been defined by the unit of analysis, the process of study, and the outcome or end product, all essentially the case (Merriam, 2009 ).

The case is an object to be studied for an identified reason that is peculiar or particular. Classification of the case and case selection procedures informs development of the study design and clarifies the research question. Stake ( 1995 ) proposed three types of cases and study design frameworks. These include the intrinsic case, the instrumental case, and the collective instrumental case. The intrinsic case is used to understand the particulars of a single case, rather than what it represents. An instrumental case study provides insight on an issue or is used to refine theory. The case is selected to advance understanding of the object of interest. A collective refers to an instrumental case which is studied as multiple, nested cases, observed in unison, parallel, or sequential order. More than one case can be simultaneously studied; however, each case study is a concentrated, single inquiry, studied holistically in its own entirety (Stake, 1995 , 1998 ).

Researchers who use case study are urged to seek out what is common and what is particular about the case. This involves careful and in-depth consideration of the nature of the case, historical background, physical setting, and other institutional and political contextual factors (Stake, 1998 ). An interpretive or social constructivist approach to qualitative case study research supports a transactional method of inquiry, where the researcher has a personal interaction with the case. The case is developed in a relationship between the researcher and informants, and presented to engage the reader, inviting them to join in this interaction and in case discovery (Stake, 1995 ). A postpositivist approach to case study involves developing a clear case study protocol with careful consideration of validity and potential bias, which might involve an exploratory or pilot phase, and ensures that all elements of the case are measured and adequately described (Yin, 2009 , 2012 ).

Current methodological issues in qualitative case study research

The future of qualitative research will be influenced and constructed by the way research is conducted, and by what is reviewed and published in academic journals (Morse, 2011 ). If case study research is to further develop as a principal qualitative methodological approach, and make a valued contribution to the field of qualitative inquiry, issues related to methodological credibility must be considered. Researchers are required to demonstrate rigour through adequate descriptions of methodological foundations. Case studies published without sufficient detail for the reader to understand the study design, and without rationale for key methodological decisions, may lead to research being interpreted as lacking in quality or credibility (Hallberg, 2013 ; Morse, 2011 ).

There is a level of artistic license that is embraced by qualitative researchers and distinguishes practice, which nurtures creativity, innovation, and reflexivity (Denzin & Lincoln, 2011b ; Morse, 2009 ). Qualitative research is “inherently multimethod” (Denzin & Lincoln, 2011a , p. 5); however, with this creative freedom, it is important for researchers to provide adequate description for methodological justification (Meyer, 2001 ). This includes paradigm and theoretical perspectives that have influenced study design. Without adequate description, study design might not be understood by the reader, and can appear to be dishonest or inaccurate. Reviewers and readers might be confused by the inconsistent or inappropriate terms used to describe case study research approach and methods, and be distracted from important study findings (Sandelowski, 2000 ). This issue extends beyond case study research, and others have noted inconsistencies in reporting of methodology and method by qualitative researchers. Sandelowski ( 2000 , 2010 ) argued for accurate identification of qualitative description as a research approach. She recommended that the selected methodology should be harmonious with the study design, and be reflected in methods and analysis techniques. Similarly, Webb and Kevern ( 2000 ) uncovered inconsistencies in qualitative nursing research with focus group methods, recommending that methodological procedures must cite seminal authors and be applied with respect to the selected theoretical framework. Incorrect labelling using case study might stem from the flexibility in case study design and non-directional character relative to other approaches (Rosenberg & Yates, 2007 ). Methodological integrity is required in design of qualitative studies, including case study, to ensure study rigour and to enhance credibility of the field (Morse, 2011 ).

Case study has been unnecessarily devalued by comparisons with statistical methods (Eisenhardt, 1989 ; Flyvbjerg, 2006 , 2011 ; Jensen & Rodgers, 2001 ; Piekkari, Welch, & Paavilainen, 2009 ; Tight, 2010 ; Yin, 1999 ). It is reputed to be the “the weak sibling” in comparison to other, more rigorous, approaches (Yin, 2009 , p. xiii). Case study is not an inherently comparative approach to research. The objective is not statistical research, and the aim is not to produce outcomes that are generalizable to all populations (Thomas, 2011 ). Comparisons between case study and statistical research do little to advance this qualitative approach, and fail to recognize its inherent value, which can be better understood from the interpretive or social constructionist viewpoint of other authors (Merriam, 2009 ; Stake, 1995 ). Building on discussions relating to “fuzzy” (Bassey, 2001 ), or naturalistic generalizations (Stake, 1978 ), or transference of concepts and theories (Ayres, Kavanaugh, & Knafl, 2003 ; Morse et al., 2011 ) would have more relevance.

Case study research has been used as a catch-all design to justify or add weight to fundamental qualitative descriptive studies that do not fit with other traditional frameworks (Merriam, 2009 ). A case study has been a “convenient label for our research—when we ‘can't think of anything ‘better”—in an attempt to give it [qualitative methodology] some added respectability” (Tight, 2010 , p. 337). Qualitative case study research is a pliable approach (Merriam, 2009 ; Meyer, 2001 ; Stake, 1995 ), and has been likened to a “curious methodological limbo” (Gerring, 2004 , p. 341) or “paradigmatic bridge” (Luck et al., 2006 , p. 104), that is on the borderline between postpositivist and constructionist interpretations. This has resulted in inconsistency in application, which indicates that flexibility comes with limitations (Meyer, 2001 ), and the open nature of case study research might be off-putting to novice researchers (Thomas, 2011 ). The development of a well-(in)formed theoretical framework to guide a case study should improve consistency, rigour, and trust in studies published in qualitative research journals (Meyer, 2001 ).

Assessment of rigour

The purpose of this study was to analyse the methodological descriptions of case studies published in qualitative methods journals. To do this we needed to develop a suitable framework, which used existing, established criteria for appraising qualitative case study research rigour (Creswell, 2013b ; Merriam, 2009 ; Stake, 1995 ). A number of qualitative authors have developed concepts and criteria that are used to determine whether a study is rigorous (Denzin & Lincoln, 2011b ; Lincoln, 1995 ; Sandelowski & Barroso, 2002 ). The criteria proposed by Stake ( 1995 ) provide a framework for readers and reviewers to make judgements regarding case study quality, and identify key characteristics essential for good methodological rigour. Although each of the factors listed in Stake's criteria could enhance the quality of a qualitative research report, in Table I we present an adapted criteria used in this study, which integrates more recent work by Merriam ( 2009 ) and Creswell ( 2013b ). Stake's ( 1995 ) original criteria were separated into two categories. The first list of general criteria is “relevant for all qualitative research.” The second list, “high relevance to qualitative case study research,” was the criteria that we decided had higher relevance to case study research. This second list was the main criteria used to assess the methodological descriptions of the case studies reviewed. The complete table has been preserved so that the reader can determine how the original criteria were adapted.

Framework for assessing quality in qualitative case study research.

Adapted from Stake ( 1995 , p. 131).

Study design

The critical review method described by Grant and Booth ( 2009 ) was used, which is appropriate for the assessment of research quality, and is used for literature analysis to inform research and practice. This type of review goes beyond the mapping and description of scoping or rapid reviews, to include “analysis and conceptual innovation” (Grant & Booth, 2009 , p. 93). A critical review is used to develop existing, or produce new, hypotheses or models. This is different to systematic reviews that answer clinical questions. It is used to evaluate existing research and competing ideas, to provide a “launch pad” for conceptual development and “subsequent testing” (Grant & Booth, 2009 , p. 93).

Qualitative methods journals were located by a search of the 2011 ISI Journal Citation Reports in Social Science, via the database Web of Knowledge (see m.webofknowledge.com). No “qualitative research methods” category existed in the citation reports; therefore, a search of all categories was performed using the term “qualitative.” In Table II , we present the qualitative methods journals located, ranked by impact factor. The highest ranked journals were selected for searching. We acknowledge that the impact factor ranking system might not be the best measure of journal quality (Cheek, Garnham, & Quan, 2006 ); however, this was the most appropriate and accessible method available.

International Journal of Qualitative Studies on Health and Well-being.

Search strategy

In March 2013, searches of the journals, Qualitative Health Research , Qualitative Research , and Qualitative Inquiry were completed to retrieve studies with “case study” in the abstract field. The search was limited to the past 5 years (1 January 2008 to 1 March 2013). The objective was to locate published qualitative case studies suitable for assessment using the adapted criterion. Viewpoints, commentaries, and other article types were excluded from review. Title and abstracts of the 45 retrieved articles were read by the first author, who identified 34 empirical case studies for review. All authors reviewed the 34 studies to confirm selection and categorization. In Table III , we present the 34 case studies grouped by journal, and categorized by research topic, including health sciences, social sciences and anthropology, and methods research. There was a discrepancy in categorization of one article on pedagogy and a new teaching method published in Qualitative Inquiry (Jorrín-Abellán, Rubia-Avi, Anguita-Martínez, Gómez-Sánchez, & Martínez-Mones, 2008 ). Consensus was to allocate to the methods category.

Outcomes of search of qualitative methods journals.

In Table III , the number of studies located, and final numbers selected for review have been reported. Qualitative Health Research published the most empirical case studies ( n= 16). In the health category, there were 12 case studies of health conditions, health services, and health policy issues, all published in Qualitative Health Research . Seven case studies were categorized as social sciences and anthropology research, which combined case study with biography and ethnography methodologies. All three journals published case studies on methods research to illustrate a data collection or analysis technique, methodological procedure, or related issue.

The methodological descriptions of 34 case studies were critically reviewed using the adapted criteria. All articles reviewed contained a description of study methods; however, the length, amount of detail, and position of the description in the article varied. Few studies provided an accurate description and rationale for using a qualitative case study approach. In the 34 case studies reviewed, three described a theoretical framework informed by Stake ( 1995 ), two by Yin ( 2009 ), and three provided a mixed framework informed by various authors, which might have included both Yin and Stake. Few studies described their case study design, or included a rationale that explained why they excluded or added further procedures, and whether this was to enhance the study design, or to better suit the research question. In 26 of the studies no reference was provided to principal case study authors. From reviewing the description of methods, few authors provided a description or justification of case study methodology that demonstrated how their study was informed by the methodological literature that exists on this approach.

The methodological descriptions of each study were reviewed using the adapted criteria, and the following issues were identified: case study methodology or method; case of something particular and case selection; contextually bound case study; researcher and case interactions and triangulation; and, study design inconsistent with methodology. An outline of how the issues were developed from the critical review is provided, followed by a discussion of how these relate to the current methodological literature.

Case study methodology or method

A third of the case studies reviewed appeared to use a case report method, not case study methodology as described by principal authors (Creswell, 2013b ; Merriam, 2009 ; Stake, 1995 ; Yin, 2009 ). Case studies were identified as a case report because of missing methodological detail and by review of the study aims and purpose. These reports presented data for small samples of no more than three people, places or phenomenon. Four studies, or “case reports” were single cases selected retrospectively from larger studies (Bronken, Kirkevold, Martinsen, & Kvigne, 2012 ; Coltart & Henwood, 2012 ; Hooghe, Neimeyer, & Rober, 2012 ; Roscigno et al., 2012 ). Case reports were not a case of something, instead were a case demonstration or an example presented in a report. These reports presented outcomes, and reported on how the case could be generalized. Descriptions focussed on the phenomena, rather than the case itself, and did not appear to study the case in its entirety.

Case reports had minimal in-text references to case study methodology, and were informed by other qualitative traditions or secondary sources (Adamson & Holloway, 2012 ; Buzzanell & D'Enbeau, 2009 ; Nagar-Ron & Motzafi-Haller, 2011 ). This does not suggest that case study methodology cannot be multimethod, however, methodology should be consistent in design, be clearly described (Meyer, 2001 ; Stake, 1995 ), and maintain focus on the case (Creswell, 2013b ).

To demonstrate how case reports were identified, three examples are provided. The first, Yeh ( 2013 ) described their study as, “the examination of the emergence of vegetarianism in Victorian England serves as a case study to reveal the relationships between boundaries and entities” (p. 306). The findings were a historical case report, which resulted from an ethnographic study of vegetarianism. Cunsolo Willox, Harper, Edge, ‘My Word’: Storytelling and Digital Media Lab, and Rigolet Inuit Community Government (2013) used “a case study that illustrates the usage of digital storytelling within an Inuit community” (p. 130). This case study reported how digital storytelling can be used with indigenous communities as a participatory method to illuminate the benefits of this method for other studies. This “case study was conducted in the Inuit community” but did not include the Inuit community in case analysis (Cunsolo Willox et al., 2013 , p. 130). Bronken et al. ( 2012 ) provided a single case report to demonstrate issues observed in a larger clinical study of aphasia and stroke, without adequate case description or analysis.

Case study of something particular and case selection

Case selection is a precursor to case analysis, which needs to be presented as a convincing argument (Merriam, 2009 ). Descriptions of the case were often not adequate to ascertain why the case was selected, or whether it was a particular exemplar or outlier (Thomas, 2011 ). In a number of case studies in the health and social science categories, it was not explicit whether the case was of something particular, or peculiar to their discipline or field (Adamson & Holloway, 2012 ; Bronken et al., 2012 ; Colón-Emeric et al., 2010 ; Jackson, Botelho, Welch, Joseph, & Tennstedt, 2012 ; Mawn et al., 2010 ; Snyder-Young, 2011 ). There were exceptions in the methods category ( Table III ), where cases were selected by researchers to report on a new or innovative method. The cases emerged through heuristic study, and were reported to be particular, relative to the existing methods literature (Ajodhia-Andrews & Berman, 2009 ; Buckley & Waring, 2013 ; Cunsolo Willox et al., 2013 ; De Haene, Grietens, & Verschueren, 2010 ; Gratton & O'Donnell, 2011 ; Sumsion, 2013 ; Wimpenny & Savin-Baden, 2012 ).

Case selection processes were sometimes insufficient to understand why the case was selected from the global population of cases, or what study of this case would contribute to knowledge as compared with other possible cases (Adamson & Holloway, 2012 ; Bronken et al., 2012 ; Colón-Emeric et al., 2010 ; Jackson et al., 2012 ; Mawn et al., 2010 ). In two studies, local cases were selected (Barone, 2010 ; Fourie & Theron, 2012 ) because the researcher was familiar with and had access to the case. Possible limitations of a convenience sample were not acknowledged. Purposeful sampling was used to recruit participants within the case of one study, but not of the case itself (Gallagher et al., 2013 ). Random sampling was completed for case selection in two studies (Colón-Emeric et al., 2010 ; Jackson et al., 2012 ), which has limited meaning in interpretive qualitative research.

To demonstrate how researchers provided a good justification for the selection of case study approaches, four examples are provided. The first, cases of residential care homes, were selected because of reported occurrences of mistreatment, which included residents being locked in rooms at night (Rytterström, Unosson, & Arman, 2013 ). Roscigno et al. ( 2012 ) selected cases of parents who were admitted for early hospitalization in neonatal intensive care with a threatened preterm delivery before 26 weeks. Hooghe et al. ( 2012 ) used random sampling to select 20 couples that had experienced the death of a child; however, the case study was of one couple and a particular metaphor described only by them. The final example, Coltart and Henwood ( 2012 ), provided a detailed account of how they selected two cases from a sample of 46 fathers based on personal characteristics and beliefs. They described how the analysis of the two cases would contribute to their larger study on first time fathers and parenting.

Contextually bound case study

The limits or boundaries of the case are a defining factor of case study methodology (Merriam, 2009 ; Ragin & Becker, 1992 ; Stake, 1995 ; Yin, 2009 ). Adequate contextual description is required to understand the setting or context in which the case is revealed. In the health category, case studies were used to illustrate a clinical phenomenon or issue such as compliance and health behaviour (Colón-Emeric et al., 2010 ; D'Enbeau, Buzzanell, & Duckworth, 2010 ; Gallagher et al., 2013 ; Hooghe et al., 2012 ; Jackson et al., 2012 ; Roscigno et al., 2012 ). In these case studies, contextual boundaries, such as physical and institutional descriptions, were not sufficient to understand the case as a holistic system, for example, the general practitioner (GP) clinic in Gallagher et al. ( 2013 ), or the nursing home in Colón-Emeric et al. ( 2010 ). Similarly, in the social science and methods categories, attention was paid to some components of the case context, but not others, missing important information required to understand the case as a holistic system (Alexander, Moreira, & Kumar, 2012 ; Buzzanell & D'Enbeau, 2009 ; Nairn & Panelli, 2009 ; Wimpenny & Savin-Baden, 2012 ).

In two studies, vicarious experience or vignettes (Nairn & Panelli, 2009 ) and images (Jorrín-Abellán et al., 2008 ) were effective to support description of context, and might have been a useful addition for other case studies. Missing contextual boundaries suggests that the case might not be adequately defined. Additional information, such as the physical, institutional, political, and community context, would improve understanding of the case (Stake, 1998 ). In Boxes 1 and 2 , we present brief synopses of two studies that were reviewed, which demonstrated a well bounded case. In Box 1 , Ledderer ( 2011 ) used a qualitative case study design informed by Stake's tradition. In Box 2 , Gillard, Witt, and Watts ( 2011 ) were informed by Yin's tradition. By providing a brief outline of the case studies in Boxes 1 and 2 , we demonstrate how effective case boundaries can be constructed and reported, which may be of particular interest to prospective case study researchers.

Article synopsis of case study research using Stake's tradition

Ledderer ( 2011 ) used a qualitative case study research design, informed by modern ethnography. The study is bounded to 10 general practice clinics in Denmark, who had received federal funding to implement preventative care services based on a Motivational Interviewing intervention. The researcher question focussed on “why is it so difficult to create change in medical practice?” (Ledderer, 2011 , p. 27). The study context was adequately described, providing detail on the general practitioner (GP) clinics and relevant political and economic influences. Methodological decisions are described in first person narrative, providing insight on researcher perspectives and interaction with the case. Forty-four interviews were conducted, which focussed on how GPs conducted consultations, and the form, nature and content, rather than asking their opinion or experience (Ledderer, 2011 , p. 30). The duration and intensity of researcher immersion in the case enhanced depth of description and trustworthiness of study findings. Analysis was consistent with Stake's tradition, and the researcher provided examples of inquiry techniques used to challenge assumptions about emerging themes. Several other seminal qualitative works were cited. The themes and typology constructed are rich in narrative data and storytelling by clinic staff, demonstrating individual clinic experiences as well as shared meanings and understandings about changing from a biomedical to psychological approach to preventative health intervention. Conclusions make note of social and cultural meanings and lessons learned, which might not have been uncovered using a different methodology.

Article synopsis of case study research using Yin's tradition

Gillard et al. ( 2011 ) study of camps for adolescents living with HIV/AIDs provided a good example of Yin's interpretive case study approach. The context of the case is bounded by the three summer camps of which the researchers had prior professional involvement. A case study protocol was developed that used multiple methods to gather information at three data collection points coinciding with three youth camps (Teen Forum, Discover Camp, and Camp Strong). Gillard and colleagues followed Yin's ( 2009 ) principles, using a consistent data protocol that enhanced cross-case analysis. Data described the young people, the camp physical environment, camp schedule, objectives and outcomes, and the staff of three youth camps. The findings provided a detailed description of the context, with less detail of individual participants, including insight into researcher's interpretations and methodological decisions throughout the data collection and analysis process. Findings provided the reader with a sense of “being there,” and are discovered through constant comparison of the case with the research issues; the case is the unit of analysis. There is evidence of researcher immersion in the case, and Gillard reports spending significant time in the field in a naturalistic and integrated youth mentor role.

This case study is not intended to have a significant impact on broader health policy, although does have implications for health professionals working with adolescents. Study conclusions will inform future camps for young people with chronic disease, and practitioners are able to compare similarities between this case and their own practice (for knowledge translation). No limitations of this article were reported. Limitations related to publication of this case study were that it was 20 pages long and used three tables to provide sufficient description of the camp and program components, and relationships with the research issue.

Researcher and case interactions and triangulation

Researcher and case interactions and transactions are a defining feature of case study methodology (Stake, 1995 ). Narrative stories, vignettes, and thick description are used to provoke vicarious experience and a sense of being there with the researcher in their interaction with the case. Few of the case studies reviewed provided details of the researcher's relationship with the case, researcher–case interactions, and how these influenced the development of the case study (Buzzanell & D'Enbeau, 2009 ; D'Enbeau et al., 2010 ; Gallagher et al., 2013 ; Gillard et al., 2011 ; Ledderer, 2011 ; Nagar-Ron & Motzafi-Haller, 2011 ). The role and position of the researcher needed to be self-examined and understood by readers, to understand how this influenced interactions with participants, and to determine what triangulation is needed (Merriam, 2009 ; Stake, 1995 ).

Gillard et al. ( 2011 ) provided a good example of triangulation, comparing data sources in a table (p. 1513). Triangulation of sources was used to reveal as much depth as possible in the study by Nagar-Ron and Motzafi-Haller ( 2011 ), while also enhancing confirmation validity. There were several case studies that would have benefited from improved range and use of data sources, and descriptions of researcher–case interactions (Ajodhia-Andrews & Berman, 2009 ; Bronken et al., 2012 ; Fincham, Scourfield, & Langer, 2008 ; Fourie & Theron, 2012 ; Hooghe et al., 2012 ; Snyder-Young, 2011 ; Yeh, 2013 ).

Study design inconsistent with methodology

Good, rigorous case studies require a strong methodological justification (Meyer, 2001 ) and a logical and coherent argument that defines paradigm, methodological position, and selection of study methods (Denzin & Lincoln, 2011b ). Methodological justification was insufficient in several of the studies reviewed (Barone, 2010 ; Bronken et al., 2012 ; Hooghe et al., 2012 ; Mawn et al., 2010 ; Roscigno et al., 2012 ; Yeh, 2013 ). This was judged by the absence, or inadequate or inconsistent reference to case study methodology in-text.

In six studies, the methodological justification provided did not relate to case study. There were common issues identified. Secondary sources were used as primary methodological references indicating that study design might not have been theoretically sound (Colón-Emeric et al., 2010 ; Coltart & Henwood, 2012 ; Roscigno et al., 2012 ; Snyder-Young, 2011 ). Authors and sources cited in methodological descriptions were inconsistent with the actual study design and practices used (Fourie & Theron, 2012 ; Hooghe et al., 2012 ; Jorrín-Abellán et al., 2008 ; Mawn et al., 2010 ; Rytterström et al., 2013 ; Wimpenny & Savin-Baden, 2012 ). This occurred when researchers cited Stake or Yin, or both (Mawn et al., 2010 ; Rytterström et al., 2013 ), although did not follow their paradigmatic or methodological approach. In 26 studies there were no citations for a case study methodological approach.

The findings of this study have highlighted a number of issues for researchers. A considerable number of case studies reviewed were missing key elements that define qualitative case study methodology and the tradition cited. A significant number of studies did not provide a clear methodological description or justification relevant to case study. Case studies in health and social sciences did not provide sufficient information for the reader to understand case selection, and why this case was chosen above others. The context of the cases were not described in adequate detail to understand all relevant elements of the case context, which indicated that cases may have not been contextually bounded. There were inconsistencies between reported methodology, study design, and paradigmatic approach in case studies reviewed, which made it difficult to understand the study methodology and theoretical foundations. These issues have implications for methodological integrity and honesty when reporting study design, which are values of the qualitative research tradition and are ethical requirements (Wager & Kleinert, 2010a ). Poorly described methodological descriptions may lead the reader to misinterpret or discredit study findings, which limits the impact of the study, and, as a collective, hinders advancements in the broader qualitative research field.

The issues highlighted in our review build on current debates in the case study literature, and queries about the value of this methodology. Case study research can be situated within different paradigms or designed with an array of methods. In order to maintain the creativity and flexibility that is valued in this methodology, clearer descriptions of paradigm and theoretical position and methods should be provided so that study findings are not undervalued or discredited. Case study research is an interdisciplinary practice, which means that clear methodological descriptions might be more important for this approach than other methodologies that are predominantly driven by fewer disciplines (Creswell, 2013b ).

