1 General
Case study research is a qualitative research design that focuses on the in‑depth investigation of one or a small number of cases in order to understand complex phenomena in context. A “case” may be, for example, an organization, a project, a team, a site, a system, or a specific implementation process. Case studies can be used both to examine theoretical assumptions in a concrete setting and to generate new insights and hypotheses (see Research Methods and Scientific Writing).
Case studies are particularly well suited for exploratory, descriptive, and explanatory research questions. Compared to purely quantitative approaches, their strength lies in the ability to capture organizational and social reality in a rich and contextualized way. This makes it possible to trace developments, processes, and cause‑and‑effect relationships and to derive data‑driven, practice‑relevant findings.
A basic distinction is typically made between two main variants:
- Single‑case study: Focuses on a single case that is, for example, critical, extreme, unique, particularly typical, previously inaccessible, or observed over an extended period of time.
- Multiple‑case study: Examines several cases that are systematically compared. The added value lies in identifying similarities and differences across cases, thereby enhancing the robustness and theoretical generalizability of the findings.
Single‑case designs enable particularly deep contextual analysis; multiple‑case designs strengthen robustness and theoretical generalizability, but come with greater demands in terms of time and resources.
Unlike quantitative methods, case studies do not support statistical generalization to a population. Generalization is usually achieved in the form of analytic generalization, that is, by linking findings to theoretical concepts or models rather than to a statistical population (see Theoretischer Rahmen und Argumentation).
2 Goal
Case studies are especially useful in new or complex research areas where existing models, indicators, or standard instruments offer only limited guidance. They can help to
- develop a nuanced understanding of the situation,
- distinguish central mechanisms from peripheral aspects,
- ground abstract concepts in concrete empirical examples,
- relate seemingly novel phenomena back to established theoretical relationships.
Even in domains with a more mature body of knowledge, case studies can open up new perspectives and generate impulses for further research, for example by examining deviant cases, edge cases, or emerging phenomena.
In combination with quantitative approaches, case studies offer additional potential. They can be used, for instance,
- for hypothesis generation (e. g., based on exploratory case studies that are later tested in surveys),
- to explain outliers or clusters identified in quantitative analyses, or
- for construct validation, by examining whether operationalized variables adequately reflect the phenomena observed in the field.
The overarching aim of a case study is to obtain theoretically informed insights into previously under‑researched or complex phenomena and to embed these insights in the existing body of literature (see Research Question and Research Gap in Information Systems).
2.1 Example application areas in Information Systems
In Information Systems, case studies are used in a variety of contexts, including:
- implementation of large ERP systems or digital platforms in single organizations or consortia,
- execution of digital transformation programs in public administration,
- development and implementation of data analytics solutions in companies,
- introduction of collaboration tools and their impact on teamwork,
- design and use of self‑service BI or low‑code platforms.
In such contexts, case studies make it possible to uncover process dynamics, negotiation processes, and local adaptations (e. g., workarounds, shadow IT) that often remain hidden in highly standardized surveys. They thus provide not only empirical evidence but also entry points for design‑oriented questions (see Design Science).
3 Execution
Conducting case study research requires a systematic planning and implementation process, which should be carefully documented, particularly when multiple cases are involved. Yin (2014) emphasizes the importance of a case study protocol that specifies the research design, data collection procedures, and analytic strategies in advance.
3.1 Planning phase and case selection
The starting point is to clarify the research problem and define the phenomenon of interest. As part of the study’s objectives, it is determined whether the case study is primarily intended to generate hypotheses or to test and refine existing assumptions, and which theory‑driven research questions it addresses (see Theses - Outline).
Based on this, the cases are selected:
- In single‑case studies, the case may be particularly typical, critical, or unique.
- In multiple‑case studies, several cases are purposefully selected to enable replication logic:
- Literal replication: additional cases are chosen such that similar conditions are present and similar results are expected.
- Theoretical replication: cases are selected such that theoretically different results are expected, but can still be explained within the same conceptual framework.
Case selection is typically guided by theoretical and substantive criteria, rather than by random sampling as in many quantitative designs. The rationale for case selection should be clearly articulated and closely aligned with the research objective.
In parallel, suitable data collection methods are determined and matched to the characteristics of each case (see Research Methods – Qualitative Methods). Common methods include:
- interviews (guided, semi‑structured, or open),
- observations (participant or non‑participant),
- document and Content Analysis (e. g., reports, minutes, artifacts),
- where appropriate, focus groups or workshops (e. g., to validate interim findings).
