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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.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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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 .

Table of contents

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.

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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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Case Study Methodology in Business Research

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The complete guide for how to design and conduct theory-testing and other case studies… Case Study Methodology in Business Research sets out structures and guidelines that assist students and researchers from a wide range of disciplines to develop their case study research in a consistent and rigorous manner. It clarifies the differences between practice-oriented and theory-oriented research and, within the latter category, between theory-testing and theory-building. It describes in detail how to design and conduct different types of case study research, providing students and researchers with everything they need for their project. The main aims are to: * present a broad spectrum of types of case study research (including practice-oriented case studies, theory-building case studies and theory-testing case studies) in one consistent methodological framework. * emphasize and clearly illustrate that the case study is the preferred research strategy for testing deterministic propositions such as those expressing a necessary condition case by case and that the survey is the preferred research strategy for testing probabilistic propositions. * stress the role of replication in all theory-testing research, irrespective of which research strategy is chosen for a specific test. * give more weight to the importance of theory-testing relative to theory-building. Case Study Methodology in Business Research is a clear, concise and comprehensive text for case study methodology. Templates are supplied for case study protocol and how to report a case study. A modular textbook primarily aimed at serving research methodology courses for final year undergraduate students and graduate students in Business Administration and Management, which is also useful as a handbook for researchers. Written by Jan Dul, Professor of Technology and Human Factors, RSM Erasmus University, Rotterdam and Tony Hak, Associate professor of Research Methodology, RSM Erasmus University, Rotterdam, in collaboration with other authors from RSM Erasmus University.

What the Case Study Method Really Teaches

• Nitin Nohria

Seven meta-skills that stick even if the cases fade from memory.

It’s been 100 years since Harvard Business School began using the case study method. Beyond teaching specific subject matter, the case study method excels in instilling meta-skills in students. This article explains the importance of seven such skills: preparation, discernment, bias recognition, judgement, collaboration, curiosity, and self-confidence.

During my decade as dean of Harvard Business School, I spent hundreds of hours talking with our alumni. To enliven these conversations, I relied on a favorite question: “What was the most important thing you learned from your time in our MBA program?”

• Nitin Nohria is the George F. Baker Jr. and Distinguished Service University Professor. He served as the 10th dean of Harvard Business School, from 2010 to 2020.

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5 Benefits of Learning Through the Case Study Method

• 28 Nov 2023

While several factors make HBS Online unique —including a global Community and real-world outcomes —active learning through the case study method rises to the top.

In a 2023 City Square Associates survey, 74 percent of HBS Online learners who also took a course from another provider said HBS Online’s case method and real-world examples were better by comparison.

Here’s a primer on the case method, five benefits you could gain, and how to experience it for yourself.

Access your free e-book today.

What Is the Harvard Business School Case Study Method?

The case study method , or case method , is a learning technique in which you’re presented with a real-world business challenge and asked how you’d solve it. After working through it yourself and with peers, you’re told how the scenario played out.

HBS pioneered the case method in 1922. Shortly before, in 1921, the first case was written.

“How do you go into an ambiguous situation and get to the bottom of it?” says HBS Professor Jan Rivkin, former senior associate dean and chair of HBS's master of business administration (MBA) program, in a video about the case method . “That skill—the skill of figuring out a course of inquiry to choose a course of action—that skill is as relevant today as it was in 1921.”

Originally developed for the in-person MBA classroom, HBS Online adapted the case method into an engaging, interactive online learning experience in 2014.

In HBS Online courses , you learn about each case from the business professional who experienced it. After reviewing their videos, you’re prompted to take their perspective and explain how you’d handle their situation.

You then get to read peers’ responses, “star” them, and comment to further the discussion. Afterward, you learn how the professional handled it and their key takeaways.

Learn more about HBS Online's approach to the case method in the video below, and subscribe to our YouTube channel for more.

HBS Online’s adaptation of the case method incorporates the famed HBS “cold call,” in which you’re called on at random to make a decision without time to prepare.

“Learning came to life!” said Sheneka Balogun , chief administration officer and chief of staff at LeMoyne-Owen College, of her experience taking the Credential of Readiness (CORe) program . “The videos from the professors, the interactive cold calls where you were randomly selected to participate, and the case studies that enhanced and often captured the essence of objectives and learning goals were all embedded in each module. This made learning fun, engaging, and student-friendly.”

If you’re considering taking a course that leverages the case study method, here are five benefits you could experience.

5 Benefits of Learning Through Case Studies

1. take new perspectives.

The case method prompts you to consider a scenario from another person’s perspective. To work through the situation and come up with a solution, you must consider their circumstances, limitations, risk tolerance, stakeholders, resources, and potential consequences to assess how to respond.

Taking on new perspectives not only can help you navigate your own challenges but also others’. Putting yourself in someone else’s situation to understand their motivations and needs can go a long way when collaborating with stakeholders.

2. Hone Your Decision-Making Skills

Another skill you can build is the ability to make decisions effectively . The case study method forces you to use limited information to decide how to handle a problem—just like in the real world.

Throughout your career, you’ll need to make difficult decisions with incomplete or imperfect information—and sometimes, you won’t feel qualified to do so. Learning through the case method allows you to practice this skill in a low-stakes environment. When facing a real challenge, you’ll be better prepared to think quickly, collaborate with others, and present and defend your solution.

3. Become More Open-Minded

As you collaborate with peers on responses, it becomes clear that not everyone solves problems the same way. Exposing yourself to various approaches and perspectives can help you become a more open-minded professional.

When you’re part of a diverse group of learners from around the world, your experiences, cultures, and backgrounds contribute to a range of opinions on each case.

On the HBS Online course platform, you’re prompted to view and comment on others’ responses, and discussion is encouraged. This practice of considering others’ perspectives can make you more receptive in your career.

“You’d be surprised at how much you can learn from your peers,” said Ratnaditya Jonnalagadda , a software engineer who took CORe.

In addition to interacting with peers in the course platform, Jonnalagadda was part of the HBS Online Community , where he networked with other professionals and continued discussions sparked by course content.

“You get to understand your peers better, and students share examples of businesses implementing a concept from a module you just learned,” Jonnalagadda said. “It’s a very good way to cement the concepts in one's mind.”

4. Enhance Your Curiosity

One byproduct of taking on different perspectives is that it enables you to picture yourself in various roles, industries, and business functions.

“Each case offers an opportunity for students to see what resonates with them, what excites them, what bores them, which role they could imagine inhabiting in their careers,” says former HBS Dean Nitin Nohria in the Harvard Business Review . “Cases stimulate curiosity about the range of opportunities in the world and the many ways that students can make a difference as leaders.”

Through the case method, you can “try on” roles you may not have considered and feel more prepared to change or advance your career .

5. Build Your Self-Confidence

Finally, learning through the case study method can build your confidence. Each time you assume a business leader’s perspective, aim to solve a new challenge, and express and defend your opinions and decisions to peers, you prepare to do the same in your career.

According to a 2022 City Square Associates survey , 84 percent of HBS Online learners report feeling more confident making business decisions after taking a course.

“Self-confidence is difficult to teach or coach, but the case study method seems to instill it in people,” Nohria says in the Harvard Business Review . “There may well be other ways of learning these meta-skills, such as the repeated experience gained through practice or guidance from a gifted coach. However, under the direction of a masterful teacher, the case method can engage students and help them develop powerful meta-skills like no other form of teaching.”

How to Experience the Case Study Method

If the case method seems like a good fit for your learning style, experience it for yourself by taking an HBS Online course. Offerings span eight subject areas, including:

• Business essentials
• Leadership and management
• Entrepreneurship and innovation
• Digital transformation
• Finance and accounting
• Business in society

No matter which course or credential program you choose, you’ll examine case studies from real business professionals, work through their challenges alongside peers, and gain valuable insights to apply to your career.

Are you interested in discovering how HBS Online can help advance your career? Explore our course catalog and download our free guide —complete with interactive workbook sections—to determine if online learning is right for you and which course to take.

About the Author

1st Edition

Case Study Methodology in Business Research

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Description

The complete guide for how to design and conduct theory-testing and other case studies… Case Study Methodology in Business Research sets out structures and guidelines that assist students and researchers from a wide range of disciplines to develop their case study research in a consistent and rigorous manner. It clarifies the differences between practice-oriented and theory-oriented research and, within the latter category, between theory-testing and theory-building. It describes in detail how to design and conduct different types of case study research, providing students and researchers with everything they need for their project. The main aims are to: * present a broad spectrum of types of case study research (including practice-oriented case studies, theory-building case studies and theory-testing case studies) in one consistent methodological framework. * emphasize and clearly illustrate that the case study is the preferred research strategy for testing deterministic propositions such as those expressing a necessary condition case by case and that the survey is the preferred research strategy for testing probabilistic propositions. * stress the role of replication in all theory-testing research, irrespective of which research strategy is chosen for a specific test. * give more weight to the importance of theory-testing relative to theory-building. Case Study Methodology in Business Research is a clear, concise and comprehensive text for case study methodology. Templates are supplied for case study protocol and how to report a case study. A modular textbook primarily aimed at serving research methodology courses for final year undergraduate students and graduate students in Business Administration and Management, which is also useful as a handbook for researchers. Written by Jan Dul, Professor of Technology and Human Factors, RSM Erasmus University, Rotterdam and Tony Hak, Associate professor of Research Methodology, RSM Erasmus University, Rotterdam, in collaboration with other authors from RSM Erasmus University.