Authors frequently omit elements of methodologies and include others to strengthen study design, and we do not propose a rigid or purist ideology in this paper. On the contrary, we encourage new ideas about using case study, together with adequate reporting, which will advance the value and practice of case study. The implications of unclear methodological descriptions in the studies reviewed were that study design appeared to be inconsistent with reported methodology, and key elements required for making judgements of rigour were missing. It was not clear whether the deviations from methodological tradition were made by researchers to strengthen the study design, or because of misinterpretations. Morse ( 2011 ) recommended that innovations and deviations from practice are best made by experienced researchers, and that a novice might be unaware of the issues involved with making these changes. To perpetuate the tradition of case study research, applications in the published literature should have consistencies with traditional methodological constructions, and deviations should be described with a rationale that is inherent in study conduct and findings. Providing methodological descriptions that demonstrate a strong theoretical foundation and coherent study design will add credibility to the study, while ensuring the intrinsic meaning of case study is maintained.

The value of this review is that it contributes to discussion of whether case study is a methodology or method. We propose possible reasons why researchers might make this misinterpretation. Researchers may interchange the terms methods and methodology, and conduct research without adequate attention to epistemology and historical tradition (Carter & Little, 2007 ; Sandelowski, 2010 ). If the rich meaning that naming a qualitative methodology brings to the study is not recognized, a case study might appear to be inconsistent with the traditional approaches described by principal authors (Creswell, 2013a ; Merriam, 2009 ; Stake, 1995 ; Yin, 2009 ). If case studies are not methodologically and theoretically situated, then they might appear to be a case report.

Case reports are promoted by university and medical journals as a method of reporting on medical or scientific cases; guidelines for case reports are publicly available on websites ( http://www.hopkinsmedicine.org/institutional_review_board/guidelines_policies/guidelines/case_report.html ). The various case report guidelines provide a general criteria for case reports, which describes that this form of report does not meet the criteria of research, is used for retrospective analysis of up to three clinical cases, and is primarily illustrative and for educational purposes. Case reports can be published in academic journals, but do not require approval from a human research ethics committee. Traditionally, case reports describe a single case, to explain how and what occurred in a selected setting, for example, to illustrate a new phenomenon that has emerged from a larger study. A case report is not necessarily particular or the study of a case in its entirety, and the larger study would usually be guided by a different research methodology.

This description of a case report is similar to what was provided in some studies reviewed. This form of report lacks methodological grounding and qualities of research rigour. The case report has publication value in demonstrating an example and for dissemination of knowledge (Flanagan, 1999 ). However, case reports have different meaning and purpose to case study, which needs to be distinguished. Findings of our review suggest that the medical understanding of a case report has been confused with qualitative case study approaches.

In this review, a number of case studies did not have methodological descriptions that included key characteristics of case study listed in the adapted criteria, and several issues have been discussed. There have been calls for improvements in publication quality of qualitative research (Morse, 2011 ), and for improvements in peer review of submitted manuscripts (Carter & Little, 2007 ; Jasper, Vaismoradi, Bondas, & Turunen, 2013 ). The challenging nature of editor and reviewers responsibilities are acknowledged in the literature (Hames, 2013 ; Wager & Kleinert, 2010b ); however, review of case study methodology should be prioritized because of disputes on methodological value.

Authors using case study approaches are recommended to describe their theoretical framework and methods clearly, and to seek and follow specialist methodological advice when needed (Wager & Kleinert, 2010a ). Adequate page space for case study description would contribute to better publications (Gillard et al., 2011 ). Capitalizing on the ability to publish complementary resources should be considered.

Limitations of the review

There is a level of subjectivity involved in this type of review and this should be considered when interpreting study findings. Qualitative methods journals were selected because the aims and scope of these journals are to publish studies that contribute to methodological discussion and development of qualitative research. Generalist health and social science journals were excluded that might have contained good quality case studies. Journals in business or education were also excluded, although a review of case studies in international business journals has been published elsewhere (Piekkari et al., 2009 ).

The criteria used to assess the quality of the case studies were a set of qualitative indicators. A numerical or ranking system might have resulted in different results. Stake's ( 1995 ) criteria have been referenced elsewhere, and was deemed the best available (Creswell, 2013b ; Crowe et al., 2011 ). Not all qualitative studies are reported in a consistent way and some authors choose to report findings in a narrative form in comparison to a typical biomedical report style (Sandelowski & Barroso, 2002 ), if misinterpretations were made this may have affected the review.

Case study research is an increasingly popular approach among qualitative researchers, which provides methodological flexibility through the incorporation of different paradigmatic positions, study designs, and methods. However, whereas flexibility can be an advantage, a myriad of different interpretations has resulted in critics questioning the use of case study as a methodology. Using an adaptation of established criteria, we aimed to identify and assess the methodological descriptions of case studies in high impact, qualitative methods journals. Few articles were identified that applied qualitative case study approaches as described by experts in case study design. There were inconsistencies in methodology and study design, which indicated that researchers were confused whether case study was a methodology or a method. Commonly, there appeared to be confusion between case studies and case reports. Without clear understanding and application of the principles and key elements of case study methodology, there is a risk that the flexibility of the approach will result in haphazard reporting, and will limit its global application as a valuable, theoretically supported methodology that can be rigorously applied across disciplines and fields.

Conflict of interest and funding

The authors have not received any funding or benefits from industry or elsewhere to conduct this study.

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Case Study: Research in Practice

SAGE Publications

Case Study Research in Practice explores the theory and practice of case study. Helen Simons draws on her extensive experience of teaching and conducting case study to provide a comprehensive and practical account of how to design, conduct and communicate case study research. It addresses questions often raised by students and common misconceptions. In four sections the book covers: Rationale, concept and design of case study research Methods, ethics and reflexivity in case study Interpreting, analysing and reporting the case Generalizing and theorizing in case study research Rich with ‘tales from the field’ and summary memos as an aide-memoire to future action, the book provides fresh insights and challenges for researchers to guide their practice of case study research. This is an ideal text for those studying and conducting case study research in education, health and social care, and related social science disciplines. Book jacket.

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Acknowledgments

A case study research paper examines a person, place, event, condition, phenomenon, or other type of subject of analysis in order to extrapolate key themes and results that help predict future trends, illuminate previously hidden issues that can be applied to practice, and/or provide a means for understanding an important research problem with greater clarity. A case study research paper usually examines a single subject of analysis, but case study papers can also be designed as a comparative investigation that shows relationships between two or more subjects. The methods used to study a case can rest within a quantitative, qualitative, or mixed-method investigative paradigm.

Case Studies. Writing@CSU. Colorado State University; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010 ; “What is a Case Study?” In Swanborn, Peter G. Case Study Research: What, Why and How? London: SAGE, 2010.

How to Approach Writing a Case Study Research Paper

General information about how to choose a topic to investigate can be found under the " Choosing a Research Problem " tab in the Organizing Your Social Sciences Research Paper writing guide. Review this page because it may help you identify a subject of analysis that can be investigated using a case study design.

However, identifying a case to investigate involves more than choosing the research problem . A case study encompasses a problem contextualized around the application of in-depth analysis, interpretation, and discussion, often resulting in specific recommendations for action or for improving existing conditions. As Seawright and Gerring note, practical considerations such as time and access to information can influence case selection, but these issues should not be the sole factors used in describing the methodological justification for identifying a particular case to study. Given this, selecting a case includes considering the following:

The case represents an unusual or atypical example of a research problem that requires more in-depth analysis? Cases often represent a topic that rests on the fringes of prior investigations because the case may provide new ways of understanding the research problem. For example, if the research problem is to identify strategies to improve policies that support girl's access to secondary education in predominantly Muslim nations, you could consider using Azerbaijan as a case study rather than selecting a more obvious nation in the Middle East. Doing so may reveal important new insights into recommending how governments in other predominantly Muslim nations can formulate policies that support improved access to education for girls.
The case provides important insight or illuminate a previously hidden problem? In-depth analysis of a case can be based on the hypothesis that the case study will reveal trends or issues that have not been exposed in prior research or will reveal new and important implications for practice. For example, anecdotal evidence may suggest drug use among homeless veterans is related to their patterns of travel throughout the day. Assuming prior studies have not looked at individual travel choices as a way to study access to illicit drug use, a case study that observes a homeless veteran could reveal how issues of personal mobility choices facilitate regular access to illicit drugs. Note that it is important to conduct a thorough literature review to ensure that your assumption about the need to reveal new insights or previously hidden problems is valid and evidence-based.
The case challenges and offers a counter-point to prevailing assumptions? Over time, research on any given topic can fall into a trap of developing assumptions based on outdated studies that are still applied to new or changing conditions or the idea that something should simply be accepted as "common sense," even though the issue has not been thoroughly tested in current practice. A case study analysis may offer an opportunity to gather evidence that challenges prevailing assumptions about a research problem and provide a new set of recommendations applied to practice that have not been tested previously. For example, perhaps there has been a long practice among scholars to apply a particular theory in explaining the relationship between two subjects of analysis. Your case could challenge this assumption by applying an innovative theoretical framework [perhaps borrowed from another discipline] to explore whether this approach offers new ways of understanding the research problem. Taking a contrarian stance is one of the most important ways that new knowledge and understanding develops from existing literature.
The case provides an opportunity to pursue action leading to the resolution of a problem? Another way to think about choosing a case to study is to consider how the results from investigating a particular case may result in findings that reveal ways in which to resolve an existing or emerging problem. For example, studying the case of an unforeseen incident, such as a fatal accident at a railroad crossing, can reveal hidden issues that could be applied to preventative measures that contribute to reducing the chance of accidents in the future. In this example, a case study investigating the accident could lead to a better understanding of where to strategically locate additional signals at other railroad crossings so as to better warn drivers of an approaching train, particularly when visibility is hindered by heavy rain, fog, or at night.
The case offers a new direction in future research? A case study can be used as a tool for an exploratory investigation that highlights the need for further research about the problem. A case can be used when there are few studies that help predict an outcome or that establish a clear understanding about how best to proceed in addressing a problem. For example, after conducting a thorough literature review [very important!], you discover that little research exists showing the ways in which women contribute to promoting water conservation in rural communities of east central Africa. A case study of how women contribute to saving water in a rural village of Uganda can lay the foundation for understanding the need for more thorough research that documents how women in their roles as cooks and family caregivers think about water as a valuable resource within their community. This example of a case study could also point to the need for scholars to build new theoretical frameworks around the topic [e.g., applying feminist theories of work and family to the issue of water conservation].

Eisenhardt, Kathleen M. “Building Theories from Case Study Research.” Academy of Management Review 14 (October 1989): 532-550; Emmel, Nick. Sampling and Choosing Cases in Qualitative Research: A Realist Approach . Thousand Oaks, CA: SAGE Publications, 2013; Gerring, John. “What Is a Case Study and What Is It Good for?” American Political Science Review 98 (May 2004): 341-354; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Seawright, Jason and John Gerring. "Case Selection Techniques in Case Study Research." Political Research Quarterly 61 (June 2008): 294-308.

Structure and Writing Style

The purpose of a paper in the social sciences designed around a case study is to thoroughly investigate a subject of analysis in order to reveal a new understanding about the research problem and, in so doing, contributing new knowledge to what is already known from previous studies. In applied social sciences disciplines [e.g., education, social work, public administration, etc.], case studies may also be used to reveal best practices, highlight key programs, or investigate interesting aspects of professional work.

In general, the structure of a case study research paper is not all that different from a standard college-level research paper. However, there are subtle differences you should be aware of. Here are the key elements to organizing and writing a case study research paper.

I. Introduction

As with any research paper, your introduction should serve as a roadmap for your readers to ascertain the scope and purpose of your study . The introduction to a case study research paper, however, should not only describe the research problem and its significance, but you should also succinctly describe why the case is being used and how it relates to addressing the problem. The two elements should be linked. With this in mind, a good introduction answers these four questions:

What is being studied? Describe the research problem and describe the subject of analysis [the case] you have chosen to address the problem. Explain how they are linked and what elements of the case will help to expand knowledge and understanding about the problem.
Why is this topic important to investigate? Describe the significance of the research problem and state why a case study design and the subject of analysis that the paper is designed around is appropriate in addressing the problem.
What did we know about this topic before I did this study? Provide background that helps lead the reader into the more in-depth literature review to follow. If applicable, summarize prior case study research applied to the research problem and why it fails to adequately address the problem. Describe why your case will be useful. If no prior case studies have been used to address the research problem, explain why you have selected this subject of analysis.
How will this study advance new knowledge or new ways of understanding? Explain why your case study will be suitable in helping to expand knowledge and understanding about the research problem.

Each of these questions should be addressed in no more than a few paragraphs. Exceptions to this can be when you are addressing a complex research problem or subject of analysis that requires more in-depth background information.

II. Literature Review

The literature review for a case study research paper is generally structured the same as it is for any college-level research paper. The difference, however, is that the literature review is focused on providing background information and enabling historical interpretation of the subject of analysis in relation to the research problem the case is intended to address . This includes synthesizing studies that help to:

Place relevant works in the context of their contribution to understanding the case study being investigated . This would involve summarizing studies that have used a similar subject of analysis to investigate the research problem. If there is literature using the same or a very similar case to study, you need to explain why duplicating past research is important [e.g., conditions have changed; prior studies were conducted long ago, etc.].
Describe the relationship each work has to the others under consideration that informs the reader why this case is applicable . Your literature review should include a description of any works that support using the case to investigate the research problem and the underlying research questions.
Identify new ways to interpret prior research using the case study . If applicable, review any research that has examined the research problem using a different research design. Explain how your use of a case study design may reveal new knowledge or a new perspective or that can redirect research in an important new direction.
Resolve conflicts amongst seemingly contradictory previous studies . This refers to synthesizing any literature that points to unresolved issues of concern about the research problem and describing how the subject of analysis that forms the case study can help resolve these existing contradictions.
Point the way in fulfilling a need for additional research . Your review should examine any literature that lays a foundation for understanding why your case study design and the subject of analysis around which you have designed your study may reveal a new way of approaching the research problem or offer a perspective that points to the need for additional research.
Expose any gaps that exist in the literature that the case study could help to fill . Summarize any literature that not only shows how your subject of analysis contributes to understanding the research problem, but how your case contributes to a new way of understanding the problem that prior research has failed to do.
Locate your own research within the context of existing literature [very important!] . Collectively, your literature review should always place your case study within the larger domain of prior research about the problem. The overarching purpose of reviewing pertinent literature in a case study paper is to demonstrate that you have thoroughly identified and synthesized prior studies in relation to explaining the relevance of the case in addressing the research problem.

III. Method

In this section, you explain why you selected a particular case [i.e., subject of analysis] and the strategy you used to identify and ultimately decide that your case was appropriate in addressing the research problem. The way you describe the methods used varies depending on the type of subject of analysis that constitutes your case study.

If your subject of analysis is an incident or event . In the social and behavioral sciences, the event or incident that represents the case to be studied is usually bounded by time and place, with a clear beginning and end and with an identifiable location or position relative to its surroundings. The subject of analysis can be a rare or critical event or it can focus on a typical or regular event. The purpose of studying a rare event is to illuminate new ways of thinking about the broader research problem or to test a hypothesis. Critical incident case studies must describe the method by which you identified the event and explain the process by which you determined the validity of this case to inform broader perspectives about the research problem or to reveal new findings. However, the event does not have to be a rare or uniquely significant to support new thinking about the research problem or to challenge an existing hypothesis. For example, Walo, Bull, and Breen conducted a case study to identify and evaluate the direct and indirect economic benefits and costs of a local sports event in the City of Lismore, New South Wales, Australia. The purpose of their study was to provide new insights from measuring the impact of a typical local sports event that prior studies could not measure well because they focused on large "mega-events." Whether the event is rare or not, the methods section should include an explanation of the following characteristics of the event: a) when did it take place; b) what were the underlying circumstances leading to the event; and, c) what were the consequences of the event in relation to the research problem.

If your subject of analysis is a person. Explain why you selected this particular individual to be studied and describe what experiences they have had that provide an opportunity to advance new understandings about the research problem. Mention any background about this person which might help the reader understand the significance of their experiences that make them worthy of study. This includes describing the relationships this person has had with other people, institutions, and/or events that support using them as the subject for a case study research paper. It is particularly important to differentiate the person as the subject of analysis from others and to succinctly explain how the person relates to examining the research problem [e.g., why is one politician in a particular local election used to show an increase in voter turnout from any other candidate running in the election]. Note that these issues apply to a specific group of people used as a case study unit of analysis [e.g., a classroom of students].

If your subject of analysis is a place. In general, a case study that investigates a place suggests a subject of analysis that is unique or special in some way and that this uniqueness can be used to build new understanding or knowledge about the research problem. A case study of a place must not only describe its various attributes relevant to the research problem [e.g., physical, social, historical, cultural, economic, political], but you must state the method by which you determined that this place will illuminate new understandings about the research problem. It is also important to articulate why a particular place as the case for study is being used if similar places also exist [i.e., if you are studying patterns of homeless encampments of veterans in open spaces, explain why you are studying Echo Park in Los Angeles rather than Griffith Park?]. If applicable, describe what type of human activity involving this place makes it a good choice to study [e.g., prior research suggests Echo Park has more homeless veterans].

If your subject of analysis is a phenomenon. A phenomenon refers to a fact, occurrence, or circumstance that can be studied or observed but with the cause or explanation to be in question. In this sense, a phenomenon that forms your subject of analysis can encompass anything that can be observed or presumed to exist but is not fully understood. In the social and behavioral sciences, the case usually focuses on human interaction within a complex physical, social, economic, cultural, or political system. For example, the phenomenon could be the observation that many vehicles used by ISIS fighters are small trucks with English language advertisements on them. The research problem could be that ISIS fighters are difficult to combat because they are highly mobile. The research questions could be how and by what means are these vehicles used by ISIS being supplied to the militants and how might supply lines to these vehicles be cut off? How might knowing the suppliers of these trucks reveal larger networks of collaborators and financial support? A case study of a phenomenon most often encompasses an in-depth analysis of a cause and effect that is grounded in an interactive relationship between people and their environment in some way.

NOTE: The choice of the case or set of cases to study cannot appear random. Evidence that supports the method by which you identified and chose your subject of analysis should clearly support investigation of the research problem and linked to key findings from your literature review. Be sure to cite any studies that helped you determine that the case you chose was appropriate for examining the problem.

IV. Discussion

The main elements of your discussion section are generally the same as any research paper, but centered around interpreting and drawing conclusions about the key findings from your analysis of the case study. Note that a general social sciences research paper may contain a separate section to report findings. However, in a paper designed around a case study, it is common to combine a description of the results with the discussion about their implications. The objectives of your discussion section should include the following:

Reiterate the Research Problem/State the Major Findings Briefly reiterate the research problem you are investigating and explain why the subject of analysis around which you designed the case study were used. You should then describe the findings revealed from your study of the case using direct, declarative, and succinct proclamation of the study results. Highlight any findings that were unexpected or especially profound.

Explain the Meaning of the Findings and Why They are Important Systematically explain the meaning of your case study findings and why you believe they are important. Begin this part of the section by repeating what you consider to be your most important or surprising finding first, then systematically review each finding. Be sure to thoroughly extrapolate what your analysis of the case can tell the reader about situations or conditions beyond the actual case that was studied while, at the same time, being careful not to misconstrue or conflate a finding that undermines the external validity of your conclusions.

Relate the Findings to Similar Studies No study in the social sciences is so novel or possesses such a restricted focus that it has absolutely no relation to previously published research. The discussion section should relate your case study results to those found in other studies, particularly if questions raised from prior studies served as the motivation for choosing your subject of analysis. This is important because comparing and contrasting the findings of other studies helps support the overall importance of your results and it highlights how and in what ways your case study design and the subject of analysis differs from prior research about the topic.

Consider Alternative Explanations of the Findings Remember that the purpose of social science research is to discover and not to prove. When writing the discussion section, you should carefully consider all possible explanations revealed by the case study results, rather than just those that fit your hypothesis or prior assumptions and biases. Be alert to what the in-depth analysis of the case may reveal about the research problem, including offering a contrarian perspective to what scholars have stated in prior research if that is how the findings can be interpreted from your case.

Acknowledge the Study's Limitations You can state the study's limitations in the conclusion section of your paper but describing the limitations of your subject of analysis in the discussion section provides an opportunity to identify the limitations and explain why they are not significant. This part of the discussion section should also note any unanswered questions or issues your case study could not address. More detailed information about how to document any limitations to your research can be found here .

Suggest Areas for Further Research Although your case study may offer important insights about the research problem, there are likely additional questions related to the problem that remain unanswered or findings that unexpectedly revealed themselves as a result of your in-depth analysis of the case. Be sure that the recommendations for further research are linked to the research problem and that you explain why your recommendations are valid in other contexts and based on the original assumptions of your study.

V. Conclusion

As with any research paper, you should summarize your conclusion in clear, simple language; emphasize how the findings from your case study differs from or supports prior research and why. Do not simply reiterate the discussion section. Provide a synthesis of key findings presented in the paper to show how these converge to address the research problem. If you haven't already done so in the discussion section, be sure to document the limitations of your case study and any need for further research.

The function of your paper's conclusion is to: 1) reiterate the main argument supported by the findings from your case study; 2) state clearly the context, background, and necessity of pursuing the research problem using a case study design in relation to an issue, controversy, or a gap found from reviewing the literature; and, 3) provide a place to persuasively and succinctly restate the significance of your research problem, given that the reader has now been presented with in-depth information about the topic.

Consider the following points to help ensure your conclusion is appropriate:

If the argument or purpose of your paper is complex, you may need to summarize these points for your reader.
If prior to your conclusion, you have not yet explained the significance of your findings or if you are proceeding inductively, use the conclusion of your paper to describe your main points and explain their significance.
Move from a detailed to a general level of consideration of the case study's findings that returns the topic to the context provided by the introduction or within a new context that emerges from your case study findings.

Note that, depending on the discipline you are writing in or the preferences of your professor, the concluding paragraph may contain your final reflections on the evidence presented as it applies to practice or on the essay's central research problem. However, the nature of being introspective about the subject of analysis you have investigated will depend on whether you are explicitly asked to express your observations in this way.

Problems to Avoid

Overgeneralization One of the goals of a case study is to lay a foundation for understanding broader trends and issues applied to similar circumstances. However, be careful when drawing conclusions from your case study. They must be evidence-based and grounded in the results of the study; otherwise, it is merely speculation. Looking at a prior example, it would be incorrect to state that a factor in improving girls access to education in Azerbaijan and the policy implications this may have for improving access in other Muslim nations is due to girls access to social media if there is no documentary evidence from your case study to indicate this. There may be anecdotal evidence that retention rates were better for girls who were engaged with social media, but this observation would only point to the need for further research and would not be a definitive finding if this was not a part of your original research agenda.

Failure to Document Limitations No case is going to reveal all that needs to be understood about a research problem. Therefore, just as you have to clearly state the limitations of a general research study , you must describe the specific limitations inherent in the subject of analysis. For example, the case of studying how women conceptualize the need for water conservation in a village in Uganda could have limited application in other cultural contexts or in areas where fresh water from rivers or lakes is plentiful and, therefore, conservation is understood more in terms of managing access rather than preserving access to a scarce resource.

Failure to Extrapolate All Possible Implications Just as you don't want to over-generalize from your case study findings, you also have to be thorough in the consideration of all possible outcomes or recommendations derived from your findings. If you do not, your reader may question the validity of your analysis, particularly if you failed to document an obvious outcome from your case study research. For example, in the case of studying the accident at the railroad crossing to evaluate where and what types of warning signals should be located, you failed to take into consideration speed limit signage as well as warning signals. When designing your case study, be sure you have thoroughly addressed all aspects of the problem and do not leave gaps in your analysis that leave the reader questioning the results.

Case Studies. Writing@CSU. Colorado State University; Gerring, John. Case Study Research: Principles and Practices . New York: Cambridge University Press, 2007; Merriam, Sharan B. Qualitative Research and Case Study Applications in Education . Rev. ed. San Francisco, CA: Jossey-Bass, 1998; Miller, Lisa L. “The Use of Case Studies in Law and Social Science Research.” Annual Review of Law and Social Science 14 (2018): TBD; Mills, Albert J., Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Putney, LeAnn Grogan. "Case Study." In Encyclopedia of Research Design , Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE Publications, 2010), pp. 116-120; Simons, Helen. Case Study Research in Practice . London: SAGE Publications, 2009; Kratochwill, Thomas R. and Joel R. Levin, editors. Single-Case Research Design and Analysis: New Development for Psychology and Education . Hilldsale, NJ: Lawrence Erlbaum Associates, 1992; Swanborn, Peter G. Case Study Research: What, Why and How? London : SAGE, 2010; Yin, Robert K. Case Study Research: Design and Methods . 6th edition. Los Angeles, CA, SAGE Publications, 2014; Walo, Maree, Adrian Bull, and Helen Breen. “Achieving Economic Benefits at Local Events: A Case Study of a Local Sports Event.” Festival Management and Event Tourism 4 (1996): 95-106.

