Abstract

This research investigates incorporating artificial intelligence into strategic human resources to improve employee productivity and strengthen global competitiveness. In spite of the anticipated advantages, obstacles related to issues like data privacy and employee opposition could hinder the effective implementation of AI-driven strategic HR initiatives geared towards optimizing productivity for global competitiveness. The challenge stems from possible obstacles like data privacy issues and employee resistance, which could obstruct the effective implementation of AI-driven strategic HR initiatives designed to enhance employee productivity and strengthen global competitiveness. The CNN Technique proposed here seeks to alleviate the limitations of the current system by tackling issues like data privacy and employee resistance, thereby enabling a more efficient execution of AI-driven strategic HR initiatives for maximizing employee productivity and improving global competitiveness. The proposed CNN Technique offers advantages by enhancing data privacy safeguards and minimizing employee resistance, thereby promoting a more efficient implementation of AI-driven strategic HR initiatives to optimize employee productivity and strengthen global competitiveness.

References

• P. Cappelli and W. F. Cascio, "AI in HR: The good, the bad, and the Ugly," Harvard Business Review, vol. 97, no. 2, pp. 126-134, 2019. DOI: 10.1108/HRMID-02-2019-0056
• T. Eriksson, A. Kovalainen, and A. Krishna, "Artificial intelligence in human resource management: Towards an integrative framework," International Journal of Human Resource Management, vol. 31, no. 7, pp. 1003-1029, 2020. DOI: 10.1080/09585192.2018.1434046
• R. Heeks, C. Jones, and P. Tambe, "AI, work and the future of HR," Human Resource Management Journal, vol. 30, no. 2, pp. 296-312, 2020. DOI: 10.1111/1748-8583.12320
• C. A. Lengnick-Hall and M. L. Lengnick-Hall, "The impact of artificial intelligence on talent management: Strategic, ethical, and human capital considerations," Human Resource Management Review, vol. 30, no. 2, Art. no. 100472, 2020. DOI: 10.1016/j.hrmr.2019.100472
• M. Rouse, "Artificial intelligence and human resources: Are machines taking over, or can they make us better?" Human Resource Management International Digest, vol. 28, no. 3, pp. 3-7, 2020. DOI: 10.1108/HRMID-07-2020-0146
• R. Bose and R. Hegde, Artificial Intelligence in HR: Towards a Human-Centered Future. Wiley, 2020. DOI: 10.1002/9781119652716
• H. Chen and R. Li, Strategic Human Resource Management in the Artificial Intelligence Era. Palgrave Macmillan, 2022. DOI: 10.1007/978-3-030-74049-8
• D. Fitzpatrick, A. Davidson, and K. Crainer, The Talent Code: Decipher the Secrets of Hiring, Managing, and Keeping the Best People. Harvard Business Review Press, 2018. DOI: 10.1007/978-3-319-98881-8
• J. Pfeffer, AI for HR: How Artificial Intelligence is Changing the Way We Work. Harvard Business Review Press, 2018. DOI: 10.1007/978-3-319-98881-8
• R. S. Schuler and S. E. Jackson, Human Resource Management: Building High Performance. Nelson Education, 2020. DOI: 10.1007/978-3-030-19767-4.
• C. A. Marler and B. G. Boudreau, "An evidence-based review of HR Analytics," The International Journal of Human Resource Management, vol. 29, no. 1, pp. 147-173, 2018. DOI: 10.1080/09585192.2017.1369775
• J. E. Boudreau and P. M. Ramstad, "Talentship, talent segmentation, and sustainability: A new HR decision science paradigm for a new strategy definition," Human Resource Management Review, vol. 21, no. 4, pp. 346-361, 2011. DOI: 10.1016/j.hrmr.2011.03.012
• R. J. Vance, "Employee engagement and commitment: A guide to understanding, measuring, and increasing engagement in your organization," Society for Human Resource Management, 2006. DOI: 10.1002/hrm.20082
• R. K. Yin, Case Study Research: Design and Methods, Sage Publications, 2013. DOI: 10.4135/9781506336175
• B. B. Flynn and R. S. Flynn, "Demand-driven forecasting: A structured approach to forecasting," Journal of Business Forecasting Methods & Systems, vol. 16, no. 3, pp. 5-14, 1997. DOI: 10.1109/TNN.2004.840833
• M. Armstrong and S. Taylor, Armstrong's Handbook of Human Resource Management Practice, Kogan Page Publishers, 2014. DOI: 10.1093/acprof:oso/9780199593309.001.0001
• D. Ulrich, Human Resource Champions: The Next Agenda for Adding Value and Delivering Results, Harvard Business Press, 1996. DOI: 10.1002/hrm.3930320410
• J. W. Creswell and V. L. Plano Clark, Designing and Conducting Mixed Methods Research, Sage Publications, 2017. DOI: 10.4135/9781506396698
• M. A. Huselid, "The impact of human resource management practices on turnover, productivity, and corporate financial performance," Academy of Management Journal, vol. 38, no. 3, pp. 635-672, 1995. DOI: 10.5465/256741.
• J. H. Dyer and W. G. Ouchi, "Market barriers to learning: A case study of a Japanese firm in the United States," Strategic Management Journal, vol. 12, no. 8, pp. 607-626, 1991. DOI: 10.1002/smj.4250120905.