case study benefits knowledge accumulation in following domains except

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

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

Research question

• Conceptual framework
• Conceptual vs. theoretical framework

Data collection

• Qualitative research methods
• Focus groups
• Observational research

What is a case study?

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

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

Case studies

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

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

Definition of a case study

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

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

Characteristics of case studies

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

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

The role of case studies in research

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

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

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

What is the purpose of a case study?

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

Why use case studies in qualitative research?

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

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

The explanatory, exploratory, and descriptive roles of case studies

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

The impact of case studies on knowledge development

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

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

Types of case studies

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

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

Exploratory case studies

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

Descriptive case studies

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

Explanatory case studies

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

Intrinsic, instrumental, and collective case studies

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

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

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

Critical information systems research

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

Health research

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

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

Asthma research studies

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

Other fields

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

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

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

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

Propositions

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

Units of analysis

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

Argumentation

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

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

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

Defining the research question

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

Selecting and defining the case

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

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

Developing a detailed case study protocol

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

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

Collecting data

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

Analyzing and interpreting data

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

Writing the case study report

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

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

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

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

Observations

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

Documents and artifacts

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

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

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

Ensuring the quality of data collection

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

Data analysis

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

Organizing the data

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

Categorizing and coding the data

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

Identifying patterns and themes

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

Interpreting the data

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

Verification of the data

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

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

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

Benefits include the following:

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

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

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

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

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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 » Case Study – Methods, Examples and Guide

Case Study – Methods, Examples and Guide

Table of Contents

A case study is a research method that involves an in-depth examination and analysis of a particular phenomenon or case, such as an individual, organization, community, event, or situation.

It is a qualitative research approach that aims to provide a detailed and comprehensive understanding of the case being studied. Case studies typically involve multiple sources of data, including interviews, observations, documents, and artifacts, which are analyzed using various techniques, such as content analysis, thematic analysis, and grounded theory. The findings of a case study are often used to develop theories, inform policy or practice, or generate new research questions.

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

A single-case study is an in-depth analysis of a single case. This type of case study is useful when the researcher wants to understand a specific phenomenon in detail.

For Example , A researcher might conduct a single-case study on a particular individual to understand their experiences with a particular health condition or a specific organization to explore their management practices. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a single-case study are often used to generate new research questions, develop theories, or inform policy or practice.

Multiple-Case Study

A multiple-case study involves the analysis of several cases that are similar in nature. This type of case study is useful when the researcher wants to identify similarities and differences between the cases.

For Example, a researcher might conduct a multiple-case study on several companies to explore the factors that contribute to their success or failure. The researcher collects data from each case, compares and contrasts the findings, and uses various techniques to analyze the data, such as comparative analysis or pattern-matching. The findings of a multiple-case study can be used to develop theories, inform policy or practice, or generate new research questions.

Exploratory Case Study

An exploratory case study is used to explore a new or understudied phenomenon. This type of case study is useful when the researcher wants to generate hypotheses or theories about the phenomenon.

For Example, a researcher might conduct an exploratory case study on a new technology to understand its potential impact on society. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as grounded theory or content analysis. The findings of an exploratory case study can be used to generate new research questions, develop theories, or inform policy or practice.

Descriptive Case Study

A descriptive case study is used to describe a particular phenomenon in detail. This type of case study is useful when the researcher wants to provide a comprehensive account of the phenomenon.

For Example, a researcher might conduct a descriptive case study on a particular community to understand its social and economic characteristics. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a descriptive case study can be used to inform policy or practice or generate new research questions.

Instrumental Case Study

An instrumental case study is used to understand a particular phenomenon that is instrumental in achieving a particular goal. This type of case study is useful when the researcher wants to understand the role of the phenomenon in achieving the goal.

For Example, a researcher might conduct an instrumental case study on a particular policy to understand its impact on achieving a particular goal, such as reducing poverty. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of an instrumental case study can be used to inform policy or practice or generate new research questions.

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

Interviews involve asking questions to individuals who have knowledge or experience relevant to the case study. Interviews can be structured (where the same questions are asked to all participants) or unstructured (where the interviewer follows up on the responses with further questions). Interviews can be conducted in person, over the phone, or through video conferencing.

Observations

Observations involve watching and recording the behavior and activities of individuals or groups relevant to the case study. Observations can be participant (where the researcher actively participates in the activities) or non-participant (where the researcher observes from a distance). Observations can be recorded using notes, audio or video recordings, or photographs.

Documents can be used as a source of information for case studies. Documents can include reports, memos, emails, letters, and other written materials related to the case study. Documents can be collected from the case study participants or from public sources.

Surveys involve asking a set of questions to a sample of individuals relevant to the case study. Surveys can be administered in person, over the phone, through mail or email, or online. Surveys can be used to gather information on attitudes, opinions, or behaviors related to the case study.

Artifacts are physical objects relevant to the case study. Artifacts can include tools, equipment, products, or other objects that provide insights into the case study phenomenon.

How to conduct Case Study Research

Conducting a case study research involves several steps that need to be followed to ensure the quality and rigor of the study. Here are the steps to conduct case study research:

• Define the research questions: The first step in conducting a case study research is to define the research questions. The research questions should be specific, measurable, and relevant to the case study phenomenon under investigation.
• Select the case: The next step is to select the case or cases to be studied. The case should be relevant to the research questions and should provide rich and diverse data that can be used to answer the research questions.
• Collect data: Data can be collected using various methods, such as interviews, observations, documents, surveys, and artifacts. The data collection method should be selected based on the research questions and the nature of the case study phenomenon.
• Analyze the data: The data collected from the case study should be analyzed using various techniques, such as content analysis, thematic analysis, or grounded theory. The analysis should be guided by the research questions and should aim to provide insights and conclusions relevant to the research questions.
• Draw conclusions: The conclusions drawn from the case study should be based on the data analysis and should be relevant to the research questions. The conclusions should be supported by evidence and should be clearly stated.
• Validate the findings: The findings of the case study should be validated by reviewing the data and the analysis with participants or other experts in the field. This helps to ensure the validity and reliability of the findings.
• Write the report: The final step is to write the report of the case study research. The report should provide a clear description of the case study phenomenon, the research questions, the data collection methods, the data analysis, the findings, and the conclusions. The report should be written in a clear and concise manner and should follow the guidelines for academic writing.

Examples of Case Study

Here are some examples of case study research:

• The Hawthorne Studies : Conducted between 1924 and 1932, the Hawthorne Studies were a series of case studies conducted by Elton Mayo and his colleagues to examine the impact of work environment on employee productivity. The studies were conducted at the Hawthorne Works plant of the Western Electric Company in Chicago and included interviews, observations, and experiments.
• The Stanford Prison Experiment: Conducted in 1971, the Stanford Prison Experiment was a case study conducted by Philip Zimbardo to examine the psychological effects of power and authority. The study involved simulating a prison environment and assigning participants to the role of guards or prisoners. The study was controversial due to the ethical issues it raised.
• The Challenger Disaster: The Challenger Disaster was a case study conducted to examine the causes of the Space Shuttle Challenger explosion in 1986. The study included interviews, observations, and analysis of data to identify the technical, organizational, and cultural factors that contributed to the disaster.
• The Enron Scandal: The Enron Scandal was a case study conducted to examine the causes of the Enron Corporation’s bankruptcy in 2001. The study included interviews, analysis of financial data, and review of documents to identify the accounting practices, corporate culture, and ethical issues that led to the company’s downfall.
• The Fukushima Nuclear Disaster : The Fukushima Nuclear Disaster was a case study conducted to examine the causes of the nuclear accident that occurred at the Fukushima Daiichi Nuclear Power Plant in Japan in 2011. The study included interviews, analysis of data, and review of documents to identify the technical, organizational, and cultural factors that contributed to the disaster.

Application of Case Study

Case studies have a wide range of applications across various fields and industries. Here are some examples:

Business and Management

Case studies are widely used in business and management to examine real-life situations and develop problem-solving skills. Case studies can help students and professionals to develop a deep understanding of business concepts, theories, and best practices.

Case studies are used in healthcare to examine patient care, treatment options, and outcomes. Case studies can help healthcare professionals to develop critical thinking skills, diagnose complex medical conditions, and develop effective treatment plans.

Case studies are used in education to examine teaching and learning practices. Case studies can help educators to develop effective teaching strategies, evaluate student progress, and identify areas for improvement.

Social Sciences

Case studies are widely used in social sciences to examine human behavior, social phenomena, and cultural practices. Case studies can help researchers to develop theories, test hypotheses, and gain insights into complex social issues.

Law and Ethics

Case studies are used in law and ethics to examine legal and ethical dilemmas. Case studies can help lawyers, policymakers, and ethical professionals to develop critical thinking skills, analyze complex cases, and make informed decisions.

Purpose of Case Study

The purpose of a case study is to provide a detailed analysis of a specific phenomenon, issue, or problem in its real-life context. A case study is a qualitative research method that involves the in-depth exploration and analysis of a particular case, which can be an individual, group, organization, event, or community.

The primary purpose of a case study is to generate a comprehensive and nuanced understanding of the case, including its history, context, and dynamics. Case studies can help researchers to identify and examine the underlying factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and detailed understanding of the case, which can inform future research, practice, or policy.

Case studies can also serve other purposes, including:

• Illustrating a theory or concept: Case studies can be used to illustrate and explain theoretical concepts and frameworks, providing concrete examples of how they can be applied in real-life situations.
• Developing hypotheses: Case studies can help to generate hypotheses about the causal relationships between different factors and outcomes, which can be tested through further research.
• Providing insight into complex issues: Case studies can provide insights into complex and multifaceted issues, which may be difficult to understand through other research methods.
• Informing practice or policy: Case studies can be used to inform practice or policy by identifying best practices, lessons learned, or areas for improvement.

Advantages of Case Study Research

There are several advantages of case study research, including:

• In-depth exploration: Case study research allows for a detailed exploration and analysis of a specific phenomenon, issue, or problem in its real-life context. This can provide a comprehensive understanding of the case and its dynamics, which may not be possible through other research methods.
• Rich data: Case study research can generate rich and detailed data, including qualitative data such as interviews, observations, and documents. This can provide a nuanced understanding of the case and its complexity.
• Holistic perspective: Case study research allows for a holistic perspective of the case, taking into account the various factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and comprehensive understanding of the case.
• Theory development: Case study research can help to develop and refine theories and concepts by providing empirical evidence and concrete examples of how they can be applied in real-life situations.
• Practical application: Case study research can inform practice or policy by identifying best practices, lessons learned, or areas for improvement.
• Contextualization: Case study research takes into account the specific context in which the case is situated, which can help to understand how the case is influenced by the social, cultural, and historical factors of its environment.