Table of Contents

Jan Dul, Professor of Technology and Human Factors, RSM Erasmus University, Rotterdam and Tony Hak, Associate Professor of Research Methodology, RSM Erasmus University, Rotterdam

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Case Studies

Case studies are a popular research method in business area. Case studies aim to analyze specific issues within the boundaries of a specific environment, situation or organization.

According to its design, case studies in business research can be divided into three categories: explanatory, descriptive and exploratory.

Explanatory case studies aim to answer ‘how’ or ’why’ questions with little control on behalf of researcher over occurrence of events. This type of case studies focus on phenomena within the contexts of real-life situations. Example: “An investigation into the reasons of the global financial and economic crisis of 2008 – 2010.”

Descriptive case studies aim to analyze the sequence of interpersonal events after a certain amount of time has passed. Studies in business research belonging to this category usually describe culture or sub-culture, and they attempt to discover the key phenomena. Example: “Impact of increasing levels of multiculturalism on marketing practices: A case study of McDonald’s Indonesia.”

Exploratory case studies aim to find answers to the questions of ‘what’ or ‘who’. Exploratory case study data collection method is often accompanied by additional data collection method(s) such as interviews, questionnaires, experiments etc. Example: “A study into differences of leadership practices between private and public sector organizations in Atlanta, USA.”

Advantages of case study method include data collection and analysis within the context of phenomenon, integration of qualitative and quantitative data in data analysis, and the ability to capture complexities of real-life situations so that the phenomenon can be studied in greater levels of depth. Case studies do have certain disadvantages that may include lack of rigor, challenges associated with data analysis and very little basis for generalizations of findings and conclusions.

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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 .

Table of contents

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.

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.

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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.

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 .

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.

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Introduction

"The case method of analysis involves studying actual business situations, written as an in-depth presentation of a company, its market, and its strategic decisions, in order to improve a manager's or a student's problem-solving ability. Cases typically investigate a contemporary issue in a real-life context. There are multiple issues to consider and many 'correct' or viable alternatives to solve the case issues are presented." (Encyclopedia of Management. (4th ed.) Detroit: Gale, 1999. (p.71))

"The term case studies can be ambiguous: it can mean specific examples from real companies or fictitious stories written to help students learn a topic." The resources mentioned in this guide provide a mix of both types of case studies.

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Case Study Method Advantages and Disadvantages: Essential Insights for Success

Ever wondered why the case study method is so popular in fields like business, psychology, and education? It’s because case studies offer a unique way to dive deep into real-world scenarios, allowing you to explore complex issues in a detailed manner. By examining specific instances, you gain insights that are often missed in broader surveys or theoretical research.

But like any other research method, the case study approach has its pros and cons. While it provides in-depth understanding and rich qualitative data, it can also be time-consuming and sometimes lacks generalizability. So, how do you decide if it’s the right method for your research? Let’s explore the advantages and disadvantages to help you make an informed choice.

Key Takeaways

• In-Depth Insights: Case studies provide comprehensive and detailed data by exploring specific real-world scenarios, often revealing nuances and contexts missed by broader research methods.
• Qualitative and Quantitative Data: This method involves gathering a mix of qualitative and quantitative data, enhancing the richness and depth of the research.
• Challenges of Generalizability: While case studies offer in-depth insight, their findings are often specific to the case and may not be broadly applicable to other settings or situations.
• Time-Consuming Process: Conducting case studies requires significant time and effort, which can be a limitation for busy entrepreneurs and researchers needing quick insights.
• Strategic Application: Use case studies when exploring unique business models, understanding specific customer behaviors, or analyzing specific scenarios, but employ strategies like diversifying sources and effective time management to mitigate potential drawbacks.

Understanding the Case Study Method

Curious about how the case study method can enhance your entrepreneurial journey? Let’s break it down.

What Is a Case Study?

A case study is an in-depth investigation of a single entity, such as an individual, group, organization, or event. By focusing on real-life contexts, it provides comprehensive insights that can unveil unique patterns typically missed by broader research methods. This method involves a mix of qualitative and quantitative data collection techniques, including interviews, observations, and document analysis.

How Case Studies Are Used in Research

Researchers use case studies extensively across various fields due to their flexibility and depth of analysis. In business, they help you understand market trends, customer behavior, and effective strategies by examining real-life examples. Successful startups often serve as case studies, providing valuable lessons for budding entrepreneurs. In education, case studies contextualize theories, making learning more relatable. In psychology, they offer detailed explorations of specific issues or treatments, which can inform broader practices.

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Advantages of the Case Study Method

The case study method stands out in business and entrepreneurial research, offering in-depth insights and practical lessons.

In-Depth Data Collection

You gather comprehensive data through case studies, using techniques like interviews, observations, and document reviews. By employing both qualitative and quantitative methods, you gain a thorough understanding of real-world applications and business strategies. This approach lets you explore each aspect of a business scenario, providing detailed evidence for your insights.

Uncovering Nuances and Context

Case studies reveal unique patterns and hidden details often missed by broader research methods. When you analyze a successful startup, you uncover specific strategies that contributed to its success. This method helps you understand the context behind decisions, market conditions, and customer behavior, offering actionable insights for your entrepreneurial endeavors. By focusing on the nuances, you can apply these learnings to your own business and side-hustles, refining your strategies for better outcomes.

Disadvantages of the Case Study Method

Despite the many advantages, the case study method also presents several challenges that can impact its effectiveness in business contexts.

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Limitations in Generalizability

Case studies provide in-depth insights into specific scenarios; however, they often lack broad applicability. When focusing on a single business or entrepreneur, findings may not represent the experiences of others. For instance, a strategy that worked for a particular online startup might not yield the same results for a different venture due to varying market conditions and business models. Limited sample sizes in case studies further constrain their generalizability, making it difficult to formulate universal conclusions or industry-wide trends.

Time-Consuming Nature

Conducting case studies requires significant time and effort. Collecting data through interviews, observations, and document reviews can be particularly exhaustive. As an entrepreneur juggling multiple side-hustles, dedicating time to such detailed research might detract from hands-on activities that directly impact your business. Long research durations also delay the availability of insights, making them less timely or relevant in rapidly evolving market conditions. This time-intensive nature can be a deterrent, especially when quick decision-making and agility are crucial for business success.

Balancing the Pros and Cons

For entrepreneurs and business enthusiasts like you, the case study method offers a way to dive deep into specific business scenarios. It’s a powerful tool, but it has both benefits and challenges.

When to Choose Case Study Method

Use the case study method when you need detailed, practical insights. This method works well when exploring unique business models, understanding specific customer behaviors, or examining the success strategies of other startups. Large-scale statistics can’t capture the nuances you get from a focused case study.

• Launching a new product and needing to understand early adopter behavior.
• Analyzing a failed competitor to learn what went wrong.

Strategies to Mitigate Disadvantages

To mitigate the limitations of case studies, employ these strategies:

• Diversify Sources : Include multiple case studies to build a broader understanding, reducing the risk of overgeneralization.
• Time Management : Allocate specific time slots for case study research to prevent it from consuming your hands-on business activities.
• Collaborate : Work with others to share the research load and bring in diverse perspectives.

These strategies help you get the most out of the case study method while addressing common drawbacks.

The case study method offers a unique way to gain in-depth insights and analyze complex scenarios in various fields. While it has its challenges like time consumption and limited generalizability you can still leverage its strengths by using diverse sources and effective time management. Collaborating with others can also help you make the most out of your case studies. By understanding when and how to use this method you’ll be better equipped to harness its full potential for your business or research endeavors.

Frequently Asked Questions

What are the primary benefits of the case study method in business.

The case study method offers detailed insights, helps analyze market trends, and understands customer behavior through both qualitative and quantitative data collection techniques.

What fields commonly use the case study method?

The case study method is popular in business, psychology, and education for its ability to provide comprehensive and detailed insights.

What are the main disadvantages of using the case study method in business?

The main disadvantages include limitations in generalizability and the time-consuming nature of conducting in-depth research.

How can businesses mitigate the limitations of the case study method?

Businesses can mitigate limitations by diversifying their data sources, managing research time effectively, and collaborating with others to enhance the quality and applicability of their case studies.

When should entrepreneurs and business enthusiasts choose the case study method?

