F
Federico Aime
Researcher at Oklahoma State University–Stillwater
Publications - 25
Citations - 1679
Federico Aime is an academic researcher from Oklahoma State University–Stillwater. The author has contributed to research in topics: Context (language use) & Team effectiveness. The author has an hindex of 14, co-authored 23 publications receiving 1270 citations. Previous affiliations of Federico Aime include Michigan State University.
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Corporate social responsibility or CEO narcissism? CSR motivations and organizational performance
TL;DR: In this paper, the authors argue that CSR can be a response to leaders' personal needs for attention and image reinforcement and hypothesize that CEO narcissism has positive effects on levels and profile of organizational CSR; additionally, they find support for their ideas with a sample of Fortune 500 CEOs.
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The riddle of heterarchy: : power transitions in cross-functional teams
TL;DR: In this article, the authors developed the concept of power heterarchy, which is a conceptualization of power structures in groups that is more dynamic and fluid than traditional hierarchical structures, and demonstrated that heterarchical structures in which the expression of power actively shifts among team members to align team member capabilities with dynamic situational demands can enhance team creativity.
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Team Microdynamics: Toward an Organizing Approach to Teamwork
TL;DR: In this article, the authors outline the limitation of team researchers in the field of organizational behavior (OB) and discuss the need to embrace the organizing nature of teams in order to understand the organizational behavior of teams.
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The routine may be stable but the advantage is not: competitive implications of key employee mobility
TL;DR: It is shown that routines are stable to the loss of key employees, but the advantages derived from them are not, which challenges the traditional argument that socially complex routines create sustainable competitive advantages.
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Is performance driven by industry‐ or firm‐specific factors? A response to Hawawini, Subramanian, and Verdin
TL;DR: It is argued that exclusion of firms from a data sample based on commonly understood standards of outlier identification leads to little change in industry and firm variance component estimates compared to full-sample estimates.