To strengthen the design, a pilot case can be conducted. Insights gained from this pilot study feed back into the refinement of the case study protocol.
3.2 Data collection
On the basis of the protocol, data collection proceeds in several steps, including:
- planning and conducting interviews, observations, and document reviews,
- documenting the process and, where possible and ethically appropriate, making audio or video recordings,
- storing the material in a structured format, for example in a case study database or dossier.
Careful documentation of data collection (time points, participants, context) is crucial to ensure transparency and traceability and to support subsequent analysis. In the context of theses, a well‑structured case study dossier also signals to examiners that the research process has been systematically planned and executed.
3.3 Data analysis
Data analysis in case study research is methodologically demanding, as there is no single standard procedure. The usual starting point is to organize and structure the material. Different analytic techniques may then be applied, for example:
- qualitative Content Analysis (e. g., structuring or summarizing Content Analysis),
- coding‑based approaches (e. g., category development, thematic analysis, open/axial Coding),
- pattern matching and explanation building,
- time‑series analysis or process tracing (e. g., for transformation projects).
The result of these analyses is a set of case reports in which each case (or group of cases) is presented and analyzed in a structured manner. These reports are often shared with practice partners as part of a member‑checking process to identify factual errors and clarify potential misunderstandings.
3.4 Case interpretation and comparison
In the case interpretation stage, the findings for each case are systematically discussed and related to the research question and the theoretical framework. In multiple‑case studies, similarities and differences across cases are additionally identified.
Typical steps include:
- presenting key findings for each case,
- comparing cases along predefined categories or dimensions,
- deriving patterns, mechanisms, or configurations,
- relating the findings back to theoretical concepts and models,
- formulating theoretical propositions or hypotheses for subsequent studies.
This provides a sound basis for drawing theoretical conclusions and deriving implications for practice and future research.
3.5 Integration of case studies into theses
In Information Systems theses, the case study design is typically presented in the methodology chapter (see Theses - Outline). Common elements include:
- justification of the case study design as appropriate for the research question,
- description of the case(s) and the selection criteria,
- presentation of the case study protocol (data collection and analysis methods),
- reflection on quality criteria (e.g., construct validity, internal validity, reliability, transferability).
The results chapter presents the case study findings in a structured way, while the discussion chapter addresses analytic generalization and integration of the findings into the broader literature (see Theoretischer Rahmen und Argumentation).
3.6 Typical pitfalls
Several recurring pitfalls can be observed in case study research. These include, among others:
- insufficient justification of the case selection and research design,
- unsystematic data collection without a clear protocol,
- mixing single‑case and multiple‑case logics without explicit methodological reflection,
- limited transparency in analytic procedures,
- weak or missing linkage between findings and theoretical concepts.
In qualitative case studies, the use of a single “N” (e.g., “N = 5”) as if it were a quantitative indicator can be misleading if no statistical analysis is conducted. In qualitative case study research, the focus is less on the number of cases and more on theoretical saturation and the depth and richness of understanding.
3.7 Short FAQ on case studies
How many cases does a “good” case study require?
The number of cases depends on the research question, the complexity of the field, and the available resources. What matters more than the sheer number of cases is a systematic selection strategy and the degree of theoretical saturation achieved.
Can a case study include quantitative data?
Yes. Case studies can integrate both qualitative and quantitative data (e. g., key performance indicators, surveys within cases). The crucial point is that the case study remains recognizable as the primary design and that the analytic strategy is coherent.
How does a case study differ from a practical project?
A case study follows an explicit research design, is embedded in theory, and aims to contribute to academic discourse. A purely practical project may be highly relevant for practice, but does not necessarily meet these requirements with respect to theoretical grounding and methodological transparency.
Core Literature
- Yin, R.K. (2014): Case Study Research: Design and Methods. (5th ed.), Sage Publications, Los Angeles [u.a.] 2014.
- Borchardt, A.; Göthlich, S.E. (2009): Erkenntnisgewinnung durch Fallstudien. In Albers, S.; Klapper, D.; Konradt, U.; Walter, A.; Wolf, J. (Eds), Methodik der empirischen Forschung (pp.33-48). Wiesbaden: Gabler.