IMPACT OF THE USE AND EASE OF USE OF ARTIFICIAL INTELLIGENCE ON EMPLOYEE EFFICIENCY IN THE UAE INSURANCE SECTOR
DOI:
https://doi.org/10.35631/IJMTSS.935003Keywords:
Artificial Intelligence, Employee Efficiency, Insurance Company, Abu DhabiAbstract
Artificial intelligence (AI) has transformed the landscape in the workplace nowadays, yet efficiency improvement is highly dependent on employee onboarding. The purpose of this research was to determine the impact of ease of use of AI (EU-AI), the use of AI tools (U-AI), and the actual use of AI (AU-AI) on employee efficiency (EE) in insurance companies in Abu Dhabi. Using quantitative methodologies, data were collected from a sample of 370 employees working at Abu Dhabi National Insurance Company (ADNIC), Daman Insurance Company, and THIQA Insurance Company. Through the use of partial least square structure equation modeling (PLS-SEM), the results showed that AU-AI, U-AI, and EU-AI have a significant positive impact on employee efficiency. Additionally, the ease of use of AI serves as an important intermediary in the relationship between actual use, perceived usability, and efficiency to the employee as a whole.
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References
Al-MSloum, A. S. (2021). Applications and trends in information management systems using AI. International Virtual Conference on Sustainable Materials, 12, 3–17.
Arslan, A. (2021). AI and human worker interaction at the team level: A conceptual assessment of potential HRM challenges and strategies. International Journal of Manpower, 13(1), 1–14.
Bloomfield, P. (2021). AI in the NHS: Climate and emissions. Journal of Climate Change and Health, 20, 56–85.
Blume, B. D., Ford, J. K., Baldwin, T. T., & Huang, J. L. (2019). Exercise transfer: A meta-analytical review. Journal of Management, 45(3), 1215–1243.
Borzillo, S. R. (2021). New employee orientation, role-related stress, and conflict in the workplace: Effects on work attitudes and performance of hospitality workers. International Journal of Hospitality Management, 35, 57–88.
Bryman, A. (2016). Social research methods (5th edition). Oxford University Press.
Chen, L., & Chang, Y. (2022). Effects of flexible work arrangements on employee efficiency. Journal of Business Research, 143, 456–467.
Creswell, J. W. (2014). Research design: A qualitative, quantitative, and mixed method approach (4th edition). SAGE Publications.
Creswell, J. W., & Creswell, J. D. (2017). Research design: A qualitative, quantitative, and mixed method approach (5th edition). SAGE Publications.
Davis, F. D. (1989). Perceived usability, ease of use, and consumer acceptance of information technology. MIS Quarterly, 13(3), 319–340.
Deci, E. L., Olafsen, A. H., & Ryan, R. M. (2022). Theories of self-determination in work organization: The state of science. Annual Review of Organizational Psychology and Organizational Behavior, 9, 19–45.
Durrani, K. (2020). The impact of AI in the human resource decision-making process. Journal of Human Resource Technology, 12, 1–22.
Edmondson, A. (2019). Bold organizations: Realizing psychological safety in the workplace for learning, innovation, and growth. Wiley.
Fredrik, D. M. P. (2019). Growing opportunities in the Internet of Things. McKinsey & Company. https://www.mckinsey.com/industries/private-equity-and-principal-investors/our-insights/growing-opportunities-in-the-internet-of-things
Giudice, M. D. (2021). Towards a human-centered approach: A revised model of AI individual acceptance. Human Resource Management Review, 78, 32–56.
Kazakovs, M. (2015). Automation of human resource development planning. Computer Science Procedures, 65, 234–239.
Kim, J., & de Dear, R. (2019). Workspace satisfaction: Privacy-communication exchanges in open offices. Journal of Environmental Psychology, 63, 19–26.
Kirkpatrick, D. L., & Kirkpatrick, J. D. (2022). Evaluating training programs: Four stages. Berrett-Koehler Publishers.
Kumar, A., & Singh, P. (2021). Exploring the acceptance of social media in education: A TAM perspective. International Journal of Educational Technology in Higher Education, 18(1), Article 10.
Likert, R. (1932). A technique for measuring attitudes. Archives of Psychology, 22(140), 1–55.
Mehmood Khan. (2021). Greenness measures: An empirical study in service supply chains in the UAE. International Journal of Production Economics, 241, Article 108261.
Mohammad Islam. (2023). AI-enhanced HRM: A literature review and a proposed multi-level framework for future research. Predicting Technology and Social Change, 193, Article 122628.
Ryan, R. M., & Deci, E. L. (2020). Intrinsic and extrinsic motivation: Classical definitions and new directions. Contemporary Educational Psychology, 61, 54–67.
Sekaran, U., & Bougie, R. (2010). Research methods for business: A skills building approach (5th edition). John Wiley & Sons.
Smith, R., Jones, K., & Taylor, P. (2022). Employee well-being and productivity: A review of recent studies. Occupational Health Psychology, 27(3), 213–230.
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2020). Consumer acceptance of information technology: Towards a unified view. Journal of the Information Systems Association, 21(2), 227–252.
Wang, Y., & Ahmed, P. K. (2023). Transformational leadership and employee efficiency: The role of employee motivation intermediaries. Journal of Leadership & Organizational Development, 44(1), 45–58.
Yang, S. J. (2021). Human-centered AI in education: Seeing the invisible through the visible. Computers and Education: Artificial Intelligence, 2, Article 100008.
Zhang, Y., Wang, J., & Li, Q. (2023). Impact of AI tools on healthcare diagnostics: A review. Journal of Medical Systems, 47(1), 1–15.
Zhao, X. (2021). An analysis of an integrated mode of mental health education for workers in electric power enterprises under the background of mass education. Energy Reports, 7, 218–229.
