Jeffrey B. Vancouver

When data contradict theory, data usually win. Yet, the conclusion of Van Iddekinge, Aguinis, Mackey, and DeOrtentiis (2018) that performance is an additive rather than multiplicative function of ability and motivation may not be valid, despite applying a meta-analytic lens to the issue. We argue that the conclusion was likely reached because of a common error in the interpretation of moderated multiple-regression results. A Monte Carlo study is presented to illustrate the issue, which is that moderated multiple regression is useful for detecting the presence of moderation but typically cannot be used to determine whether or to what degree the constructs have independent or nonjoint (i.e., additive) effects beyond the joint (i.e., multiplicative) effect. Moreover, we argue that the practice of interpreting the incremental contribution of the interaction term when added to the first-order terms as an effect size is inappropriate, unless the interaction is perfectly symmetrical (i.e., X-shaped), because of the partialing procedure that moderated multiple regression uses. We discuss the importance of correctly specifying models of performance as well as methods that might facilitate drawing valid conclusions about theories with hypothesized multiplicative functions. We conclude with a recommendation to fit the entire moderated multiple-regression model in a single rather than separate steps to avoid the interpretation error highlighted in this article. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

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.

Science is a complex task. It involves the creation and dissemination of knowledge. The creation of knowledge requires identifying and abstracting patterns (i.e., identifying phenomena and theorizing about the processes that bring it about), as well as systematically observing to better see and quantify the patterns (e.g., effect size estimating) or assess the validity of the abstractions used to explain the patterns (i.e., theory testing). To help (a) hone in on what observations would be useful and (b) communicate what the patterns mean, we are encouraged to develop and report hypotheses. That is, strategically, hypotheses facilitate the planning of data collection by helping the researcher understand what patterns need to be observed to assess the merit of an explanation. Meanwhile, tactically, hypotheses help focus the audience on the crucial patterns needed to answer a question or test a theory. When the strategic hypotheses are not supported, it raises a question regarding what to do tactically. Depending on the result (i.e., different direction; null), one might construct a hypothesis to facilitate dissemination without reporting this post hoc construction or remove mention of a hypothesis altogether. This practice is called HARKing (i.e., hypothesizing after results are known). HARKing has been so disparaged as to be considered a “detrimental research practice” (Grand et al., 2018, p. 6). As such, the Society for Industrial and Organizational Psychology's (SIOP) Robust and Reliability Science task force appears to be recommending that HARKing not be taught by educators, encouraged by reviewers or editors, or practiced by authors. I do not agree with those recommendations, and I elaborate on my position below.

Self-regulation is the dynamic process by which people manage competing demands on their time and resources as they strive to achieve desired outcomes, while simultaneously preventing or avoiding undesired outcomes. In this article, we review the current state of knowledge regarding the process by which people manage these types of demands. We review studies in the organizational, cognitive, social psychology, and human factors literatures that have examined the process by which people (a) manage task demands when working on a single task or goal; (b) select which tasks or goals they work on, and the timing and order in which they work on them; and (c) make adjustments to the goals that they are pursuing. We review formal theories that have been developed to account for these phenomena and examine the prospects for an integrative account of self-regulation that can explain the broad range of empirical phenomena examined across different subdisciplines within psychology.

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.

Research examining the relationship between self-efficacy and performance has tended to find a positive effect at the between-person level and a weak negative or null effect at the within-person level. This pattern of results has led to a belief that the level of analysis is a key factor in determining the sign of the relationship. Using a between-person experimental design, the authors demonstrate a negative effect for self-efficacy on motivation and quality of performance at the between-person level. A positive effect for self-efficacy on the quantity of tasks engaged, apparently due to the reduction in motivation on each task, was also found. The discussion focused on the relevance of the findings in the debate between social cognitive and control theorists.

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.