Research in Times of Crisis: Volume 13

This essay, invited by the editors, provides a retrospective overview of Robert Gephart's career using qualitative research methods to study disasters, and disseminating findings from the research in important management and organizational journals. Dr Gephart's work is associated with many methodological innovations. These include early use of grounded theory; early application of text analysis software to support analysis of extensive documentary data sets including legal proceedings and transcripts; development of ethnostatistics to explore risk assessment; explicating and elaborating abductive processes during the research experience; and using an autoethnographic approach to embed data from his own life in his research (before the term autoethnography was in common use). His contributions to the area of disasters and research methods innovations are wide ranging and provide tools for improving our understanding of risks and crises, and for managing them.

In this retrospective of Ann Langley's extensive career, she shares how methodological insights emerged in her research career as a qualitative researcher in strategy and management. Her retrospective provides the back story of some of her highly-cited methods papers. Ann acknowledges the role that other researchers and authors have played in her research career.

The ongoing global pandemic poses significant challenges for researchers, personally and professionally, as it does for all people. However, even if we cannot safely leave our home to gather qualitative data by directly meeting with people, opportunities abound for engaging in discourse analysis. After all, people have not stopped talking or writing, even if much of that is now via Zoom, social media or some other technology platform rather than face to face. What people are talking and writing about at this time matters greatly because language use profoundly shapes how people interpret reality, perceive themselves and others and act. Quite literally, then, the discourse people engage in and are influenced by during the pandemic may help to save or imperil lives and livelihoods. While there are many possible approaches to discourse analysis, this chapter focuses on some key insights French philosopher and social theorist Michel Foucault offers for such endeavors. It offers an introductory account of his key concepts and methods, followed by a brief case study to demonstrate their application to discourses that reject scientific knowledge and advice about COVID-19.

Organizational crises are complex events for researchers to assess. However, research in this domain remains fragmented, and advanced empirical techniques remain underutilized. In this chapter, we offer an integrated approach to assessing crises. We first specify a behavioral process model of crisis management comprised of three stages: interpretations, responses, and outcomes. Within each stage, we identify areas of opportunity and provide methodological recommendations that enhance our understanding of crises and crisis management. We also provide recommendations that could be applied across stages of the model. Taken together, we present a framework by which researchers can more effectively measure and analyze critical crisis dimensions.

Natural disasters and other crises present methodological challenges to organizational researchers. While these challenges are well canvassed in the literature, less attention has been paid to understanding how distinct crisis events may present, not only unique challenges, but also important opportunities for research. In this chapter, we draw on our collective experience of conducting post-earthquake research and compare this with the COVID-19 pandemic context in order to identify and discuss the inherent vulnerabilities associated with disaster studies and the subsequent methodological challenges and opportunities that researchers might encounter. Adopting a critical perspective, the chapter grapples with some of the more contentious issues associated with research in a disaster and crisis context including aspects of stakeholder engagement, ethics, reciprocity, inequality, and vulnerability.

In the early days of the COVID-19 pandemic, an area could only report its first positive cases if the infection had spread into the area and if the infection was subsequently detected. A standard probit model does not correctly account for these two distinct latent processes but assumes there is a single underlying process for an observed outcome. A similar issue confounds research on other binary outcomes such as corporate wrongdoing, acquisitions, hiring, and new venture establishments. The bivariate probit model enables empirical analysis of two distinct latent binary processes that jointly produce a single observed binary outcome. One common challenge of applying the bivariate probit model is that it may not converge, especially with smaller sample sizes. We use Monte Carlo simulations to give guidance on the sample characteristics needed to accurately estimate a bivariate probit model. We then demonstrate the use of the bivariate probit to model infection and detection as two distinct processes behind county-level COVID-19 reports in the United States. Finally, we discuss several organizational outcomes that strategy scholars might analyze using the bivariate probit model in future research.

In this chapter, we explicate two related techniques that help quantify the sensitivity of a given causal inference to potential omitted variables and/or other sources of unexplained heterogeneity. In particular, we describe the Impact Threshold of a Confounding Variable (ITCV) and the Robustness of Inference to Replacement (RIR). The ITCV describes the minimum correlation necessary between an omitted variable and the focal parameters of a study to have created a spurious or invalid statistical inference. The RIR is a technique that quantifies the percentage of observations with nonzero effects in a sample that would need to be replaced with zero effects in order to overturn a given causal inference at any desired threshold. The RIR also measures the percentage of a given parameter estimate that would need to be biased in order to overturn an inference. Each of these procedures is critical to help establish causal inference, perhaps especially for research urgently studying the COVID-19 pandemic when scholars are not afforded the luxury of extended time periods to determine precise magnitudes of relationships between variables. Over the course of this chapter, we define each technique, illustrate how they are applied in the context of seminal strategic management research, offer guidelines for interpreting corresponding results, and delineate further considerations.

COVID-19 has generated unprecedented circumstances with a tremendous impact on the global community. The academic community has also been affected by the current pandemic, with strategy and management researchers now required to adapt elements of their research process from study design through to data collection and analysis. This chapter makes a contribution to the research methods literature by documenting the process of adapting research in light of rapidly changing circumstances, using vignettes of doctoral students from around the world. In sharing their experience of shifting from the initially proposed methodologies to their modified or completely new methodologies, they demonstrate the critical importance of adaptability in research. In doing so, this chapter draws on core literature of adaptation and conducting research in times of crises, aiming to provide key learnings, methodological tips and a “story of hope” for scholars who may be faced with similar challenges in the future.