The Impact of Artificial Intelligence Adoption on Job Satisfaction and Productivity of Healthcare Workers in Hospitals
DOI:
https://doi.org/10.37641/jimkes.v13i6.4321Keywords:
Artificial Intelligence, Healthcare Workers, Hospital Performance, Job Satisfaction, ProductivityAbstract
The integration of artificial intelligence in healthcare is transforming hospital operations, particularly in enhancing employee satisfaction and productivity. While AI offers opportunities to reduce administrative burden, foster teamwork, and improve patient care, it also introduces challenges related to technology adaptation and employee concerns about automation. This study aims to investigate how artificial intelligence adoption influences hospital staff’s job satisfaction and productivity, linking its application not only to efficiency but also to employee engagement and service outcomes. This research employs a qualitative methodology using a case study approach. Data were collected through semi-structured interviews, observation, surveys, and document analysis to capture healthcare workers’ perceptions of artificial intelligence in daily work and its influence on work-life balance. Findings reveal that artificial intelligence enhances job satisfaction by simplifying documentation, supporting skill development, improving decision-making, and creating opportunities for innovation. Productivity improvements include faster clinical decisions, better time management, effective human-technology collaboration, and higher-quality patient care. artificial intelligence adoption contributes significantly to operational success in hospitals by fostering innovation, supporting staff development, and balancing technological advancement with human needs, provided continuous training and transparent communication are ensured.
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Abdullah, M. I., Huang, D., Sarfraz, M., Ivascu, L., & Riaz, A. (2021). Effects of internal service quality on nurses’ job satisfaction, commitment and performance: Mediating role of employee well‐being. Nursing Open, 8(2), 607-619.
Ahimaz, P., Bergner, A. L., Florido, M. E., Harkavy, N., & Bhattacharyya, S. (2024). Genetic counselors’ utilization of ChatGPT in professional practice: A cross‐sectional study. American Journal of Medical Genetics Part A, 194(4), 63-69.
Akinwale, O. E., & George, O. J. (2020). Work environment and job satisfaction among nurses in government tertiary hospitals in Nigeria. Rajagiri Management Journal, 14(1), 71-92.
Al Kurdi, B., Alshurideh, M., & Alnaser, A. (2020). The impact of employee satisfaction on customer satisfaction: Theoretical and empirical underpinning. Management Science Letters, 10(15), 3561-3570.
Ameen, N., Tarhini, A., Reppel, A., & Anand, A. (2021). Customer experiences in the age of artificial intelligence. Computers in human behavior, 114(2), 10-14.
Arifin, Z., Nirwanto, N., & Manan, A. (2019). Improving the effect of work satisfaction on job performance through employee engagement. International Journal of Multi-Discipline Science (IJ-MDS), 2(1), 1-9.
Ashraf, M. A. (2019). The mediating role of work atmosphere in the relationship between supervisor cooperation, career growth and job satisfaction. Journal of Workplace Learning, 31(2), 78-94.
Atatsi, E. A., Stoffers, J., & Kil, A. (2019). Factors affecting employee performance: a systematic literature review. Journal of Advances in Management Research, 16(3), 329-351.
Budiherwanto, I. (2025). The influence of artificial intelligence and the Technology Acceptance Model on sustainable financial governance. Jurnal Ilmiah Manajemen Kesatuan, 13(3), 1791-1802.
Bhargava, A., Bester, M., & Bolton, L. (2021). Employees’ perceptions of the implementation of robotics, artificial intelligence, and automation (RAIA) on job satisfaction, job security, and employability. Journal of Technology in Behavioral Science, 6(1), 106-113.
Bibi, M. (2019). Execution of artificial intelligence approach in human resource management functions: Benefits and challenges in Pakistan. Sarhad Journal of Management Sciences, 5(1), 113-124.
Bulińska-Stangrecka, H., & Bagieńska, A. (2021). The role of employee relations in shaping job satisfaction as an element promoting positive mental health at work in the era of COVID-19. International journal of environmental research and public health, 18(4), 19-23.