Limitations of Case Study Research

There are several limitations of case study research, including:

• Limited generalizability : Case studies are typically focused on a single case or a small number of cases, which limits the generalizability of the findings. The unique characteristics of the case may not be applicable to other contexts or populations, which may limit the external validity of the research.
• Biased sampling: Case studies may rely on purposive or convenience sampling, which can introduce bias into the sample selection process. This may limit the representativeness of the sample and the generalizability of the findings.
• Subjectivity: Case studies rely on the interpretation of the researcher, which can introduce subjectivity into the analysis. The researcher’s own biases, assumptions, and perspectives may influence the findings, which may limit the objectivity of the research.
• Limited control: Case studies are typically conducted in naturalistic settings, which limits the control that the researcher has over the environment and the variables being studied. This may limit the ability to establish causal relationships between variables.
• Time-consuming: Case studies can be time-consuming to conduct, as they typically involve a detailed exploration and analysis of a specific case. This may limit the feasibility of conducting multiple case studies or conducting case studies in a timely manner.
• Resource-intensive: Case studies may require significant resources, including time, funding, and expertise. This may limit the ability of researchers to conduct case studies in resource-constrained settings.

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• Case Study | Definition, Examples & Methods

Case Study | Definition, Examples & Methods

Published on 5 May 2022 by Shona McCombes . Revised on 30 January 2023.

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

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

Table of contents

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

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

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

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

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

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

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

If you find yourself aiming to simultaneously investigate and solve an issue, consider conducting action research . As its name suggests, action research conducts research and takes action at the same time, and is highly iterative and flexible. 

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

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

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

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

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

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

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

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

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

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

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Advancing Scientific Research in Education (2005)

Chapter: appendix b understanding and promoting knowledge accumulation: summary of workshop key points, appendix b understanding and promoting knowledge accumulation: summary of workshop key points.

T his appendix is a summary of Understanding and Promoting Knowledge Accumulation in Education: Tools and Strategies for Education Research, the second workshop in the series conducted by the Committee on Research in Education. The workshop featured a discussion of conceptual ideas about reflections, definitions, and challenges associated with knowledge accumulation, generalizability, and replication in education research. It also included a discussion of tools to promote an accumulated knowledge base derived from education research, many of which we highlight in our recommendations.

Rather than issue a separate report summarizing the workshop, the committee decided to develop a summary of key points that provide context for, and help illuminate, the conclusions and recommendations in this report. This decision was based on the recognition that the ideas discussed at this workshop—while by no means an exhaustive review of the philosophy of science or nature of education research—provide an important intellectual foundation for all of the issues and strategies that were discussed during the workshop as well as throughout the workshop series (see Appendix A for a compilation of agendas).

The workshop had two objectives. The first was to provide a context for understanding the concept of knowledge accumulation, both generally and with respect to education research. No one study or evaluation—no matter how rigorous—can single-handedly chart the path of progress for education policy and practice, nor can one study adequately sum up the

state of understanding in a field or subfield. The challenge for research fields—including education research—is to find ways of facilitating the growth and refinement of knowledge about education phenomena over time. The second objective was to focus on concrete ways this progress of scientific knowledge in education research could be facilitated.

Thus, the workshop had two main parts: the first part featured a series of presentations and discussions designed to clarify phrases and terms like knowledge accumulation, generalizability, and replication in education. The second part featured discussions of three sets of tools for developing a more coherent body of knowledge from education research: developing common measures, sharing data, and taking stock of what is known. The appendix follows this structure, summarizing key points from each workshop part. Because much of what was discussed in the second part of the workshop—specific tools and strategies for promoting knowledge accumulation—is featured in boxes or in the conclusions and recommendations in the main body of the report, that section is significantly shorter than the first.

KNOWLEDGE ACCUMULATION: WHAT DOES IT MEAN?

Kenji Hakuta, in transition at the time of the event between Stanford University and the University of California, Merced, began the day with a presentation that considered key terms and ideas associated with knowledge accumulation and then traced the example of research in bilingual education to illustrate them. Following this overview, Kenneth Howe, of the University of Colorado focused on the interaction between the progression of scientific understanding and the methodological frameworks that researchers have utilized to study education. Reflecting on these two presentations, representatives from three disciplines and fields outside of education offered their perspectives on how the nature of knowledge accumulation in their fields is similar to and different from that in education: Jay Labov, of the National Research Council (NRC), on the biological sciences; David McQueen, of the Centers for Disease Control and Prevention, on epidemiology; and Sidney Winter, of the University of Pennsylvania, on business. Finally, two presentations that traced lines of inquiry in education research illustrated these core ideas with concrete examples: David Cohen, of the University of Michigan; and Helen (Sunny) Ladd, of Duke University, on the role of resources in school and student achievement; and Barbara Rogoff, of the University of California at Santa Cruz

Foundation Professor of Psychology, on the relationship between culture and learning.

Overall, the discussion made clear that knowledge accumulation, generalizability, and replication are terms that seem to have a straightforward meaning on the surface but are less clear when examined more closely. In general, presenters seemed to agree that knowledge accumulation is a way to think about the progress of scientific research—how investigators make sense of, and build on, the studies that shed light on particular phenomena. As Cohen clarified, however, it is more than just the “heaping up” of findings. It involves extending previous findings, including the elaboration and revision of accepted theories, with an eye always toward the growth of systematic understanding. In some cases, knowledge accumulation can involve wholesale replacement of a paradigm, as pointed out by McQueen.

The progression of scientific knowledge is described in Scientific Research in Education in these terms: “the path to scientific understanding … is choppy, pushing the boundaries of what is known by moving forward in fits and starts as methods, theories, and empirical findings evolve” (National Research Council, 2002). This first workshop session elaborated this core idea of methods, theories, and empirical findings interacting and growing in nonlinear ways.

Empirical and Theoretical Work

Several presenters described the dynamic relationship between theoretical or conceptual ideas in a field and the empirical studies that test their adequacy in modeling phenomena. Scientific understanding progresses when the field attends to both theoretical development and empirical testing and analysis; one without the other is not sufficient. Theory without empirical backing lacks real-world testing of its explanatory power. And data without theory leads to “dust-bowl” empiricism—that is, data that lack meaning or relevance to describing or modeling the phenomena of interest (teaching, learning, and schooling in education).

Rogoff provided the clearest illustration of how related lines of inquiry developed in cross-cultural psychology and sociolinguistics by researchers moving back and forth between periods of empirical investigation and theory building as one informed the other over time.

As she described it, in the 1960s and 1970s, there was a great deal of empirical investigation—about 100 studies—in the area of cross-cultural

psychology (see Munroe, Munroe, and Whiting, 1981). This work applied cognitive tests in international settings and led researchers to question previous assumptions about the generalizability of developmental and learning theories that were dominant at the time. Through the multitude of studies conducted in this arena, it became clear that context mattered for evaluating learning.

Following that era, in which a great deal of empirical work was carried out, a period of theory-building ensued. During the late 1970s to the early 1990s, the field of cultural psychology developed a theory that allowed researchers to take context into account, rather than assuming that what was being examined was a window on general functioning. An influential event in this regard was the translation of Lev Vygotsky’s work into English in 1978. It demonstrated how one could use both context and individual aspects of development in the same view. In short, his theory argued that individual development is a function of social and cultural involvements. Related cultural research with underserved populations in the United States also demonstrated the importance of considering the familiarity of the context in interpreting performance on cognitive tests and other contexts. Tests themselves are being investigated as a context with which some children are unfamiliar.

In her presentation on the role of resources in school and student achievement, Ladd demonstrated how different lines of inquiry in one broad area can emanate from different theoretical orientations or questions. She described three types of questions that have been prominent in this area of research:

1. What is the impact of school resources on educational outcomes? (The “effects” question.)

Ladd identified this question as the one addressed in the so-called education production function literature. In that literature, educational outcomes (defined as student achievement, educational attainment, or subsequent wage earnings) are typically modeled as a function of school inputs and family background characteristics (see, for example, the meta analyses by Hanushek [1986, 1997]). Such models have become increasingly sophisticated over time as a result of richer data sets and enhanced methodology. Notably missing from these models are teacher practices and institutional context. Hence, to the extent that particular practices are correlated with a specific school input, such as class size, across observations within a sample, the estimated impact of class size on student achievement reflects

not only class size but also any teacher practices correlated with variations in class size.

2. What resources would be needed to achieve a particular desired educational outcome? (The “adequacy” question.)

Since the early 1990s, several economists have focused on this line of work, mainly using expenditure data at the district level. Emerging from this approach is the conclusion that some students, for example, those from low-income families, are more challenging to educate, and hence, require more resources to achieve a given educational outcome, than do students from more affluent families. (See, e.g., Duncombe and Yinger, in press; Yinger, 2004; Reschovsky, 1994; Reschovsky and Imazeki, 2003.) Research being conducted by Cohen, Raudenbush, and Ball (2002) also falls into this category of adequacy research. They start out with the notion of instructional goals, and then ask what instructional strategies are most productive in reaching those goals, how much these strategies cost, and, hence, what resources would be required.

3. What can be done to make a given level of resources more productive toward the goal of educational outcomes? (The “productivity” question.)

This line of research examines what can be done to make a given level of resources more productive in achieving educational outcome goals. Much of the effective schools literature of the 1970s and 1980s falls under this category (see Stedman, 1985, for a summary). In this line of work, researchers studied schools identified as being effective in raising achievement for disadvantaged students to determine what practices were common across effective schools. Ladd also identified other important work that addresses the productivity question, including Monk’s (1992) discussion about the importance of investigating classroom practices and work summarized in Chapter 5 of the NRC report Making Money Matter (1999).

Method Matters

In addition to empirical observation and theoretical notions, a third dimension of the progression of research knowledge is the methods used to collect, analyze, and relate the data to the conceptual framework. Indeed, the main thesis put forth at the workshop by Howe was that questions concerning knowledge accumulation are difficult to disentangle from questions concerning broader methodological frameworks. He specifically argued that the experimental paradigm (that is, relying on random assignment designs) may encourage the accumulation of knowledge about easy-

to-manipulate, simplistic instructional approaches. He also suggested that since experimental studies in education typically employ random assignment, rather than random selection (and thus are typically limited to those who volunteer), the generalizability of the findings is limited. Finally, he argued that experiments do not provide knowledge about the precise mechanisms of causal phenomena, which is necessary for deeper knowledge-building.

