Organizations grapple with how to position and manage star employees within and across workgroups. One critical question not yet well understood is how to optimize the influence of stars on non-stars and specifically whether to concentrate together or spread out stars across workgroups. Furthermore, we lack knowledge of who is more likely to benefit from stars. To that end, we develop a theoretical model that introduces a new unit-level concept—group star proportion (GSP)—to progress understanding of how the staffing of stars, within and across workgroups, influences non-star performance. Invoking theories of vicarious learning, we explicate why GSP has a curvilinear relationship with non-star performance. Specifically, GSP positively relates to non-star performance up to a point at which there are diminishing returns. In Study 1, we tested these predictions in a field dataset in a healthcare system. In Study 2, we replicated these findings in a commercial real estate firm and expanded understanding of how GSP may serve as a cross-level moderator of non-star performance and two critical individual differences germane to learning: non-star tenure and trait negative affect. Findings offer theoretical and practical insights into how stars might be of most benefit to their non-star peers.

中文翻译：

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星光闪耀？发掘群星比例如何塑造非明星表现

组织努力解决如何在工作组内和跨工作组定位和管理明星员工。一个尚未很好理解的关键问题是如何优化明星对非明星的影响，特别是是将明星集中在一起还是分散在工作组中。此外，我们不知道谁更有可能从明星中受益。为此，我们开发了一个理论模型，引入了一个新的单元级概念——群星比例（GSP）- 进一步了解工作组内部和跨工作组中明星的人员配备如何影响非明星绩效。援引替代学习理论，我们解释了为什么 GSP 与非明星表现呈曲线关系。具体来说，GSP 与非明星表现正相关，直到收益递减。在研究 1 中，我们在医疗保健系统的现场数据集中测试了这些预测。在研究 2 中，我们在一家商业房地产公司中复制了这些发现，并扩展了对 GSP 如何作为非明星绩效的跨级别调节器以及与学习密切相关的两个关键个体差异的理解：非明星任期和特质负面影响. 研究结果提供了关于明星如何为其非明星同行带来最大利益的理论和实践见解。