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How to Present the Limitations of the Study Examples

a case study limitations

What are the limitations of a study?

The limitations of a study are the elements of methodology or study design that impact the interpretation of your research results. The limitations essentially detail any flaws or shortcomings in your study. Study limitations can exist due to constraints on research design, methodology, materials, etc., and these factors may impact the findings of your study. However, researchers are often reluctant to discuss the limitations of their study in their papers, feeling that bringing up limitations may undermine its research value in the eyes of readers and reviewers.

In spite of the impact it might have (and perhaps because of it) you should clearly acknowledge any limitations in your research paper in order to show readers—whether journal editors, other researchers, or the general public—that you are aware of these limitations and to explain how they affect the conclusions that can be drawn from the research.

In this article, we provide some guidelines for writing about research limitations, show examples of some frequently seen study limitations, and recommend techniques for presenting this information. And after you have finished drafting and have received manuscript editing for your work, you still might want to follow this up with academic editing before submitting your work to your target journal.

Why do I need to include limitations of research in my paper?

Although limitations address the potential weaknesses of a study, writing about them toward the end of your paper actually strengthens your study by identifying any problems before other researchers or reviewers find them.

Furthermore, pointing out study limitations shows that you’ve considered the impact of research weakness thoroughly and have an in-depth understanding of your research topic. Since all studies face limitations, being honest and detailing these limitations will impress researchers and reviewers more than ignoring them.

limitations of the study examples, brick wall with blue sky

Where should I put the limitations of the study in my paper?

Some limitations might be evident to researchers before the start of the study, while others might become clear while you are conducting the research. Whether these limitations are anticipated or not, and whether they are due to research design or to methodology, they should be clearly identified and discussed in the discussion section —the final section of your paper. Most journals now require you to include a discussion of potential limitations of your work, and many journals now ask you to place this “limitations section” at the very end of your article. 

Some journals ask you to also discuss the strengths of your work in this section, and some allow you to freely choose where to include that information in your discussion section—make sure to always check the author instructions of your target journal before you finalize a manuscript and submit it for peer review .

Limitations of the Study Examples

There are several reasons why limitations of research might exist. The two main categories of limitations are those that result from the methodology and those that result from issues with the researcher(s).

Common Methodological Limitations of Studies

Limitations of research due to methodological problems can be addressed by clearly and directly identifying the potential problem and suggesting ways in which this could have been addressed—and SHOULD be addressed in future studies. The following are some major potential methodological issues that can impact the conclusions researchers can draw from the research.

Issues with research samples and selection

Sampling errors occur when a probability sampling method is used to select a sample, but that sample does not reflect the general population or appropriate population concerned. This results in limitations of your study known as “sample bias” or “selection bias.”

For example, if you conducted a survey to obtain your research results, your samples (participants) were asked to respond to the survey questions. However, you might have had limited ability to gain access to the appropriate type or geographic scope of participants. In this case, the people who responded to your survey questions may not truly be a random sample.

Insufficient sample size for statistical measurements

When conducting a study, it is important to have a sufficient sample size in order to draw valid conclusions. The larger the sample, the more precise your results will be. If your sample size is too small, it will be difficult to identify significant relationships in the data.

Normally, statistical tests require a larger sample size to ensure that the sample is considered representative of a population and that the statistical result can be generalized to a larger population. It is a good idea to understand how to choose an appropriate sample size before you conduct your research by using scientific calculation tools—in fact, many journals now require such estimation to be included in every manuscript that is sent out for review.

Lack of previous research studies on the topic

Citing and referencing prior research studies constitutes the basis of the literature review for your thesis or study, and these prior studies provide the theoretical foundations for the research question you are investigating. However, depending on the scope of your research topic, prior research studies that are relevant to your thesis might be limited.

When there is very little or no prior research on a specific topic, you may need to develop an entirely new research typology. In this case, discovering a limitation can be considered an important opportunity to identify literature gaps and to present the need for further development in the area of study.

Methods/instruments/techniques used to collect the data

After you complete your analysis of the research findings (in the discussion section), you might realize that the manner in which you have collected the data or the ways in which you have measured variables has limited your ability to conduct a thorough analysis of the results.

For example, you might realize that you should have addressed your survey questions from another viable perspective, or that you were not able to include an important question in the survey. In these cases, you should acknowledge the deficiency or deficiencies by stating a need for future researchers to revise their specific methods for collecting data that includes these missing elements.

Common Limitations of the Researcher(s)

Study limitations that arise from situations relating to the researcher or researchers (whether the direct fault of the individuals or not) should also be addressed and dealt with, and remedies to decrease these limitations—both hypothetically in your study, and practically in future studies—should be proposed.

Limited access to data

If your research involved surveying certain people or organizations, you might have faced the problem of having limited access to these respondents. Due to this limited access, you might need to redesign or restructure your research in a different way. In this case, explain the reasons for limited access and be sure that your finding is still reliable and valid despite this limitation.

Time constraints

Just as students have deadlines to turn in their class papers, academic researchers might also have to meet deadlines for submitting a manuscript to a journal or face other time constraints related to their research (e.g., participants are only available during a certain period; funding runs out; collaborators move to a new institution). The time available to study a research problem and to measure change over time might be constrained by such practical issues. If time constraints negatively impacted your study in any way, acknowledge this impact by mentioning a need for a future study (e.g., a longitudinal study) to answer this research problem.

Conflicts arising from cultural bias and other personal issues

Researchers might hold biased views due to their cultural backgrounds or perspectives of certain phenomena, and this can affect a study’s legitimacy. Also, it is possible that researchers will have biases toward data and results that only support their hypotheses or arguments. In order to avoid these problems, the author(s) of a study should examine whether the way the research problem was stated and the data-gathering process was carried out appropriately.

Steps for Organizing Your Study Limitations Section

When you discuss the limitations of your study, don’t simply list and describe your limitations—explain how these limitations have influenced your research findings. There might be multiple limitations in your study, but you only need to point out and explain those that directly relate to and impact how you address your research questions.

We suggest that you divide your limitations section into three steps: (1) identify the study limitations; (2) explain how they impact your study in detail; and (3) propose a direction for future studies and present alternatives. By following this sequence when discussing your study’s limitations, you will be able to clearly demonstrate your study’s weakness without undermining the quality and integrity of your research.

Step 1. Identify the limitation(s) of the study

  • This part should comprise around 10%-20% of your discussion of study limitations.

The first step is to identify the particular limitation(s) that affected your study. There are many possible limitations of research that can affect your study, but you don’t need to write a long review of all possible study limitations. A 200-500 word critique is an appropriate length for a research limitations section. In the beginning of this section, identify what limitations your study has faced and how important these limitations are.

You only need to identify limitations that had the greatest potential impact on: (1) the quality of your findings, and (2) your ability to answer your research question.

limitations of a study example

Step 2. Explain these study limitations in detail

  • This part should comprise around 60-70% of your discussion of limitations.

After identifying your research limitations, it’s time to explain the nature of the limitations and how they potentially impacted your study. For example, when you conduct quantitative research, a lack of probability sampling is an important issue that you should mention. On the other hand, when you conduct qualitative research, the inability to generalize the research findings could be an issue that deserves mention.

Explain the role these limitations played on the results and implications of the research and justify the choice you made in using this “limiting” methodology or other action in your research. Also, make sure that these limitations didn’t undermine the quality of your dissertation .

methodological limitations example

Step 3. Propose a direction for future studies and present alternatives (optional)

  • This part should comprise around 10-20% of your discussion of limitations.

After acknowledging the limitations of the research, you need to discuss some possible ways to overcome these limitations in future studies. One way to do this is to present alternative methodologies and ways to avoid issues with, or “fill in the gaps of” the limitations of this study you have presented.  Discuss both the pros and cons of these alternatives and clearly explain why researchers should choose these approaches.

Make sure you are current on approaches used by prior studies and the impacts they have had on their findings. Cite review articles or scientific bodies that have recommended these approaches and why. This might be evidence in support of the approach you chose, or it might be the reason you consider your choices to be included as limitations. This process can act as a justification for your approach and a defense of your decision to take it while acknowledging the feasibility of other approaches.

P hrases and Tips for Introducing Your Study Limitations in the Discussion Section

The following phrases are frequently used to introduce the limitations of the study:

  • “There may be some possible limitations in this study.”
  • “The findings of this study have to be seen in light of some limitations.”
  •  “The first is the…The second limitation concerns the…”
  •  “The empirical results reported herein should be considered in the light of some limitations.”
  • “This research, however, is subject to several limitations.”
  • “The primary limitation to the generalization of these results is…”
  • “Nonetheless, these results must be interpreted with caution and a number of limitations should be borne in mind.”
  • “As with the majority of studies, the design of the current study is subject to limitations.”
  • “There are two major limitations in this study that could be addressed in future research. First, the study focused on …. Second ….”

For more articles on research writing and the journal submissions and publication process, visit Wordvice’s Academic Resources page.

And be sure to receive professional English editing and proofreading services , including paper editing services , for your journal manuscript before submitting it to journal editors.

Wordvice Resources

Proofreading & Editing Guide

Writing the Results Section for a Research Paper

How to Write a Literature Review

Research Writing Tips: How to Draft a Powerful Discussion Section

How to Captivate Journal Readers with a Strong Introduction

Tips That Will Make Your Abstract a Success!

APA In-Text Citation Guide for Research Writing

Additional Resources

  • Diving Deeper into Limitations and Delimitations (PhD student)
  • Organizing Your Social Sciences Research Paper: Limitations of the Study (USC Library)
  • Research Limitations (Research Methodology)
  • How to Present Limitations and Alternatives (UMASS)

Article References

Pearson-Stuttard, J., Kypridemos, C., Collins, B., Mozaffarian, D., Huang, Y., Bandosz, P.,…Micha, R. (2018). Estimating the health and economic effects of the proposed US Food and Drug Administration voluntary sodium reformulation: Microsimulation cost-effectiveness analysis. PLOS. https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1002551

Xu, W.L, Pedersen, N.L., Keller, L., Kalpouzos, G., Wang, H.X., Graff, C,. Fratiglioni, L. (2015). HHEX_23 AA Genotype Exacerbates Effect of Diabetes on Dementia and Alzheimer Disease: A Population-Based Longitudinal Study. PLOS. Retrieved from https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001853

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10 Case Study Advantages and Disadvantages

case study advantages and disadvantages, explained below

A case study in academic research is a detailed and in-depth examination of a specific instance or event, generally conducted through a qualitative approach to data.

The most common case study definition that I come across is is Robert K. Yin’s (2003, p. 13) quote provided below:

“An empirical inquiry that investigates a contemporary phenomenon within its real-life context, especially when the boundaries between phenomenon and context are not clearly evident.”

Researchers conduct case studies for a number of reasons, such as to explore complex phenomena within their real-life context, to look at a particularly interesting instance of a situation, or to dig deeper into something of interest identified in a wider-scale project.

While case studies render extremely interesting data, they have many limitations and are not suitable for all studies. One key limitation is that a case study’s findings are not usually generalizable to broader populations because one instance cannot be used to infer trends across populations.

Case Study Advantages and Disadvantages

1. in-depth analysis of complex phenomena.

Case study design allows researchers to delve deeply into intricate issues and situations.

By focusing on a specific instance or event, researchers can uncover nuanced details and layers of understanding that might be missed with other research methods, especially large-scale survey studies.

As Lee and Saunders (2017) argue,

“It allows that particular event to be studies in detail so that its unique qualities may be identified.”

This depth of analysis can provide rich insights into the underlying factors and dynamics of the studied phenomenon.

2. Holistic Understanding

Building on the above point, case studies can help us to understand a topic holistically and from multiple angles.

This means the researcher isn’t restricted to just examining a topic by using a pre-determined set of questions, as with questionnaires. Instead, researchers can use qualitative methods to delve into the many different angles, perspectives, and contextual factors related to the case study.

We can turn to Lee and Saunders (2017) again, who notes that case study researchers “develop a deep, holistic understanding of a particular phenomenon” with the intent of deeply understanding the phenomenon.

3. Examination of rare and Unusual Phenomena

We need to use case study methods when we stumble upon “rare and unusual” (Lee & Saunders, 2017) phenomena that would tend to be seen as mere outliers in population studies.

Take, for example, a child genius. A population study of all children of that child’s age would merely see this child as an outlier in the dataset, and this child may even be removed in order to predict overall trends.

So, to truly come to an understanding of this child and get insights into the environmental conditions that led to this child’s remarkable cognitive development, we need to do an in-depth study of this child specifically – so, we’d use a case study.

4. Helps Reveal the Experiences of Marginalzied Groups

Just as rare and unsual cases can be overlooked in population studies, so too can the experiences, beliefs, and perspectives of marginalized groups.

As Lee and Saunders (2017) argue, “case studies are also extremely useful in helping the expression of the voices of people whose interests are often ignored.”

Take, for example, the experiences of minority populations as they navigate healthcare systems. This was for many years a “hidden” phenomenon, not examined by researchers. It took case study designs to truly reveal this phenomenon, which helped to raise practitioners’ awareness of the importance of cultural sensitivity in medicine.

5. Ideal in Situations where Researchers cannot Control the Variables

Experimental designs – where a study takes place in a lab or controlled environment – are excellent for determining cause and effect . But not all studies can take place in controlled environments (Tetnowski, 2015).

When we’re out in the field doing observational studies or similar fieldwork, we don’t have the freedom to isolate dependent and independent variables. We need to use alternate methods.

Case studies are ideal in such situations.

A case study design will allow researchers to deeply immerse themselves in a setting (potentially combining it with methods such as ethnography or researcher observation) in order to see how phenomena take place in real-life settings.

6. Supports the generation of new theories or hypotheses

While large-scale quantitative studies such as cross-sectional designs and population surveys are excellent at testing theories and hypotheses on a large scale, they need a hypothesis to start off with!

This is where case studies – in the form of grounded research – come in. Often, a case study doesn’t start with a hypothesis. Instead, it ends with a hypothesis based upon the findings within a singular setting.

The deep analysis allows for hypotheses to emerge, which can then be taken to larger-scale studies in order to conduct further, more generalizable, testing of the hypothesis or theory.

7. Reveals the Unexpected

When a largescale quantitative research project has a clear hypothesis that it will test, it often becomes very rigid and has tunnel-vision on just exploring the hypothesis.

Of course, a structured scientific examination of the effects of specific interventions targeted at specific variables is extermely valuable.

But narrowly-focused studies often fail to shine a spotlight on unexpected and emergent data. Here, case studies come in very useful. Oftentimes, researchers set their eyes on a phenomenon and, when examining it closely with case studies, identify data and come to conclusions that are unprecedented, unforeseen, and outright surprising.

As Lars Meier (2009, p. 975) marvels, “where else can we become a part of foreign social worlds and have the chance to become aware of the unexpected?”


1. not usually generalizable.

Case studies are not generalizable because they tend not to look at a broad enough corpus of data to be able to infer that there is a trend across a population.

As Yang (2022) argues, “by definition, case studies can make no claims to be typical.”

Case studies focus on one specific instance of a phenomenon. They explore the context, nuances, and situational factors that have come to bear on the case study. This is really useful for bringing to light important, new, and surprising information, as I’ve already covered.

But , it’s not often useful for generating data that has validity beyond the specific case study being examined.

2. Subjectivity in interpretation

Case studies usually (but not always) use qualitative data which helps to get deep into a topic and explain it in human terms, finding insights unattainable by quantitative data.

But qualitative data in case studies relies heavily on researcher interpretation. While researchers can be trained and work hard to focus on minimizing subjectivity (through methods like triangulation), it often emerges – some might argue it’s innevitable in qualitative studies.

So, a criticism of case studies could be that they’re more prone to subjectivity – and researchers need to take strides to address this in their studies.

3. Difficulty in replicating results

Case study research is often non-replicable because the study takes place in complex real-world settings where variables are not controlled.

So, when returning to a setting to re-do or attempt to replicate a study, we often find that the variables have changed to such an extent that replication is difficult. Furthermore, new researchers (with new subjective eyes) may catch things that the other readers overlooked.

Replication is even harder when researchers attempt to replicate a case study design in a new setting or with different participants.

Comprehension Quiz for Students

Question 1: What benefit do case studies offer when exploring the experiences of marginalized groups?

a) They provide generalizable data. b) They help express the voices of often-ignored individuals. c) They control all variables for the study. d) They always start with a clear hypothesis.

Question 2: Why might case studies be considered ideal for situations where researchers cannot control all variables?

a) They provide a structured scientific examination. b) They allow for generalizability across populations. c) They focus on one specific instance of a phenomenon. d) They allow for deep immersion in real-life settings.

Question 3: What is a primary disadvantage of case studies in terms of data applicability?

a) They always focus on the unexpected. b) They are not usually generalizable. c) They support the generation of new theories. d) They provide a holistic understanding.

Question 4: Why might case studies be considered more prone to subjectivity?

a) They always use quantitative data. b) They heavily rely on researcher interpretation, especially with qualitative data. c) They are always replicable. d) They look at a broad corpus of data.

Question 5: In what situations are experimental designs, such as those conducted in labs, most valuable?

a) When there’s a need to study rare and unusual phenomena. b) When a holistic understanding is required. c) When determining cause-and-effect relationships. d) When the study focuses on marginalized groups.

Question 6: Why is replication challenging in case study research?

a) Because they always use qualitative data. b) Because they tend to focus on a broad corpus of data. c) Due to the changing variables in complex real-world settings. d) Because they always start with a hypothesis.

Lee, B., & Saunders, M. N. K. (2017). Conducting Case Study Research for Business and Management Students. SAGE Publications.

Meir, L. (2009). Feasting on the Benefits of Case Study Research. In Mills, A. J., Wiebe, E., & Durepos, G. (Eds.). Encyclopedia of Case Study Research (Vol. 2). London: SAGE Publications.

Tetnowski, J. (2015). Qualitative case study research design.  Perspectives on fluency and fluency disorders ,  25 (1), 39-45. ( Source )

Yang, S. L. (2022). The War on Corruption in China: Local Reform and Innovation . Taylor & Francis.

Yin, R. (2003). Case Study research. Thousand Oaks, CA: Sage.


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Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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Case Study Research Method in Psychology

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

On This Page:

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

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

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

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

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

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

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

case study

 Famous Case Studies

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

Clinical Case Studies

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

Child Psychology Case Studies

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

Types of Case Studies

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

Where Do You Find Data for a Case Study?

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

1. Primary sources

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

2. Secondary sources

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

3. Archival records

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

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

4. Organizational records

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

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

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

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

How do I Write a Case Study in Psychology?

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

1. Introduction

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

2. Case Presentation

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

3. Management and Outcome

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

4. Discussion

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

5. Additional Items

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

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

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

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

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

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


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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Further Information

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

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  • Roberta Heale 1 ,
  • Alison Twycross 2
  • 1 School of Nursing , Laurentian University , Sudbury , Ontario , Canada
  • 2 School of Health and Social Care , London South Bank University , London , UK
  • Correspondence to Dr Roberta Heale, School of Nursing, Laurentian University, Sudbury, ON P3E2C6, Canada; rheale{at}laurentian.ca


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What is it?

Case study is a research methodology, typically seen in social and life sciences. There is no one definition of case study research. 1 However, very simply… ‘a case study can be defined as an intensive study about a person, a group of people or a unit, which is aimed to generalize over several units’. 1 A case study has also been described as an intensive, systematic investigation of a single individual, group, community or some other unit in which the researcher examines in-depth data relating to several variables. 2

Often there are several similar cases to consider such as educational or social service programmes that are delivered from a number of locations. Although similar, they are complex and have unique features. In these circumstances, the evaluation of several, similar cases will provide a better answer to a research question than if only one case is examined, hence the multiple-case study. Stake asserts that the cases are grouped and viewed as one entity, called the quintain . 6  ‘We study what is similar and different about the cases to understand the quintain better’. 6

The steps when using case study methodology are the same as for other types of research. 6 The first step is defining the single case or identifying a group of similar cases that can then be incorporated into a multiple-case study. A search to determine what is known about the case(s) is typically conducted. This may include a review of the literature, grey literature, media, reports and more, which serves to establish a basic understanding of the cases and informs the development of research questions. Data in case studies are often, but not exclusively, qualitative in nature. In multiple-case studies, analysis within cases and across cases is conducted. Themes arise from the analyses and assertions about the cases as a whole, or the quintain, emerge. 6

Benefits and limitations of case studies

If a researcher wants to study a specific phenomenon arising from a particular entity, then a single-case study is warranted and will allow for a in-depth understanding of the single phenomenon and, as discussed above, would involve collecting several different types of data. This is illustrated in example 1 below.

Using a multiple-case research study allows for a more in-depth understanding of the cases as a unit, through comparison of similarities and differences of the individual cases embedded within the quintain. Evidence arising from multiple-case studies is often stronger and more reliable than from single-case research. Multiple-case studies allow for more comprehensive exploration of research questions and theory development. 6

Despite the advantages of case studies, there are limitations. The sheer volume of data is difficult to organise and data analysis and integration strategies need to be carefully thought through. There is also sometimes a temptation to veer away from the research focus. 2 Reporting of findings from multiple-case research studies is also challenging at times, 1 particularly in relation to the word limits for some journal papers.

Examples of case studies

Example 1: nurses’ paediatric pain management practices.

One of the authors of this paper (AT) has used a case study approach to explore nurses’ paediatric pain management practices. This involved collecting several datasets:

Observational data to gain a picture about actual pain management practices.

Questionnaire data about nurses’ knowledge about paediatric pain management practices and how well they felt they managed pain in children.

Questionnaire data about how critical nurses perceived pain management tasks to be.

These datasets were analysed separately and then compared 7–9 and demonstrated that nurses’ level of theoretical did not impact on the quality of their pain management practices. 7 Nor did individual nurse’s perceptions of how critical a task was effect the likelihood of them carrying out this task in practice. 8 There was also a difference in self-reported and observed practices 9 ; actual (observed) practices did not confirm to best practice guidelines, whereas self-reported practices tended to.

Example 2: quality of care for complex patients at Nurse Practitioner-Led Clinics (NPLCs)

The other author of this paper (RH) has conducted a multiple-case study to determine the quality of care for patients with complex clinical presentations in NPLCs in Ontario, Canada. 10 Five NPLCs served as individual cases that, together, represented the quatrain. Three types of data were collected including:

Review of documentation related to the NPLC model (media, annual reports, research articles, grey literature and regulatory legislation).

Interviews with nurse practitioners (NPs) practising at the five NPLCs to determine their perceptions of the impact of the NPLC model on the quality of care provided to patients with multimorbidity.

Chart audits conducted at the five NPLCs to determine the extent to which evidence-based guidelines were followed for patients with diabetes and at least one other chronic condition.

The three sources of data collected from the five NPLCs were analysed and themes arose related to the quality of care for complex patients at NPLCs. The multiple-case study confirmed that nurse practitioners are the primary care providers at the NPLCs, and this positively impacts the quality of care for patients with multimorbidity. Healthcare policy, such as lack of an increase in salary for NPs for 10 years, has resulted in issues in recruitment and retention of NPs at NPLCs. This, along with insufficient resources in the communities where NPLCs are located and high patient vulnerability at NPLCs, have a negative impact on the quality of care. 10

These examples illustrate how collecting data about a single case or multiple cases helps us to better understand the phenomenon in question. Case study methodology serves to provide a framework for evaluation and analysis of complex issues. It shines a light on the holistic nature of nursing practice and offers a perspective that informs improved patient care.

  • Gustafsson J
  • Calanzaro M
  • Sandelowski M

Competing interests None declared.

Provenance and peer review Commissioned; internally peer reviewed.

