- Pre-registration nursing students
- No definition of master’s degree in nursing described in the publication
After the search, we collated and uploaded all the identified records into EndNote v.X8 (Clarivate Analytics, Philadelphia, Pennsylvania) and removed any duplicates. Two independent reviewers (MCS and SA) screened the titles and abstracts for assessment in line with the inclusion criteria. They retrieved and assessed the full texts of the selected studies while applying the inclusion criteria. Any disagreements about the eligibility of studies were resolved by discussion or, if no consensus could be reached, by involving experienced researchers (MZ-S and RP).
The first reviewer (MCS) extracted data from the selected publications. For this purpose, an extraction tool developed by the authors was used. This tool comprised the following criteria: author(s), year of publication, country, research question, design, case definition, data sources, and methodologic and data-analysis triangulation. First, we extracted and summarized information about the case study design. Second, we narratively summarized the way in which the data and methodological triangulation were described. Finally, we summarized the information on within-case or cross-case analysis. This process was performed using Microsoft Excel. One reviewer (MCS) extracted data, whereas another reviewer (SA) cross-checked the data extraction, making suggestions for additions or edits. Any disagreements between the reviewers were resolved through discussion.
A total of 149 records were identified in 2 databases. We removed 20 duplicates and screened 129 reports by title and abstract. A total of 46 reports were assessed for eligibility. Through hand searches, we identified 117 additional records. Of these, we excluded 98 reports after title and abstract screening. A total of 17 reports were assessed for eligibility. From the 2 databases and the hand search, 63 reports were assessed for eligibility. Ultimately, we included 8 articles for data extraction. No further articles were included after the reference list screening of the included studies. A PRISMA flow diagram of the study selection and inclusion process is presented in Figure 1 . As shown in Tables 2 and and3, 3 , the articles included in this scoping review were published between 2010 and 2022 in Canada (n = 3), the United States (n = 2), Australia (n = 2), and Scotland (n = 1).
PRISMA flow diagram.
Characteristics of Articles Included.
Author | Contandriopoulos et al | Flinter | Hogan et al | Hungerford et al | O’Rourke | Roots and MacDonald | Schadewaldt et al | Strachan et al |
---|---|---|---|---|---|---|---|---|
Country | Canada | The United States | The United States | Australia | Canada | Canada | Australia | Scotland |
How or why research question | No information on the research question | Several how or why research questions | What and how research question | No information on the research question | Several how or why research questions | No information on the research question | What research question | What and why research questions |
Design and referenced author of methodological guidance | Six qualitative case studies Robert K. Yin | Multiple-case studies design Robert K. Yin | Multiple-case studies design Robert E. Stake | Case study design Robert K. Yin | Qualitative single-case study Robert K. Yin Robert E. Stake Sharan Merriam | Single-case study design Robert K. Yin Sharan Merriam | Multiple-case studies design Robert K. Yin Robert E. Stake | Multiple-case studies design |
Case definition | Team of health professionals (Small group) | Nurse practitioners (Individuals) | Primary care practices (Organization) | Community-based NP model of practice (Organization) | NP-led practice (Organization) | Primary care practices (Organization) | No information on case definition | Health board (Organization) |
Overview of Within-Method, Between/Across-Method, and Data-Analysis Triangulation.
Author | Contandriopoulos et al | Flinter | Hogan et al | Hungerford et al | O’Rourke | Roots and MacDonald | Schadewaldt et al | Strachan et al |
---|---|---|---|---|---|---|---|---|
Within-method triangulation (using within-method triangulation use at least 2 data-collection procedures from the same design approach) | ||||||||
: | ||||||||
Interviews | X | x | x | x | x | |||
Observations | x | x | ||||||
Public documents | x | x | x | |||||
Electronic health records | x | |||||||
Between/across-method (using both qualitative and quantitative data-collection procedures in the same study) | ||||||||
: | ||||||||
: | ||||||||
Interviews | x | x | x | |||||
Observations | x | x | ||||||
Public documents | x | x | ||||||
Electronic health records | x | |||||||
: | ||||||||
Self-assessment | x | |||||||
Service records | x | |||||||
Questionnaires | x | |||||||
Data-analysis triangulation (combination of 2 or more methods of analyzing data) | ||||||||
: | ||||||||
: | ||||||||
Deductive | x | x | x | |||||
Inductive | x | x | ||||||
Thematic | x | x | ||||||
Content | ||||||||
: | ||||||||
Descriptive analysis | x | x | x | |||||
: | ||||||||
: | ||||||||
Deductive | x | x | x | x | ||||
Inductive | x | x | ||||||
Thematic | x | |||||||
Content | x |
The following sections describe the research question, case definition, and case study design. Case studies are most appropriate when asking “how” or “why” questions. 1 According to Yin, 1 how and why questions are explanatory and lead to the use of case studies, histories, and experiments as the preferred research methods. In 1 study from Canada, eg, the following research question was presented: “How and why did stakeholders participate in the system change process that led to the introduction of the first nurse practitioner-led Clinic in Ontario?” (p7) 19 Once the research question has been formulated, the case should be defined and, subsequently, the case study design chosen. 1 In typical case studies with mixed methods, the 2 types of data are gathered concurrently in a convergent design and the results merged to examine a case and/or compare multiple cases. 10
“How” or “why” questions were found in 4 studies. 16 , 17 , 19 , 22 Two studies additionally asked “what” questions. Three studies described an exploratory approach, and 1 study presented an explanatory approach. Of these 4 studies, 3 studies chose a qualitative approach 17 , 19 , 22 and 1 opted for mixed methods with a convergent design. 16
In the remaining studies, either the research questions were not clearly stated or no “how” or “why” questions were formulated. For example, “what” questions were found in 1 study. 21 No information was provided on exploratory, descriptive, and explanatory approaches. Schadewaldt et al 21 chose mixed methods with a convergent design.
