Optimizing Organizational Performance : Exploring The Integration Of AI And HR Practices

Authors

  • Cahyatih Kumandang STIE Kasih Bangsa
  • Ruslaini Ruslaini STIE Kasih Bangsa
  • Seger Santoso STIE Kasih Bangsa
  • Muhammad Rizal STIE Kasih Bangsa

DOI:

https://doi.org/10.55606/icesst.v1i2.381

Keywords:

Artificial Intelligence (AI), Human Resources (HR) Practices, Organizational Excellence, Qualitative Research, Thematic Analysis

Abstract

In the rapidly evolving landscape of artificial intelligence (AI), optimizing human resources (HR) practices is imperative to foster organizational excellence. This qualitative research aims to explore the intersection of AI and HR practices to enhance organizational performance. The research adopts a qualitative approach utilizing in-depth interviews with HR professionals, AI specialists, and organizational leaders. Sampling techniques include purposive and snowball sampling to ensure diverse perspectives are captured. Data analysis involves thematic analysis, allowing for the identification of patterns and themes within the qualitative data. Preliminary findings indicate that organizations are increasingly leveraging AI to streamline HR processes, enhance talent acquisition, and improve employee engagement. Furthermore, the research reveals the significance of ethical considerations and human oversight in AI-driven HR practices. This study contributes to the growing discourse on AI integration in HR and provides insights for organizations aiming to navigate the AI-driven landscape while fostering excellence in HR practices.

 

References

Berg, B. L. (2009). "Qualitative Research Methods for the Social Sciences." Pearson Education.

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.

Creswell, J. W., & Poth, C. N. (2018). "Qualitative Inquiry and Research Design: Choosing Among Five Approaches." Sage Publications.

Davenport, T. H., & Ronanki, R. (2018). "Artificial Intelligence for the Real World." Harvard Business Review. Retrieved from https://hbr.org/2018/01/artificial-intelligence-for-the-real-world

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

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.

Schramm, W., & Wagner, S. (2022). "AI in HR: A Critical Review of Opportunities and Challenges." Journal of Management Information Systems, 39(1), 303-341.

Strohmeier, S., & Piazza, F. (2020). "People Analytics and Talent Management in the Age of Big Data: The Digital Workplace." Springer.

World Economic Forum. (2021). "The Future of Jobs Report 2020." Geneva, Switzerland: World Economic Forum.

Yeung, R. (2017). "Algorithms Rule: The Governance Implications of AI, Machine Learning, and Big Data for HR." Journal of Organizational Effectiveness: People and Performance, 4(2), 97-116.

Downloads

Published

2022-12-31

How to Cite

Cahyatih Kumandang, Ruslaini Ruslaini, Seger Santoso, & Muhammad Rizal. (2022). Optimizing Organizational Performance : Exploring The Integration Of AI And HR Practices . The International Conference on Education, Social Sciences and Technology (ICESST), 1(2), 269–277. https://doi.org/10.55606/icesst.v1i2.381

Similar Articles

1 2 3 4 5 6 7 8 9 10 > >> 

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