Writing Tip

At Least Five Misconceptions about Case Study Research

Social science case studies are often perceived as limited in their ability to create new knowledge because they are not randomly selected and findings cannot be generalized to larger populations. Flyvbjerg examines five misunderstandings about case study research and systematically "corrects" each one. To quote, these are:

Misunderstanding 1 : General, theoretical [context-independent] knowledge is more valuable than concrete, practical [context-dependent] knowledge. Misunderstanding 2 : One cannot generalize on the basis of an individual case; therefore, the case study cannot contribute to scientific development. Misunderstanding 3 : The case study is most useful for generating hypotheses; that is, in the first stage of a total research process, whereas other methods are more suitable for hypotheses testing and theory building. Misunderstanding 4 : The case study contains a bias toward verification, that is, a tendency to confirm the researcher’s preconceived notions. Misunderstanding 5 : It is often difficult to summarize and develop general propositions and theories on the basis of specific case studies [p. 221].

While writing your paper, think introspectively about how you addressed these misconceptions because to do so can help you strengthen the validity and reliability of your research by clarifying issues of case selection, the testing and challenging of existing assumptions, the interpretation of key findings, and the summation of case outcomes. Think of a case study research paper as a complete, in-depth narrative about the specific properties and key characteristics of your subject of analysis applied to the research problem.

Flyvbjerg, Bent. “Five Misunderstandings About Case-Study Research.” Qualitative Inquiry 12 (April 2006): 219-245.

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Review Article
Published: 10 May 2022

Understanding and shaping the future of work with self-determination theory

Marylène Gagné ORCID: orcid.org/0000-0003-3248-8947 1 ,
Sharon K. Parker ORCID: orcid.org/0000-0002-0978-1873 1 ,
Mark A. Griffin ORCID: orcid.org/0000-0003-4326-7752 1 ,
Patrick D. Dunlop ORCID: orcid.org/0000-0002-5225-6409 1 ,
Caroline Knight ORCID: orcid.org/0000-0001-9894-7750 1 ,
Florian E. Klonek ORCID: orcid.org/0000-0002-4466-0890 1 &
Xavier Parent-Rocheleau ORCID: orcid.org/0000-0001-5015-3214 2

Nature Reviews Psychology volume 1 , pages 378–392 ( 2022 ) Cite this article

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Human behaviour
Science, technology and society

Self-determination theory has shaped our understanding of what optimizes worker motivation by providing insights into how work context influences basic psychological needs for competence, autonomy and relatedness. As technological innovations change the nature of work, self-determination theory can provide insight into how the resulting uncertainty and interdependence might influence worker motivation, performance and well-being. In this Review, we summarize what self-determination theory has brought to the domain of work and how it is helping researchers and practitioners to shape the future of work. We consider how the experiences of job candidates are influenced by the new technologies used to assess and select them, and how self-determination theory can help to improve candidate attitudes and performance during selection assessments. We also discuss how technology transforms the design of work and its impact on worker motivation. We then describe three cases where technology is affecting work design and examine how this might influence needs satisfaction and motivation: remote work, virtual teamwork and algorithmic management. An understanding of how future work is likely to influence the satisfaction of the psychological needs of workers and how future work can be designed to satisfy such needs is of the utmost importance to worker performance and well-being.

Structured multi-criteria model of self-managed motivation in organizations based on happiness at work: pandemic related study

Joanna Nieżurawska, Radosław A. Kycia, … Agnieszka Niemczynowicz

The future of the labor force: higher cognition and more skills

Wen Zhang, Kee-Hung Lai & Qiguo Gong

The level of conscientiousness trait and technostress: a moderated mediation model

Eva Ariño-Mateo, Matías Arriagada Venegas, … David Pérez-Jorge

Introduction

The nature of work is changing as technology enables new forms of automation and communication across many industries. Although the image of human-like robots replacing human jobs is vivid, it does not reflect the typical ways people will engage with automation and how technology will change job requirements in the future. A more relevant picture is one in which people interact over dispersed networks using continuously improving communication platforms mediated by artificial intelligence (AI). Examples include the acceleration of remote working arrangements caused by the COVID-19 pandemic and the increased use of remote control operations across many industries including mining, manufacturing, transport, education and health.

Historically, automation has replaced more routine physically demanding, dangerous or repetitive work in industries such as manufacturing, with little impact on professional and managerial occupations 1 . However, since the mid-2010s, automation has replaced many repetitive error-prone administrative tasks such as processing legal documents, directing service queries and employee selection screening 2 , 3 . Thus, work requirements for employees are increasingly encompassing tasks that cannot be readily automated, such as interpersonal negotiations and service innovations 4 : in other words, work that cannot be easily achieved through algorithms.

The role of motivation is often overlooked when designing and implementing technology in the workplace, even though technological changes can have a major impact on people’s motivation. Self-determination theory offers a useful multidimensional conceptualization of motivation that can help predict these impacts. According to self-determination theory 5 , 6 , three psychological needs must be fulfilled to adequately motivate workers and ensure that they perform optimally and experience well-being. Specifically, people need to feel that they are effective and masters of their environment (need for competence), that they are agents of their own behaviour as opposed to a ‘pawn’ of external pressures (need for autonomy), and that they experience meaningful connections with other people (need for relatedness) 5 , 7 . Meta-analytic evidence shows that satisfying these three needs is associated with better performance, reduced burnout, more organizational commitment and reduced turnover intentions 8 .

Self-determination theory also distinguishes between different types of motivation that workers might experience: intrinsic motivation (doing something for its own sake, out of interest and enjoyment), extrinsic motivation (doing something for an instrumental reason) and amotivation (lacking any reason to engage in an activity). Extrinsic motivation is subdivided according to the degree to which external influences are internalized (absorbed and transformed into internal tools to regulate activity engagement) 5 , 9 . According to meta-analytic evidence, more self-determined (that is, intrinsic or more internalized) motivation is more positively associated with key attitudinal and performance outcomes, such as job satisfaction, organizational commitment, job performance and proactivity than more controlled motivation (that is, extrinsic or less internalized) 10 . Consequently, researchers advocate the development and promotion of self-determined motivation across various life domains, including work 11 . Satisfaction of the three psychological needs described above is significantly related to more self-determined motivation 8 .

Given the impact of the needs proposed in self-determination theory on work motivation and consequently work outcomes (Fig. 1 ), it is important to find ways to satisfy these needs and avoid undermining them in the workplace. Organizational research has consequently focused on managerial and leadership behaviours that support or thwart these needs and promote different types of work motivation 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 (Fig. 2 ). There is also substantial research on the effects of work design (the nature and organization of people’s work tasks within a job or role, such as who makes what decisions, the extent to which people’s tasks are varied, or whether people work alone or in a team structure) and compensation systems on need satisfaction and work motivation 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , and how individuals can seek to meet their needs and enhance their motivation through proactive efforts to craft their jobs 38 , 39 , 40 .

According to self-determination theory, satisfaction of three psychological needs (competence, autonomy and relatedness) influences work motivation, which influences outcomes. More intrinsic and internalized motivations are associated with more positive outcomes than extrinsic and less internalized motivations. These needs and motivations might be influenced by the increased uncertainty and interdependence that characterize the future of work.

Summary of research findings 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 and available meta-analyses 8 , 10 . In cases where the evidence is mixed, a negative sign indicates a negative correlation, a positive sign indicates a positive correlation, and a zero indicates no statistically significant correlation.

Importantly, the work tasks that people are more likely to do in future work will require high-level cognitive and emotional skills that are more likely to be developed, used, and sustained when underpinned by self-determined motivation 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 . Therefore, if individuals are to be effective in future work, it is important to understand how future work might meet — or fail to meet — the psychological needs proposed by self-determination theory.

In this Review, we outline how work is changing and explain the consequences of these changes for satisfying workers’ psychological needs. We then focus on two areas where technology is already changing the worker experience: when workers apply for jobs and go through selection processes; and when the design of their work — what work they do, as well as how, when and where they do it — is transformed by technology. In particular, we focus on three domains where technology is already changing work design: remote work, virtual teams and algorithmic management. We conclude by discussing the importance of satisfying the psychological needs of workers when designing and implementing technologies in the workplace.

Future work requirements

The future workplace might evolve into one where psychological needs are better fulfilled, or one where they are neglected. In addition, there is growing concern that future work will meet the needs of people with adequate access to technology and the skills to use it, but will further diminish fulfillment for neglected and disadvantaged groups 51 (Box 1 ). To understand how future work might align with human needs, it is necessary to map key work features to core constructs of self-determination theory. Future work might be characterized by environmental uncertainty interdependence, complexity, volatility and ambiguity 52 . Here we focus on uncertainty and interdependence because these features capture core concerns about the future and its implication for connections among people in the changing context of work 53 . Higher levels of uncertainty require more adaptive behaviours, whereas higher levels of interdependence require more social, team-oriented and network-oriented behaviours 54 .

We first consider the increasing role of uncertainty in the workplace. Rapid changes in technology and global supply chains mean that the environment is more unpredictable and that there is increasing uncertainty about what activities are needed to be successful. Reducing uncertainty is central to most theories of human adaptation 55 and is a strong motivational basis for goals and behaviour 56 . If uncertainty becomes a defining and pervasive feature of organizational life, organizational leaders should think beyond reducing uncertainty and instead leverage and even create it 55 . In other words, in a highly dynamic context, it might be more functional and adaptive for employees and organizational leaders to consider more explorative approaches to coping with uncertainty, such as experimentation and improvization. All of these considerations imply that future effective work will require adaptive behaviours such as modifying the way work is done, and proactive behaviours such as innovating and creating new ways of working 54 .

Under higher levels of uncertainty, specific actions are difficult to define in advance. In contrast to action sequences that can be codified (for example, with algorithms) and repeated in predictable environments, the best action sequence is likely to involve flexibility and experimentation when the workplace is more uncertain. In this context, individuals must be motivated to explore new ideas, adjust their behaviour and engage with ongoing change. In stable and predictable environments, less self-determined forms of motivation might be sufficient to maintain the enactment of repetitive tasks and automation is more feasible as a replacement or support. However, under conditions of uncertainty, individuals will benefit from showing cognitive flexibility, creativity and proactivity, all behaviours that are more likely to emerge when people have self-determined motivation 40 , 41 , 44 , 46 , 47 , 48 , 49 , 57 .

Adaptive (coping with and responding to change) and proactive (initiating change) performance can be promoted by satisfying the needs for competence, autonomy and relatedness, and self-determined motivation 4 , 58 . For example, when individuals experience internalized motivation, they have a ‘reason to’ engage in the sometimes psychologically risky behaviour of proactivity 40 . Both adaptivity and proactivity depend on individuals having sufficient autonomy to work differently, try new ideas and negotiate multiple pathways to success. Hence, successful organizational functioning depends on people who can act autonomously to regulate their behaviour in response to a more unpredictable and changing environment 31 , 54 , 59 .

The second feature of the evolving workplace is an increasing level of interdependence among people, systems and technology. People will connect with each other in more numerous and complex ways as communication technologies become more reliable, deeply networked and faster. For example, medical teams from disparate locations might collaborate more easily in real time to support remote surgical procedures. They will also connect with automated entities such as cobots (robots that interact with humans) and decision-making aids supported by constantly updating algorithms. For example, algorithms might provide medical teams with predictive information about patient progress based on streaming data such as heart rate. As algorithms evolve in complexity and predictive accuracy, they will modify the work context and humans will need to adapt to work with the new information created 60 .

This interconnected and evolving future workplace requires individuals who can interact effectively across complex networks. The nature of different communication technologies can both increase and decrease feelings of relatedness depending on the extent to which they promote meaningful interactions. Typically, work technologies are developed to facilitate productivity and efficiency. However, given that human performance is also influenced by feelings of relatedness 8 , it is important to ensure that communication technologies and the way networks of people are managed by these technologies can fulfill this need.

The rapid growth of networks enabled by communication technologies (for example, Microsoft Teams, Slack and Webex) has produced positive and negative effects on performance and well-being. For example, these technologies can be a buffer against loneliness for remote workers or homeworkers 61 and enable stronger connections among distributed workers 62 . However, networking platforms lead some individuals to experience more isolation rather than more connectedness 63 . Workplace networks might also engender these contrasting effects by, for example, building a stronger understanding between individuals in a work group who do not usually get to interact or by limiting contact to more superficial communication that prevents individuals from building stronger relationships.

Both uncertainty and interdependence will challenge people’s feelings of competence. Uncertainty can lead to reduced access to predictable resources and less certainty about the success of work effort; the proliferation of networks and media can lead to feeling overwhelmed and to difficulties in managing communication and relationships. Moreover, technologies and automation can lead to the loss of human competencies as people stop using these skills 64 , 65 , 66 , 67 . For example, automating tasks that require humans to have basic financial skills diminishes opportunities for humans to develop expertise in financial skills.

Uncertainty and interdependence are likely to persist and increase in the future. This has implications for whether and how psychological needs will be satisfied or frustrated. In addition, because uncertainty and interdependence require people to behave in more adaptive and proactive ways, it is important to create future work that satisfies psychological needs.

Box 1 Inequalities caused by future work

Future work is likely to exacerbate inequalities. First, the digital divide (unequal access to, and ability to use, information communication technologies) 51 is likely to be exacerbated by technological advances that might become more costly and require more specialized skills. Moreover, the COVID-19 pandemic exacerbated work inequalities by providing better opportunities to those with digital access and skills 210 , 211 . The digital divide now also includes ‘algorithm awareness’ (knowing what algorithms do) which influences whether and how people are influenced by technology. Indeed, the degree to which algorithms influence attitudes and behaviours is negatively associated with the degree to which people are aware of algorithms and understand how they work 212 .

Second, future work is likely to require new technical and communication skills, as well as adaptive and proactive skills. Thus, people with such skills are more likely to find work than those who do not or who have fewer opportunities (for example, education access) to develop them. Even gig work requires that workers have access to relevant platforms and adequate skills for using them. These future work issues are therefore likely to increase gaps between skilled and non-skilled segments of the population, and consequently to increase societal pay disparities and poverty.

For example, workforce inequalities between mature and younger workers are likely to increase owing to real or perceived differences in technology-related skills, with increased disparities in the type of jobs these workers engage in 210 , 213 . Older workers might miss out on opportunities to upskill or might choose to leave the workforce early rather than face reskilling. This could decrease workforce diversity and strengthen negative stereotypes about mature workers (such as that they are not flexible, adaptable or motivated to keep up with changing times) 214 . Furthermore, inequalities in terms of pay have already been observed between men and women 215 . Increased robotization increases the gender pay gap 216 , and this gap is likely to be exacerbated as remote working becomes more common (as was shown during the pandemic) 217 . For example, one study found that salaries did not increase as much for women working flexibly compared to men 218 ; another study found that home workers tended to be employees with young children and these workers were 50% less likely to be promoted than those based in the office 140 .

To promote equality in future work and ensure that psychological needs are met, managers will need to adopt ‘meta-strategies’ to promote inclusivity (ensuring that all employees feel included in the workplace and are treated fairly, regardless of whether they are working remotely or not), individualization of work (ensuring that work is tailored to individual needs and desires) and employee integration (promoting interaction between employees of all ages, nationalities and backgrounds) 213 .

The future of employee selection

Changing economies are increasing demand for highly skilled labour, meaning that employers are forced to compete heavily for talent 68 . Meanwhile, technological developments, largely delivered online, have radically increased the reach, scalability and variety of selection methods available to employers 69 . Technology-based assessments also afford candidates the autonomy to interact with prospective employers at times and locations of their choosing 70 , 71 . Furthermore, video-based, virtual, gamified and AI-based assessment technologies 3 , 72 , 73 , 74 have improved the fidelity and immersion of the selection process. The fidelity of a selection assessment represents the extent to which it can reproduce the physical and psychological aspects of the work situation that the assessment is intended to simulate 75 . Virtual environments and video-based assessments can better reproduce working environments than traditional ‘paper and pencil’ assessments, and AI is being used to simulate social interactions in work or similar contexts 74 . Immersion represents how engrossing or absorbing an assessment experience is. Immersion is enhanced by richer media and gamified assessment elements 75 , 76 . These benefits have driven the widespread adoption of technology in recruitment practices 77 , but they have also attracted criticism. For example, the use of AI to analyse candidate data (such as CVs, social media profiles, text-based responses to interview questions, and videos) 78 raises concerns about the relevance of data being collected for selecting employees, transparency in how the data are used, and biases in selection based on these data 79 .

Candidates with a poor understanding of what data are being collected and how they are being used might experience a technology-based selection process as autonomy-thwarting. For example, the perceived job-relatedness of an assessment is associated with whether or not candidates view the assessment positively 69 , 80 . However, with today’s technology, assessments that appear typical or basic (such as a test or short recorded interview response) might also involve the collection of additional ‘trace’ data such as mouse movements and clicks (in the case of tests), or ancillary information such as ‘micro-expressions’ or candidates’ video backdrops 81 . We expect that it would be difficult for candidates to evaluate the job-relatedness of this information, unless provided with a rationale. Candidates may also feel increasing pressure to submit to employers’ requests to share personal information, such as social media profiles, which may further frustrate autonomy to the extent that candidates are reluctant to share this information 82 .

Furthermore, if candidates do not understand how technology-driven assessments work and are not able to receive feedback from assessment systems, their need for competence may be thwarted 83 . For example, initial research shows that people perceive fewer opportunities to demonstrate their strengths and capabilities in interviews they know will be evaluated by AI, compared to those evaluated by humans 83 .

Finally, because candidates are increasingly interacting with systems, rather than people, their opportunities to build relatedness with employers might be stifled. A notable exemplar is the use of asynchronous video interviews 70 , 71 , a type of video-based assessment where candidates log into an online system, are presented with a series of questions, and are asked to video-record their responses. Unlike a traditional or videoconference interview, candidates completing an asynchronous video interview do not interact directly with anyone from the employer organization, and they consequently often describe the experience as impersonal 84 . Absent any interventions, the use of asynchronous video interviews removes the opportunity for candidates to meet the employer and get a feel for what it might be like to work for the employer, or to ask questions of their own 84 .

Because technologies have changed rapidly, research on candidates’ reactions to these new selection methods has not kept up 69 . Nonetheless, to the extent that test-related and technology-related anxiety influences motivation and performance when completing an online assessment or a video interview, the performance of applicants might be adversely affected 85 . Furthermore, candidate experience can influence decisions to accept a job offer and how positively the candidate will talk about the organization to other potential candidates and even clients, thereby influencing brand reputation 86 . Thus, technology developments offer clear opportunities to improve the satisfaction of candidates’ needs and to assess them in richer environments that more closely resemble work settings. However, there are risks that technology that is needs-thwarting or is implemented in a needs-thwarting manner, will add to the uncertainty already inherent in competitive job applications. In the context of a globally competitive skills market, employers risk losing high-quality candidates.

The future of work design

Discussion in the popular press about the impact of AI and other forms of digitalization focuses on eradicating large numbers of jobs and mass unemployment. However, the reality is that tasks within jobs are being influenced by digitalization rather than whole jobs being replaced 87 . Most occupations in most industries have at least some tasks that could be replaced by AI, yet currently there is no occupation in which all tasks could be replaced 88 . The consequence of this observation is that people will need to increasingly interact with machines as part of their jobs. This raises work design questions, such as how people and machines should share tasks, and the consequences of different choices in this respect.

Work design theory is intimately connected to self-determination theory, with early scholars arguing that work arrangements should create jobs in which employees can satisfy their core psychological needs 89 . Core aspects of work design, including decision-making power, the opportunity to use skills and do a variety of tasks, the ability to ascertain the impact of one’s work, performance feedback 90 , social contact, time pressure, emotional demands and role conflict 91 are important predictors of job satisfaction, job performance 92 and work motivation 93 . Some evidence suggests that these motivating characteristics (considered ‘job resources’ according to the jobs demands–resources model) 94 are especially important for fostering motivation or reducing strain when job demands (aspects of a job that require sustained physical, emotional or mental effort) are high 93 , 95 . For example, autonomy and social support can reduce the effect of workload on negative outcomes such as exhaustion 96 .

Technology can potentially influence work design and therefore employee motivation in positive ways 1 . Increasing workers’ task variety and opportunities for more complex problem-solving should occur whenever technology takes over tasks (such as assembly line or mining work). Leaving the less routine and more interesting tasks for people to do 97 increases the opportunity for workers to fulfill their need for competence. For example, within manufacturing, complex production systems in which cyber-machines are connected in a factory-wide information network require strategic human decision-makers operating in complex, varied and high-level autonomy jobs 98 . Technology (such as social media) can also enhance social contact and support in some jobs and under some circumstances 86 , 87 (but see ref. 63 ), increasing opportunities for meeting relatedness needs.

However, new technologies can also undermine the design of motivating work, and thus reduce workers’ need satisfaction 1 . For example, in the aviation industry, manual flying skills can become degraded due to a lack of opportunity to practice when aircraft are highly automated 99 , decreasing the opportunity for pilots to meet their need for competence. As another example, technology has enabled the introduction of ‘microwork’ in which jobs are broken down into small tasks that are then carried out via information communication technologies 100 . Such jobs often lack variety, skill use and meaning 101 , again reducing the opportunity for the work to meet competence needs. In an analysis of robots in surgery, technology designed purely for ‘efficiency’ reduced the opportunities for trainee surgeons to engage in challenging tasks and resulted in impaired skill development 102 , and therefore probably reduced competence need satisfaction. Thus, poor work design might negatively influence work motivation through poor need satisfaction, especially the need for competence, owing to the lack of opportunity to maintain one’s skills or gain new ones 2 .

As the above examples show, the impact of new technologies on work design, and hence on need satisfaction, is powerful — but also mixed. That is, digital technologies can increase or decrease motivational work characteristics and can thereby influence need satisfaction (Fig. 3 ). The research shows that there is no deterministic relationship between technology and work design; instead, the effect of new technology on work design, and hence on motivation, depends on various moderating factors 1 . These moderating factors include individual aspects, such as the level of skill an individual has or the individual’s personality. Highly skilled individuals or those with proactive personalities might actively shape the technology and/or craft their work design to better meet their needs and increase their motivation 1 . For example, tech-savvy Uber drivers subject to algorithmic management sometimes resist or game the system, such as by cancelling rides to avoid negative ratings from passengers 103 .

The causal relationships among the possible (but not exhaustive) variables implicated in the influence of technology on work design and work motivation discussed in this Review.

More generally, individuals proactively seek a better fit with their job through behaviours such as idiosyncratic deals (non-standard work arrangements negotiated between an employee and an employer) and job crafting (changing one’s work design to align one’s job with personal needs, goals and skills) 39 , 40 (Box 2 ). Consequently, although there is relatively little research on proactivity in work redesign through technology, it is important to recognize that individuals will not necessarily be passive in the face of negative technologies. Just as time pressure can stimulate proactivity 104 , we should expect that technology that creates poor work design will motivate job crafting and other proactive behaviours from workers seeking to meet their psychological needs better 105 . This perspective fits with a broader approach to technology that emphasizes human agency 106 .

Importantly, mitigating and managing the impact of technology on work is not the sole responsibility of individuals. Organizational implementation factors (for example, whether technology is selected, designed and implemented in a participatory way or how much training is given to support the introduction of technology) and technological design factors (for example, how much worker control is built into automated systems) are also fundamental in shaping the effect of technology on work design. Understanding these moderating factors is important because they provide potential ‘levers’ for creating more motivating work while still capitalizing on the advantages of technologies. For example, in one case study 107 , several new digital technologies such as cobots and digital paper flow (systems that integrate and automate different organizational functions, such as sales and purchasing with accounting, inventory control and dispatch) were implemented following a strong technocentric approach (that is, highly focused on engineering solutions) with little worker participation, and with limited attention to creating motivating work design. A more human-centred approach could have prevented the considerable negative outcomes that followed (including friction, reduced morale, loss of motivation, errors and impaired performance) 107 . Ultimately, how technology is designed and implemented should be proactively adapted to better meet human competencies, needs and values.

