Methods to Improve Our Field: Volume 14

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.

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.

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.

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.

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.

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.

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.