Entrepreneurs and business enthusiasts should choose the case study method when they need detailed, qualitative insights that cannot be captured through other research methods.

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Home > Books > Entrepreneurship - Digital Transformation, Education, Opportunities and Challenges [Working Title]

Digital Transformation in Entrepreneurship Education: A Case Study of KABADA at the University of Monastir

Submitted: 18 July 2024 Reviewed: 20 July 2024 Published: 11 September 2024

DOI: 10.5772/intechopen.1006571

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This chapter explores the integration of digital tools in entrepreneurial education, specifically focusing on the digital tool KABADA (Knowledge Alliance of Business Idea Assessment: Digital Approach) and its impact on the entrepreneurial intentions of Generation Z students at the University of Monastir, Tunisia. The study situates itself within the broader context of the Sustainable Development Goals and the European Union’s Digital Education Action Plan, emphasizing the role of digital transformation in enhancing educational practices. By employing a quasi-experimental design, the research compares the outcomes of entrepreneurial workshops utilizing KABADA against traditional methods, highlighting the tool’s efficacy in fostering entrepreneurial knowledge and intentions. Key findings underscore the importance of incorporating digital technologies in higher education to align with global market demands and prepare future entrepreneurs. The chapter concludes with recommendations for educators and policymakers on leveraging digital tools to support sustainable and innovative entrepreneurial education.

• transformation
• entrepreneurship

Author Information

Fitouri mohamed *.

• Laboratory Innovation Strategy Entrepreneurship Finance and Economy LISEFE, Faculty of Economics and Management of Mahdia, University of Monastir, Tunisia

Samia Karoui Zouaoui

• Laboratory Innovation Strategy Entrepreneurship Finance and Economy LISEFE, Faculty of Economics and Management of Tunis, University of Tunis El Manar, Tunis, Tunisia

Akram Belhaj Mohamed

• Taif University, Saudi Arabia

*Address all correspondence to: [email protected]

1. Introduction

The implementation of the Sustainable Development Goals by the United Nations emphasizes investment in education to foster innovation. Entrepreneurial education is undergoing a digital transformation, integrating new technologies that significantly impact the educational process. Educational institutions are crucial in training future entrepreneurs, aiming to increase students’ entrepreneurial intention. Generation Z, embedded in today’s education system, promotes the diverse use of digital tools for learning [ 1 ].

UN’s MDG Objective 4 aims to increase by 2030 the number of people with skills necessary for employment, including entrepreneurial skills. Responding to this, the European Union launched the Digital Education Action Plan (2021–2027) to harmonize European education systems with high-quality digital education.

The adoption of ICT is vital in promoting sustainable educational practices. This study enriches theories on ICT and AI in entrepreneurial and sustainable education. While digital transformation is well-documented in finance and engineering, its adaptation in higher education is understudied.

Alenezi [ 2 ] notes that digital transformation is accelerating, prompting higher education to adopt new technologies. Research in entrepreneurial education exploring student entrepreneurship and innovation is expanding [ 3 ].

Authors like Kuratko [ 4 ], Pittaway and Cope [ 5 ], Fayolle and Gailly [ 6 ], and Lackéus [ 7 ] have deepened understanding of entrepreneurial education. Findings on its impact on entrepreneurial intent vary; some studies report positive effects [ 8 , 9 , 10 ], while others find mixed or negative results [ 11 , 12 ].

The increasing use of online learning and AI in higher education suggests AI’s potential to enhance educational processes [ 13 ]. However, the application of digital tools in entrepreneurial education remains underexplored [ 14 ].

This study evaluates the digital tool KABADA (Knowledge Alliance of Business Idea Assessment: Digital Approach) in entrepreneurial workshops during digital transformation (DT). Focused on Generation Z, known for digital immersion [ 15 ], KABADA, developed through Erasmus+, is examined for enhancing influences entrepreneurial intentions (EI) among students at the University of Monastir, Tunisia.

In Tunisia, the University of Monastir leads in integrating ICT into entrepreneurial education, aligning with MDG goals to strengthen student entrepreneurship and innovation skills. This research aims to understand KABADA’s impact on Tunisian students’ entrepreneurial intention, preparing them for global market challenges.

The chapter begins with a literature review on digital education transformation and digital tools in entrepreneurial education, followed by research methodology, results analysis at the University of Monastir, and concludes with a discussion, recommendations, and research significance.

2. Literature review

Digitization, as defined by Vial [ 16 ] and Mirzagayeva and Aslanov [ 17 ], encompasses the adoption of digital technologies across various sectors. Giuggioli and Pellegrini [ 18 ] further elaborate that digitization involves transforming analog processes and organizational tasks into digital formats, including management processes.

The concept of digital transformation and its impact on sustainable development is complex and not extensively explored in scientific literature. Holopainen et al. [ 19 ] investigate how digital transformation influences value creation, emphasizing the need for organizations to integrate digital capabilities with existing value chains.

Digitization is closely intertwined with sustainability [ 20 ]. Ionescu-Feleagă et al. [ 21 ] highlight that digitization presents new opportunities and challenges for organizations aiming to implement sustainable strategies. They find a positive correlation between the Digital Economy and Society Index (DESI) and the Sustainable Development Goals Index (SDG Index) across EU countries from 2019 to 2021.

Iannone and Mille [ 22 ] argues that digitization enhances efficiency by automating production stages and enabling precise monitoring of environmental impacts, thereby supporting sustainable development goals. From an economic perspective, digitization also boosts the demand for human capital, contributing to economic growth [ 23 , 24 ]. The COVID-19 pandemic has catalyzed a surge in studies on the digitization of higher education [ 25 ]. Benavides et al. [ 26 ] argue that higher education institutions are grappling with the impacts of Industry 4.0, necessitating comprehensive digital transformation. Many universities prioritize enhancing academic quality and global rankings through digital integration in teaching processes.

However, Rodríguez-Abitia and Bribiesca-Correa [ 27 ] find that universities lag behind other sectors in digital transformation due to ineffective leadership, cultural resistance, limited innovation, and financial constraints. Akour and Alenezi [ 28 ] highlight the increasing concerns among educational stakeholders regarding digitization, emphasizing the growing importance of digital skills in education and the workplace.

Ratten and Usmanij [ 29 ] link current trends in entrepreneurship education (EE) with emerging employment patterns like the gig economy and digital workplace transformation. They emphasize the shift toward digital entrepreneurship facilitated by digital platforms.

Five key factors driving digitalization in EE include internal culture and skills of teachers and students, cost efficiencies, and industry competition [ 30 , 31 ]. Despite advocacy for contemporary skills in education, the integration of digital skills into curricula and teaching practices remains inadequate [ 32 ].

Pan et al. [ 33 ], Cattaneo et al. [ 34 ], and Hammoda [ 35 ] highlight significant investments in technology by higher education institutions to reduce costs and enhance educational outcomes through digital tools. Frey and Osborne [ 36 ] underscore the increasing role of digital tools in distance learning, which proves crucial for cost savings and improving educational accessibility.

Artificial intelligence (AI) technologies are advancing in education, with roots in automation dating back to the 1950s for accelerating work processes. Huang et al. [ 37 ] note the prominence of Bayesian statistics in machine learning research from the 1960s. AI’s integration in education aims for personalized, effective, transformative, results-oriented, inclusive, and sustainable learning experiences [ 35 ].

AI applications include machine learning and intelligent machines, enhancing data analysis capabilities for deductive and inductive reasoning [ 35 ]. The shift toward AI-based learning tools in education is seen as transformative [ 38 ], with intelligent tutoring systems predicted to revolutionize educational practices [ 35 , 36 , 37 , 38 , 39 ].

Giuggioli and Pellegrini [ 18 ] advocate for integrating AI to offer students access to vast information resources, suggesting a shift toward innovative, practical, inclusive, and entrepreneurial-focused education [ 40 ].

Entrepreneurial intention is shaped by personal characteristics and self-analysis, influencing career choices and entrepreneurial aspirations [ 41 ]. Kasler et al. [ 42 ] highlight significant correlations between hope, courage, and perceptions of employability, while Lim et al. [ 43 ] stress the moderating role of self-efficacy in professional development outcomes.

Researchers like Lesinskis et al. [ 44 ] and Davey et al. [ 45 ] address disparities among Generation Z in different global regions, noting varying inclinations toward entrepreneurship. Ajzen’s [ 46 ] Planned Behavior Theory (1991) is widely used to understand and modify social behavior, emphasizing the influence of positive attitudes and subjective norms on behavioral intentions [ 47 ].

According to Vamvaka et al. [ 47 ], the Theory of Planned Behavior (TPB) views entrepreneurship as a deliberate, planned behavior developed over time. They advocate for further empirical studies to analyze perceptions of entrepreneurship.

Cheung [ 41 ] underscores the importance of fostering entrepreneurial thinking early in life to enhance emotional intelligence. Overall, the impact of entrepreneurship education on entrepreneurial intentions remains a complex area of study.