Box 2 The future of careers

Employment stability started to decline during the 1980s with the rise of public ownership and international trade, the increased use of performance-based incentives and contracts, and the introduction of new technologies. Employment stability is expected to continue to decline with the growth of gig work and continued technological developments 219 , 220 . Indeed, people will more frequently be asked to change career paths as work is transformed by technology, to use and ‘sell’ their transferrable skills in creative ways, and to reskill. The rise of more precarious work and new employment relationships (for example, in gig work) adds to these career challenges 221 . The current generation of workers is likely to experience career shocks (disruptive events that trigger a sensemaking process regarding one’s career) caused by rapid technological changes, and indeed many workers have already experienced career shocks from the pandemic 222 . Moreover, rapid technological change and increasing uncertainty pushes organizations to hire for skill sets rather than fitting people into set jobs, requiring people to be aware of their skills and to know how to market them.

In short, the careers of the current and future workforce will be non-linear and will require people to be more adaptive and proactive in crafting their career. For this reason, the concept of a protean career, whereby people have an adaptive and self-directed career, is likely to be increasingly important 223 . A protean career is a career that is guided by a search for self-fulfillment and is characterized by frequent learning cycles that push an individual into constant transformation; a successful protean career therefore requires a combination of adaptivity skills and identity awareness 224 , 225 . Adaptivity allows people to forge their career by using, or even creating, emerging opportunities. Having a solid sense of self helps individuals to make choices according to personal strengths and values. However, a protean career orientation might fit only a small segment of the labour market. Change-averse individuals might regard protean careers as career-destructive and the identity changes associated with a protean career might be regarded as stressful. In addition, overly frequent transitions might limit deep learning opportunities and achievements, and disrupt important support networks 221 .

Nonetheless, career-related adaptive and proactive behaviours can be encouraged by satisfying psychological needs. In fact, protean careers tend to flourish in environments that provide autonomy and allow for proactivity, with support for competence and learning 223 , 226 . Moreover, people have greater self-awareness when they feel autonomous. Indeed, self-awareness is a component of authenticity and mindfulness, both of which are linked to the satisfaction of the need for autonomy 227 , 228 . Thus, supporting psychological needs during training, development and career transitions is likely to assist people in crafting successful careers.

Applications

In what follows, we describe three specific cases where technology is already influencing work design (virtual and remote work, virtual teamwork, and algorithmic management), and consider the potential consequences for worker need satisfaction and motivation.

Virtual and remote work

Technologies have significantly altered when and where people can work, with the Covid-19 pandemic vastly accelerating the extent of working from home (Box 3 ). Remote work has persisted beyond the early stages of the Covid-19 pandemic with hybrid working — where people work from home some days a week and at the workplace on other days — becoming commonplace 108 . The development of information communication technologies (such as Microsoft Teams) has enabled workers to easily connect with colleagues, clients and patients remotely 105 , for example, via online patient ‘telehealth’ consultations, webinars and discussion forums. Technology has even enabled the remote control of other technologies, such as manufacturing machinery, vehicles and remote systems that monitor hospital ward patient vital signs through AI 1 . However, even when people are working on work premises (that is, not working remotely), an increasing amount of work in many jobs is done virtually (for example, online training or communicating with a colleague next door via email).

Working virtually is inherently tied to changes in uncertainty and interdependence. Virtual work engenders uncertainty because workplace and interpersonal cues are less available or reliable in providing virtual employees with role clarity and ensuring smooth interactions. Indeed, ‘screen’ interactions are more stressful and effortful than face-to-face interactions. It is more difficult to decipher and synchronize non-verbal behaviour on a screen than face-to-face, particularly given the lack of body language cues due to camera frame limitations, increasing the cognitive load for meeting attendees 109 , 110 , 111 , 112 . Non-verbal synchrony can be affected by the video streaming speed, which also increases cognitive load 109 , 110 , 111 , 112 . Virtual interactions involve ‘hyper gaze’ from seeing grids of staring faces, which the brain interprets as a threat 109 , 110 , 111 , 112 . Seeing oneself on screen increases self-consciousness during social interactions, which can cause anxiety, especially in women and those from minoritized groups 109 , 110 , 111 , 112 . Finally, reduced mobility from having to stay in the camera frame has been shown to reduce individual performance relative to face-to-face meetings 109 , 110 , 111 , 112 . Research on virtual interactions is still in its infancy. In one study, workers were randomly assigned to have their camera either on or off during their daily virtual meetings for a week. Those with the camera on during meetings experienced more daily fatigue and less daily work engagement than those with the camera off 113 .

Lower-quality virtual communication between managers and colleagues can leave individuals unclear about their goals and priorities, and how they should achieve them 114 . This calls for more self-regulation 115 because employees must structure their daily work activities and remind themselves of their work priorities and goals, without relying on the physical presence of colleagues or managers. If virtual workers must coordinate some of their work tasks with colleagues, it can be difficult to synchronize and coordinate actions, working schedules and breaks, motivate each other, and assist each other with timely information exchange 115 . This can make it harder for employees to acquire and share information 53 .

Virtual work also affects work design and changes how psychological needs can be satisfied and frustrated (Table 1 ), which has implications for both managers and employees. Physical workplace cues that usually guide work behaviours and routines in the office do not exist in virtual work, consequently demanding more autonomous regulation of work behaviours 116 , 117 . Some remote workers experience an increased sense of control and autonomy over their work environment 118 , 119 , 120 under these circumstances, resulting in lower family–work conflict, depression and turnover 121 , 122 . However, managers and organizations might rob workers of this autonomy by closely monitoring them, for example by checking their computer or phone usage 123 . This type of close monitoring reflects a lack of manager trust in individuals’ abilities or intentions to work effectively remotely. This lack of trust leads to decreased feelings of autonomy 124 , increased employee home–work conflict 105 and distress 125 , 126 . Surveillance has been shown to decrease self-determined motivation 127 . It is therefore important to train managers in managing remote workers in an autonomy-supportive way to avoid these negative consequences 128 . The negative effects of monitoring can also be reduced if monitoring is used constructively to help employees develop through feedback 129 , 130 , 131 , 132 , 133 , and when employees participate in the design and control of the monitoring systems 134 , 135 .

Information communication technology might satisfy competence needs by increasing access to global information and communication and the ability to analyse data 136 . For example, online courses, training and webinars can improve workers’ knowledge, skills and abilities, and can therefore help workers to carry out their work tasks more proficiently, which increases self-efficacy and a sense of competence. Furthermore, the internet allows people to connect rapidly and asynchronously with experts around the world, who may be able to provide information needed to solve a work problem that local colleagues cannot help with 136 . This type of remote work is increasingly occurring whether or not individuals themselves are based remotely, and can potentially enhance performance.

At the same time, technology might thwart competence needs, and increase fatigue and stress. For example, constant electronic messages (such as email or keeping track of online messaging platforms such as Slack or Microsoft Teams) are likely to increase in volume when working remotely, but can be distracting and prevent individuals from completing core tasks while they respond to incoming messages 136 . The frustration of the need for competence can increase if individuals are constantly switching tasks to deal with overwhelming correspondence and failing to finish tasks in a timely manner. In addition, information communication technology enables access to what some individuals might perceive as an overwhelming amount of information (for example, through the internet, email and messages) which can lead to a lot of time spent sifting and processing information. This can be interpreted as a job demand that might make individuals feel incompetent if it is not clear what information is most important. Individuals might also require training in the use of information communication technology, and even then, technology can malfunction, preventing workers from completing tasks, and causing frustration and distress 136 , 137 .

Finally, remote workers can suffer from professional isolation because there are fewer opportunities to meet or be introduced to connections that enable career development and progression 138 , which could influence their feelings of competence in the long run. Although some research suggests that those who work flexibly are viewed as less committed to their career 139 and might be overlooked for career progression 140 , other research has found no relationship between remote working and career prospects 119 .

Virtual work can also present challenges for meeting workers’ need for relatedness 141 . Remote workers can feel isolated from, and excluded by, colleagues and fail to gain the social support they might receive if co-located 142 , 143 , weakening their sense of belonging to a team or organization 144 and their job performance 145 . This effect will probably be accentuated in the future: if the current trend for working from home continues, more people will be dissociated from office social environments more often and indefinitely. Office social environments could be degraded permanently if fewer people frequent the office on a daily basis, such that workers may not be in the office at the same time as collaborators, and there might be fewer people to ask for help or talk with informally. We do not yet know the long-term implications of a degraded social environment, but some suggest that extended virtual working could create a society where people have poor communication skills and in which social isolation and anxiety are exacerbated 146 . Self-determination theory suggests that it will be critical to actively design hybrid and remote work that meets relatedness needs to prevent these long-term issues. When working remotely, simple actions could be effective, such as actively providing opportunities for connecting with others, for example, through ‘virtual coffee breaks’ 147 . Individuals could also be ‘buddied’ up into pairs who regularly check in with each other via virtual platforms.

Hybrid work seems to offer the best of both worlds, providing opportunities for connection and collaboration while in the workplace, and affording autonomy in terms of flexible working. Some research suggests that two remote workdays a week provides the optimum balance 148 . However, it is likely that this balance will be affected by individual characteristics and desires, as well as by differences in work roles and goals. For example, Israeli employees with autism who had to work from home during the COVID-19 pandemic experienced significantly lower competence and autonomy satisfaction than before the pandemic 149 . Yet remote workers high in emotional stability and job autonomy reported higher autonomy and relatedness satisfaction compared to those with low emotional stability 120 . These findings suggest that managers and individuals should consider the interplay between individual characteristics, work design and psychological need satisfaction when considering virtual and remote work.

Box 3 The ‘great resignation’

‘The great resignation’ refers to the massive wave of employee departures during the COVID-19 pandemic in several parts of the world, including North America, Europe and China 229 , 230 , that can be attributed in part to career shocks caused by the pandemic 222 . In the healthcare profession, the shock consisted of an exponential increase in workload and the resulting exhaustion, coupled with the disorganization caused by lack of resources and compounded by health fears 231 . In other industries, the pandemic caused work disruptions by forcing or allowing people to work from home, furloughing employees for varying periods of time, or lay-offs caused by an abrupt loss of business (such as in the tourism and hospitality industries).

Scholars have speculated that these shocks have resulted in a staggering number of people not wanting to go back to work or quitting their current jobs 232 . For example, the hospitality and tourism industries failed to attract employees back following lay-offs 233 . Career shocks can trigger a sensemaking process that can lead one to question how time is spent at work and the benefits one draws from it. For example, the transition to working from home made employees question how and why they work 234 . Frequent health and financial concerns, juggling school closures and complications in caring for dependents have compounded exhaustion and disorganization issues. Some have even renamed ‘the great resignation’ as ‘the great discontent’ to highlight that many people reported wanting to quit because of dissatisfaction with their work conditions 235 .

It might be helpful to understand ‘the great resignation’ through the lens of basic psychological need satisfaction. Being stretched to the limit might influence the need for competence and relatedness when workers feel they have suboptimal ways to connect with colleagues and insufficient time to balance work with other life activities that connect them to family and friends 128 , 236 . The sensemaking process that accompanies career shocks might highlight a lack of meaningful work that decreases the satisfaction of the need for autonomy. This lack of need satisfaction might lead people to take advantage of the disruption to ‘cut their losses’ by reorienting their life priorities and career goals, leading to resignation from their current jobs 237 , 238 .

Alternatively, the experiences gained from working differently during the COVID-19 pandemic might have made many workers aware of how work could be (for example, one does not have to commute), emboldening them to demand better work design and work conditions for themselves. Not surprisingly, barely a year after ‘the great resignation’ many are now talking about ‘the great reshuffle’, suggesting that many people who quit their jobs used this time to rethink their careers and find more satisfying work 239 . Generally, this has meant getting better pay and seeking work that aligns better with individual values and that provides a better work–life balance: in other words, work that better meets psychological needs for competence, autonomy and relatedness.

Virtual teamwork

Uncertainty and interconnectedness make work more complex, increasing the need for teamwork across many industries 150 . Work teams are groups of individuals that must both collaborate and work interdependently to achieve shared objectives 151 . Technology has created opportunities to develop work teams that operate virtually. Virtual teams are individuals working interdependently towards a common goal but who are geographically dispersed and who rely on electronic technologies to perform their work 152 , 153 . Thus, virtual teamwork is a special category of virtual work that also involves collective psychological experiences (that are shaped by and interact with virtual work) 154 . This adds another layer of complexity and therefore requires a separate discussion.

Most research conceptualizes team virtuality as a construct with two dimensions: geographical dispersion and reliance on technology 153 , 155 . Notably, these dimensions are not completely independent because team members require technology to communicate and coordinate tasks when working in different locations 156 , 157 . Virtuality differs between and within teams. Team members might be in different locations on some days and the same location on other days, which changes the level of team virtuality over time. Thus, teams are not strictly virtual or non-virtual. Team virtuality influences how team members coordinate tasks and share information 130 , which is critical for team effectiveness (usually assessed by a team’s tangible outputs, such as their productivity, and team member reactions, such as satisfaction with, or commitment to, the team) 158 .

Although individual team members might react differently to working in a virtual team, multi-level theory suggests that team members collectively develop shared experiences, called team emergent states 159 , 160 . Team emergent states include team cohesion (the bond among group members) 161 , team trust 162 , and team motivation and engagement 159 , 163 . These emergent states arise out of individual psychological behaviours and states 164 and are influenced by factors that are internal (for example, interactions between team members) and external (for example, organizational team rewards, organizational leadership and project deadlines) to the team, as well as team structure (for example, team size and composition). Team emergent states, particularly team trust, are critical for virtual team effectiveness because reliance on technology often brings uncertainties and fewer opportunities for social control 165 .

Team virtuality is likely to affect team functioning via its impact on psychological need satisfaction, in a fashion similar to remote work. However, the need for coordination and information sharing to achieve team goals is likely to be enhanced by how team members support and satisfy each other’s psychological needs 166 , which might be more difficult under virtual work conditions. In addition to affecting individual performance, need satisfaction within virtual teams can also influence collective-level team processes, such as coordination and trust, which ultimately affect team performance. For example, working in a virtual team might make it more difficult to feel meaningful connections because team members in different locations often have less contact than co-located team members. Virtual team members predominantly interact via technology, which — as described in the previous section — might influence the quality of relationships they can develop with their team members 141 , 167 , 168 and consequently the satisfaction of relatedness needs 169 .

Furthermore, virtual team members must master electronic communication technology (including virtual meeting and breakout rooms, internet connectivity issues, meeting across different time zones, and email overload), which can lead to frustrations and ‘technostress’ 170 . Frustrations with electronic communication might diminish the psychological need for competence because team members might feel ineffective in mastering their environment.

In sum, virtual team members might experience lower relatedness and competence need satisfaction. However, these needs are critical determinants of work motivation. Furthermore, virtual team members can also develop shared collective experiences around their need satisfaction. Thus, self-determination theory offers explanatory mechanisms (that is, team members’ need satisfaction, which influences work motivation) that are at play in virtual teams and that organizations should consider when implementing virtual teams.

Algorithmic management

Algorithmic management refers to the use of software algorithms to partially or completely execute workforce management functions (for example, hiring and firing, coordinating work, and monitoring performance) 2 , 123 , 171 , 172 . This phenomenon first appeared on gig economy platforms such as Uber, Instacart and Upwork, where all management is automated 173 . However, it is rapidly spreading to traditional work settings. Examples include monitoring the productivity, activity and emotions of remote workers 174 , the algorithmic determination of truck drivers’ routes and time targets 175 , and automated schedule creation in retail settings 176 . The constant updating of the algorithms as more data is collected and the opacity of this process makes algorithmic management unpredictable, which produces more uncertainty for workers 177 .

Algorithmic management has repercussions for work design. Specifically, whether algorithmic management systems consider human motivational factors in their design influences whether workers are given enough autonomy, skills usage, task variety, social contact, role clarity (including knowing the impact of one’s work) and a manageable workload 123 . So far, empirical evidence show that algorithmic management features predominantly reduce employees’ basic needs for autonomy, competence and relatedness because of how they influence work design (Fig. 4 ).

Summary of the features and consequences of algorithmic management on autonomy needs, relatedness needs and competence needs.

Algorithmic management tends to foster the ‘working-for-data’ phenomenon (or datafication of work) 172 , 178 , 179 , leading workers to focus their efforts on aspects of work that are being monitored and quantified at the expense of other tasks that might be more personally valued or meaningful. This tendency is reinforced by the fact that algorithms are updated with new incoming data, increasing the need for workers to pay close attention to what ‘pays off’ at any given moment. Monitoring and quantifying worker behaviours might reduce autonomy because it is experienced as controlling and narrows goal focus to only quantifiable results 127 , 180 ; there is some evidence that this is the case when algorithmic management systems are used to this end 172 , 178 , 181 . Rigid rules about how to carry out work often determine performance ratings (for example, imposing a route to deliver goods or prescribing how equipment and materials must be used) and even future task assignments and firing decisions, with little to no opportunity for employee input 182 , 183 , 184 . Thus, the combination of telling workers what to do to reach performance targets and how to get it done significantly limits their autonomy to make decisions based on their knowledge and skills.

Some algorithmic management platforms do not reveal all aspects of a given task (for example, not revealing the client destination before work is accepted) or penalize workers who decline jobs 185 , thereby severely restricting their choices. This encourages workers to either overwork to the point of exhaustion, find ways to game the system 184 , or misbehave 186 . Moreover, the technical complexity and opacity of algorithmic systems 187 , 188 , 189 deprives workers of the ability to understand and master the system that governs their work, which limits their voice and enpowerment 172 , 185 , 190 . Workers’ typical response to the lack of transparency is to organize themselves on social media to share any insights they have on what the algorithm ‘wants’ as a way to gain back some control over their work 183 , 191 .

Finally, algorithmic management usually provides comparative feedback (comparing one’s results to other workers’) and is linked to incentive pay structures, both of which reduce self-determined motivation as they are experienced as more controlling 26 , 192 . For instance, after algorithms estimated normal time standards for each ‘act’, algorithmic tracking and case allocation systems forced homecare nurses to reduce the ‘social’ time spent with patients because they were assigned more patients per day, thereby limiting nurses’ autonomy to decide how to perform their work 181 . Because these types of quantified metric are often directly linked to performance scores, pay incentives and future allocation of tasks or schedules (that is, getting future work), algorithmic management reduces workers’ freedom in decision-making related to their work, which can significantly reduce their self-determined motivation 123 .

Algorithmic management also tends to individualize work, which affects the need for relatedness. For example, algorithmic management inevitably transforms or reduces (sometimes even eliminates) contact with a supervisor 2 , 182 , 193 , leading to the feeling that the organization does not care about the worker and provides little social support 194 , 195 . ‘App-workers’, who obtain work through gig-work platforms such as Uber, reportedly crave more social interactions and networking opportunities 179 , 185 , 194 and often attempt to compensate for a lack of relatedness by creating support groups that connect virtually and physically 183 , 191 , 195 . Increased competitive climates due to comparative feedback or displaying team members’ individual rankings 175 , 196 can also hamper relatedness. Indeed, when workers have to compete against each other to rank highly (which influences their chances of getting future work and the financial incentives they receive), they are less likely to develop trusting and supportive relationships.

Researchers have formulated contradictory predictions about the potential implications of algorithmic management on competence satisfaction. On the one hand, using quantified metrics, algorithmic management systems can provide more frequent, unambiguous and performance-related feedback, often in the form of ratings and rankings 177 , and simultaneously link this feedback to financial rewards. Informational feedback can enhance intrinsic motivation because it provides information about one’s competence. At the same time, linking rewards to this feedback could decrease intrinsic motivation, because the contingency between work behaviour and pay limits worker discretion and therefore reduces their autonomy 26 . The evidence so far suggests that the mostly comparative feedback provided by algorithmic management is insufficiently informative because the value of the feedback is short-lived — continuously updating algorithms change what is required to perform well 177 , 183 , 185 . This short-lived feedback can undermine feelings of mastery or competence. In addition, algorithmic management is often associated with simplified tasks, and with lower problem-solving opportunities and job variety 123 . However, gamification features on some platforms might increase intrinsic motivation 179 , 183 .

The nascent research on the effects of algorithmic management on workers’ motivation indicates mostly negative effects on self-determined forms of motivation, because the way it is designed decreases the satisfaction of competence, autonomy and relatedness needs. Algorithmic management is being rapidly adopted across an increasing number of industries. Thus, technology developers and those who implement the technology in organizations will need to pay closer attention to how it changes work design to avoid negative effects on work motivation.

Summary and future directions

Self-determination theory can help predict the motivational consequences of future work and these motivational considerations should be taken into account when designing and implementing technology. More self-determined motivation will be needed to deal with the uncertainty and interdependence that will characterize future work. Thus, research examining how need satisfaction and work motivation influence people’s ability to adapt to uncertainty, or even leverage it, is needed. For example, future research could examine how different managerial styles influence adaptivity and proactivity in highly uncertain work environments 197 . Need-satisfying leadership, such as transformational leadership (charismatic or inspirational) 15 , can encourage job crafting and other proactive work behaviours 198 , 199 . Transactional leadership (focused on monitoring, rewarding and sanctioning) might promote self-determined motivation during organizational crises 23 . In addition, research on the quality of interconnectedness (the breadth and depth of interactions and networks) could provide insight on how to manage the increased interconnectedness workers are experiencing.

Technology can greatly assist in recruiting and selecting workers; self-determination theory can inform guidelines on how to design and use such technologies. It is important that the technology is easy to use and perceived as useful to the candidates for best representing themselves 200 , 201 . This can be done by ensuring that candidates have complete instructions before an assessment starts, even possibly getting a ‘practice run’, to improve their feelings of competence. It is also important for candidates to feel some amount of control and less pressure associated with online asynchronous assessments. Giving candidates some choice over testing platforms and the order of questions or settings, explaining how the results will be used, or allowing candidates to ask questions, could improve feelings of autonomy 70 . Finally, it is crucial to enhance perceptions that the organization cares about getting to know candidates and forging connections with them despite using these tools. For example, enhancing these tools with personalized videos of organizational members and providing candidates with feedback following selection decisions might increase feelings of relatedness. These suggestions need to be empirically tested 202 .

More research is also needed on how technology is transforming work design, and consequently influencing worker need satisfaction and motivation. Research in behavioural health has examined how digital applications that encourage healthy behaviours can be designed to fulfill the needs for competence, autonomy and relatedness 203 . Whether and how technology designed for other purposes (such as industrial robots, information communication technology, or automated decision-making systems) can be deliberately designed to meet these core human needs remains an open question. To date, little research has examined how work technologies are created, and what can be done to influence the process to create more human-centred designs. Collaborative research across social science and technical disciplines (such as engineering and computing) is needed.

In terms of implementation, although there is a long history of studies investigating the impact of technology on work design, current digital technologies are increasingly autonomous. This situation presents new challenges: a human-centred approach to automation in which the worker has transparent influence over the technical system has frequently been recommended as the optimal way to achieve high performance and to avoid automation failures 1 , 204 . But it is not clear that this work design strategy will be equally effective in terms of safety, productivity and meeting human needs when workers can no longer understand or control highly autonomous technology.

Given the likely persistence of virtual and remote work into the future, there is a critical need to understand how psychological needs can be satisfied when working remotely. Multi-wave studies that explore the boundary conditions of need satisfaction would advance knowledge around who is most likely to experience need satisfaction, when and why. Such knowledge can be leveraged to inform the design of interventions, such as supervisor training, to improve well-being and performance outcomes for virtual and remote workers. Similarly, no research to date has used self-determination theory to better understand how team virtuality affects how well team members support each other’s psychological needs. Within non-virtual teams, need satisfaction is influenced by the extent to which team members exhibit need-supportive behaviours towards each other 205 . For example, giving autonomy and empowering virtual teams is crucial for good team performance 206 . Studies that track team activities and interaction patterns, including virtual communication records, over time could be used to examine the effects of need support and thwarting between virtual team members 207 , 208 .

Finally, although most studies have shown negative effects of algorithmic management on workers’ motivation and work design characteristics, researchers should not view the effects of algorithmic management as predetermined and unchangeable. Sociotechnical aspects of the system 2 , 209 (such as transparency, privacy, accuracy, invasiveness and human control) and organizational policies surrounding their use could mitigate the motivational effects of algorithmic management. In sum, it is not algorithms that shape workers’ motivation, but how organizations design and use them 3 . Given that applications that use algorithmic management are developed mostly by computer and data scientists, sometimes with input from marketing specialists 185 , organizations would benefit from employing psychologists and human resources specialists to enhance the motivational potential of these applications.

