what is a case study in theory test

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In order to drive professionally you must also pass the driver CPC case study test (also known as the Driver CPC case study theory test - Step 2). Both the truck and the bus case study tests ask you to look at three real-life situations a professional driver might face.

Each case study test is computer-based and user-friendly, and works as follows:

• you will be asked 15 questions on each of three real-life driver situations
• to pass you must score at least 5 out of 15 in each situation and have an overall score of at least 28 out of 45.

You can prepare for the case study test by reading and learning the Rules of the Road. There are a wide range of official printed, digital and online learning materials available.

Case study test learning resources

To prepare for your case study test you should study the following:

• The Official driver theory test truck and bus book , available online or in most book shops
• The Rules of the Road

The RSA also has  approved Driver CPC Trainers  who can help you pass the CPC case study test.

How to book your case study test

If you want to become a category C (CPC truck) or category D (CPC bus) driver you must pass a case study test.

You must have passed the Driver CPC theory test (Step 1) in the relevant category before you can sit your case study test.

You have the option to sit it at any of the  40+ theory test centre locations nationwide.  

If you're ready, you can go ahead and book your case study test online now. 

Book your case study test

Book, reschedule or cancel a CPC theory (case study) test

Rescheduling or cancelling your case study test

You can reschedule or cancel your case study test online.

You must submit your cancellation or rescheduling request at least five days before your test booking. These five days must include three working days.

Your case study test result

When you complete your case study test you will be given a score report. This will tell you whether you have passed or failed your test.

If you've passed

When you pass the case study test you are issued with a driver CPC case study test certificate. This is valid for up to two years. You should apply for a learner permit within that time. After two years the certificate will no longer be valid and you will have to pay for and sit the case study test again.

If you're unsuccessful

If you're unsuccessful at your case study test, you'll get a score report with feedback telling you where you went wrong. Pay attention to these areas as they will help with your revision and give you a better chance of passing next time.

You can  book another case study  test straight away, but you can't take it for another three clear working days.

Frequently asked questions

Yes. But we would not recommend this as the multiple choice theory test can take up to 3 hours to complete (depending on which theory test you have applied for) and the case study test takes 1.5 hours to complete. 

No. Once you have passed the theory test in the relevant category you can go ahead and apply for your learner permit straight away and take your case study test at a later time.  

No, but you must pass the case study test before you can apply for and complete the CPC walk around test.

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In Your Theory Test Exam You Will Be Asked 5 Case Studies Question, Practice Online Case Study Questions and Answers For Free. Let’s start case study a questions.

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Case Study A

You plan to visit a friend who lives in a town a full day’s drive away.

Two weeks before your journey, you realise that your vehicle excise license (road tax) will expire while you’re away.

At the beginning of the journey, you reach a roundabout. Another car cuts in front of you, causing you to do an emergency stop.

Later, you join the motorway, where a red X is flashing above the outside lane.

While travelling along the motorway, you start to feel tired and stop at a service station.

It’s just after 11 pm when you park outside your friend’s house.  

Why must you avoid using your horn when parked outside your friend's house?

What should you do at the roundabout, what should you do two weeks before you leave, what should you do at the service station, what must you do on the motorway.

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Theory Test Case Study

A case study represents a driving scenario. You will need to read the case study and then answer five questions based on it.

A case study represents a driving scenario. You will need to read the case study and then answer five questions based on it. The questions test whether you have truly understood and can apply the driving theory knowledge in a practical and typical driving situation.

Case Study Example

You are going to visit your cousin who lives in the next town. You have a road atlas in your car although you have been before and know the route really well. You also have your mobile phone and have promised to call your cousin if you get delayed. On the way you find that a country lane you usually travel on is flooded and decide to turn back.

Qu.1 You are on a country lane and see that it is flooded ahead. How can you judge the depth of water? Choose one answer.

A. park at the roadside and wait for another vehicle to drive through

B. drive through slowly and keep checking through the side window

C. look for a depth gauge at then roadside

D. get out of your vehicle and wade in

Correct answer: C

Qu.2 You find that you can't judge the depth of the water so you decide to turn around. The road is quite narrow. The best method of turning would be: Choose one answer.

A. to give a signal and make a quick U-turn

B. turn around in the road using forward and reverse gears

C. reverse back down the country lane until you find a farm entrance to turn into

D. drive slowly forward to a wider section of road to turn around in.

Correct answer: B

Qu.3 You have turned around on the narrow country lane because you can't follow your usual route. The best thing to do to find a new route would be to: Choose one answer.

A. call you cousin to ask for directions as you drive back towards the main road

B. drive on slowly whilst checking your road atlas

C. find a safe place to pull in and consult your road atlas

D. wait until you are back on the main road before calling your cousin for help

Qu.4 On the way back to the main road you are delayed by a slow-moving farm vehicle ahead. You are worried about being late and should: Choose one answer.

A. sound your horn so the driver of the farm vehicle will get out of your way

B. follow the farm vehicle closely so you can overtake at the earliest opportunity

C. pull out to overtake even though the road is very narrow

D. keep well back from the farm vehicle so you can see well ahead

Correct answer: D

Qu.5 You decide to let your cousin know that you will be late. You should: Choose one answer.

A. find a safe place to pull in and make a call on your mobile phone

B. rely on your hands-free kit to keep you safe whilst you make a call

C. stop and get out of your car to make the call

D. drive slowly and send a text message to your cousin

Correct answer: A

The image below shows you how the question will appear when you actually take the theory test.

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Case Study Questions for the Theory Test

Are you ready for the new style theory test, the case study questions scenario below is no longer used on the theory test since 28th september 2020.

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Theory test changes: 28 September 2020

From 28 September 2020, the car theory test will include 3 multiple-choice questions based on a short video you'll watch.

The way the theory test works in England, Scotland and Wales will change from 28 September 2020.

The same changes will apply in Northern Ireland .

The change will make the theory test more accessible, especially to people with a:

• reading difficulty (like dyslexia)
• learning disability
• developmental condition (like autism)

The change only applies to car theory tests to begin with.

This change was due to happen on 14 April 2020 but was postponed due to coronavirus.

How the theory test is changing to use video clips instead of written case studies

Currently, you have to read a case study and then answer 5 questions about it.

This tests your knowledge and understanding of road rules.

This will change if you take your test from 28 September 2020. You’ll watch one video clip instead of reading a case study, and answer 3 questions about it.

How using a video clip will work

You’ll watch a short, silent, video clip and answer 3 multiple-choice questions about it.

You can watch the video clip as many times as you like during the multiple-choice part of the theory test.

Example You can watch the video, answer a question, and then watch the video again before you answer the next question.

What the video clip will look like

The video clip will show a situation, such as driving through a town centre, or driving on a country road.

Car theory test video clips from 28 September 2020: example clip

The type of questions you’ll answer about the video clip

You’ll answer questions like these:

• Why are motorcyclists considered vulnerable road users?
• Why should the driver, on the side road, look out for motorcyclists at junctions?
• In this clip, who can cross the chevrons to overtake other vehicles, when it’s safe to do so?

For each of the 3 questions, you’ll have to choose the correct answer from 4 possible answers.

What the screen will look like

The left-hand side of the screen will show the video clip, with controls to:

• play the video
• pause the video
• move to a specific part of the video on a progress bar
• watch the video using the full screen

The right-hand side of the screen will show the question and 4 possible answers.

Who this change will affect

All car theory tests will use video clips from 28 September 2020.

This includes if:

• you fail a test before then and retake if from 28 September 2020
• your test is cancelled or moved for any reason, and your new test date is from 28 September 2020

What’s not changing

You’ll still need to study the same books and software to prepare for your theory test.

You’ll still need to:

• answer 50 multiple-choice questions within 57 minutes
• get 43 out of the 50 questions right to pass the multiple-choice part of the test

The hazard perception part of the test is not changing. This is where you watch video clips to spot developing hazards.

Tests that are not changing

The change does not yet apply to these types of theory tests:

• bus or coach
• approved driving instructor (ADI) part 1

Other support for people with a reading difficulty, disability or health condition

You can have reasonable adjustments made to your theory test if you have a:

• reading difficulty
• health condition

These include:

• extra time to take the test
• someone to read what’s on the screen and record your answers
• someone to reword the questions for you

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PCV CPC Module 2 Case Studies

Grace, a driver of a city transit bus in the UK, regularly transports a diverse group of passengers. She has received basic first aid training and is familiar with the first aid kit on her bus. Grace knows the importance of quickly and safely responding to medical emergencies, such as falls, sudden illnesses, or injuries among passengers. She is prepared to assess danger, provide first aid using the ‘DR ABC’ approach, and handle specific situations like shock, burns, and electric shock, maintaining passenger safety until professional medical help arrives.

PCV CPC Case Study 171

There are 7 multiple choice questions in this PCV CPC case study. Read this carefully and ensure you fully understand the scenario before starting the test. You need to score 6 out of 7 to pass.

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Methodology

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

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

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

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

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

Table of contents

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Research bias

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

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• How would you go about finding a place to eat near you in Downtown Oakland? You want something kind of quick, open late, not too expensive, and with a good rating.
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• You're currently close to the 19th St Bart station, and it's 9PM. How would you get to this restaurant? Do you think you'll be able to make it before closing time?
• Your friend recommended you to check out a place called Belly while you're in Oakland. Try to find where it is, when it's open, and what kind of food options they have.
• Now go to any restaurant's page and try to leave a review (don't actually submit it).

What was the worst thing about your experience?

It was hard to find the bart station. The collections not being able to be sorted was a bit of a bummer

What other aspects of the experience could be improved?

Feedback from the owners would be nice

What did you like about the website?

The flow was good, lots of bright photos

What other comments do you have for the owner of the website?

I like that you can sort by what you are looking for and i like the idea of collections

You're going on a vacation to Italy next month, and you want to learn some basic Italian for getting around while there. You decided to try Duolingo.

• Please begin by downloading the app to your device.
• Choose Italian and get started with the first lesson (stop once you reach the first question).
• Now go all the way through the rest of the first lesson, describing your thoughts as you go.
• Get your profile set up, then view your account page. What information and options are there? Do you feel that these are useful? Why or why not?
• After a week in Italy, you're going to spend a few days in Austria. How would you take German lessons on Duolingo?
• What other languages does the app offer? Do any of them interest you?

I felt like there could have been a little more of an instructional component to the lesson.

It would be cool if there were some feature that could allow two learners studying the same language to take lessons together. I imagine that their screens would be synced and they could go through lessons together and chat along the way.

Overall, the app was very intuitive to use and visually appealing. I also liked the option to connect with others.

Overall, the app seemed very helpful and easy to use. I feel like it makes learning a new language fun and almost like a game. It would be nice, however, if it contained more of an instructional portion.

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What is a Case Study? Definition, Research Methods, Sampling and Examples

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What is a Case Study?

A case study is defined as an in-depth analysis of a particular subject, often a real-world situation, individual, group, or organization. 

It is a research method that involves the comprehensive examination of a specific instance to gain a better understanding of its complexities, dynamics, and context. 

Case studies are commonly used in various fields such as business, psychology, medicine, and education to explore and illustrate phenomena, theories, or practical applications.

In a typical case study, researchers collect and analyze a rich array of qualitative and/or quantitative data, including interviews, observations, documents, and other relevant sources. The goal is to provide a nuanced and holistic perspective on the subject under investigation.

The information gathered here is used to generate insights, draw conclusions, and often to inform broader theories or practices within the respective field.

Case studies offer a valuable method for researchers to explore real-world phenomena in their natural settings, providing an opportunity to delve deeply into the intricacies of a particular case. They are particularly useful when studying complex, multifaceted situations where various factors interact. 

Additionally, case studies can be instrumental in generating hypotheses, testing theories, and offering practical insights that can be applied to similar situations. Overall, the comprehensive nature of case studies makes them a powerful tool for gaining a thorough understanding of specific instances within the broader context of academic and professional inquiry.

