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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.

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 – Methods, Examples and Guide

Table of Contents

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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The Ultimate Guide to Qualitative Research - Part 1: The Basics

• Introduction and overview
• What is qualitative research?
• What is qualitative data?
• Examples of qualitative data
• Qualitative vs. quantitative research
• Mixed methods
• Qualitative research preparation
• Theoretical perspective
• Theoretical framework
• Literature reviews

Research question

• Conceptual framework
• Conceptual vs. theoretical framework

Data collection

• Qualitative research methods
• Focus groups
• Observational research

What is a case study?

Applications for case study research, what is a good case study, process of case study design, benefits and limitations of case studies.

• Ethnographical research
• Ethical considerations
• Confidentiality and privacy
• Power dynamics
• Reflexivity

Case studies

Case studies are essential to qualitative research , offering a lens through which researchers can investigate complex phenomena within their real-life contexts. This chapter explores the concept, purpose, applications, examples, and types of case studies and provides guidance on how to conduct case study research effectively.

Whereas quantitative methods look at phenomena at scale, case study research looks at a concept or phenomenon in considerable detail. While analyzing a single case can help understand one perspective regarding the object of research inquiry, analyzing multiple cases can help obtain a more holistic sense of the topic or issue. Let's provide a basic definition of a case study, then explore its characteristics and role in the qualitative research process.

Definition of a case study

A case study in qualitative research is a strategy of inquiry that involves an in-depth investigation of a phenomenon within its real-world context. It provides researchers with the opportunity to acquire an in-depth understanding of intricate details that might not be as apparent or accessible through other methods of research. The specific case or cases being studied can be a single person, group, or organization – demarcating what constitutes a relevant case worth studying depends on the researcher and their research question .

Among qualitative research methods , a case study relies on multiple sources of evidence, such as documents, artifacts, interviews , or observations , to present a complete and nuanced understanding of the phenomenon under investigation. The objective is to illuminate the readers' understanding of the phenomenon beyond its abstract statistical or theoretical explanations.

Characteristics of case studies

Case studies typically possess a number of distinct characteristics that set them apart from other research methods. These characteristics include a focus on holistic description and explanation, flexibility in the design and data collection methods, reliance on multiple sources of evidence, and emphasis on the context in which the phenomenon occurs.

Furthermore, case studies can often involve a longitudinal examination of the case, meaning they study the case over a period of time. These characteristics allow case studies to yield comprehensive, in-depth, and richly contextualized insights about the phenomenon of interest.

The role of case studies in research

Case studies hold a unique position in the broader landscape of research methods aimed at theory development. They are instrumental when the primary research interest is to gain an intensive, detailed understanding of a phenomenon in its real-life context.

In addition, case studies can serve different purposes within research - they can be used for exploratory, descriptive, or explanatory purposes, depending on the research question and objectives. This flexibility and depth make case studies a valuable tool in the toolkit of qualitative researchers.

Remember, a well-conducted case study can offer a rich, insightful contribution to both academic and practical knowledge through theory development or theory verification, thus enhancing our understanding of complex phenomena in their real-world contexts.

What is the purpose of a case study?

Case study research aims for a more comprehensive understanding of phenomena, requiring various research methods to gather information for qualitative analysis . Ultimately, a case study can allow the researcher to gain insight into a particular object of inquiry and develop a theoretical framework relevant to the research inquiry.

Why use case studies in qualitative research?

Using case studies as a research strategy depends mainly on the nature of the research question and the researcher's access to the data.

Conducting case study research provides a level of detail and contextual richness that other research methods might not offer. They are beneficial when there's a need to understand complex social phenomena within their natural contexts.

The explanatory, exploratory, and descriptive roles of case studies

Case studies can take on various roles depending on the research objectives. They can be exploratory when the research aims to discover new phenomena or define new research questions; they are descriptive when the objective is to depict a phenomenon within its context in a detailed manner; and they can be explanatory if the goal is to understand specific relationships within the studied context. Thus, the versatility of case studies allows researchers to approach their topic from different angles, offering multiple ways to uncover and interpret the data .

The impact of case studies on knowledge development

Case studies play a significant role in knowledge development across various disciplines. Analysis of cases provides an avenue for researchers to explore phenomena within their context based on the collected data.

This can result in the production of rich, practical insights that can be instrumental in both theory-building and practice. Case studies allow researchers to delve into the intricacies and complexities of real-life situations, uncovering insights that might otherwise remain hidden.

Types of case studies

In qualitative research , a case study is not a one-size-fits-all approach. Depending on the nature of the research question and the specific objectives of the study, researchers might choose to use different types of case studies. These types differ in their focus, methodology, and the level of detail they provide about the phenomenon under investigation.

Understanding these types is crucial for selecting the most appropriate approach for your research project and effectively achieving your research goals. Let's briefly look at the main types of case studies.

Exploratory case studies

Exploratory case studies are typically conducted to develop a theory or framework around an understudied phenomenon. They can also serve as a precursor to a larger-scale research project. Exploratory case studies are useful when a researcher wants to identify the key issues or questions which can spur more extensive study or be used to develop propositions for further research. These case studies are characterized by flexibility, allowing researchers to explore various aspects of a phenomenon as they emerge, which can also form the foundation for subsequent studies.

Descriptive case studies

Descriptive case studies aim to provide a complete and accurate representation of a phenomenon or event within its context. These case studies are often based on an established theoretical framework, which guides how data is collected and analyzed. The researcher is concerned with describing the phenomenon in detail, as it occurs naturally, without trying to influence or manipulate it.

Explanatory case studies

Explanatory case studies are focused on explanation - they seek to clarify how or why certain phenomena occur. Often used in complex, real-life situations, they can be particularly valuable in clarifying causal relationships among concepts and understanding the interplay between different factors within a specific context.

Intrinsic, instrumental, and collective case studies

These three categories of case studies focus on the nature and purpose of the study. An intrinsic case study is conducted when a researcher has an inherent interest in the case itself. Instrumental case studies are employed when the case is used to provide insight into a particular issue or phenomenon. A collective case study, on the other hand, involves studying multiple cases simultaneously to investigate some general phenomena.

Each type of case study serves a different purpose and has its own strengths and challenges. The selection of the type should be guided by the research question and objectives, as well as the context and constraints of the research.

The flexibility, depth, and contextual richness offered by case studies make this approach an excellent research method for various fields of study. They enable researchers to investigate real-world phenomena within their specific contexts, capturing nuances that other research methods might miss. Across numerous fields, case studies provide valuable insights into complex issues.

Critical information systems research

Case studies provide a detailed understanding of the role and impact of information systems in different contexts. They offer a platform to explore how information systems are designed, implemented, and used and how they interact with various social, economic, and political factors. Case studies in this field often focus on examining the intricate relationship between technology, organizational processes, and user behavior, helping to uncover insights that can inform better system design and implementation.

Health research

Health research is another field where case studies are highly valuable. They offer a way to explore patient experiences, healthcare delivery processes, and the impact of various interventions in a real-world context.

Case studies can provide a deep understanding of a patient's journey, giving insights into the intricacies of disease progression, treatment effects, and the psychosocial aspects of health and illness.

Asthma research studies

Specifically within medical research, studies on asthma often employ case studies to explore the individual and environmental factors that influence asthma development, management, and outcomes. A case study can provide rich, detailed data about individual patients' experiences, from the triggers and symptoms they experience to the effectiveness of various management strategies. This can be crucial for developing patient-centered asthma care approaches.

Other fields

Apart from the fields mentioned, case studies are also extensively used in business and management research, education research, and political sciences, among many others. They provide an opportunity to delve into the intricacies of real-world situations, allowing for a comprehensive understanding of various phenomena.

Case studies, with their depth and contextual focus, offer unique insights across these varied fields. They allow researchers to illuminate the complexities of real-life situations, contributing to both theory and practice.

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Understanding the key elements of case study design is crucial for conducting rigorous and impactful case study research. A well-structured design guides the researcher through the process, ensuring that the study is methodologically sound and its findings are reliable and valid. The main elements of case study design include the research question , propositions, units of analysis, and the logic linking the data to the propositions.

The research question is the foundation of any research study. A good research question guides the direction of the study and informs the selection of the case, the methods of collecting data, and the analysis techniques. A well-formulated research question in case study research is typically clear, focused, and complex enough to merit further detailed examination of the relevant case(s).

Propositions

Propositions, though not necessary in every case study, provide a direction by stating what we might expect to find in the data collected. They guide how data is collected and analyzed by helping researchers focus on specific aspects of the case. They are particularly important in explanatory case studies, which seek to understand the relationships among concepts within the studied phenomenon.

Units of analysis

The unit of analysis refers to the case, or the main entity or entities that are being analyzed in the study. In case study research, the unit of analysis can be an individual, a group, an organization, a decision, an event, or even a time period. It's crucial to clearly define the unit of analysis, as it shapes the qualitative data analysis process by allowing the researcher to analyze a particular case and synthesize analysis across multiple case studies to draw conclusions.

Argumentation

This refers to the inferential model that allows researchers to draw conclusions from the data. The researcher needs to ensure that there is a clear link between the data, the propositions (if any), and the conclusions drawn. This argumentation is what enables the researcher to make valid and credible inferences about the phenomenon under study.

Understanding and carefully considering these elements in the design phase of a case study can significantly enhance the quality of the research. It can help ensure that the study is methodologically sound and its findings contribute meaningful insights about the case.

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Conducting a case study involves several steps, from defining the research question and selecting the case to collecting and analyzing data . This section outlines these key stages, providing a practical guide on how to conduct case study research.

Defining the research question

The first step in case study research is defining a clear, focused research question. This question should guide the entire research process, from case selection to analysis. It's crucial to ensure that the research question is suitable for a case study approach. Typically, such questions are exploratory or descriptive in nature and focus on understanding a phenomenon within its real-life context.

Selecting and defining the case

The selection of the case should be based on the research question and the objectives of the study. It involves choosing a unique example or a set of examples that provide rich, in-depth data about the phenomenon under investigation. After selecting the case, it's crucial to define it clearly, setting the boundaries of the case, including the time period and the specific context.

Previous research can help guide the case study design. When considering a case study, an example of a case could be taken from previous case study research and used to define cases in a new research inquiry. Considering recently published examples can help understand how to select and define cases effectively.

Developing a detailed case study protocol

A case study protocol outlines the procedures and general rules to be followed during the case study. This includes the data collection methods to be used, the sources of data, and the procedures for analysis. Having a detailed case study protocol ensures consistency and reliability in the study.

The protocol should also consider how to work with the people involved in the research context to grant the research team access to collecting data. As mentioned in previous sections of this guide, establishing rapport is an essential component of qualitative research as it shapes the overall potential for collecting and analyzing data.

Collecting data

Gathering data in case study research often involves multiple sources of evidence, including documents, archival records, interviews, observations, and physical artifacts. This allows for a comprehensive understanding of the case. The process for gathering data should be systematic and carefully documented to ensure the reliability and validity of the study.

Analyzing and interpreting data

The next step is analyzing the data. This involves organizing the data , categorizing it into themes or patterns , and interpreting these patterns to answer the research question. The analysis might also involve comparing the findings with prior research or theoretical propositions.

Writing the case study report

The final step is writing the case study report . This should provide a detailed description of the case, the data, the analysis process, and the findings. The report should be clear, organized, and carefully written to ensure that the reader can understand the case and the conclusions drawn from it.

Each of these steps is crucial in ensuring that the case study research is rigorous, reliable, and provides valuable insights about the case.

The type, depth, and quality of data in your study can significantly influence the validity and utility of the study. In case study research, data is usually collected from multiple sources to provide a comprehensive and nuanced understanding of the case. This section will outline the various methods of collecting data used in case study research and discuss considerations for ensuring the quality of the data.

Interviews are a common method of gathering data in case study research. They can provide rich, in-depth data about the perspectives, experiences, and interpretations of the individuals involved in the case. Interviews can be structured , semi-structured , or unstructured , depending on the research question and the degree of flexibility needed.

Observations

Observations involve the researcher observing the case in its natural setting, providing first-hand information about the case and its context. Observations can provide data that might not be revealed in interviews or documents, such as non-verbal cues or contextual information.

Documents and artifacts

Documents and archival records provide a valuable source of data in case study research. They can include reports, letters, memos, meeting minutes, email correspondence, and various public and private documents related to the case.

These records can provide historical context, corroborate evidence from other sources, and offer insights into the case that might not be apparent from interviews or observations.

Physical artifacts refer to any physical evidence related to the case, such as tools, products, or physical environments. These artifacts can provide tangible insights into the case, complementing the data gathered from other sources.

Ensuring the quality of data collection

Determining the quality of data in case study research requires careful planning and execution. It's crucial to ensure that the data is reliable, accurate, and relevant to the research question. This involves selecting appropriate methods of collecting data, properly training interviewers or observers, and systematically recording and storing the data. It also includes considering ethical issues related to collecting and handling data, such as obtaining informed consent and ensuring the privacy and confidentiality of the participants.

Data analysis

Analyzing case study research involves making sense of the rich, detailed data to answer the research question. This process can be challenging due to the volume and complexity of case study data. However, a systematic and rigorous approach to analysis can ensure that the findings are credible and meaningful. This section outlines the main steps and considerations in analyzing data in case study research.

Organizing the data

The first step in the analysis is organizing the data. This involves sorting the data into manageable sections, often according to the data source or the theme. This step can also involve transcribing interviews, digitizing physical artifacts, or organizing observational data.

Categorizing and coding the data

Once the data is organized, the next step is to categorize or code the data. This involves identifying common themes, patterns, or concepts in the data and assigning codes to relevant data segments. Coding can be done manually or with the help of software tools, and in either case, qualitative analysis software can greatly facilitate the entire coding process. Coding helps to reduce the data to a set of themes or categories that can be more easily analyzed.

Identifying patterns and themes

After coding the data, the researcher looks for patterns or themes in the coded data. This involves comparing and contrasting the codes and looking for relationships or patterns among them. The identified patterns and themes should help answer the research question.

Interpreting the data

Once patterns and themes have been identified, the next step is to interpret these findings. This involves explaining what the patterns or themes mean in the context of the research question and the case. This interpretation should be grounded in the data, but it can also involve drawing on theoretical concepts or prior research.

Verification of the data

The last step in the analysis is verification. This involves checking the accuracy and consistency of the analysis process and confirming that the findings are supported by the data. This can involve re-checking the original data, checking the consistency of codes, or seeking feedback from research participants or peers.

Like any research method , case study research has its strengths and limitations. Researchers must be aware of these, as they can influence the design, conduct, and interpretation of the study.

Understanding the strengths and limitations of case study research can also guide researchers in deciding whether this approach is suitable for their research question . This section outlines some of the key strengths and limitations of case study research.

Benefits include the following:

• Rich, detailed data: One of the main strengths of case study research is that it can generate rich, detailed data about the case. This can provide a deep understanding of the case and its context, which can be valuable in exploring complex phenomena.
• Flexibility: Case study research is flexible in terms of design , data collection , and analysis . A sufficient degree of flexibility allows the researcher to adapt the study according to the case and the emerging findings.
• Real-world context: Case study research involves studying the case in its real-world context, which can provide valuable insights into the interplay between the case and its context.
• Multiple sources of evidence: Case study research often involves collecting data from multiple sources , which can enhance the robustness and validity of the findings.

On the other hand, researchers should consider the following limitations:

• Generalizability: A common criticism of case study research is that its findings might not be generalizable to other cases due to the specificity and uniqueness of each case.
• Time and resource intensive: Case study research can be time and resource intensive due to the depth of the investigation and the amount of collected data.
• Complexity of analysis: The rich, detailed data generated in case study research can make analyzing the data challenging.
• Subjectivity: Given the nature of case study research, there may be a higher degree of subjectivity in interpreting the data , so researchers need to reflect on this and transparently convey to audiences how the research was conducted.

Being aware of these strengths and limitations can help researchers design and conduct case study research effectively and interpret and report the findings appropriately.

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Case Study Research Method in Psychology

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

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

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

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

On This Page:

Case studies are in-depth investigations of a person, group, event, or community. Typically, data is gathered from various sources using several methods (e.g., observations & interviews).

The case study research method originated in clinical medicine (the case history, i.e., the patient’s personal history). In psychology, case studies are often confined to the study of a particular individual.

The information is mainly biographical and relates to events in the individual’s past (i.e., retrospective), as well as to significant events that are currently occurring in his or her everyday life.