The exchange that followed the presentations extended the discussion of methodology by focusing on the need to use multiple methodologies in appropriate ways to promote broad understanding of the complexities of teaching, learning, and schooling over time. Using research on class size reduction as an example, Harris Cooper, who at the time of the workshop was in transition between the University of Missouri, Columbia, and Duke University, pointed out that examining the findings from small, qualitative studies opened up the “black box” and revealed that not only were teachers spending less time on classroom management and more time on instruction in smaller classes, but also that they were conducting more enrichment activities. This finding alerted the research community to a host of potential new effects that traditional quantitative research and the main findings about the effects of class size reduction on achievement would not—could not—have illuminated.

Later in the day, other speakers picked up the same theme. Hugh (Bud) Mehan, of the University of California, San Diego, provided an example of how the skillful combination of quantitative and qualitative methodologies is not only powerful but may also be necessary in education research. In describing his work in scaling up school improvement efforts beginning in a few schools in San Diego, extending to the state of California, and then growing yet again to multiple states, Mehan argued that the methodological approaches required to conduct the research on this program and its growth were both quantitative and qualitative. He suggested that although in individual investigations, quantitative and qualitative research are typically carried out independently, in carrying out large-scale studies in which programs are introduced and studied in increasing numbers of sites, this separation can no longer be sustained.

Public Interest and Contestation

Education research is often criticized for being endlessly contested, both among researchers and in the broader public community. Several par-

ticipants mentioned the role of the public in shaping research, in both education and other fields, underscoring the point that public criticism is not unique to education and that this interest has both positive and negative effects.

Cohen argued most directly that education research both benefits and suffers as a field from high public interest. This level of involvement from the public can be trying for researchers as they try to balance their approaches to research on education issues with public concern that can often take on a highly political character. Public interest also lends the potential for greater success for the research field. The National Institutes of Health (NIH) have benefited greatly from the public demand for high-quality medical research in terms of rising appropriations. However, the high level of public interest in education is less productive for increasing the use of research findings, because the public places low value on research in education. While the public is highly interested in questions of how best to educate children, they rarely look to research to provide answers of value.

And there are opportunity costs associated with letting policy and political issues drive a research agenda. Hakuta’s depiction of the bilingual education research following publicly framed dichotomies of program options—rather than research-driven, theoretically derived models based on practice—shows that this orientation led to a great deal of work trying to explain only a small fraction of the variation in how English-language learners best learn the language. Only recently, he argued, have researchers turned their attention to studies that focus on questions most relevant to understanding this key issue.

McQueen offered another example from research on the relationship between smoking and lung cancer. Ethical considerations obviously preclude randomly assigning people to smoke, so research findings were criticized as not being valid. Couple this fact with the involvement of interested parties (cigarette manufacturers, the antismoking lobby), McQueen posited, and research findings became even more contested. Winter offered another example from the realm of business, commenting that corporate governance is a “violently” contested area right now, with implications for research.

Finally, Labov elaborated that there are both internal and external controversies that play into claims regarding the contested nature of research. For example, in biology, evolution is an area that is hotly debated. Within the field, most biologists accept the idea of evolution as a key organizing principle of the discipline, but there is debate surrounding the mechanisms

by which evolution occurs—such as Charles Darwin’s idea of incremental change and Steven Jay Gould’s idea of punctuated equilibrium. Outside the field, this debate is interpreted as a more serious controversy, and some outsiders suggest this as evidence that the theory of evolution itself is in question or is challenged by most biologists.

Contrasting Fields and Disciplines

An important contrast emerged with respect to the nature of theoretical ideas in fields of practice (like education and business) versus those in traditional scientific disciplines (like cell biology). McQueen articulated most explicitly that, in such applied fields as medicine and public health, theoretical ideas are different from those found in such disciplines as chemistry and biology. Medicine and public health are fields of action, he argued; as such, they are characterized by carrying out interventions, so practitioners draw on theories from multiple disciplines. He pointed out that when one works in a field rather than a discipline, it is difficult to set up a theoretical base whereby hypotheses and causal relationships are tested, as demanded by a more strict scientific model.

In his presentation, Hakuta provided a synopsis of the history of research on teaching students with limited English proficiency (LEP) that illustrated how education as a (contested) field of action influenced the creation of theoretical frameworks that shaped the associated field of research. In 1974, the Supreme Court decided in Lau vs. Nichols that addressing the needs of children who arrive in school not speaking English is a district and state responsibility. No particular approach was prescribed in the decision, so two general approaches for educating LEP students each developed a following: English-language immersion and a bilingual approach. LEP education became (and continues to be) a controversial issue; subsequently, a great deal of resources was invested in research to investigate the question of which was the better method. To date, the research shows a slight advantage in English-reading comprehension for LEP students who have been in transitional bilingual programs (see, e.g., Willig, 1985; Greene, 1998: National Research Council, 1997). However, comparatively very little research has focused on the gap in reading scores between LEP and non-LEP students and its growth as students progress through school. Specifically, in grade one, the gap in age equivalent scores of reading achievement between LEP and non-LEP students is equivalent to approximately one year in age. By fifth grade, that gap has increased to

two years. Nothing in the research to date can explain that gap. Public pressure in the applied field of education, Hakuta argued, has led to an overemphasis on a single research question, inhibiting the growth of knowledge on other important questions.

In his discussion of research on the effects of resources on school and student achievement, Cohen pointed to another area in which a great deal of time and effort has been devoted to researching a phenomenon in the applied field of education research that accounts for a small percentage of the differences in student achievement. In describing research that explores the relationships among resources, instruction, and learning outcomes, Cohen began by summarizing the seminal On Equality of Educational Opportunity, known as the Coleman report, of 1966. Coleman investigated differences in performance among schools and concluded that resources made little or no difference. Since that report was released, there has been a great deal of additional investigation into this topic.

However, as Cohen pointed out, 80 percent of the differences in student achievement lie within schools, not from school to school, so there is a great deal of variation that is not being examined in this line of research. While research is ongoing in examining differences in both the 80 percent and the 20 percent, the public debate framed the question and the theoretical conceptions early, persisting for decades.

Context Dependence

Workshop speakers also argued that in a field like education, which is characterized by complex human interactions and organizational, cultural, and political influences, attending to context in the research process is critical. Thus, it is unreasonable to expect simple generalizations that are broadly applicable. That said, however, the field advances when investigators strive to make generalizations that build in descriptions of how context affects results. Furthermore, variation deriving from contextual factors is helping to reveal relationships: without variation, there is no way to learn about effects and patterns among variables and concepts. This context dependence is a theme that continued throughout the day, but in this session it became clear that it is not a characteristic that is unique to research in education.

In business, as Winter described, many situations depend on the interactions between employees, investors, and customers. These interactions can be quite complex and vary from one grouping to the next. As such,

those who conduct research on business practices encounter many of the same obstacles in trying to understand the extent to which findings are applicable to multiple settings that education researchers do. In other words, the strategy that business researchers found was employed with resounding success in Site A may not be at all effective in Site B.

The importance of context dependence in the conduct of research is further demonstrated by the history of physiological experimentation at NIH. As Labov pointed out, NIH came under a great deal of criticism about 25 years ago because clinical trials were being conducted primarily on white male subjects. However, such results often do not generalize from one gender to the other. As a consequence, many of the treatments for diseases that affect both men and women, such as heart disease, were not as effective for women as they were for men, but without explicitly designing research to estimate differential effects on men and women, physicians would not know to prescribe different regimens.

In one sense, participants characterized the fact that results vary across contexts as a challenge to efforts that aim to make summary statements applicable to multiple settings, times, and populations. Mehan, for example, quipped that the one core principle of ethnographic work is “it depends,” referring to this relationship between findings and contexts. However, explaining variation is the core purpose of research, so the variation that results from this context dependence also enables attempts to model differences in outcomes. Rogoff, echoed by a few other workshop participants, argued that the field of education ought to focus its efforts on elaborating theories and crafting “universal laws that account for this context dependence and thus reflect the complexity of educational phenomena.”

Relationship to Practice

Extending the discussion of education and business as fields rather than disciplines, two dimensions of the relationship between practice and research were elaborated. First, David Klahr, of Carnegie Mellon University, when questioning the presenters, offered the idea that education research might be more comparable to an engineering discipline than a science. He continued by arguing that knowledge accumulates in engineering through practice. For example, there is a great deal of variability from one space shuttle to another, even though they are all in the same series. As one shuttle would be completed, he continued, engineers would apply what

was learned in the construction of that shuttle to the design and construction of the next.

Second, a conversation about the role of cases in education and business research further elaborated the close link between practice and research in these fields. Cohen’s description of a particular line of work in which he has been involved in the resources and student achievement area illustrated this idea in education. Along with his colleagues Steve Raudenbush and Deborah Ball, Cohen has spent considerable time examining the relationship between resources and student achievement. They have found that much of the research on school effects assumes a model in which there are desired outcomes that are directly caused by the input of resources. However, he argued, this is not plausible, because resources become active only when they are used. Therefore, in order to validly measure the effects of resources, the conditions in which they are used must be taken into account, and this requires attention to practice.

Winter also offered examples of how practice relates to research in business. First, he said that for students engaged in dissertation work, they are fortunate if they can carry out two or three years of work in an area without a merger or a regulatory incident interfering with their research site. He went on to say that the use of cases in business schools is to create effective managers that “more or less give people a vision of what it means to be pushing the levers that are available for controlling a management situation.”

Continuing to explore the idea of how theoretical ideas and research priorities can and should be driven by the practices of the field (education, business, medicine, etc.) and their surrounding political contexts, Lauress Wise pointed out that most NRC studies that integrate and summarize research on a topic across disciplines and fields do so at the request of public officials and are therefore at least partially shaped by the political and policy questions of the day.

Two talks on scaling up brought into sharp relief how research and practice can feed into one another in ways that benefit both. Robert Slavin, of Johns Hopkins University and chairman of the Success for All Foundation, illustrated the potential for mutually reinforcing relationships between educational practice and research and evaluation by detailing the history of the development of the Success for All program. According to Slavin, by the 1970s a body of evidence about the effectiveness of cooperative learning pointed to the value of such student team approaches (see Slavin, 1995).

At the same time, the idea was gaining a foothold among practitioners, and so their use became commonplace. However, the fundamental elements that research suggested needed to be in place for them to promote learning—groups were structured, purposes were clear and shared by all students, and each member had a specific task or role to play—were typically not in place in practice. Slavin told the group that the research findings and the disconnect between them and what was going on in schools was the “intellectual background” for the development of Success for All, which began in one school in Baltimore and is now operating in about 1,500 schools across the country. As the program grew, Slavin and his team have engaged in a development process of implementing programs, studying how they are used and what their influences are, and then feeding that knowledge back into program improvement but also, importantly, into the larger knowledge base on cooperative learning, comprehensive school reform, and program evaluation.