According to recent research by Asimakopoulos et al. [ 8 ], Cera et al. [ 9 ], Iwu et al. [ 48 ], Wang et al. [ 10 ], and Pan [ 33 ], entrepreneurial education demonstrates a positive correlation with entrepreneurial intentions. Akpoviroro et al. [ 49 ] highlight a significant link between understanding business models in AI studies and entrepreneurial orientation. Carvalho et al. [ 50 , 51 ] and Wibowo and Narmaditya [ 40 ] specifically focus on digital entrepreneurship, finding that it fosters intentions for digital enterprise development among students. Conversely, research by Reissová et al. [ 12 ] and Martínez-Gregorio et al. [ 11 ] challenges or restricts the perceived beneficial impact of entrepreneurial education on entrepreneurial intentions.

Generational influences such as societal factors, global developments, technology, and demographics shape each generation, contributing unique skills, individuality, and perspectives that benefit society as a whole [ 42 ]. Understanding Generation Z’s distinct characteristics, shaped by their technological experiences and socio-cultural expectations, is crucial for adapting to their needs, motivations, and interpersonal dynamics [ 45 ].

Based on an extensive literature review, a conceptual framework has been developed, depicted in Figure 1 , illustrating variables and hypothesized relationships. The framework predicts that entrepreneurship education (EE) influences entrepreneurial intentions (EI) and other outcomes, with the Theory of Planned Behavior (TPB) antecedents acting as mediators. The impact of EE is moderated by two types of workshops: traditional workshops and those utilizing the digital tool KABADA.

Conceptual framework.

Based on the comprehensive literature analysis, the following primary hypotheses and sub-hypotheses have been formulated:

Primary hypotheses:

H1. Utilizing the digital tool KABADA in entrepreneurship education (EE) workshops positively influences the EI of Generation Z.

H2. The positive impact on the EI of Generation Z is more pronounced when the digital tool KABADA is used in EE workshops compared to traditional EE workshops.

Sub-hypotheses:

H2a. The digital tool KABADA enhances entrepreneurial knowledge among Generation Z more effectively in EE workshops than traditional EE workshops.

H2b. Generation Z shows greater interest in becoming entrepreneurs when exposed to the digital tool KABADA in EE workshops compared to traditional EE workshops.

H2c. The use of the digital tool KABADA inspires Generation Z more significantly to consider entrepreneurship in EE workshops compared to traditional EE workshops.

H2d. Generation Z perceives entrepreneurship as more fulfilling when engaged with the digital tool KABADA in EE workshops than in traditional EE workshops.

H2e. Overall interest in entrepreneurship is higher among Generation Z students participating in EE workshops with the digital tool KABADA compared to traditional EE workshops.

H2f. Generation Z expresses a stronger intention to initiate entrepreneurial ventures within the next 5 years when exposed to the digital tool KABADA in EE workshops compared to traditional EE workshops.

3. Data collection and research approach

3.1 kabada digital tool for online entrepreneurship education.

In contemporary times, the utilization of automated software incorporating AI algorithms and machine learning components is prevalent across various sectors and increasingly essential in the field of education [ 52 , 53 ]. This article’s empirical section investigates an experiment examining the impact of the digital tool KABADA on the entrepreneurial enthusiasm (EI) of Generation Z students. KABADA, an acronym for Knowledge Alliance of Business Idea Assessment: Digital Approach, was developed with the support of the Erasmus+ project. The study of KABADA, which integrates AI algorithms, contributes significantly to our understanding of AI applications in entrepreneurship education. Launched in 2022 by the ERASMUS+ project group, the KABADA business planning tool provides an organized, online solution assisting students in the step-by-step creation of a business plan.

According to Ahmed et al. [ 54 ], Dasgupta [ 55 ], and Antwi and Kasim [ 56 ], students must understand the structure of a business plan and practice creating one to implement business ideas effectively. Utilizing theoretical studies, business statistics, and artificial intelligence, KABADA supports novice entrepreneurs at every stage of business plan design [ 57 ]. The tool targets entrepreneurs, financial institutions, and labor organizations but is primarily aimed at students from various degree programs, including both business and non-business students with diverse backgrounds.

The KABADA tool’s foundation lies in the structure and elements of a business plan, encompassing all critical areas of business planning. Eliades et al. [ 58 ] note that students are trained in six major stages: industry statistics, industry risks, designing a Business Model Canvas, SWOT analysis, personal characteristics analysis, and financial forecasts. Initially, KABADA introduces users to the business statistics of their chosen industry within the country where they intend to become entrepreneurs. According to Martínez-Gregorio et al. [ 11 ], the system compares national indicators with industry trends in the European Union, derived from Eurostat data.

Subsequently, KABADA educates users about various macroeconomic, industrial, and business risks faced by companies in the selected industry. Martínez-Gregorio et al. [ 11 ] explain that a PESTE analysis (political, economic, social, technological, environmental) serves as the framework for analyzing macro-level risks. ELIADES et al. [ 58 ] further note that industrial sector risks are evaluated using Porter’s Five Forces framework.

Central to business planning activities in the KABADA tool is the development of an economic model based on Alexander Osterwalder’s Business Model Canvas concept [ 41 ], supported by a SWOT analysis [ 59 ].

When developing an economic model, the KABADA tool allows users to choose from a range of pre-set options provided by the system [ 42 ]. Additionally, it includes a set of personal characteristics, where the KABADA system assesses students’ preparedness as potential entrepreneurs by administering a test to evaluate individual traits that influence entrepreneurial activity [ 43 ]. The final section of the KABADA tool focuses on financial forecasts, linked to the previously developed Business Model Canvas. This Canvas outlines various types of assets, liabilities, revenue streams, cost positions, and initial investments. Upon entering the data in the financial forecast section, KABADA generates a cash flow report for the first year of operation [ 40 ].

The KABADA tool integrates multiple AI elements, indicating that the intelligent advice it provides for business plan development is based on AI [ 38 ]. According to Hammoda [ 35 ], the KABADA tool operates on virtual servers running AI software developed with the Python programming language, using Bayesian networks to construct business plans. Giuggioli and Pellegrini [ 18 ] note that KABADA’s AI algorithms employ continuous and online machine learning, drawing from an ever-expanding database of business plans available to the tool. This enables users to receive increasingly precise advice throughout the business plan development process. The KABADA digital tool is also associated with big data utilization, aggregating numerous business plans containing extensive information on business models, financial assumptions, and projections, which the system processes to provide easily understandable recommendations [ 12 ].

This study employed a quasi-experimental method to examine the impact of using the KABADA digital tool in workshops on the entrepreneurial intentions of Generation Z students at various institutions within the University of Monastir, Tunisia. The experiment was conducted from October 2023 to February 2024. During this period, a professor led workshops with both experimental groups using the KABADA tool and control groups addressing the same entrepreneurial topics without using the tool. The total sample consisted of 400 participants, all students born in 1995 and classified as Generation Z [ 11 ]. Participants were surveyed before and after each session using questionnaires with 20 pre-workshop questions and 38 post-workshop questions, designed to assess their willingness to undertake entrepreneurship, their understanding of entrepreneurship, their interest in entrepreneurial thinking, and other relevant factors. Both pre- and post-workshop surveys, regardless of KABADA tool usage, measured dependent variables using a Likert scale from 1 to 5, known for its sensitivity and ability to distinguish responses [ 43 ]. Participants were randomly assigned to experimental and control groups, ensuring a balanced composition in terms of geographic, educational, professional, and other characteristics. Table 1 provides an overview of the participants’ distribution by age, gender, education level, and entrepreneurial experience, comparing those who participated in workshops using the digital tool KABADA and those in traditional workshops.

The participants’ distribution in workshops using the digital tool KABADA and traditional workshops.

Source: Authors (data of University of Monastir students).

To evaluate the distribution of respondents by age, gender, education level, and entrepreneurial experience before and after their participation in workshops using the digital tool KABADA and traditional workshops, we employed chi-square tests and associated p-values. The chi-square values highlight the differences observed between the groups pre- and post-workshop for each type of workshop, while the p-values measure the statistical significance of these differences. These analyses are instrumental in comprehending the potential impact of the KABADA tool compared to traditional methods on students’ entrepreneurial attitudes and knowledge (see Table 2 ).

Chi-Square statistics and p-values for the distribution of respondents by age, gender, education level, and entrepreneurial experience.

Source: Calculated by the authors based on a sample of 400 students. Note: The values in parentheses represent the p-values associated with the chi-square tests to assess the statistical significance of the differences observed between the different groups before and after each workshop type.

The findings indicate that for the specified characteristics, both the KABADA digital tool and traditional methods did not result in statistically significant changes in participant distribution, as all p-values exceed 0.05 except for the education level. There is a near-significant difference (p = 0.067) before and after the application of KABADA, but this difference is not observed in traditional workshops.

