Marketing and Management of Innovations

ISSN (print) – 2218-4511 

ISSN (online) – 2227-6718

Registered in the Media Registrants-Register

Identifier in the register: R30-01179 Decision dated August 31, 2023, No. 759

The language of publication is English. 

Issued 4 times a year (March, June, September, December) since 2010

Business Model: Golden Open Access | APC Policy

Publisher & Founder:

logo_longENG

Editor-in-Chieff             View Editorial Board

Oleksii Lyulyov

Sumy State University | Ukraine

The Nexus Between Talent Management Attention and Artificial Intelligence: Evidence from Companies Operating Within the AI Domain

Naif Fawzi Alruwaili 1 , , Khaled Mokni 2,*,   
  1. Northern Border University, Arar, Kingdom of Saudi Arabia
  2. International University of Rabat, Rabat, Morocco; University of Sousse, Tunisia

     * Corresponding author

DOI: https://doi.org/10.21272/mmi.2024.4-07

Pages: 85–98

Received: 30 May 2024

Revised: 20 December 2024

Accepted: 25 December 2024

Abstract

This study examines the relationship between talent management (TM) attention and the performance of leading artificial intelligence (AI) companies. Using Google Trends data, TM attention is quantified through search queries related to talent acquisition, employee development, and workforce planning, while additional corporate metrics, such as HR performance reports and employee retention rates, are incorporated to increase the robustness of the analysis. AI company performance is measured via the stock returns of Microsoft, Google, Amazon, and NVIDIA, which represent key players in the AI sector. A nonparametric causality-in-quantiles test is applied to capture the asymmetric and heterogeneous effects of TM attention on stock returns across different market conditions, ranging from bearish to bullish scenarios. The results reveal significant causality from TM attention to AI stock performance under bearish and normal market conditions, emphasizing the importance of TM strategies during periods of market stress or stability. In contrast, TM attention exerts limited influence during bullish conditions, where performance is likely driven by other factors, such as market sentiment and technological advancements. A facet-specific analysis highlights that talent acquisition consistently influences stock performance across all market conditions, whereas employee development has a significant effect only during bearish and normal conditions. Workforce planning has limited causal influence, suggesting that its market impact depends on company-specific factors and contextual dynamics. This study makes important contributions to theory and practice by offering a nuanced understanding of TM’s role in shaping organisational performance within the dynamic AI landscape. For companies, prioritizing effective TM strategies, particularly talent acquisition and employee development, can enhance resilience and competitiveness. Investors can leverage TM insights to refine portfolio strategies, whereas policymakers are encouraged to implement initiatives such as grants for workforce training or public‒private partnerships to foster talent pipelines in the AI sector. These findings underscore the critical interplay between TM practices and market performance, providing actionable insights for navigating the complexities of the rapidly evolving AI industry.

Keywords: talent management; artificial intelligence; nonparametric causality; AI companies; stock markets. 

How to Cite: Alruwaili, N. F., & Mokni, K. (2024). The Nexus Between Talent Management Attention and Artificial Intelligence: Evidence from Companies Operating Within AI Domain. Marketing and Management of Innovations, 15(4), 85–98. https://doi.org/10.21272/mmi.2024.4-07

Abstract Views

PDF Downloads

ccbyThis work is licensed under a Creative Commons Attribution 4.0 International License