Mehan, too, touched on this idea by offering a lesson from his experience in scaling up school reform efforts in California and the fact that the research that documented and analyzed the expansion was an iterative process. The iterations were necessary, he argued, to strike the right balance between a standard set of questions and data collection protocols and the need to recognize and articulate what he termed “emergent phenomena.” Because program elements interact with local circumstances in different ways, Mehan argued that the kinds of issues and data that are relevant to understanding the implementation and effectiveness of the program will vary to some degree across sites.

Research Community

A final theme raised in this initial workshop session was the crucial role of the community of investigators, including funding agencies, to support efforts to integrate and build on findings from related work. Hakuta said it plainly: “It is not just the methods that enable knowledge to accumulate,” but also fundamental are “the critiques and the questioning that happen in science.”

While such critique and debate in a field is healthy and promotes the growth of knowledge, workshop speakers suggested that it is important to keep the debate at a civil level. One audience member noted that a tone of derisiveness and lack of respect can creep into the discourse, especially across disciplines, which is to the detriment of the kind of building community

that can facilitate knowledge accumulation. Winter reiterated this point, suggesting that the kind of standards that would be most useful to researchers are standards for “intelligent debate.”

One issue that is closely related to community is the lack of common quality standards in education research. Hakuta suggested that standards could be helpful, but he cautioned that standards generated within the community are much more likely to be accepted by researchers than standards that are imposed from the top down. Across workshop speakers, opinions on the topic varied, with some suggesting that standards would serve as an impediment to research, and others suggesting that standards would improve research quality. Rogoff cautioned that standardization could be premature; it could short-circuit the empirical work that needs to be carried out in order to learn more about the regularities across communities and across contexts that would enable the understanding of how culture plays a role in human development. To do this, she argued, lines of research that build on prior studies are needed, because from each study, questions, theories, and ways of doing research are refined.

Other speakers addressed the idea of human capacity in research and its connections to knowledge accumulation. Mehan, for example, discussed the need for thoroughly trained research staff—preferably those who have been working with the team on the issues for some time—to collect data according to protocols and to be attuned to what he called relevant “emergent phenomena” in scaling up and studying the implementation and effects of the Achievement Via Individual Determination, or AVID, program. In a different vein, Harris Cooper, in describing the evolution of meta-analytic methods for summarizing research on effectiveness about a particular intervention, argued that “vote counting”—a way of summarizing literatures commonly used by researchers—is a demonstrably poor method for arriving at valid conclusions about what the research says collectively (in that it consistently leads to an underestimation of the program effect), suggesting that researchers with meta-analytic skills are needed for these tasks.

The discussion of human capacity extended beyond individual investigators. Daniel Berch, of the National Institute of Child Health and Human Development, offered a description of the important role of federal research agency personnel in both taking stock of what is known in an area and in using that information for setting research priorities. Depicting the unique bird’s eye view of the field or fields that agency staff has, Berch described a variety of activities that directors engage in as they work directly

with leading investigators. These include such activities as assembling panelists to participate in workshops that consider the current state of knowledge and potential areas for breakthrough, and listening in on peer review panels on which scholars review proposals for new work—all of which coalesce to inform the ongoing development of research programs.

KNOWLEDGE ACCUMULATION: HOW TO PROMOTE IT

Barbara Schneider, of the University of Chicago, began the second part of the workshop by focusing on the idea of replication, a concept, she argued, that provides an important, unifying idea for creating scientific norms that can unite a community of researchers from different disciplinary perspectives. She asserted that replication begins with data sharing—it is the sharing of information about studies, including the actual data on which findings are based, that makes replication possible. Replication involves applying the same conditions to multiple cases, as well as replicating the designs, including cases that are sufficiently different to justify the generalization of results in theories, she said. Without convergence of results from multiple studies, the objectivity, neutrality, and generalizability of research are questionable.

In addition to addressing more specific topics, David Grissmer, of the RAND Corporation, provided important insights about strategies for knowledge accumulation in education research that explicitly relate theory, data, and measures and connect to the themes described in the previous section. He argued that generating consensus is not a matter of gathering more data or generating better techniques. “It is much more a matter of whether we can get replicable and consistent measurements across social science in general, and education, as a basis for forming theories.” Until there are consistent measurements, he went on to say, it is not possible to build broader theories. Furthermore, it is the role of theory to cut down on the amount of data collected. “Without theory, you collect everything. With theory, you can design a very specific set of experiments to test.” He argued that currently the field of education research is oriented toward making more measurements. As a result, “we have much research, but little knowledge.” Grissmer suggested that progress depends on the field focusing much more on exploring and explaining why research results differ to enable nuanced generalizations that account for variations in findings and contexts.

Several of the ideas and strategies for promoting an accumulated knowledge base in education research discussed during the session are

described in the main body of this report. A very brief synopsis of issues covered and speakers featured in each session is provided here.

Common Measures

Central to the conduct of research is the gathering of data on various measures. Common measures for the types of data collected by researchers can help to promote the accumulation of knowledge by facilitating the comparison and analysis of results across studies in both similar and disparate environments. In a session dedicated to this topic, two speakers elaborated on moving toward more common definitions of important measures in education research. Claudia Buchmann, of Duke University, discussed the development of measures of family background, including socioeconomic status. Michael Nettles, who at the time of the workshop was in transition between the University of Michigan and the Educational Testing Service, discussed issues surrounding the measurement of student achievement.

In her presentation, Buchmann offered a rationale for why measures of socioeconomic status and family background are important in education research and charted the progression of measure development that reflects the challenges of developing a common core of measures in education. She argued that family background measures are required to conduct a fair assessment of educational outcomes by enabling the isolation of outcomes from differences in inputs: student populations in different schools differ from the beginning, so it is necessary to control for this variation. Giving careful thought to how to measure family background relates to the necessity to improve knowledge of the ways that the family, as an institution, affects children’s ability and motivations to learn and their academic achievement. The bulk of Buchmann’s presentation focused on tracing the evolution of the concept of family background, which she demonstrated has become increasingly complex over time. She described simple socioeconomic status measures expanding to include an array of measures targeting different dimensions of this concept: for example, family structure or demographic characteristics, as well as family social and cultural capital. Buchmann also showed, compared, and critiqued how a sampling of major surveys and data collection efforts measured these concepts and their effects on the quality of inferences that could be drawn about key questions across and within them.

Nettles approached the idea of a common set of measures from a slightly different standpoint, focusing on the benefits and drawbacks of

using the National Assessment of Educational Progress (NAEP) as a centralized measure of achievement. He argued that there is a great deal of fragmentation and questionable stability in measuring student achievement. Although NAEP is appealing for a number of reasons, Nettles raised a number of issues related to student motivation, representativeness across geographic areas and other categories, the validity of the test for making particular inferences, and equity and bias, that have significant bearing on research that relies on these measures of student achievement.

Data Sharing

Another set of tools or strategies that can facilitate the continued development of a coherent knowledge base is the sharing of data. In her introductory talk, Schneider pointed to three points of leverage for encouraging data sharing and replication: professional associations, scholarly journals, and data banks.

A panel that focused on data sharing followed consisted of five scholars from a range of positions and roles in the research community: individual investigators, senior officials from federal agencies, and journal editors. Ronald Ehrenberg, of Cornell University, discussed his experience using and reanalyzing the Coleman data. Grissmer focused on the role of NAEP. Marilyn Seastrom, of the National Center for Education Statistics, described the agency’s efforts to maximize access to data while maintaining privacy and confidentiality. Norman Bradburn, of the National Science Foundation, extended Seastrom’s presentation by focusing on broad concepts and tools associated with access, privacy, and confidentiality. And finally, Gary Natriello, of Teachers College, offered ideas on the role of journals in facilitating and promoting data sharing. Key points from these presentations are discussed in Chapter 3 .

Taking Stock

The workshop concluded with a session focused on ways of taking stock—that is, efforts by researchers to summarize what is known in topic areas or subfields. In various ways, investigators in a field periodically assess what (they believe) they know and formally or informally integrate findings from individual studies into the larger body of knowledge. The practice of researchers attempting to replicate previous studies is one way to assess the extent to which findings hold up in different times, places, and

circumstances. Similarly, a researcher who has piloted and evaluated a program at a small number of sites might scale up to a larger number of sites to see if and how results transfer to other settings. Research synthesis and meta-analysis are yet another way to summarize findings across studies of program effectiveness. Explicit efforts to engage groups of investigators (and other stakeholders) in building professional consensus can also generate summative statements that provide an indication of what is known and not known at a particular point in time.

Five speakers offered ideas for how the field can promote the accumulation of research-based knowledge through such work. Mehan and Slavin focused their talks on how scaling up programs or reform models to increasing numbers of schools offers opportunities for contributing to the advancement of scientific understanding while improving program services for participating schools. Cooper described meta-analysis, a methodology used to summarize the findings from multiple studies of program effects. Drawing on personal experience working with committees charged with developing consensus about research findings in areas of education, Wise described the consensus-building process of the NRC. Finally, Berch described the ways in which the National Institute of Child Health and Human Development attempts to understand what is known, what is not known, and how to craft research agendas and competitions based on that understanding.

The presenters seemed to agree that the accumulation of knowledge in education is possible, but challenging. The studies, methods, and activities they described together showed that careful, rigorous attempts to provide summative statements about what is known as a foundation for the continued advancement of scientific research in education are possible. To be sure, impediments exist. Cooper mentioned the tendency of advocacy groups to selectively rely on research results to support their (previously established) political positions and a lack of civility among researchers as particularly acute problems to be overcome. Summing up these sentiments, Cooper put it this way: “knowledge accumulation is possible, but it is not for the faint of heart.”

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Monk, D.H. (1992). Education productivity research: An update and assessment of its role in education finance reform. Educational Evaluation and Policy Analysis , 14 (4), 307-332.

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Transforming education into an evidence-based field depends in no small part on a strong base of scientific knowledge to inform educational policy and practice. Advancing Scientific Research in Education makes select recommendations for strengthening scientific education research and targets federal agencies, professional associations, and universities—particularly schools of education—to take the lead in advancing the field.

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The need for better methods for evaluation in health research has been widely recognised. The ‘complexity turn’ has drawn attention to the limitations of relying on causal inference from randomised controlled trials alone for understanding whether, and under which conditions, interventions in complex systems improve health services or the public health, and what mechanisms might link interventions and outcomes. We argue that case study research—currently denigrated as poor evidence—is an under-utilised resource for not only providing evidence about context and transferability, but also for helping strengthen causal inferences when pathways between intervention and effects are likely to be non-linear.

