Theories suggest that groups within organizations often develop shared values, beliefs, affect, behaviors, or agreed-on routines; however, researchers rarely study predictors of consensus emergence over time. Recently, a multilevel-methods approach for detecting and studying emergence in organizational field data has been described. This approach—the consensus emergence model—builds on an extended three-level multilevel model. Researchers planning future studies based on the consensus emergence model need to consider (a) sample size characteristics required to detect emergence effects with satisfactory statistical power and (b) how the distribution of the overall sample size across the levels of the multilevel model influences power. We systematically address both issues by conducting a power simulation for detecting main and moderating effects involving consensus emergence under a variety of typical research scenarios and provide an R-based tool that readers can use to estimate power. Our discussion focuses on the future use and development of multilevel methods for studying emergence in organizational research.

Researchers have suggested that some personality traits are associated with better team functioning when team members are homogeneous, whereas other personality traits improve team functioning when team members are heterogeneous. This article extends these ideas to team innovation and examines (a) how team variance in extraversion, agreeableness, openness, and conscientiousness relates to innovation in teams; and (b) how these relationships dynamically evolve over time. Our study included 704 surveys completed by 243 team members in 32 teams, at three time points. Results revealed that teams with less variance in extraversion showed higher levels of team innovation. For agreeableness and openness, we did not find main effects of team heterogeneity on team innovation. For teams with low heterogeneity in agreeableness, however, team innovation decreased over time. Team variance in conscientiousness was negatively associated with team innovation. Our findings provide support that team personality plays a role for innovation.

The Operant Motive Test (OMT) is a picture-based procedure that asks respondents to generate imaginative verbal behavior that is later coded for the presence of affiliation, power, and achievement-related motive content by trained coders. The OMT uses a larger number of pictures and asks respondents to provide more brief answers than earlier and more traditional picture-based implicit motive measures and has therefore become a frequently used measurement instrument in both research and practice. This article focuses on the psychometric response mechanism in the OMT and builds on recent advancements in the psychometric modeling of the response process in implicit motive measures through the use of Thurstonian item-response theory. The contribution of the article is twofold. First, the article builds on a recently developed dynamic Thurstonian model for more traditional implicit motive measures (Lang, 2014) and reports the first analysis of which we are aware that applies this model to OMT data (N = 633) and studies dynamic motive activation in the OMT. Results of this analysis yielded evidence for dynamic motive activation in the OMT and showed that simulated IRT reliabilities based on the dynamic model were .52, .62, and .73 for the affiliation, achievement, and power motive in the OMT, respectively. The second contribution of this article is a tutorial and R code that allows researchers to directly apply the dynamic Thurstonian IRT model to their data. The future use of the OMT in research and potential ways to improve the OMT are discussed.

This study extends research on the link between personality and Counterproductive Work Behavior (CWB) by investigating whether the implicit Affiliation, Achievement, and Power motives contribute to the prediction of CWB beyond basic personality traits. Employees high in Affiliation, Achievement, and Power motives may disengage from CWB because it is not rewarding and thwarts goal attainment. In Study 1 (N = 263), we found that Affiliation predicted self-rated CWB beyond traits. In Study 2 (N = 121), we found that Affiliation and Power predicted supervisor-rated CWB. Our findings thus suggest to also consider implicit motives as personality determinants of CWB.

Psychologists have long been interested in studying individual differences in implicit motives. Implicit motives are typically measured asking respondents to write fantasy-stories based on a series of pictures showing one or several persons. The stories are then coded for implicit motivational content by trained experts because researchers have long assumed that respondents have no conscious access to the motivational themes in the stories they write. However, empirical research on self-evaluation of implicit motives is scarce. In this article, we provide new insights into this topic with a new measurement procedure-the motive self-categorization (MSC) test. In the MSC, respondents first fill out an implicit motive measure and then self-code their stories using lists of picture-specific statements that are typical concrete manifestations of implicit motives in the specific picture. We studied the MSC in a sample of 247 respondents by analyzing convergence with expert codings using a latent multitrait-multimethod item response theory (IRT) model. Results showed respondents could evaluate the motivational content of their stories (latent motive-motive rs ϭ .37-.62), IRT latent motive scores based on self-categorization showed evidence of reliability (rs ϭ .42-.67), and we found small method effects. The discussion focuses on implications for theory on measuring implicit motives and the possibility that self-insight occasionally goes beyond expert insight. Public Significance StatementThis article introduces a new measurement procedure for implicit motives (achievement, power, and affiliation). Unlike earlier motive measures that required subjective expert judgment, the new test lets respondent score their stories themselves by selecting empirically developed short descriptions of typical stories that are linked to the motives.