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An Analysis of Micro-scale Conflict in Collaborative Governance

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Nicola Ulibarri, An Analysis of Micro-scale Conflict in Collaborative Governance, Journal of Public Administration Research and Theory , Volume 34, Issue 2, April 2024, Pages 316–330, https://doi.org/10.1093/jopart/muad025

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Conflict is the forgotten sibling of collaborative governance. Variably framed as an alternative to collaboration, a contextual feature shaping interpersonal interactions, or an obstacle to be overcome via deliberation, conflict lurks in the background of discourse about collaboration. However, few theories of collaboration directly address the role of conflict, and those that do focus on conflict as a macro-scale phenomenon, characteristic of a governance forum or participating organizations. Given the importance of short term, person-to-person interactions in shaping the overall trajectory of collaborative dynamics and outcomes, a micro-scale analysis of collaborative conflict is warranted. This article develops a framework for evaluating the role of micro-scale conflict in collaborative governance, drawing on the case of negotiations to relicense hydropower dams in the Central Valley of California, USA. Data sources include 4 years of meeting observations, interviews with participating stakeholders, and written comments submitted during the process. The work first classifies all instances of disagreement observed during the negotiations to develop a typology of micro-scale conflict. It then compares differences in the frequency, type, and management of disagreements in high and low collaboration relicensings to explore the interaction between conflict dynamics and overall collaborative approach. In the high collaboration case, interpersonal disagreements occurred frequently, were more dynamic and mutable over time, and served to elaborate and refine management approaches. By evaluating conflict dynamics that occur at the scale of an individual interaction and the positive and negative roles they play in shaping collaborative outcomes, this research moves conflict from being a static barrier or contextual factor to a dynamic ingredient that can be managed to shape policy outcomes.

Collaborative governance is a prominent mode of decision-making, with government agencies and civil society groups working jointly in a variety of multiagency, multilevel, and multisectoral arrangements to address public problems. A ballooning academic literature details why and how collaborations form ( Hossu et al. 2018 ; Scott and Thomas 2017 ; Sørensen and Torfing 2018 ), collaboration’s impacts on goals like learning, innovation, and policy outcomes ( Scott 2015 ; Siddiki, Kim, and Leach 2017 ; van Gestel and Grotenbreg 2021 ), and structural and process factors that support high-performing collaborations ( Douglas et al. 2020 ; Newig et al. 2018 ; Siddiki et al. 2015 ; Ulibarri and Scott 2016 ).

Lurking in the discourse around collaboration and its benefits is the idea of conflict. In literature about collaborative governance, conflict emerges in several ways. The first frame views conflict as an alternative to collaboration, wherein the same constellation of policy problems might be solved via lawsuits or external fiat versus stakeholder negotiation ( London et al. 2013 ; Pecurul-Botines, Di Gregorio, and Paavola 2014 ). Here, collaboration is often perceived as the more desirable approach, enabling a more equitable distribution of benefits and costs than the winner-take-all solution that accompanies litigation ( Feingold 2021 ; Innes and Booher 2003 ). This frame also notes that the threat of a one-sided solution can motivate individuals to collaborate, such as when landowners opt to co-create a habitat conservation plan with the government rather than having those same agencies regulate them under the Endangered Species Act ( Taylor 2022 ).

The second frame views conflict as a feature of context, wherein certain types of policy problems have greater distance between stakeholder perspectives, making agreement harder ( Sotirov et al. 2017 ; Weible, Heikkila, and Pierce 2018 ; Zachrisson and Beland Lindahl 2013 ). Establishing effective working relationships when stakeholders have an antagonistic history is harder than when they have worked together successfully in the past ( Ansell and Gash 2007 ; McLaughlin, Mewhirter, and Lubell 2021 ; Schoon et al. 2021 ). Likewise, problems that are more sociopolitically contentious, have entrenched stakeholder values, and/or involve trade-offs between multiple desirable resources have a higher inherent likelihood of drawing together people with conflicting interests ( Balint et al. 2011 ; Weible, Heikkila, and Pierce 2018 ).

The third frame views conflict as an inherent feature of collaborative processes. By definition, collaborative governance involves bringing together diverse perspectives, creating inherent divergence over how a problem can or should be addressed ( Curșeu and Schruijer 2017 ). Through conflict resolution approaches, this divergence can be managed and overcome ( Ansell and Gash 2007 ; Bryson, Crosby, and Stone 2006 ; Emerson, Nabatchi, and Balogh 2011 ; Koebele 2019 ). This common framing is reflected in the literature’s focus on consensus-building as a particularly desirable way to bring diverse stakeholders together, establish working norms, and build trust over time ( Ansell and Gash 2007 ; Bryson, Crosby, and Stone 2006 ; Connick and Innes 2003 ; Innes and Booher 1999 ; Susskind and Cruikshank 1987 ).

None of these framings, however, capture the omnipresent and dynamic nature of conflict in collaborative governance when viewed at a micro-scale. Over the course of a single meeting or series of interactions, collaborative participants might disagree on any number of topics. As these miniature conflicts shape relationship dynamics and expectations that participants hold about one another, over time they might affect the overall trajectory of the collaborative process. In addition, and in contrast to the focus on consensus in the collaborative governance literature, governance scholars in other arenas note that conflict and disagreement can play a generative role in decision-making, helping to hold decision-makers accountable and raise new perspectives into policy ( Aylett 2010 ; de Souza 2006 ; Mancilla García, Hileman, and Bodin 2019 ).

This article aims to elevate the role of conflict in collaborative governance by developing an empirically derived framework of micro-scale conflict. I draw on case studies of negotiations to develop operating licenses for two hydropower dams in the Central Valley of California, USA, with data including 4 years of meeting observations, interviews with diverse participating stakeholders, and public comments submitted through the formal relicensing process. Drawing on approaches from the team dynamics literature that defines conflict as disagreement, I identify all instances of disagreement observed during the negotiation process to develop a typology of disagreement. I then compare differences in the frequency, content, and handling of disagreements in high and low collaboration cases to explore the interaction between micro-scale conflict and overall collaborative approach. By evaluating conflict dynamics that occur at the scale of an individual interaction and the positive and negative roles they play in shaping collaborative outcomes, this research shifts conflict from being a static barrier or contextual factor to a dynamic ingredient that can be managed to shape policy outcomes.

Conflict in Governance Processes

Numerous governance-related fields, including public administration, policy sciences, and planning, have developed diverse understandings of what conflict is and how it relates to collaboration. The first set of literatures view collaboration and conflict as two opposing approaches to multi-organizational decision-making (e.g., London et al. 2013 ; Pecurul-Botines, Di Gregorio, and Paavola 2014 ), wherein organizations can work cooperatively or antagonistically. For instance, adopting a game theoretic understanding of conflict (e.g., North 1990 ), the Ecology of Games Framework (EGF) understands conflict as occurring when an individual or organization chooses to maximize their self-interest in a zero-sum manner versus to work collaboratively ( Lubell, Mewhirter, and Berardo 2020 ). EGF scholars typically measure conflict at the level of an individual policy forum, and recognize that there are numerous possible interactions between the conflict and collaboration endpoints ( Lubell, Mewhirter, and Berardo 2020 ; McLaughlin, Mewhirter, and Lubell 2021 ). The EGF tends to view conflict as neutral or perhaps negative, highlighting its influences on transaction costs ( Lubell et al. 2017 ) and policy effectiveness ( Lubell, Mewhirter, and Berardo 2020 ).

Critical planning scholars also view conflict and collaboration as distinct approaches at the policy forum level. However, they argue that across forums, conflict and consensus-building are mutually beneficial and reinforcing ( Aylett 2010 ; de Souza 2006 ). Drawing on Michel Foucault’s notion that power differentials imbue every human interaction (e.g., Foucault 1978 ), this viewpoint values deliberation because it helps raise diverse perspectives, but disagrees that all differences should (or even can) be overcome. Achieving consensus would mean not that everyone agreed, but rather that less powerful voices were shut out of the debate. By engaging in a deliberative process while also proceeding with antagonistic forms of interaction (like protests or lawsuits), organizations can work toward shared governance while also holding dominant institutions accountable for their actions ( de Souza 2006 ). Indeed, in the collaborative governance context, a study of water governance in Oregon found that concurrent litigation benefited the collaborative process because it forced more powerful groups to commit to change ( Satein and Weber 2018 ).

The advocacy coalition framework (ACF) holds that conflict and collaboration can co-occur within the same policy form. The ACF views conflict as a function of belief incompatibility, wherein policy subsystems vary according to the number of coalitions and the degree of shared beliefs ( Weible 2008 ). In high-divergence settings, conflicts can persist for years or even decades. Nevertheless, individuals from opposing coalitions can still work together toward a negotiated agreement—without fundamentally changing their beliefs or overcoming their core conflict ( Weible, Heikkila, and Pierce 2018 ). In one instance, following a 10-year negotiation, two coalitions were found to have become even more entrenched—and further apart—in their beliefs, despite negotiating a joint agreement ( Koebele and Crow 2023 ). Additionally, ACF argues that conflict will increase over time, as actors coalesce into more well-defined coalitions formed around specific beliefs ( Fidelman et al. 2014 ; Gmoser-Daskalakis et al. 2022 ).

Finally, combining insights from many of the above perspectives, the Policy Conflict Framework (PCF) views conflict as a multidimensional construct combining actors’ divergence in policy positions, degree of perceived threats from others, and unwillingness to compromise; the intensity of this “cognitive” conflict influences whether actors use antagonistic tactics to shape the policy decision ( Weible and Heikkila 2017 ). Like the EGF and ACF, the PCF views conflict as a function of individual behaviors and choices. However, by assessing more dimensions of conflict, the PCF enables a more nuanced view of why conflict arises in particular policy arenas ( Heikkila and Weible 2017 ).

Micro-scale Conflict Dynamics

Governance-focused theories of conflict tend to frame conflict as a function of the governance arena, seeing some forums—and longer-term interactions within those forums—as either conflicting or cooperative. However, considering the role of conflict at the scale of day-to-day interpersonal interactions is important given the role these interactions play in shaping collaborative outcomes. In the widely used Collaborative Governance Regimes (CGR) framework, the essence of collaborative governance is three collaborative dynamics , which describe the structure and processes shaping participants’ interaction toward a decision ( Emerson and Nabatchi 2015 ; Emerson, Nabatchi, and Balogh 2011 ). Principled engagement refers to the process used for negotiation, including a focus on face-to-face dialogue, developing a shared understanding and definition of the problem, and achieving small wins to build momentum. Shared motivation focuses on building trust and mutual understanding between participants and ensuring that participants view the process as legitimate. Capacity for joint action focuses on the institutional structure supporting the collaborative process, including the resources, leadership, knowledge, and time needed to make decisions. The three dynamics jointly facilitate dialogue across diverse viewpoints and potentially finding ways to overcome disagreement.

While all three collaborative dynamics shape the overall course of a collaboration, principled engagement is a core driver of success. Principled engagement builds the trust to support shared motivation; together, principled engagement and shared motivation allow participants to co-create the capacity for joint action that enables actual decision-making ( Emerson and Nabatchi 2015 ; Emerson, Nabatchi, and Balogh 2011 ). More specifically, deliberative, interest-based deliberation between diverse stakeholders supports numerous short- and long-term outcomes, from improved problem-solving capacity, to better integration of diverse perspectives and knowledge sources into decision-making, to more implementable and better tailored policies and programs ( Emerson et al. 2009 ; Mandarano 2008 ; Ulibarri 2015a , 2015b ). These same features also shape the long-term durability of CGRs, with principled engagement in early phases of collaboration shaping a CGR’s ability to complete its work and avoid functional decline over time ( Ulibarri et al. 2020 ).

Principled engagement also relates most directly to conflict, as deliberation entails “Hard conversations, constructive self-assertion, asking and answering challenging questions, and expressing honest disagreements” ( Emerson, Nabatchi, and Balogh 2011 , 12). But what is the process by which principled engagement enables people to overcome conflicting viewpoints and reach a shared decision? And what happens when the decision is not based on consensus? To answer these questions, it is imperative to consider conflict and cooperation at the scale of an interaction rather than a forum. Scholars working at this interpersonal scale see conflict as a dynamic construct that can ebb and flow over relatively short periods of time. One of the first management scholars to advocate for conflict, Mary Parker Follett coined the term “constructive conflict” to highlight the benefits that disagreement can provide an organization ( Follett 2011 ). She argues that allowing diverse teams to express their differences and finding ways to integrate across those differences—rather than simply finding a compromise—can help drive innovation ( Follett 2011 ; Gehani and Gehani 2007 ). This process of integration requires finding and articulating the real (rather than assumed) source of conflict and then re-evaluating each side to identify solutions that meet all parties’ interests ( Follett 2011 ).

Studies of team dynamics have developed a multidimensional understanding of micro-scale conflict. The first dimension asks whether a conflict focuses on a task to be solved or is about an interpersonal relationship ( Barki and Hartwick 2004 ; de Wit, Greer, and Jehn 2012 ). In general, conflicts directed toward a person—whether about their views, preferences, or personality—are seen to be detrimental to decision-making. In contrast, conflict (i.e., agreement or disagreement) over how a task should be done—what the task is, how to approach it, whether it is a problem—can be beneficial because it enables more diverse perspectives and creative thinking about how to address the task ( Curșeu and Schruijer 2017 ).

Second, the way a conflict is communicated, specifically the directness and intensity of expression, can shape its impact on decision-making ( Weingart et al. 2015 ). Communicating a disagreement directly—that is, explicitly stating one’s position to the other involved party, rather than signaling an ambiguous lack of agreement or communicating via a third party—avoids problems of misinterpretation and allows for open dialogue about the conflict ( Coleman, Deutsch, and Marcus 2014 ). However, communicating that conflict with high intensity (i.e., yelling) can undermine communication, lead individuals to become entrenched in their positions, and create an emotional spiral that damages the group’s ability to work together ( Brett, Shapiro, and Lytle 1998 ; Weingart et al. 2015 ). Conflict has the most potential to positively shape a team’s decision-making when it is direct but has low intensity of expression ( Somech, Desivilya, and Lidogoster 2009 ; Weingart et al. 2015 ).

Team dynamics has also explored the conditions in which intra-team conflict is beneficial or detrimental to team performance ( Bradley et al. 2015 )—bearing in mind that conflict within a team is distinct from the conflict that may arise in a multi-organization collaborative setting. Conflict is beneficial when teams face more complex or nonroutine tasks, as completing these tasks benefits from debate and critical analysis ( de Wit, Greer, and Jehn 2012 ; O’Neill, Allen, and Hastings 2013 ). Conflict is also more likely to be beneficial when a team is working face-to-face, rather than virtually ( Martínez-Moreno et al. 2009 ), as task conflict is less likely to create interpersonal conflict in person ( Holahan, Mooney, and Paul 2011 ). Finally, the timing of conflict within the problem-solving cycle also shapes its influence on performance. Conflict in early stages, especially when teams are gathering information about a task, is beneficial “because it prevents cognitive biases from interfering with information processing” ( Bradley et al. 2015 ; see also Schulz-Hardt, Jochims, and Frey 2002 ). High-performing teams also tend to have relatively higher “levels of task conflict in the middle of their interactions than at other times” ( Bradley et al. 2015 ; see also Jehn and Mannix 2001 ).

This article uses an inductive, multiple case analysis to develop a framework of micro-scale collaborative conflict. In contrast to positivist approaches where a priori hypotheses are tested and refined, inductive methods start from the data to develop a broader explanation of observed phenomena and as such are particularly useful for theory development ( Ashworth, McDermott, and Currie 2018 ; Nowell and Albrecht 2018 ). Moreover, I draw on a multiple case analysis with maximum variation, developing my understanding of collaborative conflict from both a high and low collaboration case; this multiple case design enables broader generalizability than a single case analysis ( Yin 2017 ). The resulting theoretical framework is exploratory and designed for future refinement.

Case Selection

This article studies collaborative decision-making in the US Federal Energy Regulatory Commission’s (FERC) process for licensing hydropower facilities. Hydroelectricity is a renewable, low greenhouse gas source of electricity, but the construction and operation of hydropower facilities, especially those located at storage dams, can dramatically affect the river’s natural flow regime. This has numerous impacts on other resources that depend on the river, including fish, riparian habitat, recreation, water quality, and flood control.

To balance these competing values, FERC—which regulates non-federally owned hydropower facilities—developed an “Integrated Licensing Process” (ILP), wherein the owner of the hydropower facility (hereafter “the utility”) develops its application for an operating license with substantial input from interested stakeholders. The ILP has four primary phases ( figure 1 ): (1) a scoping process to determine what resources the dam might impact, (2) development of a study plan to assess the potential impact of the hydropower facility on selected resources, (3) implementation of the scientific studies, and (4) development of the license application, including proposed “Protection, Mitigation and Enhancement Measures” (PM&Es) to reduce the project’s impact on key resources. 1 During each of these phases, FERC requires stakeholder consultation in the form of public meetings and written comments. However, some utilities opt for a more collaborative approach, jointly developing the study plan, implementing studies, and writing the PM&Es with stakeholders that represent the diversity of resources impacted by the dam, including federal and state resource agencies, tribes, local governments, and nonprofit organizations ( Ulibarri 2015a , 2015b ).

The FERC Integrated Licensing Process. FERC = Federal Energy Regulatory Commission; NEPA = National Environmental Policy Act.

This article studies two relicensings (i.e., license renewals for existing facilities) in California’s Central Valley. Water management in the Central Valley is particularly contentious ( Hanak et al. 2011 ). The region produces around 25% of the nation’s food supply ( U.S. Geological Survey n.d ), depending on a vast water infrastructure system to irrigate crops. However, this same infrastructure has led to substantial impacts on water quality and culturally and economically important fish populations. Debates about the equity of water rights allocations are widespread, with concerns that the agricultural sector uses too much water relative to residential and industrial users. With this backdrop, any water-related decision is likely to attract substantial conflict.

The relicensings were selected because they used different collaborative approaches. One of the relicensings, which I call Platinum, used a consultation-based approach to stakeholder engagement. They held around 75 public meetings, which is more than FERC requires; these meetings largely entailed one-way engagement, with the utility sharing what it was doing or planned to do and little evidence that stakeholder feedback was incorporated into the utility’s decisions. The second relicensing, which I call Golden, was highly collaborative, holding over 400 stakeholder meetings and trying to make most decisions via a deliberative, consensus-based approach.

In both cases, the overall decision about how to approach consultation versus collaboration (and therefore how many meetings to hold and how to facilitate them) were made by the utilities with substantial input from the consulting firm hired to run the process; both relicensings used the same firm, but with different lead consultants. However, Golden had a collaborative “process team” with representatives of diverse organizations that made decisions about how frequently to meet and what to discuss at each meeting; in Platinum, all such decisions were made by the utility and their consultants.

Apart from the collaborative differences, the two relicensings shared many surface characteristics. Both facilities generate relatively large amounts of electricity (over 150 MW capacity), are owned by public water utilities, and operate to supply water for cities and farms, control floods, and provide recreational opportunities in addition to generating hydropower. As the dams impact a similar set of environmental interests, the relicensings drew a similar mix of participating stakeholders, including federal land use agencies, federal and state wildlife agencies, and nongovernmental organizations (NGOs) focused on both recreation and environmental interests. Both groups of stakeholders had relatively high technical backgrounds, with most participants having training in fields like biology, economics, or hydrology. The exception to having similar stakeholders present was that the Platinum utility almost always had their lawyers present; the lawyers only spoke to introduce themselves at the start of the meetings. The Platinum relicensing was also slightly larger, with a mean of 22 attendees per meeting and a maximum of 40; Golden had a mean of 15 attendees per meeting, with a maximum of 30. I describe each case in more detail in Ulibarri (2018b) .

Data Collection and Analysis

I used an ethnographic approach—combining participant observation, semistructured interviews, and document analysis—to observe each relicensing’s collaborative dynamics. Participant observation is ideal for observing decision-making in situ, as opposed to gathering recollections of a process after it is complete. Between May 2012 and August 2016, I observed 71 relicensing meetings (11 for Platinum and 60 for Golden), totaling over 300 h of observation. In both cases, this timeline covered the second and third phases of the ILP: conducting scientific studies and developing the license application. Both utilities submitted their final license application in 2017, so I miss about 1 year of final negotiations over the proposed license. At the time of writing, neither project has received its new license, as it is common for FERC and other regulatory agencies to take multiple years to complete required environmental reviews for the new license ( Ulibarri 2018a ).

Data analysis was informed by a modified grounded theory approach ( Chun Tie, Birks, and Francis 2019 ; Corbin and Strauss 2008 ), with close reading of meeting fieldnotes and iterative, inductive development of coding categories. With inspiration from the team dynamics perspective that conflict is disagreement ( Barki and Hartwick 2004 ; Curșeu and Schruijer 2017 ; de Wit, Greer, and Jehn 2012 ), I iteratively developed criteria to identify instances of disagreement in my meeting fieldnotes. The final coding criteria required disagreements to involve two or more individuals orally expressing divergent opinions about a topic or proposed action. Because this research focuses on interpersonal dynamics of conflict, my definition of disagreement does not include instances where an individual or group expressed dissatisfaction with a person or the process, where the person disagreed with could not hear (i.e., “The consulting firm isn’t good about coordinating schedules around times when participants are available,” expressed over lunch).

Because disagreement is intimately related to agreement, I also captured all agreements: instances where two or more parties verbally stated a shared understanding or commitment, for instance agreeing on the underlying cause of a problem or desirable next step. When discussing agreements and disagreements in tandem, I call them “(dis)agreements.”

After identifying all (dis)agreements in my fieldnotes, I iteratively developed ways of categorizing these (dis)agreements. The typology (presented below) ultimately groups them by focus, magnitude, directness, and stickiness.

The primary limitation to this analysis is that it presents an incomplete record of decision-making. I did not observe all stakeholder meetings, so the typology is built on a sample from which we can generate information about the types of (dis)agreements that occur rather than a census of all in-meeting (dis)agreements. The second limitation is that I rely on explicit statements of agreement or disagreement. In many cases, agreements might simply be signaled by a lack of disagreement, as no questions or objections likely indicates that people are on the same page. However, given the focus on interpersonal dynamics, these unstated (dis)agreements are not central to my conceptualization of conflict.

As a point of comparison to in-meeting (dis)agreement, I analyzed (dis)agreements raised in comment letters submitted to FERC for each relicensing. FERC maintains an online repository of relicensing documents at https://elibrary.ferc.gov/eLibrary/ . For each relicensing, I downloaded all “Comment/Protest” documents submitted between 2011 and 2018 (a total of 656 letters). These were categorized by date, author name, organization, and issue area(s) (i.e., fish, recreation, water supply). In some cases, letters were submitted by organizations participating in person, but some letters were from purely “external” participants. I then sampled 10% of letters submitted by organizations (versus individuals) for further analysis (29 letters for Golden and 26 letters for Platinum). These letters were coded for instances of agreement or disagreement (with a decision proposed by FERC, the utility, or another organization), as well as the content of the (dis)agreement, deductively using the typology developed from the meeting fieldnotes.

Finally, to understand how disagreements varied between the two relicensings, why disagreements arose, and how they were resolved, I used a combination of pattern matching and process tracing. Besides the meeting fieldnotes, this analysis draws on documents accumulated during the process, including meeting agendas, tracking sheets, and the final license applications. I also conducted interviews with 27 individuals involved in the relicensing processes, representing the hydropower utilities, consulting firms, federal agencies, state agencies, and NGOs. The interviews and document record help me to gather a more complete picture of decisions that occurred outside formal meetings, especially as they pertain to resolving or addressing conflict, and interviewees’ reflections on why the conflict occurred or were resolved.

Figure 2 summarizes the data analysis protocol. Additional details relating to data collection are available online in Supplementary Appendix A , and the interview protocol is available in Supplementary Appendix B . This research protocol was approved by Stanford University’s Institutional Review Board (protocol # IRB-26261).

The Data Analysis Process.

Conflict Dynamics in Collaborative Settings

To illustrate the types of disagreements arising during the relicensings and how they shaped decision-making, I first present a series of vignettes around specific recurring conflicts: the spatial scope of the Platinum relicensing and fish entrainment and tunnel closure from the Golden relicensing.