The case study is not a research method, but researchers select methods of data collection and analysis that will generate material suitable for case studies.

Freud (1909a, 1909b) conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.

This makes it clear that the case study is a method that should only be used by a psychologist, therapist, or psychiatrist, i.e., someone with a professional qualification.

There is an ethical issue of competence. Only someone qualified to diagnose and treat a person can conduct a formal case study relating to atypical (i.e., abnormal) behavior or atypical development.

 Famous Case Studies

• Anna O – One of the most famous case studies, documenting psychoanalyst Josef Breuer’s treatment of “Anna O” (real name Bertha Pappenheim) for hysteria in the late 1800s using early psychoanalytic theory.
• Little Hans – A child psychoanalysis case study published by Sigmund Freud in 1909 analyzing his five-year-old patient Herbert Graf’s house phobia as related to the Oedipus complex.
• Bruce/Brenda – Gender identity case of the boy (Bruce) whose botched circumcision led psychologist John Money to advise gender reassignment and raise him as a girl (Brenda) in the 1960s.
• Genie Wiley – Linguistics/psychological development case of the victim of extreme isolation abuse who was studied in 1970s California for effects of early language deprivation on acquiring speech later in life.
• Phineas Gage – One of the most famous neuropsychology case studies analyzes personality changes in railroad worker Phineas Gage after an 1848 brain injury involving a tamping iron piercing his skull.

Clinical Case Studies

• Studying the effectiveness of psychotherapy approaches with an individual patient
• Assessing and treating mental illnesses like depression, anxiety disorders, PTSD
• Neuropsychological cases investigating brain injuries or disorders

Child Psychology Case Studies

• Studying psychological development from birth through adolescence
• Cases of learning disabilities, autism spectrum disorders, ADHD
• Effects of trauma, abuse, deprivation on development

Types of Case Studies

• Explanatory case studies : Used to explore causation in order to find underlying principles. Helpful for doing qualitative analysis to explain presumed causal links.
• Exploratory case studies : Used to explore situations where an intervention being evaluated has no clear set of outcomes. It helps define questions and hypotheses for future research.
• Descriptive case studies : Describe an intervention or phenomenon and the real-life context in which it occurred. It is helpful for illustrating certain topics within an evaluation.
• Multiple-case studies : Used to explore differences between cases and replicate findings across cases. Helpful for comparing and contrasting specific cases.
• Intrinsic : Used to gain a better understanding of a particular case. Helpful for capturing the complexity of a single case.
• Collective : Used to explore a general phenomenon using multiple case studies. Helpful for jointly studying a group of cases in order to inquire into the phenomenon.

Where Do You Find Data for a Case Study?

There are several places to find data for a case study. The key is to gather data from multiple sources to get a complete picture of the case and corroborate facts or findings through triangulation of evidence. Most of this information is likely qualitative (i.e., verbal description rather than measurement), but the psychologist might also collect numerical data.

1. Primary sources

• Interviews – Interviewing key people related to the case to get their perspectives and insights. The interview is an extremely effective procedure for obtaining information about an individual, and it may be used to collect comments from the person’s friends, parents, employer, workmates, and others who have a good knowledge of the person, as well as to obtain facts from the person him or herself.
• Observations – Observing behaviors, interactions, processes, etc., related to the case as they unfold in real-time.
• Documents & Records – Reviewing private documents, diaries, public records, correspondence, meeting minutes, etc., relevant to the case.

2. Secondary sources

• News/Media – News coverage of events related to the case study.
• Academic articles – Journal articles, dissertations etc. that discuss the case.
• Government reports – Official data and records related to the case context.
• Books/films – Books, documentaries or films discussing the case.

3. Archival records

Searching historical archives, museum collections and databases to find relevant documents, visual/audio records related to the case history and context.

Public archives like newspapers, organizational records, photographic collections could all include potentially relevant pieces of information to shed light on attitudes, cultural perspectives, common practices and historical contexts related to psychology.

4. Organizational records

Organizational records offer the advantage of often having large datasets collected over time that can reveal or confirm psychological insights.

Of course, privacy and ethical concerns regarding confidential data must be navigated carefully.

However, with proper protocols, organizational records can provide invaluable context and empirical depth to qualitative case studies exploring the intersection of psychology and organizations.

• Organizational/industrial psychology research : Organizational records like employee surveys, turnover/retention data, policies, incident reports etc. may provide insight into topics like job satisfaction, workplace culture and dynamics, leadership issues, employee behaviors etc.
• Clinical psychology : Therapists/hospitals may grant access to anonymized medical records to study aspects like assessments, diagnoses, treatment plans etc. This could shed light on clinical practices.
• School psychology : Studies could utilize anonymized student records like test scores, grades, disciplinary issues, and counseling referrals to study child development, learning barriers, effectiveness of support programs, and more.

How do I Write a Case Study in Psychology?

Follow specified case study guidelines provided by a journal or your psychology tutor. General components of clinical case studies include: background, symptoms, assessments, diagnosis, treatment, and outcomes. Interpreting the information means the researcher decides what to include or leave out. A good case study should always clarify which information is the factual description and which is an inference or the researcher’s opinion.

1. Introduction

• Provide background on the case context and why it is of interest, presenting background information like demographics, relevant history, and presenting problem.
• Compare briefly to similar published cases if applicable. Clearly state the focus/importance of the case.

2. Case Presentation

• Describe the presenting problem in detail, including symptoms, duration,and impact on daily life.
• Include client demographics like age and gender, information about social relationships, and mental health history.
• Describe all physical, emotional, and/or sensory symptoms reported by the client.
• Use patient quotes to describe the initial complaint verbatim. Follow with full-sentence summaries of relevant history details gathered, including key components that led to a working diagnosis.
• Summarize clinical exam results, namely orthopedic/neurological tests, imaging, lab tests, etc. Note actual results rather than subjective conclusions. Provide images if clearly reproducible/anonymized.
• Clearly state the working diagnosis or clinical impression before transitioning to management.

3. Management and Outcome

• Indicate the total duration of care and number of treatments given over what timeframe. Use specific names/descriptions for any therapies/interventions applied.
• Present the results of the intervention,including any quantitative or qualitative data collected.
• For outcomes, utilize visual analog scales for pain, medication usage logs, etc., if possible. Include patient self-reports of improvement/worsening of symptoms. Note the reason for discharge/end of care.

4. Discussion

• Analyze the case, exploring contributing factors, limitations of the study, and connections to existing research.
• Analyze the effectiveness of the intervention,considering factors like participant adherence, limitations of the study, and potential alternative explanations for the results.
• Identify any questions raised in the case analysis and relate insights to established theories and current research if applicable. Avoid definitive claims about physiological explanations.
• Offer clinical implications, and suggest future research directions.

5. Additional Items

• Thank specific assistants for writing support only. No patient acknowledgments.
• References should directly support any key claims or quotes included.
• Use tables/figures/images only if substantially informative. Include permissions and legends/explanatory notes.
• Provides detailed (rich qualitative) information.
• Provides insight for further research.
• Permitting investigation of otherwise impractical (or unethical) situations.

Case studies allow a researcher to investigate a topic in far more detail than might be possible if they were trying to deal with a large number of research participants (nomothetic approach) with the aim of ‘averaging’.

Because of their in-depth, multi-sided approach, case studies often shed light on aspects of human thinking and behavior that would be unethical or impractical to study in other ways.

Research that only looks into the measurable aspects of human behavior is not likely to give us insights into the subjective dimension of experience, which is important to psychoanalytic and humanistic psychologists.

Case studies are often used in exploratory research. They can help us generate new ideas (that might be tested by other methods). They are an important way of illustrating theories and can help show how different aspects of a person’s life are related to each other.

The method is, therefore, important for psychologists who adopt a holistic point of view (i.e., humanistic psychologists ).

Limitations

• Lacking scientific rigor and providing little basis for generalization of results to the wider population.
• Researchers’ own subjective feelings may influence the case study (researcher bias).
• Difficult to replicate.
• Time-consuming and expensive.
• The volume of data, together with the time restrictions in place, impacted the depth of analysis that was possible within the available resources.

Because a case study deals with only one person/event/group, we can never be sure if the case study investigated is representative of the wider body of “similar” instances. This means the conclusions drawn from a particular case may not be transferable to other settings.

Because case studies are based on the analysis of qualitative (i.e., descriptive) data , a lot depends on the psychologist’s interpretation of the information she has acquired.

This means that there is a lot of scope for Anna O , and it could be that the subjective opinions of the psychologist intrude in the assessment of what the data means.

For example, Freud has been criticized for producing case studies in which the information was sometimes distorted to fit particular behavioral theories (e.g., Little Hans ).

This is also true of Money’s interpretation of the Bruce/Brenda case study (Diamond, 1997) when he ignored evidence that went against his theory.

Breuer, J., & Freud, S. (1895).  Studies on hysteria . Standard Edition 2: London.

Curtiss, S. (1981). Genie: The case of a modern wild child .

Diamond, M., & Sigmundson, K. (1997). Sex Reassignment at Birth: Long-term Review and Clinical Implications. Archives of Pediatrics & Adolescent Medicine , 151(3), 298-304

Freud, S. (1909a). Analysis of a phobia of a five year old boy. In The Pelican Freud Library (1977), Vol 8, Case Histories 1, pages 169-306

Freud, S. (1909b). Bemerkungen über einen Fall von Zwangsneurose (Der “Rattenmann”). Jb. psychoanal. psychopathol. Forsch ., I, p. 357-421; GW, VII, p. 379-463; Notes upon a case of obsessional neurosis, SE , 10: 151-318.

Harlow J. M. (1848). Passage of an iron rod through the head.  Boston Medical and Surgical Journal, 39 , 389–393.

Harlow, J. M. (1868).  Recovery from the Passage of an Iron Bar through the Head .  Publications of the Massachusetts Medical Society. 2  (3), 327-347.

Money, J., & Ehrhardt, A. A. (1972).  Man & Woman, Boy & Girl : The Differentiation and Dimorphism of Gender Identity from Conception to Maturity. Baltimore, Maryland: Johns Hopkins University Press.

Money, J., & Tucker, P. (1975). Sexual signatures: On being a man or a woman.

Further Information

• Case Study Approach
• Case Study Method
• Enhancing the Quality of Case Studies in Health Services Research
• “We do things together” A case study of “couplehood” in dementia
• Using mixed methods for evaluating an integrative approach to cancer care: a case study

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23 Case Study Research: In-Depth Understanding in Context

Helen Simons, School of Education, University of Southampton

• Published: 02 September 2020
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This chapter explores case study as a major approach to research and evaluation. After first noting various contexts in which case studies are commonly used, the chapter focuses on case study research directly. Strengths and potential problematic issues are outlined, followed by key phases of the process. The chapter emphasizes how important it is to design the case, to collect and interpret data in ways that highlight the qualitative, to have an ethical practice that values multiple perspectives and political interests, and to report creatively to facilitate use in policymaking and practice. Finally, the chapter explores how to generalize from the single case. Concluding issues center on the need to think more imaginatively about design and the range of methods and forms of reporting required to persuade audiences to value qualitative ways of knowing in case study research.

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• Knowledge Base
• Methodology
• 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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McCombes, S. (2023, January 30). Case Study | Definition, Examples & Methods. Scribbr. Retrieved 3 September 2024, from https://www.scribbr.co.uk/research-methods/case-studies/

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• Open access
• Published: 27 June 2011

The case study approach

• Sarah Crowe 1 ,
• Kathrin Cresswell 2 ,
• Ann Robertson 2 ,
• Guro Huby 3 ,
• Anthony Avery 1 &
• Aziz Sheikh 2  

BMC Medical Research Methodology volume  11 , Article number:  100 ( 2011 ) Cite this article

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The case study approach allows in-depth, multi-faceted explorations of complex issues in their real-life settings. The value of the case study approach is well recognised in the fields of business, law and policy, but somewhat less so in health services research. Based on our experiences of conducting several health-related case studies, we reflect on the different types of case study design, the specific research questions this approach can help answer, the data sources that tend to be used, and the particular advantages and disadvantages of employing this methodological approach. The paper concludes with key pointers to aid those designing and appraising proposals for conducting case study research, and a checklist to help readers assess the quality of case study reports.

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Introduction

The case study approach is particularly useful to employ when there is a need to obtain an in-depth appreciation of an issue, event or phenomenon of interest, in its natural real-life context. Our aim in writing this piece is to provide insights into when to consider employing this approach and an overview of key methodological considerations in relation to the design, planning, analysis, interpretation and reporting of case studies.

The illustrative 'grand round', 'case report' and 'case series' have a long tradition in clinical practice and research. Presenting detailed critiques, typically of one or more patients, aims to provide insights into aspects of the clinical case and, in doing so, illustrate broader lessons that may be learnt. In research, the conceptually-related case study approach can be used, for example, to describe in detail a patient's episode of care, explore professional attitudes to and experiences of a new policy initiative or service development or more generally to 'investigate contemporary phenomena within its real-life context' [ 1 ]. Based on our experiences of conducting a range of case studies, we reflect on when to consider using this approach, discuss the key steps involved and illustrate, with examples, some of the practical challenges of attaining an in-depth understanding of a 'case' as an integrated whole. In keeping with previously published work, we acknowledge the importance of theory to underpin the design, selection, conduct and interpretation of case studies[ 2 ]. In so doing, we make passing reference to the different epistemological approaches used in case study research by key theoreticians and methodologists in this field of enquiry.

This paper is structured around the following main questions: What is a case study? What are case studies used for? How are case studies conducted? What are the potential pitfalls and how can these be avoided? We draw in particular on four of our own recently published examples of case studies (see Tables 1 , 2 , 3 and 4 ) and those of others to illustrate our discussion[ 3 – 7 ].

What is a case study?

A case study is a research approach that is used to generate an in-depth, multi-faceted understanding of a complex issue in its real-life context. It is an established research design that is used extensively in a wide variety of disciplines, particularly in the social sciences. A case study can be defined in a variety of ways (Table 5 ), the central tenet being the need to explore an event or phenomenon in depth and in its natural context. It is for this reason sometimes referred to as a "naturalistic" design; this is in contrast to an "experimental" design (such as a randomised controlled trial) in which the investigator seeks to exert control over and manipulate the variable(s) of interest.

Stake's work has been particularly influential in defining the case study approach to scientific enquiry. He has helpfully characterised three main types of case study: intrinsic , instrumental and collective [ 8 ]. An intrinsic case study is typically undertaken to learn about a unique phenomenon. The researcher should define the uniqueness of the phenomenon, which distinguishes it from all others. In contrast, the instrumental case study uses a particular case (some of which may be better than others) to gain a broader appreciation of an issue or phenomenon. The collective case study involves studying multiple cases simultaneously or sequentially in an attempt to generate a still broader appreciation of a particular issue.

These are however not necessarily mutually exclusive categories. In the first of our examples (Table 1 ), we undertook an intrinsic case study to investigate the issue of recruitment of minority ethnic people into the specific context of asthma research studies, but it developed into a instrumental case study through seeking to understand the issue of recruitment of these marginalised populations more generally, generating a number of the findings that are potentially transferable to other disease contexts[ 3 ]. In contrast, the other three examples (see Tables 2 , 3 and 4 ) employed collective case study designs to study the introduction of workforce reconfiguration in primary care, the implementation of electronic health records into hospitals, and to understand the ways in which healthcare students learn about patient safety considerations[ 4 – 6 ]. Although our study focusing on the introduction of General Practitioners with Specialist Interests (Table 2 ) was explicitly collective in design (four contrasting primary care organisations were studied), is was also instrumental in that this particular professional group was studied as an exemplar of the more general phenomenon of workforce redesign[ 4 ].

What are case studies used for?

According to Yin, case studies can be used to explain, describe or explore events or phenomena in the everyday contexts in which they occur[ 1 ]. These can, for example, help to understand and explain causal links and pathways resulting from a new policy initiative or service development (see Tables 2 and 3 , for example)[ 1 ]. In contrast to experimental designs, which seek to test a specific hypothesis through deliberately manipulating the environment (like, for example, in a randomised controlled trial giving a new drug to randomly selected individuals and then comparing outcomes with controls),[ 9 ] the case study approach lends itself well to capturing information on more explanatory ' how ', 'what' and ' why ' questions, such as ' how is the intervention being implemented and received on the ground?'. The case study approach can offer additional insights into what gaps exist in its delivery or why one implementation strategy might be chosen over another. This in turn can help develop or refine theory, as shown in our study of the teaching of patient safety in undergraduate curricula (Table 4 )[ 6 , 10 ]. Key questions to consider when selecting the most appropriate study design are whether it is desirable or indeed possible to undertake a formal experimental investigation in which individuals and/or organisations are allocated to an intervention or control arm? Or whether the wish is to obtain a more naturalistic understanding of an issue? The former is ideally studied using a controlled experimental design, whereas the latter is more appropriately studied using a case study design.

