Artificial Intelligence Adoption and Generation Z Work Adaptation

Sonya Sidjabat; Halim Tjiwidjaja; Muhammad Ramdhan; Sutariyono Sutariyono

doi:10.59653/jbmed.v4i01.2343

Authors

Sonya Sidjabat Institut Transportasi dan Logistik Trisakti, Indonesia
Halim Tjiwidjaja Sekolah Tinggi Ilmu Ekonomi Ganesha, Indonesia
Muhammad Ramdhan Sekolah Tinggi Ilmu Ekonomi Ganesha, Indonesia
Sutariyono Sekolah Tinggi Ilmu Ekonomi Ganesha, Indonesia

DOI:

https://doi.org/10.59653/jbmed.v4i01.2343

Keywords:

Artificial Intelligence Adoption, Generation Z, work adaptation, Workplace Adaptation, Digital Transformation

Abstract

The rapid development of artificial intelligence (AI) has significantly transformed workplace practices and employee work patterns. This study aims to analyze the relationship between AI adoption and Generation Z work adaptation in workplaces in Cianjur Regency. The research focuses on how AI implementation influences employee adaptation, organizational support, digital work culture, and work performance. A quantitative research approach was employed using survey data collected from Generation Z employees working in various organizations in Cianjur Regency. The data were analyzed using statistical techniques to examine the relationship between AI adoption, work adaptation, and employee performance. The findings indicate that Generation Z employees demonstrate a high level of adaptability to AI-based work environments due to their familiarity with digital technologies and openness to technological innovation. AI adoption was found to enhance work efficiency, facilitate information processing, and support data-driven decision-making. In addition, organizational support and a supportive digital work culture play an important role in strengthening employees’ ability to integrate AI tools into their daily work activities. The study also reveals that AI adoption positively influences employee work performance and contributes to improved productivity and workplace effectiveness. These findings highlight the importance of strengthening digital competencies and organizational support systems to optimize AI implementation and support sustainable workforce development in regional workplaces.

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References

Abdulla, A., & Hussain, O. Bin. (2024). AI enabled Business Process Optimization and Digital Marketing. 2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), 2, 1–5. https://doi.org/10.1109/IATMSI60426.2024.10502481

Abdulla, H., Sleptchenko, A., & Nayfeh, A. (2024). Customized Cleaning Solutions for Photovoltaic Fleets: Integrating Soiling Modeling and Optimization Techniques. 2024 IEEE 52nd Photovoltaic Specialist Conference (PVSC), 204–209. https://doi.org/10.1109/PVSC57443.2024.10748807

Bonilla-Chaves, E. F., & Palos-Sánchez, P. R. (2023). Exploring the evolution of human resource analytics: a bibliometric study. Behavioral Sciences, 13(3), 244. https://doi.org/10.3390/bs13030244

Braun, V., & Clarke, V. (2021). One size fits all? What counts as quality practice in (reflexive) thematic analysis? Qualitative Research in Psychology, 18(3), 328–352. https://doi.org/10.1080/14780887.2020.1769238

Campbell, S., Greenwood, M., Prior, S., Shearer, T., Walkem, K., Young, S., Bywaters, D., & Walker, K. (2020). Purposive sampling: complex or simple? Research case examples. Journal of Research in Nursing, 25(8), 652–661. https://doi.org/10.1177/1744987120927206

Casacio, C. A., Madsen, L. S., Terrasson, A., Waleed, M., Barnscheidt, K., Hage, B., Taylor, M. A., & Bowen, W. P. (2021). Quantum-enhanced nonlinear microscopy. Nature, 594(7862), 201–206. https://doi.org/10.1038/s41586-021-03528-w

Chatterjee, S., Rana, N. P., Khorana, S., Mikalef, P., & Sharma, A. (2023). Assessing organizational users’ intentions and behavior to AI integrated CRM systems: A meta-UTAUT approach. Information Systems Frontiers, 25(4), 1299–1313. https://doi.org/10.1007/s10796-021-10181-1

