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A multiplex view of leadership structure in management teams

机译:管理团队领导结构的多元化视图

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摘要

In the hope of complementing the structural perspective in upper echelon research and advancing a fine-grained understanding of dyadic leadership influence in management teams, the current study combines two types of intrateam structures-leadership network and friendship network-to create a multidimensional conceptualization of leadership structure in management teams. Specifically, we propose that management teams with a denser singular leadership network (i.e., a network consisting of many leadership ties that are not coupled with friendship ties) should have lower management team cohesion, which subsequently renders worse business unit performance. To contrast, management teams with a denser multiplex network (i.e., a network consisting of many leadership ties that are coupled with friendship ties) should have higher management team cohesion, which subsequently renders better business unit performance. Guided by structural contingency perspective, we further propose that management team task interdependence will strengthen team cohesion's positive impact on business unit performance. To test the hypothesized model, we collected team-level social network data and multiple-wave survey data from 697 managers nested in 148 hotels (i.e., 148 management teams) owned by a large hospitality company. We also obtained objective performance data for each hotel (i.e., occupancy percentage rate and revenue per available room per day) as the criterion measure. The data supported our hypotheses. The theoretical and practical implications of our findings are discussed.
机译:为了补充高层研究中的结构视角,推进对管理团队中二元领导影响的精细理解,本研究结合了两种类型的团队内部结构——领导网络和友谊网络,创建了管理团队中领导结构的多维概念。具体而言,我们建议,单一领导网络(即,由许多领导关系组成的网络,不与友谊关系耦合)密度更高的管理团队,其管理团队凝聚力应更低,从而导致业务部门绩效更差。相比之下,具有更密集的多元化网络(即,由许多领导关系和友谊关系组成的网络)的管理团队应该具有更高的管理团队凝聚力,从而提高业务部门的绩效。在结构权变视角的指导下,我们进一步提出管理团队任务相互依赖将增强团队凝聚力对业务部门绩效的积极影响。为了检验假设模型,我们收集了一家大型酒店公司拥有的148家酒店(即148个管理团队)的697名经理的团队级社交网络数据和多波调查数据。我们还获得了每家酒店的客观绩效数据(即入住率和每天每间可用客房的收入),作为衡量标准。这些数据支持我们的假设。讨论了我们的发现的理论和实践意义。

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