A total of 5 studies defined the case as an organizational unit. 17 , 18 - 20 , 22 Of the 8 articles, 4 reported multiple-case studies. 16 , 17 , 22 , 23 Another 2 publications involved single-case studies. 19 , 20 Moreover, 2 publications did not state the case study design explicitly.
This section describes within-method triangulation, which involves employing at least 2 data-collection procedures within the same design approach. 6 , 7 This can also be called data source triangulation. 8 Next, we present the single data-collection procedures in detail. In 5 studies, information on within-method triangulation was found. 15 , 17 - 19 , 22 Studies describing a quantitative approach and the triangulation of 2 or more quantitative data-collection procedures could not be included in this scoping review.
Five studies used qualitative data-collection procedures. Two studies combined face-to-face interviews and documents. 15 , 19 One study mixed in-depth interviews with observations, 18 and 1 study combined face-to-face interviews and documentation. 22 One study contained face-to-face interviews, observations, and documentation. 17 The combination of different qualitative data-collection procedures was used to present the case context in an authentic and complex way, to elicit the perspectives of the participants, and to obtain a holistic description and explanation of the cases under study.
All 5 studies used qualitative interviews as the primary data-collection procedure. 15 , 17 - 19 , 22 Face-to-face, in-depth, and semi-structured interviews were conducted. The topics covered in the interviews included processes in the introduction of new care services and experiences of barriers and facilitators to collaborative work in general practices. Two studies did not specify the type of interviews conducted and did not report sample questions. 15 , 18
In 2 studies, qualitative observations were carried out. 17 , 18 During the observations, the physical design of the clinical patients’ rooms and office spaces was examined. 17 Hungerford et al 18 did not explain what information was collected during the observations. In both studies, the type of observation was not specified. Observations were generally recorded as field notes.
In 3 studies, various qualitative public documents were studied. 15 , 19 , 22 These documents included role description, education curriculum, governance frameworks, websites, and newspapers with information about the implementation of the role and general practice. Only 1 study failed to specify the type of document and the collected data. 15
In 1 study, qualitative documentation was investigated. 17 This included a review of dashboards (eg, provider productivity reports or provider quality dashboards in the electronic health record) and quality performance reports (eg, practice-wide or co-management team-wide performance reports).
This section describes the between/across methods, which involve employing both qualitative and quantitative data-collection procedures in the same study. 6 , 7 This procedure can also be denoted “methodologic triangulation.” 8 Subsequently, we present the individual data-collection procedures. In 3 studies, information on between/across triangulation was found. 16 , 20 , 21
Three studies used qualitative and quantitative data-collection procedures. One study combined face-to-face interviews, documentation, and self-assessments. 16 One study employed semi-structured interviews, direct observation, documents, and service records, 20 and another study combined face-to-face interviews, non-participant observation, documents, and questionnaires. 23
All 3 studies used qualitative interviews as the primary data-collection procedure. 16 , 20 , 23 Face-to-face and semi-structured interviews were conducted. In the interviews, data were collected on the introduction of new care services and experiences of barriers to and facilitators of collaborative work in general practices.
In 2 studies, direct and non-participant qualitative observations were conducted. 20 , 23 During the observations, the interaction between health professionals or the organization and the clinical context was observed. Observations were generally recorded as field notes.
In 2 studies, various qualitative public documents were examined. 20 , 23 These documents included role description, newspapers, websites, and practice documents (eg, flyers). In the documents, information on the role implementation and role description of NPs was collected.
In 1 study, qualitative individual journals were studied. 16 These included reflective journals from NPs, who performed the role in primary health care.
Only 1 study involved quantitative service records. 20 These service records were obtained from the primary care practices and the respective health authorities. They were collected before and after the implementation of an NP role to identify changes in patients’ access to health care, the volume of patients served, and patients’ use of acute care services.
In 2 studies, quantitative questionnaires were used to gather information about the teams’ satisfaction with collaboration. 16 , 21 In 1 study, 3 validated scales were used. The scales measured experience, satisfaction, and belief in the benefits of collaboration. 21 Psychometric performance indicators of these scales were provided. However, the time points of data collection were not specified; similarly, whether the questionnaires were completed online or by hand was not mentioned. A competency self-assessment tool was used in another study. 16 The assessment comprised 70 items and included topics such as health promotion, protection, disease prevention and treatment, the NP-patient relationship, the teaching-coaching function, the professional role, managing and negotiating health care delivery systems, monitoring and ensuring the quality of health care practice, and cultural competence. Psychometric performance indicators were provided. The assessment was completed online with 2 measurement time points (pre self-assessment and post self-assessment).
This section describes data-analysis triangulation, which involves the combination of 2 or more methods of analyzing data. 6 Subsequently, we present within-case analysis and cross-case analysis.
Three studies combined qualitative and quantitative methods of analysis. 16 , 20 , 21 Two studies involved deductive and inductive qualitative analysis, and qualitative data were analyzed thematically. 20 , 21 One used deductive qualitative analysis. 16 The method of analysis was not specified in the studies. Quantitative data were analyzed using descriptive statistics in 3 studies. 16 , 20 , 23 The descriptive statistics comprised the calculation of the mean, median, and frequencies.
Two studies combined deductive and inductive qualitative analysis, 19 , 22 and 2 studies only used deductive qualitative analysis. 15 , 18 Qualitative data were analyzed thematically in 1 study, 22 and data were treated with content analysis in the other. 19 The method of analysis was not specified in the 2 studies.
In 7 studies, a within-case analysis was performed. 15 - 20 , 22 Six studies used qualitative data for the within-case analysis, and 1 study employed qualitative and quantitative data. Data were analyzed separately, consecutively, or in parallel. The themes generated from qualitative data were compared and then summarized. The individual cases were presented mostly as a narrative description. Quantitative data were integrated into the qualitative description with tables and graphs. Qualitative and quantitative data were also presented as a narrative description.