Dwivedi, Y. K., Ismagilova, E., Hughes, D. L., Carlson, J., Filieri, R., Jacobson, J., Jain, V., Karjaluoto, H., Kefi, H., & Krishen, A. S. (2021). Setting the future of digital and social media marketing research: Perspectives and research propositions. International Journal of Information Management, 59, 102168. https://doi.org/10.1016/j.ijinfomgt.2020.102168

Gao, A. (2023). Prompt engineering for large language models. Available at SSRN 4504303. https://doi.org/10.2139/ssrn.4504303

Gao, J., Zhang, Y., Xu, S., & Ma, E. (2025). Rethinking work-life integration: empowering talent in emerging hospitality and tourism work paradigms. International Journal of Contemporary Hospitality Management, 37(5), 1765–1783. https://doi.org/10.1108/IJCHM-05-2024-0736

Hay, I., & Israel, M. (2021). Caring about research ethics and integrity in human geography. In Research ethics in human geography (pp. 23–41). Routledge. https://doi.org/10.4324/9780429507366-2

Hennink, M., & Kaiser, B. N. (2022). Sample sizes for saturation in qualitative research: A systematic review of empirical tests. Social Science & Medicine, 292, 114523. https://doi.org/10.1016/j.socscimed.2021.114523

Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586. https://doi.org/10.1016/j.bushor.2018.03.007

Jooss, S., Duggan, J., & Parry, E. (2022). Technology in human resource functions: Core systems, emerging trends and algorithmic management. https://doi.org/10.1108/978-1-80071-779-420221006

Katsaros, K. K. (2024). Gen Z employee adaptive performance: The role of inclusive leadership and workplace happiness. Administrative Sciences, 14(8), 163. https://doi.org/10.3390/admsci14080163

Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1), 366–410. https://doi.org/10.5465/annals.2018.0174

Kumar, P., Dwivedi, Y. K., & Anand, A. (2023). Responsible artificial intelligence (AI) for value formation and market performance in healthcare: The mediating role of patient’s cognitive engagement. Information Systems Frontiers, 25(6), 2197–2220. https://doi.org/10.1007/s10796-021-10136-6

Lei, L., Feng, H., & Ren, J. (2025). Artificial intelligence, human capital and firm-level total factor productivity. Finance Research Letters, 85, 107897. https://doi.org/10.1016/j.frl.2025.107897

Leonardi, P. M., Parker, S. H., & Shen, R. (2024). How remote work changes the world of work. Annual Review of Organizational Psychology and Organizational Behavior, 11(1), 193–219. https://doi.org/10.1146/annurev-orgpsych-091922-015852

Li, B., Qi, P., Liu, B., Di, S., Liu, J., Pei, J., Yi, J., & Zhou, B. (2023). Trustworthy AI: From principles to practices. ACM Computing Surveys, 55(9), 1–46. https://doi.org/10.1145/3648354

Li, R., & Rosli, R. (2026). Research on Employee Training Model Transformation in the Publishing Industry amid Digital Transformation. Innovative Organizational Design, 2(1), 1–12. https://doi.org/10.63808/iod.v2i1.242

Malik, A., Budhwar, P., & Kazmi, B. A. (2023). Artificial intelligence (AI)-assisted HRM: Towards an extended strategic framework. In Human Resource Management Review (Vol. 33, Number 1, p. 100940). Elsevier. https://doi.org/10.1016/j.hrmr.2022.100940

Margherita, A. (2022). Human resources analytics: A systematization of research topics and directions for future research. Human Resource Management Review, 32(2), 100795. https://doi.org/10.1016/j.hrmr.2020.100795

Margherita, G., Caffieri, A., Mariani, R., Filosa, M., Manari, T., Lenzo, V., Quattropani, M. C., Vegni, E., Borghi, L., & Castelnuovo, G. (2023). Dreaming or daydreaming during COVID-19 lockdown: A comparison between maladaptive and nonmaladaptive daydreamers. Psychology of Consciousness: Theory, Research, and Practice, 10(4), 331. https://doi.org/10.1037/cns0000333

