Proceedings of the 2025 4th International Conference on Big Data Economy and Digital Management (BDEDM 2025)

<Previous Article In Volume
Next Article In Volume>

AI Usage, Employee Engagement, and Work Performance: Examining the Roles of Job Complexity and AI Knowledge

Authors
Muhammad Asim1, Wanling Ding1, *
1Shenzhen University, Shenzhen, China
*Corresponding author. Email: wld@szu.edu.cn
Corresponding Author
Wanling Ding
Available Online 14 May 2025.
DOI
10.2991/978-94-6463-710-6_34How to use a DOI?
Keywords
Artificial Intelligence (AI); Job Demands-Resources (JDR) Theory; Employee Engagement; Work Performance; AI Knowledge; Job Complexity; Moderation Effects; Mediation Effects; Workplace Innovation; Structural Equation Modeling (SEM)
Abstract

The integration of Artificial Intelligence (AI) in the workplace has revolutionized organizational processes, enhancing efficiency, innovation, and decision-making capabilities. Grounded in the Job Demands-Resources (JDR) theory, this study examines the complex relationships between AI usage, employee engagement, and work performance, while exploring the moderating roles of job complexity and AI knowledge. Using Structural Equation Modeling (SEM) on data collected from 500 professionals across diverse industries, the findings reveal that AI usage positively influences both employee engagement and work performance. Furthermore, employee engagement mediates this relationship, highlighting its critical role in leveraging AI’s potential. Job complexity and AI knowledge significantly moderate these effects, suggesting that individual and contextual factors amplify AI’s impact. This research contributes to the growing body of knowledge by integrating emerging technologies with established theories, providing valuable insights for academia and practitioners to optimize AI implementation in organizational contexts.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the 2025 4th International Conference on Big Data Economy and Digital Management (BDEDM 2025)
Series
Advances in Intelligent Systems Research
Publication Date
14 May 2025
ISBN
978-94-6463-710-6
ISSN
1951-6851
DOI
10.2991/978-94-6463-710-6_34How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Muhammad Asim
AU  - Wanling Ding
PY  - 2025
DA  - 2025/05/14
TI  - AI Usage, Employee Engagement, and Work Performance: Examining the Roles of Job Complexity and AI Knowledge
BT  - Proceedings of the 2025 4th International Conference on Big Data Economy and Digital Management (BDEDM 2025)
PB  - Atlantis Press
SP  - 294
EP  - 303
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-710-6_34
DO  - 10.2991/978-94-6463-710-6_34
ID  - Asim2025
ER  -