Case study research, as an overall approach, is based on in-depth explorations of complex phenomena in their natural, or real-life, settings. Empirical case studies typically enable dynamic understanding of complex challenges and provide evidence about causal mechanisms and the necessary and sufficient conditions (contexts) for intervention implementation and effects. This is essential evidence not just for researchers concerned about internal and external validity, but also research users in policy and practice who need to know what the likely effects of complex programmes or interventions will be in their settings. The health sciences have much to learn from scholarship on case study methodology in the social sciences. However, there are multiple challenges in fully exploiting the potential learning from case study research. First are misconceptions that case study research can only provide exploratory or descriptive evidence. Second, there is little consensus about what a case study is, and considerable diversity in how empirical case studies are conducted and reported. Finally, as case study researchers typically (and appropriately) focus on thick description (that captures contextual detail), it can be challenging to identify the key messages related to intervention evaluation from case study reports.

Whilst the diversity of published case studies in health services and public health research is rich and productive, we recommend further clarity and specific methodological guidance for those reporting case study research for evaluation audiences.

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The need for methodological development to address the most urgent challenges in health research has been well-documented. Many of the most pressing questions for public health research, where the focus is on system-level determinants [ 1 , 2 ], and for health services research, where provisions typically vary across sites and are provided through interlocking networks of services [ 3 ], require methodological approaches that can attend to complexity. The need for methodological advance has arisen, in part, as a result of the diminishing returns from randomised controlled trials (RCTs) where they have been used to answer questions about the effects of interventions in complex systems [ 4 , 5 , 6 ]. In conditions of complexity, there is limited value in maintaining the current orientation to experimental trial designs in the health sciences as providing ‘gold standard’ evidence of effect.

There are increasing calls for methodological pluralism [ 7 , 8 ], with the recognition that complex intervention and context are not easily or usefully separated (as is often the situation when using trial design), and that system interruptions may have effects that are not reducible to linear causal pathways between intervention and outcome. These calls are reflected in a shifting and contested discourse of trial design, seen with the emergence of realist [ 9 ], adaptive and hybrid (types 1, 2 and 3) [ 10 , 11 ] trials that blend studies of effectiveness with a close consideration of the contexts of implementation. Similarly, process evaluation has now become a core component of complex healthcare intervention trials, reflected in MRC guidance on how to explore implementation, causal mechanisms and context [ 12 ].

Evidence about the context of an intervention is crucial for questions of external validity. As Woolcock [ 4 ] notes, even if RCT designs are accepted as robust for maximising internal validity, questions of transferability (how well the intervention works in different contexts) and generalisability (how well the intervention can be scaled up) remain unanswered [ 5 , 13 ]. For research evidence to have impact on policy and systems organisation, and thus to improve population and patient health, there is an urgent need for better methods for strengthening external validity, including a better understanding of the relationship between intervention and context [ 14 ].

Policymakers, healthcare commissioners and other research users require credible evidence of relevance to their settings and populations [ 15 ], to perform what Rosengarten and Savransky [ 16 ] call ‘careful abstraction’ to the locales that matter for them. They also require robust evidence for understanding complex causal pathways. Case study research, currently under-utilised in public health and health services evaluation, can offer considerable potential for strengthening faith in both external and internal validity. For example, in an empirical case study of how the policy of free bus travel had specific health effects in London, UK, a quasi-experimental evaluation (led by JG) identified how important aspects of context (a good public transport system) and intervention (that it was universal) were necessary conditions for the observed effects, thus providing useful, actionable evidence for decision-makers in other contexts [ 17 ].

The overall approach of case study research is based on the in-depth exploration of complex phenomena in their natural, or ‘real-life’, settings. Empirical case studies typically enable dynamic understanding of complex challenges rather than restricting the focus on narrow problem delineations and simple fixes. Case study research is a diverse and somewhat contested field, with multiple definitions and perspectives grounded in different ways of viewing the world, and involving different combinations of methods. In this paper, we raise awareness of such plurality and highlight the contribution that case study research can make to the evaluation of complex system-level interventions. We review some of the challenges in exploiting the current evidence base from empirical case studies and conclude by recommending that further guidance and minimum reporting criteria for evaluation using case studies, appropriate for audiences in the health sciences, can enhance the take-up of evidence from case study research.

Case study research offers evidence about context, causal inference in complex systems and implementation

Well-conducted and described empirical case studies provide evidence on context, complexity and mechanisms for understanding how, where and why interventions have their observed effects. Recognition of the importance of context for understanding the relationships between interventions and outcomes is hardly new. In 1943, Canguilhem berated an over-reliance on experimental designs for determining universal physiological laws: ‘As if one could determine a phenomenon’s essence apart from its conditions! As if conditions were a mask or frame which changed neither the face nor the picture!’ ([ 18 ] p126). More recently, a concern with context has been expressed in health systems and public health research as part of what has been called the ‘complexity turn’ [ 1 ]: a recognition that many of the most enduring challenges for developing an evidence base require a consideration of system-level effects [ 1 ] and the conceptualisation of interventions as interruptions in systems [ 19 ].

The case study approach is widely recognised as offering an invaluable resource for understanding the dynamic and evolving influence of context on complex, system-level interventions [ 20 , 21 , 22 , 23 ]. Empirically, case studies can directly inform assessments of where, when, how and for whom interventions might be successfully implemented, by helping to specify the necessary and sufficient conditions under which interventions might have effects and to consolidate learning on how interdependencies, emergence and unpredictability can be managed to achieve and sustain desired effects. Case study research has the potential to address four objectives for improving research and reporting of context recently set out by guidance on taking account of context in population health research [ 24 ], that is to (1) improve the appropriateness of intervention development for specific contexts, (2) improve understanding of ‘how’ interventions work, (3) better understand how and why impacts vary across contexts and (4) ensure reports of intervention studies are most useful for decision-makers and researchers.

However, evaluations of complex healthcare interventions have arguably not exploited the full potential of case study research and can learn much from other disciplines. For evaluative research, exploratory case studies have had a traditional role of providing data on ‘process’, or initial ‘hypothesis-generating’ scoping, but might also have an increasing salience for explanatory aims. Across the social and political sciences, different kinds of case studies are undertaken to meet diverse aims (description, exploration or explanation) and across different scales (from small N qualitative studies that aim to elucidate processes, or provide thick description, to more systematic techniques designed for medium-to-large N cases).

Case studies with explanatory aims vary in terms of their positioning within mixed-methods projects, with designs including (but not restricted to) (1) single N of 1 studies of interventions in specific contexts, where the overall design is a case study that may incorporate one or more (randomised or not) comparisons over time and between variables within the case; (2) a series of cases conducted or synthesised to provide explanation from variations between cases; and (3) case studies of particular settings within RCT or quasi-experimental designs to explore variation in effects or implementation.

Detailed qualitative research (typically done as ‘case studies’ within process evaluations) provides evidence for the plausibility of mechanisms [ 25 ], offering theoretical generalisations for how interventions may function under different conditions. Although RCT designs reduce many threats to internal validity, the mechanisms of effect remain opaque, particularly when the causal pathways between ‘intervention’ and ‘effect’ are long and potentially non-linear: case study research has a more fundamental role here, in providing detailed observational evidence for causal claims [ 26 ] as well as producing a rich, nuanced picture of tensions and multiple perspectives [ 8 ].

Longitudinal or cross-case analysis may be best suited for evidence generation in system-level evaluative research. Turner [ 27 ], for instance, reflecting on the complex processes in major system change, has argued for the need for methods that integrate learning across cases, to develop theoretical knowledge that would enable inferences beyond the single case, and to develop generalisable theory about organisational and structural change in health systems. Qualitative Comparative Analysis (QCA) [ 28 ] is one such formal method for deriving causal claims, using set theory mathematics to integrate data from empirical case studies to answer questions about the configurations of causal pathways linking conditions to outcomes [ 29 , 30 ].

Nonetheless, the single N case study, too, provides opportunities for theoretical development [ 31 ], and theoretical generalisation or analytical refinement [ 32 ]. How ‘the case’ and ‘context’ are conceptualised is crucial here. Findings from the single case may seem to be confined to its intrinsic particularities in a specific and distinct context [ 33 ]. However, if such context is viewed as exemplifying wider social and political forces, the single case can be ‘telling’, rather than ‘typical’, and offer insight into a wider issue [ 34 ]. Internal comparisons within the case can offer rich possibilities for logical inferences about causation [ 17 ]. Further, case studies of any size can be used for theory testing through refutation [ 22 ]. The potential lies, then, in utilising the strengths and plurality of case study to support theory-driven research within different methodological paradigms.

Evaluation research in health has much to learn from a range of social sciences where case study methodology has been used to develop various kinds of causal inference. For instance, Gerring [ 35 ] expands on the within-case variations utilised to make causal claims. For Gerring [ 35 ], case studies come into their own with regard to invariant or strong causal claims (such as X is a necessary and/or sufficient condition for Y) rather than for probabilistic causal claims. For the latter (where experimental methods might have an advantage in estimating effect sizes), case studies offer evidence on mechanisms: from observations of X affecting Y, from process tracing or from pattern matching. Case studies also support the study of emergent causation, that is, the multiple interacting properties that account for particular and unexpected outcomes in complex systems, such as in healthcare [ 8 ].

Finally, efficacy (or beliefs about efficacy) is not the only contributor to intervention uptake, with a range of organisational and policy contingencies affecting whether an intervention is likely to be rolled out in practice. Case study research is, therefore, invaluable for learning about contextual contingencies and identifying the conditions necessary for interventions to become normalised (i.e. implemented routinely) in practice [ 36 ].

The challenges in exploiting evidence from case study research

At present, there are significant challenges in exploiting the benefits of case study research in evaluative health research, which relate to status, definition and reporting. Case study research has been marginalised at the bottom of an evidence hierarchy, seen to offer little by way of explanatory power, if nonetheless useful for adding descriptive data on process or providing useful illustrations for policymakers [ 37 ]. This is an opportune moment to revisit this low status. As health researchers are increasingly charged with evaluating ‘natural experiments’—the use of face masks in the response to the COVID-19 pandemic being a recent example [ 38 ]—rather than interventions that take place in settings that can be controlled, research approaches using methods to strengthen causal inference that does not require randomisation become more relevant.

