Public Perception and Confidence: How Workforce Attitudes towards AI Influence Willingness to Engage in Upskilling or Reskilling Initiatives
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V5I4P112Keywords:
Artificial Intelligence, Workforce Perception, Public Confidence, Upskilling, Reskilling, Digital Transformation, Technology Acceptance, Policy ReadinessAbstract
The rapid adoption of artificial intelligence (AI) in industries has made the question of whether the workforce is prepared to respond to the changes through upskilling and reskilling increasingly controversial. The paper discusses the impact of general opinion and trust in AI, as well as trust in the workforce, on readiness to participate in skill development programs. In light of post-pandemic dynamics of digital transformation, the research synthesizes knowledge from organizational psychology, labor economics, and the theory of technology acceptance to investigate the impact of affective and cognitive trust in AI systems on reskilling behavioral intentions. It is suggested that a conceptual framework be used to connect AI perception, confidence, and participation in learning programs as interdependent variables, mediated by organizational communication and national digital maturity. The discussion shows that a positive attitude towards AI and a sense of control over technological change are motivating upskilling. Meanwhile, unemployment and a lack of confidence in AI governance are detrimental to participation, driven by fear of being replaced by machines. The paper ends with policy suggestions that include providing inclusive digital education, communicating clearly about AI, and mechanisms to build employer-capable confidence to enhance societal preparedness for an AI-enhanced economy
Downloads
References
[1] Alaaraj, S., Mohamed, Z. A., & Bustamam, U. S. (2021). The effect of trust in government and perceived usefulness on citizens’ intentions to adopt e-government services. International Journal of Advanced Computer Science and Applications, 12(3), 84–94. https://doi.org/10.14569/IJACSA.2021.0120311
[2] Becker, G. S. (1964). Human capital: A theoretical and empirical analysis, with special reference to education. University of Chicago Press.
[3] Bughin, J., Hazan, E., Lund, S., Dahlström, P., Wiesinger, A., & Subramaniam, A. (2018). Skill shift: Automation and the future of the workforce. McKinsey Global Institute.
[4] European Commission. (2020). White paper on artificial intelligence: A European approach to excellence and trust. Publications Office of the European Union.
[5] International Labour Organization (ILO). (2021). Global framework on core skills for life and work in the 21st century. Geneva: ILO.
[6] Marangunić, N., & Granić, A. (2015). Technology acceptance model: A literature review from 1986 to 2013. Universal Access in the Information Society, 14(1), 81–95. https://doi.org/10.1007/s10209-014-0348-1
[7] Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of Management Review, 20(3), 709–734. https://doi.org/10.5465/amr.1995.9508080335
[8] McKinsey & Company. (2021). The future of work after COVID-19. McKinsey Global Institute.
[9] McKnight, D. H., Carter, M., Thatcher, J. B., & Clay, P. F. (2011). Trust in a specific technology: An investigation of its components and measures. ACM Transactions on Management Information Systems, 2(2), 1–25. https://doi.org/10.1145/1985347.1985353
[10] Organisation for Economic Co-operation and Development (OECD). (2021). AI, the future of work, and skills. Paris: OECD Publishing. https://doi.org/10.1787/1d0c92ef-en
[11] Saks, A. M., & Gruman, J. A. (2020). Organizational socialization and learning in the age of artificial intelligence. Human Resource Management Review, 30(4), 100715. https://doi.org/10.1016/j.hrmr.2019.100715
[12] Shin, D. (2020). The effects of explainability and transparency on user trust in artificial intelligence systems. Computers in Human Behavior, 102, 14–31. https://doi.org/10.1016/j.chb.2019.08.010
[13] Siau, K., & Wang, W. (2020). Building trust in artificial intelligence, machine learning, and robotics. Cutter Business Technology Journal, 33(2), 47–53.
[14] UNESCO. (2021). Recommendation on the ethics of artificial intelligence. Paris: UNESCO.
[15] Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
[16] Venkatesh, V., Thong, J. Y. L., & Xu, X. (2022). Unified theory of acceptance and use of technology: A review and future research directions. Information Systems Frontiers, 24(4), 987–1012. https://doi.org/10.1007/s10796-021-10120-2
[17] World Economic Forum. (2020). The future of jobs report 2020. Geneva: WEF.
[18] Zhang, B., & Dafoe, A. (2019). Artificial intelligence: American attitudes and trends. Center for the Governance of AI, Future of Humanity Institute, University of Oxford.
[19] European Commission. (2021). Coordinated plan on the artificial intelligence 2021 review—Publications Office of the European Union.
