MAO Jiye
Management World,2020,Vol 36,No. 03
The rigor of data analysis is critical for the quality of qualitative research, and has long been a major challenge and headache to many researchers. To help address this issue, Professor Dennis A. Gioia was invited as the keynote and main tutorial speaker at the conference to promote his data analysis method. Drawing upon his two talks, this paper presented his structured method for data analysis in qualitative research, including its assumptions based on philosophies of social science, key features, and advantages, along with the application process. In addition, this paper also highlighted three common weaknesses in resent cases studies, and provided advice for improvement. Based on relevant philosophical assumptions, three principles must be followed in qualitative data analysis, according to Gioia. First, the informants should be allowed to give an account of their experience in their terms, not the researchers’. Second, researchers are not only reporters of what informants are thinking, feeling and doing, but also should give their own voices because researchers are knowledgeable and able to see patterns, relationships, new concepts, and theoretical explanations. Third and lastly, both voices should be reported to give a multifaceted view, but ultimately it is based on the informants’ experience, not the researchers’ theories. The data structure is the core of data analysis, which shows data-to-theory connections. By a detailed analysis of an exemplar study, this paper showed that the method can facilitate presentation of qualitative data in a systematic manner, and allow the emergence of new insights and theorizing, while maintaining a strong chain of proof between raw data and the conclusions. The Gioia method was demonstrated in detail via an exemplar paper (Bisho, at al., 2019). Its data structure intuitively presented concrete results emerging from analysis of raw data by the ground theory, and findings of induction via different levels of abstraction. First-order concepts were drawn from closely related phrases and sentences from raw data grouped together and labeled with tags, based on iterative comparisons between data and theories. Each second-order theme emerged from closely related first-order concepts, based on their relationships and patterns. This process features iterative comparisons with the literature to identify convergence and differences. Aggregate constructs were organized chronically based on their emergence timeline form a process model. A table was also given to show representative instances of raw data, a couple of instances for each of the first-order concepts. In short, Gioia’s data structure emphasizes systematic presentation of evidence, which facilitates both the emergence of new insights and theory development. It establishes a solid chain of proof between raw data and findings. Moreover, this approach to qualitative data analysis is easy to imitate and has the potential of significantly improving the quality of qualitative research. Lastly, this paper identified three common weaknesses in manuscripts submitted to this conference on case study, which hinder the development of impactful theories. First, the literature review tends to be broad and thin, without sufficient depth in meaningful dialog with prior studies. Consequently, such literature review is not able to guide data analysis, nor can it help conceptualize findings. Second, there is a recent tendency of over use of concepts, which are often vague, unnecessary, self-created, and ill-defined. It creates a proliferation of new concepts and models. As a result, simple insights are buried in obscure terminologies and become incomprehensible. Third, authors misunderstood the meaning of theoretical sampling quite commonly, as they claimed to have selected typical cases for single case research, which is wrong. On the contrary, ideally, the chosen single case should be extreme and unrepresentative, which could be rare but imitable. Only in this way, can single case studies create true and exciting management insights.