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How to Write Limitations of the Study (with examples)

This blog emphasizes the importance of recognizing and effectively writing about limitations in research. It discusses the types of limitations, their significance, and provides guidelines for writing about them, highlighting their role in advancing scholarly research.

Updated on August 24, 2023

a group of researchers writing their limitation of their study

No matter how well thought out, every research endeavor encounters challenges. There is simply no way to predict all possible variances throughout the process.

These uncharted boundaries and abrupt constraints are known as limitations in research . Identifying and acknowledging limitations is crucial for conducting rigorous studies. Limitations provide context and shed light on gaps in the prevailing inquiry and literature.

This article explores the importance of recognizing limitations and discusses how to write them effectively. By interpreting limitations in research and considering prevalent examples, we aim to reframe the perception from shameful mistakes to respectable revelations.

What are limitations in research?

In the clearest terms, research limitations are the practical or theoretical shortcomings of a study that are often outside of the researcher’s control . While these weaknesses limit the generalizability of a study’s conclusions, they also present a foundation for future research.

Sometimes limitations arise from tangible circumstances like time and funding constraints, or equipment and participant availability. Other times the rationale is more obscure and buried within the research design. Common types of limitations and their ramifications include:

  • Theoretical: limits the scope, depth, or applicability of a study.
  • Methodological: limits the quality, quantity, or diversity of the data.
  • Empirical: limits the representativeness, validity, or reliability of the data.
  • Analytical: limits the accuracy, completeness, or significance of the findings.
  • Ethical: limits the access, consent, or confidentiality of the data.

Regardless of how, when, or why they arise, limitations are a natural part of the research process and should never be ignored . Like all other aspects, they are vital in their own purpose.

Why is identifying limitations important?

Whether to seek acceptance or avoid struggle, humans often instinctively hide flaws and mistakes. Merging this thought process into research by attempting to hide limitations, however, is a bad idea. It has the potential to negate the validity of outcomes and damage the reputation of scholars.

By identifying and addressing limitations throughout a project, researchers strengthen their arguments and curtail the chance of peer censure based on overlooked mistakes. Pointing out these flaws shows an understanding of variable limits and a scrupulous research process.

Showing awareness of and taking responsibility for a project’s boundaries and challenges validates the integrity and transparency of a researcher. It further demonstrates the researchers understand the applicable literature and have thoroughly evaluated their chosen research methods.

Presenting limitations also benefits the readers by providing context for research findings. It guides them to interpret the project’s conclusions only within the scope of very specific conditions. By allowing for an appropriate generalization of the findings that is accurately confined by research boundaries and is not too broad, limitations boost a study’s credibility .

Limitations are true assets to the research process. They highlight opportunities for future research. When researchers identify the limitations of their particular approach to a study question, they enable precise transferability and improve chances for reproducibility. 

Simply stating a project’s limitations is not adequate for spurring further research, though. To spark the interest of other researchers, these acknowledgements must come with thorough explanations regarding how the limitations affected the current study and how they can potentially be overcome with amended methods.

How to write limitations

Typically, the information about a study’s limitations is situated either at the beginning of the discussion section to provide context for readers or at the conclusion of the discussion section to acknowledge the need for further research. However, it varies depending upon the target journal or publication guidelines. 

Don’t hide your limitations

It is also important to not bury a limitation in the body of the paper unless it has a unique connection to a topic in that section. If so, it needs to be reiterated with the other limitations or at the conclusion of the discussion section. Wherever it is included in the manuscript, ensure that the limitations section is prominently positioned and clearly introduced.

While maintaining transparency by disclosing limitations means taking a comprehensive approach, it is not necessary to discuss everything that could have potentially gone wrong during the research study. If there is no commitment to investigation in the introduction, it is unnecessary to consider the issue a limitation to the research. Wholly consider the term ‘limitations’ and ask, “Did it significantly change or limit the possible outcomes?” Then, qualify the occurrence as either a limitation to include in the current manuscript or as an idea to note for other projects. 

Writing limitations

Once the limitations are concretely identified and it is decided where they will be included in the paper, researchers are ready for the writing task. Including only what is pertinent, keeping explanations detailed but concise, and employing the following guidelines is key for crafting valuable limitations:

1) Identify and describe the limitations : Clearly introduce the limitation by classifying its form and specifying its origin. For example:

  • An unintentional bias encountered during data collection
  • An intentional use of unplanned post-hoc data analysis

2) Explain the implications : Describe how the limitation potentially influences the study’s findings and how the validity and generalizability are subsequently impacted. Provide examples and evidence to support claims of the limitations’ effects without making excuses or exaggerating their impact. Overall, be transparent and objective in presenting the limitations, without undermining the significance of the research. 

3) Provide alternative approaches for future studies : Offer specific suggestions for potential improvements or avenues for further investigation. Demonstrate a proactive approach by encouraging future research that addresses the identified gaps and, therefore, expands the knowledge base.

Whether presenting limitations as an individual section within the manuscript or as a subtopic in the discussion area, authors should use clear headings and straightforward language to facilitate readability. There is no need to complicate limitations with jargon, computations, or complex datasets.

Examples of common limitations

Limitations are generally grouped into two categories , methodology and research process .

Methodology limitations

Methodology may include limitations due to:

  • Sample size
  • Lack of available or reliable data
  • Lack of prior research studies on the topic
  • Measure used to collect the data
  • Self-reported data

methodology limitation example

The researcher is addressing how the large sample size requires a reassessment of the measures used to collect and analyze the data.

Research process limitations

Limitations during the research process may arise from:

  • Access to information
  • Longitudinal effects
  • Cultural and other biases
  • Language fluency
  • Time constraints

research process limitations example

The author is pointing out that the model’s estimates are based on potentially biased observational studies.

Final thoughts

Successfully proving theories and touting great achievements are only two very narrow goals of scholarly research. The true passion and greatest efforts of researchers comes more in the form of confronting assumptions and exploring the obscure.

In many ways, recognizing and sharing the limitations of a research study both allows for and encourages this type of discovery that continuously pushes research forward. By using limitations to provide a transparent account of the project's boundaries and to contextualize the findings, researchers pave the way for even more robust and impactful research in the future.

Charla Viera, MS

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Strengths and Weaknesses of Case Studies

There is no doubt that case studies are a valuable and important form of research for all of the industries and fields that use them. However, along with all their advantages, they also have some disadvantages. In this article we are going to look at both.

Advantages of Case Studies

Intensive Study

Case study method is responsible for intensive study of a unit. It is the investigation and exploration of an event thoroughly and deeply. You get a very detailed and in-depth study of a person or event. This is especially the case with subjects that cannot be physically or ethically recreated.

This is one of the biggest advantages of the Genie case. You cannot lock up a child for 13 years and deprive them of everything. That would be morally and ethically wrong in every single way. So when the opportunity presented itself, researchers could not look away. It was a once in a lifetime opportunity to learn about feral children.

Genie was a feral child. She was raised in completed isolation, with little human contact. Because of the abuse she withstood, she was unable to develop cognitively. From infancy she was strapped to a potty chair, and therefore never acquired the physicality needed for walking, running and jumping.

If Genie made a noise, her father beat her. Therefore, she learned to not make a noise. Once she was found, researchers studied her language skills, and attempted to find ways to get her to communicate. They were successful. While she never gained the ability to speak, she did develop other ways to communicate. However, the public soon lost interest in her case, and with that, the funds to conduct the study.

However, her case was extremely important to child development psychology and linguistic theory. Because of her, we know that mental stimulation is needed for proper development. We also now know that there is a "critical period" for the learning of language.

Developing New Research

Case studies are one of the best ways to stimulate new research. A case study can be completed, and if the findings are valuable, they can lead to new and advanced research in the field. There has been a great deal of research done that wouldn't have been possible without case studies.

An example of this is the sociological study Nickel and Dimed. Nickel and Dimed is a book and study done by Barbara Ehrenreich. She wanted to study poverty in America, and did so by living and working as a person living on minimum wage.

Through her experiment, she discovered that poverty was almost inescapable. As soon as she saved a little money, she was hit with a crisis. She might get sick, or her car might break down, all occurrences that can be destructive when a person doesn't have a safety net to fall back on.

It didn't matter where she lived or what she did. Working a minimum wage job gave her no chances for advancement or improvement whatsoever. And she did the experiment as a woman with no children to support.

This study opened a lot of eyes to the problem of the working poor in America. By living and working as the experiment, Ehrenreich was able to show first-hand data regarding the issues surrounding poverty. The book didn't end with any solutions, just suggestions for the reader and points for them to think about.

Using this case study information, new studies could be organized to learn better ways to help people who are fighting poverty, or better ways to help the working poor.

Contradicting Established Ideas or Theories

Oftentimes there are theories that may be questioned with case studies. For example, in the John/John case study, it was believed that gender and sexual identity were a construct of nurture, not nature.

John-John focused on a set of twin boys, both of whom were circumcised at the age of 6 months. One of the twin's circumcisions failed, causing irreparable damage to the penis. His parents were concerned about the sexual health of their son, so they contacted Dr. John Money for a solution.

Dr. Money believed that sexuality came from nurture, not nature, and that the injured baby, Bruce, could be raised as a girl. His penis was removed and he was sexually reassigned to become a girl. Bruce's name was changed to Brenda, and his parents decided to raise him as a girl.

In this case, Dr. Money was dishonest. He believed that gender could be changed, which has since been proven false. Brenda's parents were also dishonest, stating that the surgery was a success, when in fact that wasn't the case.

As Brenda grew up, she always acted masculine and was teased for it at school. She did not socialize as a girl, and did not identify as a female. When Brenda was 13 she learned the truth, and was incredibly relieved. She changed her name to David, and lived the rest of her life as a male.

This case proved that the general theory was wrong, and is still valuable, even though the study author was dishonest.

Giving New Insight

Case studies have the ability to give insight into phenomena that cannot be learned in any other way. An example of this is the case study about Sidney Bradford. Bradford was blind from the age of 10 months old, and regained his sight at the age of 52 from a corneal transplant.

This unique situation allowed researchers to better learn how perception and motion changes when suddenly given sight. They were able to better understand how colors and dimensions affect the human process. For what it is worth, Bradford continued to live and work with his eyes closed, as he found sight too stimulating.

Another famous study was the sociological study of Milgram.

Stanley Milgram did a study from 1960 to 1974 in which he studied the effects of social pressure. The study was set up as an independent laboratory. A random person would walk in, and agree to be a part of the study. He was told to act as a teacher, and ask questions to another volunteer, who was the learner.

The teacher would ask the learner questions, and whenever he answered incorrectly, the teacher was instructed to give the learner an electric shock. Each time the learner was wrong, the shock would be increased by 15 volts. What the teacher didn't know was that the learner was a part of the experiment, and that no shocks were being given. However, the learner did act as if they were being shocked.

If the teachers tried to quit, they were strongly pushed to continue. The goal of the experiment was to see whether or not any of the teachers would go up to the highest voltage. As it turned out, 65% of the teachers did.

This study opened eyes when it comes to social pressure. If someone tells you it is okay to hurt someone, at what point will the person back off and say "this is not ok!" And in this study, the results were the same, regardless of income, race, gender or ethnicity.

This study opened up the sociological world of understanding the divide between social pressure and morality.

Disadvantages of Case Studies

Inability to Replicate

As demonstrated with the Genie case study, many studies cannot be replicated, and therefore, cannot be corroborated. Because the studies cannot be replicated, it means the data and results are only valid for that one person. Now, one could infer that that results of the Genie study would be the same with other feral children, without additional studies we can never be 100% certain.

Also, Genie was a white, American female. We do not know whether someone with a different gender, race or ethnicity would have a different result.

Key Term! Hawthorne Effect

The effect in which people change their behavior when they are aware they are being observed.

Researcher Bias

When conducting a case study, it is very possible for the author to form a bias. This bias can be for the subject; the form of data collection, or the way the data is interpreted. This is very common, since it is normal for humans to be subjective. It is well known that Sigmund Freud, the father of psychology, was often biased in his case histories and interpretations.

The researcher can become close to a study participant, or may learn to identify with the subject. When this happens the researcher loses their perspective as an outsider.

No Classification

Any classification is not possible due to studying a small unit. This generalization of results is limited, since the study is only focusing on one small group. However, this isn't always a problem, especially if generalization is not one of the study's goals.

Time Intensive

Case studies can be very time consuming. The data collection process can be very intensive and long, and this is something new researchers are not familiar with. It takes a long period of time to develop a case study, and develop a detailed analysis.

Many studies also require the authors to immerse themselves in the case. For example, in the Genie case, the lead researchers spent an abnormal amount of time with Genie, since so few people knew how to handle her. David Rigler, one of the lead researchers, actually had Genie live with him and his family for years. Because of this attachment, many questioned the veracity of the study data.

Possibility of Errors

Case study method may have errors of memory or judgment. Since reconstructing case history is based on memory, this can lead to errors. Also, how one person perceived the past could be different for another person, and this can and does lead to errors.

When considering various aspects of their lives, people tend to focus on issues that they find most important. This allows them to form a prejudice and can make them unaware of other possible options.

Ethical Issues

With small studies, there is always the question of ethics. At what point does a study become unethical? The Genie case was riddled with accusations of being unethical, and people still debate about it today.

Was it ethical to study Genie as deeply as she was studied?

Did Genie deserve to live out her life unbothered by researchers and academics trying to use her case to potentially further their careers?

At what point does the pursuit of scientific knowledge outweigh the right to a life free from research?

Also, because the researchers became so invested in the study, people questioned whether a researcher would report unethical behavior if they witnessed it.

Advantages and Disadvantages in Real-Life Studies

Two of these case studies are the Tylenol Scandal and the Genie language study.

Let's look at the advantages and disadvantages of these two studies.

Genie – Advantages

Uniqueness of study – Being able to study a feral child is a rare occurrence.

Genie – Disadvantages

Ethics - The lead researcher David Rigler provided a home for Genie, and was paid for being a foster parent. This is often seen as unethical, since Rigler had a financial interest in Genie and her case.

Tylenol – Advantages

Uniqueness of study – What happened to Tylenol was very unique and rare. While companies face crisis all the time, a public health crisis of this magnitude is very unique.

Tylenol – Disadvantages

Understanding the Different Types of Case Studies

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

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

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

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

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

Table of contents

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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a case study limitations

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

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

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

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

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

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

Research bias

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

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McCombes, S. (2023, November 20). What Is a Case Study? | Definition, Examples & Methods. Scribbr. Retrieved March 4, 2024, from https://www.scribbr.com/methodology/case-study/

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StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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StatPearls [Internet].

Case control studies.

Steven Tenny ; Connor C. Kerndt ; Mary R. Hoffman .


Last Update: March 27, 2023 .

  • Introduction

A case-control study is a type of observational study commonly used to look at factors associated with diseases or outcomes. [1]   The case-control study starts with a group of cases, which are the individuals who have the outcome of interest. The researcher then tries to construct a second group of individuals called the controls, who are similar to the case individuals but do not have the outcome of interest. The researcher then looks at historical factors to identify if some exposure(s) is/are found more commonly in the cases than the controls. If the exposure is found more commonly in the cases than in the controls, the researcher can hypothesize that the exposure may be linked to the outcome of interest. 

For example, a researcher may want to look at the rare cancer Kaposi's sarcoma. The researcher would find a group of individuals with Kaposi's sarcoma (the cases) and compare them to a group of patients who are similar to the cases in most ways but do not have Kaposi's sarcoma (controls). The researcher could then ask about various exposures to see if any exposure is more common in those with Kaposi's sarcoma (the cases) than those without Kaposi's sarcoma (the controls). The researcher might find that those with Kaposi's sarcoma are more likely to have HIV, and thus conclude that HIV may be a risk factor for the development of Kaposi's sarcoma.

There are many advantages to case-control studies.  First, the case-control approach allows for the study of rare diseases.   If a disease occurs very infrequently, one would have to follow a large group of people for a long period of time to accrue enough incident cases to study. Such use of resources may be impractical, so a case-control study can be useful for identifying current cases and evaluating historical associated factors.  For example, if a disease developed in 1 in 1000 people per year (0.001/year) then in ten years one would expect about 10 cases of a disease to exist in a group of 1000 people. If the disease is much rarer, say 1 in 1,000,0000 per year (0.0000001/year) this would require either having to follow 1,000,0000 people for ten years or 1000 people for 1000 years to accrue ten total cases. As it may be impractical to follow 1,000,000 for ten years or to wait 1000 years for recruitment, a case-control study allows for a more feasible approach. 

Second, the case-control study design makes it possible to look at multiple risk factors at once. In the example above about Kaposi's sarcoma, the researcher could ask both the cases and controls about exposures to HIV, asbestos, smoking, lead, sunburns, aniline dye, alcohol, herpes, human papillomavirus, or any number of possible exposures to identify those most likely associated with Kaposi's sarcoma.

Case-control studies can also be very helpful when disease outbreaks occur, and potential links and exposures need to be identified.  This study mechanism can be commonly seen in food-related disease outbreaks associated with contaminated products, or when rare diseases start to increase in frequency, as has been seen with measles in recent years.

Because of these advantages, case-control studies are commonly used as one of the first studies to build evidence of an association between exposure and an event or disease.

In a case-control study, the investigator can include unequal numbers of cases with controls such as 2:1 or 4:1 to increase the power of the study.

Disadvantages and Limitations

The most commonly cited disadvantage in case-control studies is the potential for recall bias. [2]   Recall bias in a case-control study is the increased likelihood that those with the outcome will recall and report exposures compared to those without the outcome.  In other words, even if both groups had exactly the same exposures, the participants in the cases group may report the exposure more often than the controls do.  Recall bias may lead to concluding that there are associations between exposure and disease that do not, in fact, exist. It is due to subjects' imperfect memories of past exposures.  If people with Kaposi's sarcoma are asked about exposure and history (e.g., HIV, asbestos, smoking, lead, sunburn, aniline dye, alcohol, herpes, human papillomavirus), the individuals with the disease are more likely to think harder about these exposures and recall having some of the exposures that the healthy controls.

Case-control studies, due to their typically retrospective nature, can be used to establish a correlation  between exposures and outcomes, but cannot establish causation . These studies simply attempt to find correlations between past events and the current state. 

When designing a case-control study, the researcher must find an appropriate control group. Ideally, the case group (those with the outcome) and the control group (those without the outcome) will have almost the same characteristics, such as age, gender, overall health status, and other factors. The two groups should have similar histories and live in similar environments. If, for example, our cases of Kaposi's sarcoma came from across the country but our controls were only chosen from a small community in northern latitudes where people rarely go outside or get sunburns, asking about sunburn may not be a valid exposure to investigate.  Similarly, if all of the cases of Kaposi's sarcoma were found to come from a small community outside a battery factory with high levels of lead in the environment, then controls from across the country with minimal lead exposure would not provide an appropriate control group.  The investigator must put a great deal of effort into creating a proper control group to bolster the strength of the case-control study as well as enhance their ability to find true and valid potential correlations between exposures and disease states.

Similarly, the researcher must recognize the potential for failing to identify confounding variables or exposures, introducing the possibility of confounding bias, which occurs when a variable that is not being accounted for that has a relationship with both the exposure and outcome.  This can cause us to accidentally be studying something we are not accounting for but that may be systematically different between the groups. 

The major method for analyzing results in case-control studies is the odds ratio (OR). The odds ratio is the odds of having a disease (or outcome) with the exposure versus the odds of having the disease without the exposure. The most straightforward way to calculate the odds ratio is with a 2 by 2 table divided by exposure and disease status (see below). Mathematically we can write the odds ratio as follows.

Odds ratio = [(Number exposed with disease)/(Number exposed without disease) ]/[(Number not exposed to disease)/(Number not exposed without disease) ]

This can be rewritten as:

Odds ratio = [ (Number exposed with disease) x (Number not exposed without disease) ] / [ (Number exposed without disease ) x (Number not exposed with disease) ] 

The odds ratio tells us how strongly the exposure is related to the disease state. An odds ratio of greater than one implies the disease is more likely with exposure. An odds ratio of less than one implies the disease is less likely with exposure and thus the exposure may be protective.  For example, a patient with a prior heart attack taking a daily aspirin has a decreased odds of having another heart attack (odds ratio less than one). An odds ratio of one implies there is no relation between the exposure and the disease process.

Odds ratios are often confused with Relative Risk (RR), which is a measure of the probability of the disease or outcome in the exposed vs unexposed groups.  For very rare conditions, the OR and RR may be very similar, but they are measuring different aspects of the association between outcome and exposure.  The OR is used in case-control studies because RR cannot be estimated; whereas in randomized clinical trials, a direct measurement of the development of events in the exposed and unexposed groups can be seen. RR is also used to compare risk in other prospective study designs.

  • Issues of Concern

The main issues of concern with a case-control study are recall bias, its retrospective nature, the need for a careful collection of measured variables, and the selection of an appropriate control group. [3]  These are discussed above in the disadvantages section.

  • Clinical Significance

A case-control study is a good tool for exploring risk factors for rare diseases or when other study types are not feasible.  Many times an investigator will hypothesize a list of possible risk factors for a disease process and will then use a case-control study to see if there are any possible associations between the risk factors and the disease process. The investigator can then use the data from the case-control study to focus on a few of the most likely causative factors and develop additional hypotheses or questions.  Then through further exploration, often using other study types (such as cohort studies or randomized clinical studies) the researcher may be able to develop further support for the evidence of the possible association between the exposure and the outcome.

  • Enhancing Healthcare Team Outcomes

Case-control studies are prevalent in all fields of medicine from nursing and pharmacy to use in public health and surgical patients.  Case-control studies are important for each member of the health care team to not only understand their common occurrence in research but because each part of the health care team has parts to contribute to such studies.  One of the most important things each party provides is helping identify correct controls for the cases.  Matching the controls across a spectrum of factors outside of the elements of interest take input from nurses, pharmacists, social workers, physicians, demographers, and more.  Failure for adequate selection of controls can lead to invalid study conclusions and invalidate the entire study.

  • Review Questions
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  • Comment on this article.

2x2 table with calculations for the odds ratio and 95% confidence interval for the odds ratio Contributed by Steven Tenny MD, MPH, MBA

Disclosure: Steven Tenny declares no relevant financial relationships with ineligible companies.

Disclosure: Connor Kerndt declares no relevant financial relationships with ineligible companies.

Disclosure: Mary Hoffman declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

  • Cite this Page Tenny S, Kerndt CC, Hoffman MR. Case Control Studies. [Updated 2023 Mar 27]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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a case study limitations

The Advantages and Limitations of Single Case Study Analysis

a case study limitations

As Andrew Bennett and Colin Elman have recently noted, qualitative research methods presently enjoy “an almost unprecedented popularity and vitality… in the international relations sub-field”, such that they are now “indisputably prominent, if not pre-eminent” (2010: 499). This is, they suggest, due in no small part to the considerable advantages that case study methods in particular have to offer in studying the “complex and relatively unstructured and infrequent phenomena that lie at the heart of the subfield” (Bennett and Elman, 2007: 171). Using selected examples from within the International Relations literature[1], this paper aims to provide a brief overview of the main principles and distinctive advantages and limitations of single case study analysis. Divided into three inter-related sections, the paper therefore begins by first identifying the underlying principles that serve to constitute the case study as a particular research strategy, noting the somewhat contested nature of the approach in ontological, epistemological, and methodological terms. The second part then looks to the principal single case study types and their associated advantages, including those from within the recent ‘third generation’ of qualitative International Relations (IR) research. The final section of the paper then discusses the most commonly articulated limitations of single case studies; while accepting their susceptibility to criticism, it is however suggested that such weaknesses are somewhat exaggerated. The paper concludes that single case study analysis has a great deal to offer as a means of both understanding and explaining contemporary international relations.