Key Characteristics of Case Study

Case studies are characterized by several key features that distinguish them from other research methods. Here are some essential characteristics of case studies:

• In-depth Exploration: Case studies involve a thorough and detailed examination of a specific case or instance. Researchers aim to explore the complexities and nuances of the subject under investigation, often using multiple data sources and methods to gather comprehensive information.
• Contextual Analysis: Case studies emphasize the importance of understanding the context in which the case unfolds. Researchers seek to examine the unique circumstances, background, and environmental factors that contribute to the dynamics of the case. Contextual analysis is crucial for drawing meaningful conclusions and generalizing findings to similar situations.
• Holistic Perspective: Rather than focusing on isolated variables, case studies take a holistic approach to studying a phenomenon. Researchers consider a wide range of factors and their interrelationships, aiming to capture the richness and complexity of the case. This holistic perspective helps in providing a more complete understanding of the subject.
• Qualitative and/or Quantitative Data: Case studies can incorporate both qualitative and quantitative data, depending on the research question and objectives. Qualitative data often include interviews, observations, and document analysis, while quantitative data may involve statistical measures or numerical information. The combination of these data types enhances the depth and validity of the study.
• Longitudinal or Retrospective Design: Case studies can be designed as longitudinal studies, where the researcher follows the case over an extended period, or retrospective studies, where the focus is on examining past events. This temporal dimension allows researchers to capture changes and developments within the case.
• Unique and Unpredictable Nature: Each case study is unique, and the findings may not be easily generalized to other situations. The unpredictable nature of real-world cases adds a layer of authenticity to the study, making it an effective method for exploring complex and dynamic phenomena.
• Theory Building or Testing: Case studies can serve different purposes, including theory building or theory testing. In some cases, researchers use case studies to develop new theories or refine existing ones. In others, they may test existing theories by applying them to real-world situations and assessing their explanatory power.

Understanding these key characteristics is essential for researchers and practitioners using case studies as a methodological approach, as it helps guide the design, implementation, and analysis of the study.

Key Components of a Case Study

A well-constructed case study typically consists of several key components that collectively provide a comprehensive understanding of the subject under investigation. Here are the key components of a case study:

• Provide an overview of the context and background information relevant to the case. This may include the history, industry, or setting in which the case is situated.
• Clearly state the purpose and objectives of the case study. Define what the study aims to achieve and the questions it seeks to answer.
• Clearly identify the subject of the case study. This could be an individual, a group, an organization, or a specific event.
• Define the boundaries and scope of the case study. Specify what aspects will be included and excluded from the investigation.
• Provide a brief review of relevant theories or concepts that will guide the analysis. This helps place the case study within the broader theoretical context.
• Summarize existing literature related to the subject, highlighting key findings and gaps in knowledge. This establishes the context for the current case study.
• Describe the research design chosen for the case study (e.g., exploratory, explanatory, descriptive). Justify why this design is appropriate for the research objectives.
• Specify the methods used to gather data, whether through interviews, observations, document analysis, surveys, or a combination of these. Detail the procedures followed to ensure data validity and reliability.
• Explain the criteria for selecting the case and any sampling considerations. Discuss why the chosen case is representative or relevant to the research questions.
• Describe how the collected data will be coded and categorized. Discuss the analytical framework or approach used to identify patterns, themes, or trends.
• If multiple data sources or methods are used, explain how they complement each other to enhance the credibility and validity of the findings.
• Present the key findings in a clear and organized manner. Use tables, charts, or quotes from participants to illustrate the results.
• Interpret the results in the context of the research objectives and theoretical framework. Discuss any unexpected findings and their implications.
• Provide a thorough interpretation of the results, connecting them to the research questions and relevant literature.
• Acknowledge the limitations of the study, such as constraints in data collection, sample size, or generalizability.
• Highlight the contributions of the case study to the existing body of knowledge and identify potential avenues for future research.
• Summarize the key findings and their significance in relation to the research objectives.
• Conclude with a concise summary of the case study, its implications, and potential practical applications.
• Provide a complete list of all the sources cited in the case study, following a consistent citation style.
• Include any additional materials or supplementary information, such as interview transcripts, survey instruments, or supporting documents.

By including these key components, a case study becomes a comprehensive and well-rounded exploration of a specific subject, offering valuable insights and contributing to the body of knowledge in the respective field.

Sampling in a Case Study Research

Sampling in case study research involves selecting a subset of cases or individuals from a larger population to study in depth. Unlike quantitative research where random sampling is often employed, case study sampling is typically purposeful and driven by the specific objectives of the study. Here are some key considerations for sampling in case study research:

• Criterion Sampling: Cases are selected based on specific criteria relevant to the research questions. For example, if studying successful business strategies, cases may be selected based on their demonstrated success.
• Maximum Variation Sampling: Cases are chosen to represent a broad range of variations related to key characteristics. This approach helps capture diversity within the sample.
• Selecting Cases with Rich Information: Researchers aim to choose cases that are information-rich and provide insights into the phenomenon under investigation. These cases should offer a depth of detail and variation relevant to the research objectives.
• Single Case vs. Multiple Cases: Decide whether the study will focus on a single case (single-case study) or multiple cases (multiple-case study). The choice depends on the research objectives, the complexity of the phenomenon, and the depth of understanding required.
• Emergent Nature of Sampling: In some case studies, the sampling strategy may evolve as the study progresses. This is known as theoretical sampling, where new cases are selected based on emerging findings and theoretical insights from earlier analysis.
• Data Saturation: Sampling may continue until data saturation is achieved, meaning that collecting additional cases or data does not yield new insights or information. Saturation indicates that the researcher has adequately explored the phenomenon.
• Defining Case Boundaries: Clearly define the boundaries of the case to ensure consistency and avoid ambiguity. Consider what is included and excluded from the case study, and justify these decisions.
• Practical Considerations: Assess the feasibility of accessing the selected cases. Consider factors such as availability, willingness to participate, and the practicality of data collection methods.
• Informed Consent: Obtain informed consent from participants, ensuring that they understand the purpose of the study and the ways in which their information will be used. Protect the confidentiality and anonymity of participants as needed.
• Pilot Testing the Sampling Strategy: Before conducting the full study, consider pilot testing the sampling strategy to identify potential challenges and refine the approach. This can help ensure the effectiveness of the sampling method.
• Transparent Reporting: Clearly document the sampling process in the research methodology section. Provide a rationale for the chosen sampling strategy and discuss any adjustments made during the study.

Sampling in case study research is a critical step that influences the depth and richness of the study’s findings. By carefully selecting cases based on specific criteria and considering the unique characteristics of the phenomenon under investigation, researchers can enhance the relevance and validity of their case study.

Case Study Research Methods With Examples

• Interviews:
• Interviews involve engaging with participants to gather detailed information, opinions, and insights. In a case study, interviews are often semi-structured, allowing flexibility in questioning.
• Example: A case study on workplace culture might involve conducting interviews with employees at different levels to understand their perceptions, experiences, and attitudes.
• Observations:
• Observations entail direct examination and recording of behavior, activities, or events in their natural setting. This method is valuable for understanding behaviors in context.
• Example: A case study investigating customer interactions at a retail store may involve observing and documenting customer behavior, staff interactions, and overall dynamics.
• Document Analysis:
• Document analysis involves reviewing and interpreting written or recorded materials, such as reports, memos, emails, and other relevant documents.
• Example: In a case study on organizational change, researchers may analyze internal documents, such as communication memos or strategic plans, to trace the evolution of the change process.
• Surveys and Questionnaires:
• Surveys and questionnaires collect structured data from a sample of participants. While less common in case studies, they can be used to supplement other methods.
• Example: A case study on the impact of a health intervention might include a survey to gather quantitative data on participants’ health outcomes.
• Focus Groups:
• Focus groups involve a facilitated discussion among a group of participants to explore their perceptions, attitudes, and experiences.
• Example: In a case study on community development, a focus group might be conducted with residents to discuss their views on recent initiatives and their impact.
• Archival Research:
• Archival research involves examining existing records, historical documents, or artifacts to gain insights into a particular phenomenon.
• Example: A case study on the history of a landmark building may involve archival research, exploring construction records, historical photos, and maintenance logs.
• Longitudinal Studies:
• Longitudinal studies involve the collection of data over an extended period to observe changes and developments.
• Example: A case study tracking the career progression of employees in a company may involve longitudinal interviews and document analysis over several years.
• Cross-Case Analysis:
• Cross-case analysis compares and contrasts multiple cases to identify patterns, similarities, and differences.
• Example: A comparative case study of different educational institutions may involve analyzing common challenges and successful strategies across various cases.
• Ethnography:
• Ethnography involves immersive, in-depth exploration within a cultural or social setting to understand the behaviors and perspectives of participants.
• Example: A case study using ethnographic methods might involve spending an extended period within a community to understand its social dynamics and cultural practices.
• Experimental Designs (Rare):
• While less common, experimental designs involve manipulating variables to observe their effects. In case studies, this might be applied in specific contexts.
• Example: A case study exploring the impact of a new teaching method might involve implementing the method in one classroom while comparing it to a traditional method in another.

These case study research methods offer a versatile toolkit for researchers to investigate and gain insights into complex phenomena across various disciplines. The choice of methods depends on the research questions, the nature of the case, and the desired depth of understanding.

Best Practices for a Case Study in 2024

Creating a high-quality case study involves adhering to best practices that ensure rigor, relevance, and credibility. Here are some key best practices for conducting and presenting a case study:

• Clearly articulate the purpose and objectives of the case study. Define the research questions or problems you aim to address, ensuring a focused and purposeful approach.
• Choose a case that aligns with the research objectives and provides the depth and richness needed for the study. Consider the uniqueness of the case and its relevance to the research questions.
• Develop a robust research design that aligns with the nature of the case study (single-case or multiple-case) and integrates appropriate research methods. Ensure the chosen design is suitable for exploring the complexities of the phenomenon.
• Use a variety of data sources to enhance the validity and reliability of the study. Combine methods such as interviews, observations, document analysis, and surveys to provide a comprehensive understanding of the case.
• Clearly document and describe the procedures for data collection to enhance transparency. Include details on participant selection, sampling strategy, and data collection methods to facilitate replication and evaluation.
• Implement measures to ensure the validity and reliability of the data. Triangulate information from different sources to cross-verify findings and strengthen the credibility of the study.
• Clearly define the boundaries of the case to avoid scope creep and maintain focus. Specify what is included and excluded from the study, providing a clear framework for analysis.
• Include perspectives from various stakeholders within the case to capture a holistic view. This might involve interviewing individuals at different organizational levels, customers, or community members, depending on the context.
• Adhere to ethical principles in research, including obtaining informed consent from participants, ensuring confidentiality, and addressing any potential conflicts of interest.
• Conduct a rigorous analysis of the data, using appropriate analytical techniques. Interpret the findings in the context of the research questions, theoretical framework, and relevant literature.
• Offer detailed and rich descriptions of the case, including the context, key events, and participant perspectives. This helps readers understand the intricacies of the case and supports the generalization of findings.
• Communicate findings in a clear and accessible manner. Avoid jargon and technical language that may hinder understanding. Use visuals, such as charts or graphs, to enhance clarity.
• Seek feedback from colleagues or experts in the field through peer review. This helps ensure the rigor and credibility of the case study and provides valuable insights for improvement.
• Connect the case study findings to existing theories or concepts, contributing to the theoretical understanding of the phenomenon. Discuss practical implications and potential applications in relevant contexts.
• Recognize that case study research is often an iterative process. Be open to revisiting and refining research questions, methods, or analysis as the study progresses. Practice reflexivity by acknowledging and addressing potential biases or preconceptions.

By incorporating these best practices, researchers can enhance the quality and impact of their case studies, making valuable contributions to the academic and practical understanding of complex phenomena.

