The Impact of Employees’ AI Acceptance on Productivity in the Construction of Smart Hospitals: The Mediating Role of Collaboration
Keywords:
AI acceptance, employee productivity, smart hospitals, employee-AI collaboration, Technology Acceptance Model
Abstract
Against the backdrop of China’s smart-hospital initiative, this review examines how frontline employees’ AI acceptance translates into productivity. Synthesizing 31 empirical studies published between 2020 and 2024 across clinical and managerial settings, the evidence is synthesized and organized around four acceptance dimensions—awareness, understanding, trust, and skills—as framed by the Technology Acceptance Model (TAM), Protection Motivation Theory (PMT), and Person–Environment Fit (PE Fit) theory. Across studies, understanding and trust are most consistently linked with faster decisions and higher service quality; skills underpin effective workflow integration. Crucially, employee-AI–AI collaboration mediates these effects in most samples, converting favorable mind-sets into operational gains via shared diagnosis, decision support, and scheduling optimization. We discuss boundary conditions (leadership, culture, prior experience) and propose an integrated pathway from acceptance to collaboration to productivity. The review provides a conceptual baseline for an upcoming large-scale survey and offers actionable recommendations: prioritize trust-building, formalize collaboration protocols, and invest in continuous AI-skilling to fully realize AI's potential for enhancing productivity in smart hospitals.
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References
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Chen, C. H., & Lee, W. I. (2024). Exploring Nurses’ Behavioral Intention to Adopt AI Technology: The Perspectives of Social Influence, Perceived Job Stress and Human–Machine Trust. Journal of Advanced Nursing 81?7??3739-3752. https://doi.org/10.1111/JAN.16495
Chowdhury, S., Budhwar, P., Dey, P. K., Joel-Edgar, S., & Abadie, A. (2022). AI-employee collaboration and business performance: Integrating knowledge-based view, socio-technical systems, and organizational socialization framework. Journal of Business Research, 144, 31–49. https://doi.org/10.1016/j.jbusres.2022.01.069
Chowdhury, S., Dey, P., Joel-Edgar, S., Bhattacharya, S., Rodriguez-Espindola, O., Abadie, A., & Truong, L. (2023). Unlocking the value of artificial intelligence in human resource management through an AI capability framework. Human Resource Management Review, 33(1). https://doi.org/10.1016/j.hrmr.2022.100899
Chuang, S. (2024). Indispensable skills for human employees in the age of robots and AI. European Journal of Training and Development, 48(1–2), 179–195. https://doi.org/10.1108/EJTD-06-2022-0062
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Hou, K., Hou, T., & Cai, L. (2023). Exploring Trust in Human–AI Collaboration in the Context of Multiplayer Online Games. Systems, 11(5). https://doi.org/10.3390/systems11050217
Hou, T., Li, M., Tan, Y. (Ricky), & Zhao, H. (2024). Physician Adoption of AI Assistant. Manuf. Serv. Oper. Manag., 26, 1639–1655. https://doi.org/10.1287/MSOM.2023.0093
Jiang, N., Liu, X., Liu, H., Lim, E. T. K., Tan, C. W., & Gu, J. (2023). Beyond AI-powered context-aware services: the role of human–AI collaboration. Industrial Management and Data Systems, 123(11), 2771–2802. https://doi.org/10.1108/IMDS-03-2022-0152
Kang, D. Y., Hur, W. M., & Shin, Y. (2023). Smart technology and service employees’ job crafting: Relationship between STARA awareness, performance pressure, receiving and giving help, and job crafting. Journal of Retailing and Consumer Services, 73. https://doi.org/10.1016/j.jretconser.2023.103282
Kerasidou, C., Kerasidou, A., Buscher, M., & Wilkinson, S. (2022). Before and beyond trust: reliance in medical AI. Journal of Medical Ethics, 48(11), 852–856. https://doi.org/10.1136/MEDETHICS-2020-107095