Of the multiple-case studies, 5 carried out cross-case analyses. 15 - 17 , 20 , 22 Three studies described the cross-case analysis using qualitative data. Two studies reported a combination of qualitative and quantitative data for the cross-case analysis. In each multiple-case study, the individual cases were contrasted to identify the differences and similarities between the cases. One study did not specify whether a within-case or a cross-case analysis was conducted. 23
This section describes confirmation or contradiction through qualitative and quantitative data. 1 , 4 Qualitative and quantitative data were reported separately, with little connection between them. As a result, the conclusions on neither the comparisons nor the contradictions could be clearly determined.
In 3 studies, the consistency of the results of different types of qualitative data was highlighted. 16 , 19 , 21 In particular, documentation and interviews or interviews and observations were contrasted:
Both types of data showed that NPs and general practitioners wanted to have more time in common to discuss patient cases and engage in personal exchanges. 21 In addition, the qualitative and quantitative data confirmed the individual progression of NPs from less competent to more competent. 16 One study pointed out that qualitative and quantitative data obtained similar results for the cases. 20 For example, integrating NPs improved patient access by increasing appointment availability.
Although questionnaire results indicated that NPs and general practitioners experienced high levels of collaboration and satisfaction with the collaborative relationship, the qualitative results drew a more ambivalent picture of NPs’ and general practitioners’ experiences with collaboration. 21
The studies included in this scoping review evidenced various research questions. The recommended formats (ie, how or why questions) were not applied consistently. Therefore, no case study design should be applied because the research question is the major guide for determining the research design. 2 Furthermore, case definitions and designs were applied variably. The lack of standardization is reflected in differences in the reporting of these case studies. Generally, case study research is viewed as allowing much more freedom and flexibility. 5 , 24 However, this flexibility and the lack of uniform specifications lead to confusion.
Methodologic triangulation, as described in the literature, can be somewhat confusing as it can refer to either data-collection methods or research designs. 6 , 8 For example, methodologic triangulation can allude to qualitative and quantitative methods, indicating a paradigmatic connection. Methodologic triangulation can also point to qualitative and quantitative data-collection methods, analysis, and interpretation without specific philosophical stances. 6 , 8 Regarding “data-collection methods with no philosophical stances,” we would recommend using the wording “data source triangulation” instead. Thus, the demarcation between the method and the data-collection procedures will be clearer.
Yin 1 advocated the use of multiple sources of evidence so that a case or cases can be investigated more comprehensively and accurately. Most studies included multiple data-collection procedures. Five studies employed a variety of qualitative data-collection procedures, and 3 studies used qualitative and quantitative data-collection procedures (mixed methods). In contrast, no study contained 2 or more quantitative data-collection procedures. In particular, quantitative data-collection procedures—such as validated, reliable questionnaires, scales, or assessments—were not used exhaustively. The prerequisites for using multiple data-collection procedures are availability, the knowledge and skill of the researcher, and sufficient financial funds. 1 To meet these prerequisites, research teams consisting of members with different levels of training and experience are necessary. Multidisciplinary research teams need to be aware of the strengths and weaknesses of different data sources and collection procedures. 1
When using multiple data sources and analysis methods, it is necessary to present the results in a coherent manner. Although the importance of multiple data sources and analysis has been emphasized, 1 , 5 the description of triangulation has tended to be brief. Thus, traceability of the research process is not always ensured. The sparse description of the data-analysis triangulation procedure may be due to the limited number of words in publications or the complexity involved in merging the different data sources.
Only a few concrete recommendations regarding the operationalization of the data-analysis triangulation with the qualitative data process were found. 25 A total of 3 approaches have been proposed 25 : (1) the intuitive approach, in which researchers intuitively connect information from different data sources; (2) the procedural approach, in which each comparative or contrasting step in triangulation is documented to ensure transparency and replicability; and (3) the intersubjective approach, which necessitates a group of researchers agreeing on the steps in the triangulation process. For each case study, one of these 3 approaches needs to be selected, carefully carried out, and documented. Thus, in-depth examination of the data can take place. Farmer et al 25 concluded that most researchers take the intuitive approach; therefore, triangulation is not clearly articulated. This trend is also evident in our scoping review.
Few studies in this scoping review used a combination of qualitative and quantitative analysis. However, creating a comprehensive stand-alone picture of a case from both qualitative and quantitative methods is challenging. Findings derived from different data types may not automatically coalesce into a coherent whole. 4 O’Cathain et al 26 described 3 techniques for combining the results of qualitative and quantitative methods: (1) developing a triangulation protocol; (2) following a thread by selecting a theme from 1 component and following it across the other components; and (3) developing a mixed-methods matrix.
The most detailed description of the conducting of triangulation is the triangulation protocol. The triangulation protocol takes place at the interpretation stage of the research process. 26 This protocol was developed for multiple qualitative data but can also be applied to a combination of qualitative and quantitative data. 25 , 26 It is possible to determine agreement, partial agreement, “silence,” or dissonance between the results of qualitative and quantitative data. The protocol is intended to bring together the various themes from the qualitative and quantitative results and identify overarching meta-themes. 25 , 26
The “following a thread” technique is used in the analysis stage of the research process. To begin, each data source is analyzed to identify the most important themes that need further investigation. Subsequently, the research team selects 1 theme from 1 data source and follows it up in the other data source, thereby creating a thread. The individual steps of this technique are not specified. 26 , 27
A mixed-methods matrix is used at the end of the analysis. 26 All the data collected on a defined case are examined together in 1 large matrix, paying attention to cases rather than variables or themes. In a mixed-methods matrix (eg, a table), the rows represent the cases for which both qualitative and quantitative data exist. The columns show the findings for each case. This technique allows the research team to look for congruency, surprises, and paradoxes among the findings as well as patterns across multiple cases. In our review, we identified only one of these 3 approaches in the study by Roots and MacDonald. 20 These authors mentioned that a causal network analysis was performed using a matrix. However, no further details were given, and reference was made to a later publication. We could not find this publication.
