Guidelines for Writing a Case Study Analysis
A case study analysis requires you to investigate a business problem, examine the alternative solutions, and propose the most effective solution using supporting evidence. To see an annotated sample of a Case Study Analysis, click here.
Preparing the Case
Before you begin writing, follow these guidelines to help you prepare and understand the case study:
- Read and examine the case thoroughly
- Take notes, highlight relevant facts, underline key problems.
- Focus your analysis
- Identify two to five key problems
- Why do they exist?
- How do they impact the organization?
- Who is responsible for them?
- Uncover possible solutions
- Review course readings, discussions, outside research, your experience.
- Select the best solution
- Consider strong supporting evidence, pros, and cons: is this solution realistic?
Drafting the Case
Once you have gathered the necessary information, a draft of your analysis should include these sections:
- Identify the key problems and issues in the case study.
- Formulate and include a thesis statement, summarizing the outcome of your analysis in 1–2 sentences.
- Set the scene: background information, relevant facts, and the most important issues.
- Demonstrate that you have researched the problems in this case study.
- Outline possible alternatives (not necessarily all of them)
- Explain why alternatives were rejected
- Why are alternatives not possible at this time?
- Proposed Solution
- Provide one specific and realistic solution
- Explain why this solution was chosen
- Support this solution with solid evidence
- Concepts from class (text readings, discussions, lectures)
- Outside research
- Personal experience (anecdotes)
- Determine and discuss specific strategies for accomplishing the proposed solution.
- If applicable, recommend further action to resolve some of the issues
- What should be done and who should do it?
Finalizing the Case
After you have composed the first draft of your case study analysis, read through it to check for any gaps or inconsistencies in content or structure: Is your thesis statement clear and direct? Have you provided solid evidence? Is any component from the analysis missing?
When you make the necessary revisions, proofread and edit your analysis before submitting the final draft. (Refer to Proofreading and Editing Strategies to guide you at this stage).
Research based on qualitative data has played a long and illustrious role in IB (Birkinshaw et al., 2011); yet, the proportion of qualitative research appearing in JIBS is lower than this track record might warrant. There are probably several interrelated reasons for this. There are few submissions of qualitative papers to JIBS. There is limited training in many PhD programs in qualitative methods, and so researchers may lack familiarity with them. There is a lack of established standards for analyzing and presenting data (e.g., Bansal & Corley, 2012; Pratt, 2008), which makes the research process seem uncertain. It is time-consuming to embark on the long journey involved in collecting and analyzing qualitative research, such as gaining access to research sites, conducting interviews, and analyzing interview transcripts and documents. On top of all of this, there is the language challenge: primary data from interviews and participant observation often need to be conducted in more than one language, transcriptions must be done by a native speaker and at some point translated into English for publication in JIBS, and assuring meaning congruence and functional equivalence of terms is challenging.
In addition to these supply-based reasons, we believe that a factor constraining the publication of qualitative research papers is that they are having difficulty getting through the review process successfully. While the nature of the difficulties vary, we have noticed that a weakness common to many qualitative research submissions is that the authors have not paid sufficient attention to demonstrating the trustworthiness of their research. To address this, we provide guidelines as to how qualitative researchers in IB can establish this trustworthiness in their manuscripts.
At the outset we note that researchers wishing to use qualitative methods have many resources from which to draw inspiration. There was a JIBS Special Issue on Qualitative Methods (Birkinshaw et al., 2011), and there have been recent JIBS articles on qualitative methods in general (e.g., Doz, 2011) and on specific topics related to qualitative methods such as longitudinal historical research (Burgelman, 2011), grounded theory (Gligor, Esmark, & Gölgeci, 2016), case-based research (Welch, Piekkari, Plakoyiannaki, & Paavilainen-Mäntymäki, 2011), and ethnography (Westney & Van Maanen, 2011). There are articles in other journals on topics particularly relevant to IB, such as process-based research (e.g., Langley, 1999; Welch & Paavilainen-Mäntymäki, 2014) and there are classic texts such as Corbin and Strauss (2008), Glaser and Strauss (2011), Miles and Huberman (1994), Marschan-Piekkari and Welch (2011), Piekkari, Welch and Paavilainen (2009), Van Maanen (1998) and Yin (2009). We encourage authors to consult these and other resources when they are making research design and analysis decisions, and to use them to justify these decisions when reporting research results in their manuscripts.