Interested in learning more about the fields of product, research, and design? Search our articles here for helpful information spanning a wide range of topics!

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Theory Testing Using Case Studies

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Abstract: The appropriateness of case studies as a tool for theory testing is still a controversial issue, and discussions about the weaknesses of such research designs have previously taken precedence over those about its strengths. The purpose of the paper is to examine and revive the approach of theory testing using case studies, including the associated research goal, analysis, and generalisability. We argue that research designs for theory testing using case studies differ from theory-building case study research designs because different research projects serve different purposes and follow different research paths.

Keywords: Case studies, theory testing, research paths

1. Introduction

Research based on case studies can take many forms. Case study research can depart in a positivist or interpretivist approach, it can be deductive or inductive, and it can rely on qualitative or quantitative methods. It can also be a mix of these extremes (Cavaye 1996). Using case studies is, however, still perceived as a less conventional manner of testing theories in many research communities (Cavaye 1996). This is so regardless of the fact that research capacities already in the 1970s made the following propositions: '[c]ase studies... are valuable at all stages of the theory building process, but most valuable at that stage of theory building where least value is generally attached to them: the stage at which candidate theories are "tested"' (Eckstein 1975: 80); and that case studies are useful 'particularly to examine a single exception that shows the hypothesis to be false' (Stake 1978: 7).

Today, we propose that case studies are still an overlooked source of theory refinement and development. Extending the value of case studies to that of theory development within a larger research programme is regrettably still a contested issue.

A research design based on case studies as a means for testing theories has not previously been examined comprehensively. Often the weaknesses of such research designs have taken precedence over reflections about its strengths. In this paper, we approach the debate from a different stance, arguing that case studies can indeed be a valuable tool for testing theories.

Thus, the purpose of the paper is to examine theory testing using case studies, including the associated research goal, analysis, and generalisability. In this respect, it is argued that the research design for theory testing using case studies differs from the design of theory building using case studies because different research projects serve different purposes and follow different research paths.

To promote the argument, we revitalise the notions of both 'testing' and 'theories' in a wider sense than is usually done. We suggest two specific research paths that serve as structured and legitimate frameworks for thinking about case research designs for theory testing. Finally, we elaborate on what consequences theory testing using case studies has on different elements of a research design, in particular the generalisability issues.

2. Theoretical Research Paths for Case Studies

Research based on a case study focuses on a single setting or unit that is spatially and temporally bounded (Eisenhardt 1989, Van Maanen 1979). Sometimes it can be difficult to specify where the case ends and the environment begins, but here boundedness, contexts, and experience can be useful concepts (Stake 2006).

Overall, the advantage of a case study is that it 'can "close-in" on real-life situations and test views directly in relation to phenomena as they unfold in practice' (Flyvbjerg 2004: 428).

Each case can contain embedded cases (Yin 2014). The essence of a case study is the case, understood as the choice of study object(s) and the framing of these (Stake 2000). In principle, any technique for collecting data is applicable, even if case studies are often mistakenly presented as a qualitative method (Eisenhardt 1989).

Case studies may serve different research goals (Maxwell 2005). For instance, Eisenhardt (1989) specifies three such goals: description, theory testing, and theory generation. The connection between elements in the research design is ensured by a research strategy (Maxwell 2005), which becomes 'a way of linking ideas and evidence to produce a representation of some aspect of social life' (Ragin 1994: 48).

Brinberg and McGrath (1985) suggest that research paths progress through three different domains: a substantive 'real-world' domain (S), a conceptual domain (C), and a methodological domain (M). Each domain can be a starting point for conducting research, and any study covers all three domains. Mapping out a research strategy thus starts with the choice of the primary domain of interest, then the second domain, and, finally, the third domain. The domains are interrelated and the researcher works iteratively between them, so the presentation in the following is a representation and not a cookbook recipe. Being clear about a study's starting point benefits researchers because it promotes the understanding of the study itself and gives an overview, as well as helps in expressing its results and contributions to a research programme.

For our purpose, we are interested in the type of research path that Brinberg and McGrath (1985) identify as a theoretical path leading to an end product of tested hypotheses. While many case studies take their starting point in the substantive domain, a theoretical research path also describes a situation where a study focuses on the conceptual domain and in which the cases are instrumental to the theoretical contribution. Theoretical paths are thus either concept-driven or system-driven (Brinberg and McGrath 1985). While we focus mainly on concept-driven paths in the following, the outcome of the two paths may appear similar, cf. Figure 1.

Concept-driven theoretical paths focus on understanding the explanation(s) underlying a phenomenon (Brinberg and McGrath 1985). Such a research path matches a research design built on, for instance, rival theories, a design in which multiple theories are compared in order to assess their relative value in terms of strengths, weaknesses, boundaries, and other relevant dimensions. Examples of research questions to this path could be 'Is the original theory correct? Does the original theory fit other circumstances? Are there additional categories or relationships?' (Crabtree and Miller 1999: 7).

System-driven theoretical paths focus on understanding an empirical system (Brinberg and McGrath 1985). For a system-driven theoretical path, we suggest a matching theory-testing case study research design using multiple theories to examine the system from different angles (triangulation).

Similar conceptualisations have been denoted analytic induction (Patton 2002) and explanatory case studies (Yin 2014). However, analytic induction builds on cross-case analyses, and explanatory studies examine how a particular situation or event may be explained by one or more theories. Only to a lesser degree does the latter focus on the theory as such, and therefore explanatory studies represent system-driven theoretical study paths. We argue that both types are subsets of theory-testing studies.

The line between system-driven and concept-driven case studies is blurred and several purposes may be served within the same study. A classic example from political science is the advent of revolutions and wars (George and Bennett 2005). Here the variables come partly from the cases and partly from prior theoretical explanations.

3. The Role of Theory for Theoretical Research Paths

In order to discuss the role of theories for theoretical research paths (c.f. Brinberg and McGrath 1985), of which theory testing using case studies is an example, we need to define both the concept of theory and theory-testing.

In the literature theory is defined more or less accurately (Andersen and Kragh 2010) and is often mistakenly refered to as models and propositions (Sutton and Staw 1995). Doty and Click (1994) define theory as 'a series of logical arguments that specifies a set of relationships among concepts, constructs, or variables'. Such a definition allows for theories to be conceptualised at different levels - for instance, conceptual, construct, and variable levels. The purpose of theories is to explain why (Sutton and Staw 1995) - which again explains how. So, theories are explanations.

Theories as explanations are important for case studies in several ways (c.f. Walsham 1995). First, they are always present as the researcher's implicit and explicit understanding of what is going on with the studied phenomenon. With minor differences, this role of theory is referred to as 'conceptual context' (Maxwell 2005), 'conceptual domain' (Brinberg and McGrath 1985), or 'analytical frames' (Ragin 1994).

Second, theories explicitly provide analytical guidelines and serve as 'a heuristic for collecting and organising data' (Colville et al. 1999). In a theory-testing case study, the researcher specifies a priori to data collection the types and content of data to be collected. These a priori-specified data requirements are the minimum amount of data satisfying the analytical needs. Theories are a useful tool for handling the large amounts of data in case studies because '[scanning all variables is not the same as including all variables' (Lijphart 1971: 690).

Third, theories as an object of interest can be developed, modified, and tested using case studies and thus serve as both input and output to the study (Campbell 1975, Eckstein 1975, Yin 2014). Theories-as-object is the special focus when case studies are used for theory testing, which distinguishes them from other types of case studies.

In this sense, theories allow for a focus on key variables, leading to the required parsimony of analysis. Thus, when the purpose of a case study is theory testing, not only in-depth knowledge of the case(s) and the methods is needed, but also knowledge of the theories involved.

Consider the meaning and implications of the terms 'test' and 'testing'. Many researchers seem to think of testing in a narrow sense: a specified and near-conclusive procedure for falsification or verification. We rely on a more inclusive definition. For instance, Crabtree and Miller specifically state that the goal of theory testing is 'to test explanatory theory by evaluating it in different contexts' (1999: 7). Likewise, Yin (2014) argues that theory testing is a matter of external validity and can be seen as the replication of case studies with the purpose of identifying whether previous results extend to new cases.

When a researcher conducts a theory test, then propositions, i.e., logical conclusions or predictions, are derived from the theory and are compared to observations, or data, in the case (Cavaye 1996). The more often and the more conclusively the theory is confirmed, the more faith in that the theory reflects reality (Cavaye 1996).

Theory testing is in contrast to theory-building case studies, where the latter are defined as 'the process through which researchers seek to make sense of the observable world by conceptualizing, categorizing and ordering relationships among observed elements' (Andersen and Kragh 2010). The case plays a different role whether it is used for theory testing or theory building, c.f. Figure 2. In theory testing using case studies, propositions are selected and articulated beforehand, as well as used dynamically in all other phases of the research process. The role of the case thus becomes instrumental, meaning that '[t]he case is of secondary interest, it plays a supportive role, and it facilitates our understanding of something else' (Stake 2000: 437).

The contributions from theory testing case studies can be diverse '...to strengthen or reduce support for a theory, narrow or extend the scope conditions of a theory, or determine which of two or more theories best explains a case, type, or general phenomenon' (George and Bennett 2005: 109). The classical study of theory testing using a case study is Allison's (1971) application of three different decision-making perspectives in the analysis of the Cuban Missile Crisis, but we find other types where case studies have been used for theory testing (e.g., Argyris 1979, Pinfield 1986, Jobber and Lucas 2000, Trochim 1985, Jauch et al. 1980, Brown 1999, Lee et al. 1996).

4. Theory Testing Case Study Design

When a researcher decides on theory testing using case studies, it affects different elements of a research design, as summarised in Table 1. A researcher doing a theory testing case study spends relatively more time preparing for data collection and analysis, making extant theories explicit and setting up the analytical framework.

4.1 Research goals

Consistent with the concept-driven theoretical path, testing of competing theories may be a research goal in itself. The competing perspectives approach aims to rule out less effective explanations, often looking for the one explanation that best explains a phenomenon, or to establish the boundaries of a theory's application.

The system-driven theoretical path is equivalent to theory triangulation. Comparing complementary theories is a form of triangulation (Brinberg and McGrath 1985): 'The point... is to understand how differing assumptions and premises affect findings and interpretations' (Patton 2002: 562). The complementary perspective thus sees each theory as contributing to understanding.

Identifying the relevant theories to include in a case study is a selection process: In some cases, the choice of theories to be included is unproblematic. In other instances, it may be difficult to distinguish distinct theories within a field, especially when a research field is emerging and everybody is trying to break new ground. Such diversity is one reason why theory testing using case studies is relevant, since the literature must be systematised prior to data collection.

4.2 Pre-data collection work on theories

Prior to data collection, theories are 'operationalised' in terms of the minimum data requirements and propositions to be matched with empirical data, thus addressing construct validity. Several levels and elements of the same theory, or rather the pattern that the theory constitutes, can be evaluated through multiple propositions and multiple theories because of the amount of data from the case study (Campbell 1975).

4.3 Analysis

Analytically, in-depth knowledge of theories facilitates moving from emic to etic accounts of a phenomenon. Ernie conceptualisations are those given by case informants, while etic conceptualisations are researchers' interpretations (Maxwell 2005). In terms of specific analytical techniques, Campbell (1975) suggested degrees- of-freedom analysis, which has been exemplified by, for example, Wilson and Woodside (1999). Ragin and co- workers (Ragin 1994) have developed techniques for analysing both crisp and fuzzy case data sets. Knowledge of prior theory also has potential risks. If, for instance, a researcher is too emotionally attached to certain explanations, (s)he runs the risk of ignoring conflicting information. Rival explanations might be a way of mitigating such a risk. However, knowledge of prior theory can also be argued to free 'mental' resources to look for alternative explanations and elements. Researchers will have excess information processing capacity to include additional thought experiments and iterations between theory and data (c.f. Campbell 1975), because familiar information can be processed faster and with less basic analytical work.