The term ‘case study’, John Gerring has suggested, is “a definitional morass… Evidently, researchers have many different things in mind when they talk about case study research” (2006a: 17). It is possible, however, to distil some of the more commonly-agreed principles. One of the most prominent advocates of case study research, Robert Yin (2009: 14) defines it as “an empirical enquiry that investigates a contemporary phenomenon in depth and within its real-life context, especially when the boundaries between phenomenon and context are not clearly evident”. What this definition usefully captures is that case studies are intended – unlike more superficial and generalising methods – to provide a level of detail and understanding, similar to the ethnographer Clifford Geertz’s (1973) notion of ‘thick description’, that allows for the thorough analysis of the complex and particularistic nature of distinct phenomena. Another frequently cited proponent of the approach, Robert Stake, notes that as a form of research the case study “is defined by interest in an individual case, not by the methods of inquiry used”, and that “the object of study is a specific, unique, bounded system” (2008: 443, 445). As such, three key points can be derived from this – respectively concerning issues of ontology, epistemology, and methodology – that are central to the principles of single case study research.

First, the vital notion of ‘boundedness’ when it comes to the particular unit of analysis means that defining principles should incorporate both the synchronic (spatial) and diachronic (temporal) elements of any so-called ‘case’. As Gerring puts it, a case study should be “an intensive study of a single unit… a spatially bounded phenomenon – e.g. a nation-state, revolution, political party, election, or person – observed at a single point in time or over some delimited period of time” (2004: 342). It is important to note, however, that – whereas Gerring refers to a single unit of analysis – it may be that attention also necessarily be given to particular sub-units. This points to the important difference between what Yin refers to as an ‘holistic’ case design, with a single unit of analysis, and an ’embedded’ case design with multiple units of analysis (Yin, 2009: 50-52). The former, for example, would examine only the overall nature of an international organization, whereas the latter would also look to specific departments, programmes, or policies etc.

Secondly, as Tim May notes of the case study approach, “even the most fervent advocates acknowledge that the term has entered into understandings with little specification or discussion of purpose and process” (2011: 220). One of the principal reasons for this, he argues, is the relationship between the use of case studies in social research and the differing epistemological traditions – positivist, interpretivist, and others – within which it has been utilised. Philosophy of science concerns are obviously a complex issue, and beyond the scope of much of this paper. That said, the issue of how it is that we know what we know – of whether or not a single independent reality exists of which we as researchers can seek to provide explanation – does lead us to an important distinction to be made between so-called idiographic and nomothetic case studies (Gerring, 2006b). The former refers to those which purport to explain only a single case, are concerned with particularisation, and hence are typically (although not exclusively) associated with more interpretivist approaches. The latter are those focused studies that reflect upon a larger population and are more concerned with generalisation, as is often so with more positivist approaches[2]. The importance of this distinction, and its relation to the advantages and limitations of single case study analysis, is returned to below.

Thirdly, in methodological terms, given that the case study has often been seen as more of an interpretivist and idiographic tool, it has also been associated with a distinctly qualitative approach (Bryman, 2009: 67-68). However, as Yin notes, case studies can – like all forms of social science research – be exploratory, descriptive, and/or explanatory in nature. It is “a common misconception”, he notes, “that the various research methods should be arrayed hierarchically… many social scientists still deeply believe that case studies are only appropriate for the exploratory phase of an investigation” (Yin, 2009: 6). If case studies can reliably perform any or all three of these roles – and given that their in-depth approach may also require multiple sources of data and the within-case triangulation of methods – then it becomes readily apparent that they should not be limited to only one research paradigm. Exploratory and descriptive studies usually tend toward the qualitative and inductive, whereas explanatory studies are more often quantitative and deductive (David and Sutton, 2011: 165-166). As such, the association of case study analysis with a qualitative approach is a “methodological affinity, not a definitional requirement” (Gerring, 2006a: 36). It is perhaps better to think of case studies as transparadigmatic; it is mistaken to assume single case study analysis to adhere exclusively to a qualitative methodology (or an interpretivist epistemology) even if it – or rather, practitioners of it – may be so inclined. By extension, this also implies that single case study analysis therefore remains an option for a multitude of IR theories and issue areas; it is how this can be put to researchers’ advantage that is the subject of the next section.

Having elucidated the defining principles of the single case study approach, the paper now turns to an overview of its main benefits. As noted above, a lack of consensus still exists within the wider social science literature on the principles and purposes – and by extension the advantages and limitations – of case study research. Given that this paper is directed towards the particular sub-field of International Relations, it suggests Bennett and Elman’s (2010) more discipline-specific understanding of contemporary case study methods as an analytical framework. It begins however, by discussing Harry Eckstein’s seminal (1975) contribution to the potential advantages of the case study approach within the wider social sciences.

Eckstein proposed a taxonomy which usefully identified what he considered to be the five most relevant types of case study. Firstly were so-called configurative-idiographic studies, distinctly interpretivist in orientation and predicated on the assumption that “one cannot attain prediction and control in the natural science sense, but only understanding ( verstehen )… subjective values and modes of cognition are crucial” (1975: 132). Eckstein’s own sceptical view was that any interpreter ‘simply’ considers a body of observations that are not self-explanatory and “without hard rules of interpretation, may discern in them any number of patterns that are more or less equally plausible” (1975: 134). Those of a more post-modernist bent, of course – sharing an “incredulity towards meta-narratives”, in Lyotard’s (1994: xxiv) evocative phrase – would instead suggest that this more free-form approach actually be advantageous in delving into the subtleties and particularities of individual cases.

Eckstein’s four other types of case study, meanwhile, promote a more nomothetic (and positivist) usage. As described, disciplined-configurative studies were essentially about the use of pre-existing general theories, with a case acting “passively, in the main, as a receptacle for putting theories to work” (Eckstein, 1975: 136). As opposed to the opportunity this presented primarily for theory application, Eckstein identified heuristic case studies as explicit theoretical stimulants – thus having instead the intended advantage of theory-building. So-called p lausibility probes entailed preliminary attempts to determine whether initial hypotheses should be considered sound enough to warrant more rigorous and extensive testing. Finally, and perhaps most notably, Eckstein then outlined the idea of crucial case studies , within which he also included the idea of ‘most-likely’ and ‘least-likely’ cases; the essential characteristic of crucial cases being their specific theory-testing function.

Whilst Eckstein’s was an early contribution to refining the case study approach, Yin’s (2009: 47-52) more recent delineation of possible single case designs similarly assigns them roles in the applying, testing, or building of theory, as well as in the study of unique cases[3]. As a subset of the latter, however, Jack Levy (2008) notes that the advantages of idiographic cases are actually twofold. Firstly, as inductive/descriptive cases – akin to Eckstein’s configurative-idiographic cases – whereby they are highly descriptive, lacking in an explicit theoretical framework and therefore taking the form of “total history”. Secondly, they can operate as theory-guided case studies, but ones that seek only to explain or interpret a single historical episode rather than generalise beyond the case. Not only does this therefore incorporate ‘single-outcome’ studies concerned with establishing causal inference (Gerring, 2006b), it also provides room for the more postmodern approaches within IR theory, such as discourse analysis, that may have developed a distinct methodology but do not seek traditional social scientific forms of explanation.

Applying specifically to the state of the field in contemporary IR, Bennett and Elman identify a ‘third generation’ of mainstream qualitative scholars – rooted in a pragmatic scientific realist epistemology and advocating a pluralistic approach to methodology – that have, over the last fifteen years, “revised or added to essentially every aspect of traditional case study research methods” (2010: 502). They identify ‘process tracing’ as having emerged from this as a central method of within-case analysis. As Bennett and Checkel observe, this carries the advantage of offering a methodologically rigorous “analysis of evidence on processes, sequences, and conjunctures of events within a case, for the purposes of either developing or testing hypotheses about causal mechanisms that might causally explain the case” (2012: 10).

Harnessing various methods, process tracing may entail the inductive use of evidence from within a case to develop explanatory hypotheses, and deductive examination of the observable implications of hypothesised causal mechanisms to test their explanatory capability[4]. It involves providing not only a coherent explanation of the key sequential steps in a hypothesised process, but also sensitivity to alternative explanations as well as potential biases in the available evidence (Bennett and Elman 2010: 503-504). John Owen (1994), for example, demonstrates the advantages of process tracing in analysing whether the causal factors underpinning democratic peace theory are – as liberalism suggests – not epiphenomenal, but variously normative, institutional, or some given combination of the two or other unexplained mechanism inherent to liberal states. Within-case process tracing has also been identified as advantageous in addressing the complexity of path-dependent explanations and critical junctures – as for example with the development of political regime types – and their constituent elements of causal possibility, contingency, closure, and constraint (Bennett and Elman, 2006b).

Bennett and Elman (2010: 505-506) also identify the advantages of single case studies that are implicitly comparative: deviant, most-likely, least-likely, and crucial cases. Of these, so-called deviant cases are those whose outcome does not fit with prior theoretical expectations or wider empirical patterns – again, the use of inductive process tracing has the advantage of potentially generating new hypotheses from these, either particular to that individual case or potentially generalisable to a broader population. A classic example here is that of post-independence India as an outlier to the standard modernisation theory of democratisation, which holds that higher levels of socio-economic development are typically required for the transition to, and consolidation of, democratic rule (Lipset, 1959; Diamond, 1992). Absent these factors, MacMillan’s single case study analysis (2008) suggests the particularistic importance of the British colonial heritage, the ideology and leadership of the Indian National Congress, and the size and heterogeneity of the federal state.

Most-likely cases, as per Eckstein above, are those in which a theory is to be considered likely to provide a good explanation if it is to have any application at all, whereas least-likely cases are ‘tough test’ ones in which the posited theory is unlikely to provide good explanation (Bennett and Elman, 2010: 505). Levy (2008) neatly refers to the inferential logic of the least-likely case as the ‘Sinatra inference’ – if a theory can make it here, it can make it anywhere. Conversely, if a theory cannot pass a most-likely case, it is seriously impugned. Single case analysis can therefore be valuable for the testing of theoretical propositions, provided that predictions are relatively precise and measurement error is low (Levy, 2008: 12-13). As Gerring rightly observes of this potential for falsification:

“a positivist orientation toward the work of social science militates toward a greater appreciation of the case study format, not a denigration of that format, as is usually supposed” (Gerring, 2007: 247, emphasis added).

In summary, the various forms of single case study analysis can – through the application of multiple qualitative and/or quantitative research methods – provide a nuanced, empirically-rich, holistic account of specific phenomena. This may be particularly appropriate for those phenomena that are simply less amenable to more superficial measures and tests (or indeed any substantive form of quantification) as well as those for which our reasons for understanding and/or explaining them are irreducibly subjective – as, for example, with many of the normative and ethical issues associated with the practice of international relations. From various epistemological and analytical standpoints, single case study analysis can incorporate both idiographic sui generis cases and, where the potential for generalisation may exist, nomothetic case studies suitable for the testing and building of causal hypotheses. Finally, it should not be ignored that a signal advantage of the case study – with particular relevance to international relations – also exists at a more practical rather than theoretical level. This is, as Eckstein noted, “that it is economical for all resources: money, manpower, time, effort… especially important, of course, if studies are inherently costly, as they are if units are complex collective individuals ” (1975: 149-150, emphasis added).


Single case study analysis has, however, been subject to a number of criticisms, the most common of which concern the inter-related issues of methodological rigour, researcher subjectivity, and external validity. With regard to the first point, the prototypical view here is that of Zeev Maoz (2002: 164-165), who suggests that “the use of the case study absolves the author from any kind of methodological considerations. Case studies have become in many cases a synonym for freeform research where anything goes”. The absence of systematic procedures for case study research is something that Yin (2009: 14-15) sees as traditionally the greatest concern due to a relative absence of methodological guidelines. As the previous section suggests, this critique seems somewhat unfair; many contemporary case study practitioners – and representing various strands of IR theory – have increasingly sought to clarify and develop their methodological techniques and epistemological grounding (Bennett and Elman, 2010: 499-500).

A second issue, again also incorporating issues of construct validity, concerns that of the reliability and replicability of various forms of single case study analysis. This is usually tied to a broader critique of qualitative research methods as a whole. However, whereas the latter obviously tend toward an explicitly-acknowledged interpretive basis for meanings, reasons, and understandings:

“quantitative measures appear objective, but only so long as we don’t ask questions about where and how the data were produced… pure objectivity is not a meaningful concept if the goal is to measure intangibles [as] these concepts only exist because we can interpret them” (Berg and Lune, 2010: 340).

The question of researcher subjectivity is a valid one, and it may be intended only as a methodological critique of what are obviously less formalised and researcher-independent methods (Verschuren, 2003). Owen (1994) and Layne’s (1994) contradictory process tracing results of interdemocratic war-avoidance during the Anglo-American crisis of 1861 to 1863 – from liberal and realist standpoints respectively – are a useful example. However, it does also rest on certain assumptions that can raise deeper and potentially irreconcilable ontological and epistemological issues. There are, regardless, plenty such as Bent Flyvbjerg (2006: 237) who suggest that the case study contains no greater bias toward verification than other methods of inquiry, and that “on the contrary, experience indicates that the case study contains a greater bias toward falsification of preconceived notions than toward verification”.

The third and arguably most prominent critique of single case study analysis is the issue of external validity or generalisability. How is it that one case can reliably offer anything beyond the particular? “We always do better (or, in the extreme, no worse) with more observation as the basis of our generalization”, as King et al write; “in all social science research and all prediction, it is important that we be as explicit as possible about the degree of uncertainty that accompanies out prediction” (1994: 212). This is an unavoidably valid criticism. It may be that theories which pass a single crucial case study test, for example, require rare antecedent conditions and therefore actually have little explanatory range. These conditions may emerge more clearly, as Van Evera (1997: 51-54) notes, from large-N studies in which cases that lack them present themselves as outliers exhibiting a theory’s cause but without its predicted outcome. As with the case of Indian democratisation above, it would logically be preferable to conduct large-N analysis beforehand to identify that state’s non-representative nature in relation to the broader population.

There are, however, three important qualifiers to the argument about generalisation that deserve particular mention here. The first is that with regard to an idiographic single-outcome case study, as Eckstein notes, the criticism is “mitigated by the fact that its capability to do so [is] never claimed by its exponents; in fact it is often explicitly repudiated” (1975: 134). Criticism of generalisability is of little relevance when the intention is one of particularisation. A second qualifier relates to the difference between statistical and analytical generalisation; single case studies are clearly less appropriate for the former but arguably retain significant utility for the latter – the difference also between explanatory and exploratory, or theory-testing and theory-building, as discussed above. As Gerring puts it, “theory confirmation/disconfirmation is not the case study’s strong suit” (2004: 350). A third qualification relates to the issue of case selection. As Seawright and Gerring (2008) note, the generalisability of case studies can be increased by the strategic selection of cases. Representative or random samples may not be the most appropriate, given that they may not provide the richest insight (or indeed, that a random and unknown deviant case may appear). Instead, and properly used , atypical or extreme cases “often reveal more information because they activate more actors… and more basic mechanisms in the situation studied” (Flyvbjerg, 2006). Of course, this also points to the very serious limitation, as hinted at with the case of India above, that poor case selection may alternatively lead to overgeneralisation and/or grievous misunderstandings of the relationship between variables or processes (Bennett and Elman, 2006a: 460-463).

As Tim May (2011: 226) notes, “the goal for many proponents of case studies […] is to overcome dichotomies between generalizing and particularizing, quantitative and qualitative, deductive and inductive techniques”. Research aims should drive methodological choices, rather than narrow and dogmatic preconceived approaches. As demonstrated above, there are various advantages to both idiographic and nomothetic single case study analyses – notably the empirically-rich, context-specific, holistic accounts that they have to offer, and their contribution to theory-building and, to a lesser extent, that of theory-testing. Furthermore, while they do possess clear limitations, any research method involves necessary trade-offs; the inherent weaknesses of any one method, however, can potentially be offset by situating them within a broader, pluralistic mixed-method research strategy. Whether or not single case studies are used in this fashion, they clearly have a great deal to offer.


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Gerring, J. (2006a) Case Study Research: Principles and Practices . Cambridge University Press: Cambridge.

Gerring, J. (2006b) ‘Single-Outcome Studies: A Methodological Primer’, International Sociology , 21, 5, 707-734.

Gerring, J. (2007) ‘Is There a (Viable) Crucial-Case Method?’, Comparative Political Studies , 40, 3, 231-253.

King, G., Keohane, R. O. and Verba, S. (1994) Designing Social Inquiry: Scientific Inference in Qualitative Research . Princeton University Press: Chichester.

Layne, C. (1994) ‘Kant or Cant: The Myth of the Democratic Peace’, International Security , 19, 2, 5-49.

Levy, J. S. (2008) ‘Case Studies: Types, Designs, and Logics of Inference’, Conflict Management and Peace Science , 25, 1-18.

Lipset, S. M. (1959) ‘Some Social Requisites of Democracy: Economic Development and Political Legitimacy’, The American Political Science Review , 53, 1, 69-105.

Lyotard, J-F. (1984) The Postmodern Condition: A Report on Knowledge . University of Minnesota Press: Minneapolis.

MacMillan, A. (2008) ‘Deviant Democratization in India’, Democratization , 15, 4, 733-749.

Maoz, Z. (2002) Case study methodology in international studies: from storytelling to hypothesis testing. In F. P. Harvey and M. Brecher (eds) Evaluating Methodology in International Studies . University of Michigan Press: Ann Arbor.

May, T. (2011) Social Research: Issues, Methods and Process . Open University Press: Maidenhead.

Owen, J. M. (1994) ‘How Liberalism Produces Democratic Peace’, International Security , 19, 2, 87-125.

Seawright, J. and Gerring, J. (2008) ‘Case Selection Techniques in Case Study Research: A Menu of Qualitative and Quantitative Options’, Political Research Quarterly , 61, 2, 294-308.

Stake, R. E. (2008) Qualitative Case Studies. In N. K. Denzin and Y. S. Lincoln (eds) Strategies of Qualitative Inquiry . Sage Publications: Los Angeles. Ch. 17.

Van Evera, S. (1997) Guide to Methods for Students of Political Science . Cornell University Press: Ithaca.

Verschuren, P. J. M. (2003) ‘Case study as a research strategy: some ambiguities and opportunities’, International Journal of Social Research Methodology , 6, 2, 121-139.

Yin, R. K. (2009) Case Study Research: Design and Methods . SAGE Publications Ltd: London.

[1] The paper follows convention by differentiating between ‘International Relations’ as the academic discipline and ‘international relations’ as the subject of study.

[2] There is some similarity here with Stake’s (2008: 445-447) notion of intrinsic cases, those undertaken for a better understanding of the particular case, and instrumental ones that provide insight for the purposes of a wider external interest.

[3] These may be unique in the idiographic sense, or in nomothetic terms as an exception to the generalising suppositions of either probabilistic or deterministic theories (as per deviant cases, below).

[4] Although there are “philosophical hurdles to mount”, according to Bennett and Checkel, there exists no a priori reason as to why process tracing (as typically grounded in scientific realism) is fundamentally incompatible with various strands of positivism or interpretivism (2012: 18-19). By extension, it can therefore be incorporated by a range of contemporary mainstream IR theories.

— Written by: Ben Willis Written at: University of Plymouth Written for: David Brockington Date written: January 2013

Further Reading on E-International Relations

  • Identity in International Conflicts: A Case Study of the Cuban Missile Crisis
  • Imperialism’s Legacy in the Study of Contemporary Politics: The Case of Hegemonic Stability Theory
  • Recreating a Nation’s Identity Through Symbolism: A Chinese Case Study
  • Ontological Insecurity: A Case Study on Israeli-Palestinian Conflict in Jerusalem
  • Terrorists or Freedom Fighters: A Case Study of ETA
  • A Critical Assessment of Eco-Marxism: A Ghanaian Case Study

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a case study limitations

Organizing Your Social Sciences Research Assignments

  • Annotated Bibliography
  • Analyzing a Scholarly Journal Article
  • Group Presentations
  • Dealing with Nervousness
  • Using Visual Aids
  • Grading Someone Else's Paper
  • Types of Structured Group Activities
  • Group Project Survival Skills
  • Leading a Class Discussion
  • Multiple Book Review Essay
  • Reviewing Collected Works
  • Writing a Case Analysis Paper
  • Writing a Case Study
  • About Informed Consent
  • Writing Field Notes
  • Writing a Policy Memo
  • Writing a Reflective Paper
  • Writing a Research Proposal
  • Generative AI and Writing
  • Acknowledgments

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

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

How to Approach Writing a Case Study Research Paper

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

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

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

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

Structure and Writing Style

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

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

I.  Introduction

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

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

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

II.  Literature Review

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

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

III.  Method

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

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

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

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

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

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

IV.  Discussion

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

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

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

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

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

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

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

V.  Conclusion

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

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

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

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

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

Problems to Avoid

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

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

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

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

Writing Tip

At Least Five Misconceptions about Case Study Research

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

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

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

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

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Home » Limitations in Research – Types, Examples and Writing Guide

Limitations in Research – Types, Examples and Writing Guide

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Limitations in Research

Limitations in Research

Limitations in research refer to the factors that may affect the results, conclusions , and generalizability of a study. These limitations can arise from various sources, such as the design of the study, the sampling methods used, the measurement tools employed, and the limitations of the data analysis techniques.

Types of Limitations in Research

Types of Limitations in Research are as follows:

Sample Size Limitations

This refers to the size of the group of people or subjects that are being studied. If the sample size is too small, then the results may not be representative of the population being studied. This can lead to a lack of generalizability of the results.

Time Limitations

Time limitations can be a constraint on the research process . This could mean that the study is unable to be conducted for a long enough period of time to observe the long-term effects of an intervention, or to collect enough data to draw accurate conclusions.

Selection Bias

This refers to a type of bias that can occur when the selection of participants in a study is not random. This can lead to a biased sample that is not representative of the population being studied.

Confounding Variables

Confounding variables are factors that can influence the outcome of a study, but are not being measured or controlled for. These can lead to inaccurate conclusions or a lack of clarity in the results.

Measurement Error

This refers to inaccuracies in the measurement of variables, such as using a faulty instrument or scale. This can lead to inaccurate results or a lack of validity in the study.

Ethical Limitations

Ethical limitations refer to the ethical constraints placed on research studies. For example, certain studies may not be allowed to be conducted due to ethical concerns, such as studies that involve harm to participants.

Examples of Limitations in Research

Some Examples of Limitations in Research are as follows:

Research Title: “The Effectiveness of Machine Learning Algorithms in Predicting Customer Behavior”


  • The study only considered a limited number of machine learning algorithms and did not explore the effectiveness of other algorithms.
  • The study used a specific dataset, which may not be representative of all customer behaviors or demographics.
  • The study did not consider the potential ethical implications of using machine learning algorithms in predicting customer behavior.

Research Title: “The Impact of Online Learning on Student Performance in Computer Science Courses”

  • The study was conducted during the COVID-19 pandemic, which may have affected the results due to the unique circumstances of remote learning.
  • The study only included students from a single university, which may limit the generalizability of the findings to other institutions.
  • The study did not consider the impact of individual differences, such as prior knowledge or motivation, on student performance in online learning environments.

Research Title: “The Effect of Gamification on User Engagement in Mobile Health Applications”

  • The study only tested a specific gamification strategy and did not explore the effectiveness of other gamification techniques.
  • The study relied on self-reported measures of user engagement, which may be subject to social desirability bias or measurement errors.
  • The study only included a specific demographic group (e.g., young adults) and may not be generalizable to other populations with different preferences or needs.

