Understanding employee attitudes toward artificial intelligence in the workplace: a systematic review of attitude definitions and measurements

DOI: https://doi.org/10.3846/btp.2026.25127

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.

Keywords:

artificial intelligence, AI, workplace, employee attitudes, measurements, systematic literature review

How to Cite

Bagočiūnaitė, R., & Endriulaitienė, A. (2026). Understanding employee attitudes toward artificial intelligence in the workplace: a systematic review of attitude definitions and measurements. Business: Theory and Practice, 27(1), 168–178. https://doi.org/10.3846/btp.2026.25127

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May 19, 2026
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References

Almashawreh, R., Talukder, M., Charath, S. K., & Khan, M. I. (2024). AI adoption in Jordanian SMEs: The Influence of technological and organizational orientations. Global Business Review. https://doi.org/10.1177/09721509241250273

Alrishan, A. M. H. (2023). Determinants of Intention to Use ChatGPT for professional development among Omani EFL pre-service Teachers. International Journal of Learning, Teaching and Educational Research, 22(12), 187–209. https://doi.org/10.26803/ijlter.22.12.10

Arora, S., Chaudhary, P., & Singh, R. K. (2025). Adoption of HR analytics for future-proof decision making: Role of attitude toward artificial intelligence as a moderator. International Journal of Organizational Analysis, 33(9), 3047–3063. https://doi.org/10.1108/IJOA-03-2024-4392

Babamiri, M., Heidarimoghadam, R., Ghasemi, F., Tapak, L., & Mortezapour, A. (2022). Insights into the relationship between usability and willingness to use a robot in the future workplaces: Studying the mediating role of trust and the moderating roles of age and STARA. PLoS ONE, 17(6), 1–12. https://doi.org/10.1371/journal.pone.0268942

Bakker, A. B., Demerouti, E., & Sanz-Vergel, A. I. (2023). Job demands-resources theory: Ten years later. Annual Review of Organizational Psychology and Organizational Behavior, 10, 25–53. https://doi.org/10.1146/annurev-orgpsych-120920-053933

Bankins, S., Ocampo, A. C., Marrone, M., Restubog, S. L. D., & Woo, S. E. (2023). A multilevel review of artificial intelligence in organizations: Implications for organizational behavior research and practice. Journal of Organizational Behavior, 45(2), 159–182. https://doi.org/10.1002/job.2735

Benhayoun, I., Bougrine, S., & Sassioui, A. (2025). Readiness for artificial intelligence adoption by auditors in emerging countries – a PLS-SEM analysis of Moroccan firms. Journal of Financial Reporting and Accounting, 23(4), 1486–1508. https://doi.org/10.1108/JFRA-07-2024-0448

Cao, G., Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2021). Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making. Technovation, 106, Article 102312. https://doi.org/10.1016/j.technovation.2021.102312

Chen, J., & Zhou, W. (2022). Drivers of salespeople’s AI acceptance: what do managers think? Journal of Personal Selling and Sales Management, 42(2), 107–120. https://doi.org/10.1080/08853134.2021.2016058

Daly, S. J., Wiewiora, A., & Hearn, G. (2025). Shifting attitudes and trust in AI: Influences on organizational AI adoption. Technological Forecasting and Social Change, 215, Article 124108. https://doi.org/10.1016/j.techfore.2025.124108

Darko, A., Chan, A. P. C., Adabre, M. A., Edwards, D. J., Hosseini, M. R., & Ameyaw, E. E. (2020). Artificial intelligence in the AEC industry: Scientometric analysis and visualization of research activities. Automation in Construction, 112, Article 103081. https://doi.org/10.1016/j.autcon.2020.103081

Do, H., Chu, L. X., & Shipton, H. (2025). How and when AI-driven HRM promotes employee resilience and adaptive performance: A self-determination theory. Journal of Business Research, 192, Article 115279. https://doi.org/10.1016/j.jbusres.2025.115279

Dwivedi, Y. K., Rana, N. P., Janssen, M., Lal, B., Williams, M. D., & Clement, M. (2017). An empirical validation of a unified model of electronic government adoption (UMEGA). Government Information Quarterly, 34(2), 211–230. https://doi.org/10.1016/j.giq.2017.03.001

Eftimov, L., & Kitanovikj, B. (2023). Unlocking the path to AI adoption: Antecedents to behavioral intentions in utilizing AI for effective job (re)design. Journal of Human Resource Management – HR Advances and Developments, 26(2), 122–134. https://doi.org/10.46287/OTTP6295

Emre Taşgit, Y., Baykal, Y., Aydin, U. C., Yakupoğlu, A., & Coşkuner, M. (2023). Do employees’ artificial intelligence attitudes affect individual business performance? Journal of Organisational Studies and Innovation, 10(2), 19–37. https://doi.org/10.51659/josi.22.176

Ghazy, K., & Fedorova, A. E. (2022). Hotel employees’ attitude and acceptance toward human-robot co-working based on the Industry 5.0 concept. Changing Societies and Personalities, 6(4), 906–926. https://doi.org/10.15826/csp.2022.6.4.209

Gkinko, L., & Elbanna, A. (2023). Designing trust: The formation of employees’ trust in conversational AI in the digital workplace. Journal of Business Research, 158, Article 113707. https://doi.org/10.1016/j.jbusres.2023.113707

Hmoud, B., & Várallyai, L. (2021). Artificial intelligence in talent acquisition, do we trust it? Journal of Agricultural Informatics, 12(1). https://doi.org/10.17700/jai.2021.12.1.594

