Author Identifier (ORCID)
Ahmad Khanfar: https://orcid.org/0000-0002-2696-4604
Abstract
Artificial intelligence (AI) is transforming how organisations operate, yet many implementations fail due to limited employee acceptance. While prior research has focused primarily on organisational and managerial determinants, less is known about the factors shaping employees’ behavioural intentions toward AI systems, particularly in technology-intensive industries. This study investigates the determinants of AI adoption among employees in the Electrical and Electronics (E&E) industry across South, Southeast, and Western Asia. Drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT) and Task–Technology Fit (TTF) frameworks, the model integrates individual (personal innovativeness, resistance, job replacement anxiety), social, and technological factors. Using data from 208 employees, a hybrid analytical approach combining Partial Least Squares Structural Equation Modelling (PLS-SEM) and Fuzzy-set Qualitative Comparative Analysis (fsQCA) was employed. PLS-SEM results show that performance expectancy, task–technology fit, social influence, and personal innovativeness positively influence adoption intentions, while resistance to use exerts a negative effect. The fsQCA findings reveal nine distinct configurations leading to high intention to use AI systems, highlighting behavioural heterogeneity and multiple sufficient adoption pathways. The study advances theory by integrating symmetric and configurational perspectives and offers practical insights for fostering responsible AI adoption in digitally advanced manufacturing environments.
Keywords
AI adoption framework, Artificial intelligence (AI), employee behaviour, fsQCA, task-technology fit (TTF), unified theory of acceptance and use of technology (UTAUT)
Document Type
Journal Article
Date of Publication
1-1-2026
Publication Title
Cognition Technology & Work
Publisher
Springer
School
School of Business and Law
RAS ID
88772
Creative Commons License
This work is licensed under a Creative Commons Attribution-No Derivative Works 4.0 License.
Comments
This is an Author's Accepted Manuscript of: Khanfar, A. A., Mavi, R. K., & Iranmanesh, M. (2026). Employee adoption of artificial intelligence systems in the electrical and electronics industry: A hybrid PLS–fsQCA analysis. Cognition Technology & Work. Advance online publication. https://doi.org/10.1007/s10111-025-00852-3
This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s10111-025-00852-3