How to Write Limitations in Research

When writing about the limitations of a research study, it is important to be honest and clear about the potential weaknesses of your work. Here are some tips for writing about limitations in research:

  • Identify the limitations: Start by identifying the potential limitations of your research. These may include sample size, selection bias, measurement error, or other issues that could affect the validity and reliability of your findings.
  • Be honest and objective: When describing the limitations of your research, be honest and objective. Do not try to minimize or downplay the limitations, but also do not exaggerate them. Be clear and concise in your description of the limitations.
  • Provide context: It is important to provide context for the limitations of your research. For example, if your sample size was small, explain why this was the case and how it may have affected your results. Providing context can help readers understand the limitations in a broader context.
  • Discuss implications : Discuss the implications of the limitations for your research findings. For example, if there was a selection bias in your sample, explain how this may have affected the generalizability of your findings. This can help readers understand the limitations in terms of their impact on the overall validity of your research.
  • Provide suggestions for future research : Finally, provide suggestions for future research that can address the limitations of your study. This can help readers understand how your research fits into the broader field and can provide a roadmap for future studies.

Purpose of Limitations in Research

There are several purposes of limitations in research. Here are some of the most important ones:

  • To acknowledge the boundaries of the study : Limitations help to define the scope of the research project and set realistic expectations for the findings. They can help to clarify what the study is not intended to address.
  • To identify potential sources of bias: Limitations can help researchers identify potential sources of bias in their research design, data collection, or analysis. This can help to improve the validity and reliability of the findings.
  • To provide opportunities for future research: Limitations can highlight areas for future research and suggest avenues for further exploration. This can help to advance knowledge in a particular field.
  • To demonstrate transparency and accountability: By acknowledging the limitations of their research, researchers can demonstrate transparency and accountability to their readers, peers, and funders. This can help to build trust and credibility in the research community.
  • To encourage critical thinking: Limitations can encourage readers to critically evaluate the study’s findings and consider alternative explanations or interpretations. This can help to promote a more nuanced and sophisticated understanding of the topic under investigation.

When to Write Limitations in Research

Limitations should be included in research when they help to provide a more complete understanding of the study’s results and implications. A limitation is any factor that could potentially impact the accuracy, reliability, or generalizability of the study’s findings.

It is important to identify and discuss limitations in research because doing so helps to ensure that the results are interpreted appropriately and that any conclusions drawn are supported by the available evidence. Limitations can also suggest areas for future research, highlight potential biases or confounding factors that may have affected the results, and provide context for the study’s findings.

Generally, limitations should be discussed in the conclusion section of a research paper or thesis, although they may also be mentioned in other sections, such as the introduction or methods. The specific limitations that are discussed will depend on the nature of the study, the research question being investigated, and the data that was collected.

Examples of limitations that might be discussed in research include sample size limitations, data collection methods, the validity and reliability of measures used, and potential biases or confounding factors that could have affected the results. It is important to note that limitations should not be used as a justification for poor research design or methodology, but rather as a way to enhance the understanding and interpretation of the study’s findings.

Importance of Limitations in Research

Here are some reasons why limitations are important in research:

  • Enhances the credibility of research: Limitations highlight the potential weaknesses and threats to validity, which helps readers to understand the scope and boundaries of the study. This improves the credibility of research by acknowledging its limitations and providing a clear picture of what can and cannot be concluded from the study.
  • Facilitates replication: By highlighting the limitations, researchers can provide detailed information about the study’s methodology, data collection, and analysis. This information helps other researchers to replicate the study and test the validity of the findings, which enhances the reliability of research.
  • Guides future research : Limitations provide insights into areas for future research by identifying gaps or areas that require further investigation. This can help researchers to design more comprehensive and effective studies that build on existing knowledge.
  • Provides a balanced view: Limitations help to provide a balanced view of the research by highlighting both strengths and weaknesses. This ensures that readers have a clear understanding of the study’s limitations and can make informed decisions about the generalizability and applicability of the findings.

Advantages of Limitations in Research

Here are some potential advantages of limitations in research:

  • Focus : Limitations can help researchers focus their study on a specific area or population, which can make the research more relevant and useful.
  • Realism : Limitations can make a study more realistic by reflecting the practical constraints and challenges of conducting research in the real world.
  • Innovation : Limitations can spur researchers to be more innovative and creative in their research design and methodology, as they search for ways to work around the limitations.
  • Rigor : Limitations can actually increase the rigor and credibility of a study, as researchers are forced to carefully consider the potential sources of bias and error, and address them to the best of their abilities.
  • Generalizability : Limitations can actually improve the generalizability of a study by ensuring that it is not overly focused on a specific sample or situation, and that the results can be applied more broadly.

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What are the limitations of case studies?

Case studies are in-depth analyses of a particular person, group, circumstance, or civilization. Data is frequently obtained from several sources and in various methods (e.g. observations & interviews). The patient’s medical history or personal case study is where the case study research methodology started, and case studies frequently look into one person in their investigations.

The content is mostly biographical and pertains to noteworthy events in the person’s past (i.e., retrospective) and current events in their day-to-day lives. The case study is not a research method in and of itself; rather, researchers select methods for data collection and analysis that will result in case study-worthy data.

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Limitations of Case Studies

  • There is insufficient scientific rigour and no basis for extending findings to a broader population.
  • The researchers could inject their personal opinions into the case study (researcher bias).
  • It is challenging to repeat.
  • It’s expensive and time-consuming.
  • The amount of analysis done with the instruments was constrained by the data and the time limits imposed.

It is hard to determine whether a case study represents a larger body of “similar” events because it only examines one individual, event, or group. As a result, the findings drawn in one instance might not apply in another. Since case studies are based on qualitative (descriptive) data, the psychologist’s interpretation is essential.

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What the Case Study Method Really Teaches

  • Nitin Nohria

a case study limitations

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

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

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

  • Nitin Nohria is the George F. Baker Jr. Professor at Harvard Business School and the former dean of HBS.

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What Is a Case Study?

Weighing the pros and cons of this method of research

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

a case study limitations

Cara Lustik is a fact-checker and copywriter.

a case study limitations

Verywell / Colleen Tighe

  • Pros and Cons

What Types of Case Studies Are Out There?

Where do you find data for a case study, how do i write a psychology case study.

A case study is an in-depth study of one person, group, or event. In a case study, nearly every aspect of the subject's life and history is analyzed to seek patterns and causes of behavior. Case studies can be used in many different fields, including psychology, medicine, education, anthropology, political science, and social work.

The point of a case study is to learn as much as possible about an individual or group so that the information can be generalized to many others. Unfortunately, case studies tend to be highly subjective, and it is sometimes difficult to generalize results to a larger population.

While case studies focus on a single individual or group, they follow a format similar to other types of psychology writing. If you are writing a case study, we got you—here are some rules of APA format to reference.  

At a Glance

A case study, or an in-depth study of a person, group, or event, can be a useful research tool when used wisely. In many cases, case studies are best used in situations where it would be difficult or impossible for you to conduct an experiment. They are helpful for looking at unique situations and allow researchers to gather a lot of˜ information about a specific individual or group of people. However, it's important to be cautious of any bias we draw from them as they are highly subjective.

What Are the Benefits and Limitations of Case Studies?

A case study can have its strengths and weaknesses. Researchers must consider these pros and cons before deciding if this type of study is appropriate for their needs.

One of the greatest advantages of a case study is that it allows researchers to investigate things that are often difficult or impossible to replicate in a lab. Some other benefits of a case study:

  • Allows researchers to capture information on the 'how,' 'what,' and 'why,' of something that's implemented
  • Gives researchers the chance to collect information on why one strategy might be chosen over another
  • Permits researchers to develop hypotheses that can be explored in experimental research

On the other hand, a case study can have some drawbacks:

  • It cannot necessarily be generalized to the larger population
  • Cannot demonstrate cause and effect
  • It may not be scientifically rigorous
  • It can lead to bias

Researchers may choose to perform a case study if they want to explore a unique or recently discovered phenomenon. Through their insights, researchers develop additional ideas and study questions that might be explored in future studies.

It's important to remember that the insights from case studies cannot be used to determine cause-and-effect relationships between variables. However, case studies may be used to develop hypotheses that can then be addressed in experimental research.

Case Study Examples

There have been a number of notable case studies in the history of psychology. Much of  Freud's work and theories were developed through individual case studies. Some great examples of case studies in psychology include:

  • Anna O : Anna O. was a pseudonym of a woman named Bertha Pappenheim, a patient of a physician named Josef Breuer. While she was never a patient of Freud's, Freud and Breuer discussed her case extensively. The woman was experiencing symptoms of a condition that was then known as hysteria and found that talking about her problems helped relieve her symptoms. Her case played an important part in the development of talk therapy as an approach to mental health treatment.
  • Phineas Gage : Phineas Gage was a railroad employee who experienced a terrible accident in which an explosion sent a metal rod through his skull, damaging important portions of his brain. Gage recovered from his accident but was left with serious changes in both personality and behavior.
  • Genie : Genie was a young girl subjected to horrific abuse and isolation. The case study of Genie allowed researchers to study whether language learning was possible, even after missing critical periods for language development. Her case also served as an example of how scientific research may interfere with treatment and lead to further abuse of vulnerable individuals.

Such cases demonstrate how case research can be used to study things that researchers could not replicate in experimental settings. In Genie's case, her horrific abuse denied her the opportunity to learn a language at critical points in her development.

This is clearly not something researchers could ethically replicate, but conducting a case study on Genie allowed researchers to study phenomena that are otherwise impossible to reproduce.

There are a few different types of case studies that psychologists and other researchers might use:

  • Collective case studies : These involve studying a group of individuals. Researchers might study a group of people in a certain setting or look at an entire community. For example, psychologists might explore how access to resources in a community has affected the collective mental well-being of those who live there.
  • Descriptive case studies : These involve starting with a descriptive theory. The subjects are then observed, and the information gathered is compared to the pre-existing theory.
  • Explanatory case studies : These   are often used to do causal investigations. In other words, researchers are interested in looking at factors that may have caused certain things to occur.
  • Exploratory case studies : These are sometimes used as a prelude to further, more in-depth research. This allows researchers to gather more information before developing their research questions and hypotheses .
  • Instrumental case studies : These occur when the individual or group allows researchers to understand more than what is initially obvious to observers.
  • Intrinsic case studies : This type of case study is when the researcher has a personal interest in the case. Jean Piaget's observations of his own children are good examples of how an intrinsic case study can contribute to the development of a psychological theory.

The three main case study types often used are intrinsic, instrumental, and collective. Intrinsic case studies are useful for learning about unique cases. Instrumental case studies help look at an individual to learn more about a broader issue. A collective case study can be useful for looking at several cases simultaneously.

The type of case study that psychology researchers use depends on the unique characteristics of the situation and the case itself.

There are a number of different sources and methods that researchers can use to gather information about an individual or group. Six major sources that have been identified by researchers are:

  • Archival records : Census records, survey records, and name lists are examples of archival records.
  • Direct observation : This strategy involves observing the subject, often in a natural setting . While an individual observer is sometimes used, it is more common to utilize a group of observers.
  • Documents : Letters, newspaper articles, administrative records, etc., are the types of documents often used as sources.
  • Interviews : Interviews are one of the most important methods for gathering information in case studies. An interview can involve structured survey questions or more open-ended questions.
  • Participant observation : When the researcher serves as a participant in events and observes the actions and outcomes, it is called participant observation.
  • Physical artifacts : Tools, objects, instruments, and other artifacts are often observed during a direct observation of the subject.

If you have been directed to write a case study for a psychology course, be sure to check with your instructor for any specific guidelines you need to follow. If you are writing your case study for a professional publication, check with the publisher for their specific guidelines for submitting a case study.

Here is a general outline of what should be included in a case study.

Section 1: A Case History

This section will have the following structure and content:

Background information : The first section of your paper will present your client's background. Include factors such as age, gender, work, health status, family mental health history, family and social relationships, drug and alcohol history, life difficulties, goals, and coping skills and weaknesses.

Description of the presenting problem : In the next section of your case study, you will describe the problem or symptoms that the client presented with.

Describe any physical, emotional, or sensory symptoms reported by the client. Thoughts, feelings, and perceptions related to the symptoms should also be noted. Any screening or diagnostic assessments that are used should also be described in detail and all scores reported.

Your diagnosis : Provide your diagnosis and give the appropriate Diagnostic and Statistical Manual code. Explain how you reached your diagnosis, how the client's symptoms fit the diagnostic criteria for the disorder(s), or any possible difficulties in reaching a diagnosis.

Section 2: Treatment Plan

This portion of the paper will address the chosen treatment for the condition. This might also include the theoretical basis for the chosen treatment or any other evidence that might exist to support why this approach was chosen.

  • Cognitive behavioral approach : Explain how a cognitive behavioral therapist would approach treatment. Offer background information on cognitive behavioral therapy and describe the treatment sessions, client response, and outcome of this type of treatment. Make note of any difficulties or successes encountered by your client during treatment.
  • Humanistic approach : Describe a humanistic approach that could be used to treat your client, such as client-centered therapy . Provide information on the type of treatment you chose, the client's reaction to the treatment, and the end result of this approach. Explain why the treatment was successful or unsuccessful.
  • Psychoanalytic approach : Describe how a psychoanalytic therapist would view the client's problem. Provide some background on the psychoanalytic approach and cite relevant references. Explain how psychoanalytic therapy would be used to treat the client, how the client would respond to therapy, and the effectiveness of this treatment approach.
  • Pharmacological approach : If treatment primarily involves the use of medications, explain which medications were used and why. Provide background on the effectiveness of these medications and how monotherapy may compare with an approach that combines medications with therapy or other treatments.

This section of a case study should also include information about the treatment goals, process, and outcomes.

When you are writing a case study, you should also include a section where you discuss the case study itself, including the strengths and limitiations of the study. You should note how the findings of your case study might support previous research. 

In your discussion section, you should also describe some of the implications of your case study. What ideas or findings might require further exploration? How might researchers go about exploring some of these questions in additional studies?

Need More Tips?

Here are a few additional pointers to keep in mind when formatting your case study:

  • Never refer to the subject of your case study as "the client." Instead, use their name or a pseudonym.
  • Read examples of case studies to gain an idea about the style and format.
  • Remember to use APA format when citing references .

Crowe S, Cresswell K, Robertson A, Huby G, Avery A, Sheikh A. The case study approach .  BMC Med Res Methodol . 2011;11:100.

Crowe S, Cresswell K, Robertson A, Huby G, Avery A, Sheikh A. The case study approach . BMC Med Res Methodol . 2011 Jun 27;11:100. doi:10.1186/1471-2288-11-100

Gagnon, Yves-Chantal.  The Case Study as Research Method: A Practical Handbook . Canada, Chicago Review Press Incorporated DBA Independent Pub Group, 2010.

Yin, Robert K. Case Study Research and Applications: Design and Methods . United States, SAGE Publications, 2017.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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  • Published: 18 January 2024

From interaction networks to interfaces, scanning intrinsically disordered regions using AlphaFold2

  • Hélène Bret 1 ,
  • Jinmei Gao 1 ,
  • Diego Javier Zea 1 ,
  • Jessica Andreani   ORCID: orcid.org/0000-0003-4435-9093 1 &
  • Raphaël Guerois   ORCID: orcid.org/0000-0001-5294-2858 1  

Nature Communications volume  15 , Article number:  597 ( 2024 ) Cite this article

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  • Protein–protein interaction networks
  • Protein structure predictions

The revolution brought about by AlphaFold2 opens promising perspectives to unravel the complexity of protein-protein interaction networks. The analysis of interaction networks obtained from proteomics experiments does not systematically provide the delimitations of the interaction regions. This is of particular concern in the case of interactions mediated by intrinsically disordered regions, in which the interaction site is generally small. Using a dataset of protein-peptide complexes involving intrinsically disordered regions that are non-redundant with the structures used in AlphaFold2 training, we show that when using the full sequences of the proteins, AlphaFold2-Multimer only achieves 40% success rate in identifying the correct site and structure of the interface. By delineating the interaction region into fragments of decreasing size and combining different strategies for integrating evolutionary information, we manage to raise this success rate up to 90%. We obtain similar success rates using a much larger dataset of protein complexes taken from the ELM database. Beyond the correct identification of the interaction site, our study also explores specificity issues. We show the advantages and limitations of using the AlphaFold2 confidence score to discriminate between alternative binding partners, a task that can be particularly challenging in the case of small interaction motifs.

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Protein interactions are crucial for a vast number of processes in living organisms. Strong evidence points to the biological importance of interactions mediated by intrinsically disordered protein regions (IDRs), such as short linear motifs, in particular for regulation, transport and signaling, and in a number of human pathologies 1 , 2 , 3 . Established resources exist to identify already annotated binding motifs, such as the Eukaryotic Linear Motif (ELM) repository 4 , to visualize evolutionary properties 5 and to screen full protein sequences for disordered stretches that might fold upon binding, as with the IUPred server 6 .

Protein interactions are connected within complex networks called interactomes, which can be derived from large amounts of experimental data such as proteomics. Much effort has been invested into mapping and modeling interactions at the scale of these interactomes 7 , 8 . In these networks, most protein-protein interactions evolve under negative selection to maintain function and many of them can rewire 9 , although at different evolutionary rates: stable protein complexes evolve more slowly than most domain-motif interactions 10 . Interactions in evolutionarily old, housekeeping protein complexes are conserved across different contexts (cell types, tissues and conditions) while evolutionarily young interactions and those mediated by disordered regions are more versatile 11 , 12 . Evolutionary conservation has long been recognized as relevant to detect binding motifs in disordered regions, as reviewed in 13 ; however, the quality of the multiple sequence alignment (MSA) used for detection is particularly crucial 14 .

AlphaFold2 revolutionized structure prediction for single proteins by leveraging deep learning approaches to extract signal from MSAs and output protein atomic 3D coordinates in an end-to-end manner 15 . AlphaFold2 structure predictions for the entire human proteome 16 hinted that low prediction quality could pinpoint regions likely to be intrinsically disordered. Subsequent studies confirmed that AlphaFold2, although trained only on single proteins with a folded structure, can be used as an intrinsic disorder predictor by repurposing low-confidence residue predictions 17 , 18 , 19 . AlphaFold2 low-confidence predictions on protein surfaces might also be indicative of possible binding regions 20 , 21 .

Very soon after its release, AlphaFold2 was also tested for its capacity to predict protein-protein interactions. Despite not being designed for this purpose, AlphaFold2 outperformed traditional methods for the structural prediction of complexes between globular protein domains, in terms of both success rate and model quality 22 , 23 , 24 , 25 , 26 , 27 , 28 . AlphaFold-Multimer, specifically retrained on protein complexes, displayed improved performance for interface modeling over the original AlphaFold2 22 , 23 , 29 . At a wider scale, a systematic exploration of the yeast interactome used prefiltering with a fast version of RoseTTAFold 30 followed by AlphaFold2 structure prediction 31 . This opened exciting perspectives for the use of AlphaFold2 not only for complex structure prediction, but also as an in silico screening tool for interactions, as recently illustrated by the discovery of DONSON’s role in replication initiation 32 .

AlphaFold2 predictions are sensitive to the input parameters, input MSA and protein delimitations. For instance, the quality of structural models could be significantly improved using the AFsample strategy, relying on the massive generation of up to 6000 models using different sampling schemes 33 . AlphaFold2 can also be made to predict alternative conformational states for some proteins through manipulation of the MSA 34 either by subsampling 35 or by in silico mutagenesis 36 . For complexes, the generation of a paired MSA, where species are matched between homologs of the different protein partners, was not found to be necessary for AlphaFold2 to pick up interaction signal 22 , 26 , although combining unpaired and paired MSAs gave the best results 22 . The AlphaPulldown package allows users to select or screen protein fragments for modeling protein complexes, since some interactions cannot be predicted if the full-length sequences are provided to AlphaFold2 37 .

Interactions mediated by short peptides within disordered protein regions are quite specific and thus require extra care for handling by AlphaFold2. Indeed, conformational versatility is even higher and covariation signal is weaker than for globular complexes 38 . Traditional tools to predict protein-peptide interactions include mostly docking approaches, recently reviewed in 39 , 40 ; some of these also make use of evolutionary information 13 . Several recent studies have addressed the ability of AlphaFold2 to predict protein-peptide complexes. An early implementation already showed interesting predictive capacity, including in cases where the peptide induces a large conformational change of the protein and docking therefore most likely fails, and without the need for a peptide MSA 41 . InterPepScore 42 , a graph neural network used to score protein-peptide complexes for improving Rosetta FlexPepDock refinement 43 , was also found beneficial to refine AlphaFold-Multimer models. AlphaFold-Multimer performs better than AlphaFold2 at protein-peptide complex prediction 44 , and sampling a larger part of the conformational space by enforcing dropout at inference time in AlphaFold-Multimer further increased the quality of protein-peptide complex models 45 . Finally, the importance of choosing the right delimitations to optimize the sensitivity of the AlphaFold2 predictions has recently been highlighted for a number of protein-protein interactions involving disordered regions 46 .

In the present study, we investigate how best to use AlphaFold2 to make the leap from interaction networks to interfaces when dealing with binding partners containing intrinsically disordered regions (Fig.  1a ). We carefully develop an unbiased benchmark of 42 protein-peptide complexes sharing no similarity with any complex from the AlphaFold-Multimer training dataset and assess the performance of AlphaFold-Multimer on this dataset using different MSA schemes. We show that performance is limited when full-length protein sequences are used as input and considering delimited fragments increases the success rate. We set the fragment size at 100 amino acids in order to scan potential interacting regions within full-length sequences such as those derived from large-scale interactome data. The fragment scanning approach on the 42 protein ligands reveals that in 89% of the cases the fragment with highest ipTM score matches the region containing the correct binding site. Once the correct delimitations are identified, we show a synergistic effect of combining different MSA schemes and scores, reaching more than 90.5% success rate on our benchmark dataset. We also observed this synergy when using a larger dataset of 923 protein-peptide interactions extracted from the ELM database. Finally, our study also raises the issue of prediction specificity, which may require the enumeration and ranking of potential anchoring sites, and assesses the usefulness of AlphaFold confidence scores in discriminating between possible binding regions.

figure 1

a Disentangling the complexity of a protein interaction network (sketched on the left) by analyzing binary interactions between a central gray protein and its blue binding partners can be complicated in case they contain intrinsically disordered regions. b General pipeline to select the PDB entries that can be used as test complexes from those released after May 2018. They were required to share no sequence or structural redundancy with any of the complex structures that were used for AlphaFold2-Multimer training. c Example illustrating filters used to assess the lack of redundancy between the candidate complex and structures published before May 2018. Two filters were used, one based on sequence identity using a 30% seq. id. threshold and a second retrieving all complexes involving a receptor homolog using PPI3D 57 and checking for lack of interface structural similarity using MM-align 59 . d Boxplots showing the cumulative size distribution of the 42 inputs (receptor+ligand) that were processed by AlphaFold2, either in protocols where sequences were delineated following the boundaries of the experimental structures or in those where full lengths of ligands and/or receptors were used. In the boxplot representation center line is the median, min and max limits of the box are the lower and upper quartiles, whiskers are the 1.5x interquartile range and points represent outliers. Source data are provided as a Source Data file.