Case studies may be approached in different ways depending on the epistemological standpoint of the researcher, that is, whether they take a critical (questioning one's own and others' assumptions), interpretivist (trying to understand individual and shared social meanings) or positivist approach (orientating towards the criteria of natural sciences, such as focusing on generalisability considerations) (Table 6 ). Whilst such a schema can be conceptually helpful, it may be appropriate to draw on more than one approach in any case study, particularly in the context of conducting health services research. Doolin has, for example, noted that in the context of undertaking interpretative case studies, researchers can usefully draw on a critical, reflective perspective which seeks to take into account the wider social and political environment that has shaped the case[ 11 ].

How are case studies conducted?

Here, we focus on the main stages of research activity when planning and undertaking a case study; the crucial stages are: defining the case; selecting the case(s); collecting and analysing the data; interpreting data; and reporting the findings.

Defining the case

Carefully formulated research question(s), informed by the existing literature and a prior appreciation of the theoretical issues and setting(s), are all important in appropriately and succinctly defining the case[ 8 , 12 ]. Crucially, each case should have a pre-defined boundary which clarifies the nature and time period covered by the case study (i.e. its scope, beginning and end), the relevant social group, organisation or geographical area of interest to the investigator, the types of evidence to be collected, and the priorities for data collection and analysis (see Table 7 )[ 1 ]. A theory driven approach to defining the case may help generate knowledge that is potentially transferable to a range of clinical contexts and behaviours; using theory is also likely to result in a more informed appreciation of, for example, how and why interventions have succeeded or failed[ 13 ].

For example, in our evaluation of the introduction of electronic health records in English hospitals (Table 3 ), we defined our cases as the NHS Trusts that were receiving the new technology[ 5 ]. Our focus was on how the technology was being implemented. However, if the primary research interest had been on the social and organisational dimensions of implementation, we might have defined our case differently as a grouping of healthcare professionals (e.g. doctors and/or nurses). The precise beginning and end of the case may however prove difficult to define. Pursuing this same example, when does the process of implementation and adoption of an electronic health record system really begin or end? Such judgements will inevitably be influenced by a range of factors, including the research question, theory of interest, the scope and richness of the gathered data and the resources available to the research team.

Selecting the case(s)

The decision on how to select the case(s) to study is a very important one that merits some reflection. In an intrinsic case study, the case is selected on its own merits[ 8 ]. The case is selected not because it is representative of other cases, but because of its uniqueness, which is of genuine interest to the researchers. This was, for example, the case in our study of the recruitment of minority ethnic participants into asthma research (Table 1 ) as our earlier work had demonstrated the marginalisation of minority ethnic people with asthma, despite evidence of disproportionate asthma morbidity[ 14 , 15 ]. In another example of an intrinsic case study, Hellstrom et al.[ 16 ] studied an elderly married couple living with dementia to explore how dementia had impacted on their understanding of home, their everyday life and their relationships.

For an instrumental case study, selecting a "typical" case can work well[ 8 ]. In contrast to the intrinsic case study, the particular case which is chosen is of less importance than selecting a case that allows the researcher to investigate an issue or phenomenon. For example, in order to gain an understanding of doctors' responses to health policy initiatives, Som undertook an instrumental case study interviewing clinicians who had a range of responsibilities for clinical governance in one NHS acute hospital trust[ 17 ]. Sampling a "deviant" or "atypical" case may however prove even more informative, potentially enabling the researcher to identify causal processes, generate hypotheses and develop theory.

In collective or multiple case studies, a number of cases are carefully selected. This offers the advantage of allowing comparisons to be made across several cases and/or replication. Choosing a "typical" case may enable the findings to be generalised to theory (i.e. analytical generalisation) or to test theory by replicating the findings in a second or even a third case (i.e. replication logic)[ 1 ]. Yin suggests two or three literal replications (i.e. predicting similar results) if the theory is straightforward and five or more if the theory is more subtle. However, critics might argue that selecting 'cases' in this way is insufficiently reflexive and ill-suited to the complexities of contemporary healthcare organisations.

The selected case study site(s) should allow the research team access to the group of individuals, the organisation, the processes or whatever else constitutes the chosen unit of analysis for the study. Access is therefore a central consideration; the researcher needs to come to know the case study site(s) well and to work cooperatively with them. Selected cases need to be not only interesting but also hospitable to the inquiry [ 8 ] if they are to be informative and answer the research question(s). Case study sites may also be pre-selected for the researcher, with decisions being influenced by key stakeholders. For example, our selection of case study sites in the evaluation of the implementation and adoption of electronic health record systems (see Table 3 ) was heavily influenced by NHS Connecting for Health, the government agency that was responsible for overseeing the National Programme for Information Technology (NPfIT)[ 5 ]. This prominent stakeholder had already selected the NHS sites (through a competitive bidding process) to be early adopters of the electronic health record systems and had negotiated contracts that detailed the deployment timelines.

It is also important to consider in advance the likely burden and risks associated with participation for those who (or the site(s) which) comprise the case study. Of particular importance is the obligation for the researcher to think through the ethical implications of the study (e.g. the risk of inadvertently breaching anonymity or confidentiality) and to ensure that potential participants/participating sites are provided with sufficient information to make an informed choice about joining the study. The outcome of providing this information might be that the emotive burden associated with participation, or the organisational disruption associated with supporting the fieldwork, is considered so high that the individuals or sites decide against participation.

In our example of evaluating implementations of electronic health record systems, given the restricted number of early adopter sites available to us, we sought purposively to select a diverse range of implementation cases among those that were available[ 5 ]. We chose a mixture of teaching, non-teaching and Foundation Trust hospitals, and examples of each of the three electronic health record systems procured centrally by the NPfIT. At one recruited site, it quickly became apparent that access was problematic because of competing demands on that organisation. Recognising the importance of full access and co-operative working for generating rich data, the research team decided not to pursue work at that site and instead to focus on other recruited sites.

Collecting the data

In order to develop a thorough understanding of the case, the case study approach usually involves the collection of multiple sources of evidence, using a range of quantitative (e.g. questionnaires, audits and analysis of routinely collected healthcare data) and more commonly qualitative techniques (e.g. interviews, focus groups and observations). The use of multiple sources of data (data triangulation) has been advocated as a way of increasing the internal validity of a study (i.e. the extent to which the method is appropriate to answer the research question)[ 8 , 18 – 21 ]. An underlying assumption is that data collected in different ways should lead to similar conclusions, and approaching the same issue from different angles can help develop a holistic picture of the phenomenon (Table 2 )[ 4 ].

Brazier and colleagues used a mixed-methods case study approach to investigate the impact of a cancer care programme[ 22 ]. Here, quantitative measures were collected with questionnaires before, and five months after, the start of the intervention which did not yield any statistically significant results. Qualitative interviews with patients however helped provide an insight into potentially beneficial process-related aspects of the programme, such as greater, perceived patient involvement in care. The authors reported how this case study approach provided a number of contextual factors likely to influence the effectiveness of the intervention and which were not likely to have been obtained from quantitative methods alone.

In collective or multiple case studies, data collection needs to be flexible enough to allow a detailed description of each individual case to be developed (e.g. the nature of different cancer care programmes), before considering the emerging similarities and differences in cross-case comparisons (e.g. to explore why one programme is more effective than another). It is important that data sources from different cases are, where possible, broadly comparable for this purpose even though they may vary in nature and depth.

Analysing, interpreting and reporting case studies

Making sense and offering a coherent interpretation of the typically disparate sources of data (whether qualitative alone or together with quantitative) is far from straightforward. Repeated reviewing and sorting of the voluminous and detail-rich data are integral to the process of analysis. In collective case studies, it is helpful to analyse data relating to the individual component cases first, before making comparisons across cases. Attention needs to be paid to variations within each case and, where relevant, the relationship between different causes, effects and outcomes[ 23 ]. Data will need to be organised and coded to allow the key issues, both derived from the literature and emerging from the dataset, to be easily retrieved at a later stage. An initial coding frame can help capture these issues and can be applied systematically to the whole dataset with the aid of a qualitative data analysis software package.

The Framework approach is a practical approach, comprising of five stages (familiarisation; identifying a thematic framework; indexing; charting; mapping and interpretation) , to managing and analysing large datasets particularly if time is limited, as was the case in our study of recruitment of South Asians into asthma research (Table 1 )[ 3 , 24 ]. Theoretical frameworks may also play an important role in integrating different sources of data and examining emerging themes. For example, we drew on a socio-technical framework to help explain the connections between different elements - technology; people; and the organisational settings within which they worked - in our study of the introduction of electronic health record systems (Table 3 )[ 5 ]. Our study of patient safety in undergraduate curricula drew on an evaluation-based approach to design and analysis, which emphasised the importance of the academic, organisational and practice contexts through which students learn (Table 4 )[ 6 ].

Case study findings can have implications both for theory development and theory testing. They may establish, strengthen or weaken historical explanations of a case and, in certain circumstances, allow theoretical (as opposed to statistical) generalisation beyond the particular cases studied[ 12 ]. These theoretical lenses should not, however, constitute a strait-jacket and the cases should not be "forced to fit" the particular theoretical framework that is being employed.

When reporting findings, it is important to provide the reader with enough contextual information to understand the processes that were followed and how the conclusions were reached. In a collective case study, researchers may choose to present the findings from individual cases separately before amalgamating across cases. Care must be taken to ensure the anonymity of both case sites and individual participants (if agreed in advance) by allocating appropriate codes or withholding descriptors. In the example given in Table 3 , we decided against providing detailed information on the NHS sites and individual participants in order to avoid the risk of inadvertent disclosure of identities[ 5 , 25 ].

What are the potential pitfalls and how can these be avoided?

The case study approach is, as with all research, not without its limitations. When investigating the formal and informal ways undergraduate students learn about patient safety (Table 4 ), for example, we rapidly accumulated a large quantity of data. The volume of data, together with the time restrictions in place, impacted on the depth of analysis that was possible within the available resources. This highlights a more general point of the importance of avoiding the temptation to collect as much data as possible; adequate time also needs to be set aside for data analysis and interpretation of what are often highly complex datasets.

Case study research has sometimes been criticised for lacking scientific rigour and providing little basis for generalisation (i.e. producing findings that may be transferable to other settings)[ 1 ]. There are several ways to address these concerns, including: the use of theoretical sampling (i.e. drawing on a particular conceptual framework); respondent validation (i.e. participants checking emerging findings and the researcher's interpretation, and providing an opinion as to whether they feel these are accurate); and transparency throughout the research process (see Table 8 )[ 8 , 18 – 21 , 23 , 26 ]. Transparency can be achieved by describing in detail the steps involved in case selection, data collection, the reasons for the particular methods chosen, and the researcher's background and level of involvement (i.e. being explicit about how the researcher has influenced data collection and interpretation). Seeking potential, alternative explanations, and being explicit about how interpretations and conclusions were reached, help readers to judge the trustworthiness of the case study report. Stake provides a critique checklist for a case study report (Table 9 )[ 8 ].

Conclusions

The case study approach allows, amongst other things, critical events, interventions, policy developments and programme-based service reforms to be studied in detail in a real-life context. It should therefore be considered when an experimental design is either inappropriate to answer the research questions posed or impossible to undertake. Considering the frequency with which implementations of innovations are now taking place in healthcare settings and how well the case study approach lends itself to in-depth, complex health service research, we believe this approach should be more widely considered by researchers. Though inherently challenging, the research case study can, if carefully conceptualised and thoughtfully undertaken and reported, yield powerful insights into many important aspects of health and healthcare delivery.

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Acknowledgements

We are grateful to the participants and colleagues who contributed to the individual case studies that we have drawn on. This work received no direct funding, but it has been informed by projects funded by Asthma UK, the NHS Service Delivery Organisation, NHS Connecting for Health Evaluation Programme, and Patient Safety Research Portfolio. We would also like to thank the expert reviewers for their insightful and constructive feedback. Our thanks are also due to Dr. Allison Worth who commented on an earlier draft of this manuscript.

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AS conceived this article. SC, KC and AR wrote this paper with GH, AA and AS all commenting on various drafts. SC and AS are guarantors.

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Crowe, S., Cresswell, K., Robertson, A. et al. The case study approach. BMC Med Res Methodol 11 , 100 (2011). https://doi.org/10.1186/1471-2288-11-100

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What is a Case Study? Definition & Examples

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Case Study Definition

A case study is an in-depth investigation of a single person, group, event, or community. This research method involves intensively analyzing a subject to understand its complexity and context. The richness of a case study comes from its ability to capture detailed, qualitative data that can offer insights into a process or subject matter that other research methods might miss.

A case study strives for a holistic understanding of events or situations by examining all relevant variables. They are ideal for exploring ‘how’ or ‘why’ questions in contexts where the researcher has limited control over events in real-life settings. Unlike narrowly focused experiments, these projects seek a comprehensive understanding of events or situations.

In a case study, researchers gather data through various methods such as participant observation, interviews, tests, record examinations, and writing samples. Unlike statistically-based studies that seek only quantifiable data, a case study attempts to uncover new variables and pose questions for subsequent research.

A case study is particularly beneficial when your research:

• Requires a deep, contextual understanding of a specific case.
• Needs to explore or generate hypotheses rather than test them.
• Focuses on a contemporary phenomenon within a real-life context.

Learn more about Other Types of Experimental Design .

Case Study Examples

Various fields utilize case studies, including the following:

• Social sciences : For understanding complex social phenomena.
• Business : For analyzing corporate strategies and business decisions.
• Healthcare : For detailed patient studies and medical research.
• Education : For understanding educational methods and policies.
• Law : For in-depth analysis of legal cases.

For example, consider a case study in a business setting where a startup struggles to scale. Researchers might examine the startup’s strategies, market conditions, management decisions, and competition. Interviews with the CEO, employees, and customers, alongside an analysis of financial data, could offer insights into the challenges and potential solutions for the startup. This research could serve as a valuable lesson for other emerging businesses.

See below for other examples.

Types of Case Studies

Several standard types of case studies exist that vary based on the objectives and specific research needs.

Illustrative Case Study : Descriptive in nature, these studies use one or two instances to depict a situation, helping to familiarize the unfamiliar and establish a common understanding of the topic.

Exploratory Case Study : Conducted as precursors to large-scale investigations, they assist in raising relevant questions, choosing measurement types, and identifying hypotheses to test.

Cumulative Case Study : These studies compile information from various sources over time to enhance generalization without the need for costly, repetitive new studies.

Critical Instance Case Study : Focused on specific sites, they either explore unique situations with limited generalizability or challenge broad assertions, to identify potential cause-and-effect issues.

Pros and Cons

As with any research study, case studies have a set of benefits and drawbacks.

• Provides comprehensive and detailed data.
• Offers a real-life perspective.
• Flexible and can adapt to discoveries during the study.
• Enables investigation of scenarios that are hard to assess in laboratory settings.
• Facilitates studying rare or unique cases.
• Generates hypotheses for future experimental research.
• Time-consuming and may require a lot of resources.
• Hard to generalize findings to a broader context.
• Potential for researcher bias.
• Cannot establish causality .
• Lacks scientific rigor compared to more controlled research methods .

Crafting a Good Case Study: Methodology

While case studies emphasize specific details over broad theories, they should connect to theoretical frameworks in the field. This approach ensures that these projects contribute to the existing body of knowledge on the subject, rather than standing as an isolated entity.

The following are critical steps in developing a case study:

• Define the Research Questions : Clearly outline what you want to explore. Define specific, achievable objectives.
• Select the Case : Choose a case that best suits the research questions. Consider using a typical case for general understanding or an atypical subject for unique insights.
• Data Collection : Use a variety of data sources, such as interviews, observations, documents, and archival records, to provide multiple perspectives on the issue.
• Data Analysis : Identify patterns and themes in the data.
• Report Findings : Present the findings in a structured and clear manner.

