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Summary This research investigates when certain coworkers can serve as “catalysts” to enhance the creative performance of others. We draw on social capital theory and the ability‐motivation framework to focus on the characteristics of potential catalysts and the facets of relationships between catalysts and creators. We hypothesized and found that the strength of the relationship between a catalyst and a creator has a positive association with the intensity of the catalyst's contribution to the creator's creative performance. In addition, this contribution is stronger if the catalyst himself/herself is creative, and counter to our hypothesis if the catalyst has access to the same contacts as the creator (i.e., high dyadic redundancy). Later, we turned our attention from the intensity of contribution between a catalyst and a creator to the total catalytic contribution that a creator receives from all their catalysts. We predicted and found an inverted U‐shaped relationship, such that although it is beneficial to receive help from catalysts up to a certain point, receiving excessive levels of catalytic contributions can be detrimental to employees' creative performance. We discuss the implications of our findings and explore how our results indicate a meaningful although mostly overlooked phenomenon in creativity research.

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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