The results of descriptive statistics, the Shapiro–Wilk test, the Wilcoxon–Mann–Whitney test, and the Brunner–Munzel test for dependent variables (self-assessment of entrepreneurial knowledge, intention to become an entrepreneur, interest in imagining oneself as an entrepreneur, inspiration from imagining oneself as an entrepreneur, approval of the idea that entrepreneurship could fulfill one’s life, interest in entrepreneurship, and consideration of starting a business within the next 5 years) reveal that the use of the KABADA digital tool has a modest positive impact on certain variables, such as the intention to become an entrepreneur. However, changes in other variables are less pronounced or negative. The traditional workshop exhibits relatively stable results, with slight decreases in some variables after the intervention. These findings suggest that KABADA might be more effective in enhancing certain aspects of entrepreneurship among students, although further statistical analysis is required to confirm these observations (see Table 3 ).

Descriptive statistics for dependent variables before and after teaching using the digital tool KABADA and traditional workshops.

Source: Calculated by the authors based on survey data.

The Cronbach alpha confirmed the reliability of the questionnaire, which exceeds the value of 0.760, confirming its internal consistency. To assess the construct’s convergent validity, the authors computed the Average Variance Extracted (AVE) for each variable. The obtained AVE values, which ranged above the minimum threshold of 0.50 (with a minimum of 0.625), indicate satisfactory convergent validity. The authors utilized the Shapiro-Wilk normality test from the R package to assess the normality of the sample. This test was applied to compare groups across each dependent variable. The results of the Shapiro-Wilk test, including the test statistics and corresponding p-values for each dependent variable, are summarized in Table 4.

Shapiro-Wilk test statistics and normality test p values.

Source: Authors.

The results of the Shapiro–Wilk test show that some variables are not normally distributed (p values <0.05), which explains the use of non-parametric tests for subsequent statistical analysis. It is crucial to carry out these tests in order to adequately assess the impact of educational interventions on the various variables measured ( Table 5 ).

Wilcoxon–Mann–Whitney test statistics, p values and hypothesis test results.

Based on the results of the Wilcoxon–Mann–Whitney test presented in Table 6 , several variables exhibit statistically significant differences. Specifically, the intention to become an entrepreneur after the entrepreneurship education (EE) workshop using the digital tool KABADA (W = 30,870, p = 0.005) compared to traditional EE workshops (W = 28,108, p = 0.003) shows significant differences. Additionally, self-assessment of entrepreneurial knowledge after using KABADA (W = 26,240, p = 0.045), interest in entrepreneurship (W = 24,211, p = 0.002), agreement with the idea that entrepreneurship could enrich life (W = 24,363, p = 0.006), interest in entrepreneurship (W = 24,283, p = 0.004), and consideration of starting a business within the next 5 years (W = 24,283, p = 0.004) also demonstrate notable differences between the two methods. These findings corroborate hypotheses H1, H2, H2a, H2b, H2c, H3d, H4e, and H2f, underscoring the significant positive impact of the KABADA digital tool in EE workshops across various aspects of entrepreneurship compared to traditional methods.

Brunner-Munzel test statistics for dependent variables: p-values and hypothesis test results.

Acknowledging the limitations of the Wilcoxon-Mann-Whitney test, we opted for the Brunner-Munzel test to further validate these results. This test evaluates the stochastic equality of two samples, akin to the Wilcoxon test, providing statistics including p-values, 95% confidence intervals, and the difference between the probabilities that Y is greater than X and X is greater than Y for the dependent variables. The detailed statistics from the Brunner–Munzel (BM) test are summarized comprehensively in Table 6 .

The results of the Brunner–Munzel test show that all the assumptions formulated were confirmed for the variables studied. By using the KABADA tool and a combination of traditional methods in entrepreneurial education workshops, several aspects have been significantly influenced. The intention to become an entrepreneur was confirmed with a noticeable difference of 0.128 (p = 0.0169). Similarly, the self-assessment of knowledge (difference of 0.138, p = 0.0271), the sense of interest (difference of 0.154, p = 0.0092), the agreement on the fulfillment of life (0.145, p = 0.0134), the interest in entrepreneurship (0.0149, p = 0.0115), and the consideration of starting a business in the next 5 years (0.198, p = 0,0008) all showed significant improvements. Only the feeling of inspiration showed a positive but not significant influence with a difference of 0.110 and a p-value of 0.0503. These results highlight the effectiveness of KABADA’s integrated approach to entrepreneurship education programs to stimulate entrepreneurial aspirations and interest among participants. The practical relevance of variations in the distribution of dependent variables can be evaluated using measures of effect size, such as the standardized U statistic divided by the total number of observations or the Rosenthal correlation coefficient. The Wilcoxon effect size statistics are summarized in Table 7 , including the number of participants in comparable groups and 95% confidence intervals based on 1000 bootstrap iterations of effect size values.

Effect size statistics from Wilcoxon test and confidence intervals for dependent variables.

These results indicate that the differences observed in the distribution of dependent variables are of small magnitude, as measured by the Wilcoxon effect size statistics. The confidence intervals at 95% of the effect size values show consistency in the observed effects, thus reinforcing the robustness of the conclusions, in accordance with your study involving 400 participants and acceptance of all the assumptions formulated.

4. Discussion

Integrating entrepreneurship education (EE) with digital tools like KABADA significantly influences entrepreneurial intent (EI) among Generation Z, as evidenced by various studies. Research by Kasler et al. [ 42 ], Lim et al. [ 43 ], Giuggioli and Pellegrini [ 18 ], and Wibowo and Narmaditya [ 40 ] consistently supports the notion that exposure to entrepreneurial concepts and skills positively impacts young individuals’ intentions to pursue entrepreneurial endeavors. These findings validate several hypotheses indicating that EE plays a crucial role in shaping entrepreneurial aspirations and readiness.

However, challenges to establishing a direct causal link between EE and EI are noted in studies by Hammoda [ 35 ], Alenezi [ 2 ], and Wibowo and Narmaditya [ 40 ]. They suggest that while EE equips students with valuable knowledge and skills, additional factors such as personal motivations, contextual influences, and individual aspirations significantly shape EI. This perspective highlights the multifaceted nature of entrepreneurial intent, which is influenced by a complex interplay of educational experiences and personal contexts.

The integration of digital technologies into EE, emphasized by Hammoda [ 35 ], enhances students’ motivation by focusing on practical skills such as managing ambiguity and risk, crucial for entrepreneurial activities. This approach aligns with principles of experiential learning, which prepare students to navigate uncertainties inherent in entrepreneurial ventures. Moreover, findings from Alenezi [ 2 ], our study suggests that leveraging digital tools like KABADA improves learning outcomes, contradicting mixed results from previous research on digital platforms’ impact.

Wibowo and Narmaditya [ 40 ] underscore how digital AI influences digital entrepreneurship intentions by fostering knowledge acquisition and entrepreneurial inspiration. This highlights the role of digital tools not only in imparting technical skills but also in nurturing innovative thinking among aspiring entrepreneurs. Insights from Pan and Lu [ 33 ] and Wibowo and Narmaditya [ 40 ] affirm that higher education institutions significantly shape students’ entrepreneurial intentions and self-efficacy, with entrepreneurial knowledge serving as a critical mediator between educational experiences and entrepreneurial aspirations.

Furthermore, Almeida’ et al. [ 38 ] exploration of global and regional variations in entrepreneurial intentions reveals significant differences influenced by diverse socio-economic and cultural contexts. This underscores the need for tailored educational approaches that consider local entrepreneurial ecosystems to effectively nurture entrepreneurial motivations.

Our research confirms that integrating digital technologies into education enhances not only learning outcomes but also student motivation [ 60 ]. The interactive nature of digital tools like KABADA engages students actively in learning processes, making theoretical concepts tangible through practical application and simulation exercises.

Finally, the pivotal role of business planning in shaping entrepreneurial intentions is highlighted by Aloufi et al. [ 52 ], Dasgupta and Bhattacharya [ 53 ], and others. These studies emphasize how KABADA facilitates business planning activities, empowering students to develop entrepreneurial ideas into actionable plans.

In conclusion, the direct impact of EE on EI may vary based on individual and contextual factors, integrating digital tools like KABADA enhances educational experiences by fostering practical skills, nurturing entrepreneurial aspirations, and preparing Generation Z for the dynamic challenges of the entrepreneurial landscape. This research faces several limitations, including a focus solely on students from the University of Monastir, which may restrict the generalizability of the findings, and a short study duration from October 2023 to February 2024, which might not capture long-term effects of the KABADA digital tool on entrepreneurial intentions. Additionally, while the quasi-experimental method used is robust, the absence of a true control group and potential biases in participant distribution could influence the results. Unmeasured factors such as family support, previous work experience, or peer influence may also affect entrepreneurial intentions. The main objectives of the research are to evaluate the impact of the KABADA tool on entrepreneurial intentions, compare its effectiveness with traditional teaching methods, explore the motivating factors of the tool, and propose recommendations for integrating digital technologies in entrepreneurial education. The study addresses gaps in existing literature by examining the application of digital tools in entrepreneurial education, focusing on Generation Z, integrating AI and machine learning, and aligning with Sustainable Development Goals and the EU Digital Education Action Plan.