Chatterjee, S., Rana, N. P., Khorana, S., Mikalef, P., & Sharma, A. (2023). Assessing organizational users’ intentions and behavior to AI integrated CRM systems: A meta-UTAUT approach. Information Systems Frontiers, 25(4), 1299-1313.
Chen, S., Zhang, X., Pan, L., & Hu, M. (2024). Innovative work behavior and job performance of corporate employees in the age of artificial intelligence. Appl. Math. Nonlinear Sci, 9(2), 1-17.
Çolak, O. (2023). The impact of artificial intelligence on the employment structure of the tourism industry: An interview with ChatGPT. İktisadi İdari ve Siyasal Araştırmalar Dergisi (İKTİSAD), 8(22), 919-939.
Crane, B. (2020). Revisiting who, when, and why stakeholders matter: Trust and stakeholder connectedness. Business & Society, 59(2), 263-286.
Enholm, I. M., Papagiannidis, E., Mikalef, P., & Krogstie, J. (2022). Artificial intelligence and business value: A literature review. Information systems frontiers, 24(5), 1709-1734.
Gabelaia, I., Bagočiūnaitė, R., & Navickienė, V. (2024). Exploring the intersections of artificial intelligence, organizational behavior, and communication dynamics in the modern workplace. Int. J. Manage. Knowl. Learn, 13(1), 71-80.
Ghamrawi, N., Shal, T., & Ghamrawi, N. A. (2024). Exploring the impact of AI on teacher leadership: regressing or expanding?. Education and Information Technologies, 29(7), 8415-8433.
Gosnell, G. K., List, J. A., & Metcalfe, R. D. (2020). The impact of management practices on employee productivity: A field experiment with airline captains. Journal of Political Economy, 128(4), 1195-1233.
Kalaivani, K., & Venkatachalam, S. (2023). An analysis on the productivity of employees through artificial intelligence. In 2023 7th International Conference on Electronics, Communication and Aerospace Technology (ICECA) (pp. 1590-1594). New Jersey: IEEE.
Kuru, H. (2023). Understanding employee wellness in industry 5.0: A systematic review. Journal of Ekonomi, 5(1), 32-35.
Lee, B., Lee, C., Choi, I., & Kim, J. (2022). Analyzing determinants of job satisfaction based on two-factor theory. Sustainability, 14(19), 12-15.
Mahmoud, N. N., & Rothenberger, D. (2019). From burnout to well-being: a focus on resilience. Clinics in colon and rectal surgery, 32(06), 415-423.
Malik, A., Budhwar, P., Patel, C., & Srikanth, N. R. (2023). May the bots be with you! Delivering HR cost-effectiveness and individualised employee experiences in an MNE. In Artificial intelligence and international HRM (pp. 83-113). London: Routledge.
Mayfield, M., Mayfield, J., & Ma, K. Q. (2020). Innovation matters: creative environment, absenteeism, and job satisfaction. Journal of Organizational Change Management, 33(5), 715-735.
Miles, M. B., & Huberman, A. M. (1984). Drawing valid meaning from qualitative data: Toward a shared craft. Educational Researcher, 13(5), 20-30.
Mittal, P., Jora, R. B., Sodhi, K. K., & Saxena, P. (2023, March). A review of the role of artificial intelligence in employee engagement. In 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS) (Vol. 1, pp. 2502-2506). New Jersey: IEEE.
Nalgozhina, N., Razaque, A., Raissa, U., & Yoo, J. (2023). Developing robotic process automation to efficiently integrate long-term business process management. Technologies, 11(6), 164-167.
Nurlia, N., Daud, I., & Rosadi, M. E. (2023). AI implementation impact on workforce productivity: The role of AI training and organizational adaptation. Escalate: Economics and Business Journal, 1(01), 01-13.
Quek, S. J., Thomson, L., Houghton, R., Bramley, L., Davis, S., & Cooper, J. (2021). Distributed leadership as a predictor of employee engagement, job satisfaction and turnover intention in UK nursing staff. Journal of Nursing Management, 29(6), 1544-1553.
