An Overview of Work Ability of Employees Using Artificial Intelligence: A Phenomenological Study
DOI:
https://doi.org/10.52300/jmso.v7i1.24949Keywords:
Work Ability, Artificial Intelligence, Technology, IndustryAbstract
Objective – The use of technology such as artificial intelligence in the workplace can change and reshape the job market, especially in terms of skills and knowledge that influence a person's work ability. This study aims to describe the work ability of workers after using AI in their work.
Design/Methodology/Approach – This research design uses a phenomenological approach and is conducted through semi-structured interviews, which are then analyzed using IPA. Sample selection is done using the snowball sampling technique. Interviews are conducted with 3 participants face-to-face.
Findings – The results of the interview analysis produce four themes in describing the work ability of workers, namely (1) perception of work effectiveness and efficiency using AI; (2) perception of the need for knowledge and skills development; (3) self-evaluation of AI use behavior; and (4) feelings after making decisions using AI.
Implications – This research provides an overview of the challenges faced in the era of the industrial revolution based on artificial intelligence technology so that it can help prospective workers, employees, and employers to prepare the knowledge and skills needed and help to maintain their abilities and performance.
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References
Alase, A. (2017). The Interpretative Phenomenological Analysis (IPA): A Guide to a Good Qualitative Research Approach. International Journal of Education & Literacy Studies, 5(2), 9-19. https://doi.org/10.7575/aiac.ijels.v.5n.2p.9
Association, A. P. (2025). Technology Addictions: Social Media, Online Gaming, and More.
Bizoi, C. (2024). Neuroethical Implications of AI-Driven Productivity Tools on Intellectual Capital: A Theoretical and Econometric Analysis. Journal of Intellectual Capital, 26(7), 1–23. https://doi.org/10.1108/JIC-07-2024-0218
Cabeceira, H., de Souza, D., Juliano, Y., & Veiga, D. (2019). Work Ability and Productivity in Patients with Diabetic Foot. Clinics, 74(421), 1-5. https://doi.org/10.6061/clinics/2019/e421
Cadiz, D., Brady, G., Rineer, J., Truxillo, D. (2019). A Review and Synthesis of the Work Ability Literature. Work, Aging & Retirement, 5(1), 114-138. https://doi.org/10.1093/workar/way010
Camilleri, M. (2024). Artificial Intelligence Governance: Ethical Considerations and Implication for Social Responsibility. Wiley Expert Systems, 41(7). https://doi.org/10.1111/exsy.13406
Devianto, Y., & Dwiasnati, S. (2020). Kerangka kerja sistem kecerdasan buatan dalam meningkatkan kompetensi sumber daya manusia Indonesia. IncomTech: Jurnal Telekomunikasi dan Komputer, 10(1), 19-24.
Erina, E., & Zein, A. (2023). Artificial Intelligence (AI). Purbalingga: Eureka Media Aksara.
Fiantika, F., Wasil, M., Jumiyati, S., Honesti, L., Wahyuni, S., Mouw, E., Jonata, Mashudi, I., Hasanah, N., Maharani, A., Ambarwati, K., Noflidaputri, R., Nuryami, & Waris, L. (2022). Qualitative Research Methodology. Padang: PT Global Eksekutif Teknologi.
Ghobakhloo, M. (2020). Industry 4.0, digitization, and opportunities for sustainability. Journal of cleaner production, 252, 119869.
Gusti, M., & Satrianto, A. (2024). Artificial Intelligence for Employee Engagement and Productivity. Business Perspectives: Problems and Perspectives in Management. http://dx.doi.org/10.21511/ppm.22(3).2024.14
Gumilar, B., Hariadi, E., & Achmad, I. (2024). The Mediating Effect of On The Job Training for Work Ability on Productivity Improvement Air Force Polytechnic's Cadets. Technium Social Sciences Journal, 60, 12-22.
Gould, R., Ilmarinen, J., Jarvisalo, J., & Koskinen, S. (2008). Dimensions of Work Ability. Helsinki: Terveys Halsa Health.
Hidayat, M., & Syam, A. (2019). Urgensitas Perencanaan Strategis dan Pengelolaan Sumber Daya Manusia Madrasah Era Revolusi Industri 4.0. AL-ASASIYYA: Journal Of Basic Education, 4(1), 1-13.
Huang, M., Rust, R., & Maksimovic, V. (2019). The Feeling Economy: Managing in the Next Generation of Artificial Intelligence (AI). California Management Review, 61(4), 43-65. https://doi.org/10.1177/0008125619863436
Khan, A. (2024). The Intersection of Artificial Intelligence and International Trade Laws: Challenges and Opportunities. IIUM Law Journal, 32, 103-152. https://doi.org/10.31436/iiumlj.v32i1.912
Kumar, A. (2023). Machine Learning and Artificial Intelligence - Induced Technostress in Organizations: A Study on Automation-Augmentation Paradox with Socio-Technical Systems as Coping Mechanisms. International Journal of Organizational Analysis, 32(4), 681–701. https://doi.org/10.1108/IJOA-01-2023-3581
Lampe, N., Sopather, K., Damayanti, N., Jonathan, G., Mahrita, A., & Sambung, R. (2025). Strategy of Increasing Human Resources to Improve the Performance of Village Owned Enterprises in Pulang Pisau Regency. Jurnal Manajemen Sains Dan Organisasi, 6(3), 187–194. https://doi.org/https://doi.org/10.52300/jmso.v6i3.23203
Ledoh, C., Judijanto, L., Jumiono, A., Apriyanto, A., & Hakpantria, H. (2024). Revolusi Industri 5.0: Kesiapan Generasi-Z dalam menghadapi persaingan global. PT. Sonpedia Publishing Indonesia.