5. Conclusion

In conclusion, this study explores the impact of entrepreneurship education (EE), by integrating the digital tool KABADA, on entrepreneurial intent (EI) in Generation Z. Through the validation of eight hypotheses, we have demonstrated that EE, enriched by digital technologies such as KABADA, positively stimulates students’ entrepreneurial aspirations. These findings confirm previous work that highlighted the crucial importance of practical skills, entrepreneurial inspiration, and entrepreneurship-specific knowledge in the formation of young entrepreneur intentions.

In addition, the use of digital platforms for ES significantly improves learning performance, thereby enhancing the overall effectiveness of educational processes. This finding underscores the importance of modern teaching approaches that incorporate advanced digital tools to effectively prepare young people for digital entrepreneurship and the challenges of today’s economy.

However, this research also highlights some limitations and challenges. Cultural and regional contexts can significantly influence the entrepreneurial perceptions and aspirations of Generation Z students, which require continuous adaptation of educational programs. Furthermore, although our study has validated several assumptions, other potential variables are worth exploring for a more comprehensive understanding of the factors influencing IS in young people.

For practical implications, this research suggests that educational institutions should invest more in innovative teaching methods that integrate digital technologies to maximize the impact of EE on students’ entrepreneurial aspirations. This could stimulate not only economic and social innovation but also effectively prepare the future workforce to adapt to the rapid transformation of the digital world.

Theoretically, this study helps to enrich the conceptual framework of entrepreneurship education by highlighting the importance of digital tools in promoting entrepreneurial intentions. Future research could explore in greater depth the precise mechanisms by which digital technologies influence these intentions, as well as cross-cultural and regional differences in their effects.

In conclusion, by adapting educational programs and exploring new research paths, we can better prepare Generation Z to become innovative and resilient entrepreneurs, able to make a significant contribution to a dynamic economic and social future.

The authors received no direct funding for this research.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes on contributors

Fitouri Mohamed is at the University of Monastir Tunisia. Fitouri M. has experience of about 17 years in teaching, business, and research. He has published many journal articles.

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The Narrative of Circular Economy and Sustainability -A Critical Analysis of Fashion Industry

• Original Paper
• Published: 12 September 2024

Cite this article

• Ruchi Gautam   ORCID: orcid.org/0009-0000-1551-7537 1  

Amidst growing environmental concerns, the fashion industry faces a pivotal moment marked by its substantial ecological footprint. This study delves into the intersection of circular economy (CE) and sustainable development (SD) within the fashion industry, emphasizing the urgent need for change. The research aims to identify implementation challenges while highlighting the timeliness of shifting towards sustainable fashion practices.

Methodology

This research employs a comprehensive methodology, incorporating both a systematic literature review (SLR) followed by real-world case studies. An extensive search was conducted across pertinent journals from 2010 to 2022 using Scopus, with keywords related to circular economy, sustainable development, and the fashion industry. Furthermore, eight secondary case studies were purposively selected and analysed.

Circular economy principles offer a promising avenue for advancing fashion sustainability. However, hurdles persist, including complex supply chains, mixed fabric recycling, inadequate infrastructure, and the lack of industry standards. Overcoming these challenges necessitates systemic changes, collaborative efforts, and technological investments. Nevertheless, embracing circularity presents valuable opportunities for enhancing resource efficiency and cultivating a socially and environmentally conscious fashion industry.

Significance

This study highlights the pressing need for a profound transformation within the fashion industry to mitigate its environmental impact. By adopting circular economy principles, the industry can significantly contribute to the United Nations Sustainable Development Goals. Achieving this, however, requires substantial shifts in business models and industry practices.

Originality

The research stands out for its comprehensive approach, integrating a systematic literature review with real-world case studies. By analysing a diverse array of journals and cases spanning nearly a decade, it offers a comprehensive understanding of the current state of circular economy adoption within the fashion sector.

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A multi-state evaluation of extreme risk protection orders: a research protocol

• April M. Zeoli 1 ,
• Amy Molocznik 2 ,
• Jennifer Paruk 3 ,
• Elise Omaki 2 ,
• Shannon Frattaroli 2 ,
• Marian E. Betz 4 ,
• Annette Christy 5 ,
• Reena Kapoor 6 ,
• Christopher Knoepke 7 ,
• Wenjuan Ma 8 ,
• Michael A. Norko 6 ,
• Veronica A. Pear 9 ,
• Ali Rowhani-Rahbar 10 ,
• Julia P. Schleimer 9 ,
• Jeffrey W. Swanson 11 &
• Garen J. Wintemute 9  

Injury Epidemiology volume  11 , Article number:  49 ( 2024 ) Cite this article

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Extreme Risk Protection Orders (ERPOs) are civil court orders that prohibit firearm purchase and possession when someone is behaving dangerously and is at risk of harming themselves and/or others. As of June 2024, ERPOs are available in 21 states and the District of Columbia to prevent firearm violence. This paper describes the design and protocol of a six-state study of ERPO use.

The six states included are California, Colorado, Connecticut, Florida, Maryland, and Washington. During the 3-year project period (2020–2023), ERPO case files were obtained through public records requests or through agreements with agencies with access to these data in each state. A team of over four dozen research assistants from seven institutions coded 6628 ERPO cases, abstracting 80 variables per case under domains related to respondent characteristics, events and behaviors leading to ERPO petitions, petitioner types, and court outcomes. Research assistants received didactic training through an online learning management system that included virtual training modules, quizzes, practice coding exercises, and two virtual synchronous sessions. A protocol for gaining strong interrater reliability was used. Research assistants also learned strategies for reducing the risk of experiencing secondary trauma through the coding process, identifying its occurrence, and obtaining help.

Addressing firearm violence in the U.S. is a priority. Understanding ERPO use in these six states can inform implementation planning and ERPO uptake, including promising opportunities to enhance safety and prevent firearm-related injuries and deaths. By publishing this protocol, we offer detailed insight into the methods underlying the papers published from these data, and the process of managing data abstraction from ERPO case files across the multi-state and multi-institution teams involved. Such information may also inform future analyses of this data, and future replication efforts.

Registration

This protocol is registered on Open Science Framework ( https://osf.io/kv4fc/ ).

In 2022 in the United States, over 27,000 people died by firearm suicide and more than 20,000 people were killed as a result of interpersonal firearm violence resulting in 14.2/100,000 people dying from intentional firearm injuries that year (Centers for Disease Control and Prevention, National Center for Injury Prevention and Control 2023 ). Preventing firearm access by those identified to be at risk of harming themselves and/or others is a logical strategy to reduce firearm homicide, firearm suicide, and nonfatal firearm violence. One promising and innovative opportunity to address firearm violence, therefore, is with extreme risk protection order (ERPO) laws. ERPO laws, or “red flag laws” as they are often called in popular discourse, provide a civil court process to temporarily prohibit firearm purchase and possession by individuals who are behaving dangerously and are at risk of harming themselves or others. As of June 2024, 21 states and the District of Columbia have passed ERPO-style bills into law (Johns Hopkins Bloomberg School of Public Health 2024 ). ERPOs fill an important policy gap because some individuals at risk of harming themselves and/or others, are legally able to purchase and possess firearms and cannot otherwise be disarmed. Therefore, ERPOs provide a mechanism for preventing firearm access (and potentially firearm violence) when an individual who represents a credible threat of violence is known but is not prohibited from accessing firearms by other legal mechanisms.

Research on ERPOs and their use and outcomes is in its infancy. Multiple studies have described characteristics of ERPO respondents and risk behaviors detailed in the applications in a single state or county ERPO (Barnard et al. 2021 ; Frattaroli et al. 2020 ; Pear et al. 2022 ; Rowhani-Rahbar et al. 2020 ; Swanson et al. 2019 , 2017 ; Zeoli et al. 2021 ). Few studies have examined outcomes, and those that have generally focus on suicide outcomes, with findings suggestive of a reduction in suicide risk when ERPOs are used (Swanson et al. 2019 , 2017 ; Miller et al. 2024 ) and an association at the state-level between ERPO law enactment and a reduction in firearm suicides (Kivisto and Phalen 2018 ). To our knowledge, this is the first multi-state ERPO study.