References

  1. Abdeldayem, M. M., & Aldulaimi, S. H. (2020). Trends and opportunities of artificial intelligence in human resource management: Aspirations for public sector in Bahrain. International Journal of Scientific and Technology Research, 9(1), 3867–3871. [Google Scholar]
  2. Salleh, K. A., & Janczewski, L. (2018). An Implementation of Sec-TOE Framework: Identifying Security Determinants of Big Data Solutions Adoption. [Google Scholar]
  3. Alam, M. S., Dhar, S. S., & Munira, K. S. (2020). HR Professionals’ intention to adopt and use of artificial intelligence in recruiting talents. Business Perspective Review, 2(2), 15-30. [Google Scholar] [CrossRef]
  4. Alam, M. S., Munira, K. S., Rahman, M. S., Uddin, M. A., & Akter, A. (2022). Artificial Intelligence (AI) for Talent Acquisition: Human Resource Professionals’ Perspective. International Journal of Human Capital and Information Technology Professionals (IJHCITP), 13(1), 1-18. [Google Scholar] [CrossRef]
  5. Alam, S., Ali, M. Y., & Mohd. Jani, M. F. (2011). An empirical study of factors affecting electronic commerce adoption among SMEs in Malaysia. Journal of business economics and management, 12(2), 375-399. [Google Scholar]
  6. Balcilar, M., Gupta, R., & Pierdzioch, C. (2016). Does uncertainty move the gold price? New evidence from a nonparametric causality-in-quantiles test. Resources Policy, 49, 74-80. [Google Scholar] [CrossRef]
  7. Belanche, D., Casaló, L. V., & Flavián, C. (2019). Artificial Intelligence in FinTech: understanding robo-advisors adoption among customers. Industrial Management & Data Systems, 119(7), 1411-1430. [Google Scholar]
  8. Callaghan, V., Miller, J., Yampolskiy, R., & Armstrong, S. (2017). Technological singularity. New York: Springer. [Google Scholar]
  9. Cappelli, P. (2008) Talent on Demand, Harvard Business School Press, Boston, MA.
  10. Chambers, E. G., Foulon, M., Handfield-Jones, H., Hanking, S. M., & Michaels, E. G. (1998). The global ‘war for talent’. McKinsey Quarterly, 3, 1-8.
  11. CIPD (2023). Talent management. [Link]
  12. Collings, D. G., & Mellahi, K. (2009). Strategic talent management: A review and research agenda. Human resource management review, 19(4), 304-313. [Google Scholar] [CrossRef]
  13. Cruz-Jesus, F., Oliveira, T., & Naranjo, M. (2018). Understanding the adoption of business analytics and intelligence. In Trends and Advances in Information Systems and Technologies: Volume 1 6(pp. 1094-1103). Springer International Publishing. [Google Scholar] [CrossRef]
  14. Dawson, J. Y., & Agbozo, E. (2024). AI in talent management in the digital era–an overview. Journal of Science and Technology Policy Management. [Google Scholar] [CrossRef]
  15. Del Giudice, M., Scuotto, V., Orlando, B., & Mustilli, M. (2023). Toward the human–centered approach. A revised model of individual acceptance of AI. Human Resource Management Review, 33(1), 100856. [Google Scholar] [CrossRef]
  16. Dhamija, P., & Bag, S. (2020). Role of artificial intelligence in operations environment: a review and bibliometric analysis. The TQM Journal, 32(4), 869-896. [Google Scholar] [CrossRef]
  17. El-Haddadeh, R. (2020). Digital innovation dynamics influence on organisational adoption: the case of cloud computing services. Information Systems Frontiers, 22(4), 985-999. [Google Scholar] [CrossRef]
  18. Faqihi, A., & Miah, S. J. (2023). Artificial intelligence-driven talent management system: Exploring the risks and options for constructing a theoretical foundation. Journal of Risk and Financial Management, 16(1), 31. [Google Scholar] [CrossRef]
  19. Fraij, J., Aldabbas, A., & László, V. (2022). Awareness of the impact and usage of artificial intelligence in human resources practices: a developing Country case study. International Journal of Economics and Management Systems, 7. [Google Scholar]
  20. Gallardo-Gallardo, E., Nijs, S., Dries, N., & Gallo, P. (2015). Toward an understanding of talent management as a phenomenon-driven field using bibliometric and content analysis. Human resource management review, 25(3), 264-279. [Google Scholar] [CrossRef].
  21. Giulia, M., Tangi, L., Gastaldi, L., & Benedetti, M. (2023). Exploring the factors, affordances and constraints outlining the implementation of Artificial Intelligence in public sector organizations. International Journal of Information Management, 73, 102686.
  22. Goldsmith, M., & Carter, L. (2009). Best practices in talent management: how the world’s leading corporations manage, develop, and retain top talent. John Wiley & Sons. [Google Scholar]
  23. Gonzalez, M. F., Capman, J. F., Oswald, F. L., Theys, E. R., & Tomczak, D. L. (2019). “Where’s the IO?” Artificial intelligence and machine learning in talent management systems. Personnel Assessment and Decisions, 5(3), 5. [Google Scholar] [CrossRef]
  24. Gruetzemacher, R., & Whittlestone, J. (2022). The transformative potential of artificial intelligence. Futures, 135, 102884. [Google Scholar] [CrossRef]
