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Seeking to explain the processes by which one construct causes another is a staple of psychological research. This is typically done with statistical procedures using observations of the hypothesized cause, mediator, and effect. Unfortunately, interpretations of mediation are generally weak on internal validity when using this approach. As an alternative, a causal chain approach has been advocated, but that approach is often impractical. In this article, a third method that involves measuring or manipulating a moderator is described. The core of the argument for a moderation approach hinges on the recognition that mediation refers to a mechanism or process that might be blocked or enhanced via a moderator. Thus, finding interactions with manipulations, variables, or constructs that might affect the efficiency, rate, or operation of a mechanism, or the links to or from the mechanism, implies that mechanism is involved in determining the relationship between a cause and an effect. Examples and implications regarding the potential use as well as the advantages and disadvantages of the approach for applied researchers are described.

Theorists in management and organizational science rarely use computational modeling to support theoretical development or refinement, particularly at the micro level of analysis. This article argues that organizational scholars, who strive to understand dynamic behavior in a complex context, are particularly in need of the support computational models offer. Moreover, organizational scholars can build on (a) the plethora of informal theories extant in the literature and (b) the computational architectures and model building platforms developed in recent years. To increase the number of organizational scholars building and evaluating computational models, the article provides a tutorial in model building and simulation. Specifically, a new computational model is built and assessed. Surprising realizations emerge in the process. There is also an extensive section on model evaluation involving empirical observations.

Theories are the core of any science, but many imprecisely stated theories in organizational and management science are hampering progress in the field. Computational modeling of existing theories can help address the issue. Computational models are a type of formal theory that are represented mathematically or by other formal logic and can be simulated, allowing theorists to assess whether the theory can explain the phenomena intended as well as make testable predictions. As an example of the process, Locke's integrated model of work motivation is translated into static and dynamic computational models. Simulations of these models are compared to the empirical data used to develop and test the theory. For the static model, the simulations revealed largely strong associations with robust empirical findings. However, adding dynamics created several challenges to key precepts of the theory. Moreover, the effort revealed where empirical work is needed to further refine or refute the theory. Discussion focuses on the value of computational modeling as a method for formally testing, pruning, and extending extant theories in the field.

This chapter is designed help motivate the current readers' interest in computational modeling as the authors provide a description of the myriad of values computational modeling can bring to our science. Readers are also given a brief history of computational modeling as it relates to the field of I-O psychology. This is followed by a more complete description of the goals for the book, as the authors describe the learning objectives for various levels of computational model afficionados, from the scholarly consumers to the computational model creators. Finally, an overview of the chapters is provided.

"This collection provides a primer to the process and promise of computational modeling for industrial-organizational psychologists. With contributions by global experts in the field, the book is designed to expand readers' appreciation for computational modeling via chapters focused on key modeling achievements in domains relevant to industrial-organizational psychology, including decision-making in organizations, diversity and inclusion, learning and training, leadership, and teams"--

To encourage the use of computational modeling in organizational behavior research, an example computational model is developed and rigorous tests of it presented. Specifically, a computational model based on control theory was created to test the theory's explanation of the goal-level effect (e.g., higher goals lead to higher performance). Data from simulations of the model were compared with the behavior of 32 undergraduate students performing a scheduling task under various within-subject manipulations and across time. Correlational analyses indicated that the model accounted for most of the participants'data, with coefficients between the model and each participant's behavior mostly in the high 90s.