Khaksar, S. M. S., Chu, M. T., Rozario, S., & Slade, B. (2023). Knowledge-based dynamic capabilities and knowledge worker productivity in professional service firms: The moderating role of organizational culture. Knowledge Management Research & Practice, 21(2), 241–258. https://doi.org/10.1080/14778238.2020.1794992
Kong, H., Yin, Z., Baruch, Y., & Yuan, Y. (2023). The impact of trust in AI on career sustainability: The role of employee-AI–AI collaboration and protean career orientation. Journal of Vocational Behavior, 146. https://doi.org/10.1016/j.jvb.2023.103928
Kong, H., Yuan, Y., Baruch, Y., Bu, N., Jiang, X., & Wang, K. (2021). Influences of artificial intelligence (AI) awareness on career competency and job burnout. International Journal of Contemporary Hospitality Management, 33(2), 717–734. https://doi.org/10.1108/IJCHM-07-2020-0789
Kumar, S., & Mittal, S. (2024). Employee learning and skilling in AI embedded organizations – predictors and outcomes. Development and Learning in Organizations, 38(4), 11–15. https://doi.org/10.1108/DLO-11-2023-0249
Liang, X., Guo, G., Shu, L., Gong, Q., & Luo, P. (2022). Investigating the double-edged sword effect of AI awareness on employees’ service innovative behavior. Tourism Management, 92. https://doi.org/10.1016/j.tourman.2022.104564
Makarius, E. E., Mukherjee, D., Fox, J. D., & Fox, A. K. (2020). Rising with the machines: A sociotechnical framework for bringing artificial intelligence into the organization. Journal of Business Research, 120, 262–273. https://doi.org/10.1016/j.jbusres.2020.07.045
Marikyan, D., Papagiannidis, S., Rana, O. F., Ranjan, R., & Morgan, G. (2022). “Alexa, let’s talk about my productivity”: The impact of digital assistants on work productivity. Journal of Business Research, 142, 572–584. https://doi.org/10.1016/j.jbusres.2022.01.015
McGrath, M. J., Duenser, A., Lacey, J., & Paris, C. (2025). Collaborative human-AI trust (CHAI-T): A process framework for active management of trust in human-AI collaboration. Computers in Human Behavior: Artificial Humans, 6, 100200. https://doi.org/10.1016/j.chbah.2025.100200
Morandini, S., Fraboni, F., De Angelis, M., Puzzo, G., Giusino, D., & Pietrantoni, L. (2023). The impact of artificial intelligence on workers’ skills: Upskilling and Reskilling in Organizations. Informing Science, 26, 39–68. https://doi.org/10.28945/5078
Parvez, M. O., Arasli, H., Ozturen, A., Lodhi, R. N., & Ongsakul, V. (2022). Antecedents of human-robot collaboration: theoretical extension of the technology acceptance model. Journal of Hospitality and Tourism Technology, 13(2), 240–263. https://doi.org/10.1108/JHTT-09-2021-0267
Patil, S., & Shankar, H. (2023). Transforming Healthcare: Harnessing the Power of AI in the Modern Era. International Journal of Multidisciplinary Sciences and Arts, 2(2), 60–70. https://doi.org/10.47709/IJMDSA.V2I1.2513
Pinski, M., Adam, M., & Benlian, A. (2023, April 19). AI Knowledge: Improving AI Delegation through Human Enablement. Conference on Human Factors in Computing Systems - Proceedings. https://doi.org/10.1145/3544548.3580794
Rafi, M., Aitken, J. M., Fatah, T. D., & Mailangkay, A. (2024). Analyzing The Impact of Generative AI on IT Employee Performance. 2024 3rd International Conference on Creative Communication and Innovative Technology (ICCIT), 1–7. https://doi.org/10.1109/ICCIT62134.2024.10701171
Simón, C., Revilla, E., Y Jesús Sáenz, M. (2024). Integrating AI in organizations for value creation through Human-AI teaming: A dynamic-capabilities approach. Journal of Business Research, 182?114783. https://doi.org/10.1016/j.jbusres.2024.114783
Stevens, A. F., & Stetson, P. (2023). Theory of trust and acceptance of artificial intelligence technology (TrAAIT): An instrument to assess clinician trust and acceptance of artificial intelligence. Journal of Biomedical Informatics, 148. https://doi.org/10.1016/J.JBI.2023.104550