Because it focused on the implementation of NPs in primary health care, the setting of this scoping review was narrow. However, triangulation is essential for research in this area. This type of research was found to provide a good basis for understanding methodologic and data-analysis triangulation. Despite the lack of traceability in the description of the data and methodological triangulation, we believe that case studies are an appropriate design for exploring new nursing roles in existing health care systems. This is evidenced by the fact that case study research is widely used in many social science disciplines as well as in professional practice. 1 To strengthen this research method and increase the traceability in the research process, we recommend using the reporting guideline and reporting checklist by Rodgers et al. 9 This reporting checklist needs to be complemented with methodologic and data-analysis triangulation. A procedural approach needs to be followed in which each comparative step of the triangulation is documented. 25 A triangulation protocol or a mixed-methods matrix can be used for this purpose. 26 If there is a word limit in a publication, the triangulation protocol or mixed-methods matrix needs to be identified. A schematic representation of methodologic and data-analysis triangulation in case studies can be found in Figure 2 .
Schematic representation of methodologic and data-analysis triangulation in case studies (own work).
This study suffered from several limitations that must be acknowledged. Given the nature of scoping reviews, we did not analyze the evidence reported in the studies. However, 2 reviewers independently reviewed all the full-text reports with respect to the inclusion criteria. The focus on the primary care setting with NPs (master’s degree) was very narrow, and only a few studies qualified. Thus, possible important methodological aspects that would have contributed to answering the questions were omitted. Studies describing the triangulation of 2 or more quantitative data-collection procedures could not be included in this scoping review due to the inclusion and exclusion criteria.
Given the various processes described for methodologic and data-analysis triangulation, we can conclude that triangulation in case studies is poorly standardized. Consequently, the traceability of the research process is not always given. Triangulation is complicated by the confusion of terminology. To advance case study research in nursing, we encourage authors to reflect critically on methodologic and data-analysis triangulation and use existing tools, such as the triangulation protocol or mixed-methods matrix and the reporting guideline checklist by Rodgers et al, 9 to ensure more transparent reporting.
Acknowledgments.
The authors thank Simona Aeschlimann for her support during the screening process.
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.
Supplemental Material: Supplemental material for this article is available online.
Case interviews are often challenging because they are open-ended, with limitless possibilities of where you can go. There is some truth to this predicament – there is always more than just one way to crack a case. However, there are also ways to follow a system for every case you encounter that leads to fruitful results. One key component of consistently cracking case interviews involves being hypothesis-driven. But how do you do hypothesis-driven case interviews and, to cover the bases, what is a hypothesis?
In the context of a consulting interview, a hypothesis definition is “a testable statement that needs further data for verification”. In other words, the meaning of a hypothesis is that it’s an educated guess that you think could be the answer to your client’s problem.
A hypothesis is therefore not always true. Instead, it is a starting point that ultimately leads you to the end point. Imagine your client comes to you with a problem, and the root cause is A, B, C, D, or E. Forming a hypothesis allows you to start with A, gather data to see if it’s correct, and if not, move onto B. You then keep going until you get to the right “letter” or answer to the case.
To be clear, you don’t always know your options upfront at the start of a case interview. Usually, after you gather data, you may find that option A was completely wrong, somewhat wrong, or right on track. Depending on the data, you either move onto a new hypothesis, revise it, or dig for more data, respectively. But for the purpose of a case interview, we feel this is a good hypothesis definition.
Let’s use an example to shed some more light on what a hypothesis is, and how to use them in case interviews. Imagine your client is a shoe manufacturing company that has experienced a decrease in profitability over the past 12 months.
An approach without a hypothesis might result in a laundry list of questions like in the following exchange:
I understand that our client is looking to solve its profitability issues. I have identified a few areas that I’d like to look into.
In this exchange, even though the candidate is getting closer to the right answer, there is no structure in the approach. The candidate is merely guessing potential problems rather than systematically getting to a solution.
Using a hypothesis driven approach requires the following steps:
For example, you might start your hypothesis with a focus on revenue for a profitability issue. If you find that the reason is due to a decrease in volume, you may hypothesize that the issue is due to an increase in competition. You then ask for data regarding the competition, and adjust your hypothesis accordingly to the data or lack thereof.
Let’s next see what a hypothesis driven approach looks like:
I understand that our client is looking to solve its profitability issues. My hypothesis is that the client is experiencing a decrease in revenue due to intense competition in the shoe market.
As you can see, in this exchange, the candidate is drilling down into a hypothesis and sounds structured in his or her approach. The interviewer can be sure that even if the candidate is provided with another problem, he or she would be able to systematically get to the answer.
To be clear, you don’t need to always state “my hypothesis is X.” In fact, it may sound too robotic in an actual interview. This is just an example to show you how the hypothesis-driven approach looks.
Power presentation coaching.
Using this approach ensures that you are displaying some of the key skills that consulting firms care about in the case interview: structure and clarity . If you can be hypothesis-driven in your case interview, you are displaying to your interviewer that you will be hypothesis-driven on the job. This means that you will be a much more efficient data collector, and conduct more efficient data analysis, to arrive at a solution quickly.
Do yourself a favor – use our hypothesis-driven case interview approach as you practice and watch your performance soar.
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The term case study refers to both a method of analysis and a specific research design for examining a problem, both of which are used in most circumstances to generalize across populations. This tab focuses on the latter--how to design and organize a research paper in the social sciences that analyzes a specific case.
A case study research paper examines a person, place, event, 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 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 among more than two 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.
General information about how to choose a topic to investigate can be found under the " Choosing a Research Problem " tab in this writing guide. Review this page because it may help you identify a subject of analysis that can be investigated using a single 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:
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.
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:
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:
III. Method
In this section, you explain why you selected a particular subject of analysis to study 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 frames 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; c) what were the consequences of the event.
If your subject of analysis is a person. Explain why you selected this particular individual to be studied and describe what experience he or she has had that provides 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 his/her 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 him or her 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.
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, cultural, economic, political, etc.], 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, why study 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 reveals 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? How might knowing the suppliers of these trucks from overseas 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 be linked to the findings from the literature review. Be sure to cite any prior studies that helped you determine that the case you chose was appropriate for investigating the research 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 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 more common to combine a description of the findings 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 to 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 It is important to 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 for 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.