Morlett Paredes, A., Lee, E. E., Chik, L., Gupta, S., Palmer, B. W., Palinkas, L. A., Kim, H.-C., & Jeste, D. V. (2021). Qualitative study of loneliness in a senior housing community: the importance of wisdom and other coping strategies. Aging & Mental Health, 25(3), 559–566. https://doi.org/10.1080/13607863.2019.1699022

Myers, D. (2022). Construction economics: A new approach. Routledge. https://doi.org/10.1201/9781003287513

Naoum, R. F., Szakadáti, T., & Balogh, G. (2026). Artificial Intelligence (AI) in human resource management (HRM): a systematic review of its dual impact on diversity, equity, and inclusion (DEI). Management Review Quarterly, 1–72. https://doi.org/10.1007/s11301-025-00580-y

Nowell, A. (2023). Rethinking neandertals. Annual Review of Anthropology, 52(1), 151–170. https://doi.org/10.1146/annurev-anthro-052621-024752

Palos-Sánchez, P. R., Baena-Luna, P., Badicu, A., & Infante-Moro, J. C. (2022). Artificial intelligence and human resources management: A bibliometric analysis. Applied Artificial Intelligence, 36(1), 2145631. https://doi.org/10.1080/08839514.2022.2145631

Pan, J., Ye, N., Yu, H., Hong, T., Al-Rubaye, S., Mumtaz, S., Al-Dulaimi, A., & Chih-Lin, I. (2022). AI-driven blind signature classification for IoT connectivity: A deep learning approach. IEEE Transactions on Wireless Communications, 21(8), 6033–6047. https://doi.org/10.1109/TWC.2022.3145399

Parker, S. K., & Grote, G. (2022a). Automation, algorithms, and beyond: Why work design matters more than ever in a digital world. Applied Psychology, 71(4), 1171–1204. https://doi.org/10.1111/apps.12241

Parker, S. K., & Grote, G. (2022b). More than ‘more than ever’: Revisiting a work design and sociotechnical perspective on digital technologies. Applied Psychology, 71(4), 1215–1223. https://doi.org/10.1111/apps.12425

Poth, C. N. (2023). The Sage handbook of mixed methods research design. https://doi.org/10.4135/9781529614572

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

Safshekan, M., Feili, A., Shojaeifard, A., & Sorooshian, S. (2026). Artificial intelligence in human resource management: models for recruitment, training, performance, compensation, and retention. Frontiers in Artificial Intelligence, 9, 1718244. https://doi.org/10.3389/frai.2026.1718244

Saunders, C. H., Sierpe, A., Von Plessen, C., Kennedy, A. M., Leviton, L. C., Bernstein, S. L., Goldwag, J., King, J. R., Marx, C. M., & Pogue, J. A. (2023). Practical thematic analysis: a guide for multidisciplinary health services research teams engaging in qualitative analysis. Bmj, 381. https://doi.org/10.1136/bmj-2022-074256

Shams, R., Chatterjee, S., & Chaudhuri, R. (2024). Developing brand identity and sales strategy in the digital era: Moderating role of consumer belief in brand. Journal of Business Research, 179, 114689. https://doi.org/10.1016/j.jbusres.2024.114689

Shao, X., Gomez, C. D., Kapoor, N., Considine, J. M., Grams, C., Gao, Y., & Naba, A. (2023). MatrisomeDB 2.0: 2023 updates to the ECM-protein knowledge database. Nucleic Acids Research, 51(D1), D1519–D1530. https://doi.org/10.1093/nar/gkac1009

Stahl, B. C., Antoniou, J., Bhalla, N., Brooks, L., Jansen, P., Lindqvist, B., Kirichenko, A., Marchal, S., Rodrigues, R., & Santiago, N. (2023). A systematic review of artificial intelligence impact assessments. Artificial Intelligence Review, 56(11), 12799–12831. https://doi.org/10.1007/s10462-023-10420-8

Strohmeier, S. (2020). Digital human resource management: A conceptual clarification. German Journal of Human Resource Management, 34(3), 345–365. https://doi.org/10.1177/2397002220921131