Analysts typically use thematic analysis to identify patterns and themes within the data and compare different cases.

• Qualitative Analysis : Such as coding and thematic analysis for narrative data.
• Quantitative Analysis : In cases where numerical data is involved.
• Triangulation : Combining multiple methods or data sources to enhance accuracy.

A good case study requires a balanced approach, often using both qualitative and quantitative methods.

The researcher should constantly reflect on their biases and how they might influence the research. Documenting personal reflections can provide transparency.

Avoid over-generalization. One common mistake is to overstate the implications of a case study. Remember that these studies provide an in-depth insights into a specific case and might not be widely applicable.

Don’t ignore contradictory data. All data, even that which contradicts your hypothesis, is valuable. Ignoring it can lead to skewed results.

Finally, in the report, researchers provide comprehensive insight for a case study through “thick description,” which entails a detailed portrayal of the subject, its usage context, the attributes of involved individuals, and the community environment. Thick description extends to interpreting various data, including demographic details, cultural norms, societal values, prevailing attitudes, and underlying motivations. This approach ensures a nuanced and in-depth comprehension of the case in question.

Learn more about Qualitative Research and Qualitative vs. Quantitative Data .

Morland, J. & Feagin, Joe & Orum, Anthony & Sjoberg, Gideon. (1992). A Case for the Case Study . Social Forces. 71(1):240.

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Methodologic and Data-Analysis Triangulation in Case Studies: A Scoping Review

Margarithe charlotte schlunegger.

1 Department of Health Professions, Applied Research & Development in Nursing, Bern University of Applied Sciences, Bern, Switzerland

2 Faculty of Health, School of Nursing Science, Witten/Herdecke University, Witten, Germany

Maya Zumstein-Shaha

Rebecca palm.

3 Department of Health Care Research, Carl von Ossietzky University Oldenburg, Oldenburg, Germany

Associated Data

Supplemental material, sj-docx-1-wjn-10.1177_01939459241263011 for Methodologic and Data-Analysis Triangulation in Case Studies: A Scoping Review by Margarithe Charlotte Schlunegger, Maya Zumstein-Shaha and Rebecca Palm in Western Journal of Nursing Research

We sought to explore the processes of methodologic and data-analysis triangulation in case studies using the example of research on nurse practitioners in primary health care.

Design and methods:

We conducted a scoping review within Arksey and O’Malley’s methodological framework, considering studies that defined a case study design and used 2 or more data sources, published in English or German before August 2023.

Data sources:

The databases searched were MEDLINE and CINAHL, supplemented with hand searching of relevant nursing journals. We also examined the reference list of all the included studies.

In total, 63 reports were assessed for eligibility. Ultimately, we included 8 articles. Five studies described within-method triangulation, whereas 3 provided information on between/across-method triangulation. No study reported within-method triangulation of 2 or more quantitative data-collection procedures. The data-collection procedures were interviews, observation, documentation/documents, service records, and questionnaires/assessments. The data-analysis triangulation involved various qualitative and quantitative methods of analysis. Details about comparing or contrasting results from different qualitative and mixed-methods data were lacking.

Conclusions:

Various processes for methodologic and data-analysis triangulation are described in this scoping review but lack detail, thus hampering standardization in case study research, potentially affecting research traceability. Triangulation is complicated by terminological confusion. To advance case study research in nursing, authors should reflect critically on the processes of triangulation and employ existing tools, like a protocol or mixed-methods matrix, for transparent reporting. The only existing reporting guideline should be complemented with directions on methodologic and data-analysis triangulation.

Case study research is defined as “an empirical method that investigates a contemporary phenomenon (the ‘case’) in depth and within its real-world context, especially when the boundaries between phenomenon and context may not be clearly evident. A case study relies on multiple sources of evidence, with data needing to converge in a triangulating fashion.” 1 (p15) This design is described as a stand-alone research approach equivalent to grounded theory and can entail single and multiple cases. 1 , 2 However, case study research should not be confused with single clinical case reports. “Case reports are familiar ways of sharing events of intervening with single patients with previously unreported features.” 3 (p107) As a methodology, case study research encompasses substantially more complexity than a typical clinical case report. 1 , 3

A particular characteristic of case study research is the use of various data sources, such as quantitative data originating from questionnaires as well as qualitative data emerging from interviews, observations, or documents. Therefore, a case study always draws on multiple sources of evidence, and the data must converge in a triangulating manner. 1 When using multiple data sources, a case or cases can be examined more convincingly and accurately, compensating for the weaknesses of the respective data sources. 1 Another characteristic is the interaction of various perspectives. This involves comparing or contrasting perspectives of people with different points of view, eg, patients, staff, or leaders. 4 Through triangulation, case studies contribute to the completeness of the research on complex topics, such as role implementation in clinical practice. 1 , 5 Triangulation involves a combination of researchers from various disciplines, of theories, of methods, and/or of data sources. By creating connections between these sources (ie, investigator, theories, methods, data sources, and/or data analysis), a new understanding of the phenomenon under study can be obtained. 6 , 7

This scoping review focuses on methodologic and data-analysis triangulation because concrete procedures are missing, eg, in reporting guidelines. Methodologic triangulation has been called methods, mixed methods, or multimethods. 6 It can encompass within-method triangulation and between/across-method triangulation. 7 “Researchers using within-method triangulation use at least 2 data-collection procedures from the same design approach.” 6 (p254) Within-method triangulation is either qualitative or quantitative but not both. Therefore, within-method triangulation can also be considered data source triangulation. 8 In contrast, “researchers using between/across-method triangulation employ both qualitative and quantitative data-collection methods in the same study.” 6 (p254) Hence, methodologic approaches are combined as well as various data sources. For this scoping review, the term “methodologic triangulation” is maintained to denote between/across-method triangulation. “Data-analysis triangulation is the combination of 2 or more methods of analyzing data.” 6 (p254)

Although much has been published on case studies, there is little consensus on the quality of the various data sources, the most appropriate methods, or the procedures for conducting methodologic and data-analysis triangulation. 5 According to the EQUATOR (Enhancing the QUAlity and Transparency Of health Research) clearinghouse for reporting guidelines, one standard exists for organizational case studies. 9 Organizational case studies provide insights into organizational change in health care services. 9 Rodgers et al 9 pointed out that, although high-quality studies are being funded and published, they are sometimes poorly articulated and methodologically inadequate. In the reporting checklist by Rodgers et al, 9 a description of the data collection is included, but reporting directions on methodologic and data-analysis triangulation are missing. Therefore, the purpose of this study was to examine the process of methodologic and data-analysis triangulation in case studies. Accordingly, we conducted a scoping review to elicit descriptions of and directions for triangulation methods and analysis, drawing on case studies of nurse practitioners (NPs) in primary health care as an example. Case studies are recommended to evaluate the implementation of new roles in (primary) health care, such as that of NPs. 1 , 5 Case studies on new role implementation can generate a unique and in-depth understanding of specific roles (individual), teams (smaller groups), family practices or similar institutions (organization), and social and political processes in health care systems. 1 , 10 The integration of NPs into health care systems is at different stages of progress around the world. 11 Therefore, studies are needed to evaluate this process.

The methodological framework by Arksey and O’Malley 12 guided this scoping review. We examined the current scientific literature on the use of methodologic and data-analysis triangulation in case studies on NPs in primary health care. The review process included the following stages: (1) establishing the research question; (2) identifying relevant studies; (3) selecting the studies for inclusion; (4) charting the data; (5) collating, summarizing, and reporting the results; and (6) consulting experts in the field. 12 Stage 6 was not performed due to a lack of financial resources. The reporting of the review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Review) guideline by Tricco et al 13 (guidelines for reporting systematic reviews and meta-analyses [ Supplementary Table A ]). Scoping reviews are not eligible for registration in PROSPERO.

Stage 1: Establishing the Research Question

The aim of this scoping review was to examine the process of triangulating methods and analysis in case studies on NPs in primary health care to improve the reporting. We sought to answer the following question: How have methodologic and data-analysis triangulation been conducted in case studies on NPs in primary health care? To answer the research question, we examined the following elements of the selected studies: the research question, the study design, the case definition, the selected data sources, and the methodologic and data-analysis triangulation.

Stage 2: Identifying Relevant Studies

A systematic database search was performed in the MEDLINE (via PubMed) and CINAHL (via EBSCO) databases between July and September 2020 to identify relevant articles. The following terms were used as keyword search strategies: (“Advanced Practice Nursing” OR “nurse practitioners”) AND (“primary health care” OR “Primary Care Nursing”) AND (“case study” OR “case studies”). Searches were limited to English- and German-language articles. Hand searches were conducted in the journals Nursing Inquiry , BMJ Open , and BioMed Central ( BMC ). We also screened the reference lists of the studies included. The database search was updated in August 2023. The complete search strategy for all the databases is presented in Supplementary Table B .

Stage 3: Selecting the Studies

Inclusion and exclusion criteria.

We used the inclusion and exclusion criteria reported in Table 1 . We included studies of NPs who had at least a master’s degree in nursing according to the definition of the International Council of Nurses. 14 This scoping review considered studies that were conducted in primary health care practices in rural, urban, and suburban regions. We excluded reviews and study protocols in which no data collection had occurred. Articles were included without limitations on the time period or country of origin.

Inclusion and Exclusion Criteria.

Screening process

After the search, we collated and uploaded all the identified records into EndNote v.X8 (Clarivate Analytics, Philadelphia, Pennsylvania) and removed any duplicates. Two independent reviewers (MCS and SA) screened the titles and abstracts for assessment in line with the inclusion criteria. They retrieved and assessed the full texts of the selected studies while applying the inclusion criteria. Any disagreements about the eligibility of studies were resolved by discussion or, if no consensus could be reached, by involving experienced researchers (MZ-S and RP).

Stages 4 and 5: Charting the Data and Collating, Summarizing, and Reporting the Results

The first reviewer (MCS) extracted data from the selected publications. For this purpose, an extraction tool developed by the authors was used. This tool comprised the following criteria: author(s), year of publication, country, research question, design, case definition, data sources, and methodologic and data-analysis triangulation. First, we extracted and summarized information about the case study design. Second, we narratively summarized the way in which the data and methodological triangulation were described. Finally, we summarized the information on within-case or cross-case analysis. This process was performed using Microsoft Excel. One reviewer (MCS) extracted data, whereas another reviewer (SA) cross-checked the data extraction, making suggestions for additions or edits. Any disagreements between the reviewers were resolved through discussion.

A total of 149 records were identified in 2 databases. We removed 20 duplicates and screened 129 reports by title and abstract. A total of 46 reports were assessed for eligibility. Through hand searches, we identified 117 additional records. Of these, we excluded 98 reports after title and abstract screening. A total of 17 reports were assessed for eligibility. From the 2 databases and the hand search, 63 reports were assessed for eligibility. Ultimately, we included 8 articles for data extraction. No further articles were included after the reference list screening of the included studies. A PRISMA flow diagram of the study selection and inclusion process is presented in Figure 1 . As shown in Tables 2 and ​ and3, 3 , the articles included in this scoping review were published between 2010 and 2022 in Canada (n = 3), the United States (n = 2), Australia (n = 2), and Scotland (n = 1).

PRISMA flow diagram.

Characteristics of Articles Included.

Overview of Within-Method, Between/Across-Method, and Data-Analysis Triangulation.

Research Question, Case Definition, and Case Study Design

The following sections describe the research question, case definition, and case study design. Case studies are most appropriate when asking “how” or “why” questions. 1 According to Yin, 1 how and why questions are explanatory and lead to the use of case studies, histories, and experiments as the preferred research methods. In 1 study from Canada, eg, the following research question was presented: “How and why did stakeholders participate in the system change process that led to the introduction of the first nurse practitioner-led Clinic in Ontario?” (p7) 19 Once the research question has been formulated, the case should be defined and, subsequently, the case study design chosen. 1 In typical case studies with mixed methods, the 2 types of data are gathered concurrently in a convergent design and the results merged to examine a case and/or compare multiple cases. 10

Research question

“How” or “why” questions were found in 4 studies. 16 , 17 , 19 , 22 Two studies additionally asked “what” questions. Three studies described an exploratory approach, and 1 study presented an explanatory approach. Of these 4 studies, 3 studies chose a qualitative approach 17 , 19 , 22 and 1 opted for mixed methods with a convergent design. 16

In the remaining studies, either the research questions were not clearly stated or no “how” or “why” questions were formulated. For example, “what” questions were found in 1 study. 21 No information was provided on exploratory, descriptive, and explanatory approaches. Schadewaldt et al 21 chose mixed methods with a convergent design.

Case definition and case study design

A total of 5 studies defined the case as an organizational unit. 17 , 18 - 20 , 22 Of the 8 articles, 4 reported multiple-case studies. 16 , 17 , 22 , 23 Another 2 publications involved single-case studies. 19 , 20 Moreover, 2 publications did not state the case study design explicitly.

Within-Method Triangulation

This section describes within-method triangulation, which involves employing at least 2 data-collection procedures within the same design approach. 6 , 7 This can also be called data source triangulation. 8 Next, we present the single data-collection procedures in detail. In 5 studies, information on within-method triangulation was found. 15 , 17 - 19 , 22 Studies describing a quantitative approach and the triangulation of 2 or more quantitative data-collection procedures could not be included in this scoping review.

Qualitative approach

Five studies used qualitative data-collection procedures. Two studies combined face-to-face interviews and documents. 15 , 19 One study mixed in-depth interviews with observations, 18 and 1 study combined face-to-face interviews and documentation. 22 One study contained face-to-face interviews, observations, and documentation. 17 The combination of different qualitative data-collection procedures was used to present the case context in an authentic and complex way, to elicit the perspectives of the participants, and to obtain a holistic description and explanation of the cases under study.

All 5 studies used qualitative interviews as the primary data-collection procedure. 15 , 17 - 19 , 22 Face-to-face, in-depth, and semi-structured interviews were conducted. The topics covered in the interviews included processes in the introduction of new care services and experiences of barriers and facilitators to collaborative work in general practices. Two studies did not specify the type of interviews conducted and did not report sample questions. 15 , 18

Observations

In 2 studies, qualitative observations were carried out. 17 , 18 During the observations, the physical design of the clinical patients’ rooms and office spaces was examined. 17 Hungerford et al 18 did not explain what information was collected during the observations. In both studies, the type of observation was not specified. Observations were generally recorded as field notes.

Public documents

In 3 studies, various qualitative public documents were studied. 15 , 19 , 22 These documents included role description, education curriculum, governance frameworks, websites, and newspapers with information about the implementation of the role and general practice. Only 1 study failed to specify the type of document and the collected data. 15

Electronic health records

In 1 study, qualitative documentation was investigated. 17 This included a review of dashboards (eg, provider productivity reports or provider quality dashboards in the electronic health record) and quality performance reports (eg, practice-wide or co-management team-wide performance reports).

Between/Across-Method Triangulation

This section describes the between/across methods, which involve employing both qualitative and quantitative data-collection procedures in the same study. 6 , 7 This procedure can also be denoted “methodologic triangulation.” 8 Subsequently, we present the individual data-collection procedures. In 3 studies, information on between/across triangulation was found. 16 , 20 , 21

Mixed methods

Three studies used qualitative and quantitative data-collection procedures. One study combined face-to-face interviews, documentation, and self-assessments. 16 One study employed semi-structured interviews, direct observation, documents, and service records, 20 and another study combined face-to-face interviews, non-participant observation, documents, and questionnaires. 23

All 3 studies used qualitative interviews as the primary data-collection procedure. 16 , 20 , 23 Face-to-face and semi-structured interviews were conducted. In the interviews, data were collected on the introduction of new care services and experiences of barriers to and facilitators of collaborative work in general practices.

Observation

In 2 studies, direct and non-participant qualitative observations were conducted. 20 , 23 During the observations, the interaction between health professionals or the organization and the clinical context was observed. Observations were generally recorded as field notes.

In 2 studies, various qualitative public documents were examined. 20 , 23 These documents included role description, newspapers, websites, and practice documents (eg, flyers). In the documents, information on the role implementation and role description of NPs was collected.

Individual journals

In 1 study, qualitative individual journals were studied. 16 These included reflective journals from NPs, who performed the role in primary health care.

Service records

Only 1 study involved quantitative service records. 20 These service records were obtained from the primary care practices and the respective health authorities. They were collected before and after the implementation of an NP role to identify changes in patients’ access to health care, the volume of patients served, and patients’ use of acute care services.