Selecting a test dataset of complexes not redundant with the training set of AF2-Multimer

To assess the performance of AlphaFold2 (AF2) in predicting the mode of association between a protein (hereafter called the receptor) and a small binding motif within a structurally disordered partner (the ligand), it is important to study cases of complexes that do not have homologs in the database on which AlphaFold2 has been trained. An example of how AF2 models may be biased by existing structures in the PDB is illustrated in Supplementary Fig.  1a . AF2-Multimer was trained on structures released until 30 Apr 2018. An analysis of the structures released after that date revealed that nearly 2,500 structures of complexes involving small protein motifs had been deposited in the PDB (Fig.  1b ). A large number of these structures have significant similarities in sequence or structure with structures released in the PDB before May 2018. Following a strict treatment of this sequence and structure redundancy (Fig.  1c , see Methods), we isolated a set of 42 complexes involving a receptor and a small peptide ligand that could provide an unbiased estimate of AF2 performance in different conditions (Supplementary Table  1 ). Among the 42 complexes, we observed a diversity of subunit lengths (Fig.  1d ) and a representative occurrence of peptides with sizes ranging from 6 to 39 amino acids (Supplementary Fig.  1b ) that are binding their receptors as helices, strands, coils or combination of those (Supplementary Fig.  1c ).

AF2 relies on multiple sequence alignments whose evolutionary depth on the ligand peptide region may be limited due to the difficulty of identifying homologs from a short IDR sequence. Hence, for each of the proteins in this dataset, we used the full-length sequences of the protein partners to construct MSAs and subsequently delineate the interacting domains (Supplementary Fig.  2 ). These MSAs were combined to generate mixed co-alignments in which partner sequences belonging to the same species were paired while those with a single partner homolog present in a species were added as unpaired, similarly to ref. 47 (see Methods). When the receptor and ligand are considered in their integrality, the overall length of the concatenated sequences is in majority between 1000 and 2000 amino acids, significantly larger than when the size of the inputs is delimited to the boundaries used for structural determination (Fig.  1d ). As a first analysis, we assessed whether AlphaFold2 was able to identify the correct binding site when proteins were considered in their full length. This is typical of a scenario where knowing that two proteins are interacting, we have no initial indication of which regions are involved.

Success rates of AF2-Multimer for full-length and delimited input protein partners

For each run, 25 models were generated with AF2-Multimer parameters, following the reference protocol 29 . The AF2 model confidence score (noted AF2 confidence score below), consisting of an 80:20 linear combination of ipTMscore and pTMscore, was used to rank the models and identify the best model. The accuracy of this best model was used to calculate the overall success rate for the 42 cases using the stringent criteria defined by the CAPRI community to assess the precision of protein-peptide complex models (see Methods and Supplementary Data  1 for detailed scores). With full-length protein partners, we obtained a success rate of 42.9 % (Fig.  2a ), rather low with respect to that reported in the evaluation of AF2-Multimer for protein complexes, which was benchmarked using delimited sequence inputs 29 . Analysis of the quality of the best model as a function of the size of the modeled assembly (Supplementary Figure  3a ) shows that the performance tends to decrease for large sizes above 1600 amino acids although it is still possible to observe good predictions above this size threshold. Below 1500 amino acids, the success rates do not appear correlated with the size of the assembly or the nature of the peptide secondary structure (Supplementary Figure  3a ).

figure 2

Stacked barplots reporting the success rates of AlphaFold2 prediction depending on the types of co-alignment used. All success rates are presented as the percentage of test cases in which the best AF2-Multimer model (best AF2 confidence score) is of Acceptable (light color), Medium (medium color) or High (dark color) quality according to the CAPRI criteria for protein-peptide complexes 49 . a Success rates using (from left to right): full-length partners with a mixed alignment generation mode (gray grades), delimited receptor with full-length ligand with a mixed alignment generation mode (deep purple grades), delimited partners with no evolutionary information for the peptide (cyan grades), delimited partners with unpaired co-alignment (blue grades), delimited partners with mixed alignment (blue grades). b Success rates using (from left to right): delimited partners (blue grades) (same as rightmost bar in panel a ), peptides extended by 100 or 200 amino acids (purple grades), full-length partners (gray grades) (same as leftmost bar in panel a ). Source data are provided as a Source Data file.

Next, the sequences of each binding partner were delimited according to their boundaries as observed in the experimental structure of the complex (Supplementary Fig.  2 ). When both the receptor and ligand were delimited, the overall success rate was much higher, reaching 78.6% of the 42 complexes correctly predicted (Fig.  2a ). In these first tests, the evolutionary information was integrated using the mixed co-alignment mode described above. We also tested alignment conditions in which the co-MSA is constructed from the same sequences but concatenated as an unpaired alignment (without matching homologous sequences between partners). In this unpaired mode (Supplementary Fig.  2 ), the success rate remained similar at 78.6% (Fig.  2a ), suggesting that homolog matching in the paired alignment does not provide a major gain. We assessed a third prediction mode in which no evolutionary information is added in the peptide region, as performed in refs. 41 , 45 (Supplementary Fig.  2 ). With this third approach, the performance obtained without evolutionary information on the peptide side remains high, with 71.4% of correct models for the 42 cases (Fig.  2a ).

Such a good performance in the absence of any alignment associated with the peptide confirms that the properties of the binding site in the receptor are often sufficient to guide the interaction mode of the peptide 41 , 45 . Consistently, in a situation where the receptor is delimited but the ligand is considered in its full-length sequence, the performance drops back to a lower level of 52.4%, even when using the MSA information on the ligand side (Fig.  2a , Supplementary Fig.  3b ). Hence, one of the difficulties encountered by AF2 in dealing with large IDR-containing proteins lies in its ability to identify the correct interaction region within the partner protein.

The success rates calculated above were obtained by selecting only the model with highest AF2 confidence score among the 25 sampled models. Considering the entire set of 75 models (25 models for every complex in the three alignment conditions: mixed, unpaired, no_ali) highlights a significant Pearson’s correlation of 0.84 between the AF2 confidence score and the DockQ score, a commonly used metrics to rate the accuracy of modeled interfaces with respect to the reference complex 48 (Fig.  3a ). Grouping the models according to their CAPRI quality ranks (Acceptable/Medium/High) (Fig.  3b ) using the stringent criteria used for protein-peptide complexes 49 (see Methods) highlights that above an AF2 confidence score of 0.65, the predicted models are most often correct. There is also a minority of cases with a score between 0.4 and 0.65 that are found correct (in the Acceptable category) indicating that this twilight zone may be interesting to investigate if no alternative solution has been detected. In any case, the graphs on Fig.  3 a, b confirm that the AF2 confidence score (see Methods) can be used as a reliable proxy for estimating the reliability of a protein-peptide interaction prediction.

figure 3

a Distribution of DockQ scores 48 for 75 models for every binary protein-peptide complex (25 models in the three alignment conditions: mixed, unpaired, no_ali) as a function of the AF2 model confidence score. Data points are colored according to the model quality as rated by the DockQ score from low (white) to high (dark gray) values. Pearson’s correlation is 0.84. b Boxplots of the AF2 confidence score value distributions for the same set of models, split by model quality category according to the CAPRI protein-peptide criteria: High (sample size n  = 128, dark gray), Medium ( n  = 332, medium gray), Acceptable ( n  = 106, light gray), Incorrect ( n  = 484, white). In the boxplot representation center line is the median, min and max limits of the box are the lower and upper quartiles, whiskers are the 1.5x interquartile range and points represent outliers. Source data are provided as a Source Data file.

Success rates of AF2-Multimer considering protein fragments of increasing size

When searching for an interaction site between two proteins, the region involved in the interaction is usually not known precisely. In order to use AF2 to carry out this task, and given the lower performance of AF2 with full-length proteins, we explored how AF2 predictions would be impacted by queries in which the bound motif is not perfectly delineated and is embedded in a larger fragment that may contain 100 or 200 additional amino acids. Extending the sequence containing the binding motif of each complex with up to 100 or 200 amino acids, and delimiting the alignments constructed in a mixed alignment mode (Supplementary Fig.  2 ), we obtained a decrease by 2.4 to 11.9 points with success rates of 76.2% and 66.7%, respectively for fragment size 100 and 200 (Fig.  2b ). The success rate of 66.7%, obtained for cases where the fragment extends the peptide motif by 200 amino acids, is substantially higher than the 42.9% obtained with full-length proteins. This result underscores the interest of fragment-based searching to identify potential interaction motifs between two partners and to predict their recognition mode. Previously (Fig.  2a ), we showed that the lack of evolution for the peptide was not very detrimental for a significant number of correct predictions (71.4%). This trend is less pronounced when using fragments extended by 100 or 200 amino acids as shown in Supplementary Fig.  4a . Without ligand alignment, there is a loss of performance of more than 20 points, which highlights the importance of associating evolutionary information when the binding site identification involves a systematic search within larger fragments. For fragments of length 200, without evolutionary information for the ligand, the success rate is 45.2%, almost as low as the success rates obtained for full-length proteins with evolution.

Success rates of AF2-Multimer when scanning a binding partner with overlapping fragments of fixed size

The success rates obtained with extended fragments (Fig.  2b ) prompted us to assess whether AF2-Multimer would be suitable for screening and ranking different overlapping fragments along the sequence of a binding partner. To do this, we considered each of the 42 pairs of binding partners in the benchmark dataset, delineated the receptor binding domain and generated models of the complex between this receptor domain and every fragment of the ligand protein of size 100 amino acids with overlaps of 30 amino acids between fragments (Fig.  4a ). The models were ranked according to their ipTM scores (Fig.  4b ) and discriminated based on the overlap of the fragment with the binding site motif.

figure 4

a Protocol used to screen the complete ligand sequence of a binding partner by analyzing all 100 amino acid long fragments against the receptor delimited by the length of its interaction domain. The fragment overlapping the correct binding site shown in orange is colored red while the other fragments are blue. b The predicted ipTM score is used to rank the different fragments and evaluate those that overlap or not with the correct binding site. c Scatter plot showing the highest ipTM score for the model containing a fragment overlapping the correct binding site compared to the highest ipTM score among models with no overlap. 35 points are represented and not 42 since 7 ligands have less than 100 amino acids. d Detailed distribution of ipTM scores for the 42 PDB cases of the benchmark with the fragment overlapping the correct binding site as a red diamond and the non-overlapping ones as blue diamonds. If two fragments overlap with the binding site, only the model with highest ipTM score is represented in panels c and d . Source data are provided as a Source Data file.

For 35 cases in which the ligand protein was larger than 100 amino acids, the fragment with the highest ipTM score overlapped with the correct region of binding in 31 cases (89 % success rate) (Fig.  4c and Supplementary Data  2 ). Four cases were incorrectly predicted, i.e. one fragment not overlapping the binding site had the highest ipTM score. In three of these four cases, the correct models had low ipTM scores. However, the fourth case, 39_7O6N, was incorrectly predicted despite the correct model having an ipTM score of 0.646. In PDB 7O6N, the receptor is in the form of a dimer. Since its ligand binds as a helix away from the dimer interface, the receptor was modeled as a monomer in the benchmark (Supplementary Fig.  5a ). However, the surface of the receptor involved in homodimer formation tends to create an interaction surface that traps the 100 amino acid long fragments and generates ipTM scores higher than that of the correct interface (Supplementary Fig.  5 d, e). When the ligand was delimited as in the PDB, AF2-multimer managed to predict the correct interface even with the receptor as a monomer (Supplementary Fig.  5 b, c). This example highlights the importance of modeling the receptors with as many permanent binding partners as possible (either as homomers or heteromers) to prevent large hydrophobic surfaces from misleading AF2-multimer predictions with extended fragments containing short linear binding motifs.

Advantage of combining different alignment modes

The performance obtained using different alignment modes and input lengths suggests that some complexes can be correctly predicted regardless of the protocol used, while others may be sensitive to these input conditions. Overall, for 35.7% (15 complexes), a correct model could be ranked first using the AF2 confidence score with any of the input conditions, even using full-length alignments (Supplementary Fig.  6 ). In contrast, other complexes could only be predicted correctly with a limited set of conditions (Fig.  5a ), suggesting a potential interest for combining different strategies. Instead of considering 25 models generated with every protocol, we analyzed a pool of 100 models generated with four different protocols and ranked them according to the highest AF2 confidence score. The resulting success rate improves significantly, rising up to 90.5% (Fig.  5b ). The AF2 model confidence score is sufficiently correlated with the accuracy of the models that it can be used to identify correct assemblies in much larger model sets. We verified that sampling 100 models rather than 25 did not change the success rates of single protocols, whereas generating 100 models through a combination of four protocols increased the success rate by almost 12 points, highlighting the value of increasing the sampling by varying the properties of multiple sequence alignments (Supplementary Fig.  4b ).

figure 5

a UpSet diagram 69 displaying the number of successful cases (out of 42) found by either a single or several prediction mode(s) among the following: delimited peptide with no peptide multiple sequence alignment (MSA), peptide extended by 100 residues with a mixed MSA, delimited peptide with a mixed MSA, delimited peptide with an unpaired MSA. 5 cases that can be identified in none of these conditions are highlighted in red. b Success rates for the four protocols shown in panel a (values are the same as in Fig.  2 ) and for a combined protocol taking the best AlphaFold2 (AF2) confidence score value out of 100 models (25 for each condition) (green grades). Source data are provided as a Source Data file.

In an attempt to interpret the failures and successes of the tested protocols, we performed a detailed analysis of complexes that specifically succeeded with only a subset of the protocols and those that did not succeed with any. A typical case is when the conformation of the bound peptide is best predicted in the absence of evolutionary information. The absence of evolutionary information was found to be favorable for complexes such as 6ICV or 6YN0 that were not correctly predicted when MSA was added to the peptide. In the case of 6ICV (Fig.  6a ), the peptide (blue) is predicted to adopt a helix-and-turn conformation with high confidence when the evolutionary information of the MSA is included. This local structure is incompatible with the extended bound conformation. In contrast, in the absence of evolutionary information, the predicted structure of the peptide (light blue) is in very good agreement with the experimental structure (red), suggesting that the geometric constraints have been relaxed sufficiently for the peptide to sample an extended geometry that was well evaluated by the AF2 confidence score.

figure 6

The receptor is represented as a gray surface, the native ligand as a red cartoon, the predicted peptides in shades of blue: bright blue for the predictions in mixed multiple sequence alignment (MSA) mode, and light blue for the prediction with no peptide MSA ( a ) or for the peptide within the prediction of a fragment extended by 200 residues in dark blue ( b–d ). PDB identifiers of represented cases are 6ICV ( a ), 7F2D ( b ), 6ZW0 ( c ) and 6JLH ( d ).

Other differences between the tested protocols could be observed in case a motif, well predicted in a short fragment, was not correctly predicted in longer ones. This is observed in 5 cases including PDB cases 7F2D, 6ZW0 and 6JLH illustrated in Fig.  6 b–d, respectively. For these systems, considering the ligand peptide in the context of a larger fragment with 200 additional amino-acids (dark blue models) never led to a correct prediction by AF2, while the delimited peptides (blue) were always modeled in good agreement with the experimental reference structure (red). In almost all of these complexes, the origin of the failure in the larger fragments seems to be due to the presence of intramolecular contacts involving the peptide and surrounding regions. In the case of 7F2D and 6ZW0, the peptide is located in the vicinity of a globular domain with which it forms contacts of relatively low confidence. However, these appear to be sufficient to interfere with the generation of the native complex. In the third case, 6JLH, the binding peptide is embedded in a longer coiled-coil that masks the surface found to bind the receptor experimentally. This prediction would be consistent with the experimental study that showed the interaction to be observed only in specific physiological contexts 50 . This example together with another case also involving long coiled-coils (7MU2) highlights the value of exploring different fragment lengths to reveal the appropriate binding epitopes. Therefore, in the cases where prediction performance varies between alignment content and delineation protocols, a common explanation is that the binding motif may be trapped or masked in a conformational state that prevents prediction of its correct binding mode.

Four cases out of 42 failed, regardless of the alignment protocol. In one of these cases (PDB: 7CZM), the receptor itself was not quite well folded, which may have made it difficult to sample a correct binding mode. For one case (PDB: 6A30) where none of the tested protocols converged to a correct model, we tested whether reducing the size of the receptor itself would help. We reran this case with the same alignments, testing if reduction in the size of the receptor could have an impact. Splitting the receptor as two inputs of similar size led to the generation of a correct model with a high AF2 confidence score with one of those inputs, reaching 0.8 when using the protocol with no peptide alignment but below 0.5 with all the other protocols. With this additional complex, the percentage of cases that could be predicted using AF2 rises above 92%. Hence, there is room for further improvement by sampling simple alterations of the input MSAs and using the AF2 model confidence score as a guide for identification of the correct protocol.

Specificity for similar binding motifs recognized by receptors

Out of the 42 cases in the test set, AF2 is able to correctly predict the binding mode of a peptide to its receptor without any evolutionary information for the peptide in 71.4% of the cases. Such a performance suggests that the structural and evolutionary properties of the receptor match well with the peptide sequence irrespective of its conservation pattern. This calls into question the ability of AF2 to distinguish cognate binding peptides from non-binding ones. This issue may be particularly difficult in the challenging cases where two short fragments embedded in long disordered regions of different binding partners need to be discriminated while they tend to adopt a similar local conformation. To address this issue, we distinguished different classes of complexes based on the secondary structure adopted by the peptide in its bound conformation in order to create 83 challenging cross-partners predictions between 23 receptors and cognate or non-cognate ligands selected from the 42 cases of our test set (Supplementary Table  2 ). We then assessed whether AF2 could specifically predict the binding mode of receptors with their respective peptides and distinguish them from potentially misleading peptides taken from unrelated structures but sharing similar bound conformations.

In total, 7 categories of peptide conformations were considered (Supplementary Fig.  7 ). We distinguished those binding through a small, medium, or long helix, those showing no canonical secondary structure and those binding through the formation of a combination of helix and strand or a single or two beta-strands (Supplementary Fig.  7 a–g). To run the cross-partners interaction tests, we used the protocol with no MSA in the peptide region. Over the 23 selected cases for cross-partners analysis, 16 were successfully predicted by AF2 (70%) in agreement with the performance obtained with this protocol on the 42 test cases. In Fig.  7a , the distribution of AF2 confidence scores obtained for the models rated as correct using the CAPRI protein-peptide criteria (darker blue distribution) differs significantly from the distribution of the scores obtained with non-native peptide ligands (light blue distribution). The AF2 confidence score of the specific peptide was superior to any of the non-specific peptides in 15 out of the 16 correctly predicted complexes. Figure  7b illustrates one of these 15 cases, using the receptor of 7CFC, highlighting that even if the non-specific peptides tend to interact in the same region as the specific one, the AF2 confidence score is higher for the specific peptide (reaching 0.75) and can be used as a proxy to discriminate between several likely binders.

figure 7

a Distribution of AF2 confidence scores obtained for the models involving the native peptide and rated as correct using the CAPRI protein-peptide criteria (darker blue distribution) for 16 out of 23 cases selected for cross-partners evaluation and for the models obtained with non-native peptide ligands (light blue distribution). Cross-partners predictions were performed using delimited partners with no evolutionary information for the peptide. Specific predictions illustrated in panels b – d are drawn from the relevant distributions at the indicated score values. Illustration of specific cases discussed in the text for native PDB identifiers: 7CFC ( b ), 6IDX ( c ), 6J0W ( d ). The receptor is shown as a gray surface, the native peptide as a red cartoon, the best predicted model involving the native peptide in bright blue cartoon and the best predicted models involving non-native peptides in light blue. AF2sc values indicate the best AF2 confidence score values obtained for each complex.

Based on the distribution in Fig.  7a , a minority of models (approximately 10%) would give a misleading assignment for AF2 confidence scores greater than 0.6 and could prevent identification of a correct binding site. This is illustrated by the case of the 6IDX complex in Fig.  7c in which an incorrect binder (6KPB Ligand) is predicted to form a complex with the receptor with high confidence (as indicated by an AF2 confidence score of 0.83) while the specific ligand was not correctly predicted (AF2 confidence score = 0.46 and wrong binding mode).

Last, there are also alternative situations as illustrated in Fig.  7d in which the score of the specific binder is mild (below 0.6) but still among the highest scores obtained in the set of potential binders. This was observed for 3 of the 16 cases where the specific receptor-ligand pair was correctly predicted by AF2 (6YN0 in Supplementary Fig.  7c and 7F2D, 6J0W in Supplementary Fig.  7f ). With the 6J0W receptor (Fig.  7d ), the AF2 confidence score of the specific ligand is 0.53, whereas it reaches 0.54 with another non-specific ligand of 7CZM. Such a situation highlights the specificity issue that may arise in the case where the peptide is not accurately modeled in the receptor binding site. It can be noted in Supplementary Fig.  7f that the misleading 7CZM ligand tends to have higher AF2 confidence scores than the other ligands on average with all the non-specific receptors. Such promiscuity indicates the risk that some sequences may systematically bias the specificity analysis and that normalization or the use of an alternative scoring scheme might be useful to further disentangle the complexity of protein-protein interaction networks involving unstructured regions. For a few specific classes of binding motifs, a recent comparison with experimental data also indicated a lack of specificity for AF2 predictions 51 . However, in the absence of further biophysical experiments, we cannot completely rule out that, for the misleading assignments discussed above, non-specific peptides may indeed exhibit detectable binding to their non-cognate receptors.

Extension to a larger dataset from the Eukaryotic Linear Motif database

In the previous sections, we assessed the value of the fragment scanning strategy using a dataset of 42 receptor/ligand pairs, ensuring that this assessment was unbiased with respect to AF2 training process. One drawback is that this dataset is limited in size, and we wondered whether the performance of the method would be maintained if we used data from the larger Eukaryotic Linear Motif (ELM) database 4 with the risk that the predicted cases would be biased by their similarity to the cases used in the AF2-Multimer parameter training. The ELM database contains a very large number of binding motifs identified in the disordered regions of proteins. These ligands are generally identified based on a consensus sequence motif established by experimental characterization of the interaction specificities of the protein domains specialized in recognition of these linear motifs. For many of the linear motifs listed in the ELM database, an experimental reference is provided to validate the existence of the binding motif. We extracted a list of 1884 receptor/ligand pairs with defined delimitations from the linear motifs associated with a reference in PubMed on July 3, 2023 (Supplementary Data  3 ). Among these pairs, the subset possessing (i) a unique binding site in the ligand and (ii) a PDB reference (either exact or homologous) contains 923 cases divided into 84 categories of ELM types, corresponding to different families of domains and their associated consensus motifs (Fig.  8a ) (see Methods). The different protocols discussed above for using AF2 were applied to assess AF2’s ability to model the bound motif correctly. To correct for the unbalanced distribution of ELM motifs within the 84 categories, we evaluated the predicted success rates by repeated stratified sampling with 1000 repeats of randomly selecting one ELM motif from each of the 84 categories.

figure 8

a Distribution of the 923 ELM cases that could be modeled and evaluated among 84 ELM categories (ELM types), highlighting the unbalanced distribution, biased towards some receptor/ligand couples such as the LIG_LIR_Gen_1, LIG_Actin_CPI_1 and LIG_Rb_LxCxE_1 categories comprising 132, 62 and 40 cases, respectively. Dark gray bars indicate the cases with an exact reference PDB structure while light gray bars indicate the cases that could be evaluated using a homologous PDB structure (see Methods). b Histogram representing the success rates obtained with six different protocols applied to the dataset extracted from the ELM database. The average success rates are reported with same color codes as in Figs.  2 and 5 but using white hatched bars (full-length ligand delimitation (deep purple), with extension of 100 amino acids (purple), with no alignment (cyan), mixed or unpaired co-alignments (blue), and combined (green)). The average success rates were calculated from a repeated stratified sampling of 1000 iterations over the 84 ELM categories, randomly selecting one ELM complex in each of the 84 categories. At every iteration, success rates were calculated and the mean success rate value is reported in the histogram where the best AlphaFold2 (AF2)-Multimer model (best AF2 confidence score) is of Acceptable (light color), Medium (medium color) or High (dark color) quality according to the CAPRI criteria for protein-peptide complexes 49 . Source data are provided as a Source Data file.