4.4 Validity and generalisation

The majority of the methodological literature describes quantitative and qualitative studies as associated with generalisation and particularisation, respectively. The result is that notions of theory testing, which are associated with generalisation purposes, in small-N studies, which are associated with particularisation purposes, are controversial topics in many research communities. Such scepticism includes theory testing using case studies. In a case study design, however, the methods applied for data collection do not determine whether the purpose is generalisation or particularisation. Therefore, the assumption that case studies cannot be applied for generalisation purposes is questionable.

Still, there are different views as to whether generalisations from case studies are possible. Some claim that inference is possible (e.g., Yin), whereas others reject this (e.g., Stake, Kennedy, Lincoln & Cuba) and argue that readers of case study reports are themselves responsible for whether there can be a 'transferability' of findings from one situation to another (Gomm et al. 2000).

Yin (2014) considers the case as an experiment and claims that case studies can lead to analytic generalisations, Le., a generalisation on a conceptual higher level than the case. Such analytical generalisation is based on 'a) corroborating, modifying, rejecting, or otherwise advancing theoretical concepts that you referenced in designing your case study or b) new concepts that arose upon the completion of your case study' (Yin 2014: 40). Thus, according to Yin, a case study is generalisable to theoretical propositions and not populations (Swanborn 2010: 66). Analytical generalisations involve a judgement about whether the findings of one study can be a guide to what occurs in another situation and include a comparison of the two situations (Brinkman and Kvale 2015).

Stake rejects a scientific induction from case studies but talks about a naturalistic generalizability which is developed within people based on their experience (Stake 1978, 1995). Often these generalisations are not predictions, but rather lead to expectations (Stake 1978). 'They may become verbalized, passing of course from tacit knowledge to propositional; but they have not yet passed the empirical and logical tests that characterize formal (scholarly, scientific) generalizations' (Gomm et al. 2000).

Kennedy (1979) claims that generalisation is a judgment of degree. The researcher should produce and share the information, and after this the receiver judges whether the findings can be generalised to the receiver's situation. Here it is of course essential that the researcher makes a detailed description of the specific case characteristics, because s(he) does not know who the receivers are (Kennedy 1979).

The logic behind theory testing supports the idea of generalisation from prior studies and their outcomes to an actual study, and from an actual study to theories (Lee and Baskerville 2003, Yin 2014). Two assumptions are present here. First, verification in the sense of 'ultimately true' is not possible except for trivial facts (Lakatos 1970). Second, verification and falsification are not opposites. The opposite of falsification is confirmation or corroboration-words that more accurately denote the outcome, namely an ongoing process of theory testing and theory development.

In terms of internal validity, rival explanations can be included because: 'the more rivals that your analysis addresses and rejects, the more confidence you can place in your findings' (Yin 2003: 113). Explicit consideration of alternative explanations for findings increases the credibility of a study, and here we use the entire range of rival explanations, not just theories (see Yin 2000, 2003).

The pursuit of generalisations typically goes together with a search for causes (Stake 2006). It is often claimed that case studies contribute by giving the opportunity of identifying causalities because cases are examined deeply and longitudinal (Gomm et al. 2000). Unlike an experiment, cases 'investigate causal processes "in the real world" rather than in artificially created settings' (Gomm et al. 2000).

However, we know that case studies are useful when the phenomenon under investigation is complex. Such complexity has many faces and can be seen, for instance, when phenomena interrelate, occur at multiple levels of analysis (e.g., span individual and group levels), or when they can only be understood as embedded in a larger context.

If the phenomenon being studied is too complex, a search for 'simple' causalities, however, becomes hopeless.

Nevertheless, cases can be seen as manifestations of more general phenomena and therefore embody the essence of those phenomena (c.f. Becker 1998, Gerring 2004).

4.4.1 Sampling

Generalisation in theory-testing case studies is closely related to the issue of sampling. It is, however, not merely a function of the number of cases observed, but rather the range of characteristics of the units and the range of conditions occurred under observation (Kennedy 1979). 'The range of characteristics included in a sample increases the range of population characteristics to which generalization is possible' (Kennedy 1979).

In line with this, we suggest that the number of cases is considered from what is added from each new case in terms of analytical benefits. Figure 3 outlines the relationship between the number of theories and the number of cases. When the number of theories and their propositions or 'variables' to be tested is small, multiple case studies are an obvious choice to investigate the boundaries of those theories in different settings. As the number of theories to be evaluated grows, a single case study may yield more credibility in the findings, because all theories are evaluated against the same material. The 'efficiency boundary' represents the interaction points where a thorough analysis is feasible and credible. It may be shifted outwards if more researchers are involved in a study, or if a researcher has special skills, experience, or insights with respect to the cases or the theories, because the pooled information processing capacity is increased and more relationships between theory and data can be handled.

Besides choosing the number of cases, strategic or purposeful case selection is essential for generalisation (Flyvbjerg 2004). Theory testing using case studies relies by definition on theoretical sampling where cases are chosen on the basis of theoretical criteria (Eisenhardt 1989, Patton 2002, Wilson and Vlosky 1997). In addition, we rely on information-oriented selection where '[cjases are selected on the basis of expectations about their information content' (Flyvbjerg 2004: 426), i.e., their potential for learning (Stake 2000: 446). Diverging ideas prevail regarding the benefits of one or multiple cases relative to generalisation. Yin argues that more cases provide stronger conclusions (2014), while Stake claims 'conclusions from differences between any two cases are less to be trusted than conclusions about one' and argues that comparisons might impede learning from the particular (Stake 2000: 444). In short, the number and type of cases relative to generalisability depends upon research questions and goals.

5. Theory Development

Case studies contribute to the development and refinement of research programmes along with other types of research. Much methodological literature seems to favour the use of cases for descriptive and explorative purposes, that is, early in the development of a research field or a study. Case studies are, however, sources of learning throughout the progress of a larger research programme (Campbell 1975, Eckstein 1975). For instance, case studies add depth to understanding that may arise from completed large-N studies (Patton 2002), they illuminate mechanisms and relations (Gerring 2004), and, of course, they are unique for learning from the particular (Stake 2000).

Research programmes 'connect' researchers through series of related theories and have a 'hard core' of assumptions which are not questioned and a protective belt of auxiliary hypotheses (Lakatos 1970). Findings in theory testing using case studies will often be related to the protective belt, but as fundamentally different ('paradigmatic') theories are evaluated, outcomes may also be related to the hard core.

Critiques have been made against case studies for 'having a bias towards verification, understood as a tendency to confirm the researcher's preconceived notions' (Flyvbjerg 2004: 428). The risk of confirming existing ideas and beliefs does not, however, seem to be an observed problem in case study research. Researchers often report that they change or discharge their original ideas as they gain additional insights during a case study (c.f. Campbell 1975, Flyvbjerg 2004, Lee and Baskerville 2003).

So rather than seeing one piece of counterevidence (or one case) as falsification, we use Lakatos' distinction between naïve and sophisticated falsification. Lakatos (1970: 120) suggests: 'falsification is not simply a relation between a theory and the empirical basis, but a multiple relation between competing theories, the original "empirical basis", and the empirical growth resulting from the competition'.

Lakatos nuances the issue of falsification by introducing the notion of sophisticated falsification, in which 'a theory is "acceptable" or "scientific" only if it has corroborated excess empirical content over its predecessor (or rival), that is, only if it leads to the discovery of novel facts' (Lakatos 1970:116).

In comparison, naïve falsificationists, following a Popperian argument, claim that a theory can be falsified by an 'observational' conflicting statement. To sophisticated falsificationists, a theory is falsified if another theory is proposed, fulfilling the requirements mentioned. Naïve falsification is therefore not a sufficient condition for eliminating a theory: 'in spite of hundreds of known anomalies we do not regard it as falsified (that is, eliminated) until we have a better one' (Lakatos 1970: 121).

When results in case studies indicate falsification or anomalies, we have two options: Either to suggest a new theory within a different research programme (abandon the hard core), or to explain anomalies using auxiliary hypotheses (modify the protective belt). In this sense theory testing using case studies is valuable at any stage in the theory development process as long as it has a positive and progressive effect on a research programme.

6. Concluding Remarks

We conclude that a research design built on theory testing using case studies contributes in several ways. Theory testing using case studies evaluates the explanatory power of theories and their boundaries, thus assessing external validity. In summary, the range of theories' applicability and usability is explored, thereby positioning such research in a stream of cumulative research that refines and develops the knowledge of a field. Theory testing using case studies is an overlooked and undervalued research strategy that can follow different paths. We seek to fill a gap in the literature to facilitate the understanding of this research strategy and to make it a legitimate research path. With this paper, we encourage researchers to experiment more with theory testing using case studies and to engage in the formulation of the epistemology of the particular.

Acknowledgements

We would like to thank the following people for their comments and feedback during the writing process: At Aalborg University, Professor Poul Houman Andersen; at the University of Southern Denmark, Associate Professors Bo Eriksen, Karsten Boye Rasmussen, and Per 0stergaard, as well as Head of Department Jeanette Lemmergaard; and, at Aarhus University, Associate Professor Emeritus Erik Maalpe.

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Ann-Kristina Lokke1 and Pernille Dissing S0rensenz

department of Economics and Business,School of Business and Social Sciences, Aarhus University

interdisciplinary Centre for Organizational Architecture (ICOA), School of Business and Social Sciences, Fuglesangs Allé 4

[email protected]

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Copyright Academic Conferences International Limited Jul 2014

The appropriateness of case studies as a tool for theory testing is still a controversial issue, and discussions about the weaknesses of such research designs have previously taken precedence over those about its strengths. The purpose of the paper is to examine and revive the approach of theory testing using case studies, including the associated research goal, analysis, and generalisability. We argue that research designs for theory testing using case studies differ from theory-building case study research designs because different research projects serve different purposes and follow different research paths.

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The case study approach

Sarah crowe.

1 Division of Primary Care, The University of Nottingham, Nottingham, UK

Kathrin Cresswell

2 Centre for Population Health Sciences, The University of Edinburgh, Edinburgh, UK

Ann Robertson

3 School of Health in Social Science, The University of Edinburgh, Edinburgh, UK

Anthony Avery

Aziz sheikh.

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.

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 ​ Tables1, 1 , ​ ,2, 2 , ​ ,3 3 and ​ and4) 4 ) and those of others to illustrate our discussion[ 3 - 7 ].

Example of a case study investigating the reasons for differences in recruitment rates of minority ethnic people in asthma research[ 3 ]

Example of a case study investigating the process of planning and implementing a service in Primary Care Organisations[ 4 ]

Example of a case study investigating the introduction of the electronic health records[ 5 ]

Example of a case study investigating the formal and informal ways students learn about patient safety[ 6 ]

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 ​ (Table5), 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.

Definitions of a case study

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 ​ (Table1), 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 ​ Tables2, 2 , ​ ,3 3 and ​ and4) 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 ​ (Table2) 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 ​ Tables2 2 and ​ and3, 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 ​ (Table4 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 ​ (Table6). 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 ].

Example of epistemological approaches that may be used in case study research

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 ​ Table7 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 ].

Example of a checklist for rating a case study proposal[ 8 ]

For example, in our evaluation of the introduction of electronic health records in English hospitals (Table ​ (Table3), 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 ​ (Table1) 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 ​ Table3) 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 ​ (Table2 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 ​ (Table1 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 ​ (Table3 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 ​ (Table4 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 ​ Table3, 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 ​ (Table4), 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 ​ Table8 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 ​ (Table9 9 )[ 8 ].

Potential pitfalls and mitigating actions when undertaking case study research

Stake's checklist for assessing the quality of a case study report[ 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.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

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.

Pre-publication history

The pre-publication history for this paper can be accessed here:

http://www.biomedcentral.com/1471-2288/11/100/prepub

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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Theory Test Case Study

Part of the multiple choice section of the DVSA theory test is presented as a case study. Case studies are also part of this mock theory test designed to replicate the real test.