Questionnaires/Assessment

In 2 studies, quantitative questionnaires were used to gather information about the teams’ satisfaction with collaboration. 16 , 21 In 1 study, 3 validated scales were used. The scales measured experience, satisfaction, and belief in the benefits of collaboration. 21 Psychometric performance indicators of these scales were provided. However, the time points of data collection were not specified; similarly, whether the questionnaires were completed online or by hand was not mentioned. A competency self-assessment tool was used in another study. 16 The assessment comprised 70 items and included topics such as health promotion, protection, disease prevention and treatment, the NP-patient relationship, the teaching-coaching function, the professional role, managing and negotiating health care delivery systems, monitoring and ensuring the quality of health care practice, and cultural competence. Psychometric performance indicators were provided. The assessment was completed online with 2 measurement time points (pre self-assessment and post self-assessment).

Data-Analysis Triangulation

This section describes data-analysis triangulation, which involves the combination of 2 or more methods of analyzing data. 6 Subsequently, we present within-case analysis and cross-case analysis.

Mixed-methods analysis

Three studies combined qualitative and quantitative methods of analysis. 16 , 20 , 21 Two studies involved deductive and inductive qualitative analysis, and qualitative data were analyzed thematically. 20 , 21 One used deductive qualitative analysis. 16 The method of analysis was not specified in the studies. Quantitative data were analyzed using descriptive statistics in 3 studies. 16 , 20 , 23 The descriptive statistics comprised the calculation of the mean, median, and frequencies.

Qualitative methods of analysis

Two studies combined deductive and inductive qualitative analysis, 19 , 22 and 2 studies only used deductive qualitative analysis. 15 , 18 Qualitative data were analyzed thematically in 1 study, 22 and data were treated with content analysis in the other. 19 The method of analysis was not specified in the 2 studies.

Within-case analysis

In 7 studies, a within-case analysis was performed. 15 - 20 , 22 Six studies used qualitative data for the within-case analysis, and 1 study employed qualitative and quantitative data. Data were analyzed separately, consecutively, or in parallel. The themes generated from qualitative data were compared and then summarized. The individual cases were presented mostly as a narrative description. Quantitative data were integrated into the qualitative description with tables and graphs. Qualitative and quantitative data were also presented as a narrative description.

Cross-case analyses

Of the multiple-case studies, 5 carried out cross-case analyses. 15 - 17 , 20 , 22 Three studies described the cross-case analysis using qualitative data. Two studies reported a combination of qualitative and quantitative data for the cross-case analysis. In each multiple-case study, the individual cases were contrasted to identify the differences and similarities between the cases. One study did not specify whether a within-case or a cross-case analysis was conducted. 23

Confirmation or contradiction of data

This section describes confirmation or contradiction through qualitative and quantitative data. 1 , 4 Qualitative and quantitative data were reported separately, with little connection between them. As a result, the conclusions on neither the comparisons nor the contradictions could be clearly determined.

Confirmation or contradiction among qualitative data

In 3 studies, the consistency of the results of different types of qualitative data was highlighted. 16 , 19 , 21 In particular, documentation and interviews or interviews and observations were contrasted:

• Confirmation between interviews and documentation: The data from these sources corroborated the existence of a common vision for an NP-led clinic. 19
• Confirmation among interviews and observation: NPs experienced pressure to find and maintain their position within the existing system. Nurse practitioners and general practitioners performed complete episodes of care, each without collaborative interaction. 21
• Contradiction among interviews and documentation: For example, interviewees mentioned that differentiating the scope of practice between NPs and physicians is difficult as there are too many areas of overlap. However, a clear description of the scope of practice for the 2 roles was provided. 21

Confirmation through a combination of qualitative and quantitative data

Both types of data showed that NPs and general practitioners wanted to have more time in common to discuss patient cases and engage in personal exchanges. 21 In addition, the qualitative and quantitative data confirmed the individual progression of NPs from less competent to more competent. 16 One study pointed out that qualitative and quantitative data obtained similar results for the cases. 20 For example, integrating NPs improved patient access by increasing appointment availability.

Contradiction through a combination of qualitative and quantitative data

Although questionnaire results indicated that NPs and general practitioners experienced high levels of collaboration and satisfaction with the collaborative relationship, the qualitative results drew a more ambivalent picture of NPs’ and general practitioners’ experiences with collaboration. 21

Research Question and Design

The studies included in this scoping review evidenced various research questions. The recommended formats (ie, how or why questions) were not applied consistently. Therefore, no case study design should be applied because the research question is the major guide for determining the research design. 2 Furthermore, case definitions and designs were applied variably. The lack of standardization is reflected in differences in the reporting of these case studies. Generally, case study research is viewed as allowing much more freedom and flexibility. 5 , 24 However, this flexibility and the lack of uniform specifications lead to confusion.

Methodologic Triangulation

Methodologic triangulation, as described in the literature, can be somewhat confusing as it can refer to either data-collection methods or research designs. 6 , 8 For example, methodologic triangulation can allude to qualitative and quantitative methods, indicating a paradigmatic connection. Methodologic triangulation can also point to qualitative and quantitative data-collection methods, analysis, and interpretation without specific philosophical stances. 6 , 8 Regarding “data-collection methods with no philosophical stances,” we would recommend using the wording “data source triangulation” instead. Thus, the demarcation between the method and the data-collection procedures will be clearer.

Within-Method and Between/Across-Method Triangulation

Yin 1 advocated the use of multiple sources of evidence so that a case or cases can be investigated more comprehensively and accurately. Most studies included multiple data-collection procedures. Five studies employed a variety of qualitative data-collection procedures, and 3 studies used qualitative and quantitative data-collection procedures (mixed methods). In contrast, no study contained 2 or more quantitative data-collection procedures. In particular, quantitative data-collection procedures—such as validated, reliable questionnaires, scales, or assessments—were not used exhaustively. The prerequisites for using multiple data-collection procedures are availability, the knowledge and skill of the researcher, and sufficient financial funds. 1 To meet these prerequisites, research teams consisting of members with different levels of training and experience are necessary. Multidisciplinary research teams need to be aware of the strengths and weaknesses of different data sources and collection procedures. 1

Qualitative methods of analysis and results

When using multiple data sources and analysis methods, it is necessary to present the results in a coherent manner. Although the importance of multiple data sources and analysis has been emphasized, 1 , 5 the description of triangulation has tended to be brief. Thus, traceability of the research process is not always ensured. The sparse description of the data-analysis triangulation procedure may be due to the limited number of words in publications or the complexity involved in merging the different data sources.

Only a few concrete recommendations regarding the operationalization of the data-analysis triangulation with the qualitative data process were found. 25 A total of 3 approaches have been proposed 25 : (1) the intuitive approach, in which researchers intuitively connect information from different data sources; (2) the procedural approach, in which each comparative or contrasting step in triangulation is documented to ensure transparency and replicability; and (3) the intersubjective approach, which necessitates a group of researchers agreeing on the steps in the triangulation process. For each case study, one of these 3 approaches needs to be selected, carefully carried out, and documented. Thus, in-depth examination of the data can take place. Farmer et al 25 concluded that most researchers take the intuitive approach; therefore, triangulation is not clearly articulated. This trend is also evident in our scoping review.

Mixed-methods analysis and results

Few studies in this scoping review used a combination of qualitative and quantitative analysis. However, creating a comprehensive stand-alone picture of a case from both qualitative and quantitative methods is challenging. Findings derived from different data types may not automatically coalesce into a coherent whole. 4 O’Cathain et al 26 described 3 techniques for combining the results of qualitative and quantitative methods: (1) developing a triangulation protocol; (2) following a thread by selecting a theme from 1 component and following it across the other components; and (3) developing a mixed-methods matrix.

The most detailed description of the conducting of triangulation is the triangulation protocol. The triangulation protocol takes place at the interpretation stage of the research process. 26 This protocol was developed for multiple qualitative data but can also be applied to a combination of qualitative and quantitative data. 25 , 26 It is possible to determine agreement, partial agreement, “silence,” or dissonance between the results of qualitative and quantitative data. The protocol is intended to bring together the various themes from the qualitative and quantitative results and identify overarching meta-themes. 25 , 26

The “following a thread” technique is used in the analysis stage of the research process. To begin, each data source is analyzed to identify the most important themes that need further investigation. Subsequently, the research team selects 1 theme from 1 data source and follows it up in the other data source, thereby creating a thread. The individual steps of this technique are not specified. 26 , 27

A mixed-methods matrix is used at the end of the analysis. 26 All the data collected on a defined case are examined together in 1 large matrix, paying attention to cases rather than variables or themes. In a mixed-methods matrix (eg, a table), the rows represent the cases for which both qualitative and quantitative data exist. The columns show the findings for each case. This technique allows the research team to look for congruency, surprises, and paradoxes among the findings as well as patterns across multiple cases. In our review, we identified only one of these 3 approaches in the study by Roots and MacDonald. 20 These authors mentioned that a causal network analysis was performed using a matrix. However, no further details were given, and reference was made to a later publication. We could not find this publication.

Case Studies in Nursing Research and Recommendations

Because it focused on the implementation of NPs in primary health care, the setting of this scoping review was narrow. However, triangulation is essential for research in this area. This type of research was found to provide a good basis for understanding methodologic and data-analysis triangulation. Despite the lack of traceability in the description of the data and methodological triangulation, we believe that case studies are an appropriate design for exploring new nursing roles in existing health care systems. This is evidenced by the fact that case study research is widely used in many social science disciplines as well as in professional practice. 1 To strengthen this research method and increase the traceability in the research process, we recommend using the reporting guideline and reporting checklist by Rodgers et al. 9 This reporting checklist needs to be complemented with methodologic and data-analysis triangulation. A procedural approach needs to be followed in which each comparative step of the triangulation is documented. 25 A triangulation protocol or a mixed-methods matrix can be used for this purpose. 26 If there is a word limit in a publication, the triangulation protocol or mixed-methods matrix needs to be identified. A schematic representation of methodologic and data-analysis triangulation in case studies can be found in Figure 2 .

Schematic representation of methodologic and data-analysis triangulation in case studies (own work).

Limitations

This study suffered from several limitations that must be acknowledged. Given the nature of scoping reviews, we did not analyze the evidence reported in the studies. However, 2 reviewers independently reviewed all the full-text reports with respect to the inclusion criteria. The focus on the primary care setting with NPs (master’s degree) was very narrow, and only a few studies qualified. Thus, possible important methodological aspects that would have contributed to answering the questions were omitted. Studies describing the triangulation of 2 or more quantitative data-collection procedures could not be included in this scoping review due to the inclusion and exclusion criteria.

Conclusions

Given the various processes described for methodologic and data-analysis triangulation, we can conclude that triangulation in case studies is poorly standardized. Consequently, the traceability of the research process is not always given. Triangulation is complicated by the confusion of terminology. To advance case study research in nursing, we encourage authors to reflect critically on methodologic and data-analysis triangulation and use existing tools, such as the triangulation protocol or mixed-methods matrix and the reporting guideline checklist by Rodgers et al, 9 to ensure more transparent reporting.

Supplemental Material

Acknowledgments.

The authors thank Simona Aeschlimann for her support during the screening process.

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.

Supplemental Material: Supplemental material for this article is available online.

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Case Study Research Design

The case study research design have evolved over the past few years as a useful tool for investigating trends and specific situations in many scientific disciplines.

This article is a part of the guide:

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• 3.1 Case Study
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• 7.1 Literature Review
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• 7.3 Meta Analysis

The case study has been especially used in social science, psychology, anthropology and ecology.

This method of study is especially useful for trying to test theoretical models by using them in real world situations. For example, if an anthropologist were to live amongst a remote tribe, whilst their observations might produce no quantitative data, they are still useful to science.

What is a Case Study?

Basically, a case study is an in depth study of a particular situation rather than a sweeping statistical survey . It is a method used to narrow down a very broad field of research into one easily researchable topic.

Whilst it will not answer a question completely, it will give some indications and allow further elaboration and hypothesis creation on a subject.

The case study research design is also useful for testing whether scientific theories and models actually work in the real world. You may come out with a great computer model for describing how the ecosystem of a rock pool works but it is only by trying it out on a real life pool that you can see if it is a realistic simulation.

For psychologists, anthropologists and social scientists they have been regarded as a valid method of research for many years. Scientists are sometimes guilty of becoming bogged down in the general picture and it is sometimes important to understand specific cases and ensure a more holistic approach to research .

H.M.: An example of a study using the case study research design.

The Argument for and Against the Case Study Research Design

Some argue that because a case study is such a narrow field that its results cannot be extrapolated to fit an entire question and that they show only one narrow example. On the other hand, it is argued that a case study provides more realistic responses than a purely statistical survey.

The truth probably lies between the two and it is probably best to try and synergize the two approaches. It is valid to conduct case studies but they should be tied in with more general statistical processes.

For example, a statistical survey might show how much time people spend talking on mobile phones, but it is case studies of a narrow group that will determine why this is so.

The other main thing to remember during case studies is their flexibility. Whilst a pure scientist is trying to prove or disprove a hypothesis , a case study might introduce new and unexpected results during its course, and lead to research taking new directions.

The argument between case study and statistical method also appears to be one of scale. Whilst many 'physical' scientists avoid case studies, for psychology, anthropology and ecology they are an essential tool. It is important to ensure that you realize that a case study cannot be generalized to fit a whole population or ecosystem.

Finally, one peripheral point is that, when informing others of your results, case studies make more interesting topics than purely statistical surveys, something that has been realized by teachers and magazine editors for many years. The general public has little interest in pages of statistical calculations but some well placed case studies can have a strong impact.

How to Design and Conduct a Case Study

The advantage of the case study research design is that you can focus on specific and interesting cases. This may be an attempt to test a theory with a typical case or it can be a specific topic that is of interest. Research should be thorough and note taking should be meticulous and systematic.

The first foundation of the case study is the subject and relevance. In a case study, you are deliberately trying to isolate a small study group, one individual case or one particular population.

For example, statistical analysis may have shown that birthrates in African countries are increasing. A case study on one or two specific countries becomes a powerful and focused tool for determining the social and economic pressures driving this.

In the design of a case study, it is important to plan and design how you are going to address the study and make sure that all collected data is relevant. Unlike a scientific report, there is no strict set of rules so the most important part is making sure that the study is focused and concise; otherwise you will end up having to wade through a lot of irrelevant information.

It is best if you make yourself a short list of 4 or 5 bullet points that you are going to try and address during the study. If you make sure that all research refers back to these then you will not be far wrong.

With a case study, even more than a questionnaire or survey , it is important to be passive in your research. You are much more of an observer than an experimenter and you must remember that, even in a multi-subject case, each case must be treated individually and then cross case conclusions can be drawn .

How to Analyze the Results

Analyzing results for a case study tends to be more opinion based than statistical methods. The usual idea is to try and collate your data into a manageable form and construct a narrative around it.

Use examples in your narrative whilst keeping things concise and interesting. It is useful to show some numerical data but remember that you are only trying to judge trends and not analyze every last piece of data. Constantly refer back to your bullet points so that you do not lose focus.

It is always a good idea to assume that a person reading your research may not possess a lot of knowledge of the subject so try to write accordingly.

In addition, unlike a scientific study which deals with facts, a case study is based on opinion and is very much designed to provoke reasoned debate. There really is no right or wrong answer in a case study.

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Lecturers’ perceptions of the influence of AI on a blended learning approach in a South African higher education institution

• Open access
• Published: 02 September 2024
• Volume 3 , article number  135 , ( 2024 )

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• Debbie A. Sanders 1 , 2 &
• Shirley S. Mukhari 1 , 3  

In this study, the researchers explore lecturers’ perspectives on the impact artificial intelligence (AI) has on blended learning within the context of South African higher education. AI is transforming traditional teaching and learning by enabling academic institutions to offer computerised, effective, and objective educational processes. The research was conducted to address the growing need to understand lecturers’ viewpoints on how AI can enhance educational practices and overcome existing challenges in blended learning environments. To investigate this phenomenon, the researchers applied the Substitution, Augmentation, Modification, and Redefinition (SAMR) model as theoretical framework for the study. Their qualitative research undertaking employed a singular case study design focusing on 15 lecturers from the College of Education at a selected academic institution, to arrive at an in-depth understanding of lecturers’ experiences and perceptions of how AI is integrated in blended learning. The researchers examined both the benefits and challenges associated with a blended teaching and learning mode, in the context of AI integration. The data collection process involved semi-structured focus group interviews that allowed for in-depth discussions to be conducted. This was complemented by detailed document analysis to analyse the course materials and teaching methods used by the lecturers. Homogeneous, purposeful sampling was applied to select participating lecturers who shared specific characteristics relevant to the study. Data analysis involved coding through the induction method, which helped to reveal relevant codes that were subsequently categorised. The study also included a comprehensive literature review of recent research findings, which were correlated with the collected data. The findings underscored the critical need for supportive measures, such as management backing, enhanced training opportunities, professional development initiatives, reliable technological infrastructure, improved internet connectivity, and additional time allocation, for the successful implementation of blended learning which integrates AI. This study contributes valuable insights into, and discussions on, the implications of adopting AI in a hybrid learning environment.