Theis, S., Jentzsch, S., Deligiannaki, F., Berro, C., Raulf, A. P., & Bruder, C. (2023). Requirements for Explainability and Acceptance of Artificial Intelligence in Collaborative Work. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14050 LNAI, 355–380. https://doi.org/10.1007/978-3-031-35891-3_22
Tian, J., Li, H., Qi, Y., Wang, X., & Feng, Y. (2024). Intelligent medical detection and diagnosis assisted by deep learning. Applied and Computational Engineering, 64(1), 121–126. https://doi.org/10.54254/2755-2721/64/20241356
Upadhyay, N., Upadhyay, S., & Dwivedi, Y. K. (2022). Theorizing artificial intelligence acceptance and the digital entrepreneurship model. International Journal of Entrepreneurial Behavior and Research, 28(5), 1138–1166. https://doi.org/10.1108/IJEBR-01-2021-0052
Wang, F. J., Chiu, W., Tseng, K. F., & Cho, H. (2023). The roles of employee–employee collaboration and employee–customer collaboration in fitness service innovation: a comparison of frontline and non-frontline employees. International Journal of Sports Marketing and Sponsorship, 24(4), 793–813. https://doi.org/10.1108/IJSMS-12-2022-0206
Wang, P., Keat, O. B., & Talib, Z. M. (2024). Evaluation and Regional Differences of Urban Green Development in Henan Province. International Journal of Social Sciences and Public Administration, 3(1), 247–256. https://doi.org/10.62051/ijsspa.v3n1.36
Weisz, E., Herold, D. M., Ostern, N. K., Payne, R., & Kummer, S. (2024). Artificial intelligence (AI) for supply chain collaboration: implications on information sharing and trust. Online Information Review?49?1??164-181. https://doi.org/10.1108/OIR-02-2024-0083
Zhang, G. W., Gong, M., Li, H. J., Wang, S., & Gong, D. X. (2023). The “Trinity” smart hospital construction policy promotes the development of hospitals and health management in China. Frontiers in Public Health, 11. https://doi.org/10.3389/FPUBH.2023.1219407/PDF
Baroni, I., Re Calegari, G., Scandolari, D., & Celino, I. (2022). AI-TAM: a model to investigate user acceptance and collaborative intention in human-in-the-loop AI applications. Hum. Comput., 9(1), 1–21. https://doi.org/10.15346/HC.V9I1.134
Cadavid, J., Piana, D., & Abherve, A. (2024). Building Trust in AI Deployments in Healthcare. 2024 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA), 1–6. https://doi.org/10.1109/ACDSA59508.2024.10467853
Chandio, S. A., Rehman, A. U., Bano, S., Hammed, A., & Hussain, A. (2024). Enhancing Trust in Healthcare: The Role of AI Explainability and Professional Familiarity. The Asian Bulletin of Big Data Management, 4(1). https://doi.org/10.62019/ABBDM.V4I1.100
Chen, C. H., & Lee, W. I. (2024). Exploring Nurses’ Behavioral Intention to Adopt AI Technology: The Perspectives of Social Influence, Perceived Job Stress and Human–Machine Trust. Journal of Advanced Nursing 81?7??3739-3752. https://doi.org/10.1111/JAN.16495
Chowdhury, S., Budhwar, P., Dey, P. K., Joel-Edgar, S., & Abadie, A. (2022). AI-employee collaboration and business performance: Integrating knowledge-based view, socio-technical systems, and organizational socialization framework. Journal of Business Research, 144, 31–49. https://doi.org/10.1016/j.jbusres.2022.01.069
Chowdhury, S., Dey, P., Joel-Edgar, S., Bhattacharya, S., Rodriguez-Espindola, O., Abadie, A., & Truong, L. (2023). Unlocking the value of artificial intelligence in human resource management through an AI capability framework. Human Resource Management Review, 33(1). https://doi.org/10.1016/j.hrmr.2022.100899
Chuang, S. (2024). Indispensable skills for human employees in the age of robots and AI. European Journal of Training and Development, 48(1–2), 179–195. https://doi.org/10.1108/EJTD-06-2022-0062
Ding, L. (2021). Employees’ challenge-hindrance appraisals toward STARA awareness and competitive productivity: a micro-level case. International Journal of Contemporary Hospitality Management, 33(9), 2950–2969. https://doi.org/10.1108/IJCHM-09-2020-1038
Dutta, D., & Mishra, S. K. (2024). Artificial intelligence-based virtual assistant and employee engagement: an empirical investigation. Personnel Review?54?3??913-934. https://doi.org/10.1108/PR-03-2023-0263
