Understanding Employee Attitudes Toward Artificial Intelligence in the Workplace: A Systematic Review of Attitude Definitions and Measurements Cover Image

Understanding Employee Attitudes Toward Artificial Intelligence in the Workplace: A Systematic Review of Attitude Definitions and Measurements
Understanding Employee Attitudes Toward Artificial Intelligence in the Workplace: A Systematic Review of Attitude Definitions and Measurements

Author(s): Ramunė Bagočiūnaitė, Auksė Endriulaitienė
Subject(s): Business Economy / Management, Organizational Psychology, Human Resources in Economy, ICT Information and Communications Technologies
Published by: Vilnius Gediminas Technical University
Keywords: artificial intelligence; AI; workplace; employee attitudes; measurements; systematic literature review;

Summary/Abstract: This study aims to provide a comprehensive synthesis of existing empirical research on employee attitudes toward artificial intelligence (AI) in the workplace, with a particular focus on how these attitudes are defined and measured. Therefore, systematic literature review was performed. Following PRISMA guidelines, search conducted in two databases, EBSCOhost and Scopus, yielded 642 records, of which 29 met the inclusion criteria. The included studies, published between 2021–2025, covered a broad geographic and sectoral range, encompassing the United States, Europe, Asia, the Middle East, and involved various occupational groups, most commonly general employees, managers, and HR professionals. Review findings indicate that employee attitudes are conceptualized as a multi-dimensional phenomenon, most commonly situated within a cognitive–affective framework. However, the methods used to measure attitudes vary widely, with studies drawing on a combination of established models, adapted scales, and context-specific instruments, which in turn restricts the comparability of results across studies. Drawing from human-centered perspective, this study is timely in highlighting the need to clearly define fundamental employee attitudes toward AI, to employ a validated method to measure them, thereby enabling comparability in future research, and supporting organizations to implement and use AI effectively.

  • Issue Year: 27/2026
  • Issue No: 1
  • Page Range: 168-178
  • Page Count: 11
  • Language: English
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