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1 Introduction

Over the past few years there have been notable advances in supporting lecturers to enhance their teaching methods, and in improving students’ learning experiences through the adoption of blended learning. Defined as a combination of face-to-face (F2F) and online learning, blended learning offers more flexible learning experiences that are also deemed to be more effective. Also known as "brick-and-click" instruction, hybrid learning, dual-mode instruction, blended pedagogies, or HyFlex, targeted, multimodal or flipped learning [ 5 , 38 ], this approach is becoming increasingly popular. The approach, which combines traditional classroom F2F learning with online components, facilitates the application of asynchronous teaching and learning in educational settings [ 16 ]. In recent years, educational institutions have widely embraced blended learning as the preferred teaching method, expressing appreciation for its flexibility, timeliness, and uninterrupted learning opportunities. As hybrid learning gains popularity, so it has become increasingly important to find new ways of improving the effectiveness thereof [ 17 ]. Recent developments in artificial intelligence (AI) are one way of enhancing the efficacy of blended learning approaches. With the integration of AI into academic environments, individualised learning experiences can be provided, administrative tasks can be automated, and such systems can be adapted to student needs [ 20 , 44 ]. For these reasons, the researchers sought to understand lecturers’ views on the relationship between AI and blended learning, as those perspectives are crucial for developing effective teaching and learning practices in higher education contexts.

AI involves the study and development of computer programs that display ‘intelligent’ behaviour, mindful of the fact that machine intelligence is distinct from the natural intelligence that is inherent in humans and animals. Other definitions of AI examine efforts to enable computers to possess intelligence [ 19 ]. Ultimately, AI extends much further than just robotics, however, to include the human capacity to program computers and other technology-enabled devices, so that they comprehend the principles of intelligent thought and behaviour. As a key invention of the Fourth Industrial Revolution (4IR), AI is considered one of the most influential technologies of our time [ 19 ]. For the purposes of this research, AI will be taken to refer to the development of computer systems capable of performing tasks that typically require human intelligence, such as learning, problem solving, and decision making. From the point of view of lecturers, such integration would require them to adapt their teaching and learning approaches, to make them more efficient and effective in addressing diverse student needs [ 25 ].

It is against this background that the researchers felt the need to investigate what impact AI has on blended learning, which includes lecturers having to revisit the way in which they usually lecture (the educator teaching, and students listening and regurgitating what they have been taught), to scenarios where AI is infused into a hybrid learning approach. It is crucial to emphasise that, in the context of this paper, blended learning is deemed to comprise more than the mere incorporation of technology into an academic programme. The adoption of the term, in this instance, aligns with what Lee [ 24 ] describes as a hybrid teaching approach, integrating traditional F2F lecturing with the latest, updated technologies. This mode aims to enhance student success and promote the relevance of the course content. Interaction among students, and between lecturers and the student cohort, is accomplished through various internet-enabled learning technologies, including platforms such as online discussion forums [ 3 ]. These technologies play a crucial role in promoting communication between educational stakeholders. Consequently, the smooth integration of conventional classroom instruction with e-learning offers valuable support for students’ asynchronous and collaborative learning [ 15 ]. In addition, the use of AI supplements these interactions by providing personalised feedback, allowing for two-way discussions, and for learning resources to be adapted to individual students’ needs. This combination of traditional and e-learning environments through the adoption of AI technologies makes for a more engaging and effective educational experience. It improves educational access, and promotes inclusive and equitable education, resulting in a sustainable, efficient, and accessible system of blended learning [ 3 ].

Although blended learning is not a novel concept, its use has remained largely unchanged. Its numerous challenges require further and more in-depth research into its efficacy [ 5 , 38 ]. Various aspects, including the specific technological tools and learning approaches used, and the overall quality of the teaching and learning on offer, need examination [ 5 ]. While blended learning has long been used as an approach to enhance students’ learning experiences, much of the research has focused on countries in the Global North, such as Belgium, the United Kingdom (UK), and Italy [ 6 ]. Limited research has been conducted in the South African context in this regard [ 43 ]. Notably, a search on Google Scholar revealed that only minimal related research has been published in the past 5 years (only eight research resources), with none of them originating from South Africa. Despite the increased uptake of hybrid learning in academia, AI is often perceived as a separate technological tool with limited influence on teaching and learning approaches. To enhance the effectiveness of blended learning in higher education contexts, it is essential to identify and understand lecturers’ views on the incorporation of AI into their teaching and learning, taking into account the SAMR model [ 34 ].

The significance of the study thus lies in elucidating lecturers’ viewpoints on the impact which AI and blended learning have on teaching and learning. The researchers also set out to assist higher education institutions (HEIs) in creating, adapting or changing conditions so that they are more relevant and meaningful, and ultimately enable lecturers to ensure that students are more successful in achieving specific learning outcomes. Clearly, AI is a tool that must be embraced in this modern, ever-evolving technological world.

The main research question designed to guide the study, was:

How do lecturers perceive the influence of AI on blended learning in the context of a South African higher education institution?

Four sub-questions were also formulated in this regard:

How do lecturers in South African higher education institutions perceive and integrate AI technologies into blended learning lessons within the SAMR framework?

What challenges do lecturers identify when incorporating AI into blended learning lessons, considering the SAMR levels of substitution, augmentation, modification, and redefinition?

How does AI influence student engagement, interaction, and learning outcomes in blended learning environments?

What support mechanisms do lecturers require to ensure the successful incorporation of AI into blended learning lessons?

Following the above introductory discussion on lecturers’ perspectives on blended learning and AI integration, the sections which follow focus on a comprehensive literature review on the topic, the theoretical framework chosen for this research, an exploration of the selected research methodology, the findings, and recommendations for the successful implementation of blended learning infused with AI. Lastly, concluding remarks summarise the key findings, and outline implications for future research and educational practice.

After exploring the background and rationale for this study, it is crucial that this study examines the existing body of research related to blended learning and Artificial Intelligence integration in higher education which is the focus of the next section.

2 Literature review

2.1 blended learning as an approach to teaching and learning.

In recent years, the educational domain has experienced significant transformations, driven by the continued evolution of information technology. One notable outcome is the emergence of blended learning, a pedagogical approach that integrates diverse methods of delivering information, such as web-based software courses, coupled with the management of practical knowledge [ 33 ]. According to Damanik [ 12 ], Choi and Park [ 10 ], and Qiu et al. [ 35 ], blended learning can be implemented both on- and offline. Bozkurt [ 8 ] expands on this, emphasising that blended learning encompasses F2F interactions and online engagement through specific mediums. The positive impact of the blended learning model on students’ learning outcomes, through fostering heightened engagement, is echoed by Santosa et al. [ 39 ]. This model, as observed by Nugraha et al. [ 31 ], also enhances students’ problem-solving abilities and understanding of the module content. This ensures adaptability and flexibility that caters to individual students’ needs, preferences, and schedules [ 43 ]. While initially designed for specific modules and their content, this approach prioritises student-centred satisfaction [ 43 ], thereby supporting HEIs in pursuing their goals and ultimately achieving the successful attainment of the learning outcomes set [ 38 ]. At its core, the concept of blended learning is built on the understanding that learning is not a singular, isolated event, but rather an ongoing, continuous process [ 33 ]. This transformational shift aligns with the modification level of the SAMR model, as it goes beyond merely substituting traditional teaching methods with technology, instead modifying the entire learning experience. Admittedly, the development of efficient blended learning systems can be demanding, particularly in respect of their endurance and flexibility to adapt to modern technological developments [ 3 ].

The integration of Artificial Intelligence (AI) in blended learning environments has been the subject of increasing debate in recent years. A review of the literature reveals that while there are some global studies completed which have explored various aspects of AI in education, research originating from South Africa is notably sparse. Alshahrani [ 3 ], Ferry et al. [ 13 ], and Rahman et al. [ 37 ] have all examined the impact of AI on student engagement and learning outcomes in blended learning, highlighting the potential benefits of AI-driven feedback and personalised learning experiences. However, research from a South African context is underrepresented, which may limit the generalisability of these findings to local settings. This gap highlights the need for more region-specific studies, particularly in HEIs.

The year 2017 marked a significant milestone, with extraordinary and unique developments in our understanding of the possibilities of the merging of technology and AI. As a rapidly advancing field, AI has the potential to influence the future of information technology and, for this reason, training in that regard is imperative [ 33 ]. The study of AI is fascinating and intriguing, representing the future of information technology. AI has the potential to enhance people’s lives by ensuring that tasks are accomplished more rapidly and more accurately. Petrova [ 33 ] suggests that soon AI will be integrated into all platforms and technologies, across different spheres. This development represents a shift toward the redefinition level of Puentedura’s [ 34 ] SAMR model. It transcends the traditional roles of both lecturers and students and introduces new possibilities for teaching and learning through the use of technology. While there is still substantial work ahead, AI empowers lecturers to achieve more—and with greater efficiency—than ever before. In the past, AI was a technology that instilled fear in many. The notion that computers could think and learn like humans raised concerns about our ability to comprehend and constrain machines. However, as we move away from the pursuit of human-like AI, we can now view its progress as a tool serving to develop and enhance every industry [ 33 ].

AI stands out as a potential answer to improve the efficiency and durability of blended learning systems [ 3 , 23 ]. Through the use of AI techniques such as machine learning (ML), natural language processing (NLP), and chatbots, opportunities are created which allow for the automation of diverse features of the learning journey, including content delivery, assessment, and feedback [ 3 , 22 ]. Furthermore, AI allows for the customisation of the learning experience for individual students, ensuring increased engagement and enhancing learning outcomes [ 3 ]. The fact that AI makes it possible for lecturers to adapt and automate their teaching, represents a change in traditional teaching and learning methods, aligning with the substitution as well as modification levels of the SAMR model, as technology can be used as a direct substitute for conventional teaching and learning methods, while also accommodating or revealing new capabilities. It offers a vast range of new possibilities to help ensure the successful achievement of a module’s learning outcomes—something that was not possible with conventional approaches.

It became clear to the researchers that while relevant, limited studies on this theme exist in South Africa, Mhlanga [ 27 ] and Mokoena [ 28 ] explored the challenges and opportunities of implementing AI in South African HEIs. These studies highlight the need for more specific approaches that consider the unique socio-economic and technological constraints, such as limited access to high-speed internet and the variability in digital literacy among both students and staff. These insights are crucial for understanding how AI can be effectively integrated into blended learning environments in South Africa, ensuring that such integration is successful, equitable and sustainable.

Moreover, there is a critical need for research that addresses the localised implementation of AI-driven blended learning solutions, particularly in rural and under-resourced areas where access to technology is inconsistent [ 27 ]. Such studies would provide valuable insights into how AI can be utilised not only to enhance learning outcomes but also to bridge the educational disparities that persist across different regions of the country. While limited, some relevant studies do exist. Mhlanga [ 27 ] and Mokoena [ 28 ] explored the challenges and opportunities of implementing AI in South African HEIs. These studies highlight the need for more specific approaches that consider the unique socio-economic and technological constraints, such as limited access to high-speed internet and the variability in digital literacy among both students and staff. These insights are crucial for understanding how AI can be effectively integrated into blended learning environments in South Africa, ensuring that such integration is successful, equitable and sustainable.

Integrating AI into blended learning systems offers the potential to establish an education system that is not only efficient, but also sustainable. The use of AI in education, particularly in blended learning, revolves around delivering personalised learning experiences, and optimising course delivery [ 3 ,  2 , 24 ]. Through the adoption of AI technology in hybrid learning systems, it is easier for lecturers to analyse student performance data for a personalised learning experience which aligns with individual strengths, weaknesses, and interests [ 3 , 25 ]. The implementation of AI in blended learning streamlines tailored assistance for students. Alshahrani [ 3 ] concurs that AI allows for responsive interaction. This corresponds to the augmentation (A) level of the SAMR [ 34 ] model, where technology is used to improve the learning experience, exceeding what was achievable with traditional methods. Personalised support can easily be based on individual student needs. The personalised approach assists students in navigating complex concepts, thus helping to ensure the achievement of learning outcomes, and ultimate success.

AI is also conducive to enhancing teaching and learning methods, increasing efficiency through automated administrative tasks, and refining content delivery. Introducing AI tools to ensure a sustainable and efficient blended learning system allows lecturers to lessen the strain on the environment, by reducing paper usage and minimising the carbon dioxide emissions associated with physical (F2F) lectures or meetings. This not only improves educational effectiveness and accessibility, but also empowers students to acquire the essential knowledge and skills for building a sustainable future [ 3 ,  36 ].

Viktorivna et al. [ 40 ] point out that AI serves to enhance student engagement, and the effectiveness of their learning. AI also facilitates a more straightforward explanation of subject matter [ 32 ], thereby encouraging students to develop and enhance skills required in the twenty-first century [ 11 , 42 ]. AI is a valuable educational resource for blended learning, as it grants access to an ever-expanding range of learning materials. Furthermore, AI helps in the creation of lessons, quizzes, and rubrics which allow lecturers to reorganise the curriculum and content of a module. AI-generated resources can even be customised to align with students’ instructional preferences, thereby fostering a flexible and inclusive learning experience [ 3 ].

Various studies have shown that the infusion of AI in a blended learning module enriches the learning process for students, helping them attain specific learning outcomes [ 13 , 14 , 37 , 39 ]. The collaborative and conversational capabilities of AI enhance the overall learning experience, resulting in an enjoyment of the course and heightening active participation among the student cohort. Concerted engagement delivers improved learning outcomes, and a more profound understanding of the subject matter [ 3 ]. This aligns with the Augmentation (A) level of the SAMR model, as technology (AI in this instance) goes beyond merely enhancing traditional methods, to develop a more interactive and engaging learning environment, thus fostering increased student participation and leading to a better grasp of the subject matter.

The AI-based blended learning model boosts students’ digital literacy levels as well as their 21st-century thinking skills [ 37 ]. This innovative approach helps to improve their critical thinking skills, for use in the learning process [ 18 ]. Ultimately, models can be created using a variety of AI-based technologies, thereby saving lecturers time and enhancing students’ learning opportunities [ 37 ].

In higher education, large class sizes make it difficult for lecturers to offer individualised teaching and can impede swift and direct student support. AI negates this challenge by rendering personalised support. As such, AI delivers real-time answers and support, easing the workload on lecturers and enriching the learning experience. The rise in popularity of AI has initiated extensive discourse and research regarding its potential influence in the education sector, particularly in higher education where limited lecturer–student ratios present unique challenges [ 3 ]. Thus, it is clear that AI serves as a valuable asset in blended learning.

AI also enables the delivery of customised support, feedback, and motivation to students. Investigating these aspects will further our understanding of AI’s integration in blended learning, unveiling fresh insights to guide the design, implementation, and ethical use of related technologies in educational environments that adopt a hybrid learning approach [ 3 ]. Since this is a relatively new technological development and thus a relatively novel approach to teaching and learning, further research is needed, especially at HEIs, to analyse the exact impact on students’ performance. As Rahman et al. [ 37 ] concur, this approach needs further development. This research paper extends the current knowledge base in the field of blended learning, particularly in higher education, by providing insights into the integration of AI for enhanced student literacy, thereby filling a significant gap in the existing literature. Closing this gap will not only expand our understanding of emerging educational practices, but also provide valuable insights for educators, institutions, and policymakers aiming to optimise the student learning experience.

The follow section reviews the theoretical framework that guided this research.