A second challenge for improving the use of case study evidence in evaluative health research is that, as we have seen, what is meant by ‘case study’ varies widely, not only across but also within disciplines. There is indeed little consensus amongst methodologists as to how to define ‘a case study’. Definitions focus, variously, on small sample size or lack of control over the intervention (e.g. [ 39 ] p194), on in-depth study and context [ 40 , 41 ], on the logic of inference used [ 35 ] or on distinct research strategies which incorporate a number of methods to address questions of ‘how’ and ‘why’ [ 42 ]. Moreover, definitions developed for specific disciplines do not capture the range of ways in which case study research is carried out across disciplines. Multiple definitions of case study reflect the richness and diversity of the approach. However, evidence suggests that a lack of consensus across methodologists results in some of the limitations of published reports of empirical case studies [ 43 , 44 ]. Hyett and colleagues [ 43 ], for instance, reviewing reports in qualitative journals, found little match between methodological definitions of case study research and how authors used the term.

This raises the third challenge we identify that case study reports are typically not written in ways that are accessible or useful for the evaluation research community and policymakers. Case studies may not appear in journals widely read by those in the health sciences, either because space constraints preclude the reporting of rich, thick descriptions, or because of the reported lack of willingness of some biomedical journals to publish research that uses qualitative methods [ 45 ], signalling the persistence of the aforementioned evidence hierarchy. Where they do, however, the term ‘case study’ is used to indicate, interchangeably, a qualitative study, an N of 1 sample, or a multi-method, in-depth analysis of one example from a population of phenomena. Definitions of what constitutes the ‘case’ are frequently lacking and appear to be used as a synonym for the settings in which the research is conducted. Despite offering insights for evaluation, the primary aims may not have been evaluative, so the implications may not be explicitly drawn out. Indeed, some case study reports might properly be aiming for thick description without necessarily seeking to inform about context or causality.

Acknowledging plurality and developing guidance

We recognise that definitional and methodological plurality is not only inevitable, but also a necessary and creative reflection of the very different epistemological and disciplinary origins of health researchers, and the aims they have in doing and reporting case study research. Indeed, to provide some clarity, Thomas [ 46 ] has suggested a typology of subject/purpose/approach/process for classifying aims (e.g. evaluative or exploratory), sample rationale and selection and methods for data generation of case studies. We also recognise that the diversity of methods used in case study research, and the necessary focus on narrative reporting, does not lend itself to straightforward development of formal quality or reporting criteria.

Existing checklists for reporting case study research from the social sciences—for example Lincoln and Guba’s [ 47 ] and Stake’s [ 33 ]—are primarily orientated to the quality of narrative produced, and the extent to which they encapsulate thick description, rather than the more pragmatic issues of implications for intervention effects. Those designed for clinical settings, such as the CARE (CAse REports) guidelines, provide specific reporting guidelines for medical case reports about single, or small groups of patients [ 48 ], not for case study research.

The Design of Case Study Research in Health Care (DESCARTE) model [ 44 ] suggests a series of questions to be asked of a case study researcher (including clarity about the philosophy underpinning their research), study design (with a focus on case definition) and analysis (to improve process). The model resembles toolkits for enhancing the quality and robustness of qualitative and mixed-methods research reporting, and it is usefully open-ended and non-prescriptive. However, even if it does include some reflections on context, the model does not fully address aspects of context, logic and causal inference that are perhaps most relevant for evaluative research in health.

Hence, for evaluative research where the aim is to report empirical findings in ways that are intended to be pragmatically useful for health policy and practice, this may be an opportune time to consider how to best navigate plurality around what is (minimally) important to report when publishing empirical case studies, especially with regards to the complex relationships between context and interventions, information that case study research is well placed to provide.

The conventional scientific quest for certainty, predictability and linear causality (maximised in RCT designs) has to be augmented by the study of uncertainty, unpredictability and emergent causality [ 8 ] in complex systems. This will require methodological pluralism, and openness to broadening the evidence base to better understand both causality in and the transferability of system change intervention [ 14 , 20 , 23 , 25 ]. Case study research evidence is essential, yet is currently under exploited in the health sciences. If evaluative health research is to move beyond the current impasse on methods for understanding interventions as interruptions in complex systems, we need to consider in more detail how researchers can conduct and report empirical case studies which do aim to elucidate the contextual factors which interact with interventions to produce particular effects. To this end, supported by the UK’s Medical Research Council, we are embracing the challenge to develop guidance for case study researchers studying complex interventions. Following a meta-narrative review of the literature, we are planning a Delphi study to inform guidance that will, at minimum, cover the value of case study research for evaluating the interrelationship between context and complex system-level interventions; for situating and defining ‘the case’, and generalising from case studies; as well as provide specific guidance on conducting, analysing and reporting case study research. Our hope is that such guidance can support researchers evaluating interventions in complex systems to better exploit the diversity and richness of case study research.

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Abbreviations

Qualitative comparative analysis

Quasi-experimental design

Randomised controlled trial

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This work was funded by the Medical Research Council - MRC Award MR/S014632/1 HCS: Case study, Context and Complex interventions (TRIPLE C). SP was additionally funded by the University of Oxford's Higher Education Innovation Fund (HEIF).

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Paparini, S., Green, J., Papoutsi, C. et al. Case study research for better evaluations of complex interventions: rationale and challenges. BMC Med 18 , 301 (2020). https://doi.org/10.1186/s12916-020-01777-6

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Knowledge management

Ivan Andreev

Demand Generation & Capture Strategist, Valamis

February 22, 2022 · updated April 2, 2024

14 minute read

Taking advantage of all the expertise within an organization is a great way to maximize its potential. Companies have a well of untapped knowledge within their workforce that is lying dormant or siloed to individual staff or departments.

With the proper management structures in place, this knowledge can be found, stored, and made accessible to the wider workforce, offering tangible business benefits.

What is knowledge management?

Knowledge management in practice, types of knowledge, examples of knowledge management.

• Importance of knowledge management

Benefits of knowledge management

Knowledge management process, knowledge management methods, what are knowledge management systems.

Knowledge management is the conscious process of defining, structuring, retaining, and sharing the knowledge and experience of employees within an organization.

As organizations evolve, expand into new areas, and define their approach to business, they develop significant institutional knowledge . This information is invaluable to the company. Imparting it to new or less experienced staff is vital for maintaining successful operations.

The primary goal of knowledge management is facilitating the connection of staff looking for information, or institutional knowledge, with the people who have it.

With practical knowledge management in place, organizations can spread information and raise the level of expertise held by specific individuals or teams to improve the efficiency of their practices.

It often refers to training and learning in an organization or of its customers. It consists of a cycle of creating, sharing, structuring, and auditing knowledge to maximize the effectiveness of an organization’s collective expertise.

Knowledge management can be separated into three main areas:

• Accumulating knowledge
• Storing knowledge
• Sharing knowledge

By accumulating and storing the staff’s knowledge, companies hold onto what has made them successful in the past. In addition, sharing this information throughout the organization informs staff of past approaches that improve performance or better inform new strategies.

To achieve the goal of knowledge management, companies have to enable and promote a culture of learning and development, creating an environment where employees are encouraged to share information to better the collective workforce.

How to conduct a skills gap analysis and what to do next

Start building your foundation for strategic workforce development.

When discussing knowledge management, it is helpful to consider the different types of knowledge and how it is possible to share them within an organization.

The information knowledge management covers can generally be broken down into three main types:

1. Explicit knowledge is knowledge and information that can be easily codified and taught, such as how to change the toner in a printer and mathematical equations.

2. Implicit knowledge is knowledge that explains how best to implement explicit knowledge. For example, consider discussing a task with an experienced co-worker. They may provide explicit steps detailing how to complete the job. But they may also use their understanding of the situation to consider different options and decide the best approach for your given circumstances. The experienced employee utilizes and shares their implicit knowledge to improve how the team operates.

3. Tacit knowledge is knowledge gained through experience. Therefore, it is more intuitive and less easy to share with others. Examples of tacit knowledge are “know-hows”, innovative thinking, and understanding body language.

While knowledge management for implicit and tacit knowledge can be harder to implement, with correct procedures in place, you can ensure all relevant information is shared around the company and retained as staff retire or move on.

Utilizing all the expertise in your company benefits the business as a whole, creating best practices for everyday tasks, improving situational awareness, developing employee intuition for course corrections, and enhancing organizational capacity.

Staff retiring

An employee’s knowledge and skillset grow as they spend time with an organization. As a result, staff typically retire with a wealth of expertise that the company needs to mine using efficient knowledge management processes in order to reduce disruption and prevent workforce knowledge gaps.

This means identifying and capturing the meaningful information that needs to be retained by the organization and determining the best approach for storing and distribution.

Employee transfer or promotion

When staff change positions within a company, they must develop additional skillsets and expertise to match their new role.

Efficient knowledge management procedures simplify delivering this information to create a seamless transition from one position to another.

Why is knowledge management important?

Knowledge management is important because it boosts the efficiency of an organization’s decision-making ability.

By making sure that all employees have access to the overall expertise held within the organization, a smarter workforce is built that is more able to make quick, informed decisions, benefiting the entire company.

Knowledge management allows innovation to grow within the organization, customers benefit from increased access to best practices, and employee turnover is reduced.

The importance of knowledge management is growing every year. As the marketplace becomes ever more competitive, one of the best ways to stay ahead of the curve is to build your organization in an intelligent, flexible manner. You must have the ability to spot issues from a distance and be able to respond quickly to new information and innovations.

Companies begin the knowledge management process for many different reasons.

• A merger or acquisition could spur the need for codifying knowledge and encouraging teams to share their expertise.
• The imminent retirement of key employees could demonstrate the need to capture their knowledge.
• An upcoming recruitment drive shows the wisdom in using knowledge management to assist in training new employees.

52% of respondents in Deloitte’s 2021 Global Human Capital Trends Survey stated workforce movement as the driving force behind proactively developing knowledge management strategies.

Whatever the reason is, implementing knowledge management processes offers tangible benefits that drive value. This is backed up by research , showing knowledge management positively influences dynamic capabilities and organizational performance.

A survey of over 286 people working in knowledge management across a range of industries, locations, and company sizes found the most significant benefits to be:

• Reduced time to find information
• Reduced time for new staff to become competent
• Reduced operational costs
• Improved customer satisfaction
• Improved bid win/loss ratio

Making knowledge management a significant part of a company’s leadership approach produces a more streamlined workforce with faster onboarding and well-informed staff that provide a better experience for customers.

Knowledge management is a critical tool for any company that wants to increase its bottom line and market share .

IDC estimates that Fortune 500 companies lose $31 billion from not sharing knowledge within their organization every year. Studies estimate improving employee access to information and tools could save organizations roughly $2 million a month for every 4000 employees.

