Research summary: Multi‐party alliances rely on partners' willingness to commit and pool their efforts in joint endeavors. However, partners face the dilemma of how much to commit to the alliance. We shed light on this issue by analyzing the relationship between partners' free‐riding—defined as their effort‐withholding—and their perceptions of alliance effectiveness and peers' collaboration. Specifically, we posit a U‐shaped relationship between partners' subjective evaluations of alliance effectiveness and their free‐riding. We also hypothesize a negative relation between partners' perceptions of the collaboration of peer organizations and their free‐riding. Results from a mixed‐method study—combining regression analysis of primary data on a major inter‐organizational research consortium and evidence from two experimental designs—support our hypotheses, bearing implications for the multi‐party alliances literature. Managerial summary: Free‐riding is a major concern in multi‐party alliances such as large research consortia, since the performance of these governance forms hinges on the joint contribution of multiple partners that often operate according to different logics (e.g., universities, firms, and government agencies). We show that, in such alliances, partners' perceptions have relevant implications for their willingness to contribute to the consortium's shared goals. Specifically, we find that partners free‐ride more—that is, contribute less—when they perceive the effectiveness of the overall alliance to be either very low or very high. Partners also gauge their commitment to the alliance on the perception of the effort of their peers—that is, other organizations similar to them. These findings provide managers of multi‐party alliances with additional levers to motivate partners to contribute fairly to such joint endeavor. Copyright © 2015 John Wiley & Sons, Ltd.

Structuration theory (ST) and network analysis are promising approaches for studying the emergence of communication networks. We offer a model that integrates the conceptual richness of structuration with the precision of relevant concepts and mechanisms offered from communication network research. We leverage methodological advancements (i.e., stochastic actor-oriented models) to test hypotheses deduced from ST using longitudinal communication network data collected over a two-year period. Results indicate that while structural rules external to a social network play a significant role, internal structural rules that emerge from the aggregate of individual actions during previous time periods also predict current structures, and that the reification influence of the latter is greater than that of the external factors.

This study simultaneously tested 2 theories that attempt to explain differences in job satisfaction: job characteristics theory (Hackman & Oldham, 1976) and social information processing theory (Salancik & Pfeffer, 1978). The theories were tested using data collected from the civilian employees of the public works division at a U.S. military base. The results indicated that individuals' social environments had significant effects upon their attitudes. Multiple social networks were used to operationalize individuals' social environments. The results also suggested that job characteristics had an independent main effect upon job satisfaction, in addition to the effects of the social environment. Based on prior research, employees' past experience and self-monitoring were tested as moderators of the effects of the social environment, and growth need strength was tested as a moderator of the effects of job characteristics upon job satisfaction. Only self-monitoring was found to have a significant moderating effect on the relationship between information from the social environment and job satisfaction, and growth need strength had no significant moderating effect.

Current understanding of the emergence of communication networks is hindered by at least two limitations: (a) studies tend to incorporate only 1 theoretical mechanism, and (b) studies tend to be cross-sectional. We address these concerns by analyzing the influence of 11 factors on the evolution of a communication network over 2 years. Our model is grounded in the multitheory, multilevel framework, and our analysis uses recently developed actor-oriented techniques for statistically modeling network emergence. Results suggest accurate and complete understanding of network evolution depends on studies simultaneously incorporating multiple theories that offer both complementary and contrasting explanations of the phenomenon.