  25. Haefner, N., Wincent, J., Parida, V., & Gassmann, O. (2021). Artificial intelligence and innovation management: A review, framework, and research agenda✰. Technological Forecasting and Social Change, 162, 120392. [Google Scholar] [CrossRef]
  26. Han, S., & Yang, H. (2018). Understanding adoption of intelligent personal assistants: A parasocial relationship perspective. Industrial Management & Data Systems, 118(3), 618-636. [Google Scholar] [CrossRef]
  27. Huang, T. L., & Liao, S. (2015). A model of acceptance of augmented-reality interactive technology: the moderating role of cognitive innovativeness. Electronic Commerce Research, 15, 269-295. [Google Scholar] [CrossRef]
  28. Jeong, K., Härdle, W. K., & Song, S. (2012). A consistent nonparametric test for causality in quantile. Econometric Theory, 28(4), 861-887. [Google Scholar] [CrossRef]
  29. Jha, R. (2024). Incorporating Generative Ai Into Human Resource Practices. Available at SSRN 4817287. [Google Scholar]
  30. Kashi, K., Zheng, C., & Molineux, J. (2016). Exploring factors driving social recruiting: The case of Australian organizations. Journal of Organizational Computing and Electronic Commerce, 26(3), 203-223. [Google Scholar] [CrossRef]
  31. Khan, M. R. (2024). Application of artificial intelligence for talent management: Challenges and opportunities. Intelligent Human Systems Integration (IHSI 2024): Integrating People and Intelligent Systems, 119(119). [Google Scholar] [CrossRef]
  32. Khan, S., Faisal, S., & Thomas, G. (2024). Exploring the nexus of artificial intelligence in talent acquisition: Unravelling cost‒benefit dynamics, seizing opportunities, and mitigating risks. Problems and Perspectives in Management, 22(1), 462. [Google Scholar]
  33. Kumar, S. (2019). Artificial intelligence divulges effective tactics of top management institutes of India. Benchmarking: An International Journal, 26(7), 2188-2204. [Google Scholar] [CrossRef]
  34. Kurgat, A. (2016). Talent management and its importance for today’s organizations in Kenya perspective; a critical review. International journal of advances in management and economics, 5(5), 1-8.
  35. Laelawati, K. (2024). The New Hr Paradigm: Integrating Ai And Human Insight For Superior Talent Management. Journal of Economic, Bussines and Accounting (COSTING), 7(5), 1481-1490. [Google Scholar] [CrossRef]
  36. Lin, H., Chi, O. H., & Gursoy, D. (2020). Antecedents of customers’ acceptance of artificially intelligent robotic device use in hospitality services. Journal of Hospitality Marketing & Management, 29(5), 530-549. [Google Scholar] [CrossRef]
  37. Liu, S., Li, G., & Xia, H. (2021, February). Analysis of talent management in the artificial intelligence era. In 5th Asia-Pacific Conference on Economic Research and Management Innovation (ERMI 2021)(pp. 38-42). Atlantis Press. [Google Scholar] [CrossRef]
  38. Maestro, S., & Rana, P. (2024). Variables Impacting the AI Adoption in Organizations. International Journal of Science and Research Archive, 12(2), 1055-1060. [Google Scholar] [CrossRef]
  39. Maya, M., & Thamilselvan, R. (2013). Impact of talent management on employee performance and organisational efficiency in ITSPs – With reference to Chennai City. International Journal of Economic Research, 10(2), 453–461. [Google Scholar]
  40. McDonald, K., Fisher, S., & Connelly, C. E. (2017). E-HRM systems in support of “smart” workforce management: An exploratory case study of system success. In Electronic HRM in the smart era(pp. 87-108). Emerald Publishing Limited. [Google Scholar] [CrossRef]
  41. Michaels, E. (2001). The war for talent. Harvard Business Review Press. [Google Scholar]
  42. Odugbesan, J. A., Aghazadeh, S., Al Qaralleh, R. E., & Sogeke, O. S. (2023). Green talent management and employees’ innovative work behavior: the roles of artificial intelligence and transformational leadership. Journal of Knowledge Management, 27(3), 696-716. [Google Scholar][CrossRef]
  43. Paramita, D., Okwir, S., & Nuur, C. (2024). Artificial intelligence in talent acquisition: exploring organisational and operational dimensions. International Journal of Organizational Analysis, 32(11), 108-131. [Google Scholar] [CrossRef]
  44. Parvez, M. O., Öztüren, A., Cobanoglu, C., Arasli, H., & Eluwole, K. K. (2022). Employees’ perception of robots and robot-induced unemployment in hospitality industry under COVID-19 pandemic. International Journal of Hospitality Management, 107, 103336. [Google Scholar] [CrossRef]
  45. Phahlane, M. M. (2017). A multidimensional framework for human resource information systems adoption and use in a South African university(Doctoral dissertation, University of the Witwatersrand, Faculty of Commerce, Law and Management, School of Economic & Business Sciences). [Google Scholar]
  46. Pillai, R., & Sivathanu, B. (2020). Adoption of artificial intelligence (AI) for talent acquisition in IT/ITeS organizations. Benchmarking: An International Journal, 27(9), 2599-2629. [Google Scholar] [CrossRef]