Strohmeier, S. (2020c). Smart HRM–a Delphi study on the application and consequences of the Internet of Things in Human Resource Management. The International Journal of Human Resource Management, 31(18), 2289–2318. https://doi.org/10.1080/09585192.2018.1443963

Sumantri, O. R., & Saraswati, K. D. H. (2025). AI And Gen Z: Enhancing Workplace Well-Being through Informal Learning. Tarumanagara International Conference on the Applications of Social Sciences and Humanities (TICASH 2024), 123–132. https://doi.org/10.2991/978-2-38476-446-4_14

Tarafdar, M., Shan, G., Bennett Thatcher, J., & Gupta, A. (2022). Intellectual diversity in IS research: Discipline-based conceptualization and an illustration from information systems research. Information Systems Research, 33(4), 1490–1510. https://doi.org/10.1287/isre.2022.1176

Tarafdar, P., Nobleson, K., Rana, P., Singha, J., Krishnakumar, M. A., Joshi, B. C., Paladi, A. K., Kolhe, N., Batra, N. D., & Agarwal, N. (2022). The indian pulsar timing array: First data release. Publications of the Astronomical Society of Australia, 39, e053. https://doi.org/10.1017/pasa.2022.46

Triansyah, F. A., Hejin, W., & Stefania, S. (2023). Factors affecting employee performance: A systematic review. Journal Markcount Finance, 1(3), 150–159. https://doi.org/10.55849/jmf.v1i2.102

Tuffaha, M., & Perello-Marin, M. R. (2023). Artificial intelligence definition, applications and adoption in human resource management: a systematic literature review. International Journal of Business Innovation and Research, 32(3), 293–322. https://doi.org/10.1504/IJBIR.2023.134887

Usman, M. B., Naveed, S., Nazeer, S., Siddique, N., & rana, A. H. (2026). Navigating the intellectual terrain of AI–HRM integration: a bibliometric review (2000–2024). Journal of Organizational Effectiveness: People and Performance, 1–24. https://doi.org/10.1108/JOEPP-08-2025-0670

Venkatesh, V. (2022). Adoption and use of AI tools: a research agenda grounded in UTAUT. Annals of Operations Research, 308(1), 641–652. https://doi.org/10.1007/s10479-020-03918-9

Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J. Q., Fabian, N., & Haenlein, M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, 889–901. https://doi.org/10.1016/j.jbusres.2019.09.022

Vrontis, D., Hulland, J., Shaw, J. D., Gaur, A., Czinkota, M. R., & Christofi, M. (2022a). Guest editorial: systematic literature reviews in international marketing: from the past to the future and beyond. International Marketing Review, 39(5), 1025–1028. https://doi.org/10.1108/IMR-09-2022-390

Wang, H., Wu, W., Dou, Z., He, L., & Yang, L. (2023). Performance and exploration of ChatGPT in medical examination, records and education in Chinese: pave the way for medical AI. International Journal of Medical Informatics, 177, 105173. https://doi.org/10.1016/j.ijmedinf.2023.105173

Wang, J., Chitsaz, F., Derbyshire, M. K., Gonzales, N. R., Gwadz, M., Lu, S., Marchler, G. H., Song, J. S., Thanki, N., & Yamashita, R. A. (2023). The conserved domain database in 2023. Nucleic Acids Research, 51(D1), D384–D388. https://doi.org/10.1093/nar/gkac1096

Wang, Y., Kim, S., Rafferty, A., & Sanders, K. (2020). Employee perceptions of HR practices: A critical review and future directions. The International Journal of Human Resource Management, 31(1), 128–173. https://doi.org/10.1080/09585192.2019.1674360

Wang, Y., Pan, Y., Yan, M., Su, Z., & Luan, T. H. (2023). A survey on ChatGPT: AI–generated contents, challenges, and solutions. IEEE Open Journal of the Computer Society, 4, 280–302. https://doi.org/10.1109/OJCS.2023.3300321

Yang, P., & Li, J. (2025). Construction and application of intelligent human resource management system based on machine learning algorithm. Journal of Computational Methods in Sciences and Engineering, 25(4), 3684–3696. https://doi.org/10.1177/14727978251318814