Hemmer, P., Schemmer, M., Kuhl, N., Vossing, M., & Satzger, G. (2025). Complementarity in Human-AI Collaboration: Concept, Sources, and Evidence. ArXiv, abs/2404.00029. https://doi.org/10.48550/ARXIV.2404.00029
Hornikel, A., Greiner, C., & Peisl, T. (2021). The Need to Collaborate: Opportunities for Human and AI Co-workers. International Conference on Computer-Human Interaction Research and Applications, CHIRA - Proceedings, 2021-October, 139–148. https://doi.org/10.5220/0010643700003060
Hou, K., Hou, T., & Cai, L. (2023). Exploring Trust in Human–AI Collaboration in the Context of Multiplayer Online Games. Systems, 11(5). https://doi.org/10.3390/systems11050217
Hou, T., Li, M., Tan, Y. (Ricky), & Zhao, H. (2024). Physician Adoption of AI Assistant. Manuf. Serv. Oper. Manag., 26, 1639–1655. https://doi.org/10.1287/MSOM.2023.0093
Jiang, N., Liu, X., Liu, H., Lim, E. T. K., Tan, C. W., & Gu, J. (2023). Beyond AI-powered context-aware services: the role of human–AI collaboration. Industrial Management and Data Systems, 123(11), 2771–2802. https://doi.org/10.1108/IMDS-03-2022-0152
Kang, D. Y., Hur, W. M., & Shin, Y. (2023). Smart technology and service employees’ job crafting: Relationship between STARA awareness, performance pressure, receiving and giving help, and job crafting. Journal of Retailing and Consumer Services, 73. https://doi.org/10.1016/j.jretconser.2023.103282
Kerasidou, C., Kerasidou, A., Buscher, M., & Wilkinson, S. (2022). Before and beyond trust: reliance in medical AI. Journal of Medical Ethics, 48(11), 852–856. https://doi.org/10.1136/MEDETHICS-2020-107095
Khaksar, S. M. S., Chu, M. T., Rozario, S., & Slade, B. (2023). Knowledge-based dynamic capabilities and knowledge worker productivity in professional service firms: The moderating role of organizational culture. Knowledge Management Research & Practice, 21(2), 241–258. https://doi.org/10.1080/14778238.2020.1794992
Kong, H., Yin, Z., Baruch, Y., & Yuan, Y. (2023). The impact of trust in AI on career sustainability: The role of employee-AI–AI collaboration and protean career orientation. Journal of Vocational Behavior, 146. https://doi.org/10.1016/j.jvb.2023.103928
Kong, H., Yuan, Y., Baruch, Y., Bu, N., Jiang, X., & Wang, K. (2021). Influences of artificial intelligence (AI) awareness on career competency and job burnout. International Journal of Contemporary Hospitality Management, 33(2), 717–734. https://doi.org/10.1108/IJCHM-07-2020-0789
Kumar, S., & Mittal, S. (2024). Employee learning and skilling in AI embedded organizations – predictors and outcomes. Development and Learning in Organizations, 38(4), 11–15. https://doi.org/10.1108/DLO-11-2023-0249
Liang, X., Guo, G., Shu, L., Gong, Q., & Luo, P. (2022). Investigating the double-edged sword effect of AI awareness on employees’ service innovative behavior. Tourism Management, 92. https://doi.org/10.1016/j.tourman.2022.104564
Makarius, E. E., Mukherjee, D., Fox, J. D., & Fox, A. K. (2020). Rising with the machines: A sociotechnical framework for bringing artificial intelligence into the organization. Journal of Business Research, 120, 262–273. https://doi.org/10.1016/j.jbusres.2020.07.045
Marikyan, D., Papagiannidis, S., Rana, O. F., Ranjan, R., & Morgan, G. (2022). “Alexa, let’s talk about my productivity”: The impact of digital assistants on work productivity. Journal of Business Research, 142, 572–584. https://doi.org/10.1016/j.jbusres.2022.01.015
McGrath, M. J., Duenser, A., Lacey, J., & Paris, C. (2025). Collaborative human-AI trust (CHAI-T): A process framework for active management of trust in human-AI collaboration. Computers in Human Behavior: Artificial Humans, 6, 100200. https://doi.org/10.1016/j.chbah.2025.100200
Morandini, S., Fraboni, F., De Angelis, M., Puzzo, G., Giusino, D., & Pietrantoni, L. (2023). The impact of artificial intelligence on workers’ skills: Upskilling and Reskilling in Organizations. Informing Science, 26, 39–68. https://doi.org/10.28945/5078