  47. Puklavec, B., Oliveira, T., & Popovič, A. (2018). Understanding the determinants of business intelligence system adoption stages: An empirical study of SMEs. Industrial Management & Data Systems, 118(1), 236-261. [Google Scholar] [CrossRef]
  48. Rahman, M., Mordi, C., & Nwagbara, U. (2018). Factors influencing E-HRM implementation in government organisations: Case studies from Bangladesh. Journal of Enterprise Information Management, 31(2), 247-275. [Google Scholar] [CrossRef]
  49. Scullion, H., Collings, D. G., & Caligiuri, P. (2010). Global Talent Management. Journal of World Business, 45(2), 105–108. [Google Scholar]
  50. Selamat, S. M., Baharuddin, F. N., Hayati, A., Musa, P. M., Beta, R. M. D. M., & Ali, A. (2024). Challenges and Opportunities in the Adoption of AI in Talent Acquisition and Retention. International Journal of Academic Research in Business and Social Sciences, 14(9), 100-104. [Google Scholar]
  51. Shaltoni, A. M. (2017). From websites to social media: exploring the adoption of internet marketing in emerging industrial markets. Journal of Business & Industrial Marketing, 32(7), 1009-1019. [Google Scholar] [CrossRef]
  52. Sithambaram, R. A., & Tajudeen, F. P. (2023). Impact of artificial intelligence in human resource management: a qualitative study in the Malaysian context. Asia Pacific Journal of Human Resources, 61(4), 821-844. [Google Scholar] [CrossRef]
  53. Strohmeier, S. (2007). Research in e-HRM: Review and implications. Human resource management review, 17(1), 19-37. [Google Scholar][CrossRef]
  54. Sundarapandiyan Natarajan, D. K. S., Subbaiah, B., Dhinakaran, D. P., Kumar, J. R., & Rajalakshmi, M. (2024). AI-Powered Strategies for Talent Management Optimization. Journal of Informatics Education and Research, 4(2). [Google Scholar] [CrossRef]
  55. Tarique, I., & Schuler, R. S. (2010). Global talent management: Literature review, integrative framework, and suggestions for further research. Journal of world business, 45(2), 122-133. [Google Scholar] [CrossRef]
  56. Tavana, M., & Hajipour, V. (2020). A practical review and taxonomy of fuzzy expert systems: methods and applications. Benchmarking: An International Journal, 27(1), 81-136. [Google Scholar] [CrossRef]
  57. Upadhyay, A. K., & Khandelwal, K. (2018). Applying artificial intelligence: implications for recruitment. Strategic HR Review, 17(5), 255-258. [Google Scholar]
  58. Urme, U. N. (2023). The impact of talent management strategies on employee retention. International Journal of Science and Business, 28(1), 127-146. [Google Scholar]
  59. Virdyananto, A. L., Dewi, M. A. A., Hidayanto, A. N., & Hanief, S. (2016, October). User acceptance of the human resource information system: An integrated model of the unified theory of acceptance and use of technology (UTAUT), task technology fit (TTF), and symbolic adoption. In 2016 International Conference on Information Technology Systems and Innovation (ICITSI)(pp. 1-6). IEEE. [Google Scholar][CrossRef]
  60. Waheed, S., Zaim, A. H., Zaim, H., Sertbas, A., & Akyokus, S. (2014, February). Application of neural networks in talent management. In 2013 International Conference on Electrical Information and Communication Technology (EICT)(pp. 1-4). IEEE. [Google Scholar] [CrossRef]
  61. Wiradendi Wolor, C. (2020). Implementation talent management to improve organization’s performance in Indonesia to fight industrial revolution 4.0. International journal of scientific & technology research. [Google Scholar]
  62. World Economic Forum. (2018). The Future of Jobs Report 2018. [Link]
  63. Yadav, M. P., Abedin, M. Z., Sinha, N., & Arya, V. (2024). Uncovering dynamic connectedness of Artificial intelligence stocks with agri-commodity market in wake of COVID-19 and Russia-Ukraine Invasion. Research in International Business and Finance, 67, 102146. [Google Scholar] [CrossRef]
  64. Yang, Z., Sun, J., Zhang, Y., & Wang, Y. (2015). Understanding SaaS adoption from the perspective of organizational users: A tripod readiness model. Computers in Human Behavior, 45, 254-264. [Google Scholar] [CrossRef]
  65. Yousaf, I., Ijaz, M. S., Umar, M., & Li, Y. (2024). Exploring volatility interconnections between AI tokens, AI stocks, and fossil fuel markets: evidence from time and frequency-based connectedness analysis. Energy Economics, 133, 107490. [Google Scholar] [CrossRef]
  66. Zhang, E. Y. A. (2024). Digitalization’s Enhancement in HR Practices: The Impact of Incorporating AI in the Process of Recruitment and Selection. Advances in Economics, Management and Political Sciences, 120, 41-47. [Google Scholar] [CrossRef]

View articles in other formats

License

ccby This work is licensed under a Creative Commons Attribution 4.0 International License

Coyright

Copyright (c) 2024 The Author(s).

Published by Sumy State University

Issue

Contacts

Sumy State University
116, Kharkivska st., 40007 Sumy, Ukraine

e-mail: mmi@fem.sumdu.edu.ua

About us

About publisher Sumy State University

Journal’s history

Advertising 

Follow us

LinkedIn

Twitter

 

Company 2023 © Sumy State University