Characterized simply as “the absence of leadership,” scholars have generally written off “laissez-faire leadership” as the inaction of poor managers disinterested in their followers and organizational outcomes. In this study, we question whether this simple understanding of the construct is always true, arguing that delegation, a conceivably positive behavior, is sometimes perceived as laissez-faire leadership by subordinates. We examine how perceived leader competence and subordinate gender determine how delegation affects perceptions of laissez-faire leadership and, indirectly, dysfunctional subordinate resistance. Our test of this model reveals a significant three-way interaction between delegation, perceived leader competence, and gender in influencing perceptions of laissez-faire leadership and dysfunctional resistance. We conclude that laissez-faire leadership is a more complex phenomenon than is often assumed in research. The theoretical and practical implications of these findings are discussed in this study.

Social network analysis has been increasingly used by researchers to operationalize team processes and emergent states. Despite their advantages over aggregate measures, the most frequently used network measures such as density and centrality are agnostic to potentially meaningful elements reflecting the patterns of ties between team members. Specifically, intangible resources transmitted within team networks are often more particularistic, such that the value of the shared resource is dependent upon who gives it. We use shared leadership as an exemplar case for explaining this issue and proposing a solution in the form of a new network measure, importance-weighted density (IWD). Combining logic from the principles of density, decentralization, and eigenvector centralization, IWD provides a more detailed understanding of network tie patterns by taking into account the degree to which ties emerge from members who are themselves well connected. We test the measure’s validity in a series of Monte Carlo simulations and laboratory and field studies. We find that IWD has high convergent, discriminant, and criterion validities and discuss how this statistic might help to enhance the study of several other team constructs. We provide access to a downloadable tool for the calculation of IWD and other network statistics discussed within this article.

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