Here we describe the protocol we used to conduct a six-state study of ERPO case files designed to characterize ERPO petitions, petitioners and respondents (individual parties in the ERPO petition), court outcomes, and identify whether ERPOs are associated with reductions in suicide across geographically, demographically, and politically diverse states. The protocol described in this manuscript details (1) how we accessed ERPO case files in six states; (2) an explanation of the process of standardizing data from official records across the six states; (3) guidance for training research assistants (RAs) and maintaining consistent data abstraction practices across a multi-state, multi-institution RA team; and (4) strategies for reducing and responding to secondary trauma risk experienced by RAs as a result of reading ERPO narratives, which can include graphic descriptions of violence and crises.

During the 3-year project period (2020–2023), we conducted a multi-state study (Zeoli et al. 2022 ) of ERPO use with data from six states (California, Colorado, Connecticut, Florida, Maryland, and Washington). We selected these states for three reasons. First, all are engaged in efforts to implement ERPOs, and those implementation efforts are either yielding a critical mass of ERPO petitions filed or an informative implementation context. Second, these states are geographically and politically diverse, which may impact implementation and use. Third, we were able to access ERPO case files in the selected states. While ERPO statutes differ in some ways across the six states (Smart et al. 2020 ), all share a general process that involves a petition, court hearing, and court decision about whether to temporarily prohibit the individual named in the order from purchasing and possessing firearms.

ERPO court records are publicly available for all study states except Maryland. In the five states where ERPO data are public, we requested ERPO court records through public records searches or through agencies with access to these data. For California and Washington, ERPO case numbers and non-public identifying information such as respondent name, county, and ERPO date and type were first obtained from the Department of Justice (DOJ) (for California) and the Administrative Office of the Courts (for Washington) through a special request; this information was then used to request the publicly available court records from individual local and county courts throughout the two states.

In Colorado, a local team member contacted each county court to request ERPO records. In Connecticut, the ERPO statute (Connecticut General Assembly 2023 ) specifically requires the court to give notice of the court order to the Department of Mental Health and Addiction Services, and it is through these court notices (that have been maintained since 2013) that the study team accessed the public records. In Florida, we obtained most of the case files through Florida’s secure Comprehensive Case Information System (CCIS), a centralized database of court case information, which streamlined the process of accessing these publicly available records. For a few counties, we obtained the publicly available case files directly from the County Clerks of Court.

In Maryland, at the time of the study, ERPO records were restricted to select entities named in the statute (Brown 2022 ). Working with the Maryland Attorney General’s Office, we requested and obtained ERPO case files from District Courts throughout the State.

It should be noted that ERPO court records are often paper documents and may not be digitally accessible. This is true for California, Maryland, and Washington. Accessing paper copies of ERPO case files in these three states required a significant amount of time and coordination to collect the documents and scan and upload them to secure, password protected file storage systems housed at the collaborating universities in each state. The study teams in Colorado, Connecticut, and Florida gained access to digital copies of case files.

We requested ERPO case files for the time period beginning at ERPO enactment in each state through June 30, 2020 (see Table  1 ) with the exception of Connecticut, where the law took effect in 1999 but full ERPO case reports were only available beginning January 1, 2013. For California, the request process differed slightly. We first obtained identifying information on ERPO respondents through California DOJ and used that information to request the publicly available case files. However, due to California DOJ’s process of overwriting respondents’ older orders with newer orders in the primary file every 3 weeks, it is possible that, in the early days of collecting California’s ERPO case files, we missed cases when an individual was a respondent to more than one ERPO action. Once the California team learned of the California DOJ process in mid-2019, we started requesting ERPO case numbers and respondent identifying information from the California DOJ every 21 days so that we would not miss any order data due to the data overwriting process.

This effectively means we were unable to get case-level data for California prior to mid-2019 and therefore cannot distinguish the number of cases filed. Instead, the California data reflects the number of respondents from ERPO enactment through June 30, 2020, and the number of respondents for whom we coded cases for that timeframe. Additionally, in California, we received few requested case files from the court for cases involving only emergency ERPOs (i.e., those not followed by a temporary or final order) because these orders are granted remotely while the petitioning officer is in the field. As a result, they are typically filed at the local police station or sheriff’s office rather than the courthouse.

We abstracted data from all cases received from each state except Florida. In Florida, the large number of case files received (n = 4695) exceeded our available coding resources; therefore, we abstracted data from a random sample of 50% of cases from all counties with greater than 10 ERPO case files based on the case counts by the Office of the State Court Administrator (OSCA). Fifteen Florida counties had a small number (< 10) of cases based on OSCA counts, and we coded all of those. In total, RAs abstracted data from 6,628 ERPO case files (see Table  1 ) under the 10 domains listed in Table  2 (e.g., criminal legal system; firearm access and possession; and court decisions).

Training and coding procedures

The research team included investigators from nine universities, with members located in each selected state and two additional states. Starting with data collection instruments from two prior ERPO studies (Frattaroli et al. 2020 ; Zeoli et al. 2021 ), we collectively developed the data abstraction instrument for the project by comparing the data elements included on each state’s ERPO petition form and the ERPO eligibility criteria listed in each state’s statute against the existing instruments. This process was lengthy due to the vast differences in ERPO petition forms between, and sometimes within, states. The Principal Investigator (PI) and Co-PI curated a list of common and state-specific candidate abstraction variables and shared it with the state PIs and their teams. After the initial draft of the instrument was created, the PI and Co-PI added, removed, edited, and adjusted the items as necessary given feedback from the research team. Through a series of discussions, the multi-state team refined and finalized the list of data elements that comprised the final data collection instrument.

The goal was to create an instrument that would capture the data needed to understand ERPO use. The final instrument had robust sections related to suicide and interpersonal violence risk, among others (see Appendix A in Supplementary material). For suicide risk, we distinguished among ideation, threats, plans, aborted attempts, and attempts where data were available to disambiguate them. For interpersonal violence risk, we abstracted data on threats and uses of violence, separately, with queries capturing the target of the violence or threat. For both suicide and interpersonal violence risk, we captured whether any of the acts or threats of violence involved a firearm. We also included a variable to specify whether these risk behaviors were part of the event that motivated someone to file an ERPO petition (termed the “precipitating event”). Other sections of the data collection instrument specified the risk context of the situation and captured information about substance use, mental health, criminal history, firearm possession or access, and whether a respondent brandished a firearm. Finally, we included sections about ERPO court processes, whether the ERPO was granted, and whether firearms were removed.

State PIs had the option of adding state-specific variables to the instrument and in Maryland, California, and Connecticut, the PIs did. After agreeing on a good working draft of the instrument, we developed training materials that defined each variable and provided examples of coded excerpts from case files and guidance for abstracting the data that the entire research team reviewed, refined, and approved. We then programmed the data collection instrument in Qualtrics, an online survey software program to which all sites had access. Each state PI was then asked to abstract data from a small number of ERPO petitions from their state to ensure suitability of the instrument (the Maryland team was not able to complete this task due to not yet having access to their state’s ERPO casefiles). Feedback was then incorporated into the instrument.

Each state PI staffed their teams according to their state’s volume of ERPO cases. Due to the differences among state’s ERPO petitions and associated forms within the case files, and the need to include RAs on the Institutional Review Board protocol used by their state PIs, we initially planned for each RA to abstract data only from the state they were hired to staff. In practice, some RAs worked across states to manage the variation in access to case files during the study period. Having RAs who were able to code across states allowed us to keep RAs continuously coding even when files were not available in their home states. Specifically, RAs for Maryland and Florida were combined and coded Florida case files while we waited for access to Maryland case files. When Florida was completed, the RAs moved to code Maryland cases. Importantly, RAs coded only one state at a time to avoid introducing errors associated with switching between state case files and differing forms. The project employed 59 RAs over 17 months to code the 6415 cases.

RAs completed didactic training created by the two project PIs via an online learning management system. The training, a mix of videos, readings, and quizzes, included information about ERPOs, the study aims, the data collection instrument and associated definitions, the process for abstracting data, and information about strategies to reduce the risk and impact of secondary trauma. RAs completed the virtual training modules and passed the quizzes before advancing to practice coding two ERPO case files. After coding two case files, RAs participated in two one-hour synchronous sessions hosted by the project PIs to reinforce the online training, give them an opportunity to ask questions, and to review and discuss the test case coding. Once RAs completed these steps, they were cleared by the PIs to code.

The state PIs then trained RAs cleared for coding in the specifics of each state’s case files and variables. The California team held synchronous training sessions until questions had been resolved and RAs felt comfortable proceeding. For Florida and Maryland, RAs attended two virtual synchronous training sessions, one for each state’s ERPO process. In Colorado, RAs were trained using synchronous training sessions and participated in standing biweekly meetings to discuss abstraction issues and element definitions. For Connecticut, the PI developed a state-specific coding manual instructing RAs where to find data elements in the case files. In Washington, RAs were trained using synchronous training sessions and participated in standing weekly meetings to discuss abstraction discrepancies and definitional disagreements.