We evaluated six protocols using an exact or a homologous PDB structure to rate model accuracy (see Methods) and the results are presented as a histogram in Fig.  8b and detailed in (Supplementary Data  3 ) (detailed performance with respect to each ELM types are provided in Supplementary Data  4 ).

The first protocol evaluates predictive performance in the case where the ligand is considered to be full-length and the receptor is delimited around the binding domain (see Methods). For all 84 categories, the repeated stratified sampling procedure on the 923 ELM motifs converges to a success rate of 52.7 ± 4%, very similar to the value of 52.4% obtained for the dataset of 42 non-redundant cases (Fig.  2a ). Similar to what was observed for the dataset of 42 non-redundant cases, success rates increase significantly when the size of the fragments used for prediction is restricted around the interaction region. Similar values to those obtained in Fig.  5b are observed, with the best performance being obtained when unpaired or mixed alignments are used (80.7 ± 3.0% and 77.9 ± 3.4%, respectively). Taking the best AF2 confidence score obtained among the four protocols, the success rate increases up to 87.3 ± 2.7% reaching a value close to the 90.5% obtained for the non-redundant dataset. The lower performance of the predictions for the protocol with a fragment size extended to 100 amino acids is discussed on an analysis of 30 representative cases in Supplementary Data  5 : 20% of failures are due to the existence of several nearby consensus binding sites, and 50% belong to specific domain categories such as SH2, integrins or NRP domains, for which the binding motif is very short (3 amino acids). The specific success rates obtained for ELM cases for which the reference structure is an exact PDB structure (differentiated in Supplementary Fig.  8 ) are higher than those for which only a homologous PDB structure is known.

AF2 has shown remarkable performance for predicting the structure of multi-subunit machineries only known so far through PPI maps 27 , 28 , 31 . In this study, we explored the potential of AlphaFold2 to further exploit the wealth of data contained in proteomics experiments and to enable a more comprehensive characterization of protein-protein interaction networks. We focused on interactions mediated by unstructured regions that are a cornerstone of the functional and dynamic organization of most cellular processes. The capacity of AF2 to perform well with small disordered regions binding a structured domain was established on different datasets 18 , 41 , 45 built from the structures available in the PDB 52 . However, in most proteomics experiments, the boundaries of the interacting regions are not precisely known. In addition, physical interactions can be indirect and it is crucial to disentangle the regions involved in direct interactions.

To further assess the use of AF2 for this purpose, we built a dataset of complexes consisting of a folded receptor bound to a short protein fragment and evaluated several protocols representative of challenges faced following proteomics analyses. Because AlphaFold2-multimer was trained on complexes whose structures were published before May 2018, we carefully removed any homologs of the complex to ensure that our conclusions could not be biased by similarities in sequence or 3D geometry with the training dataset. For the 42 test cases selected in the benchmark, we first evaluated the ability of AF2 to discriminate the binding site when proteins are provided in their full length as in the output of a proteomic experiment. We achieved a rather low success rate of 42.9%, in agreement with the observations made in a recent report dealing with another set of interactions involving short linear motifs 46 . We noted that above 1600 amino acids, the method gave poorer predictions, with the exception of two impressive cases above 2500 amino acids. The use of input fragments delimited as in the experimental structures significantly increased performance by more than 35 points and the combination of different MSA construction modes led to an overall success rate of 90.5%. If the binding region is unknown, scanning multiple small peptides can be computationally demanding and we found that a reasonable trade-off in accuracy could be achieved with a fragment length of about 100 amino acids. We show that using a fragment scanning strategy with fragments of 100 amino acids overlapping by 30 amino acids, the correct fragment could be identified in 89% of the tested cases. This result indicates that in most cases, it is unlikely that a wrong competitive binding site may be found in a ligand protein with ipTM scores as high as the cognate binding fragment.

The length of the scanning fragment might need to be reduced if a very short binding motif of about 3 amino acids is expected, as we found in the ELM database analysis that some of these short motifs, especially when formed by polar residues, were more difficult to predict in longer fragments (Supplementary Data  5 ). To increase success rate of the fragment scanning approach, our study also underlines the importance of representing the biological context as closely as possible, taking into account the homomeric and heteromeric assemblies pre-existing the formation of an interaction with a disordered region. A recent in-depth study of the cohesin interaction network using AF2 also led to conclusions along the same lines 53 . When using fragments of size larger than 100, evolutionary information was key to reaching the best results and for larger fragments involving more than 200 amino acids, a decrease in performance was observed which could originate from intramolecular contacts that tend to mask the binding region or hinder the sampling of the bound conformation. Finally, scanning strategies taking into account the location of globular domains to correctly delineate and cut the intrinsically disordered regions have also proven useful in increasing the success rate of predictions 46 .

In the case of delimited peptides, it is remarkable that evolutionary information in the peptide region did not prove to be as crucial as for longer fragments for generating accurate models and scoring them reliably. We found that in specific cases where the bound conformation of the peptide was rather extended, the addition of evolutionary information was even detrimental to the identification of a correct solution. Such a detrimental effect of MSA was also reported in ref. 51 for the structural prediction of complexes between MHC receptors and various sets of short peptides by AF2. In these systems, the local conformation of the bound peptides is also fully extended. Our analysis suggests that the inclusion of the MSA for the disordered short peptide may lock the local conformations of the peptides and prevent them from adopting a different bound conformation. In any case, sampling these different possibilities was considered worthwhile, as the AF2 confidence score is sufficiently reliable to pick out the correct solution among those sampled. To further enhance the chance of generating a correct solution using short delimited peptides as ligands, we also explored how the AFsample strategy would perform on some of the difficult cases found among the 42 cases in our non-redundant dataset (see Methods). Our results highlight a complementarity of the two approaches, where some cases were successful with our combined protocol but not with AFsample, while others were unsuccessful in our combined protocol and solved by AFsample, albeit at a much larger computational cost (Supplementary Data  6a ). A comparative analysis of the two approaches suggests a few guidelines that could be used to further increase success rates: the use of templates, of a combination of multimer_v1 and multimer_v2 parameters and a larger sampling for a given condition (up to 200 models per condition instead of 25). In contrast, on our dataset, using a larger number of recycles as in AFsample was never necessary to obtain successful predictions (using 9 or 21 recycles did not improve success rates). Additionally, if we had stopped sampling after 200 models for each condition instead of 1000 as implemented by default in AFsample, we would have obtained the same best models.

Beyond the remarkable ability of AF2 to generate correct conformations of protein-peptide complexes, we confirmed the reliability of the combined ipTMscore and pTMscore as an estimate of model accuracy. We also evaluated AF2 as a tool to discriminate a native ligand from other ligands potentially difficult to discriminate because adopting the same local conformation among diverse binding partners. The obtained results were satisfactory in a majority of cases where the AF2 confidence score correctly singled out the native binding peptide, but also highlighted several misleading situations that call for vigilance in the exploitation of specificity results. It certainly should be possible to reinforce the applicability of AF2 for the exploitation of more complex interactomes in which the interaction with unstructured regions plays a major role. Recent efforts in that direction have shown that AF2 parameters which were trained only with positive examples could be further fine-tuned for specificity combining positive and negative examples of receptor-peptide interactions 51 . So far, this fine-tuning was achieved in a receptor-specific manner focusing on MHC, PDZ or SH3 domains, but it might be expanded further to address other specificity issues.

The ability of AF2 to discriminate the native peptide from similar alternative binders when the native bound conformation is correctly predicted supports the conclusions that an energetic function of the protein structure may have been learned by AF2 independently of evolutionary information 54 . This ability to discriminate specific native binders is also consistent with the principle of using AF2 to discriminate peptide binders from competitive simulations 55 or for the design of high-affinity binders for their targets 56 . Using the strategy described in 55 could be a way to circumvent some specificity issues. Alternatively, rescoring complex models for different peptides using the updated AF2Rank program may provide complementary discriminative power 54 . Some receptors may also show more promiscuous binding properties than others when assessed from AF2 confidence score as shown in the case of 7CZM. Using a set of representative peptides such as those used in the present study, it may be possible to spot out receptors more prone to interacting non-specifically with various motifs and improve normalization of the confidence score. On the other hand, the fact that with larger fragments (>200), the ability to identify the correct binding site decreases significantly and requires evolutionary information is also in agreement with the proposal that AF2 needs coevolution data to search for global minima in the learned function 54 . To progress from interactomes to the identification of all potential binding sites within disordered regions, a robust strategy will benefit from systematically scanning fragments of sequences of limited length and sampling different types of evolutionary information, such as the four combined in this study.

Building the dataset of protein-peptide complexes non-redundant with the AF2 training structural dataset

An initial list of protein-peptide complexes was retrieved from the PDB server 52 on April 1, 2022 with the following request: 1) Release date after May 1st, 2018 to exclude complexes present in the AlphaFold2 v.2.2 training set; 2) The longest protein (called the receptor) must contain at least 60 amino acids and the smallest chain (called the peptide) must contain at most 40 amino acids. 3) The ‘Number of Polymer Instances (Chains) per Assembly’ has to be between 2 and 4 and should contain heteromeric assemblies. 4) The assemblies should not contain RNA or DNA chains. The initial request led to 2484 potential candidates. Using a sequence identity threshold of 30%, we discarded all candidates for which a homolog of the receptor protein was released before May 1st, 2018 and bound to a ligand partner in the same region. From the list of selected candidates, an additional filter was used to check the absence of redundant assembly modes. For each of the selected complexes, the receptor sequence was used as a query of the PPI3D server 57 , in single sequence mode, to recover all the PDB codes of complexes involving homologs of the receptor (date of PPI3D query August 2022, on the PDB updated July 20, 2022). In PPI3D, distant receptor homologs were retrieved using PSI-BLAST 58 with 2 iterations and an E-value cutoff of 0.002. For every candidate complex, PPI3D provided a detailed list of PDB codes with the chain ids involving the receptor or its homolog. We used the full list of interactions provided by PPI3D, except when it exceeded 2500 interfaces in which case the clustered list was chosen (95% sequence similarity and 50% similarity for residues in the binding region). Only the interfaces annotated as ‘hetero’ or ‘hetero-peptide’ released before May 1, 2018 were considered as potentially redundant. Their structures were compared to the candidate complex using the MM-align program (Version 20191021) 59 (option “-a”) and the maximum of the three TM-scores calculated was considered. Receptor-peptide candidates with a TM-score greater than 0.5 with any other potentially redundant interface extracted from the PPI3D results were considered redundant with a previously known structure and were discarded. This latter condition only applied to structures for which MM-align successfully aligned at least 5 consecutive amino acids on the ligand side (detected by ‘:’ in the output pair alignment corresponding to residue distance pairs <5.0 Angstrom), otherwise the interface was not considered redundant. In the end, we retained a set of 42 receptor-peptide cases to form the reference database.

Generation of the alignments for the 42 database cases

Sequences of all the chains in the dataset of 42 complexes were retrieved from the UniProt database 60 and were submitted to three iterations of MMseqs2 61 against the uniref30_2103 database 47 . The resulting alignments were filtered using hhfilter 62 using parameters (‘id’=100, ‘qid’=25, ‘cov’=50) and the taxonomy assigned to every sequence keeping only one sequence per species. Full-length sequences in the alignments were then retrieved and the sequences were realigned using MAFFT 63 with the default FFT-NS-2 protocol to generate the multiple sequence alignments (MSA) of every individual subunit. These MSAs, generated with full-length sequences, were then trimmed to match the delineations of the receptor and ligand parts, which vary according to the protocols used. The sequence boundaries defined in the PDB SEQRES parameter were used to delineate the receptor and peptide binding regions. To generate the extended 100 or 200 amino acid ligands, the peptide sequence was extended in both directions, unless a chain end was encountered, in which case extension was continued in one direction only. From the individual MSAs of receptors and ligands, different types of co-alignments were generated and assessed. First, the so-called mixed co-alignments, standing for paired+unpaired co-alignments, was built by concatenating the receptor and ligand MSAs so that homologous receptor and ligand sequences were paired when they belonged to the same species (joined as if they were part of the same sequence in the alignment) and left unpaired if no common species was found (adding gaps in place of the missing homolog) 47 . Unpaired co-alignments were obtained by unpairing the paired part of the mixed co-alignments. Last, co-alignments with no evolutionary information in the ligand part were obtained from the mixed co-alignment by leaving the ligand region as a single sequence and adding gaps in the rest of the ligand alignment. In case the receptor was a heteromer or assembled as a homodimer, the multimeric assembly complex was modeled by concatenating the alignments of the receptor subunits in the same way as described above. The different concatenated co-alignments generated using the different delimitations and pairing protocols were used as input MSA to AlphaFold2.

Generation of the input data for the scanning of the 42 ligand partners with overlapping fragments

The sequences of the 35 ligands in the non-redundant dataset that were longer than 100 amino acids, were fragmented into 100 amino acid long segments with an overlap of 30 amino acids to ensure that the binding region was entirely contained in at least one fragment. The delimitations of the tested fragments are reported in Supplementary Data  2 . A multiple sequence alignment associated with each ligand fragment was built following the same protocol as above and concatenated with the alignment of the delineated binding domain of the receptor protein.

Generation of input data for cross-partners evaluation

To generate the dataset mixing receptors and their non-cognate ligands, a subset of complexes that could be clustered according to the similarity of the type and length of the secondary structure of their ligand (reported in Supplementary Table  1 ) was defined (Supplementary Data  1 ). We selected 23 complexes with a monomeric receptor and a ligand that could be clustered into one of the 7 groups distinguished in Supplementary Data  1 . The MSA of each receptor was concatenated with each ligand in the same cluster without adding MSA information on the ligand side. These alignments were used as input to generate structural models by AlphaFold2 following the protocol described below.

Generation of the input data for the 923 cases of the ELM database

A list of ELM binding motifs and of their binding domain receptor was downloaded on July 3, 2023 from http://elm.eu.org/downloads.html . 1884 entries of receptor/ligand pairs from the ELM classes ‘LIG’ and ‘DOC’ were extracted, documented by at least one PubMed ID reference and defined delimitations for receptor and ligand binding regions (Supplementary Data  3 ). Of these, 492 pairs were not associated with a reference PDB structure and 469 pairs corresponded to a ligand containing multiple ELM binding motifs of the same type. The remaining 923 pairs could be used to evaluate the success rate of AlphaFold2 with protocols combining different delimitations and MSA sampling. Sequences and multiple sequence alignments of all pairs were obtained using the same procedure as described above for the 42 test cases. To define the boundaries of the binding domain in the receptor, we could not rely on ELM delimitations as they did not always match the structural boundaries of the globular domain. Instead, we took advantage of the recently developed Chainsaw method 64 (commit tag 1ec2be5 from Jul 20, 2023) to automatically parse and assign domain boundaries from predicted receptor structures in the AlphaFold Protein Structure Database 65 (see Supplementary Data  3 ). The delimitations of the ELM motif in the ligand were taken from the ELM database except for motifs with less than 5 residues which were extended up to 5 amino acids (see Supplementary Data  3 ). Five different concatenated MSAs were generated for all receptor/ligand pairs, always using a delimited receptor domain. The ligand side was either delimited with 3 possible MSA modes (mixed, unpaired or single sequence), extended by 100 amino acids with a mixed MSA mode, or full-length ligand with a mixed MSA mode. Concatenated MSAs were used as input of AlphaFold2 to generate 25 models following the protocol described below.

Generation of the structural models

The concatenated MSAs were used as input to run 5 independent runs of the AlphaFold2 algorithm with 3 recycles each 15 , 29 generating 5 structural models (25 models in total) using a local version of ColabFold v.1.3 47 with the Multimer v.2.2 model parameters 29 on NVidia V100 and A100 GPUs since the training of the v.2.2 parameters excludes complexes released after May 1st, 2018. In the case of the ELM dataset, for which the potential bias effect is present anyway, the structural models were generated using AF2-Multimer version v.2.3 and ColabFold v.1.5 with the benefit of saving time. Four scores were provided by AlphaFold2 to rate the quality of the models, the pLDDT, the pTMscore 15 , the ipTMscore and the model confidence score (weighted combination of pTM and ipTM scores with a 20:80 ratio) 29 . The scores obtained for all the generated models are reported in Supplementary Data  2 . The sampling of 100 models instead of 25 with a single protocol, whose performance is shown in Supplementary Fig.  4b , was achieved by increasing the number of independent runs from 5 to 20. No relaxation step was performed consistently with our own approach and since relaxation has been found to be computationally costly with little added value to the quality of results 15 . To evaluate the models generated from the sampling of fragments of 100 amino acids along the full ligand sequences, we selected the model with the highest ipTM score (see Supplementary Data  2 ), in order to focus on the interface and be less dependent on the degree of folding in the fragment itself, reflected in the pTM score. To evaluate the models from the ELM dataset, we selected the model with the highest AF2 confidence score among the 25 models generated with each of the tested protocols (scores reported in Supplementary Data  3 ).

Testing of the AFsample protocol

AFsample was cloned from https://github.com/bjornwallner/alphafoldv2.2.0 (commit tag 9f76c2a from Dec 24, 2022). AFsample was run on a selection of cases (see below) following the procedure outlined in the provided pipeline script (run_afsample.sh). (i) Create the MSAs and search for templates (hence the MSAs used are different in the AFsample runs and in our approach). (ii) Generate 6000 models (or as many as was possible given the computational cost) sampled following four schemes (a) 10 ×200 models using multimer_v1 & multimer_v2 parameters, using dropout and templates, (b) 10 ×200 models using multimer_v1 & multimer_v2 parameters, using dropout and no templates, (c) up to 5 ×200 models using multimer_v1 parameters, using dropout, no templates, 21 recycles (denoted r21), and (d) up to 5 ×200 models using multimer_v2 parameters, using dropout, no templates, 9 recycles (denoted r9). (iii) Sort all models according to their combined score. Since AFsample is very computationally intensive, we targeted two lists of cases for testing, with the goal to answer two questions about the complementarity between AFsample and our approach: (i) 17 cases with combined score lower than 0.8 for the rank 1 model in our mixed-delim-delim approach (independently of whether this approach succeeds or fails). This list addresses the question of the relative success rates for AFsample and our approach, following the recommendation of the AFsample publication that their pipeline should be run only when the best ranked solution has combined score lower than 0.8. (ii) 10 cases failing with our mixed-delim-fl pipeline and with total size (cumulated over the complex partners) smaller than 1000 amino acids (above 1000, the AFsample runtime becomes prohibitive, see Supplementary Data  6 ). This list addresses the question whether the exhaustive AFsample approach can succeed where our approach fails.

Evaluation and visualization of the structural models

The structural models generated with every alignment protocols were compared to their reference structure defined in Supplementary Table  1 for the 42 cases of the non-redundant dataset and in Supplementary Data  3 for the ELM dataset. The structural models were first cut following the delimitations of the reference experimental structure to ensure proper superposition of receptors and ligands models. For the evaluation of the ELM models dataset, the reference structure was, if available, the PDB structure exactly matching the sequence of the receptor/ligand couple. In case such an experimental structure was not available, we tested all possible reference PDB structures belonging to the same ELM TYPE category and evaluated the accuracy of the models using the reference PDB with the highest DockQ score (listed in Supplementary Data  6 ). In case there was internal symmetry in the receptor bound asymmetrically by a ligand (as in the case of coiled coils), we used different reference structures for each symmetrical arrangement (which we provide in the Zenodo archive) and selected the one that provided the best DockQ score.

The accuracy of all models was assessed using two related measures (i) the DockQ score, which provides a continuous value between 0 and 1, with limits of 0.23, 0.49, and 0.8 defining Acceptable, Medium, and High quality thresholds for protein-protein complexes 48 (ii) the more stringent conditions established by the CAPRI community to rate the specific cases of receptor-peptide complexes using ligand and interface Root-Mean-Square Deviation (L- and iRMSD) and the Fraction of native contacts (fnat). Ranks are assigned depending on the following criteria: High (fnat in [0.8, 1.0] and (L-RMSD ≤ 1.0 Å or iRMSD ≤ 0.5 Å)), Medium (fnat in [0.5, 0.8] and (L-RMSD ≤ 2.0 Å or iRMSD ≤ 1.0 Å) or fnat in [0.8, 1.0] and (L-RMSD > 1.0 Å and iRMSD > 0.5 Å)) and Acceptable (fnat in [0.2, 0.5] and (L-RMSD ≤ 4.0 Å or iRMSD ≤ 2.0 Å) or fnat [0.5, 1.0] and (L-RMSD > 2.0 Å AND iRMSD > 1.0 Å)) 49 . Additional analyses were performed following the standard metrics calculated by CAPRI assessors to rate the similarity between the models and their reference structure (such as the fraction of interface residues FRIR or the fraction of non-native contacts FRNNAT) and are also available in Supplementary Data  1 and Supplementary Data  3 for the 42 non-redundant and the ELM dataset, respectively. 3D structures were visualized and represented using ChimeraX 66 .

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

Source data used to generate all Figures and Supplementary Figures are provided as a Source Data file. The reference PDB files of the 42 test cases, the multiple sequence alignments built for all ten protocols and the corresponding PDB files of the predicted models have been deposited 67 in the ZENODO database under the accession DOI code https://doi.org/10.5281/zenodo.7838023 [ https://zenodo.org/doi/10.5281/zenodo.7838023 ]. The reference PDB files used to evaluate the predictions of the 923 cases from the ELM database, the multiple sequence alignments built for all five protocols and the PDB files of the models predicted with highest scores have been deposited 67 in the ZENODO database under the accession DOI code https://doi.org/10.5281/zenodo.7838023 [ https://zenodo.org/doi/10.5281/zenodo.7838023 ].  Source data are provided with this paper.

Code availability

The code for processing, analyzing and visualizing the results is available at: https://github.com/i2bc/SCAN_IDR and the version used was also deposited 68 in the ZENODO database under the accession DOI code https://doi.org/10.5281/zenodo.10213747 [ https://zenodo.org/doi/10.5281/zenodo.10213747 ].

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We thank Sjoerd de Vries and Isabelle Callebaut for useful discussions. The work was supported by grants from Agence Nationale de la Recherche (ANR-21-CE44-0009-01 to R.G. and ANR-18-CE45-0005-01 ESPRINet to J.A.). This work was granted access to the HPC resources of IDRIS under the allocation 2023-AD010314343 to R.G. made by GENCI and to the BIOI2 platform resources at the I2BC.

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Hélène Bret, Jinmei Gao, Diego Javier Zea, Jessica Andreani & Raphaël Guerois

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H.B., J.A. and R.G. designed the study. H.B. built the dataset and developed the scripts to generate the alignments, ran AlphaFold2 and analyzed the outputs. J.G. and D.J.Z. contributed, respectively, to the scanning protocol and to the analysis of the ELM database. J.A. and R.G. contributed to the script development and data analysis, jointly supervised the work and wrote the manuscript together with H.B.

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Correspondence to Jessica Andreani or Raphaël Guerois .

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Bret, H., Gao, J., Zea, D.J. et al. From interaction networks to interfaces, scanning intrinsically disordered regions using AlphaFold2. Nat Commun 15 , 597 (2024). https://doi.org/10.1038/s41467-023-44288-7

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Title: how multimodal integration boost the performance of llm for optimization: case study on capacitated vehicle routing problems.