Project Scope

In the consultation-oriented Platinum relicensing, a recurring topic of disagreement was whether/how to consider the impacts of an upstream dam (owned by a different utility) on resources the stakeholders cared about. Before my meeting observations started, the utility had decided that because they did not control the upstream dam, they would not consider any of its impacts in their analyses. However, relicensing participants regularly argued that this decision should be reconsidered. The typical interaction would start with one of the utility’s consultants presenting an in-progress study, and then a relicensing participant asking whether the upstream dam was included in the analysis. For instance:

NGO1: Why are we not looking at [upstream dam’s] power production? Consultant: It’s not under FERC’s jurisdiction, so we’re not including it. NGO2: We requested [upstream dam’s] inclusion. Even though FERC doesn’t have jurisdiction, it will matter because this license will impact their power production and water storage. We might want to consider including that output. Plus, it is in the interest of several relicensing participants. Consultant: Our [the utility’s] decision has been made. If you can make the case to FERC, we will reconsider our stance.

Here, the NGOs requested that the scope of a hydrology study be expanded to include the upstream facility, with the view that the facility would interact with the proposed license. The consultants held a competing stance that they did not need to study the other dam because it was not in their or FERC’s jurisdiction. The consultant then shut down the conversation by refusing to discuss it further.

While the exact words and context varied, this conversation occurred repeatedly, with both NGOs and federal or state agency representatives asking for consideration of a broader project scope and the utility dismissing the request. There was never any evolution toward a shared understanding and rarely acknowledgement that the same disagreement had occurred previously.

Entrainment

Entrainment refers to fish being carried with the flow of water out of a river and into a reservoir or hydropower facility. At minimum, entrained fish are removed from their natural habitat, but depending on where the fish are carried, they can be injured or killed. In the Golden relicensing, entrainment was a concern because tunnels were used to divert water from nearby streams into a single reservoir. The study conducted to measure how many fish were entrained through the tunnels was a highly collegial, collaborative effort that took place over 2 years. A consulting firm contracted by the utility led the study, working in close collaboration with fish biologists from the California Department of Fish & Wildlife (CDFW) and the US Forest Service (USFS), who “were in the boat on the times when they were tagging fish” to give suggestions on how to conduct the study (Federal agency interview). All meetings about the entrainment study design and implementation involved conversation resulting in agreement, and the agencies were pleased with the study that resulted: “I think they did a great job in trying to work with us and provide the best sample size available.”

However, when the conversation shifted to study interpretation (and therefore whether/how the utility needed to mitigate against entrainment), disagreements became the norm—I recorded no agreements about entrainment after the shift. Essentially, the utility and agencies agreed on how many fish they had observed going through the tunnels, but disagreed whether that represented a high or low amount of entrainment. For instance, the utility said, “We think the entrainment is low. Of days when we were diverting, we only saw tagged fish on around 10% of days.” In contrast, the USFS calculated that about 30% of trout were being entrained, and said,

We consider these numbers to be conservative estimates. We only looked at one species (rainbow trout) and didn’t track all lifestages of the fish (no juveniles) because we didn’t want extra mortality. Additionally, the study was in a dry year and higher flows might lead to higher entrainment.

Because of these differences, the agencies felt that the utility needed to install fish screens at the tunnels, which would keep fish out when they were diverting water. The utility felt that spending several million dollars on a screen was unnecessary because entrainment was an insignificant problem.

Tunnel Closure

A second, more controversial topic in Golden was the idea of closing the diversion tunnels that would carry water from the source streams into the reservoir. The idea was first proposed by a group of resource agencies and NGOs as a way to address a variety of resource needs at once. In years with high precipitation, the reservoir would fill quickly and often spill—sending water over an emergency spillway rather than through the powerhouse. The agency-NGO caucus argued that it did not make sense to divert water that would just spill, suggesting that instead the utility could close the diversion tunnels and keep the water in the natural stream, where it would restore natural flows, carry sediment throughout the system, help plants to germinate, and provide habitat for juvenile fish. The agencies and NGOs were very excited about the proposal. As one NGO put it, “Of all the relicensings I’ve seen, bringing back the full natural flow of the river for only 2% generation loss is a great deal.”

At a follow-up meeting to continue the conversation on tunnel closure, the utility opposed the idea, arguing that “Complete tunnel closure is unprecedented and may not actually achieve the relicensing participants’ goals.” They pushed back on the agency/NGO proposal with a thoroughly researched presentation showing how closing the tunnels would not achieve the resource objectives the agencies and NGOs said it would. In this presentation, the utility and their consultants evaluated impacts resource-by-resource, for example, the impact of closure on specific fish species, on tree recruitment, etc., and concluded that the marginal gain did not warrant the power generation loss closure would cause.

After the presentation, the meeting adjourned for lunch. At lunch, the agencies and NGOs complained that the utility’s approach was reductionist, showing that the sum of the parts did not merit closure. In contrast, the agencies and NGOs felt that the river and ecosystem needed to be viewed holistically, saying that scientists know that natural flow is good for rivers. One of the NGOs realized that the conflict might not actually be about science, but rather about philosophy. He asked the facilitator to convey this question to the utility: “I need to ask if this is a question of science and economics or of theology: of water infrastructure versus having a natural river. A theological discussion is totally different than just cost versus benefit.” (In his interview, the NGO elaborated on this perceived divergence: “I think, in general, there’s this cultural sense on [the utility’s part], that closing the tunnel is just not a good thing to do. Why would you have a diversion if you didn’t use it?” This perspective aligns strongly with an ACF conception of conflict as belief divergence.)

Eventually, after returning from lunch and with the agencies sharing their concerns about the reductionist approach, the meeting reached a breaking point, with voices and tempers starting to rise. Noting the escalating conflict, the facilitator stepped in:

Facilitator: I think you’re at an agree to disagree. The question is if you want to continue discussing, to see if there’s a direction forward. Also, we need to take time, let the proposals percolate, and see if other ideas come. Is it a binary yes/no, or is there a compromise? StateAgency: We still would like to hear alternatives. USFS: We’re interested. We still want tunnel closure but only in absence of other ideas. Utility: I like the idea of talking about spill recession [a related idea the agencies had proposed]—that might get us close.

By pausing the group, the facilitator helped calm the room. The participants’ responses to the facilitator’s question (whether this was an agree to disagree), also show the relicensing’s larger emphasis on collaboration and consensus-based negotiation helping participants move forward despite the disagreement.

Interestingly, throughout these disagreements, participants still emphasized that they respected one another. For instance, at the lunchtime conversation, the agencies emphasized how much they trusted the consultant who gave the presentation, emphasizing that he’s a “real biologist” and recognizing that he was just doing his job.

After several internal conversations following this highly controversial meeting, the utility decided they were willing to actively negotiate the tunnel closure, with the caveat that, “Our belief is that there isn’t evidence that what you think will occur will actually occur, but we recognize that is a difference of opinion.” They presented a counter proposal, which included closing just one tunnel (rather than two) and in a smaller proportion of years than the agency/NGO proposal. At this point, disagreements shifted from being about whether to close the tunnels to how frequently they would close and how long they would remain shut. In response, agencies and NGOs were very grateful, with one USFS representative saying, “[Utility], thank you for coming this far, for continuing to work with us.”

After several months of negotiation, the group decided that they could not resolve either entrainment or tunnel closure internally, agreeing that they wanted to have a joint meeting with their managers in the room: “All of these are above our pay grade, and we want to see if we’re at an agree to disagree.” The managers included the utility’s General Manager, the head of the National Forest unit, the regional director of the US Fish & Wildlife Service (USFWS), and the regional director of CDFW. The managers meeting started with explicit statements by each of the managers that they hoped to be able to reach agreement, rather than go to FERC for a hearing (e.g., “I say we avoid a trial-type hearing if at all possible. It would divert lots of staff and resources”), signaling that the group felt that big disagreements were undesirable.

At the meeting, the relicensing participants (agency, NGO, and utility staff) shared what they had negotiated so far and where they still had to reach agreement. A representative from USFS first shared the joint agency/NGO proposal, and then a consultant shared the utility’s perspective. There was then an opportunity for the managers to ask questions in closed caucuses for each side. In a full-group closing discussion, the managers decided to schedule a follow-up conversation without their staff present.

After several managers-only meetings, the utility’s Board of Directors stated that the full package (tunnel closure and entrainment) was too expensive and they were willing to go to a FERC hearing. However, the agency managers felt they could negotiate an agreement, and with a deadline extension from FERC, eventually brokered a deal: the utility would accept tunnel closure, provided that closing the tunnels in wet years would be sufficient for addressing entrainment (i.e., no fish screens were necessary). This decision was presented in the utility’s Final License Application, with a note that “[the utility], the Forest Service and [an NGO consortium] agreed” on the proposed action. USFS wrote an identical measure into their mandatory 4(e) requirements 2 for the project.

However, USFWS and CDFW did not feel that the proposed closure adequately addressed entrainment. Their proposed 10(j) measures called for closures to occur more often than the utility/USFS agreement, as well as to close in fall to protect steelhead trout. They did not request installation of a fish screen. (As FERC has not issued the final license, it is not clear which set of measures will be required.)

Typology of Collaborative Disagreement

The vignettes demonstrate the diverse types and trajectories of disagreement. Disagreements arose about diverse topics—everything from framing the scope of a study to how to manage the dams—and were managed (or not managed) in a variety of ways. Disagreements also varied in their trajectory, with some increasing over time, some decreasing, and some remaining static. The next section presents a typology of disagreement to aggregate these patterns across all observed interactions.

Content of Disagreement

Disagreements can first be described by the content that was being agreed upon. In order of frequency, relicensing participants disagreed over proposed management approaches for the hydropower dams, the design or implementation of scientific studies, the way study results were interpreted, the process used by the collaborative group, and the framing of the problem they were attempting to solve ( figure 3 ).

Count of Observed Disagreements and Agreements by Topic, With Shading Signaling Whether (Dis)agreement Occurred in Platinum or Golden Relicensing.

Forty-six percent of observed disagreements (i.e., 28 of 60 total disagreements) related to developing the “Protection, Mitigation and Enhancement Measures” (PM&Es) to be proposed for the new license. For example, all of the discussions about whether and how to close the tunnels in Golden relate to PM&Es. Discussions of PM&Es were also prominent among agreements (53% of agreements). Of content discussed in a FERC relicensing, PM&Es most directly affect the way the dam will be operated. It therefore makes sense that they were the focus of substantial attention during the negotiation, and that there were many small-scale (dis)agreements as the large-scale decisions about PM&Es were made.

The design and implementation of scientific studies was slightly less frequent as a topic of disagreement (33%). These included discussions about selecting model parameters, where and when to sample for field studies, and when to stop collecting data. However, I coded twice as many disagreements than agreements relating to study design, suggesting that study design was more contentious than development of the PM&Es.

Disagreements over problem framing (10% of disagreements) reflected instances when participants disagreed about what problem they were trying to solve through the relicensing or held divergent views on the causes of those problems. These disagreements were more common in the less-collaborative Platinum relicensing, when stakeholders’ stated concerns about the utility’s proposed actions would be shut down by saying they were not on the table for discussion:

CDFW: I would like to add the dam to the [fish populations] model, since it blocks upstream spawning habitat. Consultant: That gets us into questions of FERC jurisdiction. We’re not getting into upstream access issues as part of this study.

Seven percent of disagreements were about the interpretation of scientific study results, reflecting instances when participants had previously agreed to the design of the study, but later disagreed on what the study concluded about the state of a resource. The most prominent and repeated disagreement related to interpretation of the fish entrainment study in Golden (described earlier). There were no explicit agreements about study results, likely an instance where concurrence was signaled by lack of disagreement.

Finally, 3% of disagreements were about the decision-making process. The vast majority of process-related discussions were agreements, including discussions about scheduling meetings, setting meeting agendas, and deciding when to return to particular topics.

Magnitude of Disagreement

Disagreements can also be described by their magnitude. I define a small disagreement as one that remained within the internal collaboration, and a large disagreement as a decision that would be reported to higher authorities (most frequently FERC) for resolution.

There were numerous “small” disagreements, reflecting similarities or differences in how people viewed problems or thought the group should proceed. For instance, there were many small disagreements about the exact parameter to use in a model or the location where they would like to see species restoration. Small disagreements helped participants to refine content of negotiation by generating ideas and testing what was possible or acceptable to other parties:

US Forest Service (USFS): I’m curious to hear from [the utility] what you think—is our [gravel placement] proposal moving in a direction that you can accept? Utility: It’s very interesting that flows are too cold. But, as we’re looking at building roads to bring gravel and logs in instead of higher flows, I have a concern about moving heavy stuff in the mountains. … Consultant: If you have examples of gravel/sediment injection for lessons learned from other relicensings, that’d be great NGO: [shares example from other relicensing] Consultant: Forest Service, how would you feel about the road? USFS1: We haven’t completely explored that. We have access, but our land doesn’t border the river. StateAgency: There may be an opportunity to share both roads. Facilitator: A map of ownership would be useful; it’s come up multiple times, whether it’s utility or USFS land. … USFS2: Is [the utility] even considering the gravel placement? Should we continue discussing the concept? Are we on a path together? Utility: I don’t think we’re on a path together. We may converse. But we don’t plan to extend the road to the river.

In this example, the agencies and NGOs proposed an idea to inject gravel into the river to provide fish habitat, which they thought might help the utility justify lower water flows below the dam. However, through this discussion, they learned that the utility objected to building a new road and was not interested in the idea of gravel injection.

“Big” disagreements—instances where the participants felt they would never reach a joint decision, choosing to defer to outside authorities—were relatively more rare. In these cases, the utility and other parties agreed to send multiple proposals and let FERC or regulatory bodies decide which to approve.

Directness of Disagreement

Overall, disagreements were communicated in a professional and civil manner. While my definition of disagreement requires that they were stated in a manner and venue that the individual or organization who was the target of disagreement could hear the content (i.e., that it was interpersonal), some instances of dissatisfaction or disgruntlement were communicated indirectly, most often by way of a venue that would not reach the target of disagreement. For instance, attendees would sometimes email one another with concerns during meetings. Other times, concerns would be raised at lunch, with a subset of the group eating together outside the meeting venue. I never saw evidence of these indirectly stated disagreements spilling over into the formal meetings; indirectly stated disagreements seemed to be a way for participants to express frustration without derailing the collaborative trajectory.

Disagreements were also usually stated with low intensity. Across the many meetings I observed, voices were only raised twice: the controversial meeting following the agency/NGO proposal to close tunnels and one instance where a participant stormed out of the room after being told a plan they had worked on would be consolidated into another document.

Stickiness of Disagreement

The disagreements lastly varied in how permanent they were over time. There were some topics that participants disagreed about from the start and continued to disagree throughout their discussion, and there were some topics where participants always agreed. More commonly, though, disagreements were mutable: instances where the conversation started as a disagreement and evolved into an agreement, or instances where an initial agreement later became a disagreement. In some instances, this evolution occurred over the course of a single meeting, such as this discussion that followed an agency proposal to use river flow forecasts as a trigger for a management action:

Consultant: We propose to look at a downstream gauge rather than forecasting river flows, since that’s a compliance nightmare. Our thought is to look for 2200 at the gage. When it hits that, we open the outlet the next day. USFS: But then you miss the first day of an already short time period. Consultant: We don’t agree to forecasting, so we need to talk about other options. … Consultant: Why don’t you [the agencies] come back with a trigger that’s something we already do? USFS: OK. We need to figure out a way to do it adaptively. NGO1: Maybe a specific flow level with an accompanying rate of change? NGO2: What about including any rain that is forecast? Consultant: You can think about that. Facilitator: You’re in agreement, you just need to figure out how to do it.

Here, the facilitator kept people talking such that they could articulate what their interests were (an easy-to-comply-with trigger for the consultant, a trigger that would be quick to implement for the agencies), allowing them to realize they could reach an agreement with a little more brainstorming.

More frequently, however, the evolution took place over the course of weeks or even months, with participants learning more about their potential options and one another’s interests and eventually either reaching a compromise or realizing they were not going to compromise in the timeframe available. In particular, almost all big agreements relating to PM&E development were iterative, in that the parties would (e.g.) agree to a particular version of a resource management plan, but would raise a concern or come up with a new idea in a future meeting that would require updating the plan, articulating a new agreement, and resubmitting it to FERC.

This evolution is visible in a formal tracking document that Golden used to monitor the development of PM&Es. Each meeting, every PM&E was coded as being “active” (scheduled for the current or near-future meetings), “agreed & done,” “disagree, no further discussion,” or left blank (i.e., inactive). Aggregating these over time ( figure 4 ) shows that the number of PM&Es actively being discussed varied from month to month. More notably, the number of PM&Es in the formal “agreed & done” and “disagree, no further discussion” categories also fluctuated upward and downward, signaling that finished measures were brought back to the negotiation table. In two instances, a PM&E in the “disagree” category—indicating that the group thought they would never reach agreement—was later placed in the “agreed & done” category after further negotiation, with the jointly negotiated agreement appearing in the final license application.

Count of PM&Es by Status (Active, Agreed & Done, Disagree) Over Time in the Golden Relicensing. Right: “final” (dis)agreement codified in the Final License Application.

Conflict and Level of Collaboration

To explore the interaction between conflict dynamics and overall collaborative approach, I next compare how the types of disagreement and their management varied across the two relicensings. Both agreement and disagreement occurred far less frequently in low collaboration Platinum relative to high collaboration Golden, with only 16 observed (dis)agreements in Platinum versus 124 in Golden. While this is partially explained by the relative lack of meetings in Platinum, (dis)agreements were also less frequent on a per-meeting basis (an average of 1.5 versus 2.1 (dis)agreements per meeting). The relicensings also varied in the relative frequency of agreement versus disagreement: agreements were more likely in Golden and disagreements more likely in Platinum, with 58% of (dis)agreements coded as agreement in Golden versus 44% in Platinum.

The two relicensings also varied in the types of disagreement that arose. In Golden, the content of disagreement covered all potential topics, with PM&Es and study design/implementation; both topics, along with the decisions about the process, featured in agreements as well. In less-collaborative Platinum, disagreements focused on study implementation and problem framing, with no disagreements on process, study interpretation, or PM&Es. Golden had both large and small disagreements arise regularly in meetings, while Platinum had no “big” disagreements or agreements—decisions that would be sent to FERC—reached in the meetings. There were also fewer directly stated disagreements in Platinum than in Golden, reflected in the lower counts of disagreements overall and per meeting. However, I observed a similar number of indirect disagreements for each case—suggesting that people still disagreed with one another in the low collaboration case, but were not stating it outright.

The stickiness of disagreements is one of the factors where the two relicensings diverged most prominently. In low collaboration Platinum, disagreements were incredibly immutable: the first stated position was the only stated position. There was no evolution of disagreement toward agreement, nor agreements that became disagreement. This was even the case for disagreements that were raised repeatedly, such as the discussions about whether to include the upstream dam in decision-making discussed earlier. In contrast, in high collaboration Golden, disagreements were regularly discussed and negotiated, sometimes resulting in a later agreement, and some agreements were later rescinded.

Comparing temporal trends between the two relicensings ( figure 5 ), the cases were similar in the first year of observation, when negotiations focused on study implementation, especially when comparing the number of (dis)agreements per meeting (1.5 in Platinum versus 1.2 in Golden). The major divergence started after the studies were completed and negotiation moved into developing PM&Es. In Platinum, (dis)agreements in the license development phase fell to zero; stakeholder meetings ceased after the final Study Report was released, so there were no agreements or disagreements about PM&Es raised in multiparty deliberation. In Golden, (dis)agreements increased substantially (to around 3 per meeting)—showing the amount of deliberation happening about the PM&Es—and then declined as final agreements or disagreements were reached. This latter pattern appears to follow the ebb-and-flow shown by high-performing teams, wherein task conflict is highest at a midpoint and later is resolved ( Bradley et al. 2015 ; Jehn and Mannix 2001 ), signaling (dis)agreement’s role in generating new solutions.

Count of Agreements and Disagreements by Month, Comparing Low Collaboration Platinum (Top) to High Collaboration Golden (Bottom).

Finally, the two relicensings managed disagreement very differently. In Golden, conflict was a frequent topic of discussion. The facilitator, consultants, and participants in the Golden relicensing had a number of conversations about agreement/consensus as a goal. For example, meeting agendas would include topic headings like “Attempt to Reach Agreement on Erosion Plan” (much more explicit than “Discuss Erosion Plan” would be); the facilitator might kick off a meeting with the reminder that, “Today’s expectation is to land on something everyone can live with.” The group also arrived at a shared definition of what a “big” agreement meant in the context of PM&Es: that the utility would include the PM&E as worded in their license application, and the resource agencies would include the PM&E as worded in their proposed requirements (whether a permit or public comments, depending on their authority). They also had an explicit understanding that disagreements could happen, with the formal category of “agree to disagree” signaling that the group would not reach consensus and each party would propose their own preferred solution to FERC.

In Platinum, managing conflict was never discussed explicitly. The facilitator—the lead consultant hired by the utility—managed the meetings such that there were presentations from the utility, then opportunities for questions or comments from other stakeholders. When divergent views were raised, the facilitator did not probe for further understanding or dialogue, instead moving the conversation on to the next comment or presentation.

Table 1 summarizes key differences in the types of (dis)agreement that arose and how it was handled in the two case studies.

Comparing (Dis)agreements by Level of Collaboration

(Dis)agreements in Comment Letters

In the FERC relicensing process, in-meeting negotiations are complemented by formal comment letters reflecting participants’ interests and concerns in the relicensing. As an alternative venue in which (dis)agreements might be voiced, understanding who submits letters and what types of (dis)agreements they raise contributes a more holistic picture of conflict in collaborative processes.

Comparing the volume of comment letters submitted for each relicensing, low collaboration Platinum had almost twice as many as high collaboration Golden (427 versus 229). However, the difference is primarily attributable to individuals without an organizational affiliation, with Platinum receiving 254 letters submitted by individuals, compared with only 71 in Golden. Absent these individuals, the relicensings are similar, with 173 organization-led letters submitted for Platinum and 158 for Golden. In both relicensings, most organization-led comment letters came from federal agencies, with NGOs being second most prominent in Platinum and state agencies being second in Golden. One-third of letters were submitted by individuals or organizations who also attended relicensing meetings (30% in Platinum and 40% in Golden).

In both relicensings, the comment letters contained a relatively low ratio of agreements to disagreements, with 16% of (dis)agreements signaling agreement with a proposed decision (whereas the meetings were 56% agreement). In general, people write letters when they are upset with a decision, rather than when they support it. Unlike in-meeting (dis)agreements, both relicensings have relatively similar ratios of agreement to disagreement in the comment letters, with 19% of (dis)agreements in Platinum and 14% in Golden being agreements. These ratios varied, however, by organization type, with sampled local government comments being 100% agreement, whereas businesses, federal governments, state governments, and water agencies (including the hydropower utilities) all had agreement rates below 18%.

The relicensings diverged somewhat in the focus of (dis)agreements raised in the comment letters. Both relicensings were equally likely to receive comments on management approaches, and 25%–30% of these were agreements with a proposed approach; about 10% of comments in both relicensings focused on problem framing, and these were entirely disagreements. However, letters submitted for Platinum were more likely to raise concerns about the process (15% versus 1%); Platinum process letters were entirely disagreements and Golden process letters were entirely agreements. Letters submitted for Golden were more likely to focus on study design (50% versus 37%), but with similar ratios of agreement (8%–13%). Temporal trends in the comment letters were similar between the two relicensing, with the exception of Golden receiving more letters in later years (2016–18), with those letters focusing mostly on management approaches.

This article develops a framework for understanding the role of micro-scale conflict in collaborative processes and assesses how large and small disagreements interact with collaborative dynamics. Disagreements occurred in every meeting observed, signaling their ubiquity, but they were not uniformly distributed. Disagreements overall were more prevalent when participants were discussing how they would actually manage the dams versus studying the impact of the dams. And disagreements also occurred more often in the high collaboration case, with distinct patterns in the types of disagreements that arose ( table 1 ). Because Platinum and Golden were highly similar relicensings—addressing a similar set of issues (from fish passage to water quality to recreational access) and with a similar organizational and technical background of participants—their divergence in frequency, type, and management of disagreement is most likely the result of their collaborative approach.

