JOURNAL ARTICLE

Positioning context front and center in international human resource management research.

  • Published In: Human Resource Management Journal, 2023, v. 33, n. 1. P. 1 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Farndale, Elaine; Bonache, Jaime; McDonnell, Anthony; Kwon, Bora 3 of 3

Abstract

The international human resource management (IHRM) field naturally lends itself to spotlighting the importance of internal and external organizational contexts to help understand how to manage employees in organizations effectively. However, we argue that the range of opportunities that the field creates to understand this context has not yet been fully embraced by IHRM scholars. To address this gap, this special issue explores: (a) the variety of approaches to theorizing how contexts promote or constrain organizational practice; and (b) relevant methodologies that might allow us to unearth novel context‐dependent theory in international HRM. We propose a distinction between variable‐oriented theorizing (that explains the effects of internal and external contexts on the phenomena under study) and context‐dependent theorizing (that requires researchers become intimately familiar with the setting under study to understand context as a shaper of meaning). This editorial also highlights how the articles in the special issue contribute to stimulating further context‐dependent IHRM research. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Human Resource Management Journal. 2023/01, Vol. 33, Issue 1, p1
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2023
  • ISSN:0954-5395
  • DOI:10.1111/1748-8583.12483
  • Accession Number:161103302
  • Copyright Statement:Copyright of Human Resource Management Journal is the property of Wiley-Blackwell 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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