A case study is a scenario involving a set of circumstances that are based around events that might happen in real life. You will be asked some questions based on these circumstances.

As you move through the case study, you will be presented with multiple choice questions but the same scenario also presented on screen. Questions may also be accompanied by a picture.  If you wish, you can leave a question and come back to it later, just like the real theory test.

Below is an example of what you can expect in the case study part of the theory test.

Case Study Questions

Below are two case study scenario and question examples from the mock theory test.

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Theory of Planned Behavior

What is the theory of planned behavior.

The Theory of Planned Behavior (TPB) is a widely recognized psychological framework that explains how human behavior is influenced by individual intentions. According to this theory, a person’s behavioral intentions are determined by three key factors: their attitudes toward the behavior, subjective norms (also known as social norms), and perceived behavioral control. These components work together to predict whether an individual will engage in a specific behavior. TPB is often used in various fields such as psychology, health, marketing, and social sciences to understand and influence behavior.

The Basic Idea

Imagine that your friend is trying to start exercising more regularly. According to the Theory of Planned Behavior (TPB) , her decision to engage in this new routine is influenced by three key factors: attitude , subjective norms , and perceived behavioral control . 

Let’s first examine her attitude. Her new positive feelings toward exercise might stem from an article she read about how regular workouts will improve her physical health, boost her mood, and help her regulate hormonal fluctuations. Obviously, these all sound great, so she’s excited and highly motivated to start the new workout program. 

How about subjective norms—how will the environment affect her behavior? Your shared social circle has also likely played a significant role in her decision. Maybe a lot of your friends are fitness-focused—posting their achievements on Strava, going to yoga in the park, and joining run clubs. She might feel encouraged (or even slightly pressured) to join along, knowing that her friends will support her fitness journey and cheer her on as she pursues her athletic goals.

Lastly, let’s consider her perceived behavioral control. Your friend’s actual ability to start a new behavior (in this case, going to the gym) will also influence her decision. Luckily, she lives close to a gym, has a flexible work schedule, can easily afford a membership, and has plenty of friends who can offer up training advice. With all of this, she now feels confident that she can start incorporating regular exercise into her week!

The Theory of Planned Behavior would suggest that your friend’s positive attitude towards exercising, the social encouragement she receives from her environment, and her confidence in overcoming any logistical barriers all align to form her intention to start working out. This theory builds on the Theory of Reasoned Action (TRA) by adding the concept of perceived behavioral control, which addresses factors outside of the individual's control that might influence behavior.

The core component of the Theory of Planned Behavior (TPB)

Attitudes toward the behavior  .

This refers to the individual's positive or negative evaluations of performing a specific behavior. If a person believes that the behavior will lead to favorable outcomes, then they’re more likely to have a positive attitude towards it—contributing to their willingness. For example, if you think that cycling to the office will get you there faster than driving, then you might be more motivated to cycle. 

Subjective Norms

Subjective norms refer to the social pressure to perform or not perform a particular behavior. Our comportment is influenced by what we believe others expect of us, this can mean the perceived expectations of our friends, family, and society as a whole. For example, you may work in an office with a big cycle culture—with many coworkers using this as their main transport to get to work. The presence of social and cultural pressure may contribute to your inclination to follow suit.

Perceived Behavioral Control

Perceived behavioral control is a key component that distinguishes the Theory of Planned Behavior from the Theory of Reasoned Action. It refers to an individual’s perception of how easy or difficult it will be to perform a specific behavior, based on their past experiences, anticipated obstacles, and available resources. 2

This concept encompasses both internal factors, like confidence in one's abilities, and external factors, such as the presence of barriers or facilitators. For example, if you’re deciding between cycling or driving to work you might be more inclined to cycle if you realize your car is out of gas or if there’s a major traffic jam, as these obstacles make driving seem more difficult.

Together, these components shape an individual's intention to perform a behavior, which is the antecedent to the actual implementation. The stronger the intention, the more likely the behavior will be performed, provided that the individual has adequate control over the behavior. Even if someone has a strong intention to perform a certain action, they must feel genuinely capable of overcoming any obstacles that might arise. For example, no matter how much your friend may want to take up running, if she trips and breaks her leg she will have to put off achieving this goal. 

Behavioral Intention : The motivational factors that influence a given behavior; an indication of an individual's readiness to perform the behavior.

Social Norms : These are the informal rules that guide behavior within society. Generally, they are a means of constraining behavior.

Self-Efficacy: The belief in one's ability to successfully execute a specific task or achieve a goal. It influences how individuals approach challenges, with higher self-efficacy leading to greater confidence and persistence, while lower self-efficacy can result in doubt and avoidance.

Behavioral Beliefs : Beliefs about the likely outcomes of the behavior and the evaluations of these outcomes.

Normative Beliefs: An individual’s perceptions about the expectations and behaviors of specific referent people or groups concerning a particular behavior.

Control Beliefs : Beliefs about the presence of factors that may facilitate or impede performance of the behavior and the perceived power of these factors.

Theory of Reasoned Action: TRA posits that when both the attitude toward the behavior and the subjective norms are favorable, the intention to perform the behavior is strong, which increases the likelihood of the behavior actually being carried out. However, TRA assumes that behavior is under volitional control, meaning that individuals can freely decide whether to engage in the behavior without significant external constraints.

Icek Ajzen, a social psychologist, developed the Theory of Planned Behavior in 1985. This theory was actually an extension of the Theory of Reasoned Action, which he had co-developed with psychologist Martin Fishbein in the mid-70s. 

The Theory of Reasoned Action (TRA) was focused on attitude and attitude change, and combined learning theories, expectancy-value theories, attribution theory, and consistency theories. The basic idea behind TRA is that attitudes and subjective norms are highly correlated with behavioral intention, and behavioral intention is correlated with actual behavior. 1,2 This means that individuals are more likely to engage in a particular behavior if they have a positive attitude toward it and perceive that important others expect them to do it.

But, Ajzen later recognized that this theory didn’t account for behaviors over which individuals have incomplete control, where external factors, situational constraints, or limitations in personal resources influence the ability to perform the behavior. To address this limitation, he introduced the concept of perceived behavioral control in the Theory of Planned Behavior. This addition aimed to capture a more nuanced understanding of behavior by incorporating elements of self-efficacy and control beliefs. 

Since its inception, the Theory of Planned Behavior has undergone various refinements and has been applied across diverse domains. Its versatility and robustness have made it one of the most influential models in behavioral science, offering valuable insights into the prediction and understanding of human behavior.

Consequences

The application of the TPB has had profound implications for designing interventions and policies aimed at changing behavior. When we understand the factors that influence behavioral intentions, we can develop targeted strategies to promote desired behaviors. Let’s look at some of the main areas where the theory has been effectively applied:

Health Psychology

The Theory of Planned Behavior has been used to help us understand so much about how patients handle illness, why some people don't follow medical advice, and finding the most effective ways to control pain. 

It has helped design interventions to increase physical activity 3 , promote smoking cessation 4 , and enhance adherence to medical treatments. 5 For example, in promoting physical activity, interventions based on the theory might focus on enhancing positive attitudes by highlighting the health benefits of exercise. This can also lean on the power of subjective norms by encouraging social support for physical activity: if everyone is talking about how good it feels to exercise, how much fun they’re having, or all the new friends they’ve made in their exercise classes, these may be powerful influences! 

Following the same example, we could also focus on increasing perceived behavioral control by providing information on how to overcome common barriers. Maybe someone feels like they simply don’t have enough time to exercise. Are they aware they could get to work faster by cycling instead of driving? Or that exercise doesn’t have to mean training like an olympian? Maybe they don’t currently own a bike. Could we let people know about certain bike-rental schemes across the city? Addressing the key barriers can be incredibly helpful in empowering people to take charge of their health.

TPB for the Environment 

The Theory of Planned Behavior has been instrumental in promoting environmentally friendly behaviors like recycling, energy conservation, and using sustainable transportation. 6 Understanding the psychological determinants of these behaviors allows policymakers and practitioners to craft messages or design initiatives that resonate with the target audience in mind. For instance, campaigns to reduce energy consumption might focus on changing attitudes by emphasizing the environmental and financial benefits of energy-saving behaviors. They might ask people ‘do you know how much money you can save on gas by cycling to work?’ or ‘Do you know how many gallons of water you could save by choosing a plant-based meal?’, and then educating them on how these small changes could lead to large impacts. 6  

Additionally, educational and informative campaigns can influence subjective norms by showcasing the widespread social approval of such behaviors, like calling attention to the fact that over 1.5 billion people are vegetarian or that 30% of people in Germany regularly bike to work. 8 Campaigns can also increase perceived behavioral control by providing easy-to-follow energy-saving tips, like suggesting that employees choose lunch items that don’t include meat or dairy and bike to work on days when it’s nice outside.

Promoting a Behavior as an Organization 

In organizational settings like offices, the theory has been applied to understand and influence behaviors like employee productivity, adherence to safety protocols, and participation in training programs. By identifying the attitudes, subjective norms, and perceived behavioral controls that influence these behaviors, organizations can develop targeted interventions to enhance employee engagement and performance. 9 For instance—if we return to the biking example—to improve adherence to safety protocols, an organization might focus on changing attitudes by communicating the importance of safety (wearing a helmet while biking can mean the difference between a scratched noggin and permanent brain damage), leveraging subjective norms by fostering a safety culture (a sign could state that 80% of fellow employees wear their helmets when biking to work) and increasing perceived behavioral control by providing the necessary training and resources (teaching road safety and turn signals for bikers).

Controversies

Despite its widespread application and empirical support, the Theory of Planned Behavior has faced several criticisms and controversies. One of the first main points of contention is that the theory assumes behavior is the result of a “rational” decision-making process. Critics argue that this overlooks the role of emotions, habits, and unconscious influences on behavior. 

For example, habitual behaviors or impulsive actions may not always align with the “rational deliberation” posited by the Theory of Planned Behavior. Just like how behavioral economics recognizes that we are human beings who are always making choices in the context of a much broader environment (social, cultural, emotional, physical), we must acknowledge that there are many forces that affect our behavior which may not be captured by a singular theory, and our internal emotions could definitely be one such force. 

Structural barriers such as socioeconomic status, access to resources, and policy frameworks can significantly impact behavior, and these factors may not be fully captured by the components of the theory. If we think back on our example of driving versus cycling to work, how might social class impact this decision? While cycling to work may be cheaper in the long run (by saving gas money over the course of several weeks or months), someone living with a lower income may not be able to afford the upfront costs associated with purchasing a bike. 

The measurement of perceived behavioral control has also been a point of controversy. Some researchers argue that this construct is often conflated with self-efficacy, leading to inconsistencies in its operationalization and measurement. “Self-efficacy” refers to an individual's belief in their ability to perform a specific task, while “perceived behavioral control” encompasses both the individual's belief in their ability and the perceived availability of resources and opportunities. This distinction is important, but not always clearly maintained in empirical research, leading to results and interpretations which can be misinterpreted or even purposely misconstrued.

Lastly, the predictive power of the Theory of Planned Behavior has been questioned, with some studies showing that the theory explains only a modest proportion of the variance in behavior. While the theory has been successful in predicting intentions, the translation of these intentions into actual behavior is not always straightforward. Various external factors and unmeasured variables can influence whether intentions lead to behavior, highlighting the need for a more comprehensive approach to understanding and predicting behavior. No theory is perfect, and there will always be gaps when trying to explain or understand something as complex as human behavior through just one perspective. 

In 2013, a group of researchers tried to predict the physical activity intention and behavior of middle and high school students in Hong Kong by applying the Theory of Planned Behaviour. These researchers were trying to address the growing concern of physical inactivity among adolescents, as regular physical activity is linked to various health benefits and chronic disease prevention. The study used the theory to understand and potentially improve the physical activity levels of students.