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 needs for further research.
The function of your paper's conclusion is to: 1) restate the main argument supported by the findings from the analysis of your case; 2) clearly state 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 for you 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:
Note that, depending on the discipline you are writing in and your professor's preferences, the concluding paragraph may contain your final reflections on the evidence presented applied 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 on 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 differently 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.
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.
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.
2024 Theses Doctoral
Mitropolsky, Daniel
Obtaining a computational understanding of the brain is one of the most important problems in basic science. However, the brain is an incredibly complex organ, and neurobiological research has uncovered enormous amounts of detail at almost every level of analysis (the synapse, the neuron, other brain cells, brain circuits, areas, and so on); it is unclear which of these details are conceptually significant to the basic way in which the brain computes. An essential approach to the eventual resolution of this problem is the definition and study of theoretical computational models, based on varying abstractions and inclusions of such details. This thesis defines and studies a family of models, called NEMO, based on a particular set of well-established facts or well-founded assumptions in neuroscience: atomic neural firing, random connectivity, inhibition as a local dynamic firing threshold, and fully local plasticity. This thesis asks: what sort of algorithms are possible in these computational models? To the extent possible, what seem to be the simplest assumptions where interesting computation becomes possible? Additionally, can we find algorithms for cognitive phenomena that, in addition to serving as a "proof of capacity" of the computational model, otherwise reflect what is known about these processes in the brain? The major contributions of this thesis include: 1. The formal definition of the basic-NEMO and NEMO models, with an explication of their neurobiological underpinnings (that is, realism as abstractions of the brain). 2. Algorithms for the creation of neural \emph{assemblies}, or highly dense interconnected subsets of neurons, and various operations manipulating such assemblies, including reciprocal projection, merge, association, disassociation, and pattern completion, all in the basic-NEMO model. Using these operations, we show the Turing-completeness of the NEMO model (with some specific additional assumptions). 3. An algorithm for parsing a small but non-trivial subset of English and Russian (and more generally any regular language) in the NEMO model, with meta-features of the algorithm broadly in line with what is known about language in the brain. 4. An algorithm for parsing a much larger subset of English (and other languages), in particular handling dependent (embedded) clauses, in the NEMO model with some additional memory assumptions. We prove that an abstraction of this algorithm yields a new characterization of the context-free languages. 5. Algorithms for the blocks-world planning task, which involves outputting a sequence of steps to rearrange a stack of cubes in one order into another target order, in the NEMO model. A side consequence of this work is an algorithm for a chaining operation in basic-NEMO. 6. Algorithms for several of the most basic and initial steps in language acquisition in the baby brain. This includes an algorithm for the learning of the simplest, concrete nouns and action verbs (words like "cat" and "jump") from whole sentences in basic-NEMO with a novel representation of word and contextual inputs. Extending the same model, we present an algorithm for an elementary component of syntax, namely learning the word order of 2-constituent intransitive and 3-constituent transitive sentences. These algorithms are very broadly in line with what is known about language in the brain.
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The adoption of digital technologies by small and medium-sized enterprises for sustainability and value creation in pakistan: the application of a two-staged hybrid sem-ann approach.
2.1. social media applications (smas), 2.2. ai-enabled applications (aeas), 2.3. big data analytics (bda), 2.4. iot applications (ioas), 2.5. blockchain applications (bcas), 2.6. economic value (ecv), 2.7. social value (sov), 2.8. sme performance, 3. hypothesis development, 3.1. social media applications (smas) and economic value and social value, 3.2. ai-enabled applications (aeas) and economic value and social value, 3.3. big data analytics (bda) and economic value and social value, 3.4. iot applications (ioas) and economic value and social value, 3.5. blockchain applications (bcas) and economic value and social value, 3.6. economic value (ecv) and sme performance, 3.7. social value (sov) and sme performance, 4. research methodology, 4.1. research design, 4.2. data collection: procedure and sample, 4.3. measures, 4.4. statistical analysis, 5.1. demographic characteristics, 5.2. measurement model evaluation, 5.3. structural model evaluation, 5.4. artificial neural network analysis, 5.5. ranking of predictors, 6. discussion, 6.1. theoretical implications, 6.2. practical implications, 7. conclusions, 7.1. limitations and future research, 7.2. future research directions, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.
