JOURNAL ARTICLE

Have Your Cake and Eat It Too? Understanding Leisure-Work Synergizing and Its Impact on Employee Thriving.

  • Published In: Organization Science (INFORMS), 2025, v. 36, n. 4. P. 1574 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Zipay, Kate P.; Rodell, Jessica 3 of 3

Abstract

This article focuses on the concept of leisure-work synergizing, defined as the intentional integration of work-related elements into leisure activities to build work-relevant skills and competencies during employees' free time. Drawing on a model of thriving at work, the research examines how this agentic, boundary-blurring practice influences employee thriving by fostering positive emotions like self-assurance, while also considering potential negative effects such as fatigue. Empirical studies, including validation of a leisure-work synergizing measure and a multiwave experience sampling study with employees, found that leisure-work synergizing positively relates to next-day self-assurance and thriving at work, with little evidence of increased fatigue; moreover, employees who prefer integrating work and nonwork domains experience less fatigue from this practice. The findings challenge traditional views of strict work-leisure separation by highlighting leisure-work synergizing as a proactive, enriching strategy that supports employee development and well-being, with implications for both individuals and managers in managing work-nonwork boundaries.

Additional Information

  • Source:Organization Science (INFORMS). 2025/07, Vol. 36, Issue 4, p1574
  • Document Type:Article
  • Subject Area:Sports and Leisure
  • Publication Date:2025
  • ISSN:1047-7039
  • DOI:10.1287/orsc.2021.15472
  • Accession Number:187706259
  • Copyright Statement:Copyright of Organization Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences 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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