Abstract: Recently, large language models (LLMs) have notably positioned them as capable tools for addressing complex optimization challenges. Despite this recognition, a predominant limitation of existing LLM-based optimization methods is their struggle to capture the relationships among decision variables when relying exclusively on numerical text prompts, especially in high-dimensional problems. Keeping this in mind, we first propose to enhance the optimization performance using multimodal LLM capable of processing both textual and visual prompts for deeper insights of the processed optimization problem. This integration allows for a more comprehensive understanding of optimization problems, akin to human cognitive processes. We have developed a multimodal LLM-based optimization framework that simulates human problem-solving workflows, thereby offering a more nuanced and effective analysis. The efficacy of this method is evaluated through extensive empirical studies focused on a well-known combinatorial optimization problem, i.e., capacitated vehicle routing problem. The results are compared against those obtained from the LLM-based optimization algorithms that rely solely on textual prompts, demonstrating the significant advantages of our multimodal approach.

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Addressing Wicked Problems (SDGs) Through Community Colleges: Leveraging Entrepreneurial Leadership for Economic Development Post-COVID

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  • Samantha Bryant Steidle   ORCID: orcid.org/0000-0002-6532-153X 1 ,
  • Christopher Glass 2 ,
  • Macie Rice 1 &
  • Dale A Henderson   ORCID: orcid.org/0000-0003-4137-3009 1  

This qualitative case study aims to explore community colleges’ role in addressing wicked problems of economic development post-COVID through entrepreneurial leadership. The key research question is, “How do entrepreneurial leaders describe the role of community colleges in addressing wicked problems of economic development post-COVID.” The study interviewed 28 entrepreneurial leaders regarding the role(s) of community colleges in addressing wicked problems of economic development. The study addresses a critical gap in the literature. Researchers have yet to explore the role of associate degree-granting institutions, namely community colleges. Notably, the over 1200 U.S. community colleges serve nearly 12 million or half of America’s students (American Association of Community Colleges, 2011 ). From a theoretical perspective, the study leverages complexity science, complex adaptive systems, and systemic innovation to address wicked problems of economic development. Future researchers can build on these theoretical insights for future studies across many disciplines.

The results provide ten key roles community colleges can potentially take on as economic development partners, making them ideal institutions to serve as incubators of post-COVID recovery. They include revitalizing communities post-COVID, performing post-COVID business triage, modeling sustainability, creating jobs, championing entrepreneurship-led economic development, increasing tax revenue, pipelining talent, supporting talent retention, supporting main street businesses, and reducing entrepreneurial risks. Researchers recommend that policymakers and other funders allocate funding to help community colleges address wicked problems through systemic innovation labs (I-Labs). Interestingly, the roles identified appear to be moderated by proximity and trust.

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In recent years, concerns about social, environmental, and economic issues have increasingly gained attention (World Economic Forum, 2020 ). The World Economic Forum (WEF) has been sounding the alarm for years, warning of increased poverty, economic inequality, infectious disease, climate change, and many other wicked problems despite efforts to mitigate their effects (Deming, 1994 ). Researchers made the predictions before the COVID-19 pandemic, which researchers label as a “super wicked problem” due to the multifaceted and interconnected nature of the pandemic’s challenges (Auld et al., 2021 ).

What makes this study different is the focus on community colleges, their proximity to communities across the USA, and their ability to leverage trusted relationships within these communities for localized impact. While the findings indicate significant overlap between the roles of universities versus community colleges, the entrepreneurial leaders emphasized that trusted relationships and proximity were vital to maximizing the economic impact. Many scholars believe higher education institutions are well-positioned to serve as incubators of innovation for complex social, economic, and environmental challenges. The existing literature surrounding academic institutions addressing wicked problems focuses on the role of universities, businesses, and non-governmental organizations (NGOs) (Mena & Palazzo, 2012 ; Schouten & Glasbergen, 2011 ). Researchers have explored the role of baccalaureate-granting institutions in addressing wicked problems (Dentoni & Bitzer, 2015 ). However, researchers have yet to explore the role of community colleges in addressing wicked problems.

Community colleges play a critical role in the US higher education landscape, enrolling nearly half of all college students, often of socially and economically disadvantaged populations (Kolesnikova, 2009 ). These associate degree-granting institutions encompass over 1200 entities serving over 12 million students across the USA. With a mission of open access, affordable costs, workforce training, and educating both urban and rural communities, community colleges serve students often left behind by traditional institutions of higher education (Kasper, 2002 ). Community colleges nationwide also provide an opportunity for significant impact locally since 90% of the US population lives within 25 miles of a community college (American Association of Community Colleges, 2011 ). While previous researchers identified similar roles from the university’s broad impact perspective, community colleges offer a more trusted, relationship-based pathway to addressing wicked problems (Dentoni & Bitzer, 2015 ). Existing research needs to address the critical role these institutions of higher education could play in addressing wicked problems of economic development. In the next section, the authors describe the need to examine wicked problems from the community college perspective by utilizing broader theoretical frameworks.

Social enterprise literature underscores a need for more literature related to the research question. For example, according to Zivkovic ( 2017 ), traditional problem-solving methods need to be revised to address wicked problems and instead require a more comprehensive approach. The literature advises a more holistic approach, combining systemic innovation and complexity science when addressing wicked problems (Zivkovic, 2017 ). Complexity science emphasizes interactions and interconnectedness, which offers a deeper understanding of these complex issues (Phelan, 2001 ; Sturmberg & Narduzzi, 2014 ; Uhl-Bien et al., 2008 ). Similarly, complex adaptive systems theory considers interdependencies critical for addressing wicked problems (Australian Public Service Commission, 2007 ). Systemic innovation aims to maximize the value of social innovation and improve outcomes across domains (Davies et al., 2012 ). Systemic innovation labs operate across various levels of analysis (Zivkovic, 2018 ). Collectively, complexity science, complex adaptive systems, systemic innovation, and systemic design offer a theoretical framework for addressing wicked problems, providing a comprehensive and systems-oriented approach (Davies et al., 2012 ; Jones, 2014 ; Surie & Hazy, 2006 ; Zivkovic, 2017 ). The researchers have employed a qualitative approach to capture the contextual nuances and social complexities of wicked problems.

This research study explores the role of community colleges in addressing wicked problems post-COVID by asking, “How do entrepreneurial leaders describe the role of community colleges in addressing wicked problems of economic development post-COVID.” The study provides a literature review surrounding higher education’s role(s) in addressing wicked problems of economic development and a theoretical lens to explore this question.

The study is structured as follows: After a brief introduction, the authors introduce the theoretical lens employed and review the literature. The literature review includes (a) sustainability in higher education, (b) the evolution of skills when managing knowledge in the sharing economy, (c) the role of technology in achieving the Sustainable Development Goals (SDGs), (d) The role and challenges of big data in achieving the SDGs, (e) COVID-19’s impact on entrepreneurial motivation, and (f) the roles of higher education in addressing wicked problems. Next, the study includes a discussion of the qualitative methodology employed, the sample, the findings and results, the study’s limitations, future research, and conclusive remarks.

Background Literature and Theoretical Framework

Zivkovic ( 2017 ) advocated for a holistic, blended approach of systemic innovation and complexity science when addressing wicked problems. Complexity science involves the interactions between small actions that lead to large-scale effects within a given situation due to complex and multi-dimensional interconnectedness (Phelan, 2001 ). The theory of complexity science is well-documented throughout the literature (Cohen & Stewart, 1994 ; Ewing Kauffman Foundation, 1993 , 1995 , 2007 ; Gell-Mann & Tsallis, 2004 ). Researchers define complexity as “the formation and reformation of patterns and structures whether in companies, research and development teams, communities, or cities and nations” (Brett, 2019 , p. 19). The most basic unit of analysis in complexity science is the complex adaptive systems (CASs) (Uhl-Bien et al., 2008 ). CASs are individuals, agents, or groups (Lichtenstein & Plowman, 2009 ; Uhl-Bien et al., 2008 ) that are open, non-linear systems and often adapt or evolve as needed (Merali, 2006 ) and show emergent behavior (Sturmberg et al., 2014 , p. 66). This emergent behavior can utilize self-organization in recombining new patterns, thus impacting performance (Lichtenstein & Plowman, 2009 ). Researchers highly recommend employing the complex adaptive systems theory when addressing wicked problems (Elia & Margherita, 2018 ), considering the interdependencies and the ever-changing nature of wicked problems (Australian Public Service Commission, 2007 ).

Building on this argument, Davies et al. ( 2012 ) asserted that systemic innovation is the preferred style of social innovation when addressing wicked problems, as the approach incorporates concepts surrounding complexity science, including complex adaptive systems. Systemic innovation is “a set of interconnected innovations where each depends on the other, with innovation both in the parts of the system and in the way they interact” (Davies et al., 2012 , p. 4). Systemic innovation aims to maximize the value of social innovation by improving outcomes, such as higher graduation rates or lower unemployment (Davies et al., 2012 ).

According to Davies et al. ( 2012 ), wicked problems can be better addressed through systems innovation when practitioners understand the concepts surrounding complexity and complex adaptive systems. When addressing wicked problems within complex adaptive systems, Zivkovic ( 2018 ) advocates using a systemic innovation lab, a complexity-science-informed solution ecosystem. Systemic innovation labs possess certain key features, including (a) focusing on addressing complex problems, (b) emphasizing place-based local approaches, (c) enabling coherent action by diverse actors, (d) involving users as co-creators, (e) supporting a networked governance approach, and (f) recognizing government as an enabler of change. Additionally, systemic innovation labs often shift between macro, meso, and micro levels of analysis and action due to the systemic design nature of the work. In this context, design is imagining something that does not yet exist and bringing it to life (Nelson & Stolterman, 2012 ).

Systemic innovation labs embrace core principles of systemic design like purpose-finding, boundary framing, and feedback coordination to compel collective action on wicked problems. Systemic design is governed by a set of core principles, including (1) compelling collective action toward a desirable outcome, (2) appreciating complexity, (3) purpose-finding, (4) boundary framing, (5) feedback coordination, (6) system ordering, (7) generative emergence, (8) continuous adaptation, (9) self-organizing, and (10) requisite variety (Jones, 2014 ). Finally, leaders within systemic innovation labs often adopt the complex systems leadership style of “generative leadership,” which emphasizes the need for aligning and understanding collective goals before advancing action to stay aligned (Surie & Hazy, 2006 , p. 17). Complexity science, complex adaptive systems, and systemic design collectively provide the ideal theoretical frameworks for addressing wicked problems.

Researchers define entrepreneurial leadership as influencing and directing the performance of group members toward achieving organizational goals that involve recognizing and exploiting entrepreneurial opportunities (Renko et al., 2015 ). Research indicates that wicked problems are best addressed collaboratively alongside entrepreneurial leaders in today’s rapidly changing volatile, uncertain, complex, and ambiguous (VUCA) environments (Iordanoglou, 2018 ). This assertion was made before the COVID-19 pandemic and is arguably more relevant today.

Research suggests many of today’s problems are far too complex to be solved by an individual and points out the importance of a collective leadership model, which is critical for success (Iordanoglou, 2018 ). Hence, future organizations will not be as hierarchical, so future leaders will need to be less authoritative and more participative to engage their teams.

Wicked problems like poverty, hunger, and climate change are shared throughout society and demand entrepreneurial leadership skills (United Nations Assembly, 2015 ). For this reason, wicked problems have become prominent in academic conversations (Dentoni & Bitzer, 2015 ). Rittel and Webber ( 1973 ) expressed concern about the approach to public planning when dealing with problems of various wickedness dimensions.

Wicked problems have three similarities: they change over time (Weber & Khademian, 2008 ), social scientists are uncertain about their root causes due to social complexity (Lazarus, 2009 ), and stakeholders hold different values regarding the challenges, which often cause conflict (Conklin, 2006 ). In addition, the properties of wicked problems often demand collective action across several sectors to create transformative and impactful change throughout the system (Waddock, 2013 ). Furthermore, the effort of individuals to combat wicked problems has minimal impact without collective and coordinated action with others, which is why these collaborative initiatives play an essential role (Batie, 2008 ; Conklin, 2006 ; Weber & Khademian, 2008 ). In addition, the nature of wicked problems requires the acceptance that there are no absolute solutions or definite answers (Rittel & Webber, 1973 ), rather a need for goals that are on a scale of improvement.

Wicked problems have no solution, resist linear-logic models, and are not comprehensible based solely on quantitative and objective data. Researchers emphasized that wicked problems cannot be “solved” because they are unsolvable (Rittel & Webber, 1973 ). Conklin ( 2006 ) asserted, “You don’t so much solve a wicked problem as you help stakeholders negotiate shared understanding and shared meaning about the problem and its possible solution” (p. 4). The objective of the work is coherent action, not the final solution. While wicked problems are dynamic and complex, Sustainable Development Goals offer a shared framework to drive coherent action and create an understanding between stakeholders seeking substantial improvement rather than a set of shared objective solutions.

The United Nations Assembly ( 2015 ) captured the systemic nature of global challenges in the Transforming our World: The 2030 Agenda for Sustainable Development report. The report highlights the importance of addressing wicked problems. In response to the global challenges outlined in the report, the United Nations launched the 2030 Agenda for Sustainable Development. The initiative is a universal agenda outlining a plan of action to stimulate systems change between 2015 and 2030 in five areas of crucial importance: people, planet, prosperity, peace, and partnership (United Nations Assembly, 2015 ). The report outlined 27 principles, 17 goals, and 169 actions to impact societal change’s economic, social, and environmental aspects. The effort aims to tackle systemic challenges, local needs, interests, and resources for transformative change using innovative approaches and long-term investments (United Nations Assembly, 2015 ). The 2030 Sustainable Development Goals are considered a “blueprint for global development, which represents a fundamental shift in thinking, explicitly acknowledging the interconnectedness of prosperous business, a thriving society, and a healthy environment” (Stibbe et al., 2019 , para. 2). Due to the interconnected nature of the goals, researchers advocate for addressing the challenges holistically, rather than individually in isolation (Catalyst, 2030a, 2020 ). The SDGs most directly aligned with the current research question relate to SDGs no. 4 and no.8.

SDG no. 8 represents good jobs and economic growth. Target 8.3 promotes decent job creation, entrepreneurship, creativity, and innovation, emphasizing individuals aged 15–24. Additionally, the target encourages micro, small, and medium businesses to formalize and grow their enterprises through increasing access to financial services and support (Sustainable Development Solutions Network [SDSN], 2023 ).

SDG no. 4 represents quality education. Target 4.7 of the Sustainable Development Goals is also relevant to community colleges addressing wicked problems of economic development through the SDG framework. Target 4.7 encourages inclusive and equitable quality education and lifelong learning opportunities for all students. According to the target, by 2030, all learners should be able to acquire sustainable development skills and knowledge through educational opportunities. The indicator suggests an achievement of the target be measured based on the extent of (a) educating for global citizenship, (b) mainstreaming education for sustainable development, (c) promoting national education policies, (d) incorporating sustainability into the curricula, (e) providing teacher education, and (f) assessing the students (United Nations Statistics Division, 2023 ). The following section provides a review of the role(s) of higher education in addressing wicked problems.

The Role(s) of Higher Education in Addressing Wicked Problems

Within the broader discourse on the roles of educational institutions in addressing societal challenges, it is essential to consider the multifaceted responsibilities of academic institutions and their potential to contribute to solving wicked problems (Ferrer-Balas et al., 2010 ; Manring, 2014 ; Trencher et al., 2014 ). Researchers have called for academics to reflect on their responsibility in society (Ferrer-Balas et al., 2010 ) and the university’s role in addressing wicked problems (Manring, 2014 ), both of which often fit the objectives of the institution (Trencher et al., 2014 ). While some researchers have asserted that higher education institutions serve a public purpose and should contribute to solving societal problems (Shapiro, 2005 ), the issue has been debated for years. The literature reveals six themes regarding the role(s) of baccalaureate-granting institutions in addressing wicked problems. The roles include planning, convening (Innes & Booher, 2016 ; Morrison et al., 2019 ), learning (Lozano et al., 2013 ; Peer & Stoeglehner, 2013 ; Senge, 2006 ), implementation (Lozano et al., 2013 ; Stoeglehner et al., 2009 ), catalyzing, leading, facilitating (Boone, 1992 ), communicating (Adomßent et al., 2007 ; Burritt & Tingey-Holyoak, 2012 ), and as an interlocutor (Fowler, 2014 ; Fowler & Biekart, 2016 ; Fowler & Biekart, 2017 ; Turner et al., 2012 ).

Building upon this discourse, the review examines how community colleges, mainly through entrepreneurial leadership, can contribute to addressing wicked problems in economic development post-COVID. Specific topics included the importance of entrepreneurial leadership skills, such as creativity and innovation when tackling wicked problems, and how technology, big data, and higher education institutions contribute to achieving the SDGs. Finally, the study considered the impact of COVID-19 on entrepreneurial motivation and how intrapreneurial leaders also contribute. The literature review addresses many aspects of addressing wicked problems in this rapidly changing world. To provide an adequate review of the literature within the context of this changing world, the researchers include topics surrounding modern issues of sustainability, the sharing economy, technology, big data, and COVID-19’s impact on entrepreneurial motivation.

Sustainability in Higher Education

The concept of sustainability catalyzed higher education institutions to consider themselves as drivers of innovation through public-private partnerships with stakeholders (Chatterton & Goddard, 2000 ). According to the literature, European academic institutions are leading this movement thanks to the supra-national European Policy outlining sustainable development frameworks to abide by (Kommission der Europäischen Gemeinschaft, 2001 ). The purpose of the sustainable policy mandates is to encourage the creation of regions across Europe that are competitive, knowledge-based, and innovative (Kommission der Europäischen Gemeinschaft, 2001 ). In the context of higher education institutions, sustainability processes focus on empowering local populations by providing broad access to education (ÖROK, 2002 ), opportunities to overcome spatial barriers (Schnell & Held, 2005 ), and reframing institutions of education as incubators of learning and innovation (Schnell & Held, 2005 ; Streich, 2005 ). By nature, wicked problems have no absolute solutions or definite answers (Rittel & Webber, 1973 ) and no formula for resolving them. While similar studies have explored addressing wicked problems, existing studies have primarily focused on the role of 4-year universities and non-governmental organizations. To date, no published research has focused on the role(s) of community colleges in addressing wicked problems of economic development.

The Evolution of Skills—Managing Knowledge in the Sharing Economy

According to a systematic literature review by Ben Slimane et al. ( 2022 ), specific skills are crucial when managing knowledge in the sharing economy. They include strong collaboration, communication, data analytics, communication technology, and networking capabilities (Ben Slimane et al., 2022 ). Additionally, an understanding of knowledge management systems will allow for managing the sharing economy efficiently, allowing for better understanding across all platforms. The COVID-19 pandemic only accelerated digital transformation in the sharing economy, increasing the need for skills for managing knowledge such as remote collaboration, cybersecurity, digital marketing, resilience, and adaptability (Ben Slimane et al., 2022 ). Likewise, Pizzi et al. ( 2020 ) identified technological innovation, non-financial reporting, education, and developing countries as crucial categories in extant literature when examining the relationship between SDGs and business entities. This information creates opportunities for researching the future impact of SDGs, as well as helps identify education and policy gaps that may exist.

Specific skills in collaboration, communication, technology, and knowledge management systems are critical for knowledge sharing and can strengthen businesses’ economic performance and progress in emerging markets (Girón et al. ( 2021 ). Girón et al. ( 2021 ) examined 366 large Asian and African companies that addressed SDGs in their sustainability reports to determine the relationship between sustainability reporting and a firm’s performance. The findings suggested that understanding and reporting on sustainability is an essential practice for companies in emerging markets, and it can positively impact their economic performance.

Similarly, Huang ( 2023 ) examined how removing barriers to the sharing economy can promote sustainable development goals (SDGs), specifically in the ASEAN region. The study identifies the specific economic, social, and technical barriers that hinder SDG achievement and suggests that a supportive organizational climate can help mediate this relationship. The authors also offer policy recommendations for regulators looking to promote SDG achievement through the sharing economy.

Building on the previous study’s findings, Tu et al. ( 2023 ) empirically examined the impact of sharing economy platforms on achieving sustainable development goals (SDGs) in the manufacturing industry of developing economies. Results of the study show that sharing economy activities such as corporate social responsibility, eco-design, and supplier management positively impact sustainable development. The article concludes by presenting solutions to these countries’ social and environmental issues that restrict sustainable development. Thus, technology is critical in enabling sharing platforms to achieve the SDGs.

The Role of Technology in Achieving the SDGs

Digital innovation is changing the way we do business. Not only does it require new skills and competencies, but it can change how and where people work. Entrepreneurial leaders must ensure employees have the correct skills to succeed in such an environment. Educators can ensure that the curriculum exposes students to an entrepreneurial and innovative mindset. More specifically, Ben Slimane et al. ( 2022 ) suggest that data collection, analysis, communication, and collaboration skills are necessary components. Likewise, understanding technologies such as blockchain, artificial intelligence, and the Internet of Things (IoT) can promote sustainable agriculture, renewable energy, and responsible consumption and production. These technologies can play a crucial role when addressing SDGs. For example, digital innovation has the potential to accelerate progress toward the SDGs; it also draws attention to the challenges of equitable access and the vulnerability of small and medium enterprises (SMEs). While digital innovation and technologies like AI may accelerate progress on Sustainable Development Goals, the pandemic underscored inequitable access and structural vulnerabilities many SMEs face in leveraging these technologies.

The pandemic has proliferated the need for new business models. It caused a massive acceleration of the digital transformation of businesses in all industries in achieving the SDGs, particularly in areas such as healthcare, education, and digital connectivity (Ben Slimane et al., 2022 ). Likewise, Pizzi et al. ( 2020 ) suggest a need for studies conducted on single organizations that could benefit from case studies and content analysis in extending the richness of qualitative research. However, the pandemic also highlighted the need for equitable access to technology and the importance of reducing the structural vulnerability of SMEs from a lack of financial resources or specialized knowledge (Klein & Todesco, 2021 ). Considering the critical role of technology, the next logical question is, what are the role(s) and challenges of big data in achieving the SDGs?

The Role and Challenges of Big Data in Achieving the SDGs

Big data can be used to achieve the SDGs by offering statistical insights regarding sustainability. Chopra et al. ( 2022 ) delineated multiple ways big data can provide crucial insights into poverty reduction, healthcare access, and environmental sustainability. Big data can also support efforts to monitor progress toward achieving the SDGs. During the COVID-19 pandemic and possible future pandemics, these statistical insights can help researchers identify trends related to the virus’s spread, most affected regions, and predicted trajectory. Policymakers and other stakeholders can take more targeted actions to accelerate progress toward achieving the SDGs and mitigating the pandemic’s impact. Overcoming significant data challenges better inform stakeholder decisions to accelerate Sustainable Development Goals.

Research indicates that the challenges of using big data for sustainable development include solidifying statistical volume, developing data literacy within every stage of the decision-making process, and ensuring the quality, timeliness, and relevance of the data (Chopra et al., 2022 ). Along with these challenges, data creators must collaborate with other data creators for more efficient use (Gupta et al., 2022 ). When used efficiently, SDG indicators like age, gender, and location will allow for more accurate results to be presented (Chopra et al., 2022 ). Capitalizing on big data will allow policymakers and stakeholders to make informed decisions and target actions to reduce the impact of the pandemic on sustainable development and instead further the process of achieving the SDGs.

Another related consideration is COVID-19’s impact on entrepreneurial motivation. After all, the mindset of entrepreneurial leaders and students is central to their ability to contribute to addressing wicked problems.