Korayim, D., Bodhi, R., Badghish, S., Yaqub, M. Z., & Bianco, R. (2025). Do generative artificial intelligence related competencies, attitudes and experiences affect employee outcomes? An intellectual capital perspective. Journal of Intellectual Capital, 26(3), 596–615. https://doi.org/10.1108/JIC-09-2024-0295

Łukasik-Stachowiak, K. (2023). Uncertainties and challenges in human resource management in the era of artificial intelligence. Scientific Papers of Silesian University of Technology: Organization and Management Series, 181, 341–356. https://doi.org/10.29119/1641-3466.2023.181.23

Manresa, A., Sammour, A., Mas-Machuca, M., Chen, W., & Botchie, D. (2025). Humanizing GenAI at work: Bridging the gap between technological innovation and employee engagement. Journal of Managerial Psychology, 40(5), 472–492. https://doi.org/10.1108/JMP-05-2024-0356

Marimon, F., Mas-Machuca, M., & Akhmedova, A. (2025). Trusting in generative AI: Catalyst for employee performance and engagement in the workplace. International Journal of Human-Computer Interaction, 41(11), 7076–7091. https://doi.org/10.1080/10447318.2024.2388482

Navarro, C. G., Flores, N. H., Lozano, C. P., Brown, L. (2023). How can Artificial Intelligence improve organizational psychology?: A systematic review. Journal of Psychological Science and Research, 3(1), 1–15. https://doi.org/10.53902/JPSSR.2023.03.000537

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., ... & Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372(71). https://doi.org/10.1136/bmj.n71

Papakonstantinidis, S., Kwiatek, P., & Spathopoulou, F. (2024). Embrace or resist? Drivers of artificial intelligence writing software adoption in academic and non-academic contexts. Contemporary Educational Technology, 16(2), Article e495. https://doi.org/10.30935/cedtech/14250

Park, J., Woo, S. E., & Kim, J. (2024). Attitudes towards artificial intelligence at work: Scale development and validation. Journal of Occupational and Organizational Psychology, 97(3), 920–951. https://doi.org/10.1111/joop.12502

Presbitero, A., & Teng-Calleja, M. (2023). Job attitudes and career behaviors relating to employees’ perceived incorporation of artificial intelligence in the workplace: A career self-management perspective. Personnel Review, 52(4), 1169–1187. https://doi.org/10.1108/PR-02-2021-0103

Ratta, R., Sodhi, J., & Saxena, U. (2025). The relevance of trust in the implementation of AI-driven clinical decision support systems by healthcare professionals: An extended UTAUT model. Electronic Journal of Knowledge Management, 23(1), 47–66. https://doi.org/10.34190/ejkm.23.1.3499

Revillod, G. (2024). Why do Swiss HR departments dislike algorithms in their recruitment process? An empirical analysis. Administrative Sciences, 14(10), Article 253. https://doi.org/10.3390/admsci14100253

Singh, V. K., Singh, P., Karmakar, M., Leta, J., & Mayr, P. (2021). The journal coverage of Web of Science, Scopus and Dimensions: A comparative analysis. Scientometrics, 126, 5113–5142. https://doi.org/10.1007/s11192-021-03948-5

Suseno, Y., Chang, C., Hudik, M., & Fang, E. S. (2022). Beliefs, anxiety and change readiness for artificial intelligence adoption among human resource managers: The moderating role of high-performance work systems. International Journal of Human Resource Management, 33(6), 1209–1236. https://doi.org/10.1080/09585192.2021.1931408

Swinger, N., Baseman, C. M., Ryu, M., Abdullah, S., Wiese, C. W., Sherrill, A. M., & Arriaga, R. I. (2025). There’s No “I” in TEAMMAIT: Impacts of domain and expertise on trust in AI teammates for mental health work. Proceedings of the ACM on Human-Computer Interaction, 9(2), 1–36. https://doi.org/10.1145/3710917

Tandon, A., Dhir, A., Malik, A., Budhwar, P., & Kaur, P. (2025). Exploring the duality of perceptions: Insights into uncertainties, aversion and appreciation towards algorithmic HRM. Human Resource Management, 64(2), 583–616. https://doi.org/10.1002/hrm.22263

Uttley, L., Quintana, D. S., Montgomery, P., Carroll, C., Page, M. J., Falzon, L., Sutton, A., & Moher, D. (2023). The problems with systematic reviews: A living systematic review. Journal of Clinical Epidemiology, 156, 30–41. https://doi.org/10.1016/j.jclinepi.2023.01.011

Wang, X., Lin, X., & Shao, B. (2023). Artificial intelligence changes the way we work: A close look at innovating with chatbots. Journal of the Association for Information Science and Technology, 74(3), 339–353. https://doi.org/10.1002/asi.24621

Wang, X., Lin, X., & Shao, B. (2024). Security and privacy protection in developing ethical AI: A mixed-methods study from a marketing employee perspective. Journal of Business Ethics, 200, 373–392. https://doi.org/10.1007/s10551-024-05894-7

Yang, H.-H. (2024). The acceptance of AI tools among design professionals: Exploring the moderating role of job replacement. The International Review of Research in Open and Distributed Learning, 25(3), 326–349. https://doi.org/10.19173/irrodl.v25i3.7811

Zhang, P. (2024). Application of artificial intelligence (AI) in recruitment and selection: The case of company A and company B. Journal of Business and Management Studies, 6(3), 224–255. https://doi.org/10.32996/jbms.2024.6.3.18

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2026-05-19

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How to Cite

Bagočiūnaitė, R., & Endriulaitienė, A. (2026). Understanding employee attitudes toward artificial intelligence in the workplace: a systematic review of attitude definitions and measurements. Business: Theory and Practice, 27(1), 168–178. https://doi.org/10.3846/btp.2026.25127

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