Machine learning (ML) has recently gained momentum as a method for measurement in strategy research. Yet, little guidance exists regarding how to appropriately apply the method for this purpose in our discipline. We address this by offering a guide to the application of ML in strategy research, with a particular emphasis on data handling practices that should improve our ability to accurately measure our constructs of interest using ML techniques. We offer a brief overview of ML methodologies that can be used for measurement before describing key challenges that exist when applying those methods for this purpose in strategy research (i.e., sample sizes, data noise, and construct complexity). We then outline a theory-driven approach to help scholars overcome these challenges and improve data handling and the subsequent application of ML techniques in strategy research. We demonstrate the efficacy of our approach by applying it to create a linguistic measure of CEOs' motivational needs in a sample of S&P 500 firms. We conclude by describing steps scholars can take after creating ML-based measures to continue to improve the application of ML in strategy research.
A growing body of research outlines how to best facilitate and ensure methodological rigor when using dictionary-based computerized text analyses (DBCTA) in organizational research. However, these best practices are currently scattered across several methodological and empirical manuscripts, making it difficult for scholars new to the technique to implement DBCTA in their own research. To better equip researchers looking to leverage this technique, this methodological report consolidates current best practices for applying DBCTA into a single, practical guide. In doing so, we provide direction regarding how to make key design decisions and identify valuable resources to help researchers from the beginning of the research process through final publication. Consequently, we advance DBCTA methods research by providing a one-stop reference for novices and experts alike concerning current best practices and available resources.
The use of artificial intelligence (AI) in management research is still nascent and has primarily focused on content analyses of text data. Some method scholars have begun to discuss the potential benefits of far broader applications; however, these discussions have not led yet to a wave of corresponding AI applications by management researchers. This chapter explores the feasibility and the potential value of using AI for a very specific methodological task: the reliable and efficient capturing of higher-level psychological constructs in management research. It introduces the capturing of basic emotions and emotional authenticity of entrepreneurs based on their macro- and microfacial expressions during pitch presentations as an illustrative example of related AI opportunities and challenges. Thus, this chapter provides both motivation and guidance to management scholars for future applications of AI to advance management research.
Interpretative Phenomenological Analysis (IPA) offers management researchers an approach which allows deep examination of the relationship between individuals and their environments, particularly in complex social situations. Phenomenology studies phenomena, or things and events, as they are perceived by people's consciousness. Interpretivism allows researchers to access such internal awareness of research participants by attempting to understand the words used by subjects to describe their experiences and perceptions. Inherently subjective, this approach requires self-awareness by the researcher and the willingness to abandon preconceived notions in favor of interactive listening and exploration, relying on terms and concepts volunteered by participants rather than nominated by theory or preceding literature. Qualitative text analysis software can be utilized to facilitate aggregation and distillation of the voluminous narratives that result from the open-ended semi-structured interviews typically employed to collect data for IPA. However, impartiality and discernment on the part of the researcher remain essential in interpreting any automated analytical results. The researcher becomes in essence a second-hand observer, peering through windows voluntarily opened by participants, attempting to understand their understanding of their world. This chapter introduces IPA, providing an overview of its rationale and approach, and illustrates its application in a management-related setting, focusing on cultural adaptation of immigrant professionals.
In this chapter, I explore traditional notions of secondary data in qualitative research and consider the ways in which these are continually being reimagined in the digital age. I situate this discussion in respect to data typologies and, more reflexively, in relation to our need as researchers to make data real. I consider contemporary understandings of reuse in relation to secondary data, focusing particularly on qualitative interview data. Recognizing those who are already forging a path, I then suggest how we might move beyond notions of reuse and reimagine secondary data in the digital age. To illustrate these points, I highlight relevant studies drawing data from a range of online spaces, and finally summarize key considerations and challenges.
This chapter presents a novel method for using PechaKucha presentations to generate and analyze participant-generated video data. As a data source, participatory video (PV) differs from ethnographic or archival video by relying on participants to tell their own stories. As a presentation technique, PechaKucha produces six-minute-and-forty-second, narrated slideshow presentations. The slideshows or recordings from live PechaKucha presentations are a dense form of PV that is easier to code and analyze than traditional sources of PV. This chapter describes the procedures to capture and analyze PechaKucha-based PV and illustrates considerations for researchers who plan to use PV to gather data.
Panel data, where observations of entities are repeated over time, are common in strategic management research. However, explorations of the role of time on predictors of interest are often unexplored. In this chapter, we illustrate how the use of mixed-effect growth models can enhance theory and research in strategic management by exploring changes in outcomes of interest over time. Mixed-effects models allow for testing both within and between effects, while also calculating specific intercepts (firm average values) and slopes (trajectories of specific firms over time) using empirical Bayes estimates. We also illustrate how a discontinuous growth model could be used to assess differences in firm intercepts and slopes surrounding exogenous events (e.g., global pandemics) without requiring a control group.
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.
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.
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.
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.
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.
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.
