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Many scholars and practitioners lament female pay gaps and the ethical issues they pose; yet several studies provide supporting evidence showing that the female CEOs earn more than men. However, other studies find an insignificant difference between male and female CEO pay. 10; The purpose of this study is to re-investigate this question to uncover the root of the divergent findings and thereby clarify our understanding of this important issue of CEOs’ gender pay gaps.

Evidence suggests the CEO position is at times a rare instance where typical pay gaps for female workers reverse such that these executives earn pay premiums. Recently, Gupta et al. (2018) called findings for female CEO pay premiums into question, failing to find differences despite using data similar to prior studies. The authors investigated the discrepant findings, identifying and showing that the use of an analytical approach to account for unobserved differences (i.e. fixed effects) are inappropriate for the data structure drives’ divergent findings. The authors also find that results are affected by the industries and time-frames used in the analyses.

The authors find that female CEOs outearn their male counterparts. However, the authors also show that the significance of results is affected by the industries and time-frames used in the analyses.

It is an original work that reexamines a somewhat controversial issue on the gender differences in CEO pay.

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.

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.

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.

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.