Integrasi Artificial Intelligence dalam Talent Management: Tantangan dan Peluang Menuju Ekosistem Ekonomi Global
DOI:
https://doi.org/10.47065/arbitrase.v6i1.2493Keywords:
Artificial Intelligence; Talent Management; Digital Competency; Organizational Readiness; Human ResourcesAbstract
Digital transformation through the adoption of Artificial Intelligence (AI) has become a highly strategic issue in human resource management, particularly in the context of talent management systems. This study aims to analyze the influence of perceptions toward AI, organizational readiness, and digital competency of human resources on the effectiveness of talent management in the industrial sector in Medan City. The research employed a quantitative approach with a survey method, involving 120 respondents consisting of managers and human resource staff in technology and manufacturing companies. Data analysis was conducted using multiple linear regression to examine the causal relationships between variables. The results indicate that the three independent variables simultaneously have a significant influence on the effectiveness of talent management, with a coefficient of determination (R²) of 64 percent, suggesting that these variables explain the majority of the variability in talent management effectiveness. Partially, digital competency proved to be the most dominant factor affecting talent management effectiveness, followed by perceptions toward AI and organizational readiness. These findings affirm that the successful integration of AI in talent management is highly influenced by the organization’s technological readiness as well as the quality and capability of human resources in optimally utilizing digital technologies. The study recommends that organizations prioritize strengthening digital competencies through intensive training and enhancing internal organizational readiness in terms of infrastructure and digital culture, in order to strategically optimize AI utilization. This step is considered crucial in building a competitive and adaptive economic ecosystem in the global digital era. Thus, this study not only provides empirical contributions to the literature on technology-based human resource management but also offers practical implications for organizational policymaking in facing digital transformation.
Downloads
References
Anderson, M. H., & Sun, P. Y. T. (2020). Reviewing leadership styles: Overlaps and the need for a new full?range theory. International Journal of Management Reviews, 22(1), 76–96. https://doi.org/10.1111/ijmr.12208
Baker, J. (2012). The Technology–Organization–Environment Framework. University of Hamburg https://doi.org/10.1007/978-1-4419-6108-2_12
Binns, R. (2018). Fairness in Machine Learning: Lessons from Political Philosophy. Proceedings of the 2018 Conference on Fairness, Accountability and Transparency, 149–159.
Dawson, J. Y., & Agbozo, E. (2024). AI in talent management in the digital era – an overview. Journal of Science and Technology Policy Management. https://doi.org/10.1108/JSTPM-06-2023-0104
Dessler, G. (2020). Human resource management (16th ed.). Pearson Education.
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5
Forum, W. E. (2023). The Future of Jobs Report 2023. https://www.weforum.org/reports/
Galanaki, E., & Papalexandris, A. (2021). Artificial Intelligence in talent management: A systematic literature review and research agenda. Journal of Business Research, 132, 771–784. https://doi.org/10.1016/j.jbusres.2020.10.042
Ghozali, I. (2018). Aplikasi Analisis Multivariate dengan Program IBM SPSS 25 (9th ed.). Badan Penerbit Universitas Diponegoro.
Hair, J. ., Black, W. ., Babin, B. ., & Anderson, R. . (2019). Multivariate Data Analysis (8th ed.). Cengage Learning.
Hunkenschroer, A., & Luetge, C. (2022). Ethics of AI-enabled recruiting and selection: A review and research agenda. Journal of Business Ethics, 178(4), 977–1007. https://doi.org/10.1007/s10551-021-04845-0
Indonesia, K. K. B. P. R. (2023). Peta Jalan Indonesia Emas 2045: Transformasi Ekonomi Menuju Negara Maju. https://www.ekon.go.id
Jain, P., & Sharma, S. (2021). Exploring the role of organizational readiness in digital transformation: A human capital perspective. Journal of Business Research, 124, 315–324.
Lo, F.-Y., & Liao, P.-C. (2021). Rethinking financial performance and corporate sustainability: Perspectives on resources and strategies. Technological Forecasting and Social Change, 162(162), 120346. https://doi.org/10.1016/j.techfore.2020.120346
Murugesan, U., Subramanian, P., Srivastava, S., & Dwivedi, A. (2023). A study of artificial intelligence impacts on human resource digitalization in Industry 4.0. Decision Analytics Journal, 7, 100249. https://doi.org/10.1016/j.dajour.2023.100249
Noe, R. A., Hollenbeck, J. R., Gerhant, B., & Wright, P. M. (2020). Fundamentals of Human Resource Management (8th ed.). McGraw-Hill Education.
Oke, A. E., Aigbavboa, C. O., & Ochieng, E. G. (2021). Advances in Human Factors and Ergonomics in Healthcare and Medical Devices (J. Kalra, N. J. Lightner, & R. Taiar (eds.); Vol. 263). Springer International Publishing. https://doi.org/10.1007/978-3-030-80744-3
Papathanassis, A. (2020). e-Human Resource Management: A strategic approach to talent acquisition using AI. E-Journal of Organizational Development, 11(3), 101–118.
Raghavan, M., M, B. S., Kleinberg, J., & Levy, K. (2020). Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 469–481.
Sekaran, U., & Bougie, R. (2019). Research methods for business: A skill-building approach (8th ed.). Wiley.
Setiawan, A., & Hermawan, D. (2022). Transformasi digital dan pengelolaan sumber daya manusia. Penerbit Mitra Ilmu.
Sugiyono. (2021). Metode penelitian kuantitatif, kualitatif, dan R&D (26th ed.). Alfabeta.
Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial intelligence in human resources management: Challenges and a path forward. California Management Review, 61(4), 15–42. https://doi.org/10.1177/0008125619867910
Van Rooyen, J., Shrestha, P., & De Beer, E. (2021). Crisis on Human Resources: Airline Companies in Thailand. Journal of Human Resource Management, 9(2), 39. https://doi.org/10.11648/j.jhrm.20210902.12
Zikmund, W. G., Babin, B. J., Carr, J. C., & Griffin, M. (2020). Business research methods (10th ed.). Cengage Learning.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Integrasi Artificial Intelligence dalam Talent Management: Tantangan dan Peluang Menuju Ekosistem Ekonomi Global
ARTICLE HISTORY
Issue
Section
Copyright (c) 2025 Dewi Shinta Lubis, Adrianto
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