Implementing effective knowledge management requires proactive strategies and incorporating multiple new processes.

Companies have to uncover the existing knowledge available to them, understand how to spread this information to produce additional value, and plan what this looks like in action.

Knowledge management process. Credit: Valamis. ( CC BY 4.0 )

1. Discovery

Every organization has multiple sources of knowledge, from employees to data and records.

This could be the education and skillsets staff bring to the job, the experience and unique expertise they develop on the job, or hard drives of data that can positively affect the business with proper analysis.

During the discovery process, organizations must identify all the available sources of knowledge, with a particular emphasis on information that could be easily lost.

This process is simplified by a strong understanding of where and how knowledge flows around the organization.

2. Collection

Collecting all the available knowledge and data creates the foundation from which future processes build.

Sloppy or incorrect knowledge collection leads to decisions without a complete understanding of the organization and its capabilities.

Companies must audit their existing staff expertise, documentation, and external knowledge sources. A range of tools is available to help, including automated surveys, document scanning, and metadata.

Post-implementation, many organizations redefine internal processes to make capturing institutional knowledge a part of everyday processes. This could be through continual employee feedback systems or more in-depth offboarding procedures.

3. Assessment

This process involves the deep analysis of the knowledge gathered in the previous two steps. Data must be assessed and organized into a structured, searchable, and easily accessible form.

Assessment of the gathered knowledge is required to ensure it is accurate, offers value, and is up to date.

Then teams can determine how best to share information to improve company performance and give staff the knowledge they need to maximize performance.

Utilizing the right knowledge management system simplifies this process by allowing leadership to organize, assess, segment, and store a comprehensive knowledge database.

The whole point of knowledge management is to give staff the expertise and information they need to do their job to the best of their ability.

Once you have built a detailed and accurate body of knowledge related to your company, you need to plan how it will be shared.

See the “Knowledge management methods” section below for examples of how to share information around your company.

While there are many examples of sharing information, one thing that should be universal is creating a cultural shift towards learning and development .

Leadership must prioritize and reward knowledge sharing, creating an atmosphere where team members are actively encouraged to both teach each other and learn from one another.

5. Application

This is the step where organizations reap the rewards of knowledge management. Discovering and storing institutional knowledge is just the beginning.

Staff utilizing newly acquired expertise in their tasks brings a range of benefits in productivity, accuracy, decision-making, and more innovative employees.

6. Creation

The final stage of knowledge management is to create more knowledge.

It should never be considered a one-and-done process. A single audit and rollout won’t deliver the results you are looking for.

Knowledge management is a continual process that maximizes a company’s performance for the expertise available to it.

Whether it is a team discovering a new, more efficient approach to a task or a better way of capturing data related to company performance, organizations should constantly be innovating and creating new knowledge to pass on to future employees.

Depending on what the company needs, their knowledge management will look different.

Below we have listed common examples of knowledge management methods in action:

1. Tutoring & training, communities of practice, and Q&A

These examples all involve transferring information directly from the knowledge holder to other employees.

This could be through in-person tutoring, company-wide training sessions, online chats, and group discussions – or a mix of these options and others.

Many companies value building a skills matrix that maps each employee’s expertise. This simplifies finding the employee with the most experience or knowledge in a given field. In addition, it identifies knowledge gaps within the workforce and shows areas requiring focus for specific knowledge management methods and training.

Some examples of this type of knowledge management may not require a formalized structure. For example, perhaps your company is having problems with a new project, which reminds you of a previous situation. Using the company Slack, for example, you can search for similar questions and find old threads discussing how you overcame the problem last time. Prior expertise that you may not have thought about in years is stored and discovered in old communications to help you right now.

• Questions can be immediately answered
• Clarifications can be made if the material is not understood
• Brainstorming sessions can be facilitated, taking advantage of the combined power of the group’s experience and knowledge
• In-person learning tends to be remembered more clearly
• It can be time-consuming and take away from the tasks the knowledge holder is trying to complete
• A system of expertise location can be time-consuming to build and maintain
• It can be challenging to document and save for future use
• Difficulty finding the right expert with good communication skills and knowledge of the company
• You can lose the knowledge if the knowledge holder leaves the company

2. Documentations, guides, guidelines, FAQ, and tutorials

Written communications are great for storing and transferring knowledge.

With text-based knowledge management, a system to store, categorize and navigate subjects is always available.

In many cases, metadata is a great help for this.

• The company has an invaluable source of information of up to date information
• Easy to find and share online (when organized well)
• Can easily combine multiple people’s expertise into one packet
• Requires a lot of time to create and keep up-to-date
• Must be appropriately managed to ensure relevant knowledge is easily found
• Requires infrastructure (internet access, etc.)
• It takes time to consume

3. Forums, intranets, and collaboration environments

These online resources spark conversation and bring many knowledge holders into the same place.

Threads, subforums, and groups can be divided by topic, level of expertise, or any number of other classifications.

• Collaboration drives innovation
• Many experts can be brought together into one place, no matter their location globally
• Facilitating contact with remote teams helps teamwork and knowledge transfer
• It can be a chaotic, noisy environment
• Knowledge is not actively being vetted as it is added to discussions
• Searching through many messages and threads for relevant answers is time-consuming
• Messages and threads might not be archived

4. Learning and development environments

Creating an environment where learning is considered an asset will continuously drive employees to educate themselves.

Incentivizing them to take advantage of your knowledge management systems will result in upskilled employees ready to take on leadership roles in your organization.

For this to happen, there must be structured and accessible learning and development technology in place that employees can use.

• Motivated employees can develop themselves at will
• Training pathways can be set out
• Wide range of resources available to produce a constant flow of fresh content
• The structure allows for easier discovery of subjects
• Authoring tools available such that internal experts can build company-specific courses
• Analytic tools are available to help find knowledge gaps inside the company
• Requires a lot of effort to develop and maintain in house
• Readily available solutions may be too generic to add real value for your company
• Content must be created and continually updated
• Requires an influential learning culture to motivate staff to participate

5. Case studies

These in-depth studies into particular areas serve as complete guides to a subject.

Looking at the actions taken, the results they produce, and any lessons learned is extremely valuable.

• Allow for complete documentation and archiving of lessons learned
• Easily shareable
• Efficient for communicating complex information
• It takes a lot of time and skill to create
• The case study may have limitations or require approval from the parties involved
• Can be too specialized to apply the knowledge broadly
• In fast-paced fields that are constantly innovating, case studies can become out of date quickly

6. Webinars

These online seminars can be beneficial in widely disseminating ideas throughout teams, branches, or the entire company.

• Accessible for all interested employees to attend
• Potential for interactivity where attendees can ask questions specific to issues they are having
• Can be recorded and reused
• Planning, finding the right speakers, and settling on a topic is time-consuming
• Requires organization
• External experts can cost a lot
• Requires time to find answer

Knowledge management systems are IT solutions that allow for the storage and retrieval of the information stored within the company, allowing for better collaboration and more efficient problem-solving.

Depending on what your company needs, they will have different features.

Examples of knowledge management systems are:

• Feedback database – Everyone involved in a product, from designers to salespeople to customers, can share their feedback with the organization. All stakeholders can access the feedback and thus quickly make fundamental changes armed with better information.
• Research files – In developing projects and ideas, a company does market and consumer research to determine what is needed, what niches are yet to be filled in the market, and what trends can be forecasted. The files are then shared within the organization to allow all departments to benefit from the research conducted.

Shared project files – This system allows for greater collaboration and teamwork, especially across distances.

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Balancing knowledge exploration and exploitation within and across technological and geographical domains

• Published: 12 November 2013
• Volume 12 , pages 123–132, ( 2014 )

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• Antonio Messeni Petruzzelli 1  

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This paper deals with the knowledge exploration-exploitation framework by investigating the performance implications of balancing these two activities within and across domains. Specifically, we focus on firms’ strategies for new knowledge searching and acquisition, and analyse how the development of valuable innovations is positively influenced by firms’ capability to find a trade-off between knowledge exploration and exploitation within and across technological and geographical domains. The empirical analysis of 5,575 patented biotechnology inventions provides strong support for the proposed theoretical arguments, by revealing that balancing exploration and exploitation is always beneficial. Furthermore, findings demonstrate that balancing across domains produces greater advantages than balancing within domains, since this allows organizational impediments and cognitive constraints in resource allocation to be overcome.

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Petruzzelli, A. Balancing knowledge exploration and exploitation within and across technological and geographical domains. Knowl Manage Res Pract 12 , 123–132 (2014). https://doi.org/10.1057/kmrp.2012.46

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Knowledge Domains

Research has shed light onto the ways in which the brain changes and creates neural networks through experience. There are many different ways that the brain is organized, such as different knowledge domains.

The knowledge domains serve different functions based on the ways that the brain receives and organizes information and transforms that information into knowledge.

The Different Knowledge Domains

• declarative knowledge
• procedural knowledge
• strategic knowledge
• self-knowledge
• tacit knowledge
• integrated knowledge

Different types of knowledge domains serve different functions.

For example, declarative knowledge serves to provide factual information such as specific facts or theories. Procedural knowledge stores the ways in which to do procedures and processes. If you are assessing what a student knows about a particular theory, then you would want to ask questions that prompted the declarative knowledge, while if you want the student to talk about a procedure, then you would ask a different set of questions.

The different knowledge domains and interview and assessment strategies that can be used to target specific domains are located below.

Declarative Knowledge

Expand to view the indicators, strategies and level of knowledge.

Declarative: Indicators of Student Knowledge

• specific facts
• terminology
• trends and sequences
• classification and categories
• principles and generalizations
• theories and structures.
• The student can communicate information, arguments and theories.

Declarative: Interview and Evaluative Strategies

• Ask for specific declarative information.
• Have the student use the information to explain theories and arguments.

Declarative: Level of Knowledge

• lower-level undergraduate
• upper-level undergraduate

Procedural Knowledge

Procedural: indicators of student knowledge.

• The student can describe and demonstrate procedures or tasks.
• The student is able to address methodology and apply pertinent information based on specific criteria.

Procedural: Interview and Evaluative Strategies

• Have the student describe a process.
• Ask the student about pertinent information and to apply within a given situation.
• Have the student present situations where there were problems to be solved within the process/procedure.
• Propose situations for the student to solve (e.g., a case study) or ask the student to solve problems which apply their knowledge within situational contexts.
• Observe the student demonstrating a procedure or a performance-based problem or task.
• Ask the student to employ key techniques of the discipline.

Procedural: Level of Knowledge

Strategic knowledge (metacognitive knowledge), strategic: indicators of student knowledge.