Our intention in this editorial is to highlight the importance of making explicit and consistent choices in order to establish trustworthiness in a qualitative manuscript submitted for publication. This requires rigor from the start of a research project, because the conceptualization and design of a project influences the nature of the analysis that can be undertaken, and therefore the findings that constitute a scholarly contribution to the field. There are three well-known paths that are unlikely to lead to successful outcomes. One such path is converting a teaching case into a research case, which is problematic because a teaching case will rarely have the theoretical relevance and the rich data required of a research case. A second questionable path can occur in situations where it is difficult to collect data from a sample large enough to establish statistical significance, and so a researcher collects data from several companies and attempts to establish generalization by showing that multiple companies are engaged in the same strategies. This use of case studies is an example of a theoretical contribution that small n studies cannot make. Small n studies cannot make frequency-based insights, such as the propensity to engage in a particular firm behavior, because the frequency observed is highly dependent on the particular cases selected for examination. Moreover, small n studies can rarely explain outcomes such as performance, which are affected by many factors, because they cannot control for these factors as can large-scale quantitative studies. Finally, a third questionable path is “convenient sample driven” research, or “squat ethnography” (Van Maanen, 1998), where a researcher has access to a subject (individual, team, company, country) and starts collecting data. Once collected, the researcher starts analyzing the data and thinking about what to do with it, hoping to have a eureka moment in which something that seems to be different emerges from the data. This approach tends to be justified with an argument along the lines of “with an open mind and with no prior biases I studied company x to be able to identify new patterns.” However, such an approach mistakes having an open mind with having no clue about what to do!
Recommendations for establishing trustworthiness in qualitative research
Trustworthiness in Research Context
Qualitative methods are inherently embedded in context and so it is critical that the context of studies based on qualitative methods be explicitly defined. The type of context that is relevant to one study may be different from the type of context relevant to another study – for example, it could be an event, a type of environment, or a particular situational strength (Johns, 2006) – but it is important that the contextual nature of the research be consistent across all aspects of the manuscript – the research question, the literature review, methodological choices and the theoretical interpretation of the findings. If this is done effectively, then the contextual delineation of the study bounds the theoretical claims that can be made, thereby providing clarity around what is and what is not explained.
Because context is so central to the theoretical and empirical aspects of qualitative research, it is incumbent on authors to justify the particular context they are studying. At a basic level, authors should consult the JIBS Statement of Editorial Policy, which describes the meaning of IB with respect to submissions to the journal. Beyond this, it is advantageous for authors to show that the specific context they are studying is theoretically interesting and relevant to current scholarly IB conversations.
In many qualitative studies, the motivation to study a particular context is based on observations of real world phenomena. For example, Brannen and Peterson (2009) justify their study of a Japanese acquisition in the US by highlighting the high failure rate of cross-border mergers and acquisitions and the lack of theory to explain them. In the absence of prior theory, such as this, it is difficult to develop hypotheses to be tested in a large scale study, and so inductive, qualitative methods are used to generate or create theory (Edmondson & McManus, 2007). Sometimes, however, the motivation to select a particular context is based on prior research and the questions it leaves unaddressed. For example, Jonsson and Foss (2011) justify their study of the Swedish furniture retailer IKEA by noting that although scholars understand the trade-offs between replication (scale) and local adaption, little is known about the processes through which both can be accomplished. It is interesting to note that in both of these papers, the context is just one organization. That is not always the case in qualitative studies, of course. For example, Caprar’s (2011) study of the culture of local employees of MNEs is based on focus groups of employees of American MNEs in Romania. He frames this choice of context as relevant to culture – the key theoretical construct – since Romanians are both welcoming of foreign investment and sufficiently culturally distant from Americans to be theoretically interesting.
These examples illustrate that in justifying a research context, it is important to clarify what is and what is not known about the phenomena under investigation, and to be explicit about why a qualitative research approach is used. The first task, positioning a scholarly paper in prior literature, is beneficial regardless of the empirical method. However, Pratt (2008) points out that a particular challenge for qualitative researchers is to manage the tension between recognizing and drawing on existing theory, while also distancing from it to show that new theory has been generated. He suggests developing open theoretical frameworks that describe prior research while highlighting where prior research has been largely silent, in order to create a new space for an author’s contribution. In creating these boundaries between what is known and what is not yet known, an author can credibly signal that alternative explanations for the paper’s findings are unlikely.
The second task, justifying the use of qualitative methods, is important in conveying the overall theoretical objectives of the research. While articulating an explicit research question is beneficial in conveying the specific focus of the research, communicating the nature of the findings in theoretical terms helps readers to follow the thread of the storyline. Are you using qualitative methods to extend theory in a particular direction or are you building new theory? Are you generating variance theory or process theory (Langley, 1999)? Are you intending to develop testable propositions or reveal new interpretations of theoretical constructs or relationships? An important dimension of communicating the nature of your findings is being precise with respect to the outcome you are explaining; for example, learning processes within MNEs (e.g., Jonsson & Foss, 2011), variation in SME internationalization practices (e.g., Lamb, Sandberg, & Liesch, 2011) or variation in managerial narratives (e.g., Haley & Boje, 2014). Since choices among these theoretical objectives are connected with choices related to research design, empirical analysis and reporting of findings, expressing them clearly and early in the paper helps the reader understand the subsequent choices you make. This consistency therefore enhances the trustworthiness of the explanations offered as theoretical contributions.