The study was made up of 486 students, aged 11 to 18, and collected data through self-administered questionnaires that measured demographic information, past physical activity, and variables such as attitude, subjective norms, and perceived behavioral control (the components related to the TPB). The study was conducted in two phases: the first phase involved the initial distribution of questionnaires, while the second phase, conducted eight weeks later, measured the physical activity behavior of the students.

The study found that approximately 75% of students did not meet the recommended physical activity standards, with males showing higher behavioral intentions and perceived behavior control compared to females. The Theory of Planned Behavior variables explained 53% of the variance in physical activity intentions, meaning that the differences in their intents to exercise could be explained but the theory. However, when predicting actual physical activity behavior, the theory accounted for only 26% of the variance, but this is still powerful. 

The findings confirmed that the Theory of Planned Behavior model is a useful framework for predicting physical activity intentions (at least among adolescents in Hong Kong). It suggests that public health interventions should focus on enhancing perceived control and leveraging past behaviors to promote physical activity and highlights the importance of addressing gender differences and ensuring that health promotion strategies are tailored to effectively target both boys and girls. 10

Related TDL Content 

The Intention Action Gap : The intention-action gap describes the discrepancy between our intentions, or what we plan to achieve, and our actual actions, or what we ultimately do. This occurs because we are naturally inclined toward immediate gratification, which can lead us to prioritize short-term rewards, regardless of our underlying attitudes, beliefs, and values.

Disgusting decision-making with Yoel Inbar : In this podcast episode of the Decision Corner, listen to Brooke as he speaks with Yoel Inbar – professor of psychology at the University of Toronto and expert in how the feeling of disgust influences human judgment and decision-making. Together they define what it really means to feel a sense of disgust and its evolutionary purpose as a means of preventing risk or harm (like stopping us from eating rotten food!). On the flip-side, we hear about the negative consequences of disgust and why it can lead to biased or flawed judgements. 

• Ajzen, Icek. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes. 50. 179-211. 10.1016/0749-5978(91)90020-T. 
• Madden, T. J., Ellen, P. S., & Ajzen, I. (1992). A Comparison of the Theory of Planned Behavior and the Theory of Reasoned Action. Personality and Social Psychology Bulletin, 18(1), 3-9. https://doi-org.gate3.library.lse.ac.uk/10.1177/0146167292181001  
• Shafieinia, M., Hidarnia, A., Kazemnejad, A., & Rajabi, R. (2016). Effects of a Theory Based Intervention on Physical Activity Among Female Employees: A Quasi-Experimental Study. Asian journal of sports medicine , 7 (2), e31534. https://doi-org.gate3.library.lse.ac.uk/10.5812/asjsm.31534  
• Tapera, R., Mbongwe, B., Mhaka-Mutepfa, M., Lord, A., Phaladze, N. A., & Zetola, N. M. (2020). The theory of planned behavior as a behavior change model for tobacco control strategies among adolescents in Botswana. PloS one , 15 (6), e0233462. https://doi-org.gate3.library.lse.ac.uk/10.1371/journal.pone.0233462  
• M. Kopelowicz, A., Zarate, R., Wallace, C. J., Liberman, R. P., Lopez, S. R., & Mintz, J. (2015). Using the theory of planned behavior to improve treatment adherence in Mexican Americans with schizophrenia. Journal of consulting and clinical psychology , 83 (5), 985–993. https://doi-org.gate3.library.lse.ac.uk/10.1037/a0039346  
• Yuriev, A., Dahmen, M., Paillé, P., Boiral, O., & Guillaumie, L. (2020). Pro-environmental behaviors through the lens of the theory of planned behavior: A scoping review. Resources, Conservation and Recycling, 155 , 104660. https://doi.org/10.1016/j.resconrec.2019.104660
• Phoenix, Sam. (2024, March 29). Vegetarian statistics 2024: Surprising facts & data . Great Green Wall. https://www.greatgreenwall.org/supplements/vegetarian-statistics/
• Marshall, John. (2021, September 18). Germans and their beloved bike paths . Deutsche Welle. https://www.dw.com/en/germans-and-their-beloved-bike-paths/a-59215483
• Hemsworth, D., Muterera, J., Khorakian, A., & Garcia-Rivera, B. R. (2024). Exploring the Theory of Employee Planned Behavior: Job Satisfaction as a Key to Organizational Performance. Psychological Reports, 0(0). https://doi-org.gate3.library.lse.ac.uk/10.1177/00332941241252784
• Mok, V., & Lee, A. (2013). A case study on application of the theory of planned behaviour: Predicting physical activity of adolescents in Hong Kong. Journal of Community Medicine & Health Education, 3 (231). https://doi.org/10.4172/2161-0711.1000231

Case studies

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Annika completed her Masters at the London School of Economics in an interdisciplinary program combining behavioral science, behavioral economics, social psychology, and sustainability. Professionally, she’s applied data-driven insights in project management, consulting, data analytics, and policy proposal. Passionate about the power of psychology to influence an array of social systems, her research has looked at reproductive health, animal welfare, and perfectionism in female distance runners.

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• Volume 14, Issue 9
• Alternative payment models in Dutch hospital care: what works, how, why and under what circumstances? Protocol for a realist evaluation study
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• http://orcid.org/0000-0002-9401-7983 Celine Maria Rosanne Hendriks 1 ,
• Miel Antonius Petrus Vugts 2 ,
• Frank Eijkenaar 1 ,
• Jeroen Nathan Struijs 2 , 3 ,
• Daniëlle Cattel 1
• 1 Erasmus School of Health Policy & Management , Erasmus University Rotterdam , Rotterdam , Netherlands
• 2 Department of Quality of Care and Health Economics , National Institute for Public Health and the Environment , Bilthoven , Netherlands
• 3 Health Campus The Hague/ Department of Public Health and Primary Care , Leiden University Medical Center , Leiden , Netherlands
• Correspondence to Ms Celine Maria Rosanne Hendriks; hendriks{at}eshpm.eur.nl

Introduction The predominant provider payment models in healthcare, particularly fee-for-service, hinder the delivery of high-value care and can encourage healthcare providers to prioritise the volume of care over the value of care. To address these issues, healthcare providers, payers and policymakers are increasingly experimenting with alternative payment models (APMs), such as shared savings (SS) and bundled payment (BP). Despite a growing body of literature on APMs, there is still limited insight into what works in developing and implementing successful APMs, as well as how, why and under what circumstances. This paper presents the protocol for a study that aims to (1) identify these circumstances and reveal the underlying mechanisms through which outcomes are achieved and (2) identify transferrable lessons for successful APMs in practice.

Methods and analysis Drawing on realist evaluation principles, this study will employ an iterative three-step approach to elicit a programme theory that describes the relationship between context, mechanisms and outcomes of APMs. The first step involves a literature review to identify the initial programme theory. The second step entails empirical testing of this theory via a multiple case study design including seven SS and BP initiatives in Dutch hospital care. We will use various qualitative and quantitative methods, including interviews with involved stakeholders, document analysis and difference-in-differences analyses. In the final step, these data and the applicable formal theories will be combined to test and refine the (I)PT and address the research objectives.

Ethics and dissemination Ethical approval has been granted by the Research Ethics Review Committee of Erasmus School of Health Policy and Management (Project ID ETH2122-0170). Where necessary, informed consent will be obtained from study participants. Among other means, study results will be disseminated through a publicly available manual for stakeholders (eg, healthcare providers and payers), publications in peer-reviewed scientific journals and (inter)national conference presentations.

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Data availability statement

All data relevant to the study are included in the article or uploaded as supplementary information.

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/ .

https://doi.org/10.1136/bmjopen-2023-082372

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Strengths and limitations of this study

This study employs a rigorous realist evaluation approach to elicit, test and refine an (initial) programme theory explaining how alternative payment models (APMs) for healthcare providers can be successfully developed and implemented to achieve their intended outcomes under varying circumstances.

The involvement of relevant stakeholders in the design of the study provides confidence that the evaluation will generate meaningful findings for policy and practice, guiding effective payment reforms that could ultimately enhance healthcare value.

This study uses a mixed methods approach to enhance the validity of the findings and to foster the generation of in-depth insights.

The seven APM initiatives studied vary greatly in key contextual and outcome dimensions, including the medical conditions covered, enabling a comprehensive testing of the programme theory under development.

The focus on condition-specific bundled payment and shared savings models in the Dutch healthcare system may limit the generalisability of our findings to other APM types and other contexts.

Introduction

There is a growing consensus that the predominant provider payment models, particularly fee-for-service (FFS), contribute to healthcare systems performing suboptimally. 1–3 Under FFS, healthcare providers are compensated for each service rendered, regardless of the quality of care provided. 4–8 This payment model undermines healthcare system performance in two ways. First, FFS does not reward (and may even hinder) the delivery of high-value care. 6 9 Because provider revenue depends on the number of services provided, providers that reduce unnecessary services, prevent illness and/or achieve lower complication rates are penalised in financial terms. 1 5–8 10 In addition, FFS does not financially reward clinical excellence and collaboration between providers. 1–3 8 11 Second, and relatedly, FFS may create perverse incentives that encourage providers to prioritise the volume of care over the value of care. 1 2 6–9

Therefore, healthcare providers, payers and policymakers are experimenting with alternative payment models (APMs) that enable and incentivise providers to increase value by linking reimbursement to higher quality and/or lower medical spending. 1 2 6 12 The introduction of APMs may be an effective strategy for improving value, as providers are often in the best position to identify potential areas for improvement. 13 APMs can vary in design, depending on the specific aims. This paper focuses on APMs targeting specific medical conditions, which can be designed as either a shared savings (SS) or a bundled payment (BP) model. Under an SS model, payers retain the FFS architecture and continue to reimburse providers on an FFS basis. Afterward, total FFS spending is compared with a prospective spending benchmark. If spending is below the benchmark, providers receive a share of the savings. Providers may also bear a share of the losses if spending exceeds the benchmark. Providers’ share of savings/losses may depend on their performance on predefined quality measures. 14 15 Under a BP model, the FFS architecture is abandoned, and a provider-led entity receives a fixed, predetermined payment intended to cover the expected costs of all the services associated with a clinically defined episode of care. 16 17 A key difference with an SS model lies in the providers’ full financial responsibility for all spending covered by the BP model, while under an SS model this responsibility is only partial. Both models enable and incentivise providers to act cost-consciously, to coordinate care across the episode and to ensure high quality. 18

SS and BP models are widely scrutinised and implemented in numerous countries, including the USA, the UK and the Netherlands. 18 19 With the growing prevalence of these APMs, a growing body of knowledge has emerged regarding their empirical effects on care quality, utilisation and medical spending. Current evidence suggests that both SS and BP models can reduce spending growth, while improving or maintaining quality. However, the effects vary in size, and between specific APM designs and settings. 13 18 20–22 An important limitation of this evidence is that studies have mainly focused on APMs within the US healthcare system. 18 23 In addition, although an increasing number of studies provide insights into whether specific payment models ‘work’ in terms of reducing spending growth and/or improving quality, 18 20 21 23–25 they often add little information on how, why and under what circumstances these models work. More research on the role of context is essential to advance our understanding of how causal influences are sensitive to varying circumstances. 26

In addition to assessing quantitative effects, researchers have focused on analysing the complex process of developing and implementing APMs that may potentially hinder practical adoption. Efforts have been made to document the contextual factors that shape this process. 9 27 28 Despite some valuable insights, these studies have not substantially contributed to our understanding of causal pathways, including the underlying generative mechanisms. For example, though divergent individual interests and goals within provider organisations are commonly cited as potential barriers to transitioning payment incentives from volume to value, 9 27 it remains largely unclear which specific thought processes and interactions among stakeholders are triggered or blocked by these factors. A better understanding of these underlying processes may inform those involved in payment reforms, helping them reason and interact effectively to increase the likelihood of success. Such insights are crucial for APMs to reach their full potential of improving healthcare value.