3 Theoretical framework

For this research, the Substitution, Augmentation, Modification, and Redefinition (SAMR) theoretical model was selected, to establish a solid foundation for investigating intricate aspects of AI’s influence on blended learning in HEIs. The model was chosen for its applicability to an understanding of the transformative impact of AI on blended learning, within the South African higher education milieu.

As per Puentedura’s [ 34 ] SAMR model, digital technologies can either enhance or transform educational practice. Enhancement involves substitution without functional change, or augmentation with functional improvement. Transformation, by contrast, requires significant task redesign or redefinition, leading to the creation of new tasks that were previously inconceivable. The model, which explores the creative application of technology to enhance the learning experience, serves as a useful guide for lecturers facing pedagogical changes as a result of using new learning technologies in their courses [ 30 ].

The SAMR model comprises four hierarchical levels. Firstly, the substitution level in which technology is used as a direct substitute for a traditional tool, with no functional change. At this level, the lecturer is tasked with substituting an older technology to perform the same activities as previously. While this may set the stage for future development, it is unlikely to have a significant impact on student outcomes at this stage [ 30 ]. The second level, augmentation, prompts lecturers to consider whether or not the available technology improves their teaching and learning. Instead of merely observing how students performed a given task before, lecturers must now focus on specific features of the technology, to accomplish the task more effectively, informatively, and swiftly. This approach aims to enhance students’ performance in completing assigned tasks [ 30 ]. Thus, technology acts as a direct substitute, with some functional improvement. The third level is the modification level in which technology allows for significant task redesign and, during modification, the lecturers’ objectives are to successfully achieve lesson outcomes with technological assistance. Teaching methods are thus adapted to ensure the incorporation of technology. While the syllabus remains unchanged, teaching approaches are modified to enable students to attain new goals that were previously deemed challenging [ 30 ]. The final level of redefinition empowers lecturers to replace older teaching techniques with newer, more effective teaching ideas. This is achieved through the use of technology, which allows for the creation of tasks once deemed inconceivable [ 7 ]. These teaching methods mainly seek to capture and retain students’ attention [ 30 ].

The SAMR framework enhances the value of the accumulated data, by offering a decision-making model for assessing the design of research interventions. The lowest levels—substitution and augmentation—encourage participants to actively engage, thus overcoming challenges related to technology, pedagogy, and their consequences. At the higher levels—modification and redefinition—the design of research questions becomes crucial for considering potential challenges in participants’ understanding of increasingly complex topics. This approach aims to purposefully overcome obstacles associated with the evolving nature of the scheduled tasks [ 4 ].

The application of the SAMR model in the context of this research involved a comprehensive examination of how AI influences blended learning practices. At the Substitution level, the study explored how AI replaces or replicates traditional teaching methods, offering insights into its role in directly substituting conventional approaches. Moving to the Augmentation level, the research assessed how AI enhances or improves existing educational practices, particularly in terms of providing additional features or functionalities that support teaching and learning. The Modification level focused on analysing how AI introduces significant changes in the execution of educational tasks, transforming traditional methods into more dynamic and effective practices. Finally, at the Redefinition level, the study evaluated how AI facilitates entirely new and transformative educational practices that were previously unattainable, showcasing its potential to revolutionise blended learning environments in ways that were not possible before.

Using this framework ensured that the research could follow a systematic approach to assessing the influence AI exerts on blended learning. It allowed the research to progress from simple enhancements to transformative changes. By offering a structured method for assessing the extent of AI integration in various facets of teaching and learning, the researchers gained valuable insights into the evolving landscape of educational technologies.

It is against this background that the chosen methodology is discussed next.

4 Methodology

4.1 research approach.

This study applied a qualitative methodology to investigate lecturers’ views on the influence AI has on blended learning. A qualitative approach involves a thorough exploration and grasp of phenomena, using non-numerical data and highlighting context, meanings, and subjective experiences [ 21 ]. The researchers deemed this method best suited for its exploratory nature of extracting relevant information. Focus group interviews were conducted, as Islam and Aldaihani [ 21 ] suggest, to allow for the coordination of discussions among a small group of participants, to mine and gather their views on a specific topic or phenomenon. For this research the focus was on the modules, lessons, and assessments of the participating lecturers. In addition, the researchers employed document analysis, which enabled them to explore the actual course content, lesson plans, and discussion forums.

In this way, the researchers arrived at an in-depth understanding of the specific phenomenon under investigation, as proposed by Morgan [ 29 ]. This approach enabled the researchers to scrutinise the lecturers’ experiences and opinions, focusing on their knowledge of, and encounters with, AI and blended learning.

The researchers applied a singular case study research design. This involved focusing on a single participant or unit of analysis, for an in-depth exploration of the intricacies and dynamics of a specific case [ 1 ]. This approach made it possible to conduct a thorough examination of the perceptions of lecturers employed in the College of Education at the HEI in question. The choice of design was prompted by ongoing developments in both AI and blended learning, which enabled the researchers to gain insights from lecturers actively engaged in related emerging educational practices.

4.2 Population and sample

Identifying a population for a particular research study enables the researchers to gather pertinent information from a smaller representative sample. This ensures that each distinct element of the collected information with similar characteristics is given the opportunity to be part of the sample. The researchers opted to employ a homogeneous purposeful sampling technique, intentionally selecting a group of participants who shared specific characteristics or traits deemed relevant to the research objectives. Participants were thus chosen based on shared traits, including gender, age, years of experience, the college in which they lectured, and their use of AI and blended learning, in order to align with the study’s purpose and objectives (Table 1 ).

Here, the group of participants selected were part of the same college at the specific HEI. The criteria for selection encompassed their approachability, availability to actively participate in the study, responsiveness to the interview questions, and willingness to share the content of their modules, lessons, and assessments. For this study, 15 lecturers agreed to participate: two males and 13 females, ranging in age between 32 and 63. Importantly, age has an impact on a user’s acceptance and embrace of AI in teaching and learning. Older lecturers often express discomfort with new technology adoption, and tend to be resistant to change. They are usually more comfortable with traditional ways of teaching and are fearful of using cutting-edge technological innovations. The participants’ readiness to openly share their course content, lessons and assessments, assisted the researchers in effectively analysing the collected data through the chosen document analysis data-collection technique. Consequently, the participants contributed valuable information that enhanced the depth of the study. Their active involvement in university affairs (especially the teaching and learning programmes) provided information that was highly relevant to this research .

4.3 Data collection

For this study, data were acquired by conducting interviews with the participating lecturers, enhanced by document analysis (see appendices A and B). The application of these data-collection techniques enabled the researchers to gather pertinent insights into the lecturers’ practical encounters with AI and blended learning in their teaching and learning. The use of open-ended, semi-structured interviews, along with document analysis, facilitated the analysis of the data, thus ensuring a thorough and precise in-depth study of the subject matter. The thematic approach adopted in this research aimed to pinpoint repeated topics identified in the data gathered. This enabled the researchers to concentrate on emerging themes specific to the realm of AI and blended learning, rather than providing mere synopses of the data [ 9 ].

4.4 Data analysis

To gain valuable insights from the participants’ answers to the interview questions, and information derived from the document analysis, a thorough study and interpretation of the collected data was imperative—an analytical process which is crucial for answering the research questions effectively. The researchers actively engaged in interpreting, consolidating, and synthesising the lecturer participants’ statements, to assign meaning to the data. This involved transcribing, comparing, and scrutinising the interview responses, along with the content of the modules, lessons, and assessments. The participant responses were coded manually, using letters of the alphabet, to ensure anonymity. Each response was tagged with a corresponding letter, making it possible to trace every piece of data back to the specific participant who supplied it. Each statement was carefully linked to specific codes and themes, especially given the fact that AI does not replace F2F lecturing, but rather augments teaching and learning. The coding process involved categorising data into the SAMR [ 34 ] levels, to reach conclusions about how lecturers perceive AI's influence on different aspects of blended learning.

The thematic approach was used to identify patterns and themes in the data, which were then related back to Puentedura’s [ 34 ] SAMR model. This allowed for a comprehensive review of how AI is being used at different levels of integration in a specific hybrid learning environment. An inductive approach, specifically axial coding, was followed to analyse the data collected. This involved a systematic comparison of the gathered data to identify codes, categories, and subcategories. A natural analysis of the data, without preconceived notions, was achieved by using an inductive approach, which enabled an unbiased analysis of the lecturers’ actual experiences. Through this comparative analysis, the researchers aligned the collected data with information derived from the literature review. The adoption of these methodologies facilitated the analysis of findings, reinforcing the credibility and reliability of the data. The theoretical justifications for this approach included grounding the findings within the SAMR framework, to enable the data-analysis process to align with the study objectives and research questions throughout.

4.5 Trustworthiness in data collection and analysis

Ensuring the credibility and trustworthiness of research findings is the prerogative of every qualitative researcher. In this study, the researchers developed a lasting, reliable, and open relationship with the participants. This approach guaranteed the latter’s willingness to actively participate in the study, and to share their personal experiences of the impact which AI has on blended learning. Moreover, the lecturers were encouraged to review and offer feedback on the researchers’ summary of the interview responses, further confirming the accurate representation of all data, and strengthening the trustworthiness of the research.

The coding process for this study was primarily conducted by Researcher A who began the initial coding of the qualitative data, identifying preliminary themes and patterns. To enhance the reliability of the analysis, researcher B participated in the second phase, where both researchers reviewed and validated the initial codes and themes. This collaborative approach involved both open coding and axial coding and ensured a thorough and unbiased interpretation of the data. A critical reader provided feedback and suggestions, which helped refine the coding framework and resolve any discrepancies. This process promoted credibility by introducing different perspectives, which prevented individual prejudice and improved the accuracy of the data interpretation. Transparency was achieved by clearly documenting each researcher’s role and contributions, making the process open to scrutiny and validation by other future researchers. The method ensured the reliability and comprehensiveness of the data analysis and actual results.

The researchers adhered strictly to qualitative research principles, ensuring transparency in their data-collection methods and meticulousness in their data-analysis techniques. Participants were continually asked to check the researchers’ notes, interpretation of the interviews, and transcriptions (member checking). Detailed descriptions of the participants’ experiences were provided to enable the transferability of the findings. This precise approach guaranteed the reliability and validity of the findings. By integrating the findings from the interviews, document analysis, and literature review, the validity and trustworthiness of the conclusions were further enhanced. Through this methodological approach, the researchers ensured the trustworthiness of the research findings and were able to make informed recommendations based on the results reported on here.

4.6 Ethical issues

The chair of the department in which the research was undertaken, obtained comprehensive ethical clearance covering the entire department from the Research Ethics Review Committee of the College of Education of the particular HEI. This clearance authorises all researchers in the department to conduct research within the institution, under ethics clearance number 90060059MC.

In ensuring that the highest ethical standards were maintained, the researchers pledged to use codes to protect the identity and privacy of the participating lecturers. The lecturers were also required to give the researchers permission to record the interviews, and to analyse their module content, lessons, and assignments. They were explicitly informed that their participation was voluntary, and that they were free to withdraw from the study at any stage without fear of penalty.

4.7 Research findings

Here, the researchers summarise the outcomes of the research based on insights derived from the responses provided during the interviews with the participating lecturers, and the document analysis. The findings are organised to address the main research question and sub-questions.

4.8 Lecturers’ perceptions of incorporating AI technologies in their blended learning and teaching approach

Of the 15 participants interviewed, 12 reported using AI to ensure that student queries were answered, and that they could find additional information as required, thus personalising the entire academic journey. In the words of Lecturer H:

I use AI in my modules to ensure that students can easily obtain answers to their questions. It is an amazing tool which helps suggest supplementary resources based on students' progress. This ensures a learning experience which is better, as it is adapted to my students’ progress.

Lecturer C corroborated this:

These systems can answer questions, provide information, and simulate conversation, creating an amazing and enjoyable interactive environment.

The same 12 lecturers deemed AI very useful for facilitating discussions between lecturers and students, and students amongst themselves. This was achieved because AI streamlined communication, enhanced interaction, and provided valuable support. Lecturer F said:

AI has significantly improved communication channels; it allows me to develop interactive and engaging discussions between students and between students and myself, and even encourages students to discuss the course content amongst themselves.

Lecturer H concurred:

The use of AI chatbots has created a space for students to collaborate effectively. This offers immediate assistance and helps develop a sense of collaboration in our blended learning environment.

All the interviewees maintained that the use of AI to generate relevant and customised learning materials and assessments was a very useful feature that could easily be adopted in blended learning modules. In this regard, Lecturer C said:

I use AI to create customised learning materials, quizzes and even games that align with the specific learning outcomes of my modules.

Lecturer G stated:

I find that the fact that AI can create adaptive assessments that adjust difficulty levels based on the individual performance of my students, is very useful.

Five participants highlighted the value of AI for translation. This was considered extremely useful, particularly in the South African context with 11 official languages. Lecturer M explained:

The ability of AI to facilitate translation greatly benefits our students from diverse backgrounds. It is so easy for any of us [lecturers and students] to quickly translate a word or even a whole paragraph, which makes the understanding of the module so much easier.

Lecturer H added:

I find that it helps students who are more comfortable in their home language to participate in the course content. This ensures that learning materials are accessible to everyone, regardless of their language preference.

The researchers’ document analysis showed that lecturers who mentioned the benefits of AI for creating customised learning materials and adaptive assessments had indeed merged these elements into their module sites. This correlated with the findings obtained from the interviews, where 12 of the 15 participating lecturers highlighted the positive impact AI had on facilitating communication, enhancing interaction, and offering support in hybrid learning environments. In addition, the analysis revealed instances where AI tools were used to support F2F classes by providing real-time feedback and interactive activities, thus enriching the blended learning experience. Using technology to individualise learning experiences and adapt teaching strategies in real-time helps students adapt to such approaches, thereby supporting traditional teaching methods and enriches learning environments.

Lecturer C had integrated AI-generated, scenario-based case studies into the course material. The document analysis revealed a scenario related to cultural integration through language teaching and learning. Students were presented with a case study involving a classroom with learners from diverse linguistic and cultural backgrounds. They were tasked with designing a language lesson that not only focused on language acquisition, but also promoted cultural understanding and integration. AI was used to evaluate the students’ answers to the case study. Based on individual performance, the system provided feedback to each individual student, and suggested additional resources or challenges to focus on specific areas of improvement in designing the language lesson.

The document analysis (as outlined in the second criterion, which aimed to “examine evidence of how assessments reflect the unique contributions of AI to student learning outcomes”) also ascertained the presence of adaptive assessments that were able to adjust complexity levels based on individual student performance. Lecturer G, who felt that AI was beneficial for creating such assessments, had incorporated quizzes with dynamic difficulty levels into the module site. Students were able to complete personalised assessments, with questions based on their previous performance.

The researchers noted the integration of AI-based translation services. Lecturer M, who highlighted the value of AI for translation, had implemented an AI-driven language translation tool on the module site. The researchers noted that some students had translated sections of the course content into their preferred language, promoting inclusivity and ensuring that the specific learning materials were clear to everyone, regardless of their language preference.

4.9 Lecturers’ perspectives on the challenges of incorporating AI into blended learning

Four of the lecturers interviewed, described the adaptation of new methods of teaching and learning, when using AI in their blended learning modules, as a challenge. In response to interview question 5 (What challenges have you encountered when incorporating AI into blended learning, and how did you overcome them?), Lecturer H commented:

Incorporating AI into my modules requires a delicate balance. I found that at times AI tends to minimise the importance of traditional teaching and learning methods, and not actually enhance them.

Lecturer B said:

Finding the right blend is crucial, so students benefit from the best of both worlds. AI must enrich my module and definitely not disrupt it … [We have to find] a balance between the technology and the personalised touch.

Lecturer C indicated:

… it can be a challenge to decide exactly where AI should be incorporated into the actual content of the course. Determining this often requires me to rethink my learning outcomes and approaches to teaching the content of my modules.

Twelve participants expressed the view that resistance to change was a major impediment to the successful adaptation of AI in blended learning modules. This aligns with responses to Interview Question 9 (In your experience, what support or resources do lecturers currently require when implementing AI in blended learning?) where Lecturer G noted:

Change is always met with resistance, especially when it comes to technology, particularly amongst us older lecturers. Some may see AI as a threat to the traditional way of teaching.

Lecturer H stated:

There's a comfort in the familiar, and AI represents a significant shift. Overcoming resistance requires effective communication. It also requires practically exploring the uses and benefits of AI.