Rahmadani, V. G., Schaufeli, W. B., Ivanova, T. Y., & Osin, E. N. (2019). Basic psychological need satisfaction mediates the relationship between engaging leadership and work engagement: A cross‐national study. Human Resource Development Quarterly, 30(4), 453-471.
Rahman, M. H., Fatema, M., & Ali, M. H. (2019). Impact of motivation and job satisfaction on employee’s performance: an empirical study. Asian Journal of Economics, Business and Accounting, 10(4), 1-10.
Robinson, O. P., Bridges, S. A., Rollins, L. H., & Schumacker, R. E. (2019). A study of the relation between special education burnout and job satisfaction. Journal of Research in Special Educational Needs, 19(4), 295-303.
Rotz, S., Gravely, E., Mosby, I., Duncan, E., Finnis, E., Horgan, M., ... & Fraser, E. (2019). Automated pastures and the digital divide: How agricultural technologies are shaping labour and rural communities. Journal of Rural Studies, 68(12), 112-122.
Rožman, M., Oreški, D., & Tominc, P. (2022). Integrating artificial intelligence into a talent management model to increase the work engagement and performance of enterprises. Frontiers in psychology, 13(3), 104-121.
Ruel, H., & Njoku, E. (2021). AI redefining the hospitality industry. Journal of Tourism Futures, 7(1), 53-66.
Sarker, I. H. (2021). Data science and analytics: an overview from data-driven smart computing, decision-making and applications perspective. SN Computer Science, 2(5), 377-380.
Shatila, K., & Alozian, M. (2019). Factors affecting employee turnover: the case of Lebanese retail companies. Journal of Human Resources, 7(2), 5-13.
Soori, M., Arezoo, B., & Dastres, R. (2023). Artificial intelligence, machine learning and deep learning in advanced robotics, a review. Cognitive Robotics, 3(2), 54-70.
Stankevičiūtė, Ž., Staniškienė, E., & Ramanauskaitė, J. (2021). The impact of job insecurity on employee happiness at work: a case of robotised production line operators in furniture industry in Lithuania. Sustainability, 13(3), 15-18.
Stone, M., Aravopoulou, E., Ekinci, Y., Evans, G., Hobbs, M., Labib, A., ... & Machtynger, L. (2020). Artificial intelligence (AI) in strategic marketing decision-making: a research agenda. The Bottom Line, 33(2), 183-200.
Tschang, F. T., & Almirall, E. (2021). Artificial intelligence as augmenting automation: Implications for employment. Academy of Management Perspectives, 35(4), 642-659.
Valeriya, G., John, V., Singla, A., Devi, J. Y., & Kumar, K. (2024). AI-powered super-workers: An experiment in workforce productivity and satisfaction. In BIO Web of Conferences (Vol. 86, p. 01065). Jean Perrin: EDP Sciences.
Wahyudi, W. (2018). The influence of job satisfaction and work experience on lecturer performance of Pamulang University. Scientific Journal of Reflection: Economic, Accounting, Management and Business, 1(2), 221-230.
Wamba-Taguimdje, S. L., Fosso Wamba, S., Kala Kamdjoug, J. R., & Tchatchouang Wanko, C. E. (2020). Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects. Business process management journal, 26(7), 1893-1924.
Wijayanti, N. H., & Hamami, T. (2024). Implementation of Merdeka Curriculum development for Islamic religious education: A case study in a junior high school. Edunesia: Jurnal Ilmiah Pendidikan, 5(1), 129-142.
Yusuf, M. (2021). The effects of the intrinsic motivation and extrinsic motivation on employee performance with job satisfaction as an intervening variable at PT. Alwi Assegaf Palembang. Mbia, 20(1), 18-31.
Muzaini, A., Kelana, B. W. Y., Mahmod, A., & Nordin, F. H. (2025). Understanding Nurse Turnover Intentions: The role of perceived HRM, leader‐member exchange, and presenteeism in Malaysia. International Journal of Academic Research in Business and Social Sciences, 15(6), 1613-1629.
