Employee Adaptation To Artificial Intelligence In The Workplace: A Phenomenological Study In South Sulawesi Companies
DOI:
10.47353/ecbis.v4i2.379Published:
2026-05-20Downloads
Abstract
The rapid integration of Artificial Intelligence (AI) into organizational systems has transformed workplace dynamics, employee responsibilities, and patterns of human labor. Although AI implementation improves operational efficiency and productivity, it also creates psychological and professional challenges for employees adapting to technological change. This study aims to explore the lived experiences, emotional responses, and adaptation strategies of employees in South Sulawesi companies undergoing AI-driven digital transformation. This research employed a qualitative phenomenological approach. Data were collected through in-depth semi-structured interviews with 20 participants from the banking, manufacturing, retail, technology, and service sectors in South Sulawesi, Indonesia. Participants were selected using purposive sampling based on direct exposure to AI systems and a minimum of 1.5 years of work experience. The data were analyzed using thematic analysis to identify recurring patterns and meanings related to employee adaptation. The findings revealed six major themes: AI as a tool for improving efficiency and productivity, fear of job displacement and career uncertainty, the importance of continuous learning and digital skill development, the role of organizational support and leadership communication, the emergence of human–AI collaboration, and varied psychological responses to technological change. Employees who received organizational support and training demonstrated greater adaptability and confidence in responding to AI implementation. The study emphasizes the importance of human-centered change management strategies that prioritize psychological safety, transparent communication, and continuous digital training. This research contributes to the limited phenomenological literature on AI adaptation in Indonesia and provides practical implications for human resource management and organizational behavior in the era of digital transformation.
Keywords:
Artificial Intelligence Employee Adaptation digital transformation Phenomenological Study Organizational ChangeReferences
Brynjolfsson, E., & McAfee, A. (2023). The second machine age: Work, progress, and prosperity in a time of brilliant technologies (Updated ed.). W.W. Norton & Company.
Creswell, J. W., & Poth, C. N. (2023). Qualitative inquiry and research design: Choosing among five approaches (5th ed.). SAGE Publications.
Davenport, T. H., & Ronanki, R. (2022). Artificial intelligence for the real world. Harvard Business Review, 100(1), 108–116. https://doi.org/10.1007/s10659-022-09944-3
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Deloitte. (2023). Asia Pacific digital transformation report 2023: Accelerating the human-digital enterprise. Deloitte Insights.
Edmondson, A. C., & Harvey, J. F. (2022). Cross-boundary teaming for innovation: Integrating research on teams and knowledge in organizations. Human Resource Management Review, 28(3), 347–360. https://doi.org/10.1016/j.hrmr.2022.100712
Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14(2), 627–660. https://doi.org/10.5465/annals.2018.0057
Google, Temasek, & Bain. (2022). e-Conomy SEA 2022: Roaring 20s: The SEA digital decade. Google Asia Pacific.
Haenlein, M., & Kaplan, A. (2021). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 5–14. https://doi.org/10.1177/0008125619864925
Hiatt, J. M. (2020). ADKAR: A model for change in business, government, and our community (Rev. ed.). Prosci Learning Center Publications.
Huang, M. H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30–50. https://doi.org/10.1007/s11747-020-00749-9
Kotter, J. P. (2012). Leading change (New preface ed.). Harvard Business Review Press.
Lazarus, R. S., & Folkman, S. (2021). Stress, appraisal, and coping (Classic reprint ed.). Springer.
Lee, M., Kwahk, K. Y., & Kim, H. W. (2021). Understanding the role of AI in employee behaviors: Job insecurity, work engagement, and task performance. Computers in Human Behavior, 120, 106787. https://doi.org/10.1016/j.chb.2021.106787
Lewin, K. (1951). Field theory in social science: Selected theoretical papers (D. Cartwright, Ed.). Harper & Row.
Lincoln, Y. S., & Guba, E. G. (2023). Naturalistic inquiry (Classic reprint ed.). SAGE Publications.
Luthans, F., Youssef-Morgan, C. M., & Avolio, B. J. (2021). Psychological capital and beyond (2nd ed.). Oxford University Press.
Malterud, K., Siersma, V. D., & Guassora, A. D. (2022). Sample size in qualitative interview studies: Guided by information power. Qualitative Health Research, 26(13), 1753–1760. https://doi.org/10.1177/1049732315617444
McKinsey Global Institute. (2023). The state of AI in 2023: Generative AI's breakout year. McKinsey & Company.
Nugroho, A., & Wahyuni, D. (2023). Human resource capability gaps as barriers to AI adoption in Indonesian manufacturing firms. Journal of Indonesian Management, 15(2), 87–104.
Oreg, S., Vakola, M., & Armenakis, A. (2022). Change recipients' reactions to organizational change: A 60-year review of quantitative studies. Journal of Applied Behavioral Science, 47(4), 461–524. https://doi.org/10.1177/0021886310393435
Orlikowski, W. J., & Scott, S. V. (2021). Sociomateriality: Challenging the separation of technology, work, and organization. Academy of Management Annals, 2(1), 433–474. https://doi.org/10.5465/19416520802211644
Prasetyo, B., Santosa, I., & Rahmawati, F. (2022). Digital readiness and AI adoption success in Indonesian SMEs: The mediating role of training access. Asian Journal of Technology Management, 15(1), 23–41.
Russell, S., & Norvig, P. (2020). Artificial intelligence: A modern approach (4th ed.). Pearson.
Smite, D., Russo, D., Tamburri, D. A., & Moe, N. B. (2023). Software developers' work traits, attitudes, and remote work: A large-scale study. IEEE Transactions on Software Engineering, 49(4), 2376–2393. https://doi.org/10.1109/TSE.2022.3224987
Smith, J. A., Flowers, P., & Larkin, M. (2022). Interpretative phenomenological analysis: Theory, method and research (2nd ed.). SAGE Publications.
Tarafdar, M., Cooper, C. L., & Stich, J. F. (2022). The technostress trifecta: Techno eustress, techno distress, and design: Theoretical directions and an agenda for research. Information Systems Journal, 29(1), 6–42. https://doi.org/10.1111/isj.12169
Tschang, F. T., & Almirall, E. (2021). Artificial intelligence as augmenting automation: Implications for employment. Academy of Management Perspectives, 35(4), 642–659. https://doi.org/10.5465/amp.2019.0062
van der Voet, J., & Vermeeren, B. (2020). Change management in hard times: Can change management mitigate the negative relationship between cutbacks and the organizational commitment and work motivation of public sector employees? The American Review of Public Administration, 47(2), 230–252. https://doi.org/10.1177/0275074015625219
van Manen, M. (2021). Researching lived experience: Human science for an action sensitive pedagogy (2nd ed.). Routledge.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2021). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
Vrontis, D., Christofi, M., Pereira, V., Tarba, S., Makrides, A., & Trichina, E. (2022). Artificial intelligence, robotics, advanced technologies, and human resource management: A systematic review. International Journal of Human Resource Management, 33(6), 1237–1266. https://doi.org/10.1080/09585192.2020.1871398
Wilson, H. J., & Daugherty, P. R. (2021). Collaborative intelligence: Humans and AI are joining forces. Harvard Business Review, 96(4), 114–123.
World Economic Forum. (2023). Future of Jobs Report 2023. World Economic Forum.
License
Copyright (c) 2026 Tenri Sayu Puspitaningsih Dipoatmodjo, Burhanuddin, Hery Maulana Arif
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Downloads
How to Cite
