内隐追随作为一种关于追随角色的认知结构,包括追随原型和反原型两种。其中,追随原型表征了个体对追随角色的积极预期。本研究基于角色理论,考察了领导者-追随者的追随原型一致性对工作绩效的影响及工作投入的中介作用。采用跨层次多项式回归和响应面分析技术,对64个工作团队的数据进行分析,发现追随原型能否提升工作绩效主要取决于领导-追随双方的匹配情况。具体而言,(1)领导-追随双方的追随原型越一致,关系绩效越高。但上述结论并不适用于任务绩效。(2)在一致情况下,与"低-低"一致相比,任务绩效和关系绩效在双方追随原型的"高-高"一致时更高。(3)在不一致情况下,与"领导者的追随原型高-追随者的追随原型低"相比,任务绩效和关系绩效在"领导者的追随原型低-追随者的追随原型高"时相对更高。(4)追随原型一致性通过工作投入影响任务绩效和关系绩效。
In organizational settings, scholars have suggested that individuals naturally tend to classify people into two types: leader and follower. While a substantial body of research has established implicit leadership theories (ILTs) in the past three decades, the corresponding notion of implicit followership theories (IFTs) has relatively received little research attention (Sy, 2010). IFTs are defined as individuals’ personal assumptions about the traits that characterize followers, which include followership prototype and anti-prototype. To date, most research focuses on the consequence of followership prototype and suggests that followership prototype could enhance job performance through leader’s performance expectations, leader-member exchange and liking for followers. From these aforementioned studies, however, some research gaps have not been addressed. Firstly, the previous research on followership prototype becomes less convincing for they failed to integrate the follower’s followership prototype into the model for examination. Secondly, prior studies have predominantly focused on the effect of followership prototype on task performance and organizational citizenship behavior, while ingoring contextual performance. Thirdly, very few studies discuss the mediating role of job engagement in the relationship between followership prototype and performance. To fill such research gaps, the present study aims to examine the effects of leader-follower congruence in followership prototype on task and contextual performance, as well as the mediating role of job engagement. Data were collected from 243 leader-follower dyads in 64 teams of nine companies in China. Since our data contained a hierarchical structure in which individual scores were nested within teams, we used hierarchical linear modeling (HLM) to conduct cross level polynomial regression combining with response surface analysis. Based on the results, our research presents four conclusions: (1) In terms of the effects on differen