Trustworthiness in Research Design
We have come across misperceptions that research based on quantitative data and deductive reasoning is empirical research, while research based on qualitative data and inductive reasoning is conceptual research. These perceptions are wrong. Both are empirical studies and in both the quality of the research design is crucial for establishing trustworthiness. Moreover, it is crucial to check for data quality in qualitative research, because there are no statistical tests to provide assurances about the operationalization of theoretical constructs and the strength of the relationships among them.
Three aspects of the design of qualitative research can substantially influence perceptions of its trustworthiness: site selection, data replication, and data triangulation. First, with respect to site or sample selection, the researcher needs to justify how and why they chose a single site (one case), or how and why they constructed a sample of multiple cases, such as individuals, teams, organizations, events, regions or countries. Whether one case or a sample of cases is selected, the basis of selection needs to be tightly coupled with the theoretical context of the study and the interpretation of its findings in order for the choice to be seen as trustworthy. Single cases can be justified because they are extreme, unique, representative, revelatory or longitudinal (Yin, 2009: 47–49) and it is important to embed the justification in the theoretical contribution of the paper. As Siggelkow (2007) points out, it is easier to justify a special case than a representative case because you can show that it was selected to allow you to gain insights that other cases would not provide. For example, in order to reveal insights about the liability of foreignness, Brannen (2004) chose the US entertainment firm Walt Disney Company as a research site because it was an extreme case of paradoxes regarding foreignness. When the objective is to investigate variance, it is important to justify the selection of several cases on the basis of theoretical diversity, so individual cases can serve as replications, contrasts and extensions to the emerging theory (Eisenhardt & Graebner, 2007). For example, Lamb et al. (2011) wanted to capture the greatest possible variation in small firm internationalization and so they justified their cases by emphasizing that they reflected a variety of international experiences and histories within and across different wine export networks that helped better understand internationalization. In the field of IB it is not unusual to combine a single site with a theoretical diverse sample within that site. For example, Jonsson and Foss (2011) chose IKEA as a site because it exhibits a unique combination of format standardization and local adaptation, but to investigate variance in learning within IKEA, they interviewed employees in three markets (China, Japan and Russia) whose differing degrees of development were likely to be associated with variance in learning.
A second aspect of research design that influences the trustworthiness of a manuscript is data replication. Replication adds credibility to findings because it provides support that they are deeply grounded in diverse empirical evidence and not idiosyncratic to one particular case (Eisenhardt & Graebner, 2007). As we have already pointed out, including multiple cases (interviewees, firms) in a sample provides replication. Researchers can also provide replication by collecting data more than once. For example, in Caprar’s (2011) study of the culture of local employees, he conducted three focus groups, varying their composition and timing in order to be able to assess whether these factors impacted the findings. In process studies, replication can be provided through data collected on multiple observations longitudinally (Langley, Smallman, Tsoukas, & Van de Ven, 2013). For example, Bingham (2009) captured data on processes associated with multiple foreign entries over time. In this case, the study was designed with replication across organizations (cases) and within organizations (entries), but longitudinal data collection can also provide within-case replication when the study is based on a single organization. In ethnographies, which are designed specifically to describe and understand how groups of individuals (cultures) function; their norms and patterns of behavior, values and basic assumptions, replication is characterized by its continuous nature. The research outcomes of ethnography are detailed narrative accounts of cultural phenomena told as much as possible from the native’s point of view, and so participant observation is a key aspect of the methodology. The ethnographer needs to find a role within the group under observation from which to participate in some manner, even if only as “outside observer.” Participant observation, therefore, is limited to contexts where the community under study understands and permits it. Further, since the ethnographer’s aim is to understand predominantly tacit, complex, contextually embedded, existential phenomena, the amount of time spent in the field must be substantial – to an anthropologist this means at least 1 year, though a year may be too brief if the research involves learning or perfecting a new language on the part of the researcher. Thus, rather than being characterized by discrete replications, ethnographic research is characterized by diverse and continuous data collection and it is important for the ethnographer to describe in detail both the research data and how data collection took place. For example, in studying a Japanese acquisition of an American manufacturing plant, Brannen and Peterson (2009) provide a rich description of the plant before and after the acquisition, as well as the nature of their participant observation activities and other data collection techniques that were used.