Parvez, M. O., Arasli, H., Ozturen, A., Lodhi, R. N., & Ongsakul, V. (2022). Antecedents of human-robot collaboration: theoretical extension of the technology acceptance model. Journal of Hospitality and Tourism Technology, 13(2), 240–263. https://doi.org/10.1108/JHTT-09-2021-0267
Patil, S., & Shankar, H. (2023). Transforming Healthcare: Harnessing the Power of AI in the Modern Era. International Journal of Multidisciplinary Sciences and Arts, 2(2), 60–70. https://doi.org/10.47709/IJMDSA.V2I1.2513
Pinski, M., Adam, M., & Benlian, A. (2023, April 19). AI Knowledge: Improving AI Delegation through Human Enablement. Conference on Human Factors in Computing Systems - Proceedings. https://doi.org/10.1145/3544548.3580794
Rafi, M., Aitken, J. M., Fatah, T. D., & Mailangkay, A. (2024). Analyzing The Impact of Generative AI on IT Employee Performance. 2024 3rd International Conference on Creative Communication and Innovative Technology (ICCIT), 1–7. https://doi.org/10.1109/ICCIT62134.2024.10701171
Simón, C., Revilla, E., Y Jesús Sáenz, M. (2024). Integrating AI in organizations for value creation through Human-AI teaming: A dynamic-capabilities approach. Journal of Business Research, 182?114783. https://doi.org/10.1016/j.jbusres.2024.114783
Stevens, A. F., & Stetson, P. (2023). Theory of trust and acceptance of artificial intelligence technology (TrAAIT): An instrument to assess clinician trust and acceptance of artificial intelligence. Journal of Biomedical Informatics, 148. https://doi.org/10.1016/J.JBI.2023.104550
Theis, S., Jentzsch, S., Deligiannaki, F., Berro, C., Raulf, A. P., & Bruder, C. (2023). Requirements for Explainability and Acceptance of Artificial Intelligence in Collaborative Work. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14050 LNAI, 355–380. https://doi.org/10.1007/978-3-031-35891-3_22
Tian, J., Li, H., Qi, Y., Wang, X., & Feng, Y. (2024). Intelligent medical detection and diagnosis assisted by deep learning. Applied and Computational Engineering, 64(1), 121–126. https://doi.org/10.54254/2755-2721/64/20241356
Upadhyay, N., Upadhyay, S., & Dwivedi, Y. K. (2022). Theorizing artificial intelligence acceptance and the digital entrepreneurship model. International Journal of Entrepreneurial Behavior and Research, 28(5), 1138–1166. https://doi.org/10.1108/IJEBR-01-2021-0052
Wang, F. J., Chiu, W., Tseng, K. F., & Cho, H. (2023). The roles of employee–employee collaboration and employee–customer collaboration in fitness service innovation: a comparison of frontline and non-frontline employees. International Journal of Sports Marketing and Sponsorship, 24(4), 793–813. https://doi.org/10.1108/IJSMS-12-2022-0206
Wang, P., Keat, O. B., & Talib, Z. M. (2024). Evaluation and Regional Differences of Urban Green Development in Henan Province. International Journal of Social Sciences and Public Administration, 3(1), 247–256. https://doi.org/10.62051/ijsspa.v3n1.36
Weisz, E., Herold, D. M., Ostern, N. K., Payne, R., & Kummer, S. (2024). Artificial intelligence (AI) for supply chain collaboration: implications on information sharing and trust. Online Information Review?49?1??164-181. https://doi.org/10.1108/OIR-02-2024-0083
Zhang, G. W., Gong, M., Li, H. J., Wang, S., & Gong, D. X. (2023). The “Trinity” smart hospital construction policy promotes the development of hospitals and health management in China. Frontiers in Public Health, 11. https://doi.org/10.3389/FPUBH.2023.1219407/PDF
Published
2025-11-23
How to Cite
Song, T., Abdullah, N. ‘Ain B., & Mohd Talib, Z. (2025). The Impact of Employees’ AI Acceptance on Productivity in the Construction of Smart Hospitals: The Mediating Role of Collaboration. Information Management and Business Review, 17(4(I), 212-221. https://doi.org/10.22610/imbr.v17i4(I).4779
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Copyright (c) 2025 Tang Song, Nor ‘Ain Bt Abdullah, Zunirah Mohd Talib
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