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Comparing the effects of interpersonal and intergroup relative leader–member exchange in nested workgroups.

  • Published In: Social Behavior & Personality: an international journal, 2023, v. 51, n. 1. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Li, Hu; Huang, Sihong; Jiang, Jane Yan 3 of 3

Abstract

In this study we explored the influence on employees of interpersonal relative leader–member exchange and intergroup relative leader–member exchange in regard to work behaviors with different levels of risk and uncertainty (i.e., task performance vs. innovative behavior), depending on subgroup uncertainty. We examined our theoretical model by surveying 309 employees in 58 member subgroups. Results showed that both interpersonal and intergroup relative leader–member exchange were positively associated with internal and temporary workers' task performance, interpersonal relative leader–member exchange was positively associated with the innovative behavior of internal workers but not temporary workers, and intergroup relative leader–member exchange was positively related to temporary workers' innovative behavior but negatively linked to internal workers' innovative behavior. Our findings suggest that the effects of interpersonal and intergroup relative leader–member exchange on members' behaviors may vary with the subgroup identities. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Social Behavior & Personality: an international journal. 2023/01, Vol. 51, Issue 1, p1
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2023
  • ISSN:0301-2212
  • DOI:10.2224/sbp.11978
  • Accession Number:161309707
  • Copyright Statement:Copyright of Social Behavior & Personality: an international journal is the property of Scientific Journal Publishers Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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