The observed differences in disagreement between Platinum and Golden provide empirical support to the CGR framework’s assertion that disagreement is a valuable component of principled engagement ( Emerson, Nabatchi, and Balogh 2011 ). While the literature sometimes equates consensus with collaboration, in reality building that consensus requires a lot of disagreement as participants learn about and debate over one another’s interests. In Golden, the dialogue and deliberation required for principled engagement meant that disagreements were raised frequently, were dynamic and mutable; the facilitator and lead consultant spent time developing a shared understanding of how disagreements would be addressed. In contrast, in consultation-oriented Platinum, there was less space created for disagreements to be raised, with both fewer meetings overall and facilitation that emphasized one-way communications. When disagreements were raised, they were cataloged and dismissed, rather than building into larger deliberation. As a result, fewer meaningful decisions were jointly determined in the meetings.

Interestingly, when assessing disagreements in the comment letters submitted to FERC, the two relicensings look relatively similar. Similar numbers of letters were submitted for each relicensing, following a similar timeline and from a similar distribution of stakeholders (with the exception of Platinum receiving more letters from individuals). And letters tended to be far more negative than in-person discussions, with only 16% signaling agreement with another organization’s position. This suggests that comment letters alone may not be an ideal data source for studying collaborations, as they did not capture the full range of topics or disagreements covered in the in-person meetings. At the same time, for less-collaborative Platinum, the absence of directly stated disagreements in-meeting means that many stakeholders only communicated their preferences through the comment letters.

Handling of disagreement in the more collaborative case also highlights an intersection with authoritative power ( Purdy 2012 ). In the resolution of the disagreements over entrainment and tunnel closure—which reflected fairly dramatic differences of opinion and possibly even fundamental beliefs—it is interesting to note that the utility ultimately bargained with the USFS, which has mandatory conditioning authority to require specific activities on their land. The USFS representatives were appeased with just tunnel closure, whereas the fish and wildlife agencies (USFWS and CDFW) wanted more protections against fish entrainment—but their authority under the Federal Power Act ended with consultation rather than conditioning. In other words, the utility was willing to negotiate with USFS because they wanted to avoid a decision that would potentially be more stringent if USFS were to work directly with FERC, but did not consider the wildlife agencies enough of a threat to work with them. Outside the discussions on entrainment and tunnel closure, disagreements stated by NGOs—which lack any formal authority—tended to shape the overall conversation regarding the decision at hand, but did not necessarily translate into large agreements to be sent to FERC unless they were made in conjunction with a federal and/or state agency.

Micro-level conflict is a prominent factor shaping collaboration and its outcomes. The literature on collaborative governance—and governance more broadly—tends to focus on conflict as a feature of the policy forum, and generally sets conflict as an alternative to collaboration. Zooming in to look at micro-scale disagreements suggests that conflict is very dynamic and is a key feature of the deliberation inherent in a high-quality collaboration. Indeed, this article argues that the frequency and type of disagreement can indicate whether a CGR is truly collaborative or is using a more consultation-based approach.

As a foundational effort to develop a theory of micro-scale conflict, this work will benefit from future research to refine and extend the typology. First, while I employed a comparative case study approach, this work focused on a single context—hydropower relicensing in California. Applying the typology to CGRs in other geographic contexts, policy sectors, and operating at larger (regional, national, or international) scales can elaborate our understanding of the role micro-scale conflict plays in collaboration. In particular, this was a highly professional, science-based negotiation with disagreement rooted in technical discussions; a community-based collaboration might see more disagreement targeted at people or values, rather than science. Second, I studied the relicensings as isolated fora, but in reality they exist in a broader ecology of games ( Lubell 2013 ) wherein the same sets of actors interact around water and energy management questions in the Central Valley. Just as macro-level conflict can spillover between individual fora ( McLaughlin, Mewhirter, and Lubell 2021 ), disagreements in one forum might shape dynamics in another forum. I did not observe such interactions in these cases, but I was not explicitly looking for inter-forum effects. Finally, there are numerous methods not employed here that could fruitfully explore different dimensions of collaborative disagreement. For instance, applying network analysis to map who is involved in disagreement or using text mining to aggregate disagreements from meeting minutes would enable a more quantitative assessment of disagreement and its impacts.

Supplementary data is available at the Journal of Public Administration Research and Theory online.

The author is grateful to the participants in the Golden and Platinum relicensings for welcoming me into the world of hydropower; Amanda Cravens, Rebecca Nelson, and A.R. Siders for workshopping drafts of this manuscript; Karina Jhaj for assistance assembling the dataset of public comment letters; and Ryan McCarty for making the figures shine.

Research travel was supported by grants from the Stanford Center for International Conflict and Negotiation, the School of Earth, Energy, & Environmental Sciences, and the Emmett Interdisciplinary Program in Environment and Resources at Stanford University. Interview transcription was funded by the School of Social Ecology at UC Irvine.

Apart from paraphrased quotes provided in the text, the data presented in this article are not publicly available, because the meeting fieldnotes and interview transcripts cannot be sufficiently anonymized to ensure the privacy of research participants. The online appendices provide additional details relating to the research design, including the positionality of the researcher, data collection approaches, and the interview guide.

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Yin , R. K. 2017 . Case study research and applications: Design and methods . Los Angeles, CA : SAGE Publications .

Zachrisson , A. , and K. Beland Lindahl . 2013 . Conflict resolution through collaboration: Preconditions and limitations in forest and nature conservation controversies . Forest Policy and Economics 33 : 39 – 46 .

After the utility submits the final license application, FERC conducts an environmental review under the National Environmental Policy Act, and other agencies issue permits as applicable ( Ulibarri 2018a ). At this point, the decision is out of the hands of the utility and other organizations participating in the collaboration.

The Federal Power Act gives differing authority to federal and state agencies regarding what they can require of hydropower projects. Under §4(e), federal landowners (in this case USFS) can require specific management actions, while §10(j) requires FERC to consider recommendations from fish and wildlife agencies. In other words, the USFS can mandate actions, while the USFWS and CDFW can only propose them.

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Chapter 11 Case Research

Case research, also called case study, is a method of intensively studying a phenomenon over time within its natural setting in one or a few sites. Multiple methods of data collection, such as interviews, observations, prerecorded documents, and secondary data, may be employed and inferences about the phenomenon of interest tend to be rich, detailed, and contextualized. Case research can be employed in a positivist manner for the purpose of theory testing or in an interpretive manner for theory building. This method is more popular in business research than in other social science disciplines.

Case research has several unique strengths over competing research methods such as experiments and survey research. First, case research can be used for either theory building or theory testing, while positivist methods can be used for theory testing only. In interpretive case research, the constructs of interest need not be known in advance, but may emerge from the data as the research progresses. Second, the research questions can be modified during the research process if the original questions are found to be less relevant or salient. This is not possible in any positivist method after the data is collected. Third, case research can help derive richer, more contextualized, and more authentic interpretation of the phenomenon of interest than most other research methods by virtue of its ability to capture a rich array of contextual data. Fourth, the phenomenon of interest can be studied from the perspectives of multiple participants and using multiple levels of analysis (e.g., individual and organizational).

At the same time, case research also has some inherent weaknesses. Because it involves no experimental control, internal validity of inferences remain weak. Of course, this is a common problem for all research methods except experiments. However, as described later, the problem of controls may be addressed in case research using “natural controls”. Second, the quality of inferences derived from case research depends heavily on the integrative powers of the researcher. An experienced researcher may see concepts and patterns in case data that a novice researcher may miss. Hence, the findings are sometimes criticized as being subjective. Finally, because the inferences are heavily contextualized, it may be difficult to generalize inferences from case research to other contexts or other organizations.

It is important to recognize that case research is different from case descriptions such as Harvard case studies discussed in business classes. While case descriptions typically describe an organizational problem in rich detail with the goal of stimulating classroom discussion and critical thinking among students, or analyzing how well an organization handled a specific problem, case research is a formal research technique that involves a scientific method to derive explanations of organizational phenomena.

Case research is a difficult research method that requires advanced research skills on the part of the researcher, and is therefore, often prone to error. Benbasat et al. (1987) [8] describe five problems frequently encountered in case research studies. First, many case research studies start without specific research questions, and therefore end up without having any specific answers or insightful inferences. Second, case sites are often chosen based on access and convenience, rather than based on the fit with the research questions, and are therefore cannot adequately address the research questions of interest. Third, researchers often do not validate or triangulate data collected using multiple means, which may lead to biased interpretation based on responses from biased interviewees. Fourth, many studies provide very little details on how data was collected (e.g., what interview questions were used, which documents were examined, what are the organizational positions of each interviewee, etc.) or analyzed, which may raise doubts about the reliability of the inferences. Finally, despite its strength as a longitudinal research method, many case research studies do not follow through a phenomenon in a longitudinal manner, and hence present only a cross-sectional and limited view of organizational processes and phenomena that are temporal in nature.

Key Decisions in Case Research

Several key decisions must be made by a researcher when considering a case research method. First, is this the right method for the research questions being studied? The case research method is particularly appropriate for exploratory studies for discovering relevant constructs in areas where theory building at the formative stages, for studies where the experiences of participants and context of actions are critical, and for studies aimed at understanding complex, temporal processes (why and how of a phenomenon) rather than factors or causes (what). This method is well-suited for studying complex organizational processes that involve multiple participants and interacting sequences of events, such as organizational change and large-scale technology implementation projects.

Second, what is the appropriate unit of analysis for a case research study? Since case research can simultaneously examine multiple units of analyses, the researcher must decide whether she wishes to study a phenomenon at the individual, group, and organizational level or at multiple levels. For instance, a study of group decision making or group work may combine individual-level constructs such as individual participation in group activities with group-level constructs, such as group cohesion and group leadership, to derive richer understanding than that can be achieved from a single level of analysis.

Third, should the researcher employ a single-case or multiple-case design? The single case design is more appropriate at the outset of theory generation, if the situation is unique or extreme, if it is revelatory (i.e., the situation was previously inaccessible for scientific investigation), or if it represents a critical or contrary case for testing a well-formulated theory. The multiple case design is more appropriate for theory testing, for establishing generalizability of inferences, and for developing richer and more nuanced interpretations of a phenomenon. Yin (1984) [9] recommends the use of multiple case sites with replication logic, viewing each case site as similar to one experimental study, and following rules of scientific rigor similar to that used in positivist research.

Fourth, what sites should be chosen for case research? Given the contextualized nature of inferences derived from case research, site selection is a particularly critical issue because selecting the wrong site may lead to the wrong inferences. If the goal of the research is to test theories or examine generalizability of inferences, then dissimilar case sites should be selected to increase variance in observations. For instance, if the goal of the research is to understand the process of technology implementation in firms, a mix of large, mid-sized, and small firms should be selected to examine whether the technology implementation process differs with firm size. Site selection should not be opportunistic or based on convenience, but rather based on the fit with research questions through a process called “theoretical sampling.”

Fifth, what techniques of data collection should be used in case research? Although interview (either open-ended/unstructured or focused/structured) is by far the most popular data collection technique for case research, interview data can be supplemented or corroborated with other techniques such as direct observation (e.g., attending executive meetings, briefings, and planning sessions), documentation (e.g., internal reports, presentations, and memoranda, as well as external accounts such as newspaper reports), archival records (e.g., organization charts, financial records, etc.), and physical artifacts (e.g., devices, outputs, tools). Furthermore, the researcher should triangulate or validate observed data by comparing responses between interviewees.

Conducting Case Research

Most case research studies tend to be interpretive in nature. Interpretive case research is an inductive technique where evidence collected from one or more case sites is systematically analyzed and synthesized to allow concepts and patterns to emerge for the purpose of building new theories or expanding existing ones. Eisenhardt (1989) [10] propose a “roadmap” for building theories from case research, a slightly modified version of which is described below. For positivist case research, some of the following stages may need to be rearranged or modified; however sampling, data collection, and data analytic techniques should generally remain the same.

Define research questions. Like any other scientific research, case research must also start with defining research questions that are theoretically and practically interesting, and identifying some intuitive expectations about possible answers to those research questions or preliminary constructs to guide initial case design. In positivist case research, the preliminary constructs are based on theory, while no such theory or hypotheses should be considered ex ante in interpretive research. These research questions and constructs may be changed in interpretive case research later on, if needed, but not in positivist case research.

Select case sites. The researcher should use a process of “theoretical sampling” (not random sampling) to identify case sites. In this approach, case sites are chosen based on theoretical, rather than statistical, considerations, for instance, to replicate previous cases, to extend preliminary theories, or to fill theoretical categories or polar types. Care should be taken to ensure that the selected sites fit the nature of research questions, minimize extraneous variance or noise due to firm size, industry effects, and so forth, and maximize variance in the dependent variables of interest. For instance, if the goal of the research is to examine how some firms innovate better than others, the researcher should select firms of similar size within the same industry to reduce industry or size effects, and select some more innovative and some less innovative firms to increase variation in firm innovation. Instead of cold-calling or writing to a potential site, it is better to contact someone at executive level inside each firm who has the authority to approve the project or someone who can identify a person of authority. During initial conversations, the researcher should describe the nature and purpose of the project, any potential benefits to the case site, how the collected data will be used, the people involved in data collection (other researchers, research assistants, etc.), desired interviewees, and the amount of time, effort, and expense required of the sponsoring organization. The researcher must also assure confidentiality, privacy, and anonymity of both the firm and the individual respondents.

Create instruments and protocols. Since the primary mode of data collection in case research is interviews, an interview protocol should be designed to guide the interview process. This is essentially a list of questions to be asked. Questions may be open-ended (unstructured) or closed-ended (structured) or a combination of both. The interview protocol must be strictly followed, and the interviewer must not change the order of questions or skip any question during the interview process, although some deviations are allowed to probe further into respondent’s comments that are ambiguous or interesting. The interviewer must maintain a neutral tone, not lead respondents in any specific direction, say by agreeing or disagreeing with any response. More detailed interviewing techniques are discussed in the chapter on surveys. In addition, additional sources of data, such as internal documents and memorandums, annual reports, financial statements, newspaper articles, and direct observations should be sought to supplement and validate interview data.

Select respondents. Select interview respondents at different organizational levels, departments, and positions to obtain divergent perspectives on the phenomenon of interest. A random sampling of interviewees is most preferable; however a snowball sample is acceptable, as long as a diversity of perspectives is represented in the sample. Interviewees must be selected based on their personal involvement with the phenomenon under investigation and their ability and willingness to answer the researcher’s questions accurately and adequately, and not based on convenience or access.

Start data collection . It is usually a good idea to electronically record interviews for future reference. However, such recording must only be done with the interviewee’s consent. Even when interviews are being recorded, the interviewer should take notes to capture important comments or critical observations, behavioral responses (e.g., respondent’s body language), and the researcher’s personal impressions about the respondent and his/her comments. After each interview is completed, the entire interview should be transcribed verbatim into a text document for analysis.

Conduct within-case data analysis. Data analysis may follow or overlap with data collection. Overlapping data collection and analysis has the advantage of adjusting the data collection process based on themes emerging from data analysis, or to further probe into these themes. Data analysis is done in two stages. In the first stage (within-case analysis), the researcher should examine emergent concepts separately at each case site and patterns between these concepts to generate an initial theory of the problem of interest. The researcher can interview data subjectively to “make sense” of the research problem in conjunction with using her personal observations or experience at the case site. Alternatively, a coding strategy such as Glasser and Strauss’ (1967) grounded theory approach, using techniques such as open coding, axial coding, and selective coding, may be used to derive a chain of evidence and inferences. These techniques are discussed in detail in a later chapter. Homegrown techniques, such as graphical representation of data (e.g., network diagram) or sequence analysis (for longitudinal data) may also be used. Note that there is no predefined way of analyzing the various types of case data, and the data analytic techniques can be modified to fit the nature of the research project.

Conduct cross-case analysis. Multi-site case research requires cross-case analysis as the second stage of data analysis. In such analysis, the researcher should look for similar concepts and patterns between different case sites, ignoring contextual differences that may lead to idiosyncratic conclusions. Such patterns may be used for validating the initial theory, or for refining it (by adding or dropping concepts and relationships) to develop a more inclusive and generalizable theory. This analysis may take several forms. For instance, the researcher may select categories (e.g., firm size, industry, etc.) and look for within-group similarities and between-group differences (e.g., high versus low performers, innovators versus laggards). Alternatively, she can compare firms in a pair-wise manner listing similarities and differences across pairs of firms.

Build and test hypotheses. Based on emergent concepts and themes that are generalizable across case sites, tentative hypotheses are constructed. These hypotheses should be compared iteratively with observed evidence to see if they fit the observed data, and if not, the constructs or relationships should be refined. Also the researcher should compare the emergent constructs and hypotheses with those reported in the prior literature to make a case for their internal validity and generalizability. Conflicting findings must not be rejected, but rather reconciled using creative thinking to generate greater insight into the emergent theory. When further iterations between theory and data yield no new insights or changes in the existing theory, “theoretical saturation” is reached and the theory building process is complete.

Interpretive Case Research Exemplar

Perhaps the best way to learn about interpretive case research is to examine an illustrative example. One such example is Eisenhardt’s (1989) [11] study of how executives make decisions in high-velocity environments (HVE). Readers are advised to read the original paper published in Academy of Management Journal before reading the synopsis in this chapter. In this study, Eisenhardt examined how executive teams in some HVE firms make fast decisions, while those in other firms cannot, and whether faster decisions improve or worsen firm performance in such environments. HVE was defined as one where demand, competition, and technology changes so rapidly and discontinuously that the information available is often inaccurate, unavailable or obsolete. The implicit assumptions were that (1) it is hard to make fast decisions with inadequate information in HVE, and (2) fast decisions may not be efficient and may result in poor firm performance.

Reviewing the prior literature on executive decision -making, Eisenhardt found several patterns, although none of these patterns were specific to high-velocity environments. The literature suggested that in the interest of expediency, firms that make faster decisions obtain input from fewer sources, consider fewer alternatives, make limited analysis, restrict user participation in decision-making, centralize decision-making authority, and has limited internal conflicts. However, Eisenhardt contended that these views may not necessarily explain how decision makers make decisions in high-velocity environments, where decisions must be made quickly and with incomplete information, while maintaining high decision quality.

To examine this phenomenon, Eisenhardt conducted an inductive study of eight firms in the personal computing industry. The personal computing industry was undergoing dramatic changes in technology with the introduction of the UNIX operating system, RISC architecture, and 64KB random access memory in the 1980’s, increased competition with the entry of IBM into the personal computing business, and growing customer demand with double-digit demand growth, and therefore fit the profile of the high-velocity environment. This was a multiple case design with replication logic, where each case was expected to confirm or disconfirm inferences from other cases. Case sites were selected based on their access and proximity to the researcher; however, all of these firms operated in the high-velocity personal computing industry in California’s Silicon Valley area. The collocation of firms in the same industry and the same area ruled out any “noise” or variance in dependent variables (decision speed or performance) attributable to industry or geographic differences.

Initial interviews with Chief Executive Officers: CEOs were asked questions about their firm’s competitive strategy, distinctive competencies, major competitors, performance, and recent/ongoing major strategic decisions. Based on these interviews, several strategic decisions were selected in each firm for further investigation. Four criteria were used to select decisions: (1) the decisions involved the firm’s strategic positioning,

(2) the decisions had high stakes, (3) the decisions involved multiple functions, and (4) the decisions were representative of strategic decision-making process in that firm.

Interviews with divisional heads: Each divisional head was asked sixteen open-ended questions, ranging from their firm’s competitive strategy, functional strategy, top management team members, frequency and nature of interaction with team, typical decision making processes, how each of the previously identified decision was made, and how long it took them to make those decisions. Interviews lasted between 1.5 and 2 hours, and sometimes extended to 4 hours. To focus on facts and actual events rather than respondents’ perceptions or interpretations, a “courtroom” style questioning was employed, such as when did this happen, what did you do, etc. Interviews were conducted by two people, and the data was validated by cross-checking facts and impressions made by the interviewer and note-taker. All interview data was recorded, however notes were also taken during each interview, which ended with the interviewer’s overall impressions. Using a “24-hour rule”, detailed field notes were completed within 24 hours of the interview, so that some data or impressions were not lost to recall.
Questionnaires: Executive team members at each firm were completed a survey questionnaire that captured quantitative data on the extent of conflict and power distribution in their firm.
Secondary data: Industry reports and internal documents such as demographics of the executive teams (responsible for strategic decisions), financial performance of firms, and so forth, were examined.
Personal observation: Lastly, the researcher attended a 1-day strategy session and a weekly executive meeting at two firms in her sample.

Data analysis involved a combination of quantitative and qualitative techniques. Quantitative data on conflict and power were analyzed for patterns across firms/decisions. Qualitative interview data was combined into decision climate profiles, using profile traits (e.g., impatience) mentioned by more than one executive. For within-case analysis, decision stories were created for each strategic decision by combining executive accounts of the key decision events into a timeline. For cross-case analysis, pairs of firms were compared for similarities and differences, categorized along variables of interest such as decision speed and firm performance. Based on these analyses, tentative constructs and propositions were derived inductively from each decision story within firm categories. Each decision case was revisited to confirm the proposed relationships. The inferred propositions were compared with findings from the existing literature to reconcile examine differences with the extant literature and to generate new insights from the case findings. Finally, the validated propositions were synthesized into an inductive theory of strategic decision-making by firms in high-velocity environments.

Inferences derived from this multiple case research contradicted several decision-making patterns expected from the existing literature. First, fast decision makers in high-velocity environments used more information, and not less information as suggested by the previous literature. However, these decision makers used more real-time information (an insight not available from prior research), which helped them identify and respond to problems, opportunities, and changing circumstances faster. Second, fast decision makers examined more (not fewer) alternatives. However, they considered these multiple alternatives in a simultaneous manner, while slower decision makers examined fewer alternatives in a sequential manner. Third, fast decision makers did not centralize decision making or restrict inputs from others, as the literature suggested. Rather, these firms used a two-tiered decision process in which experienced counselors were asked for inputs in the first stage, following by a rapid comparison and decision selection in the second stage. Fourth, fast decision makers did not have less conflict, as expected from the literature, but employed better conflict resolution techniques to reduce conflict and improve decision-making speed. Finally, fast decision makers exhibited superior firm performance by virtue of their built-in cognitive, emotional, and political processes that led to rapid closure of major decisions.

Positivist Case Research Exemplar

Case research can also be used in a positivist manner to test theories or hypotheses. Such studies are rare, but Markus (1983) [12] provides an exemplary illustration in her study of technology implementation at the Golden Triangle Company (a pseudonym). The goal of this study was to understand why a newly implemented financial information system (FIS), intended to improve the productivity and performance of accountants at GTC was supported by accountants at GTC’s corporate headquarters but resisted by divisional accountants at GTC branches. Given the uniqueness of the phenomenon of interest, this was a single-case research study.

To explore the reasons behind user resistance of FIS, Markus posited three alternative explanations: (1) system-determined theory: resistance was caused by factors related to an inadequate system, such as its technical deficiencies, poor ergonomic design, or lack of user friendliness, (2) people-determined theory: resistance was caused by factors internal to users, such as the accountants’ cognitive styles or personality traits that were incompatible with using the system, and (3) interaction theory: resistance was not caused not by factors intrinsic to the system or the people, but by the interaction between the two set of factors. Specifically, interaction theory suggested that the FIS engendered a redistribution of intra-organizational power, and accountants who lost organizational status, relevance, or power as a result of FIS implementation resisted the system while those gaining power favored it.

In order to test the three theories, Markus predicted alternative outcomes expected from each theoretical explanation and analyzed the extent to which those predictions matched with her observations at GTC. For instance, the system-determined theory suggested that since user resistance was caused by an inadequate system, fixing the technical problems of the system would eliminate resistance. The computer running the FIS system was subsequently upgraded with a more powerful operating system, online processing (from initial batch processing, which delayed immediate processing of accounting information), and a simplified software for new account creation by managers. One year after these changes were made, the resistant users were still resisting the system and felt that it should be replaced. Hence, the system-determined theory was rejected.

The people-determined theory predicted that replacing individual resistors or co-opting them with less resistant users would reduce their resistance toward the FIS. Subsequently, GTC started a job rotation and mobility policy, moving accountants in and out of the resistant divisions, but resistance not only persisted, but in some cases increased! In one specific instance, one accountant, who was one of the system’s designers and advocates when he worked for corporate accounting, started resisting the system after he was moved to the divisional controller’s office. Failure to realize the predictions of the people-determined theory led to the rejection of this theory.

