Employee Behavior on Digital-AI Transformation

Dear Colleagues,

The rapid advancement of digital technologies and artificial intelligence (AI) is fundamentally reshaping industries worldwide. As enterprises increasingly integrate digital technologies and AI-driven solutions, understanding their implications for employee behavior becomes paramount. This special column in the Behavioral Sciences journal aims to explore and elucidate the multifaceted dynamics of employee behavior concerning digital-AI transformation.

Scholars have now explored the impact of the use of AI in organizations on employees (Budhwar et al., 2022). For example, current research has identified that AI-driven automation reshapes job roles, leading to a shift in employee responsibilities and skill requirements. This shift tends to affect job satisfaction, motivation, and engagement (Fosslien and Duffy, 2021; Li er al., 2023; Wu et al., 2023). The implementation of AI technologies also affects workplace dynamics, changing interpersonal relationships and communication patterns (Li et al., 2024; Tschang and Almirall, 2021; Wu et al., 2024). Furthermore, leadership and organizational culture play a key role in moderating the impact of AI on employee behavior. Effective leaders who promote transparency and provide adequate support for technology integration can moderate employee resistance and foster acceptance (Huang and Rust, 2018; Raisch and Krakowski, 2021). In summary, the literature has extensively documented the profound effects of digitalization and AI on organizational structures, processes, and strategic initiatives. However, the behavioral aspects of this transformation—particularly how employees perceive, adapt to, and engage with these technologies—remain relatively underexplored. Understanding employee behaviors in the context of digital-AI transformation is crucial for effectively managing change, enhancing productivity, and ensuring sustainable organizational development.

Contributions are encouraged to address, but are not limited to, the following topics:

• Impact of digital-AI transformation on employee motivation, job satisfaction, and organizational commitment.
• Skills development and training programs required for employees to effectively utilize AI technologies.
• Evolution of job roles and responsibilities in response to AI integration.
• Ethical considerations surrounding AI adoption and their implications for employee behavior and organizational culture.
• Effects of AI-driven guest interactions on employee–customer relationships and service delivery.

We encourage original research articles, case studies, theoretical perspectives, and empirical studies that contribute to a deeper understanding of how digital-AI transformation influences employee behavior. Submitted manuscripts should be innovative, well-researched, and offer practical insights for both academia and industry.

Reference

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.

Fosslien, L., & Duffy, M. W. (2021). No Hard Feelings: The Secret Power of Embracing Emotions at Work. Penguin Books.

Huang, M. H., & Rust, R. T. (2018). Artificial Intelligence in Service. Journal of Service Research, 21(2), 155-172.

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.

Li, J. M., Zhang, R. X., Wu, T. J., & Mao, M. (2024). How does work autonomy in human-robot collaboration affect hotel employees’ work and health outcomes? Role of job insecurity and person-job fit. International Journal of Hospitality Management, 117, 103654.

Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192-210.

Tschang, F. T., & Almirall, E. (2021). Artificial intelligence as augmenting automation: Implications for employment. Academy of Management Perspectives, 35(4), 642-659.

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.

Wu, T. J., Liang, Y., Duan, W. Y., & Zhang, S. D. (2024). Forced shift to teleworking: how after-hours ICTs implicate COVID-19 perceptions when employees experience abusive supervision. Current Psychology, 1-15.

Wu, T. J., Zhang, R. X., & Li, J. M. (2024). How does emotional labor influence restaurant employees’ service quality during COVID-19? The roles of work fatigue and supervisor–subordinate Guanxi. International Journal of Contemporary Hospitality Management, 36(1), 136-154.

Dr. Tungju Wu
Guest Editor

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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.

• digital-AI transformation
• employee behavior
• employee engagement
• job redesign
• artificial intelligence
• organizational culture
• ethical implications

Title: A systematic review of artificial intelligence in organizations: A Bibliometric Analysis and Future Research Agenda
Authors: Yang Yang; Teng Wang; Tung-Ju Wu
Affiliation: School of Management, Harbin Institute of Technology, Harbin, China
Abstract: With the rapid development of Artificial Intelligence (AI) technology, its application in the field of Organizational Behavior (OB) has become increasingly widespread, exerting a profound impact on both theory and practice. This study aims to systematically organize the relevant literature on AI in the OB field, explore its research topics, theoretical foundations, research methods, and propose future research directions. Through a systematic review of articles from the Web of Science Core Collection database spanning from 2018 to 2024, we categorized previous studies into three main themes based on bibliometric analysis: work, HRM, and algorithm. Subsequently, we conducted an in-depth exploration of the theoretical frameworks, including Organizational Behavior (OB) theories, Information Systems (IS) theories, and other pertinent paradigms, and assessed their application, extension, and expansion. Concurrently, we analyzed the research methods used in the literature, encompassing qualitative and quantitative. Finally, we summarized the future research directions mentioned in the literature and proposed future research directions and paths from both theoretical and methodological perspectives, hoping to provide implications for research and practice.