Acemoglu, D., & Restrepo, P. (2019). The wrong kind of AI? Artificial intelligence and the future of labor demand. Available online:
https://doi.org/10.3386/w25682 (accessed on 25 April 2024).
Akerman, A., Gaarder, I., & Mogstad, M. (2015). The Skill Complementarity of Broadband Internet *. The Quarterly Journal of Economics, 130(4), 1781–1824.
https://doi.org/10.1093/qje/qjv028
Arslan, A., Cooper, C., Khan, Z., Golgeci, I., & Ali, I. (2021). Artificial intelligence and human workers interaction at team level: a conceptual assessment of the challenges and potential HRM strategies. International Journal of Manpower, 43(1), 75–88.
https://doi.org/10.1108/ijm-01-2021-0052
Bartel, A., Ichniowski, C., & Shaw, K. (2007). How Does Information Technology Affect Productivity? Plant-Level Comparisons of Product Innovation, Process Improvement, and Worker Skills. The Quarterly Journal of Economics, 122(4), 1721–1758.
https://doi.org/10.1162/qjec.2007.122.4.1721
Boden, M. A. (2016). AI: Its Nature and Future. Oxford University Press.
Brougham, D., & Haar, J. (2020). Technological disruption and employment: The influence on job insecurity and turnover intentions: A multi-country study. Technological Forecasting and Social Change, 161, 120276.
https://doi.org/10.1016/j.techfore.2020.120276
Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Company.
Budhwar, P., Chowdhury, S., Wood, G., et al. (2023). Human resource management in the age of generative artificial intelligence: Perspectives and research directions on ChatGPT. Human Resource Management Journal, 33(3), 606–659.
https://doi.org/10.1111/1748-8583.12524
Collis, J., & Hussey, R. (2003). Business research: A practical guide for undergraduate and postgraduate students. Palgrave Macmillan.
Danaher, J., Hogan, M. J., Noone, C., et al. (2017). Algorithmic governance: Developing a research agenda through the power of collective intelligence. Big Data & Society, 4(2).
https://doi.org/10.1177/2053951717726554
Enholm, I. M., Papagiannidis, E., Mikalef, P., & Krogstie, J. (2021). Artificial Intelligence and Business Value: a Literature Review. Information Systems Frontiers, 24(5), 1709–1734.
https://doi.org/10.1007/s10796-021-10186-w
Marshall, C., & Rossman, G. (2011). Designing Qualitative Research, 5th ed. SAGE Publications.
Oganezi, B. U., Lozie, D. R. (2017). Employee retention strategy and performance of commercial banks in Ebonyi state, Nigeria. FUNAI Journal of Accounting, Business & Finance (Fujabf). 1(1), 279–287.
Sarfo, J. O., Gbordzoe, N. I., Debrah, T. P., et al. (2021). Qualitative research designs, sample size and saturation: is enough always enough? Journal of Advocacy, Research and Education, 8(3).
https://doi.org/10.13187/jare.2021.3.60
Schwab, K. (2017). The fourth industrial revolution, 1st ed. World Economic Forum.
Shrinivaas K., Sudarmani, B., & Gopinathan, N. (2021). A study on the implementation of AI in an organization and its effects on employee morale. Turkish Online Journal of Qualitative Inquiry (TOJQI). 12(6): 5109–5112.
Stanford University. (2023). Generative AI: perspectives from Stanford HAI. Stanford University Human-Centered Artificial Intelligence. Available online:
https://hai.stanford.edu/sites/default/files/2023- 03/Generative_AI_HAI_Perspectives.pdf (accessed on 25 April 2024).
von Krogh, G., Roberson, Q., & Gruber, M. (2023). Recognizing and Utilizing Novel Research Opportunities with Artificial Intelligence. Academy of Management Journal, 66(2), 367–373.
https://doi.org/10.5465/amj.2023.4002
Walsh, T., Levy, N., Bell, G., et al. (2019). The effective and ethical development of artificial intelligence. Available online: acola.org/wp-content/uploads/2019/07/hs4_artificial-intelligence-report.pdf (accessed on 25 April 2024).
Yadav, R., Chaudhary, N. S., Kumar, D., & Saini, D. (2022). Mediating and moderating variables of employee relations and sustainable organizations: a systematic literature review and future research agenda. International Journal of Organizational Analysis, 31(7), 3023–3050.
https://doi.org/10.1108/ijoa-12-2021-3091
Yapo, A., & Weiss, J. (2018). Ethical implications of bias in machine learning. In: Proceedings of the 51st Hawaii International Conference on System Sciences; 3–6 January 2018; Hilton Waikoloa Village, Hawaii, USA. pp. 5365–5372.
https://doi.org/10.24251/hicss.2018.668
Yu, X., Xu, S., & Ashton, M. (2022). Antecedents and outcomes of artificial intelligence adoption and application in the workplace: the socio-technical system theory perspective. Information Technology & People, 36(1), 454–474.
https://doi.org/10.1108/itp-04-2021-0254