When coding began in earnest, the process for reaching reliability differed slightly from state to state, depending on the number of RAs and number of cases to be coded. In Washington, for example, a total of 10% of cases were randomly sampled and coded by all RAs to ensure reliability and consistency. In Colorado, 10% of cases were randomly sampled to be double-coded. In Florida, which had the largest number of cases, coding proceeded one county at a time, and RAs double-coded cases until they graduated to single-coder status. For RAs to graduate, they needed to achieve at least a 0.80 inter-rater reliability score. New RAs and those whose scores were below the target were paired with primary RAs (who had reached the 0.80 threshold) until they, too, reached 0.80.

Data quality and maintaining fidelity to the coding procedures

Because RAs generally coded one state (with the exception of Florida and Maryland RAs), we were unable to quantitatively test reliability of coding between states. Our multiple coding training procedures in which all RAs participated were designed to help ensure consistency. However, due to differences in ERPO documents across states and the lengthy duration of our coding period, it was possible that variations in understanding of variable definitions might have developed among state teams. To combat this possibility, the PI and Co-PI instituted systems to maintain coding pace and consistency among RAs.

Weekly videoconference check-in meetings were implemented, with RAs required to attend at least one meeting each week. Online moderated group chats were used to allow RAs to ask questions as they arose, tagging team members to alert them to the question, enabling them to get answers relatively quickly. The California, Florida, and Maryland teams kept a running document of frequently asked questions that all RAs across states could access during coding. The meetings and group chats served as forums to reinforce training, the coding instrument definitions, troubleshoot coding of complex cases, share consensus with RAs about larger coding questions raised in the online group chats, and develop an inclusive and communicative team dynamic. The check-in meetings and online group chats reduced the number of RA questions needing to be elevated to the PI and Co-PI, maintaining coding pace and consistency.

Prevention and reduction of secondary trauma

Due to the sometimes detailed and graphic descriptions of crises and violence contained in ERPO case files, there was a risk that RAs would experience secondary trauma through reading them. Secondary trauma, also called vicarious trauma, are the effects of indirect exposure to trauma (McCann and Pearlman 1990 ). For example, researchers have reported experiencing physical and emotional symptoms (e.g., sleeplessness, an increased awareness of safety) when conducting research on violence and suicide (Mckenzie et al. 2017 ; Campbell 2002 ). To minimize the risk of secondary trauma, we instituted protocols to limit RA exposure to cases when needed. For example, the protocol dictated that if an RA decided they could not code a specific case, for any reason, that case was reassigned, no questions asked. By guaranteeing we would not ask for an explanation as to why an RA could not code a case, we allowed them to switch out a case without sharing what might be personal information they did not want to disclose to their supervisors. We also encouraged RAs to shift to completing other study tasks when they needed a break from the intensity of coding. In this way, RAs could request time off from coding case files and shift to completing other research-related tasks until they were ready to re-engage with coding. Additionally, at the weekly check-in meetings, space was held to discuss how RAs were handling the emotional and psychological aspects of coding ERPO case files, cultivating an inclusive and communicative environment where RAs would be comfortable sharing with each other. Importantly, PIs and other meeting leads often began the meetings by sharing what they found emotionally difficult in specific cases to set the tone for the meetings and demonstrate that it is normal to be bothered by the case narratives being read.

Furthermore, the online coding training course completed by all RAs included a module on recognizing signs that might indicate secondary trauma and information on what to do when experiencing such symptoms. A licensed clinical social worker on staff with one of the state teams was available to RAs at some RA meetings and on call for individual appointments, should an RA need it. While the social worker did not establish a therapeutic relationship with RAs, they listened, made suggestions and indicated when it might be necessary to seek other resources to help with the psychological load of coding. Additionally, each state team developed a list of available resources (mainly through their universities, for whom the RAs worked) to which RAs could refer. While this research focused on the possibility that RAs might experience vicarious trauma due to their role in reading and abstracting data from the ERPO casefiles, it is important to recognize that even the most seasoned researcher can experience vicarious trauma and benefit from the steps detailed here.

By coordinating data collection on ERPO cases across states, we efficiently achieved greater explanatory power through pooled analyses and direct comparisons than would be possible if we had examined ERPO use in each of these states independently. Analyzing the breadth of violence risks and contexts in which the risks occur in ERPO case files requires attention to detail and standard data collection protocols to be in place and followed. Considering ERPO petitions describe the ways in which the respondent is at risk of harming themselves and/or others, and therefore can contain graphic descriptions of violence and threats (including mass shooting threats, suicide attempts, and domestic violence) conducting research about ERPOs carries risks of secondary trauma. This account of our processes can inform future firearm violence prevention research by providing a reference for how to undertake similar projects in terms of data acquisition, coding, data quality, and strategies to promote health wellness among RAs.

The study used cross-sectional administrative data. Relying on administrative data meant that the processes described are for coding data reported in the case files only. We did not seek out information beyond what was provided (typically solely from the petitioner's perspective) through the ERPO case files. We note that the structure and level of information available in the case files varied across and within states, as well as between petitioner types (law enforcement or civilian). Comparisons of ERPO use across states requires consideration of this variability. In states where law enforcement officers are the only authorized petitioners, information reported about respondents and precipitating events followed a relatively uniform reporting style, although the narrative style of these reports meant that the content was not uniformly consistent in relation to the data points to be abstracted. Where civilians, mainly family members and intimate partners, were authorized to petition, the presentation and type of information included in the petitions varied more significantly.

To our knowledge, this study is the first of its kind to analyze a multi-state sample of ERPOs. The process of standardizing information and abstracting data across states consistently to describe state-level ERPO implementation and assess impacts of the law offers researchers some insight into what such an undertaking involves and provides a foundation on which to interpret findings reported from the six-state study.

Availability of data and materials

A limited dataset generated from ERPO case files will be available at ICPSR upon publication of research from the multi-state study.

Abbreviations

• Extreme risk protection order

Research assistant

Department of Justice

Comprehensive case information system

Principal investigator

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Acknowledgements

We would like to acknowledge and thank our many esteemed research assistants for their dedication and hard work on this project. We could not have done this important work without you!

This project was supported by a grant from the National Collaborative on Gun Violence Research. The funder had no role in the science of the project. The views expressed in this manuscript are the authors’ and do not necessarily reflect the view of the National Collaborative on Gun Violence Research. The views expressed do not represent the Connecticut Department of Mental Health & Addiction Services or Yale University.

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April M. Zeoli

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Amy Molocznik, Elise Omaki & Shannon Frattaroli

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AMZ performed study conceptualization, investigation, methodology, formal analysis, supervision, data curation, funding acquisition, and was a major contributor in writing and editing the manuscript. AM performed data curation, supervision, project administration, and wrote, reviewed and edited the manuscript. JP performed study investigation, data curation, supervision, project administration, and was a major contributor in writing and editing the manuscript. EO performed data curation, supervision, project administration, and was a major contributor in writing and editing the manuscript. SF performed study conceptualization, investigation, methodology, formal analysis, supervision, data curation, funding acquisition, and was a major contributor in writing and editing the manuscript. MEB performed study investigation, data curation, supervision, project administration, and was a major contributor in writing and editing the manuscript. AC performed study investigation, data curation, supervision, project administration, contributed resources, and was a major contributor in writing and editing the manuscript. RK performed study investigation, data curation, supervision, project administration, and was a major contributor in writing and editing the manuscript. CK performed study investigation, data curation, supervision, and was a major contributor in writing and editing the manuscript. WM performed study investigation, data curation, data analysis, and was a major contributor in writing and editing the manuscript. MN performed study investigation, data curation, supervision, project administration, and was a major contributor in writing and editing the manuscript. VAP performed study investigation, data curation, supervision, project administration, and was a major contributor in writing and editing the manuscript. ARR performed study investigation, data curation, supervision, project administration, and was a major contributor in writing and editing the manuscript. JWS performed study investigation, data curation, and was a major contributor in writing and editing the manuscript. JPS performed study investigation, data curation, and was a major contributor in writing and editing the manuscript. GJW performed study investigation, data curation, and was a major contributor in writing and editing the manuscript. All authors read and approved the final manuscript.

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This protocol was approved by the Institutional Review Boards at Michigan State University, Yale University, the Connecticut Department of Mental Health and Addiction Services, the Johns Hopkins Bloomberg School of Public Health, University of Colorado Denver Anschutz Medical Campus, Duke University Health System, University of South Florida, University of California Davis, and University of Washington. The authors certify that the study was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.

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Zeoli, A.M., Molocznik, A., Paruk, J. et al. A multi-state evaluation of extreme risk protection orders: a research protocol. Inj. Epidemiol. 11 , 49 (2024). https://doi.org/10.1186/s40621-024-00535-z

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Received : 06 June 2024

Accepted : 29 August 2024

Published : 09 September 2024

DOI : https://doi.org/10.1186/s40621-024-00535-z

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