SMAs | Social media applications are very popular among younger people. | 1 | 2 | 3 | 4 | 5 |
I believe different social media applications provide business value to our enterprise. | 1 | 2 | 3 | 4 | 5 | |
I think SMEs are dependent on social media to fulfill their marketing requirements. | 1 | 2 | 3 | 4 | 5 | |
I believe that social media helps develop business activities for SMEs. | 1 | 2 | 3 | 4 | 5 | |
AEAs | SMEs apply AI technologies to help them remodel various business activities. | 1 | 2 | 3 | 4 | 5 |
I believe applications of AI can help in the supply chain activities of SMEs. | 1 | 2 | 3 | 4 | 5 | |
Applications of AI can reduce the operational cost of SMEs. | 1 | 2 | 3 | 4 | 5 | |
I believe that SMEs can use AI applications to develop their customer interaction process. | 1 | 2 | 3 | 4 | 5 | |
BDA | I believe that data analytics has gained huge momentum in recent years. | 1 | 2 | 3 | 4 | 5 |
The application of big data analytics helps in the real-time analysis of customer data. | 1 | 2 | 3 | 4 | 5 | |
I believe that applications of big data analytics help in the decision-making process. | 1 | 2 | 3 | 4 | 5 | |
I think SMEs should adopt big data analytics technology to gain a competitive advantage. | 1 | 2 | 3 | 4 | 5 | |
IoT | The IoT can facilitate the rapid exchange of data in a real-time scenario. | 1 | 2 | 3 | 4 | 5 |
I believe that applications of the IoT can help in improving the upscaling process in the SMEs. | 1 | 2 | 3 | 4 | 5 | |
Applications of the IoT can provide a scalable information system that helps SMEs exchange information quickly. | 1 | 2 | 3 | 4 | 5 | |
Applications of the IoT help SMEs sense, seize, and reconfigure external opportunities. | 1 | 2 | 3 | 4 | 5 | |
BCAs | Blockchain is considered a digital ledger, which presents the detailed history of various transactions. | 1 | 2 | 3 | 4 | 5 |
I believe blockchain technology can save operational costs for SMEs. | 1 | 2 | 3 | 4 | 5 | |
I think applications of blockchain are secured for SMEs. | 1 | 2 | 3 | 4 | 5 | |
I believe that SMEs should adopt blockchain technology to gain a competitive advantage. | 1 | 2 | 3 | 4 | 5 | |
ECV | SMEs can gain economic value through profit maximization. | 1 | 2 | 3 | 4 | 5 |
The adoption of different technologies can provide economic value to SMEs. | 1 | 2 | 3 | 4 | 5 | |
The economic value changes if the price of the good or the service changes. | 1 | 2 | 3 | 4 | 5 | |
I believe that SME leadership should focus more on adopting new-edge technologies. | 1 | 2 | 3 | 4 | 5 | |
I believe product development costs can be significantly reduced if SMEs adopt appropriate technologies. | 1 | 2 | 3 | 4 | 5 | |
SOV | SMEs can gain social benefits if they perform their work to benefit society. | 1 | 2 | 3 | 4 | 5 |
I believe that social value emerges from the concept of corporate social responsibility programs. | 1 | 2 | 3 | 4 | 5 | |
Improving social value is an important aspect of SMEs. | 1 | 2 | 3 | 4 | 5 | |
Customers may favor those SMEs that spend more to uplift the society. | 1 | 2 | 3 | 4 | 5 | |
I believe that social values are shared values among the employees of the SMEs. | 1 | 2 | 3 | 4 | 5 | |
SMP | I believe that the performance of SMEs can be improved by appropriately adopting modern technologies. | 1 | 2 | 3 | 4 | 5 |
The social value of SMEs can impact the overall performance of SMEs. | 1 | 2 | 3 | 4 | 5 | |
Leadership support can play a crucial role in improving SME performance. | 1 | 2 | 3 | 4 | 5 |
Click here to enlarge figure
Demographic Characteristics | N (305) | (%) |
---|---|---|
Male | 238 | 78.0 |
Female | 67 | 22.0 |
Total | 305 | 100 |
20–30 years | 81 | 26.6 |
31–40 years | 102 | 33.4 |
41–50 years | 53 | 17.4 |
51–60 years | 55 | 18.0 |
61 years and above | 14 | 4.60 |
Total | 305 | 100 |
Never attended school | 46 | 15.1 |
Primary | 34 | 11.1 |
Secondary | 116 | 38.0 |
Tenth Grade | 40 | 13.0 |
Twelfth Grade | 33 | 10.8 |
Graduation | 15 | 4.90 |
Graduation and higher | 21 | 6.90 |
Total | 305 | 100 |
Sukkur | 164 | 53.8 |
Larkana | 95 | 31.1 |
Jacobabad | 17 | 05.6 |
Khairpur | 29 | 09.5 |
Total | 305 | 100 |
Manufacturing | 60 | 19.7 |
Retail | 91 | 29.8 |
Wholesale | 32 | 10.5 |
Agriculture | 39 | 12.8 |
Livestock | 21 | 6.90 |
Poultry | 24 | 7.90 |
Services | 18 | 5.90 |
Other | 20 | 6.60 |
Total | 305 | 100 |
Constructs | Factor Loading | CR | AVE | A |
---|---|---|---|---|
AI-Enabled Application (AEA) | 0.89 | 0.95 | 0.77 | 0.91 |
0.91 | ||||
0.87 | ||||
0.86 | ||||
Blockchain Application (BCA) | 0.75 | 0.88 | 0.64 | 0.82 |
0.78 | ||||
0.86 | ||||
0.82 | ||||
Big Data Analysis (BDA) | 0.86 | 0.92 | 0.74 | 0.88 |
0.86 | ||||
0.85 | ||||
0.86 | ||||
Economic Value (ECV) | 0.81 | 0.90 | 0.63 | 0.86 |
0.77 | ||||
0.77 | ||||
0.87 | ||||
0.77 | ||||
IoT Application (IoA) | 0.82 | 0.88 | 0.65 | 0.82 |
0.78 | ||||
0.86 | ||||
0.76 | ||||
Social Media Application (SMA) | 0.82 | 0.88 | 0.65 | 0.82 |
0.83 | ||||
0.83 | ||||
0.76 | ||||
SME Performance (SMP) | 0.77 | 0.87 | 0.69 | 0.77 |