All the participants mentioned that, although AI definitely saved time, problems were experienced with finding additional time to investigate new technologies and adapt their modules accordingly. In the words of Lecturer A:

While AI streamlines certain processes, the challenge lies in actually finding dedicated time for exploring its full potential to ensure that AI helps both me and my students successfully achieve the outcomes of the specific module.

Lecturer G mentioned:

Despite the efficiency AI brings, we must confront the reality of time constraints. It is essential to find a balance between adopting new technologies and meeting existing teaching demands.

Eight participants mentioned that it was becoming increasingly challenging to cope with the problem of the “digital divide”, which pertains to the technological proficiency of the students. Lecturer E noted:

There's a noticeable difference in access to technology among our students, and it's becoming increasingly challenging for us lecturers to bridge this gap as a result of the fast pace of new technological developments.

Lecturer F concurred, adding:

The issue of unequal access is growing. We need effective strategies to ensure all students are given equal learning experiences, regardless of their experience using computers for actual learning.

Several lecturers discussed ethical and privacy-related challenges with regard to the integration of AI in their blended learning module. As Lecturer F indicated:

I find that a huge challenge is that of ethical considerations, especially with regard to the privacy of student data. Finding the correct balance between using AI and protecting our students' privacy is an ongoing challenge. Additionally, there's a need for clear guidelines from management on how AI should be used ethically in our teaching, to avoid unintended consequences.

Lecturer G opined:

The challenge lies in providing the benefits of using AI to achieve the outcomes of our modules without compromising the privacy rights of our students. Open discussions on ethical guidelines and continuous awareness among lecturers and management as well as lecturers and students [are] essential to overcoming these challenges successfully.

4.10 Lecturers’ perspectives on AI's impact on student engagement

All 15 participating lecturers noted that using AI in their blended learning modules was beneficial, but not all believed they were using AI to its full potential, admitting there was room for improvement. Lecturer F stated:

While AI has enhanced certain aspects of my lecturing and interaction with my students, I really feel that there's much further potential for the use of AI in my modules, especially with regard to the advanced AI functionalities and typing in the correct prompts.

Lecturer O opined:

Integrating AI into blended learning helps me improve the actual teaching of the content of my modules. This allows me to individualise the learning experiences of each of my students, to ensure that their needs and preferences are met.

Lecturer A agreed:

Using AI in my blended learning course helps me adapt to my students' needs. This makes the teaching and learning much more flexible and meaningful, as it allows me to develop an individualised teaching approach to each student's strengths and weaknesses.

Nine of the participants highlighted the significance of AI’s prompt feedback to the inputs provided and queries posted on the AI system. In response to interview question 6 ("Have you received any feedback from students regarding their experiences with AI-infused blended learning?"), Lecturer B mentioned,

The quick feedback of AI has really changed the learning experience. Students receive real-time feedback [on] their progress, allowing them to make [the] necessary changes immediately.

Lecturer K echoed this:

I see AI as a game changer. Its ability to offer instant, personalised feedback has been a real […] eye-opener. It helps students understand their strengths and weaknesses without delay. This helps ensure a more integrated and authentic learning environment. It helps in identifying gaps in understanding and adapting teaching strategies.

Lecturer N concurred, adding:

From where I stand, AI's ability to analyse student data can provide valuable insights for personalised teaching and learning, and allows for instantaneous feedback. As a result, students' entire learning process is enhanced, resulting in an improved ability to achieve their learning goals.

Lecturer B, who viewed the instant feedback of AI as beneficial for enhancing teaching and learning, had used AI to create scenario-based feedback activities. The document analysis identified instances where students were presented with virtual scenarios representing diverse language teaching situations, such as classroom settings, one-on-one tutoring sessions, and language immersion programmes. AI was able to instantaneously analyse students' responses and actions in each scenario, providing immediate, real-time personalised feedback on their answers. This integration of AI thus enhanced both asynchronous learning and synchronous F2F interactions, by offering immediate feedback during live sessions.

The interview responses of seven of the participants revealed that AI is able to easily automate administrative tasks, through machine learning algorithms and natural language processing. This analytical capability allows instructional approaches to be adapted to individual student needs, ensuring that they successfully attain the learning outcomes of the module. Lecturer C said:

AI tools can streamline administrative tasks, allowing me to devote more time to my students and support them, especially where they are encountering challenges.

Lecturer F added:

I've used AI to analyse student performance data, which helps me adapt the content of my modules and teaching methods to make them more interactive. This can easily be based on my individual students’ needs.

The document analysis, which aimed to examine evidence of how assessments reflect the unique contributions of AI to student learning outcomes (the fourth criterion on the document analysis) also showed that modules where AI was integrated into feedback mechanisms saw improved student engagement. Studying the module site of Lecturer F, the researchers discovered that s/he used AI to automatically grade assignments (multiple-choice and written) and give immediate feedback. The reports generated were instantaneous and showed specific trends which helped the lecturer adapt the teaching and learning of this particular module.

It is indeed important to note how AI supports F2F teaching in class. As a result of this approach, learning during live lectures is made more dynamic and responsive to student needs. This point was highlighted by Lecturer M, who said:

The use of AI tools allows for instantaneous feedback to my students’ questions during lectures. It can give them various suggestions for additional materials and let them engage in interactive activities during face-to-face classes that will allow them to engage more deeply with the material.

4.11 Lecturers’ perspectives on the support they require to successfully implement AI in blended learning

All the participating lecturers confirmed the importance of comprehensive training and professional development. The need for comprehensive training and institutional support emerged as a critical theme. Interview Question 8 ("What kind of training or professional development opportunities do you believe are necessary for lecturers to effectively integrate AI into their blended teaching methods?") prompted responses highlighting the importance of ongoing professional development. In the words of Lecturer G:

Access to ongoing professional development courses focused on AI is essential for us lecturers to keep up to date with the latest developments in this field.

Lecturer M noted:

Professional development should include […] theoretical knowledge of AI as well as, specifically for us, its practical application in blended learning contexts.

Four participants stated that technological support was imperative if AI was to be instituted successfully. Lecturer O suggested:

Dedicated support teams must be specifically set up to assist with any technical challenges we may come across during the implementation of AI into our teaching and learning. This includes prompt responses to technical glitches and troubleshooting, to ensure that everything works properly for both me and my students.

We need assistance with initial setup and implementation, and with ongoing technical issues that may arise. This could be problematic as our IT help desk is already so overburdened. More IT staff definitely need to be employed.

Having institutional support for incorporating AI into the curriculum, is crucial. This involves not only providing resources, but also creating a culture that values and encourages the integration of AI technologies into teaching practices. This was echoed by all the lecturers interviewed. In the words of Lecturer A:

Having institutional support for incorporating AI into the curriculum is crucial. This involves providing resources as well as creating an institution that values and encourages the integration of AI into our teaching and learning.

Lecturer O echoed this:

Institutional commitment is key to the successful integration of AI. This should also include dedicated policies, so that we lecturers know exactly the correct process of AI.

Additionally, setting aside dedicated time for lecturers to adopt AI technologies was deemed imperative, as mentioned by ten of the participants. Lecturer H opined:

Allocating specific time for training and hands-on experience with AI tools is crucial. We need the opportunity to explore and familiarise ourselves with this new, exciting technology. This will definitely help us.

Lecturer E noted:

Having dedicated time for learning and experimentation is essential. This would give us more confidence in the actual implementation. But our schedules are already so busy that I have to wonder if this is at all possible.

Next, we examine the findings of the research.

5 Discussion of research findings

Using the research findings as a starting point for drawing meaningful conclusions and contributing to scholarly discourse on the subject, this section provides a summary of the findings that correlate with the literature review. From the utterances of many of the participants it became clear that there is a positive attitude towards AI, its significance for blended learning, and the benefits for tertiary students, as long as HEIs make certain adaptations. This aligns with the Redefinition and Modification aspects of the SAMR [ 34 ] model used for this study.

The research questions sought to explore how AI influences student engagement, interaction, and learning outcomes in blended learning environments. Lecturer N’s opinion, that AI boosts the learning process as a whole, resulting in an improved ability to successfully complete the course , is consistent with the findings of Alshahrani [ 3 ], Ferry et al. [ 13 ], Fradila et al. [ 14 ], Rahman et al. [ 37 ] and Santosa et al. [ 39 ], who found that infusing AI into a blended learning module enriches the learning process for students, helping them to achieve the specified learning outcomes. The collaborative and conversational capabilities of AI enhance the overall learning experience, leading to an enjoyment of the course, and active participation by students. These findings support the SAMR [ 34 ] model’s Redefinition level, where AI transforms the learning experience. Accordingly, the researchers of this study recognised that while AI does enhance learning experiences, its integration must be carefully managed to avoid over-reliance on technology at the expense of fundamental pedagogical principles.

The research findings corroborate the potential benefits AI holds for blended learning, as identified by the interviewees. Lecturer H's use of AI for immediate student support aligns with the views of Alsaleem and Alghalith [ 2 ], Alshahrani [ 3 ] and Lee [ 24 ], who emphasise AI’s capacity for personalising learning experiences. Moreover, Lecturer B's opinion on the importance of using AI for the prompt integration of AI-driven feedback, is consistent with the findings of Alshahrani [ 3 ] and Khosravi and Heidari [ 22 ], which emphasise AI’s functionality of supplying instantaneous feedback to enhance the learning experience. This aligns with the Augmentation level of the SAMR [ 34 ] model, where AI enhances existing teaching and learning practices. This made it clear to the researchers that while AI-driven feedback can significantly improve learning efficiency, it also raises concerns about data privacy and the need for transparent feedback mechanisms.

The views of Weber et al. [ 41 ]—that resistance to change may be an obstacle to the effective implementation of AI—are consistent with the opinions of 12 of the study participants. Specifically, Lecturer G noted that transformation is often met with resistance, especially when it comes to technology, and AI may be perceived as a risk to the conventional mode of teaching. Addressing this resistance requires policy interventions and professional development programs to ease the transition and encourage AI adoption. This indicated to the researchers that creating a culture of continuous improvement and gradually embracing this new approach may prevent resistance to adopting AI by lecturers and their higher education institutions.

The perspectives of all the participants, as regards the significance of tailored training and professional development which are customised to their specific needs, align with the findings of Luckin et al. [ 26 ]. According to that study, training should be more specific, and be contextually relevant to the unique demands and settings of the educational environment. This approach encourages active engagement and participation. Lecturer M specifically noted that any related training should focus mainly on its application to blended learning, to be successful. This highlights the importance of ongoing professional development to keep pace with technological advances. Clearly, HEIs need to adapt their policies to integrate AI tools that support personalised and interactive learning experiences. This suggested to the researchers that for AI technologies to be successful in higher education, professional development programmes must be made easily accessible for lecturers.

Finally, as featured in Alshahrani’s [ 3 ] study, the ethical use of AI in educational environments that adopt a blended learning approach, must be considered. Two participants (F and G) expressed the same sentiment, stating that open discussions on ethical guidelines and continuous dialogue among lecturers, management, and students are essential for navigating these issues. This suggests that policy should include ethical guidelines for AI use in education, ensuring that such integration supports not only academic integrity, but also responsible teaching and learning practices. In view of these findings, the researchers concluded that there was a distinct need for the creation of specific ethical frameworks that would assist all stakeholders to address the emerging ethical concerns associated with AI use in higher education institutions.

5.1 Limitations of the research

It is important to note the limitations of this study, which affect the generalisability of the findings. First, the study was restricted to a single South African higher HEI and one specific college, which may limit the applicability of the results to other contexts or institutions. Additionally, the full impact of AI on the blended learning approach may only become apparent in the future, as the students from this cohort progress in their careers and enter their respective professions. Furthermore, AI is a rapidly evolving field, and its continual advancements could mean that the study’s findings might become outdated relatively quickly. Finally, the successful implementation of AI in blended learning modules may be hindered by the lack of requisite technological resources and infrastructure in some educational institutions, potentially affecting the feasibility and effectiveness of AI integration.

6 Conclusion and recommendations

This paper discussed the impact of AI on a blended approach to teaching and learning in a particular HEI. It was based on the perceptions of 15 participating lecturers who lecture in the same college, albeit in different departments. The insights were based on the lecturers’ familiarity, experiences of, and involvement with, AI, and its impact on their teaching and learning. This positioned them to discuss the perceived advantages, disadvantages and supportive measures needed for such an approach to be successful. The use of focus group interviews and document analysis enabled the researchers to correlate what was actually taking place in this field of research, with the literature review undertaken.

Puentedura’s [ 34 ] SAMR model was chosen as theoretical framework to guide this undertaking, since it enabled the researchers to investigate how AI could bring about transformative changes in blended learning within the domain of higher education. The results highlight the significance of using AI in hybrid learning contexts, which has great potential for transforming traditional teaching methods. The study highlighted the implications of adopting AI to enhance the effectiveness of blended learning which offers personalised feedback, interactive discussions, and adaptive resources to cater to individual student needs. The findings draw attention to the crucial role of supportive measures such as management backing, improved training and professional development opportunities, reliable technological infrastructure, and improved internet connectivity, in ensuring the successful use of AI for blended learning modules. The findings thus enhance the knowledge base of this emerging field of study, by clarifying the perspectives of the lecturer participants at a particular HEI. Moreover, the findings can support future research on this topic, and may be used by other educational institutions—even those catering for different age groups.

6.1 Recommendations for further research

Recommendations for further research include several key areas to enhance the understanding and implementation of AI in blended learning environments. First, investigating AI and blended learning across various HEIs, both within South Africa and internationally, would provide a more comprehensive understanding of lecturers' perceptions of AI's impact. Additionally, research should focus on the effect of AI on students’ achievement of learning outcomes, their engagement with modules, and their overall enjoyment of learning within hybrid environments. Examining specific support measures, particularly relevant training, could further assist lecturers in effectively integrating AI into their modules. Longitudinal studies are also recommended to track changes in lecturers’ perceptions as they adapt to and integrate AI over time. A thorough exploration of the challenges HEIs face during the implementation process should be considered to address potential barriers. Furthermore, research into the ethical implications of AI in education, including the development of necessary guidelines, is essential. Finally, future studies should aim to validate and expand upon these findings using quantitative methods, as this study was purely qualitative.

Data availability

The data that support the findings of this study are not openly available due to the privacy and confidentiality agreements with the participants. However, the data will be made available by the corresponding author upon reasonable request, subject to review and approval by the research ethics committee of the involved institution. Requests for data access can be made by contacting the corresponding author at [email protected].

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The authors acknowledge the cooperation of the lecturers who participated in the data-collection process, and the HEI under study, for allowing the research to be conducted.

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1.1 Appendix A: Interview question

The following is the set of open-ended interview questions the researchers used by the researchers to assess the lecturer’s view of the impact of AI on blended learning:

Can you describe your experience incorporating AI technologies into your blended learning lessons?

What specific AI technologies or tools have you used in your blended learning approach?

Can you share examples of instances where AI enhanced the effectiveness of your blended learning lessons?

In your opinion, what are the key advantages of integrating AI into blended learning?

What challenges have you encountered when incorporating AI into blended learning, and how did you overcome them?

Have you received any feedback from students regarding their experiences with AI-infused blended learning?

Have you noticed any differences in student performance or understanding between traditional and AI-infused blended learning?

What kind of training or professional development opportunities do you believe are necessary for lecturers to effectively integrate AI into their blended teaching methods?

In your experience, what support or resources do lecturers currently require when implementing AI in blended learning?

1.2 Appendix B: document analysis guide

The researchers used the following guidelines when analysing the module contents, lessons and assessments:

Assess whether the content and learning objectives of the module feature the integration of AI technologies ─ look for objectives that explicitly mention the use of AI to enhance specific skills or competencies.

Identify specific occurrences where AI enhances interactivity within lessons.

Look for evidence that assessments capture the unique contributions of AI to student learning outcomes.

Search for features that assist in the immediacy and effectiveness of feedback mechanisms through AI.

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Sanders, D.A., Mukhari, S.S. Lecturers’ perceptions of the influence of AI on a blended learning approach in a South African higher education institution. Discov Educ 3 , 135 (2024). https://doi.org/10.1007/s44217-024-00235-2

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DOI : https://doi.org/10.1007/s44217-024-00235-2

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• Artificial Intelligence (AI)
• Blended learning
• Face-to-face learning
• Online learning
• Substitution, Augmentation, Modification, and Redefinition (SAMR) theoretical model

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