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Impact of task-technology fit on proactive behavior: boredom at work and core self-evaluation effects

  • Zhaoyu Sun*
  • , Xue Chang
  • , Yezhuang Tian
  • , Yifei Wu
  • *Corresponding author for this work
  • Business School, Harbin Institute of Technology
  • Northeastern University China
  • School of Management, Harbin Institute of Technology

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose – Against the backdrop of corporate digital transformation, this study aims to explore the mechanism through which task-technology fit (TTF) influences employee proactive behavior (PB), based on the conservation of resources (COR) theory, with a focus on the mediating role of boredom at work (BW) and the moderating role of core self-evaluation (CSE). Design/methodology/approach – A three-wave time-lagged survey was conducted among 419 employees from diverse Chinese organizations. Hierarchical regression, bootstrap mediation testing and conditional process modeling were used for data analysis. Findings – TTF has a significant positive linear relationship with employee PB; BW partially mediates this relationship. CSE moderates the relationship between BW and PB, weakening the inhibitory effect of BW on employee PB. Practical implications – Organizations should optimize TTF through training and talent matching, mitigate work boredom by enhancing task challenge and value employees with high CSE to promote PB during digital transformation. Originality/value – This study extends TTF theory to the research field of employee PB, constructs a multilevel mechanism where job attribute affects emotional experience, which in turn affects individual traits, and enriches the contextual application of COR theory in digital transformation.

Original languageEnglish
Pages (from-to)1-23
Number of pages23
JournalChinese Management Studies
DOIs
StateAccepted/In press - 2026
Externally publishedYes

Keywords

  • Boredom at work
  • Core self-evaluation
  • Digital transformation
  • Employee proactive behavior
  • Task-technology fit

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