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Peer monitoring, which occurs when individuals notice and respond to their peers' behavior or performance results, is an informal organizational control that has not been extensively studied. Agency theory suggests that peer monitoring should be associated with higher performance because it allows workers whose interests are aligned with those of the organization to encourage their peers to perform well and deters inappropriate behavior by increasing the chances that it would be detected.This paper considers two research questions. The first is, "What is peer monitoring?" We found two types of peer monitoring. Direct peer monitoring occurs when workers notice their peers' behavior or results and respond in a forthright way, such as correcting coworkers who make mistakes. Indirect peer monitoring occurs when workers gossip about or avoid poorly performing peers. The second research question is, "Is peer monitoring associated with higher work-unit performance?" We examined the effects of both types of peer monitoring on the performance of 67 theme park work units. We considered supervisory monitoring, task interdependence, and cohesiveness as moderators.We measured performance as the degree to which the work unit is free of employee behavior problems and found a positive main effect of direct peer monitoring. The effect was stronger when supervisory monitoring was low or task interdependence was high. Indirect peer monitoring did not have a main effect on work-unit performance, but when supervisory monitoring or cohesiveness was high, indirect peer monitoring was associated with lower performance. We discuss these results and suggest areas for future research.

AbstractCapstone classes can help MBA programs achieve student learning goals and meet accreditation standards. However, such programs often fail to build the skills they are intended to develop. Technology transfer projects can be an effective way to build these skills while creating a positive economic impact. This article describes Crummer Graduate School of Business at Rollins College's MBA program's integrative capstone experience that partners with NASA to allow student teams to commercialize unused or underutilized NASA patents to develop new products and create business startups using a 4Ps model. First, students examine Patents for intellectual property that could be transferred to new applications with commercial potential. Next, they develop a knowledge network of Partners who could assist them to commercialize the technology. Then, students identify Prospects for the proposed commercial use of the technology. Finally, they Package a newly developed, market‐ready product with strategy, financial, sales and marketing plans, and pitch these to angel investors who might fund the startup and assist them in starting the business. This article details the 20 steps in the 4Ps model and discusses student outcomes, as well as the business focus of the 29 teams that have completed the course since 2012. Benefits, challenges, and advice for delivering the 4Ps model are discussed.

This study focuses on team members' perceptions of organizational support as antecedents of team processes and potency. Potency is team members' collective belief that the team can be effective. In a field study of 39 work teams in six organizations, the authors find a positive relationship between organizational support and potency that is mediated by effective team processes. The authors also find a positive relationship between team processes and managers' ratings of team performance that is mediated by potency. Understanding how organizational support affects potency is important because research has consistently found that potency is strongly associated with team performance, yet little is known about how organizational context affects potency. Therefore, this research has practical as well as theoretical implications.

Emergent states are team-level attributes that reflect team members' collective attitudes, values, cognitions, and motivations and influence team effectiveness. When measuring emergent states (e.g., cohesion, conflict, satisfaction), researchers frequently collect ratings from individual group members and aggregate them to the team level. After aggregating to the team level, researchers typically focus on mean differences across teams and ignore variability within teams. Rather than focusing on the mean level of emergent states, this study draws on recent advances in multilevel theory and describes an approach for examining the specific patterns of dispersion (i.e., disagreement) across five emergent states. Our findings suggest that teams reliably demonstrate different patterns of rating dispersion that are consistent with existing theoretical frameworks and typologies of dispersion, yet have not previously been empirically demonstrated. We also present evidence that the different patterns of dispersion in emergent states are significantly related to key team outcomes, even after controlling for the mean levels of those emergent states. These findings underscore the importance of exploring additional forms of team-level constructs and highlight ways of extending our understanding of group-level phenomena.

Management researchers often use consensus-based composition models to examine the antecedents and effects of higher-level constructs. Typically, researchers present three indices, rwg, ICC(1), and ICC(2), to demonstrate agreement and consistency among lower-level units when justifying aggregation. Nevertheless, researchers debate what values for these indices are sufficient. This study examines the distributional characteristics of ICCs and rwg values from three sources: the multilevel literature, a large multinational sample of student teams, and a large sample of randomly generated "pseudo teams." Our results support existing cutoff criteria for ICCs but suggest that generally accepted values for rwg may, under certain circumstances, reflect pseudo-agreement (i.e., agreement observed among two raters not attributable to the same target). Thus, when there is minimal between-group variance (i.e., low ICCs), it is difficult to determine whether high rwg values reflect agreement or pseudo-agreement. Based on these findings, we provide recommendations to help researchers interpret aggregation indices.