0.87 | ||||
0.84 | ||||
Social Value (SOV) | 0.77 | 0.88 | 0.59 | 0.82 |
0.81 | ||||
0.83 | ||||
0.73 | ||||
0.68 |
Constructs | AEA | BCA | BDA | ECV | IoT | SMA | SMP | SOV |
---|---|---|---|---|---|---|---|---|
AEA | ||||||||
BCA | 0.07 | |||||||
BDA | 0.05 | 0.12 | ||||||
ECV | 0.08 | 0.83 | 0.20 | |||||
IoA | 0.05 | 0.70 | 0.15 | 0.78 | ||||
SMA | 0.07 | 0.60 | 0.07 | 0.71 | 0.78 | |||
SMP | 0.07 | 0.64 | 0.12 | 0.78 | 0.75 | 0.63 | ||
SOV | 0.07 | 0.96 | 0.20 | 0.84 | 0.82 | 0.71 | 0.78 |
Constructs | AEA | BCA | BDA | ECV | IoT | SMA | SMP | SOV |
---|---|---|---|---|---|---|---|---|
AEA | ||||||||
BCA | 0.06 | |||||||
BDA | −0.03 | 0.10 | ||||||
ECV | 0.02 | 0.70 | 0.17 | |||||
IoA | 0.02 | 0.58 | 0.13 | 0.66 | ||||
SMA | −0.05 | 0.49 | 0.04 | 0.60 | 0.65 | |||
SMP | 0.01 | 0.51 | 0.10 | 0.63 | 0.60 | 0.50 | ||
SOV | 0.05 | 0.80 | 0.18 | 0.71 | 0.67 | 0.59 | 0.61 |
Variables | R | Adjusted R | Remarks |
---|---|---|---|
Economic Value (ECV) | 0.62 | 0.61 | Substantial |
SME Performance (SMP) | 0.45 | 0.44 | Moderate |
Social Value (SOV) | 0.73 | 0.72 | Substantial |
Hypotheses | β | t-Value | Decision |
---|---|---|---|
H1a = SMAs → ECV | 0.44 | 8.72 | Supported |
H1b = SMAs → SOV | 0.49 | 8.50 | Supported |
H2a = AEAs → ECV | 0.01 | 0.17 | Unsupported |
H2b = AEAs → SOV | 0.02 | 0.65 | Unsupported |
H3a = BDA → ECV | 0.22 | 4.23 | Supported |
H3b = BDA → SOV | 0.16 | 3.07 | Supported |
H4a = IoAs → ECV | 0.25 | 4.07 | Supported |
H4b = IoAs→ SOV | 0.22 | 4.14 | Supported |
H5a = BCAs → ECV | 0.09 | 2.23 | Supported |
H5b = BCAs → SOV | 0.08 | 2.26 | Supported |
H6 = ECV → SMP | 0.40 | 5.34 | Supported |
H7 = SOV → SMP | 0.32 | 4.01 | Supported |
Training | Testing | |||||
---|---|---|---|---|---|---|
N | SSE | RMSE | N | SSE | RMSE | Total Samples |
271 | 79.418 | 1.8472 | 34 | 07.932 | 0.4830 | 503 |
277 | 88.778 | 1.7663 | 28 | 11.026 | 0.6275 | 503 |
278 | 86.833 | 1.7892 | 27 | 10.170 | 0.6137 | 503 |
271 | 83.905 | 1.7971 | 34 | 8.602 | 0.5030 | 503 |
265 | 80.203 | 1.8177 | 40 | 11.350 | 0.5327 | 503 |
275 | 83.102 | 1.8191 | 30 | 12.765 | 0.6523 | 503 |
266 | 79.519 | 1.8289 | 39 | 12.562 | 0.5675 | 503 |
274 | 79.674 | 1.8544 | 31 | 17.014 | 0.7408 | 503 |
271 | 83.813 | 1.7981 | 34 | 07.280 | 0.4627 | 503 |
277 | 87.066 | 1.7836 | 28 | 04.042 | 0.3799 | 503 |
271 | 79.418 | 1.8472 | 34 | 07.932 | 0.4830 | 503 |
277 | 88.778 | 1.7663 | 28 | 11.026 | 0.6275 | 503 |
Mean | 83.231 | 1.8102 | Mean | 10.274 | 0.5563 | |
SD | 3.3075 | 0.0268 | SD | 3.3865 | 0.1001 |
Neural Network (NN) | BCA | BDA | IoA | SMA |
---|---|---|---|---|
NN (i) | 0.15 | 0.56 | 1.00 | 0.52 |
NN (ii) | 0.03 | 0.95 | 1.00 | 0.97 |
NN (iii) | 0.25 | 0.44 | 1.00 | 0.39 |
NN (iv) | 0.04 | 0.70 | 1.00 | 0.59 |
NN (v) | 0.17 | 0.40 | 1.00 | 0.52 |
NN (vi) | 0.10 | 0.95 | 1.00 | 0.83 |
NN (vii) | 0.10 | 0.11 | 1.00 | 0.23 |
NN (viii) | 0.22 | 0.64 | 1.00 | 0.56 |
NN (ix) | 0.08 | 0.35 | 1.00 | 0.48 |
NN (x) | 0.19 | 0.77 | 1.00 | 0.63 |
Average importance | 0.58 | 0.52 | 0.72 | 0.36 |
Normalized importance (%) | 19% | 77% | 100% | 63% |
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Soomro, R.B.; Memon, S.G.; Dahri, N.A.; Al-Rahmi, W.M.; Aldriwish, K.; A. Salameh, A.; Al-Adwan, A.S.; Saleem, A. The Adoption of Digital Technologies by Small and Medium-Sized Enterprises for Sustainability and Value Creation in Pakistan: The Application of a Two-Staged Hybrid SEM-ANN Approach. Sustainability 2024 , 16 , 7351. https://doi.org/10.3390/su16177351
Soomro RB, Memon SG, Dahri NA, Al-Rahmi WM, Aldriwish K, A. Salameh A, Al-Adwan AS, Saleem A. The Adoption of Digital Technologies by Small and Medium-Sized Enterprises for Sustainability and Value Creation in Pakistan: The Application of a Two-Staged Hybrid SEM-ANN Approach. Sustainability . 2024; 16(17):7351. https://doi.org/10.3390/su16177351
Soomro, Raheem Bux, Sanam Gul Memon, Nisar Ahmed Dahri, Waleed Mugahed Al-Rahmi, Khalid Aldriwish, Anas A. Salameh, Ahmad Samed Al-Adwan, and Atif Saleem. 2024. "The Adoption of Digital Technologies by Small and Medium-Sized Enterprises for Sustainability and Value Creation in Pakistan: The Application of a Two-Staged Hybrid SEM-ANN Approach" Sustainability 16, no. 17: 7351. https://doi.org/10.3390/su16177351
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IMAGES
VIDEO
COMMENTS
Case study is unbounded and relies on gathering external information; case analysis is a self-contained subject of analysis. The scope of a case study chosen as a method of research is bounded. However, the researcher is free to gather whatever information and data is necessary to investigate its relevance to understanding the research problem.
Case study protocol is a formal document capturing the entire set of procedures involved in the collection of empirical material . It extends direction to researchers for gathering evidences, empirical material analysis, and case study reporting . This section includes a step-by-step guide that is used for the execution of the actual study.
