The Acceptance of Technology in Human Resource Management : Readiness e-HRD Among Healthcare Workers Using Binary Logistic Regression

  • Dina Mellita Postgraduate Program, Master of Management, University of Bina Darma (ID)
  • Rabin Ibnu Zainal Faculty of Economics, Management Study Program, University of South Sumatra (ID)
  • M. Darrel Rafiansyah Faculty of Economics, University of Pembangunan Nasional “Veteran” Yogyakarta (ID)
Keywords: Technology Readiness Index, Health Professionals, Logistic Regression, UTAUT Method

Viewed = 0 time(s)


This research aims to identify the readiness of healthcare professionals in regional hospitals to use e-HRD technology. The use of technology in HRM functions is essential to maintain the efficiency and capabilities of human resources within an organization. E-HRD platforms are computerized systems that support the effective and efficient management of HR processes within an organization. To assess the efficiency of employing this technology, it is essential to determine the preparedness of the users. The Technology Readiness Index (TRI) proposed by Parasuraman is applied to assess healthcare workers' preparedness for using e-HRD. The UTAUT (Unified theory of acceptance and use of integrated technology) theory is employed to identify technological readiness. A quantitative descriptive research design is employed to identify the readiness of hospital healthcare workers. The data source for this research is primary data obtained through a questionnaire. The population in this study consists of 420 healthcare workers employed in regional hospitals in the city of Palembang. The sampling technique used in this research is proportional stratified random sampling. Logistic regression is employed to assess the preparedness of healthcare professionals in regional hospitals. The research findings indicate that male healthcare workers have a tendency to be 1.843 times more prepared to use e-HRD compared to female healthcare workers. The likelihood of healthcare workers using e-HRD tends to decrease by 0.002 times with increasing age. The research concludes that healthcare professionals in regional hospitals are moderately prepared to use e-HRD technology. The study recommends that healthcare organizations should provide training and support to enhance healthcare workers' readiness to use e-HRD technology.


Athithya, E., Kavitha, A. C., & Muralidhar, S. (2020). Electronic-Human Resource Development (e-HRD) Software for Workforce Training Management–A Case Study of CVRDE. DESIDOC Journal of Library & Information Technology, 40(4), 197–203.
Berkowitz, E. N. (2021). Essentials of health care marketing. Jones & Bartlett Learning.
Blut, M., & Wang, C. (2020). Technology readiness: a meta-analysis of conceptualizations of the construct and its impact on technology usage. Journal of the Academy of Marketing Science, 48, 649–669.
Chung, J. E., Park, N., Wang, H., Fulk, J., & McLaughlin, M. (2010). Age differences in perceptions of online community participation among non-users: An extension of the Technology Acceptance Model. Computers in Human Behavior, 26(6), 1674–1684.
Kaushik, M. K., & Agrawal, D. (2021). Influence of technology readiness in adoption of e-learning. International Journal of Educational Management, 35(2), 483–495.
Li, G., Miao, J., Wang, H., Xu, S., Sun, W., Fan, Y., ... & Wang, W. (2020). Psychological impact on women health workers involved in COVID-19 outbreak in Wuhan: a cross-sectional study. Journal of Neurology, Neurosurgery & Psychiatry.
Li, Song & Jing, L. (2018). Building HR information modelling and risk management: A bayesian networks approach. In 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC 2018), 2001–2004. 10.1109/IMCEC.2018.8469751
Martín, H. S., & Herrero, Á. (2012). Influence of the user ’ s psychological factors on the online purchase intention in rural tourism : Integrating innovativeness to the UTAUT framework. . . Tourism Management, 33(2), 341–350.
Mazman Akar, S. G. (2019). Does it matter being innovative: Teachers’ technology acceptance. Education and Information Technologies, 24(6), 3415–3432.
Mlekus, L., Bentler, D., Paruzel, A. et al. (2020). How to raise technology acceptance: user experience characteristics as technology-inherent determinants. Gruppe. Interaktion. Organisation. Zeitschrift Für Angewandte Organisationspsychologie (GIO), 51(3), 273–283.
Nawaz, N., & Gomes, A. M. (2017). ). Human resource information system: a review of previous studies. Journal of Management Research, 9(3).
Odendaal, W. A., Watkins, J. A., Leon, N., Goudge, J., Griffiths, F., Tomlinson, M., & Daniels, K. (2020). Health workers’ perceptions and experiences of using mHealth technologies to deliver primary healthcare services: a qualitative evidence synthesis. Cochrane Database of Systematic Reviews, 3.
Padilla-Meléndez, A., Del Aguila-Obra, A. R., & Garrido-Moreno, A. (2013). Perceived playfulness, gender differences and technology acceptance model in a blended learning scenario. Computers & Education, 63, 306–317.
Rony, Z. T. (2019). An effective promotion strategy for managers in Era Disruption. Asia Proceedings of Social Sciences, 4(2), 57–59.
Rony, Z. T., Mangkupradja, D. R., & Pramukty, R. (2023). THE ROLE OF TRANSFORMATIONAL LEADERSHIP IN EMPLOYEE PERFORMANCE: A SYSTEMATIC LITERATURE REVIEW AT XYZ UNIVERSITY. International Journal of Accounting, Management, Economics and Social Sciences (IJAMESC), 1(4), 331–342.
Sharma, S., & Pratt, S. (2020). Does Consumers ’ Intention to Purchase Travel Online Differ Across Generations ? Empirical Evidence from. Australasian Journal of Information Systems, 24, 1–31.
Stone, D. L., Deadrick, D. L., Lukaszewski, K. M., & Johnson, R. (2015). The influence of technology on the future of human resource management. . . Human Resource Management Review, 25(2), 216–231.
V. Venkatesh, M. G. Morris, G. B. Davis, and F. D. D. (2003). User acceptance of information technology: oward a unified view. Management Information Systems, 27(3), 425–478.
V. Venkatesh and F. D. Davis. (2000). Theoretical extension of the technology acceptance model: four longitudinal field studies. Management Science, 46(2), 186–204.
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178.
Venkatesh, V. (2000). Determinants of Perceived Ease of Use : Integrating Control , Intrinsic Motivation , and Emotion into the Technology Acceptance Model. Information Systems Research, 11(4), 342–365.
How to Cite
Mellita, D., Zainal, R. I., & Rafiansyah, M. D. (2023). The Acceptance of Technology in Human Resource Management : Readiness e-HRD Among Healthcare Workers Using Binary Logistic Regression . JINAV: Journal of Information and Visualization, 4(2).