COVID-19’s Impact on Entrepreneurial Motivation

During COVID-19, both entrepreneurial and intrapreneurial leadership were highly valued qualities to possess (Ratten, 2021 ). These leaders bring distinctive value to business organizations (González-Tejero et al., 2022 ), contributing skills like creativity, entrepreneurial thinking, and an entrepreneurial mindset. In the context of the COVID-19 pandemic, both entrepreneurial and intrapreneurial leadership qualities gained significant recognition. Next, the following section provides an overview of the methodology and sample.

Model and Sample

The study builds on prior research by Dentoni and Bitzer ( 2015 ) titled “The role(s) of universities in dealing with global wicked problems through multi-stakeholder initiatives.” While Dentoni and Bitzer ( 2015 ) focused on the role(s) of universities, this article focuses on the role(s) of community colleges. The researchers employed a single case study method to understand the role of US community colleges in addressing wicked problems through entrepreneurial leadership and entrepreneurial programs. Using a qualitative, interview-based case study method, researchers aimed to explore how entrepreneurial leaders describe community colleges’ role in addressing wicked economic development problems.

The researchers employed a single case-study design with semi-structured interviews between August 1, 2020 and January 31, 2021, so that the data collected from the entrepreneurial leaders could better focus on their experiences, opinions, and knowledge related to the research question. The researchers supplemented the interviews by retrieving artifacts from research studies, government reports, and related websites. The qualitative research design was chosen for the case study, as it offered an opportunity to explore a topic from the perspective of participant experiences. The design offers several benefits, such as providing a more profound perspective than quantitative research surrounding complex real-life issues (Eisenhardt & Graebner, 2007 ; Yin, 2009 ) and providing methods that enable researchers to explore how individuals make sense of the world around them (Bogdan & Biklen, 2007 ).

This qualitative case study explores how entrepreneurial leadership addresses wicked problems of economic development. The study brought together the collective wisdom of 28 entrepreneurial leaders for programs that have (a) addressed social, economic, and environmental wicked problems, (b) included community colleges as stakeholders, (c) yielded impressive measurable outcomes that are documented, and (d) incorporated entrepreneurial leadership and entrepreneurial problem-solving. The study concluded with recommendations to help inform policymakers how community colleges can help address wicked problems of economic development post-COVID through entrepreneurial leadership.

Answering the research question required a top-down approach, with the initial entrepreneurial leaders acting as gatekeepers. Entrepreneurial leaders typically hold systemic knowledge about the efforts and impact generated through their programs. This level provided visibility, knowledge, and awareness about the initiatives that have been most successful in addressing wicked problems through entrepreneurial leadership. The interviewee’s deep exploration of personal experiences and perspectives provides insight into how we can collectively apply these strategies to address wicked problems across higher education. The entrepreneurial leaders recommended an additional 18 interviewees informed at a national level for a total sample of 28 interviewees across the USA with experience working with community colleges.

The appropriate sample size in quantitative research is more precisely defined than in qualitative research, where the goal is “saturation.” According to Smith ( 2015 ), saturation results when collecting more data will result in no new insights. Additionally, qualitative research typically results in smaller sample sizes. While 28 interviews might be considered minimal for a quantitative study, 28 is often considered appropriate for this qualitative case study, which met saturation.

The study’s goal was not generalizability but rather to provide replication logic (Johnson, 1997 ) and increase transferability through persistent observation, providing a thick description of the research and answering descriptive questions. Transferability is similar to external validity in quantitative research (Lincoln & Guba, 1985 ). The study was IRB-approved with written consent of all participants as required by data protection law.

Discussion of Results

This qualitative study explored the roles of community colleges in addressing wicked problems of economic development post-COVID through entrepreneurial leadership. Based on the participant interviews, ten roles emerged, including revitalizing communities post-COVID, performing post-COVID business triage, modeling sustainability, creating jobs, championing entrepreneurship-led economic development, increasing tax revenue, pipelining talent, supporting talent retention, supporting main street businesses, and reducing entrepreneurial risks. This section explains the activities of serving as an economic development partner when addressing wicked problems.

Revitalizing Communities Post-COVID

Participants described re-vitalizing communities as an important activity the community colleges play when addressing post-COVID challenges. Ms. Flaherty, the National Director of Engagement and Partnerships for an economic development-focused program, asserted:

Right now, we’re seeing a lot of conversation and heightened awareness of the importance and value of job creation and community vitality. And it’s really become apparent because of your main street businesses. For instance, when you’re driving down a corridor and you see vacancy signs and all of a sudden, your dry-cleaning service and your favorite pizza place no longer exist.

Post-COVID Business Triage

Businesses have been devastated by COVID-19 due to closures, restrictions, and new technological requirements. Participants emphasized business triage as crucial to community colleges when addressing complex post-COVID challenges. Ms. Flaherty maintained:

It’s really important to have a network in place that can support the increase and activities associated with the startup space. [During post-COVID,] we're seeing an increased need for education, training, coaching, and mentoring around reopening and everything from growing the customer base to how to keep your employees safe. So, how can you create an environment where you have an understanding of what your small businesses need, and you're able to quickly act on those needs? The communities that were organized to do this type of thing before COVID could accelerate their conversations.

Modeling Sustainability

The literature identifies three primary components for sustainability within the context of the Sustainable Development Goals (SDGs). They include environmental, social, and economic sustainability. During the interviews, these components were highlighted by participants as a role the community colleges play in addressing wicked problems. For example, Mr. Nelms, the Director of a community college program with 6 years of experience, pointed out:

When it comes to the environment, every roof of every building [is solar]... and all the money from that solar provides scholarships to students to go to college. So, modeling these behaviors as an institution is one of the things that we need to do as well…

Ms. Clark, a program instructor at the same community college, described why environmental sustainability is essential to their rural community college.

The climate affects our air quality, soil, and water systems. The ag system is completely dependent on the quality of the environment- the air, soil, and water. Without a healthy environment for [farmers] to grow their goods… that will truly be the death of our rural economies.

Finally, Mr. Brand mentioned economic sustainability, stating:

Our job [as community colleges] is mostly the first pillar… the economic, and then secondarily the social piece as a community convener. When you have a land grant, there’s an emphasis on community more than it is on college.

Creating Jobs

The participants often referenced the activities related to creating jobs when addressing wicked problems. For example, Dr. Mattox recently met the program’s goal of training 10,000 small businesses. He explained:

Even though we didn’t set out on a social mission… We've provided a tremendous social impact for groups that would not typically have access.

Ms. Flaherty added:

If you look at the trends and headlines about jobs.... they talk about how we’re going to create 250 new jobs for your community over time. With the exception of a few, like Amazon, they’re [actually] shedding jobs. They’re not creating new jobs. It’s your startups and small businesses that are creating the net new jobs... In one city we’ve worked with, over the last five or six years, young and new firms are creating between 14,000 and 15,000 net new jobs every year.

According to a jobs report, the same organization in a different county but comparable in size found that in 2018, over 25,000 new jobs were created by firms less than 1 year old, with an average wage of $34,000. This accounted for 10% of the total new jobs. The report emphasizes that “startups play a significant role in job creation”.

Entrepreneurship-Led Economic Development

The entrepreneurial leaders emphasized the value of entrepreneurship-led economic development. Ms. Flaherty contended:

The thing with COVID that has really captured the attention of economic developers… All you have to do is drive up and down your main streets and see shuttered businesses and vacancies. And they start to really understand the importance of these small businesses.

Tax Revenue

Many participants went further, connecting education to job creation and increased tax revenue. Ms. Clark, a community college program instructor, explained:

I think the community college plays a vital role in providing an affordable education so that people can either create jobs or find a better job in the community, so that their tax base stays here. Keeping people here is a really big issue for us.

Ms. Flaherty agreed, stating:

[Communities] are seeing a significant drop in sales tax revenue and in those kinds of things.

Pipelining Talent

One of the programs, through the community college, actively served as a talent pipeline for public service jobs across the state. Dr. Delgado, a Systemwide Dean for Workforce Development, described the wicked workforce challenge the program aimed to solve.

There are 2.1 million students in the state, which had occupational openings to fill. If we could create alignments, we could identify a local supply chain or pathway for students to find occupations in need and in demand across the state.

Talent Retention

Talent attraction and retention were also suggested by several participants to be an activity within the community college’s role as an economic development partner. Mr. Brand suggested that economic developers specialize in business retention and attraction. He believes their strength is in business attraction but lacks talent retention strategies. This [entrepreneurship program] is a business retention strategy.

Main Street Business Support

Several participants mentioned activities where the community college supported main street small entrepreneurs. Ms. Love, a business counselor with 10 years of experience with the community college, pointed out:

Some community colleges have incubators to which the Small Business Development Centers (SBDCs) are tied. That's an opportunity we could fulfill, especially with a commercial kitchen.

Similarly, Mr. Brand mentioned:

One of the groups I’m leading right now is an effort to bring together all of the entrepreneurial services for main street businesses and startups in a region to increase access and quality of service and the number of businesses served.

Reducing Entrepreneurial Risk

Community colleges can help reduce the risk of entrepreneurship thanks to entrepreneurial partnerships. Dr. Sampson explained:

One of my favorite ventures is the Everyday Entrepreneur Venture Fund (EEVF), which was started by two people who put up a million dollars of their own money to test a proof of concept [in partnership with community colleges across the United States]. What we found through the EEVF's proof of concept is, if you give a would-be entrepreneur, maybe someone from skilled trades or someone with a barbershop idea, between $7,000-$8,000 of capital, they can buy a barber chair, they can get a license, they can buy some tools and in six months, they can be cash positive. We’ve seen that with the proof of concept with [over] 50 businesses. We profile some of the entrepreneurs in the [2020] book, Impact ED. I think almost all of them are still in operation. Why? Because they got [entrepreneurial] mentorship and support through the community college.

Discussion—Role of Economic Development Partner

Past literature and the current study indicate that academic institutions act as economic development partners when addressing wicked problems (Batie, 2008 ; Mars, 2013 ; Weber & Khademian, 2008 ). Revitalizing the local community was referenced throughout the literature and during the interviews. For example, community colleges are described as “the nation’s overlooked asset” based on their ability to “retain displaced workers and serve the community during turbulent times” (College Board’s National Commission on Community Colleges, 2008 , p. 5). Ms. Flaherty explained, “We are seeing a lot of conversation and heightened awareness of the importance and the value of job creation and community vitality.” She added, “Economic developers have started to take note that when you are driving down a corridor, and you see vacancy signs and all of a sudden, your dry-cleaning service and your favorite pizza place no longer exists,” entrepreneurial vitality becomes a priority. Ms. Flaherty noted, this is a “great opportunity to rebuild better and differently” alongside economic development partners.

Modeling sustainability is particularly aligned with the mission of community colleges. Academic institutions face increasing pressure to lead change by adopting sustainable strategies (American Association of Community Colleges, 2011 ; White & Cohen, 2014 ). Over 700 college and university presidents, representing six million students, have committed to addressing global climate change by signing the American College and University Presidents’ Climate Commitment (Sustainable Development Goals, 2020 ). Researchers proposed four roles in academic and regional sustainability initiatives. Mr. Nelms explained, “We don’t always rally many people around [sustainability]; we often do it through modeling.” Ms. Henderson contended

Our job [as community colleges] is mostly that first pillar… the economic, and then secondarily the social piece as a community convener. We try to be present at everything, and when it's not happening, we convene and facilitate it or model it.

Mr. Nelms offered specific examples of modeling sustainability, stating, “Every roof of every building we have and all of our extra land in the back is now solar. All the money from that solar provides scholarships to students to go to college”. During another interview, the researcher asked the interviewee to dive deeper into why issues like climate change matter to the community college. Ms. Clark explained how the climate affects the air, soil, and water, which can negatively impact the agriculture system and farmers as they are central to healthy rural economies. So, the community college invests in solar. Ms. Henderson added that the local community college is modeling wicked problems surrounding workforce and racial inequities, in addition to the environmental issues.

Job creation was mentioned several times during the interviews. In fact, one of the program websites stated that out of the 10,000 small businesses participating in the program, “47% of the businesses created jobs after 6 months, 53% of the businesses created jobs after 18 months, and 56% of the businesses created jobs after 30 months.” Additionally, Ms. Flaherty specified that “over the last 5 or 6 years, young and new firms are creating between 14,000 and 15,000 net new jobs every year” in just one city they work with. In a separate county, the same organization touted 25,000 new jobs created by firms less than 1 year old, with an average wage of $34,000. According to the founder of the Center for American Entrepreneurship, John Dearie ( 2021 ), this is critical. He explained, “If it were not for businesses younger than five years old, the job base in this country would actually shrink. New businesses are the principal source of innovation, which drives economic growth and job creation.”

Triaging businesses post-COVID was also an activity Ms. Flaherty described, stating,

“We are seeing an increased need for education, training, coaching, and mentoring around reopening and everything from growing the customer base to how to keep your employees safe” and “[community colleges] act as that neutral party. Their role is to help entrepreneurs’ triage where they are at and what type of assistance they need.”

The interviewees emphasized the value of entrepreneurship-led economic development. Ms. Flaherty explained what seemed to motivate economic developers to support small businesses after COVID, “all you have to do is drive up and down your main streets and see shuttered businesses and vacancies. They start to really understand the importance of these small businesses.” Community colleges often partner with these economic developers to promote entrepreneurship-led economic development.

Increasing and retaining tax revenue was an activity mentioned during the interviews. For example, Ms. Clark stated, “Community colleges play a vital role in providing an affordable education so that people can either create jobs or find a better job in the community so that their tax base stays here.” Ms. Flaherty explained, “[communities] are seeing a significant drop in revenues and sales tax. Those are all measurable things [connecting back to the programming].”

Participants described talent recruitment and pipelining as an activity community colleges played when addressing wicked problems, especially economic growth and jobs. Dr. Delgado emphasized the state’s challenge in filling public sector jobs:

There are 2.1 million students in the state, which had occupational openings to fill. If we could create alignments, we could identify a local supply chain or pathway for students to find occupations in need and in demand across the state. So that somewhat [addressed] the state’s problem.

Talent attraction and retention were also cited as community colleges’ activities when addressing wicked problems surrounding economic growth. Several entrepreneurial leaders viewed their programs at the community college as “retention strategies” that were a powerful support mechanism for economic developers. Mr. Brand explained, “[economic developers] don’t have a business retention strategy. This is a business retention strategy [for them].” Mr. Nelms stated, “[This program, in partnership with the community college] works very well for retaining, helping business and industry be innovative and grow from new products and innovations.”

Supporting main street businesses was an activity mentioned by the participants. For example, Mr. Brand said the college brought “together entrepreneurial services for main street businesses and startups in the region to be able to increase access and quality of service and number of businesses served.” Ms. Love also mentioned that in addition to partnering to offer incubator and accelerator services, some colleges launched commercial kitchens designed to help culinary entrepreneurs launch businesses. Helping to reduce the risk of entrepreneurship through entrepreneurial programming and mentorship was also emphasized during the interviews.

Key Point: Community Colleges Can be Engines of Recovery Post-COVID

For the study, researchers aimed to explore the role of community colleges in addressing wicked problems of economic development post-COVID. However, as the research progressed, it became clear: (a) the entrepreneurial leaders were a critical component for community colleges addressing wicked problems, (b) technology and big data may potentially level the playing field, and (c) community colleges are located within a short drive of most American households increasing their potential for localized impact, compared to universities.

Conclusions and Limitations

In conclusion, the study contributes to the existing literature both from an empirical and theoretical perspective by providing insights into how community colleges address wicked problems of economic development. The entrepreneurial leaders interviewed provide insights into the ten roles community colleges take on when addressing wicked problems of economic development. The roles identified include revitalizing communities post-COVID, performing post-COVID business triage, modeling sustainability, creating jobs, championing entrepreneurship-led economic development, increasing tax revenue, pipelining talent, supporting talent retention, supporting main street businesses, and reducing entrepreneurial risks.

What makes this study different is the focus on community colleges, their proximity to communities across the USA, and their ability to leverage trusted relationships within these communities for localized impact. With over 1200 U.S. community colleges educating nearly 12 million students, or half of America’s college students (American Association of Community Colleges, 2011 ), these institutions are critical economic development partners to consider. While the findings indicate significant overlap between the roles of universities versus community colleges, the entrepreneurial leaders emphasized that trusted relationships and proximity were vital to maximizing the economic impact. Future researchers can build on the theoretical application for future studies across many disciplines. Collectively, these implications offer both value and originality.

The evidence presented in this paper contributes to the emerging body of knowledge surrounding the role(s) of academic institutions in addressing wicked problems in a post-COVID context. The study highlighted the importance of leveraging entrepreneurial leadership, systems innovation, and complexity science when addressing these interconnected, complex issues. Additionally, an overview of the characteristics of wicked problems and the role(s) universities play in addressing them was provided.

The researchers have created a table to compare the role of community colleges with similar higher educational bound, baccalaureate-granting (BG) institutions, as well as the opposing research on non-educational bound entities, namely innovation labs. The table presents several clear themes. For example, innovation labs or I-Labs can be seen as a conduit from which many roles can be leveraged to improve economic outcomes. Innovation and entrepreneurs, specifically, can be supported to enhance intellectual capital for economic development. While BGs focus on general knowledge and career preparation, community colleges can provide workforce training and skills development better aligned with local labor market needs.

Another difference identified in the leading role comes from BGs having limited local stakeholder engagement, while I-Labs have a more natural link with community colleges in facilitating collaborations based on trust between local stakeholders. This supports and increases multi-stakeholder engagement which is critical to addressing wicked problems. Similarly, in the roles of connecting and change-making, community colleges appear best suited to leverage the benefits through their strong connections to local high schools and the local workforce. Thus, the researchers argue that community colleges are better catalysts for fostering social mobility and economic development in local communities. While stand-alone innovation labs can certainly add to economic development, this research suggests that benefits can be leveraged with the advantageous strengths of community colleges (Table 1 ).

Following the literature review, excerpts from the qualitative interviews were synthesized and summarized. Ultimately, the researchers assert that community colleges can play a critical role in addressing wicked problems of economic development through entrepreneurial leadership. In other words, they can and should serve as engines of recovery post-COVID.

These findings align with scholarly recommendations that global challenges are best addressed at local levels (Hanson, 2008 ). In summary, community colleges are well suited for serving as engines of recovery post-COVID through entrepreneurial leadership incorporating the SDGs. The study’s participants overwhelmingly agreed that community colleges could be engines of recovery post-COVID through entrepreneurial thinking, programs, and processes. Policymakers and funders are well-positioned to support these economic development efforts through community colleges.

Recommendations for Policymakers

Entrepreneurial leaders from programs and community colleges provided insights into the role of community colleges in addressing wicked problems of economic development. The researcher recommends that policymakers allocate pilot funding for creating an innovation lab (I-Lab) to address these complex challenges. The overarching goal of the I-Lab would be to address wicked problems in partnership with community colleges through scalable, localized, complexity-informed strategies.

Recommendations for Funders

Researchers also recommend that funders provide funding for an innovation lab. Policymakers allocate pilot funding for creating an innovation lab (I-Lab) to address these complex challenges. The overarching goal of the I-Lab would be to address wicked problems in partnership with community colleges through scalable, localized, complexity-informed strategies.

Launching an Innovation Lab (I-Lab) to Address Wicked Problems

America experienced a plethora of interconnected challenges in 2020, including a global pandemic, inequality, poverty, hunger, racism, climate change, and economic growth, to name a few. Academic institutions have the potential to support America's post-COVID while also aligning with the mission of community colleges, becoming engines of scalable post-COVID recovery. Ultimately, the I-Lab would serve as an ecosystem of entrepreneurial partners committed to collaboratively tackling wicked problems locally with trusted networks. Addressing these challenges will require adequate funding.

However, funders must determine how to allocate post-COVID relief funding and donations for maximum societal return on investment. The researcher recommends that pilot funding be allocated to an academic institution for building a scalable Innovation Lab (I-Lab) model, which, after validation, can expand the open-access model throughout the nation in partnership with a national community college association. A publicly funded principal-investigator framework may be ideal for leading the initiative. According to Cunningham et al. ( 2019 ), principal investigators (PI) are “influential ecosystem agents whose behaviors shape and influence” economic and social change through complex research projects. Cunningham et al. ( 2016 ) studied the allocation of time for publicly funded principal investigators supporting public-sector entrepreneurship activities. In the study, the researcher identified ten roles and responsibilities PIs take on in academia, focusing on problem-based activities and value creation (p. 546).

By allocating funding to support academic institutions acting intentionally and entrepreneurially in this capacity at a state and nationwide level, the funding will holistically address post-COVID challenges through open access, streamlined, scalable, and complexity-informed pathways through localization. Additionally, the budget would ensure that entrepreneurship educators are trained on the ideal evidence-based programming for their local needs. Finally, the funding could prioritize rural and urban underserved institutions, which were already stretched thin before the pandemic. The efforts are less likely to achieve broad adoption without the appropriate funding incentives.

The recommended I-Lab represents an opportunity to promote positive societal impact by addressing wicked problems of economic development. The study outlined the activities under the role of economic development, which include (1) revitalizing communities post-COVID, (2) post-COVID business triage, (3) modeling sustainability, (4) creating jobs, (5) generating tax revenue, (6) pipelining talent, (7) talent retention, (8) main street business support, and (9) reducing entrepreneurial risk. These activities could be the basis for more profound impact measurement efforts related to various grant opportunities to address wicked problems in a solution ecosystem.

While many community colleges struggle with budgetary issues, the alignment and opportunity are clear. Community colleges could partner with bachelorette-granting institutions for win-win economic development outcomes. This collaborative approach could create a more significant collective impact for the SDGs. Alternatively, if funding for the I-Lab cannot be found, educators can incorporate technology and innovation competencies for sustainability and the sharing economy into entrepreneurial courses.

The study’s limitations are based on a need for more stakeholder feedback. For example, the study did not include the perspectives of academic business leaders regarding (1) the pedagogical role(s) of higher education in addressing wicked problems post-COVID, (2) the strategic value of accelerators to address wicked problems in business post-COVID, and (3) the perspectives of how entrepreneurial faculty influencers in the USA view societal impact, and (4) entrepreneurial faculty influencers regarding the ecosystem network of wicked problems for business leaders post-COVID? The representative sample may only apply to some countries, cultures, and less developed entrepreneurial ecosystems. Additionally, incorporating quantitative research using the role variables identified in the current study can strengthen future findings and recommendations.

The findings suggest that entrepreneurial leadership addressing wicked problems (i.e., SDGs), such as quality education, decent work, gender equality, and economic growth, may provide significant value for students, community colleges, and society. The study’s findings provide the groundwork for future research.

Future research related to this study would benefit from exploring the following research questions: How do business leaders describe higher education’s pedagogical role(s) in addressing wicked problems post-COVID? How do business leaders explain the strategic value of accelerators in addressing wicked problems? How do entrepreneurial faculty influencers in the USA view societal impact? What is the ecosystem network of wicked problems for business leaders post-COVID?

Policy experts, scientists, entrepreneurs, and business leaders have warned of global social, economic, and environmental risks for years (World Economic Forum, 2020 ). Specific risks include poverty, inequality, climate change, and infectious disease, to name a few (Deming, 1994 ). While academic researchers have debated the role of universities in addressing wicked problems (Dentoni & Bitzer, 2015 ), the role of community colleges was largely unknown due to a gap in the research. With over 1200 community colleges across America, the institutions are well-suited to serve as incubators of post-COVID recovery to help communities build back better and more equitable. Together, we can better tackle these wicked problems, positively impacting local communities across America.

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Steidle, S.B., Glass, C., Rice, M. et al. Addressing Wicked Problems (SDGs) Through Community Colleges: Leveraging Entrepreneurial Leadership for Economic Development Post-COVID. J Knowl Econ (2024). https://doi.org/10.1007/s13132-024-01890-4

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