This paper presents a protocol of a realist evaluation (RE) study of condition-specific SS and BP models, including case studies of seven initiatives in the setting of hospital care in the Netherlands (see Methods and analysis). The study aims to enhance our understanding of ‘what works’ in developing and implementing successful APMs, as well as how, why and under what circumstances they work. To this end, the study will draw on RE principles and elicit context-mechanism-outcome (CMO) configurations. 26 Our primary objective is to develop a programme theory describing the circumstances under which condition-specific SS and BP models can be successfully developed and implemented, uncovering the mechanisms triggered or blocked by these circumstances. The secondary objective is to identify transferrable lessons for successful SS and BP models in practice.

The study is expected to generate valuable insights for providers, payers, policymakers and other stakeholders that may support the development and implementation of successful SS and BP models, ultimately contributing to improved healthcare value. Additionally, among those evaluating provider payment models, this study may lead to greater awareness of the context in which these models are developed and implemented, emphasising the need for comprehensive evaluation of this context to take account of the relevant causal pathways.

Methods and analysis

Realist evaluation.

This study will apply an RE approach, which is a theory-driven evaluation approach well-suited for evaluating complex interventions. 26 29–31 RE seeks to provide an in-depth understanding of what works, for whom, under what circumstances, how and why? Rooted in scientific realism, this approach recognises the potential impact of unobservable factors like culture and institutions on intervention outcomes and acknowledges that individuals respond differently to interventions in varying contexts. 26 31 32 To understand how and why interventions yield different outcomes in different populations and settings, RE examines causal pathways including underlying generative mechanisms. 26 31

In RE, interventions are presumed to be underpinned by a programme theory that explains how change is enacted and outcomes are produced. This theory is explicated by developing CMO configurations, which describe how a contextual factor (C) activates or blocks a certain mechanism (M), resulting in a certain outcome (O). These configurations are the main structure of analysis in RE and can be elicited, tested and refined through an iterative research process. 26 31 Table 1 summarises the RE concepts used in our study.

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Definitions of the different realist evaluation concepts applied in this study

Study design

Following the framework for RE described by Pawson and Tilley, 26 this study will be conducted in three steps ( figure 1 ): (1) elicit the initial programme theory (IPT), (2) test the (initial) programme theory ((I)PT) using empirical data and (3) refine the (initial) programme theory. These steps are presented here as being sequential, while in practice PT development is iterative, informed by theory and interim findings to allow the gathering of additional data on emerging themes. 33 RE is a method-neutral approach, resulting in variations in design and research methods applied in previous studies. 29 31 34 35 Our study will use a literature review to elicit the IPT and a multiple case study to test and refine the (I)PT, followed by a synthesis of findings into a final PT. 36 We will investigate seven SS and BP initiatives in Dutch hospital care using a combination of qualitative and quantitative methods. The study will be conducted in accordance with the RAMESES (Realist And Meta-narrative Evidence Syntheses: Evolving Standards) II reporting standards for REs. 31 37 Data collection for this study is expected to be completed by September 2024.

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Realist evaluation research cycle with a three-step approach. Adapted from Pawson and Tilley. 26

Step 1: elicit the initial programme theory

In step 1, we will develop the IPT that clarifies under what circumstances, how, and why SS and BP models can be successfully developed and implemented and contribute to the effects observed. This IPT will include different CMO configurations and will form the starting point for our empirical research.

We will build the IPT based on literature that is relevant to our research questions. This includes literature that provides an insight into any element of the IPT. Following RE principles, 26 31 37 38 literature from a wide range of sources, including both empirical and conceptual and scientific and grey literature, will be searched and considered. Relevant literature may include systematic reviews on APMs and conceptual papers about the development and/or implementation of APMs. Additionally, relevant conceptual work on applicable (grand) theoretical approaches from various social science disciplines will be searched and studied. These approaches are particularly useful for understanding the underlying mechanisms driving APM outcomes and for explaining the observed patterns of contexts, mechanisms and outcomes at a more general level. 38 This may include, among others, contract theory, behavioural economics, institutional theory and the theory of planned behaviour.

We will start by drawing up a list of potentially relevant documents known to our interdisciplinary research team. This will include scientific papers from the APM literature (not limited to SS or BP models) that were previously identified through systematic searches and research conducted by our team (eg, 18 19 23 25 27 39–41 ). Furthermore, we will search Google Scholar to identify studies that have applied social science theories to study the development and implementation of APMs, as well as provider payment reform more broadly. To achieve this, we will create multiple search strings incorporating combinations of keywords related to health, payment and a social science theory (eg, theory of planned behaviour). The first 10 pages per search string will be screened by title and abstract for potential relevance. Documents will be included for analysis based on full-text assessment if they contribute to theory building, which is the case if they shed light on the relationships between context, mechanisms and/or outcomes within the IPT. Our experience with pilot searches and realist research indicates that the most relevant and contributory studies typically appear within the first 5–10 pages of search results. However, if important concepts or aspects have not yet reached saturation, 42 we will extend our search beyond the initial 10 pages and/or adapt our search strategy to better address the missing data, as prescribed by good realist research practices. 42 Literature searches are likely to be iterative because, as the formation of the IPT progresses, specific elements of the IPT may require further exploration of the literature, focusing on particular concepts or aspects in more detail. 38 42 Searches will continue until the IPT is saturated, meaning that the list of CMO configurations is exhaustive and each element is thoroughly described. 38 42

For each document identified, data indicative of a causal path will be extracted and analysed using a standardised extraction form (more details are provided in online supplemental appendix 1.1 ). Data extraction will be done by author pairs, with one author taking the lead to elicit CMO configurations and another checking for missing information and inconsistencies. Any discrepancies will be resolved through discussion. The IPT will be developed and refined in weekly meetings with our research team. Additionally, we will seek opinions from at least three external experts on provider payment reform to assess the list of included articles and the IPT, identifying any missing documents or elements.

Supplemental material

At the time of writing, data collection for the IPT has started. Initial collection and searches have identified over 60 relevant documents yielding a list of over 400 data rows. These rows contain background information on the study type (including the APM type that the findings apply to, where applicable), rigour appraisals and findings with respect to context, mechanisms and outcomes. Initial steps have been undertaken to condense this information, involving the clustering of rows that exhibit substantial regularities in content, with a primary focus on assessing the similarity of contextual factors. 43 This process will ultimately result in a set of CMO configurations of a middle-range level of abstraction. 43 One example of a preliminary CMO configuration that has resulted from this process is: if providers participate actively in developing the APM, development and implementation are more likely to be successful because the design of the payment model is expected to be better aligned with providers’ professional values and intrinsic motivation.

Step 2: test the (initial) programme theory

In step 2, we will gather data to test the (I)PT using a multiple case study design. This design is well-suited for studying phenomena in their real-life context 36 and is common practice in RE. 34 44 45

Case selection

At the time of writing, case selection has been completed. We have included seven cases (see table 2 ), all of which are SS or BP initiatives in the Dutch hospital care setting. This selection is the result of a combination of convenience and purposive sampling. 46 The criteria for case selection included: (1) stakeholders that were willing to cooperate in data collection (eg, representatives available for interviewing); (2) availability of data to assess the effects on quality, care utilisation and medical spending; and/or (3) variation on important elements of the IPT. The third criterion was deemed necessary to enable the IPT to be comprehensively tested. 31 37 The cases vary on several context and outcome dimensions that are relevant to the interventions of interest, such as the condition or care covered by the SS or BP model, and the stage of payment reform (eg, still in the development stage or was implemented several years ago).

General characteristics of selected study cases

Data collection and analysis

For each case, multiple data sources will be used and analysed. Quantitative data will be used to assess the effects on quality, utilisation and medical spending. Qualitative data will be used to understand how these effects occurred and ‘what works’ in developing and implementing successful SS and BP models, and how, why and under what circumstances these models work.

Qualitative methods

Qualitative data collection.

Qualitative data collection will include document analysis and interviews. First, for each case, we will obtain the payment contract and other available documentation, like meeting minutes and reports substantiating the choices made during development. These documents will be searched for information on the SS or BP model itself (eg, contract terms, payment model specifications, reform strategies and timelines) as well as on the intended outcomes (eg, aims of the payment reform and outcome measures used).

Second, we will conduct semi-structured interviews with representatives from purposively sampled stakeholders involved in each case (henceforth referred to as respondents). Interviews are a suitable and widely used method for data collection to test and refine a realist PT. 47 For each case, we will strive to maximise the variation observed in the context and the outcomes between sampled providers and insurers. We aim to interview respondents in various roles within the organisations sampled, including executive managers, medical specialists, healthcare purchasers and sellers and business controllers. Additionally, although patient involvement in the initiatives is often limited, we will interview representatives from patient organisations to learn about their potential roles in developing and implementing APMs. Finally, respondents working for providers who ultimately decided not to participate in the studied SS and BP initiatives will be interviewed to understand their reasons. Interview invitations will be sent via email by our research team. We aim to conduct approximately 65 interviews in total.

An interview guide will be developed, informed by the IPT and document data. This guide will cover three main domains: (1) development, (2) implementation and (3) (un)intended effects and will be tailored to the specific stakeholder group (ie, insurer, provider or patient organisation). The interview guides will be pilot-tested with stakeholders. During the interviews, a ‘realist interviewing style’ will be adopted, 47 using general phrases like ‘what external factors’ and ‘what thoughts were on your mind?’ to inquire about context and mechanisms, respectively. We will also actively explain the concepts of interest to respondents to ensure a shared understanding of the terminology and purpose of the questions. 34 47 Moreover, we will present parts of the IPT to jointly understand what happened throughout the stages of payment reform, allowing us to refine elements of the (I)PT. 47 The interview guide may be modified during our RE based on emerging insights gained in previous interviews that were initially overlooked or unknown during the initial development of the guide.

We will conduct two rounds of interviews with different respondents in each round. After both rounds, we will refine the (I)PT as outlined in step 3. The first interview round will help us explore the richness of case data regarding various IPT elements and identify any potentially missing elements. Subsequently, the second round can focus on further developing emergent findings and testing and refining key elements. 31 37

Before each interview, the respondent(s) will be informed of the study’s objectives and written informed consent will be obtained. Interviews will be approximately 60 min in duration, conducted in author pairs and will be audio-recorded and verbatim transcribed.

Qualitative data analysis

All documents and interview transcripts will be coded using Atlas.ti (V.9). According to the recently published principles for analysing qualitative data transparently within RE, 48 a structure of codes will be created, to which theoretical memos about PT development will be attached to. Each CMO configuration in our IPT will receive a title and a corresponding code in Atlas.ti. Each code will link to a theoretical memo containing the CMO configuration as formulated in the IPT. 48 If new information arises during coding that is not covered by existing codes, a new code will be created and linked to a new memo.

Coding will involve selecting relevant text fragments in the documents and transcripts and assigning them to applicable codes. Four members of the research team will conduct this coding, in pairs. To ensure consistency during coding, the four members will independently code several documents and transcripts, after which they will compare and discuss codes until coding practices are aligned. Next, the remaining documents and transcripts will be allocated to the two pairs for independent coding. The pairs will meet regularly to review and maintain coding consistency.

Table 3 illustrates a potential data structure resulting from this coding process.

Example data structure resulting from the coding procedure

Quantitative methods

Quantitative data collection.

Administrative data from participating insurers and providers will be used to assess the effects of the seven SS and BP initiatives on care quality, utilisation and medical spending. These data will include information on patient characteristics (eg, age, sex, comorbidities) and insurance claims (to be used for defining medical spending, utilisation and quality measures) for patients treated for the relevant condition and enrolled with the relevant insurer. We will collect data for periods both before and after implementing the SS and BP model and for patients treated by participating providers (the intervention group) and non-participating providers (the control group).