• relate relevant declarative and procedural knowledge structures to create and implement a plan
• employ translation, interpretation, extrapolation techniques to solve problems
• analyze elements, relationships and principles
• synthesize (production of a unique communication, plan, set of operations, abstract relations)
• employ appropriate evaluation strategies by using internal evidence and external criteria.
• The student applies effective research strategies.

Strategic: Interview and Evaluative Strategies

• Ask the student to describe how she or he has solved problems or situations within multiple applications and/or when multiple solutions were possible.
• Ask the student to describe how she or he has applied her/his learning in different situations, especially when novel and/or when solutions may not have clear outcomes.
• Have the student evaluate evidence, arguments and assumptions and ways these are applied to solve problems.
• Have the student justify conclusions drawn from information and research.

Strategic: Level of Knowledge

Self-knowledge, self: indicators of student knowledge.

The student:

• can reflect upon her or his abilities as a learner in various areas
• is able to engage self-assessments strategies (evaluations in terms of one’s self beliefs, attitudes, emotions)
• manages her or his learning to make use of the environment, information and feedback
• is able to situate her or his knowledge within the context and discipline
• is able to situate self within the broader context of society and cultures.

Self: Interview and Evaluative Strategies

• Have the student engage self-assessments.
• Utilize reflective dialogue with the student around her or his learning.
• Have the student demonstrate self-regulated learning and autonomy by tackling and solving problems, advancing her or his knowledge and developing new skills (lifelong learning).
• Have the student provide originality and creativity and describe her or his thinking process.

Self: Level of Knowledge

Tacit knowledge, tacit: indicators of student knowledge.

• can reflect upon how the different knowledge bases are linked and related
• can perform action-based skills within novel situations
• engages reflection and creates links among the different levels of knowledge bases
• relates different connections, even with missing or incomplete information
• demonstrates more advanced levels of proficiencies within an area.

Tacit: Interview and Evaluative Strategies

• Have the student provide situational problems and reflect on how they solve these types of problems.
• Ask the student to think aloud while solving situational problems to determine how the student links different knowledge structures and the extent that uncertainty, ambiguity and knowledge limits are integrated into schemes.
• Observe the student engaged in performance-based problems or task.

Tacit: Level of Knowledge

Integrated knowledge, integrated: indicators of student knowledge.

• can link different knowledge structures and create new interpretations, strategies and/or new knowledge during novel situations
• creates new knowledge structures from novel situations.

Integrated: Interview and Evaluative Strategies

• Through multiple methods, have the student link different knowledge structures during unfamiliar situations or procedures.
• Have the student describe new knowledge and how it is applied into novel situations.

Integrated: Level of Knowledge

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In-depth Guide to Knowledge Graph: Benefits, Use Cases & Examples

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

The rapid growth of data in today’s digital world has made data governance a challenging task. According to McKinsey, even the global leading firms can waste between 5-10% of employee time on non-value-added tasks due to poor data governance. 1 This can rise up to %29 in average across enterprises.

Knowledge graphs have emerged as a powerful solution to address these issues. A knowledge graph integrates data from diverse sources into a unified, structured, and interconnected representation, offering a more comprehensive view of information. 

By doing so, knowledge graphs not only streamline data governance but also unlock numerous benefits and use cases across various domains. In this article, we explain what a knowledge graph is with its benefits, use cases and real life examples.

What is a knowledge graph?

Figure 1. A simple knowledge graph representation

Source: Wikipedia

A knowledge graph is a structured representation of information and knowledge in the form of a graph. It consists of nodes (real world entities or concepts) connected by edges (relationships or associations). The purpose of a knowledge graph is to model, store, and organize complex information in a way that makes it easy for both humans and machines to understand, navigate, and use the knowledge it contains.

Powered by machine learning algorithms, knowledge graphs employ natural language processing (NLP) to create an extensive representation of nodes, edges, and labels via a technique known as semantic enrichment. As data is ingested, this method enables knowledge graphs to recognize distinct entities and comprehend the connections between various entities.

How does a knowledge graph work?

Figure 2. Steps involved in the construction of a knowledge graph

Source: ResearchGate

Creating a knowledge graph involves several steps that help in organizing and representing the information effectively:

• Data collection : Gathering the underlying data from various sources like databases, websites, or documents.
• Entity identification : Recognizing and distinguishing entities (people, places, etc.) in the collected data model.
• Relationship extraction: Determining the connections between the identified entities.
• Ontology creation: Developing a well-defined structure (ontology) to organize the entities and their relationships.
• Data storage: Storing the knowledge graph in a specialized database designed for handling graph data.
• Querying: Using graph query languages to search, navigate, and explore the connections in the network.
• Inference: Performing advanced tasks like discovering new relationships or identifying inconsistencies within the graph.

What are the benefits of knowledge graphs?

Knowledge graphs offer several general benefits that apply across various applications and industries. Some key general benefits include:

1- Data integration

Knowledge graphs can help link and harmonize data from diverse sources, fostering data sharing and collaboration among organizations, and allowing for a more comprehensive view of the information.

2- Enhanced understanding

Knowledge graphs provide a richer context and understanding of information by representing entities and their relationships, enabling both humans and machines to better interpret and interact with the data.

3- Flexibility

Knowledge graphs can be tailored to various domains and industries, allowing for a wide range of applications and customization to meet specific needs.

4- Improved search and discovery

By modeling relationships between entities, knowledge graphs can deliver more relevant, accurate, and comprehensive search results and facilitate the discovery of new knowledge and insights.

5- Inference and reasoning

Knowledge graphs support various inference and reasoning tasks, enabling the discovery of new relationships, identification of inconsistencies, and validation of existing knowledge.

6- Structured representation

Knowledge graphs provide a structured way of organizing and representing information, making it more accessible and easier to work with, especially for artificial intelligence and machine learning applications.

7- Scalability

Knowledge graphs are efficient at representing and storing large amounts of interconnected information, making them suitable for handling vast datasets and large-scale applications such as data science.

What are the applications of knowledge graphs?

1- semantic search.

By understanding the context and relationships between entities via the semantic web, knowledge graphs can enhance search engine results , offering more relevant and comprehensive information to users.

2- Question answering

Knowledge graphs can facilitate answering questions by identifying relevant information and connections within the graph, making them valuable for virtual assistants and chatbots .

Figure 3. The effect of knowledge graph on chatbot systems

Source: Onlim

3- Recommendation systems

By understanding user preferences, interests, and behavior, knowledge graphs can provide personalized recommendations in areas such as e-commerce , content discovery, and entertainment.

Knowledge graphs are utilized in AI-driven recommendation systems for content platforms, such as Netflix, SEO , or social media. By analyzing users’ clicks and other online engagement activities, these platforms can suggest new content for users to read or view.

Check out our article on recommendation systems to learn more.

4- Natural language processing

The relationship between knowledge graphs and NLP can be described as a mutually beneficial interaction. This relationship leads to more effective language processing and a better understanding of the connections and context within the data.

Knowledge graphs provide structured information about entities and their relationships, which helps NLP systems better understand and process textual data. On the other hand, NLP techniques are used to extract entities and relationships from unstructured text , contributing to the creation and expansion of knowledge graphs.

By incorporating background knowledge and context from knowledge graphs, NLP models can perform more effectively and accurately across a range of tasks such as:

• Entity recognition
• Relation extraction
• Text summarization

Figure 4. Knowledge graphs are used as semantic triples for NLP

Source: Accenture

5- Enterprise knowledge management

In businesses and organizations, an enterprise knowledge graph can help capture, store, and organize knowledge, improving the accessibility of information for employees through data management.

6- Biomedical research

Knowledge graphs are employed to represent complex relationships between genes, proteins, diseases, and drugs in biomedical fields. So, they facilitate the discovery of new insights and aid drug development.

If you are interested in the use of high tech in this field, you can check our article on the use of generative AI in life sciences .

Figure 5. An example of a constructed biomedical knowledge graph

7- Fraud detection and security

Knowledge graphs can help identify unusual behavior or connections by modeling relationships and patterns within large datasets of transactions and organizations.

By analyzing these relationships and patterns in the knowledge graph, it becomes easier to spot potential fraud or security threats , such as: 

• Suspicious transactions
• Fake accounts
• Abnormal user behavior

Knowledge graphs can be an effective tool for detecting and preventing fraud and enhancing overall security in various industries, including finance, e-commerce, and social networks.

What are some examples of knowledge graphs?

DBpedia is a large-scale, open-source knowledge base derived from the structured information available in Wikipedia. It represents information as a knowledge graph.

It aims to make Wikipedia’s content more accessible, machine-readable, and useful for various applications and research purposes. Launched in 2007, DBpedia knowledge graph acquires structured data from Wikipedia infoboxes, categories, links, and other elements, converting it into a standardized format by DBpedia ontology.

Figure 7. DBpedia ontology overview with classes and instances in each class

Source: DBpedia

In addition to being used for research and academic purposes, DBpedia also serves as a foundational resource for various applications, including search engines, recommendation systems, and natural language processing tools.

2- Google Knowledge Graph

Launched in 2012, Google’s Knowledge Graph is designed to provide users with more relevant, contextual, and informative search results by understanding the relationships between different entities. 

It helps power Google search, Google Assistant, and various other Google services. When users search for a topic or entity, the Google Knowledge Graph can display a knowledge panel alongside the search results, providing quick access to key information, facts, and related entities (Figure 6). This not only improves the overall search experience but also helps users explore and discover new information more efficiently.

Figure 6. Google knowledge panel

3- Microsoft Satori

Microsoft Satori is a knowledge graph developed by Microsoft that powers various applications and services, such as the Bing search engine and the Cortana virtual assistant. Similar to Google’s Knowledge Graph, Satori aims to provide a comprehensive understanding of entities, their attributes, and the relationships between them in order to enhance search results.

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External Links

• 1. “Designing data governance that delivers value.” McKinsey , 26 June 2020, https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/designing-data-governance-that-delivers-value. Accessed 26 April 2023.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

AIMultiple.com Traffic Analytics, Ranking & Audience , Similarweb. Why Microsoft, IBM, and Google Are Ramping up Efforts on AI Ethics , Business Insider. Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are , Washington Post. Data management barriers to AI success , Deloitte. Empowering AI Leadership: AI C-Suite Toolkit , World Economic Forum. Science, Research and Innovation Performance of the EU , European Commission. Public-sector digitization: The trillion-dollar challenge , McKinsey & Company. Hypatos gets $11.8M for a deep learning approach to document processing , TechCrunch. We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million , Business Insider.

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