Empowering Talent In The Age Of Artificial Intelligence: Innovations In Human Resource Management

Authors

  • Ekawahyu Kasih STIE Kasih Bangsa
  • Farah Qalbia STIE Kasih Bangsa
  • Novrizal Novrizal STIE Kasih Bangsa

DOI:

https://doi.org/10.55606/icesst.v1i2.383

Keywords:

Talent Empowerment, Artificial Intelligence (AI) Innovations, Human Resource Management (HRM), Qualitative Research, Organizational Strategies

Abstract

In the dynamic landscape of Human Resource Management (HRM), the emergence of Artificial Intelligence (AI) technologies presents both challenges and opportunities. This research aims to explore strategies for empowering talent amidst AI innovations in HRM. The research adopts a phenomenological approach to delve into the lived experiences of HR professionals and employees within organizations integrating AI technologies. Through purposive sampling, data were collected via in-depth interviews and focus group discussions. Thematic analysis was employed to identify patterns, themes, and insights from the narratives. The findings reveal a nuanced understanding of how AI impacts talent management practices, including recruitment, training, performance evaluation, and career development. Moreover, the research elucidates the importance of human-centric approaches in leveraging AI to augment rather than replace human capabilities. These insights contribute to enhancing organizational strategies for talent empowerment in the era of AI-driven HRM innovations.

References

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101.

Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.

Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116.

Feher, A., & Ivens, B. S. (2021). Talent management and artificial intelligence: A systematic review and future research agenda. Human Resource Management Review, 31(2), 100748.

Kudyba, S., & Hopton, M. (2018). Predictive analytics, data mining and big data: Myths, misconceptions and methods. Springer.

Kusnanto, E. (2022). Performance Measurement Based on Balance Scorecard Perspective of Sustainable Leadership, Corporate Governance and Human Capital in Banking Industry. International Journal of Contemporary Accounting, 4(1), 41–58. https://doi.org/10.25105/ijca.v4i1.13916

Moustakas, C. (1994). Phenomenological research methods. Sage.

Palinkas, L. A., Horwitz, S. M., Green, C. A., Wisdom, J. P., Duan, N., & Hoagwood, K. (2015). Purposeful sampling for qualitative data collection and analysis in mixed method implementation research. Administration and Policy in Mental Health and Mental Health Services Research, 42(5), 533-544.

Parry, E., & Tyson, S. (2019). Managing talent in the digital age: Current issues and future directions. Human Resource Management Journal, 29(2), 177-193.

Van Laar, E., Van Den Bergh, H., & Midden, C. J. (2017). Tailoring persuasive electronic health strategies for older adults on the basis of personal motivation: Web-based survey study. Journal of Medical Internet Research, 19(8), e274.

Downloads

Published

2022-12-31

How to Cite

Ekawahyu Kasih, Farah Qalbia, & Novrizal Novrizal. (2022). Empowering Talent In The Age Of Artificial Intelligence: Innovations In Human Resource Management. The International Conference on Education, Social Sciences and Technology (ICESST), 1(2), 287–295. https://doi.org/10.55606/icesst.v1i2.383

Similar Articles

1 2 3 4 5 6 7 8 9 10 > >> 

You may also start an advanced similarity search for this article.