A third element of research design that enhances the trustworthiness of a manuscript is data triangulation. It is common for authors to state that they have supplemented interviews with archival data about the entities they study, but positioning such data as supplemental detracts from their credibility. If the data are not relevant to the analysis and the findings, it is preferable to leave them out of the discussion. It is rare when authors show how they incorporated diverse types of data in their analysis. If the data are relevant, it is important to justify both how they were collected and how they were used. For example, in their study of MNE’s storytelling, Haley and Boje (2014) describe their diverse data sources – including onsite observation, interviews, videos, TV commercials, and transcripts of legal disputes – and weave all of these into their discussion of the study’s findings. Likewise, in their study of Englishization in the provision of cross-border services, Boussebaa, Sinha and Gabriel (2014) carefully detail and justify collecting interview data from different types of employees, as well as data from internal documents, company intranet pages and onsite observation. In discussing their findings, they are able to deepen their interpretation of interview data by portraying it in conjunction with the company`s human resources policies and with the physical work set-up that they observed.
Trustworthiness in Empirical Analysis
The empirical analysis of qualitative data can be enhanced, and thus the confidence of the scholarly IB community in the interpretation of the data presented, in three ways: navigating multilingual and multicultural boundaries, establishing clarity in the analysis, and reporting both evidence and theory and the links between the two.
First, multilingual and multicultural boundaries are particularly prevalent in the field of IB because much of the scholarly inquiry crosses national, cultural or linguistic lines. It is important for researchers to show how they navigate such boundaries effectively, because accurate data interpretation is so important in establishing the credibility of qualitative research findings. This navigation involves accurate translation of documents and interview transcripts. However, most qualitative IB researchers do not discuss their translation decisions in their manuscripts, even though there are substantial theoretical differences among approaches to translation (Chidlow, Plakoyiannaki, & Welch, 2014). It also involves an intimate knowledge of the cultural milieus being examined, both to be sufficiently accepted to be able to collect meaningful data and to be sufficiently acclimatized to be able to interpret that data. This is often achieved by ensuring that someone on the research team has the required language skills and cultural familiarity.
Second, with respect to providing a clear analysis, authors can be overwhelmed by the quantity of data to be analyzed and by the lack of prescriptions for how the analysis should be conducted, and for this reason they need to pay particular attention as to how to analyze data in the most effective way. In contrast to quantitative studies, in qualitative studies there are no standard formats for discussing the methods and findings sections (e.g., Bansal & Corley, 2012; Pratt, 2008). However, this does not mean that any approach for analyzing data is valid. Indeed, qualitative researchers are recognizing that there are templates for distinct styles of qualitative research (e.g., Gioia, Corley, & Hamilton, 2012; Langley & Abdallah, 2011). Regardless of the type of analysis used, it is important that the reader understand in detail what was done and why. Too often, manuscripts go from a description of the sample to a description of the findings and provide little detail on how data were analyzed. One way to show how data analysis was conducted is to show examples of work products, such as the coding schemes developed. This not only helps increase confidence in the analysis, but can also help other researchers improve their own research designs.
While data analysis in qualitative studies tends to be focused on identifying dominant patterns in the data, it is also important to recognize that there may be “negative cases” (Corbin & Strauss, 2008: 84); i.e., cases that do not fit the dominant pattern. These are important to acknowledge and explain. Rather than detracting from a study’s credibility, they can signal analytic rigor because rarely are dominant patterns universal. Moreover, negative cases can provide an opportunity to deepen the theoretical claims that are being made by taking exceptions into account.
Third, deciding how to report the findings of a qualitative study can be challenging, because there are no standardized tables that are expected, and because qualitative data do not always lend themselves to being summarized. One of the key issues that an author faces is deciding what to show and what to tell (Pratt, 2009). Focusing on showing the data (the evidence for theoretical claims) can make the paper seem overly descriptive, while focusing on telling about the data (the theoretical interpretations) can make the theory seem unsubstantiated. Successful qualitative researchers address this difficulty by coming up with creative ways to display their data (Bansal & Corley, 2012). It is important for the reports of the findings to transcend description and indicate clearly the new theory that was generated from the investigation.
Towards More Trustworthy Qualitative Manuscripts
In Part A of this editorial we have provided suggestions for how IB scholars can enhance readers’ confidence in research findings that are based on qualitative data. Scholarly insights are more trustworthy when they take into account extraneous factors that may have affected research results. As is discussed in Part B, on controls in large sample quantitative studies, the ruling out of alternative explanations is handled by controlling for them. In qualitative research, however, the likelihood and magnitude of alternative explanations cannot be measured. Instead, as we have explained, there are multiple and integrated mechanisms to strengthen a reader’s belief that the explanations presented in a qualitative research study are accurate and valid. These mechanisms include ensuring that the boundaries of the theoretical claims are delineated, the research site is appropriate, the data are rich and robust and there is transparency in data analysis and the interpretation of the findings. Moreover, it is important that there be coherence and consistency across these mechanisms so that the thread from theoretical purpose to method to findings to theoretical contribution is clearly visible and easy to follow. We hope that these suggestions are useful for producing more sophisticated and trustworthy qualitative studies.