Quantitative data analysis

We will assess the effectiveness of the seven APM initiatives using a difference-in-differences (DiD) design, 49 which is a quasi-experimental approach commonly used for impact evaluations of complex health interventions. We will use regression modelling to analyse trends in outcomes over time for patients treated by participating and non-participating providers. This approach enables researchers to account for trends in the outcomes that are unrelated to the introduction of the intervention, isolating the causal effect on outcomes. 49 We will analyse the effects on various quality measures (eg, complication and readmission rates), care utilisation (eg, diagnostic tests and length of stay) and medical spending (in total and in subcategories), adjusting for provider fixed effects and patient characteristics. 50

DiD analyses rely on the parallel trends assumption (PTA), 49 which asserts that the trends in outcomes of the intervention and control group are similar before the intervention. If this is true, it is reasonable to assume that these trends would continue without the intervention, making the control group’s trend a valid counterfactual for the intervention group’s trend in the absence of the intervention. The PTA will be assessed by visually inspecting trends and conducting falsification tests for all outcomes in each case. 49 To test the robustness of our results, we will perform sensitivity analyses, including using alternative specifications of control groups and outcomes (eg, yes/no truncation of spending variables).

Step 3: refine the (initial) programme theory

Bi-monthly team meetings will be organised to refine the preliminary CMO configurations through discussion. We will use coded text fragments as the primary inputs for refining the (I)PT. Theoretical memos will be used to capture all the refinements made to CMO configurations based on coded data. These memos will provide a detailed rationale for all refinements, explicitly capturing the full team’s reasoning in developing the PT. The (I)PT will initially be refined after coding the first round of interviews. We will adjust existing codes based on the preliminary findings and create new codes (and linked theoretical memos) if necessary. These adjusted codes and memos will be used for the second round of interviews, for which the same coding and refinement procedure will be followed. Additionally, we will (re)examine the relevant formal theories during theory refinement to better understand the observed CMO configurations and enrich the causal path descriptions. For example, when respondents mention ‘trust’ repeatedly as a trigger for certain behaviour, formal definitions and taxonomies of ‘trust’ will be explored from theoretical sources. In addition to the qualitative data, quantitative data will be used to refine the (I)PT. Various models for combining these data exist, which vary with respect to, for example, the timing of data collection (ie, concurrently or sequentially) and the approaches used for combining the data (eg, mixing results during interpretation or analysis). 51 We will explore different models for mixing findings. Based on the research objectives, the timing of data collection and analyses and the resources available across cases, the appropriate model(s) will be selected (more details are provided in online supplemental appendix 1.2 ). Lastly, refined CMO configurations will be discussed with (groups of) experts in the field of APMs and related areas (eg, quality measurement, competition, shared decision-making and organisation studies). Experts will be asked to identify missing or inadequately refined elements in the PT. Approximately 10–15 experts will be consulted. Based on their inputs, coded data and formal theories will be reevaluated and additional literature will be explored to refine the PT.

Patient and public involvement

The input of representatives of various stakeholder organisations, including healthcare providers, health insurers, patient organisations and policymakers, has been collected and integrated into the design of this protocol. For example, the research questions and outcomes of interest have been collaboratively formulated with these representatives, drawing on their experience in developing and implementing the initiatives of interest to this study. These stakeholders will continue to be involved in the research, for instance in terms of contributing to the data collection and interpretation of results.

Ethics and dissemination

This study will be conducted in accordance with the existing guidelines on good research practices and integrity (Netherlands Code of Conduct for Research Integrity, 2018). 52 The study has received approval from the Research Ethics Review Committee of Erasmus School of Health Policy and Management (registration number ETH2122-0170; 14 December 2021) and data providers. A data management plan, overseen by a data steward from Erasmus University Rotterdam, ensures secure data collection and storage. Informed consent will be obtained for interviews, with alternative data collection methods considered if consent is declined. Any unnecessary collection of personal data will be minimised, individual-level data will be pseudonymised for analysis and any personally identifiable information will be removed before reporting study findings.

Dissemination

To ensure the adoption of our findings in practice, results will be disseminated through a hands-on manual for policy and practice describing the building blocks of SS and BP models, the steps required and the conditions for their successful development and implementation. This manual will be publicly accessible and distributed widely to relevant stakeholders, for example, providers, payers, patient associations and policymakers. Our findings will be presented at various (inter)national conferences and published in peer-reviewed open-access journals. We will present our (preliminary) findings, among other venues, at an annual conference organised by our research group, which will be accessible to all stakeholders. To reach a broader audience, we will produce three explanatory videos and an infographic elucidating our findings. These materials will be available on a dedicated website. Finally, findings will be integrated into undergraduate, graduate, postgraduate, doctoral and online education programmes provided by our institutions.

This paper outlines the protocol for an RE based on a literature review and seven case studies of APMs in practice.

Traditionally, evaluations of complex (policy) interventions in healthcare have focused mainly on quantifying the causal effects on outcomes using (quasi-)experimental research designs. Although these designs are suitable for assessing the effectiveness of interventions, scholars increasingly emphasise the complex and dynamic nature of health systems and the healthcare organisations that shape intervention effects, which cannot be fully captured by these research designs. This limits drawing context-sensitive conclusions, 29 34 53 which is an important limitation because effective, scalable and sustainable reform requires a deeper understanding of the causal mechanisms that underly (changes in) outcomes and their interaction with context. An increasingly applied approach in recent years for evaluating health policy interventions that aims to meet these requirements is RE. 26 29 31 34 35 45 53 54 To our knowledge, our study is the first to use an RE approach to evaluate APMs in healthcare.

Our study aims to develop a broadly applicable PT providing insights into under what circumstances, how and why APMs for providers can be developed and implemented successfully. This PT is expected to offer valuable lessons for healthcare providers, payers, policymakers and researchers who are interested in (future) payment reforms and methods and strategies that contribute to the sustainability of healthcare systems. In particular, the PT will provide in-depth insights into the mechanisms driving (un)successful APM development and implementation, and the contextual circumstances that trigger or block these mechanisms. Regardless of our focus on condition-specific APMs, we expect our theory to apply to APMs beyond SS and BP models. For example, a CMO configuration explaining the prerequisites for the successful incorporation of quality measures into SS and BP models will be relevant to all APMs—in particular pay-for-performance schemes—in which the reimbursement of providers is linked explicitly to quality. The same applies to CMO configurations covering more general themes, such as how the degree of mutual trust influences collaboration. Finally, we expect our approach and findings to provide guidance to others evaluating APMs.

RE poses numerous challenges for researchers, 29 53 55 three of which have already been encountered while designing this study and collecting data for the IPT. First, identifying generative mechanisms in the existing literature on APMs has been difficult because mechanisms are often not (explicitly) described. In addition, the description of mechanisms is complicated by a lack of shared conceptions among scholars regarding what exactly constitutes a mechanism, which means that various definitions are used. 53 Similarly, differentiating between mechanisms and context can be difficult because some phenomena serve as both. For example, (emergence of) trust can act as a mechanism in explaining stakeholder responses in collaborative activities or as a contextual factor when considering the preconditions for initiating these activities. 56 To address these issues, we will explore literature beyond APMs (eg, from the field of behavioural economics and organisation studies) and prioritise the uncovering of mechanisms during the interviews.

A second challenge is that some of our cases involve APM models that were introduced years ago and have since then undergone changes. For example, the objectives and focus of the SS model for knee and hip replacement surgery and cataract surgery have been significantly revised. Furthermore, within cases the design of the payment contracts varies between providers. Documenting these changes and variations and understanding their impact on the outcomes studied will be challenging but essential. Therefore, we will pay close attention to contractual details and changes in the interventions studied over time and maintain close contact with the providers and insurers that have been involved in these changes.

A final challenge relates to RE being a resource-intensive approach that demands substantial methodological and content expertise from the researchers involved. 53 55 57 These issues have been considered when estimating the capacity required to carry out this study and the recruitment of personnel.

To enhance the rigour of this study, the RE quality standards for realist synthesis and realist evaluation (eg, RAMESES II) will be followed during data collection, analyses and reporting. 31 37 38 We will develop topic lists and coding schemes to facilitate and standardise data collection and analyses. During and after interviews, team members will verify the respondents’ responses for clarity, and if needed, schedule follow-up interviews. Quantitative data analysis will include sensitivity checks to confirm the robustness of DiD estimates against analytical choices made. We expect that leveraging various data sources, theories and methods will enhance the validity of our findings and the same applies for involving researchers with diverse backgrounds and perspectives (ie, medical, health economics, health policy, public health and organisational sciences) in the study. Lastly, refined CMO configurations will be discussed with experts from various fields, including APMs.

Although we anticipate that our findings will offer generalisable insights for the development and implementation of SS and BP models, as well as APMs more broadly, we acknowledge that the generalisability of our findings may be limited by the fact that all initiatives were developed and implemented within the Dutch context and primarily pertain to hospital care services. For instance, findings may differ between countries due to variations in payer type (ie, public vs private), cultural factors and providers’ experience with bearing financial accountability. 58 A second limitation stems from the potential scope of effects associated with the implementation of APMs, which may extend beyond what our data sources or analytical focus can (fully) capture. Examples of such effects include provider cost-shifting (ie, the shift to services and care settings outside the bundle, as a strategy to limit spending), 24 as well as broader implications such as possible changes in patient cost-sharing schemes resulting from the implementation of APMs. 1 It is important to bear this in mind when interpreting our findings.

In summary, despite a growing body of literature on APMs in healthcare, important questions remain with regard to under what circumstances, how and why these models can improve value in healthcare. Our study seeks to develop a broadly applicable PT that provides answers to these questions. This PT is expected to offer valuable lessons for healthcare providers, payers, policymakers and researchers interested in payment reforms, as well as methods and strategies that could improve the sustainability of healthcare systems.

Ethics statements

Patient consent for publication.

Not applicable.

Acknowledgments

We are grateful to the Netherlands Organization for Health Research and Development (ZonMw) for their funding. In addition, we acknowledge those representatives of healthcare providers, health insurers, in particular Menzis Health Insurance Company, and patient organisations that commented on drafts of the funding proposal and made valuable suggestions for improving our study design. We thank Anna Volkova for her contributions in drafting the data management plan. We also want to thank Chandeni Gajadien, Peter Dohmen and Nèwel Salet for contributing to the data collection for the initial programme theory thus far.

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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

• Data supplement 1

Contributors All the authors have participated in designing the study and data collection for the initial programme theory thus far. CMRH, DC and MAPV drafted the manuscript for this study protocol and FE and JNS provided feedback for the refinement of the protocol. All the authors have read, revised and approved the final manuscript. CMRH is responsible for the overall content as guarantor. The AI toolbox of ChatGPT (based on the GPT-4 architecture) was used for shortening the manuscript and text editing.

Funding This study was funded by a research grant from the Netherlands Organization for Health Research and Development (ZonMw) under grant number 516008005. The funding organisation was not involved in the study design, nor will it be involved in data collection, analyses, interpretation or manuscript writing.

Competing interests None declared.

Patient and public involvement Patients and/or the public were involved in the design, or conduct, or reporting, or dissemination plans of this research. Refer to the Methods section for further details.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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Pavić, L.; Rančić Demir, M. Agrowellness Goods Distribution in the Light of Sustainability: The Consumer Perspective and the Case of Slovenia’s Eastern Cohesion Region. Agriculture 2024 , 14 , 1698. https://doi.org/10.3390/agriculture14101698

Pavić L, Rančić Demir M. Agrowellness Goods Distribution in the Light of Sustainability: The Consumer Perspective and the Case of Slovenia’s Eastern Cohesion Region. Agriculture . 2024; 14(10):1698. https://doi.org/10.3390/agriculture14101698

Pavić, Lazar, and Milica Rančić Demir. 2024. "Agrowellness Goods Distribution in the Light of Sustainability: The Consumer Perspective and the Case of Slovenia’s Eastern Cohesion Region" Agriculture 14, no. 10: 1698. https://doi.org/10.3390/agriculture14101698

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