Justin M. Weinhardt

A Formal, Computational Theory of Multiple-Goal Pursuit: Integrating Goal-Choice and Goal-Striving Processes

Understanding the processes involved when pursuing multiple goals over time is a central question for motivational theorists. A dynamic, computational model integrating theories of goal striving and goal choice is presented to account for data emerging from Schmidt and DeShons (2007) multiple-goal-pursuit protocol. The simulated results match the results reported in their study, including the finding that relative discrepancy from the goals positively predicted resource allocation early on but negatively predicted it toward the end of the session. Variance in parameters in the model also accounted for individual differences found in the data. Discussion focuses on the theoretical contribution of formally integrating elements of self-regulation theories, further empirical work needed to test the model, and further theoretical work needed to continue the integration process exemplified here.

Training Engagement Theory A Multilevel Perspective on the Effectiveness of Work-Related Training

Training engagement theory provides a multilevel depiction of the antecedents of training effectiveness. By multilevel, we are referring both to the hierarchical nature of constructs—such that employees are embedded in organizations and workgroups—and the temporal nature of processes—emphasizing that macro and within-person processes are not static phenomena. The hierarchical nature of training engagement theory provides a broad account of how processes at various levels in the organizational hierarchy influence one another and contribute to the success or failure of training programs. The temporal nature of the theory advocates for examining the processes that occur from before training is conceptualized until the completion of training when examining the antecedents of training effectiveness. Thus, training engagement theory proposes a sequence model of the independent and joint effects of establishing training goals, prioritizing those goals, and persisting during goal striving on training effectiveness. Finally, we propose testable multilevel propositions to spur future research.

Modeling the Mind and the Milieu: Computational Modeling for Micro-Level Organizational Researchers

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.

Computational models and organizational psychology: Opportunities abound

Computational modeling has long been advocated as an important tool in the scientist’s tool shed. They are common in physical and biological sciences, but are very rare in organizational psychology. This paper describes the role computational models might play in informing theory and science in organizational psychology. After describing the major advantages of computational models, architectures, examples, and resources are described. This is followed by a comprehensive review of where computational models have been applied and where they might be profitably applied in each of the major domains of organizational psychology.

Cross‐Cultural Measurement Invariance of the Employment Opportunity Index (EOI) in Mexican and Brazilian Professionals

Perceived employment alternatives have been shown to add incremental variance in understanding voluntary turnover as well as factors related to recruiting and staffing. The Employment Opportunity Index (EOI) is a multidimensional assessment of perceived employment alternatives that has exhibited predictive validity across multiple job types and populations. This study assessed the measurement invariance of the EOI in American, Mexican, and Brazilian professionals. We found support for configural and metric invariance in all five dimensions of the EOI with these populations, and support for scalar invariance three of the five EOI dimensions. The construct validity of the EOI appears to be relatively robust in Latin American populations, although cultural and macroeconomic factors may impose some response bias in these groups.

When daily planning improves employee performance: the importance of planning type, engagement, and interruptions

Does planning for a particular workday help employees perform better than on other days they fail to plan? We investigate this question by identifying 2 distinct types of daily work planning to explain why and when planning improves employees’ daily performance. The first type is time management planning (TMP)—creating task lists, prioritizing tasks, and determining how and when to perform them. We propose that TMP enhances employees’ performance by increasing their work engagement, but that these positive effects are weakened when employees face many interruptions in their day. The second type is contingent planning (CP) in which employees anticipate possible interruptions in their work and plan for them. We propose that CP helps employees stay engaged and perform well despite frequent interruptions. We investigate these hypotheses using a 2-week experience-sampling study. Our findings indicate that TMP’s positive effects are conditioned upon the amount of interruptions, but CP has positive effects that are not influenced by the level of interruptions. Through this study, we help inform workers of the different planning methods they can use to increase their daily motivation and performance in dynamic work environments.

Use of the familiarity difference cue in inferential judgments

The familiarity difference cue has been regarded as a general cue for making inferential judgments (Honda, Abe, Matsuks, & Yamagishi in Memory and Cognition, 39(5), 851–863, 2011; Schwikert & Curran in Journal of Experimental Psychology: General, 143(6), 2341–2365, 2014). The current study tests a model of inference based on familiarity differences that encompasses the recognition heuristic (Goldstein & Gigerenzer, 1999, Goldstein & Gigerenzer in Psychological Review, 109(1), 75–90, 2002). In two studies, using a large pool of stimuli, participants rated their familiarity of cities and made choices on a typical city-size task. The data were fitted with the r-s model (Hilbig, Erdfelder, & Pohl in, Journal of Experimental Psychology: Learning Memory and Cognition, 37(4), 827–839, 2011), which was adapted to include familiarity differences. The results indicated that people used the familiarity difference cue because the participants ignored further knowledge in a substantial number of cases when the familiarity difference cue was available. An analysis of reaction-time data further indicated that the response times were shorter for heuristic judgments than for knowledge-only-based judgments. Furthermore, when knowledge was available, the response times were shorter when knowledge was congruent with a heuristic cue than when it was in conflict with it. Differences between the familiarity difference cue and the fluency heuristic (Schooler & Hertwig, 2005, Psychological Review, 112, 610–628) are discussed.