PENGALAMAN ADAPTASI KARYAWAN TERHADAP IMPLEMENTASI ARTIFICIAL INTELLIGENCE DALAM PROSES KERJA: STUDI INTERPRETATIVE PHENOMENOLOGICAL ANALYSIS
DOI:
https://doi.org/10.58174/1mqphb25Keywords:
Work Adaptation, Artificial Intelligence, Interpretative Phenomenological Analysis, Employee Experience, Digital TransformationAbstract
This research aims to explore in depth the life experiences of employees in the process of adapting to the implementation of artificial intelligence (AI) in the workplace. The phenomenon of work digitalization accelerated by the dynamics of industry 4.0 has prompted organizations to integrate AI systems into their daily operational workflows, but individual employee responses to these changes are still very poorly explored qualitatively. This study uses the Interpretative Phenomenological Analysis (IPA) approach developed by Smith, Flowers, and Larkin as the main methodological framework to understand the subjective meaning of the adaptation experience. Data collection was conducted through in-depth semi-structured interviews with eight purposively selected participants based on criteria of direct involvement with AI systems in their work for a minimum of six months. The data analysis yielded four superordinate themes, namely: (1) the shock of professional identity, (2) the negotiation of new competencies, (3) ambivalence towards work autonomy, and (4) the reconstruction of the meaning of work post-AI. The findings of the study indicate that adaptation to AI is not just a technical process, but rather a complex psychological journey that involves cognitive, emotional, and social dimensions simultaneously. This research makes a theoretical contribution to the development of an AI-based work adaptation model as well as practical implications for human resource management in designing a more humanistic change assistance program centered on the subjective experience of employees.
References
cemoglu, D., & Restrepo, P. (2020). Robots and jobs: Evidence from US labor markets. Journal of Political Economy, 128(6), 2188–2244. https://doi.org/10.1086/705716
Autor, D. H. (2022). The labor market impacts of technological change: From unbundling tasks to labor market segmentation. Annual Review of Economics, 14, 1–32. https://doi.org/10.1146/annurev-economics-051420-024722
Bankins, S., & Formosa, P. (2023). The ethical implications of artificial intelligence (AI) for meaningful work. Journal of Business Ethics, 185(4), 725–740. https://doi.org/10.1007/s10551-023-05339-5
Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton & Company. https://doi.org/10.7551/mitpress/10956.001.0001
Creswell, J. W., & Poth, C. N. (2018). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). SAGE Publications. https://doi.org/10.1177/1049732317722630
Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116. https://hbr.org/2018/01/artificial-intelligence-for-the-real-world
Dawis, R. V., & Lofquist, L. H. (1984). A psychological theory of work adjustment. University of Minnesota Press. https://doi.org/10.1037/h0070704
Deci, E. L., & Ryan, R. M. (2000). The "what" and "why" of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/S15327965PLI1104_01
Frankl, V. E. (1985). Man's search for meaning. Washington Square Press. https://doi.org/10.1037/14862-000
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
Lent, R. W., Brown, S. D., & Hackett, G. (1994). Toward a unifying social cognitive theory of career and academic interest, choice, and performance. Journal of Vocational Behavior, 45(1), 79–122. https://doi.org/10.1006/jvbe.1994.1027
Maier, C., Laumer, S., & Weinert, C. (2023). Enterprise resource planning systems-related technostress and employee work engagement: A contextual theory perspective. Journal of Management Information Systems, 40(1), 91–127. https://doi.org/10.1080/07421222.2023.2166559
Molina-Azorin, J. F. (2022). Understanding how mixed methods research is undertaken within a specific research community. International Journal of Multiple Research Approaches, 6(1), 75–86. https://doi.org/10.5172/mra.2012.6.1.75
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/10.5465/19416520.2008.10049027
Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation-augmentation paradox. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/amr.2018.0072
Smith, J. A., Flowers, P., & Larkin, M. (2009). Interpretative phenomenological analysis: Theory, method and research. SAGE Publications. https://doi.org/10.4135/9781473921344
Tajfel, H., & Turner, J. C. (1979). An integrative theory of intergroup conflict. In W. G. Austin & S. Worchel (Eds.), The social psychology of intergroup relations (pp. 33–47). Brooks/Cole. https://doi.org/10.1002/9780470693643.ch4
Tursunbayeva, A., Di Lauro, S., & Pagliari, C. (2021). People analytics-from hype to value: A longitudinal case study. Technology Analysis & Strategic Management, 33(3), 341–355. https://doi.org/10.1080/09537325.2020.1799680
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
World Economic Forum. (2023). The future of jobs report 2023. World Economic Forum. https://doi.org/10.3389/fpsyg.2020.01659
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