The purpose of case study research is twofold: (1) to provide descriptive information and (2) to suggest theoretical relevance. Rich description enables an in-depth or sharpened understanding of the case. It is unique given one characteristic: case studies draw from more than one data source. Case studies are inherently multimodal or mixed ...
Identify the key problems and issues in the case study. Formulate and include a thesis statement, summarizing the outcome of your analysis in 1-2 sentences. Background. Set the scene: background information, relevant facts, and the most important issues. Demonstrate that you have researched the problems in this case study. Evaluation of the Case
Defnition: 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.
4. Refine your hypothesis. You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain: The relevant variables; The specific group being studied; The predicted outcome of the experiment or analysis; 5.
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 ...
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 ...
Step 5: Phrase your hypothesis in three ways. To identify the variables, you can write a simple prediction in if … then form. The first part of the sentence states the independent variable and the second part states the dependent variable. If a first-year student starts attending more lectures, then their exam scores will improve.
tion of case study analysis with a qualitative approach is a "methodological affinity, not a definitional entailment." Typology of Case Studies Most typologies of case studies reflect some variation of Lijphart's (1971:691) categories of atheoretical, interpretive, hypothesis-generating, theory-confirming, theory-informing, and
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 ...
Data Analysis in of Case Study Research-Practice oriented- Theory oriented (Dul & Hak, 2008)- Exploration- Theory building- Theory testing ... Research phase which entails defining the type of case study based on the propositions and hypothesis (single case study or comparative case study) and selecting cases, data collection, and data analysis
INTRODUCTION. Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses.1,2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results.3,4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the ...
23.1 How Hypothesis Tests Are Reported in the News 1. Determine the null hypothesis and the alternative hypothesis. 2. Collect and summarize the data into a test statistic. 3. Use the test statistic to determine the p-value. 4. The result is statistically significant if the p-value is less than or equal to the level of significance.
The Case Study was used to understand the overview of the hypothesis testing for data analysis on two independent samples. I feel the case study approach can help cement your understanding of hypothesis testing theory and look at real-life problems. As a disclaimer, I would like to highlight that this was purely an academic project and the ...
A case study is an in-depth investigation of a single person, group, event, or community. This research method involves intensively analyzing a subject to understand its complexity and context. The richness of a case study comes from its ability to capture detailed, qualitative data that can offer insights into a process or subject matter that ...
Step 5: Present your findings. The results of hypothesis testing will be presented in the results and discussion sections of your research paper, dissertation or thesis.. In the results section you should give a brief summary of the data and a summary of the results of your statistical test (for example, the estimated difference between group means and associated p-value).
A case study is a research approach that is used to generate an in-depth, multi-faceted understanding of a complex issue in its real-life context. It is an established research design that is used extensively in a wide variety of disciplines, particularly in the social sciences. A case study can be defined in a variety of ways (Table 5), the ...
Briefly introduce the problems and issues found in the case study. Discuss the theory you will be using in the analysis; Present the key points of the study and present any assumptions made during the analysis. Findings. This is where you present in more detail the specific problems you discovered in the case study.
The thesis differs from the hypothesis in that the thesis is the statement that is proven true with the case study. The hypothesis is the question or idea that the researcher had going into the study. It is possible the hypothesis and thesis are the same. However, it is also possible that once all the research has been completed, the thesis ...
Abstract. This article is premised on the understanding that there are multiple dimensions of the case-theory relation and examines four of these: theory of the case, theory for the case, theory from the case, and a dialogical relation between theory and case. This fourth dimension is the article's key contribution to theorizing case study.
Within-case analysis . In 7 studies, a within-case analysis was performed. 15-20,22 Six studies used qualitative data for the within-case analysis, and 1 study employed qualitative and quantitative data. Data were analyzed separately, consecutively, or in parallel. The themes generated from qualitative data were compared and then summarized.
Hypothesis Definition. In the context of a consulting interview, a hypothesis definition is "a testable statement that needs further data for verification". In other words, the meaning of a hypothesis is that it's an educated guess that you think could be the answer to your client's problem. A hypothesis is therefore not always true.
Quantum computing, with its potential to expedite specific tasks, requires a more precise definition of its benefits in early research. This paper introduces the Quantum DNA Encoder (QDE), a novel approach for encoding genetic information efficiently and effectively. Utilizing a simple circuit suitable for a 4-qubit system, QDE surpasses One-Hot Encoding (OHE) in creating better class ...
A case study 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 among more than two subjects. ... 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 ...
Obtaining a computational understanding of the brain is one of the most important problems in basic science. However, the brain is an incredibly complex organ, and neurobiological research has uncovered enormous amounts of detail at almost every level of analysis (the synapse, the neuron, other brain cells, brain circuits, areas, and so on); it is unclear which of these details are ...
First, this study verifies the spatial nexus between the key independent and dependent variables through spatial autocorrelation tests. Fig. 2 illustrates the global Moran index results. The l n T C E E and l n D T I are positively significant at the 1% level for all periods. This indicates a significant spatial dependence characteristic of the clustering of TCEE and digital technology ...
Cultural heritage crowdsourcing has emerged as a promising approach to address the challenges of digitizing and preserving cultural heritage, contributing to the sustainable development goals of cultural preservation and digital inclusivity. However, the long-term sustainability of these projects faces numerous obstacles. This study explores the key configurational determinants and dynamic ...
Children's picture books, as a form of text interwoven with vision and language, carry rich cultural information and educational functions. The purpose of this article is to explore the phenomenon of multimodal metaphor in children's picture books and reveal its influence on ideological construction. Firstly, the article compiles the research background and puts forward the research value ...
Digital technologies have revolutionized the business field, offering significant opportunities for small and medium-sized enterprises (SMEs) to enhance sustainability and value creation. This study investigates the impact of digital technology adoption on economic and social value creation, as well as SME performance. Specifically, it examines how social media applications, big data analytics ...