3.4 Methodology Selection
Marcos Antonio de Lima Filho, PhD.
This section covers the range of potential methodologies, the shortcomings of case studies as a methodological approach, and the reasons for selecting grounded theory for this study.
Potencial Methodologies
Those undertaking qualitative studies have a baffling number of choices of methodological approaches (Creswell & Ploth, 2018). Several classifications have identified a wide variety of qualitative methods and research traditions, with numbers ranging from a dozen to 28 types. Creswell and Ploth analysed these classifications and found that some approaches appear consistently over the years, like ethnography, grounded theory, phenomenology, case studies, and narratives (Creswell & Ploth, 2018, p. 37).
However, as a researcher, my interest leaned toward the processes of design and technological innovation. For this purpose, case studies and grounded theory seemed to fit better. Furthermore, I felt compelled to approach this issue using a mixed methods approach, using quantitative and qualitative data. The value of mixed methods resides in the idea that all methods have biases and weaknesses, and the collection of both quantitative and qualitative data can neutralise the shortcomings of each (Creswell & Creswell, 2018). Since Grounded Theory is about concepts, it directly applies to both qualitative and quantitative data approaches (Glaser, 2008).
Shortcomings of Case Study Methodology
Case studies can involve single or multiple cases, as well as qualitative and quantitative data (Yin, 2018). However, while a case study may provide a wealth of descriptive details about a company or industry, this could be problematic or even inadequate for this research’s purpose.
Case study research, like any other, has its strengths and limitations. The problem of knowing whether a study’s findings are generalisable beyond the immediate study (external validity) is a recurrent limitation of case study research (Yin, 2018). To remedy this limitation, researchers often adopt a multiple-case study design. Although single-case studies can yield invaluable insights, most multiple-case studies are likely to be stronger than single-case studies (Yin, 2018). A multiple-case design creates a more robust theory because the propositions are more deeply grounded in varied empirical evidence (Eisenhardt & Graebner, 2007). By doing so, the researcher can look for common patterns and themes across different cases, which expands the possibility of making generalisations.
This PhD dissertation was initially planned to be a multiple-case study. It was not until much later, in the third year of investigation, that it became a grounded theory. As part of the first Annual Progression, I prepared a case study protocol. This is a formal document capturing the entire set of procedures involved in the collection of data for a case study (Yin, 2018). The protocol stated the case’s purpose, the rationale of case selection, the theoretical framework, and the study’s audience. It also covered data collection procedures, such as the events to be observed, sources of information, data collection questions, and an outline for the case study report.
Robert Yin developed this protocol to enhance the reliability of case study research methods: “In the past, case study research procedures were poorly documented, making external reviewers suspicious of the reliability of the case study method” (Yin, 2018). Yin had noble intentions, but his focus on procedural protocols may have unintentionally caused the case study approach to move towards a preconceived, deductive logic. Originally, case studies could be either primarily inductive, with the case serving as a source of data for conceptualisation and theory generation, or primarily deductive, in which cases are used to test pre-existing theories (Gummesson, 2006).
Another issue with the protocol is that it lacks procedures or strategies for extracting theory from the data. As a researcher, I was concerned about the possibility of amassing large amounts of data that would provide a lot of description but little theory. It is not surprising to see that researchers often import several strategies from grounded theory to develop theory out of case study data (e.g., Eisenhardt & Graebner, 2007; Gehman et al., 2018).
Kathy Eisenhardt explains that “theory building from cases is based on inductive-based theory, which is very much in line with the work of Glaser and Strauss” (Gehman et al., 2018). This differs from Yin’s deductive case study approach, in which theoretical codes are selected and imposed on data within a preconceived theoretical framework. Yin used to state that “theory development prior to the collection of any case study data is an essential step in doing case studies” (2009, p. 36). Such preconception, by its logic, leads to a structured forcing of data in confirmation of existing theories, frequently “adding very little or nothing to them”:
Many case studies merely embroider major theories, adding very little or nothing to them. Some fail to generate anything new, if the researcher solves his explanatory problems by merely relating his findings back to a major theory. Other case studies can generate considerable theory by using a major theory as a springboard. But, as we have often remarked in this book, this latter strategy frequently works to hamper or cripple the innovative capacities of the researcher (Glaser & Strauss, 1967/2006, p. 151).
Why Grounded Theory?
While narrative research focuses on individual stories told by participants and phenomenology emphasises shared experiences, a grounded theory study intends to move beyond description and generate or discover a theory (Creswell & Poth, 2018). Instead of description, grounded theory enables the researcher to investigate patterns, facilitating the discovery of theory from data. In this section, I go over my reasoning for switching from a multiple-case study to a grounded theory.
As previously said, I became interested in grounded theory after becoming dissatisfied with the multiple-case study approach. I was concerned about the possibility of writing extensive, thorough descriptions of a case that would result in “very small amounts of theory, if any" (Glaser & Strauss, 1967/2006, p. 15). Grounded theory, on the other hand, aims to “abstractly transcend all description” and, as a result, its findings are “never stale dated as descriptions soon are” (Glaser, 2016). Grounded theory is the study of abstract problems and their processes, not units of analysis (Glaser, 1992).
Personally, I was under the spirit that “developing theory is what we are meant to do as academic researchers and it sets us apart from practitioners and consultants” (Gregor, 2006, p. 613). Any research about an industry requires some level of description. However, technical trends change so quickly that any description would be outdated on the first day of publication. These goals are more suited to market research than an academic investigation. Grounded theory is unique because it solves this inherent tendency towards description. It guides the researcher in making the transition from analysing descriptive data to developing abstract patterns, concepts, and theories. This is why I recognised grounded theory as a methodological choice more suitable for me and for the research.
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