Littlejohn, S., & Foss, K. (2010). Theories of Human Communication, 7th Edition. Long Grove: Waveland Press.
Malik, N., & Tripathi, S. (2021). Impact of Artificial Intelligence on Employees Working in Industry 4.0 Led Organizations. International Journal of Manpower. https://doi.org/10.1108/IJM-03-2021-0173
McGonagle, A., Barnes-Farrell, J., Fisher, G., Grosch, J. (2015). Individual and Work Factors Related to Perceived Work Ability and Labor Force Outcomes. Journal of Applied Psychology, 100 (2), 376-398. https://doi.org/10.1037/a0037974
Nandini, W., Gustomo, A., Sushandoyo, D. (2022). The Antecedents and Consequences of Individual Adaptive Performance: A Systematic Literature Study. Business & Bureaucracy: Journal of Administrative and Organizational Sciences, 29(2), 77-89. https://doi.org/10.20476/jbb.v29i2.1271
Neuman, W. (2014). Social Research Methods: Qualitative and Quantitative Approaches Seventh Edition. Harlow: Pearson Education Limited.
Nishu, S., & Lalrinzuala, A. (2021). Employees' Knowledge and Skills on Work Productivity at the Manpower Department. La Bisecoman Journal, 2(3), 1-5. https://doi.org/10.37899/journallabisecoman.v2i3.408
Popescu, C., & Sebestova, J. (2024). The Impact of Artificial Intelligence on Intellectual Capital Development: Shifting Requirements for Professions and Processes in the Non-Profit Sector. Journal of Infrastructure, Policy and Development, 8(10), 3899. https://doi.org/10.24294/jipd.v8i10.3899
Pulakos, E., Arad, S., Donovan, M., Plamondon, K. (2000). Adaptability in the Workplace: Development of a Taxonomy of Adaptive Performance. Journal of Applied Psychology, 85(4), 612-624. https://doi.org/10.1037/0021-9010.85.4.612
Punnett, B., Duffy, A., Fox, S., Gregory, A., Lituchi, T., Miller, J., Monserrat, S., Olivas-Rujan, M., Santos, N. (2007). Career Success and Satisfaction: A Comparative Study in Nine Countries. Women in Management Review, 22(5), 371-390. https://doi.org/10.1108/09649420710761446
Rich, E., Knight, K., & Nair, S. (2009). Artificial Intelligence Third Edition. New Delhi: Tata McGraw-Hill.
Rich, E., & Knight, K. (1991). Artificial Intelligence. New York: McGraw-Hill Inc.
Rusyanti, M. (2024). Industry 5.0: Embracing Human-Machine Collaboration. Unairnews.
Sabrina, R. (2021). Manajemen sumber daya manusia: unggul, kreatif, dan inovatif di era revolusi industri 4.0. Jurnal Ilmiah Manajemen Dan Bisnis, 22(2), 216-222.
Sadowski, C., & Zimmermann, T. (2019). Rethinking Productivity in Software Engineering. Apress. https://doi.org/10.1007/978-1-4842-4221-6
Sharma, S. (2024). Benefits or Concerns of AI: A Multistakeholder Responsibility. Futures, 157, 1-10. https://doi.org/10.1016/j.futures.2024.103328
Siska, M., Siregar, I., Saputra, A., Juliana, M., & Afifudin, M. (2023). Kecerdasan buatan dan big data dalam industri manufaktur: Sebuah tinjauan sistematis. Nusantara Technology and Engineering Review, 1(1), 41-53.
Smith, J. A., Flowers, P., & Larkin, M. (2009). Interpretative Phenomenological Analysis: Theory, Method and Research. Los Angeles: SAGE.
Suci, R. P., Adya, H., & Suwarta. (2020). Strategi peningkatan kinerja Sumber Daya Manusia ( SDM ) pada UMKM di Malang dengan Metode SWOT Analisis Faktor Eksternal Strategis ( EFAS ) dan Ringkasan Analisis Faktor Strategis Internal. 1(2), 64–74. https://doi.org/https://doi.org/10.52300/jmso.v1i2.2374
Tuomi, K., Ilmarinen, J., Jahkola, A., Katajarinne, L. and Tulkki, A. (1998). Work Ability Index 2nd Edition. Helsinki: Finnish Institute of Occupational Health.
Valeriya, G., John, V., Singla, A., Devi, J. Y., & Kumar, K. (2024). AI-Powered Super-Workers: An Experiment in Workforce Productivity and Satisfaction. BIO Web of Conferences. https://doi.org/10.1051/bioconf/20248601065
Waluyo, D. (2024). Making Indonesia 4.0, Indonesia's Steps Towards the Digital and Automation Era. Editorial Indonesia.go.id . Jakarta.
Washington, M. (2021). The Mediated Effect of Social Media Technology Addiction on Productivity. Business Management Dynamics, 10(12), 01-06.
Werens, S., von Garrel, J. (2023). Implementation of Artificial Intelligence at the Workplace, Considering the Work Ability of Employees. Journal for Technology Assessment in Theory and Practice, 32(2), 43-49.
Zhang, X., Liao, H., Li, N., & Colbert, A. (2020). Playing It Safe for My Family: Exploring The Dual Effects of Family Motivation on Employee Productivity and Creativity. Academy of Management Journal, 63(6), 1923-1950. https://doi.org/10.5465/amj.2018.0680
Zhao, C., Cooke, F., & Wang, Z. (2021). Human resource management in China: What Are the Key Issues Confronting Organizations and How Can Research Help? Asia Pacific Journal of Human Resources, 59(3), 357-373. https://doi.org/10.1111/1744-7941.12295
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