Impacts of Employee–AI Collaboration on Work Behavior—Second Edition

Dear Colleagues,

As digital technologies and artificial intelligence (AI) continue to transform workplaces at an unprecedented pace, the interaction between employees and AI systems is becoming an increasingly central component of modern organizational life (Li et al., 2025; Wu et al., 2024). While AI tools promise enhanced productivity, accuracy, and efficiency, their integration also poses significant implications for employee behavior, job design, workplace dynamics, and organizational culture (Huang & Rust, 2018; Wu et al., 2025).

While prior studies have examined the organizational impacts of AI implementation (Budhwar et al., 2022), there remains a pressing need to investigate the human side of AI integration. For instance, AI-driven automation is not only transforming tasks but also redefining the skills and mindsets employees need to thrive (Fosslien & Duffy, 2021; Li et al., 2023). The success of AI collaboration often hinges on employees’ trust in technology, their readiness for change, and the support systems provided by organizational leadership (Raisch & Krakowski, 2021; Wu & Zhang, 2024).

This Special Issue of Behavioral Sciences invites contributions that explore the evolving nature of employee–AI collaboration and its behavioral consequences. We aim to deepen the understanding of how employees adapt to, work alongside, and are influenced by AI technologies in the workplace. We welcome original research, theoretical papers, empirical studies, case analyses, and interdisciplinary perspectives that examine the behavioral dimensions of employee–AI collaboration. Topics of interest include, but are not limited to, the following:

• Employee perceptions, attitudes, and adaptation toward working with AI systems;
• Impact of AI–human collaboration on job satisfaction, engagement, and motivation;
• Trust-building mechanisms and psychological safety in AI-augmented work environments;
• New skill requirements and continuous learning in AI-integrated roles;
• The role of leadership, communication, and organizational culture in shaping effective AI collaboration.
• Ethical implications and behavioral responses to AI surveillance, decision making, and bias.
• Effects of AI in service industries: employee–customer interactions and frontline behavior.
• Case studies of successful or failed employee–AI collaborations and lessons learned

We encourage submissions that offer both practical insights and theoretical advancements, contributing to a richer understanding of how employees and AI can work together to achieve organizational success.

References

1. Budhwar, P., Malik, A., De Silva, M. T., & Thevisuthan, P. (2022). Artificial intelligence–challenges and opportunities for international HRM: a review and research agenda. The International Journal of Human Resource Management, 33(6), 1065-1097.
2. Fosslien, L., & Duffy, M. W. (2021). No Hard Feelings: The Secret Power of Embracing Emotions at Work. Penguin Books.
3. Huang, M. H., & Rust, R. T. (2018). Artificial Intelligence in Service. Journal of Service Research, 21(2), 155-172.
4. Li, J. M., Wu, T. J., Wu, Y. J., & Goh, M. (2023). Systematic literature review of human–machine collaboration in organizations using bibliometric analysis. Management Decision, 61(10), 2920-2944.
5. Li, J. M., Wu, H. Y., Zhang, R. X., & Wu, T. J. (2025). How employee-generative AI collaboration affects employees work and family outcomes? The relationship instrumentality perspective. The International Journal of Human Resource Management, 1-27. https://doi.org/10.1080/09585192.2025.2512555
6. Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192-210.
7. Wu, T. J., Liang, Y., & Wang, Y. (2024). The Buffering Role of Workplace Mindfulness: How Job Insecurity of Human-Artificial Intelligence Collaboration Impacts Employees’ Work–Life-Related Outcomes. Journal of Business and Psychology, 1-17.
8. Wu, T. J., & Zhang, R. X. (2024). Exploring the impacts of intention towards human-robot collaboration on frontline hotel employees’ positive behavior: An integrative model. International Journal of Hospitality Management, 123, 103912.
9. Wu, T. J., Zhang, R. X., & Li, J. M. (2025). When employees meet digital-intelligence transformation: Unveiling the role of employee intentions. International Journal of Information Management, 84, 102912.

Dr. Tungju Wu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Behavioral Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• employee–AI collaboration
• work behavior
• artificial intelligence
• digital transformation
• human–machine interaction
• job redesign
• employee adaptation
• trust in AI
• organizational change
• employee motivation

• Employee Behavior on Digital-AI Transformation in Behavioral Sciences (20 articles)