John R Busenbark

Fit is an essential consideration for organizations, and extensive research has explored its various types. We build on and extend fit research by advancing an important form of fit—organization‐investor (O‐I) fit, which reflects the compatibility between an organization and its investors. We argue that investors tend to be attracted to organizations whose preferences already “fit” their own and, in so doing, provide a relational perspective to the corporate governance literature that often views the relationship between investors and organizational managers as purely transactional. We focus on the fit of one of the most important factors shaping both organizational and investor behavior––temporal preferences. Specifically, we argue that investors are attracted to firms that fit with their temporal preferences and that high O‐I fit leads to better organizational performance. We tested our hypotheses in a longitudinal archival study of S&P 500 firms and supplemented these findings with two experiments. Together, we find strong support for our hypotheses. Our study showcases the critical role of O‐I fit in shaping the makeup of an organization's investors and its subsequent performance, suggesting the value for scholars, managers, and investors to consider the mutual benefits offered by fit in these relationships.

Organizational researchers have increasingly noted the problems associated with nonnormal dependent variable distributions. Most of this scholarship focuses on variables with positive values and long tails, such as employee performance, capital expenses, and assets. However, scholars frequently test organizational theories using dependent variables that include negative values, which is perhaps most prominently the case as it relates to measures of firm performance. Over the course of two studies, we investigate the implications of such nonnormally distributed dependent variables in organizational research. In Study 1, we examine the nonnormality of firm performance measures and uncover extreme levels of skewness and kurtosis that vary substantially across measures, samples, and years. We also illustrate that many transformations scholars use to address nonnormality are ineffective. In Study 2, we create simulations that seek to mirror these distributions, and we find that such extreme nonnormality reduces efficiency and increases Type II errors with most statistical approaches. Our analyses also reveal the effectiveness of quantile regression when modeling dependent variables that exhibit the nonnormal distributions often found in organizational research.

Bayesian analysis offers strategy scholars numerous benefits. In addition to aligning empirical and theoretical endeavors by incorporating prior knowledge, the Bayesian approach allows researchers to estimate and visualize relationships that reflect the probability distributions many strategy researchers mistakenly interpret from conventional techniques. Yet, strategy scholars have proven hesitant to adopt Bayesian methods. We suggest that this is because there is no accessible template for employing the technique with the types of data strategy researchers tend to encounter. The central objective of our research is to synthesize disparate contributions from the Bayesian literature that are relevant for strategy scholarship, especially for nested data. We provide an intuitive overview of Bayesian thinking and illustrate how scholars can employ Bayesian techniques to analyze nested data using an example dataset involving CEO compensation. Our results show how using Bayesian models may lead to substantively different interpretations and conclusions compared to traditional approaches based on frequentist techniques.