Finally, the interaction theory predicted that neither changing the system or the people (i.e., user education or job rotation policies) will reduce resistance as long as the power imbalance and redistribution from the pre-implementation phase were not addressed. Before FIS implementation, divisional accountants at GTC felt that they owned all accounting data related to their divisional operations. They maintained this data in thick, manual ledger books, controlled others’ access to the data, and could reconcile unusual accounting events before releasing those reports. Corporate accountants relied heavily on divisional accountants for access to the divisional data for corporate reporting and consolidation. Because the FIS system automatically collected all data at source and consolidated them into a single corporate database, it obviated the need for divisional accountants, loosened their control and autonomy over their division’s accounting data, and making their job somewhat irrelevant. Corporate accountants could now query the database and access divisional data directly without going through the divisional accountants, analyze and compare the performance of individual divisions, and report unusual patterns and activities to the executive committee, resulting in further erosion of the divisions’ power. Though Markus did not empirically test this theory, her observations about the redistribution of organizational power, coupled with the rejection of the two alternative theories, led to the justification of interaction theory.

Comparisons with Traditional Research

Positivist case research, aimed at hypotheses testing, is often criticized by natural science researchers as lacking in controlled observations, controlled deductions, replicability, and generalizability of findings – the traditional principles of positivist research. However, these criticisms can be overcome through appropriate case research designs. For instance, the problem of controlled observations refers to the difficulty of obtaining experimental or statistical control in case research. However, case researchers can compensate for such lack of controls by employing “natural controls.” This natural control in Markus’ (1983) study was the corporate accountant who was one of the system advocates initially, but started resisting it once he moved to controlling division. In this instance, the change in his behavior may be attributed to his new divisional position. However, such natural controls cannot be anticipated in advance, and case researchers may overlook then unless they are proactively looking for such controls. Incidentally, natural controls are also used in natural science disciplines such as astronomy, geology, and human biology, such as wait for comets to pass close enough to the earth in order to make inferences about comets and their composition.

The problem of controlled deduction refers to the lack of adequate quantitative evidence to support inferences, given the mostly qualitative nature of case research data. Despite the lack of quantitative data for hypotheses testing (e.g., t-tests), controlled deductions can still be obtained in case research by generating behavioral predictions based on theoretical considerations and testing those predictions over time. Markus employed this strategy in her study by generating three alternative theoretical hypotheses for user resistance, and rejecting two of those predictions when they did not match with actual observed behavior. In this case, the hypotheses were tested using logical propositions rather than using mathematical tests, which are just as valid as statistical inferences since mathematics is a subset of logic.

Third, the problem of replicability refers to the difficulty of observing the same phenomenon given the uniqueness and idiosyncrasy of a given case site. However, using Markus’ three theories as an illustration, a different researcher can test the same theories at a different case site, where three different predictions may emerge based on the idiosyncratic nature of the new case site, and the three resulting predictions may be tested accordingly. In other words, it is possible to replicate the inferences of case research, even if the case research site or context may not be replicable.

Fourth, case research tends to examine unique and non-replicable phenomena that may not be generalized to other settings. Generalizability in natural sciences is established through additional studies. Likewise, additional case studies conducted in different contexts with different predictions can establish generalizability of findings if such findings are observed to be consistent across studies.

Lastly, British philosopher Karl Popper described four requirements of scientific theories: (1) theories should be falsifiable, (2) they should be logically consistent, (3) they should have adequate predictive ability, and (4) they should provide better explanation than rival theories. In case research, the first three requirements can be increased by increasing the degrees of freedom of observed findings, such as by increasing the number of case sites, the number of alternative predictions, and the number of levels of analysis examined. This was accomplished in Markus’ study by examining the behavior of multiple groups (divisional accountants and corporate accountants) and providing multiple (three) rival explanations.

Popper’s fourth condition was accomplished in this study when one hypothesis was found to match observed evidence better than the two rival hypotheses.

[8] Benbasat, I., Goldstein, D. K., and Mead, M. (1987). “The Case Research Strategy in Studies of Information Systems,” MIS Quarterly (11:3), 369-386.

[9] Yin, R. K. (2002), Case Study Research: Design and Methods . Thousand Oaks, CA: Sage Publications.

[10] Eisenhardt, K. M. (1989). “Building Theories from Case Research,” Academy of Management Review

(14:4), 532-550.

[11] Eisenhardt, K. M. (1989). “Making Fast Strategic Decisions in High-Velocity Environments,” Academy of Management Journal (32:3), 543-576.

[12] Markus, M. L. (1983). “Power, Politics, and MIS Implementation,” Communications of the ACM (26:6), 430-444.

Social Science Research: Principles, Methods, and Practices. Authored by : Anol Bhattacherjee. Provided by : University of South Florida. Located at : http://scholarcommons.usf.edu/oa_textbooks/3/ . License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

Case Summary: Brigidi, Gian-Stefano

Gian-Stefano Brigidi, Ph.D. University of California San Diego and University of Utah : Based on the report of an assessment conducted by the University of Utah (UU), and inquiry conducted by the University of California San Diego (UCSD), the Respondent’s admission, and additional analysis conducted by the Office of Research Integrity (ORI) in its oversight review, ORI found that Gian-Stefano Brigidi, Ph.D. (Respondent), who was a Postdoctoral Fellow, Department of Neurobiology, UCSD, and was an Assistant Professor, Department of Neurobiology, UU, engaged in research misconduct in research supported by U.S. Public Health Service (PHS) funds, specifically National Institute of Mental Health (NIMH), National Institutes of Health (NIH), grant F32 MH110141, National Human Genome Research Institute (NHGRI), NIH, grant T32 HG000044, National Institute of Neurological Disorders and Stroke (NINDS), NIH, grant P30 NS047101, and National Library of Medicine (NLM), NIH, grant T15 LM011271. The research was included in grant applications submitted for PHS funds, specifically R01 NS131809-01, R01 NS133405-01, DP2 NS127276-01, and R01 NS111162-01A1 submitted to NINDS, NIH, and R21 MH121860-01, R21 MH121860-01A1, F32 MH110141-01, F32 MH110141-01A1, and F32 MH110141-01AS1 submitted to NIMH, NIH.

ORI found that Respondent engaged in research misconduct by knowingly or intentionally falsifying and/or fabricating data and results by manipulating primary data values to falsely increase the n-value, manipulating fluorescence micrographs and their quantification graphs to augment the role of ITFs in murine hippocampal neurons, and/or manipulating confocal images that were obtained through different experimental conditions in twenty (20) figures of one (1) published paper and four (4) PHS grant applications, one (1) panel of one (1) poster, and seven (7) slides of one (1) presentation:

Genomic Decoding of Neuronal Depolarization by Stimulus-Specific NPAS4 Heterodimers. Cell . 2019 Oct 3;179(2):373-391.e27. doi: 10.1016/j.cell.2019.09.004 (hereafter referred to as “ Cell 2019”).
Genomic mechanisms linking neuronal activity history with present and future functions. Poster for “The Brigidi Lab – a neuronal activity lab in the Department of Neurobiology at the University of Utah” (hereafter referred to as the “UU Department of Neurobiology poster”).
Decoding neural circuit stimuli into spatially organized gene regulation. Presentation presented to the UU Department of Neurobiology & Anatomy on January 23, 2020 (hereafter referred to as “UU Department of Neurobiology presentation”).
DP2 NS127276-01, “Decoding neuronal activity history at the genome through the spatially segregated inducible transcription factors,” submitted to NINDS, NIH, on August 20, 2020, Awarded Project Dates: September 15, 2021-August 1, 2023.
F32 MH110141-01, “Regulation of excitatory-inhibitory balance by the local translation of the immediate early gene Npas4,” submitted to NIMH, NIH, on August 10, 2015.
F32 MH110141-01A1, “Regulation of Excitatory-Inhibitory Balance by Local Translation of the Immediate Early Gene Npas4,” submitted to NIMH, NIH, on December 8, 2015, Awarded Project Dates: August 1, 2016-July 31, 2018.
F32 MH110141-01A1S1, “Regulation of Excitatory-Inhibitory Balance by Local Translation of the Immediate Early Gene Npas4,” submitted to NIMH, NIH, on December 8, 2016, Awarded Project Dates: December 1, 2016-July 31, 2017.

The falsified and/or fabricated data also were included in twenty-three (23) figures in the following five (5) PHS grant applications:

R01 NS131809-01, “Regulation and function of dendritic mRNA that encodes the neuronal transcription factor Npas4,” submitted to NINDS, NIH, on June 6, 2022.
R01 NS133405-01, “Assessing the impact of the inducible transcription factor NPAS4 on spatial tuning in the mouse hippocampus,” submitted to NINDS, NIH, on October 5, 2022.
R01 NS111162-01A1, “Molecular and cellular mechanisms underlying activity dependent gene regulation in neurons,” submitted to NINDS, NIH, on March 5, 2019, Awarded Project Dates: December 15, 2019-November 30, 2024.
R21 MH121860-01, “Identification of dendritically-localized transcription factor mRNAs as a mechanism for conveying multiple streams of information to the nucleus,” submitted to NIMH, NIH, on February 19, 2019.
R21 MH121860-01A1, “Identification of dendritically-localized transcription factor mRNAs,” submitted to NIMH, NIH, on March 16, 2020.

Specifically, ORI found that:

Respondent knowingly or intentionally combined two to three real data sets and two to five fabricated data sets to falsely increase the n-values reported in:
Figures 1B, 1D, 1E, 1G, 1I, 1J, 1M-1O, 1Q-1T, S2B-S2D, S2F-S2H, S3I, S3L, S3M, and S6H of Cell 2019 and Slides 6-10, 13, and 28 of the UU Department of Neurobiology presentation representing the quantification of NPAS4 immunohistofluorescence

Figures 2H, 2I, 2K, 2P, 3C, 3E, 4D-4G, 4K-4N, 4P-4Q, S3G, S5B, and S5C of Cell 2019 representing the quantification of Npas4 mRNA or puro-PLA puncta

Figures S1E, S1G, and S1H of Cell 2019 representing the quantification of whole-cell clamp recordings of CA1 PN
Figures 2 (lower panel) and 3c of F32 MH110141-01, Figures 1g, 2b, 2d, and 4 of F32 MH110141-01A1S1, and Figures 1g, 2b, 2d, and 4 of F32 MH110141-01A1 representing time points of NPAS4 quantification after no stimulation or post-stimulation in the alveus or radiatum SR, SO, SP, SLM, with or without the addition of an inhibitor
Respondent knowingly or intentionally manipulated confocal images that were obtained through different experimental conditions in:

Figures 1A, 1C, and 1F of Cell 2019 and Slides 6-9 of the UU Department of Neurobiology presentation representing confocal images of hippocampal slices immunostained for NPAS4 and Neu

Figures S2A and S2E of Cell 2019 by manipulating and misrepresenting the GFP signals as NPAS4 signals in wildtype mice

Figures 1H, 1L, 1P, S3K, S6F, and S6G of Cell 2019 and Slides 9 and 28 of the UU Department of Neurobiology presentation by manipulating the raw images of hippocampal slices immunostained with NPAS4 and Neu and/or ARNT1 or ARNT2 by generating a mask of NPAS4 immunofluorescent signal through GFP signal from tissue obtained from Thy1-GFP mice to intentionally enhance the appearance of the dendritic NPAS4 signal

Figures S6F and S6G of Cell 2019 by manipulating the raw images of hippocampus slices by overlaying a GFP channel over ARNT1 channel and using the multiply feature in Photoshop to restrict ARNT1 signal through GFP to enhance the ARNT1 signal in three panels

Slides 7, 9, and 28 of the UU Department of Neurobiology presentation by manipulating six images representing post-stimulation with different time points by using a GFP mask overlaid on top of raw NPAS4 immunofluorescence

Figure 4 of DP2 NS127276-01 and panel 1 of the UU Department of Neurobiology poster representing twelve images in columns 2-4 labeled EGR, FOS, ATF4 by mislabeling the microscope images as immunofluorescent stained with antibodies against EGR, FOS, and ATF4 when they actually were stained with anti-NPAS4 and selected images to support the immunofluorescence data in the ITF induction graphs

Figure 5 of DP2 NS127276-01 representing two confocal images in the far-right column by intentionally and selectively enhancing the brightness of the anti-NPAS4 immunofluorescent channel within the dashed box and left brightness unchanged in surrounding areas of the images

Figure 6 of DP2 NS127276-01 in twelve images in columns 2-5 labeled Egr2 , Fos , and Atf4 by intentionally mislabeling the microscope images as RNA in situ hybridization with probes against Egr2, Fos, and Atf4 when they actually were stained with NPAS4 probes and intentionally selecting and quantifying images in the quantification graphs to support the conclusions of the grant application

Respondent knowingly or intentionally manipulated the fluorescence micrographs and their quantification graphs to augment the role of ITFs in murine hippocampal neurons in Figures 2B-2G, 2J, 2L-2O, 3B, 3D, 3F-3H, 4C, 4J, 4O, S1A-S1D, S1F, S1I-S1J, S3A-S3F, S3H, S3J, S3N-S3T, S5D-S5G, and S6A-S6E of Cell 2019; the falsified/fabricated data also were included in Figures 2B-2H, 3, 4B-4E, and 5C-5G of R21 MH121860-01, Figures 2, 3B-3E, 4B-4C, 4E-4I, and 5B-5E of R21 MH121860-01A1, Figures 3, 5, 6B, 7, 8, 10B-10D, 11A-11C, and 11E-11F in R01 NS131809-01, Figure 8 of R01 NS133405-01, and Figures 3B-3C, 3E-3I, 4B-4I, 5, 9, 10B-10E, and 11-12 of R01 NS111162-01A1.

Respondent entered into a Voluntary Settlement Agreement (Agreement) and voluntarily agreed to the following:

Respondent will have his research supervised for a period of five (5) years beginning on March 24, 2024 (the “Supervision Period”). Prior to the submission of an application for PHS support for a research project on which Respondent’s participation is proposed and prior to Respondent’s participation in any capacity in PHS-supported research, Respondent will submit a plan for supervision of Respondent’s duties to ORI for approval. The supervision plan must be designed to ensure the integrity of Respondent’s research. Respondent will not participate in any PHS-supported research until such a supervision plan is approved by ORI. Respondent will comply with the agreed-upon supervision plan.
A committee of 2-3 senior faculty members at the institution who are familiar with Respondent’s field of research, but not including Respondent’s supervisor or collaborators, will provide oversight and guidance for a period of five (5) years from the effective date of the Agreement. The committee will review primary data from Respondent’s laboratory on a quarterly basis and submit a report to ORI at six (6) month intervals setting forth the committee meeting dates and Respondent’s compliance with appropriate research standards and confirming the integrity of Respondent’s research.

The committee will conduct an advance review of each application for PHS funds, or report, manuscript, or abstract involving PHS-supported research in which Respondent is involved. The review will include a discussion with Respondent of the primary data represented in those documents and will include a certification to ORI that the data presented in the proposed application, report, manuscript, or abstract are supported by the research record.

During the Supervision Period, Respondent will ensure that any institution employing him submits, in conjunction with each application for PHS funds, or report, manuscript, or abstract involving PHS-supported research in which Respondent is involved, a certification to ORI that the data provided by Respondent are based on actual experiments or are otherwise legitimately derived and that the data, procedures, and methodology are accurately reported and not plagiarized in the application, report, manuscript, or abstract.
If no supervision plan is provided to ORI, Respondent will provide certification to ORI at the conclusion of the Supervision Period that his participation was not proposed on a research project for which an application for PHS support was submitted and that he has not participated in any capacity in PHS-supported research.
During the Supervision Period, Respondent will exclude himself voluntarily from serving in any advisory or consultant capacity to PHS including, but not limited to, service on any PHS advisory committee, board, and/or peer review committee.
Respondent will request that the following paper be corrected or retracted:
Cell. 2019 Oct 3;179(2):373-391.e27. doi: 10.1016/j.cell.2019.09.004

Respondent will copy ORI and the Research Integrity Officer at UCSD on the correspondence with the journal.

A Federal Register notice (FRN) has been submitted to the Federal Register for this case. When the FRN is published in the Federal Register , the link will be provided here.

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ORIGINAL RESEARCH article

This article is part of the research topic.

Low-Carbon Oriented Market Mechanism and Reliability Improvement of Multi-energy Systems

The search method for key transmission sections based on an improved spectral clustering algorithm Provisionally Accepted

1 Energy Power Research Center, Jinan University, China

The final, formatted version of the article will be published soon.

With the increased complexity of power systems stemming from the connection of highproportion renewable energy sources, coupled with the escalating volatility and uncertainty, the key transmission sections that serve as indicators of the power grid's security status are also subject to frequent changes, posing challenges to grid monitoring. The search method for key transmission sections based on an improved spectral clustering algorithm is proposed in this paper.A branch weight model, considering the impact of node voltage and power flow factors, is initially established to comprehensively reflect the electrical connectivity between nodes. Subsequently, a weighted graph model is constructed based on spectral graph theory, and an improved spectral clustering algorithm is employed to partition the power grid. Finally, a safety risk indicator is utilized to identify whether the partitioned sections are key transmission sections. Results from case studies on the IEEE39-node system and actual power grid examples demonstrate that the proposed method accurately and effectively searches for all key transmission sections of the system and identifies their security risks. The application in real power grid scenarios validates its ability to screen out some previously unrecognized key transmission sections.

Keywords: Renewable Energy, power grid partitioning, Key transmission section, spectral clustering, Normalized cut, security risk index

Received: 18 Feb 2024; Accepted: 08 Apr 2024.

Copyright: © 2024 Jiliang and Min. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Prof. Liu Min, Energy Power Research Center, Jinan University, Zhuhai, Guangdong Province, China

People also looked at

Is Antifa a gang? Trial in street brawl at pro-Trump rally opens a landmark legal test

The trial of two men, charged in a 3-year-old street brawl in a California beach town, could soon reshape the image of one of the most debated political movements in America: Antifa.

Opening arguments Tuesday laid out conflicting views about the roles Brian Lightfoot and Jeremy White played in a heated protest in the San Diego neighborhood of Pacific Beach on Jan. 9, 2021, three days after the Capitol insurrection.

That day, far-right protesters ended up in a string of violent brawls with anti-fascist counterprotesters. Prosecutors, however, focused solely on Antifa, charging 11 defendants with dozens of felonies.

The prosecution's case rests not just on proving that Lightfoot and White engaged in the acts they’re accused of – hardly in dispute, as they’re caught on camera from multiple angles – but that they did so as part of a broader conspiracy to commit violence. That conspiracy, Harvey and her team have argued, was organized under and inspired by the banner of Antifa, a political movement of far-left militants who oppose neo-Nazis and white supremacists at demonstrations and other events.

But defense attorneys argued the case opens the door for conspiracy allegations against anyone who protests against fascism, or even dresses in black. “It has a chilling effect on political expression,” said attorney John Hamasaki, who is representing Lightfoot.

Of 11 original defendants, nine made deals with prosecutors. White and Lightfoot, represented by activist attorneys, took the case to trial, stressing its importance not just to them, but to protesters against fascism across the country.

A gang-style conspiracy conviction could effectively double any prison sentence. If successful, it could be used as a blueprint for prosecutors across the country, according to legal experts. It could also bolster a right-wing political narrative that has claimed Antifa is not just a political ideology, but a dangerous group.

“This case opens the door for any prosecutor to approach any protest where people are either anti-fascist or dressed in black and to pursue them under a conspiracy,” said attorney John Hamasaki, who is representing Lightfoot. “It has a chilling effect on political expression.”

But the prosecution’s high-profile approach has been criticized, in part because the two-sided street brawls only led to charges for one side. Video from the day showed fights involving people affiliated with the extremist group the Proud Boys and local white supremacist factions.

Yet the office of San Diego District Attorney Summer Stephan chose to prosecute only black-clad anti-fascists. Stephan, a longtime Republican who left the party and ran in the nonpartisan DA election, has a history of supporting conspiracy theories about Antifa.

On Tuesday morning, prosecutors painted Antifa as a menace to society.

In her opening statement, Deputy District Attorney Makenzie Harvey played numerous videos of the 11 incidents of violence from Jan. 9 that prosecutors have focused on, some of which only tangentially involved, or didn’t at all involve, either of the defendants.

White, Lightfoot, and their alleged co-conspirators came to San Diego “with the intention to shut down the speech and the assembly of a patriot group, for the simple fact that they didn’t agree with their beliefs,” Harvey told the jury. “You will see that these defendants and their co-conspirators arrived in San Diego dressed and armed and prepared for violence.”

Harvey said prosecutors will show the jury secret communications between anti-fascists from San Diego and Los Angeles that prove the defendants were always intent on committing violence against peaceful, patriotic protestors.

Attorneys for the two defendants sought to poke holes in the broader conspiracy argument and to distance their clients from the violence.

Hamasaki described his client as a wildland firefighter in training, who grew up in poverty in South-Central Los Angeles. Leading up to, and during, the social justice uprisings following the murder of George Floyd, Lightfoot became fascinated by the anti-fascist movement, Hamasaki said. So he bought some equipment and hit the streets to protest against what he believes are malevolent racist and fascist far-right actors.

“Anti-fascism is an ideology, it’s a belief system – It’s saying ‘I am opposed to fascism,'” Hamasaki said. “The prosecution said the net is pretty wide for who they consider fascists – that it’s not just white supremacists and neo-Nazis but also Trump supporters – I think you’re going to find in this case there’s some overlap in that.”

Representing White, San Francisco attorney Curtis Briggs said his client came to Pacific Beach equipped as a medic and ready to help fellow anti-fascists who might be injured during the protests. White attended protests and rallies across the country, Briggs said, even winning a Certificate of Congressional Recognition for his activism on behalf of Sen. Bernie Sanders.

As he witnessed fellow protesters increasingly attacked by far-right agitators, White “put on a suit of armor” which included a helmet emblazoned with the message “When the shooting starts, get behind me,” and stepped into the fray, Briggs said.

And Briggs noted that the people listed as victims in the DA’s case carried weapons of their own: at least two knives, a replica gun, gloves with kevlar-reinforced knuckles and pepper spray.

As USA TODAY first reported in 2022, those victims include people identified by activists as white supremacist agitators notorious for spurring fights in neighborhoods where they're not welcome. At least one has a criminal record and has long been involved with neo-Nazi groups.

“Please pay close attention to whether the people on the stand have vice – whether they’re really victims or not – and approach the prosecution’s case with healthy skepticism,” Briggs told the jury.

Prosecutors, however, filed a successful, wide-ranging motion in the case a few weeks ago that severely limits how the defense attorneys may describe the pro-Trump protesters from the day of the crimes.

Throughout the opening arguments, the prosecutors objected to every mention by defense attorneys of well-known groups including the Proud Boys, the Oath Keepers, the Three Percenters and local San Diego neo-Nazi organizations. At one point, Judge Daniel Goldstein halted proceedings and dismissed the jury before chastising Hamasaki for using such terms, noting that he had ruled on this matter “at least seven times.”

Investigation: California statehouse candidate says she didn't join Capitol riot. Video shows otherwise

San Diego community advocate Tasha Williamson, who attended trial in support of White and Lightfoot, said she was upset by what she saw as inequity.

“Clearly, the district attorney only believes in the First Amendment rights of white supremacists, not the rights of people who look like me,” Williamson, who is Black, said. “I heard ‘Antifa’ so many times, but not 'Proud Boys,' or ‘American Guard’ or ‘Defend East County’ or any of these people who have threatened my life, my family’s life, who are known white supremacists.”

She added: “You can’t even utter those words in that courtroom, and I’m ashamed to have to walk through these doors, where it says justice everywhere. This isn’t justice.”

Members of the prosecution said they could not comment on the case.

While the two face charges with varying sentences, Lightfoot could serve up to 18 years if convicted. The trial is expected to continue through the rest of the month.

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A Necessary Dialogue: Theory in Case Study Research
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A case study is one of the most commonly used methodologies of social research. This article attempts to look into the various dimensions of a case study research strategy, the different epistemological strands which determine the particular case study type and approach adopted in the field, discusses the factors which can enhance the effectiveness of a case study research, and the debate ...
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Case Study – Methods, Examples and Guide

Types of Case Study

Single-Case Study

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Descriptive Case Study

Instrumental Case Study

Case Study Data Collection Methods

Observations

How to conduct Case Study Research

Examples of Case Study

Application of Case Study

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Social Sciences

Law and Ethics

Purpose of Case Study

Advantages of Case Study Research

Limitations of Case Study Research

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Muhammad Hassan

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