Behavioral Intentions of Radio Frequency Identification Users at Hospitals in Thailand An Application of the Unified Theory of Acceptance

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Passarin Phalitnonkiat
Siwaporn Kunnapapdeelert
Krittipat Pitchayadejanant


The aims of this study were to investigate the behavioral intentions of hospital staff in using Radio Frequency Identification by applying the Unified Theory of Acceptance and Use of Technology 2. Data were collected from 404 respondents who worked in the hospitals and had experience using Radio Frequency Identification. Confirmatory Factor Analysis and the Structure Equation Modelling technique, based on the Unified Theory of Acceptance and Use of Technology 2 model, were used to test among seven hypotheses (performance expectancy, effort expectancy, social influence, facilitating condition, hedonic motivation, price value, and habit) affecting Radio Frequency Identification users’ behavioral intentions. The results revealed that perceived utility and hedonic motivation had a strong positive influence over behavioral intention. The current findings could help hospital management teams understand the use of Radio Frequency Identification in healthcare.


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Brown, S. & Venkatesh, V. (2005). Model of adoption of technology in households: A baseline model test and extension incorporating household life cycle. MIS quarterly, 29(4), 399–426.

Carvalho, J., Rocha, Á., Van De Wetering, R., & Abreu, A. (2019). A maturity model for hospital information systems. Journal of Business Research, 94, 388–399.

Cheng, C., & Kuo, Y. (2016). RFID analytics for hospital ward management. Flexible Services and Manufacturing Journal, 28(4), 593–616.

Cheung, R., & Vogel, D. (2013). Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers & Education, 63(1), 160–175.

Daim, T., Basoglu, N., & Tanoglu, I. (2010). A critical assessment of information technology adoption: Technical, organizational and personal perspectives. International Journal of Business Information Systems, 6(3), 315–335.

Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 13(3), 319–340.

Davis, F., Bagozzi, R., & Warshaw, P. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003.

Duroc, Y., & Tedjini, S. (2018). RFID: A key technology for humanity. Comptes Rendus Physique, 19(1–2), 64–71.

Fisher, J., & Monahan, T. (2008). Tracking the social dimensions of RFID systems in hospitals. International Journal of Medical Informatics, 77, 176–183.

Gulcharan, N., Daud, H., Nor, N., Ibrahim, T., & Nyamasvisva, E. (2013). Limitation and solution for healthcare network using RFID technology: A review. Procedia Technology, 11, 565–571.

Haddara, M., & Staaby, A. (2018). RFID applications and adoptions in healthcare: a review on patient safety. Procedia Computer Science, 138, 80–88.

Hair, J., Black, W., Babin, B., Anderson, R., & Tatham, R. (2006). Multivariate data analysis (6th ed.). Prentice Hall.

Hatz, M., Sonnenschein, T., & Blankart, C. (2017). The PMA scale: A measure of physicians’ motivation to adopt medical devices. Value in Health, 20(4), 533–541.

He, X., & Mu, Q. (2012). How Chinese firms learn technology from transnational corporations: A comparison of the telecommunication and automobile industries. Journal of Asian Economics, 23(3), 270–287.

Islam, Z., Low, P., & Hasan, I. (2013). Intention to use advanced mobile phone services (AMPS). Management Decision, 51(4), 824–838.

Jambulingam, M. (2013). Behavioural intention to adopt mobile technology among tertiary students. World Applied Sciences Journal, 22(9), 1262–1271.

Kim, S., & Malhotra, N. (2005). A longitudinal model of continued IS use: An integrative view of four mechanisms underlying post-adoption phenomena. Management Science, 51(5), 741–755.

Lai, H., Lin, I., & Tseng, L. (2014). High-level managers’ considerations for RFID adoption in hospitals: An empirical study in Taiwan. Journal of Medical Systems, 38(3), 1–17.

Leong, L., Hew, T., Tan, G., & Ooi, K. (2013). Predicting the determinants of the NFC-enabled mobile credit card acceptance: A neural networks approach. Expert Systems with Applications, 40(14), 5604–5620.

Lu, H., & Weng, C. (2018). Smart manufacturing technology, market maturity analysis and technology roadmap in the computer and electronic product manufacturing industry. Technological Forecasting and Social Change, 133(C), 85–94.

Macedo, I. (2017). Predicting the acceptance and use of information and communication technology by older adults: An empirical examination of the revised UTAUT2. Computers in Human Behavior, 75, 935–948.

Mafe, C., Blas, S., & Tavera-Mesías, J. (2010). A comparative study of mobile messaging services acceptance to participate in television programmes. Journal of Service Management, 21(1), 69–102.

Namahoot, K., & Laohavichien, T. (2018). Assessing the intentions to use internet banking: The role of perceived risk and trust as mediating factors. International Journal of Bank Marketing, 36(2), 256–276.

Patcharanarumol, W., Pongutta, S., Witthayapipopsakul, W., Viriyathorn, S., & Tangcharoensathien, V. (2018). In H. Legido-Quigley & N. Asgari-Jirhandeh (Eds.), Resilient and people-centred health systems: Progress, challenges and future directions in Asia. Comparative Country Studies, 3(1), 346–373. iris/handle/10665/276045

Rabaa’i, A. (2017). The use of UTAUT to investigate the adoption of e-government in Jordan: A cultural perspective. International Journal of Business Information Systems, 24(3), 285–315.

Roper, K., Sedehi, A., & Ashuri, B. (2015). A cost-benefit case for RFID implementation in hospitals: Adapting to industry reform. Facilities, 33(5/6), 367–388.

Saafein, O., & Shaykhian, G. (2014). Factors affecting virtual team performance in telecommunication support environment. Telematics and Informatics, 31(3), 459–462.

Sin, A., Zailani, S., Iranmanesh, M., & Ramayah, T. (2015). Structural equation modelling on knowledge creation in Six Sigma DMAIC project and its impact on organizational performance. International Journal of Production Economics, 168, 105–117.

Taylor, D., Voelker, T., & Pentina, I. (2011). Mobile application adoption by young adults: A social network perspective. International Journal of Mobile Marketing, 6(2), 60–70.

Teo, T., & Noyes, J. (2014). Explaining the intention to use technology among pre-service teachers: A multi-group analysis of the unified theory of acceptance and use of technology. Interactive Learning Environments, 22(1), 51–66.

Tonglet, M., Phillips, P., & Bates, M. (2004). Determining the drivers for householder pro-environmental behaviour: Waste minimisation compared to recycling. Resources, Conservation and Recycling, 42(1), 27–48.

Van der Togt, R., Bakker, P., & Jaspers, M. (2011). A framework for performance and data quality assessment of Radio Frequency IDentification (RFID) systems in health care settings. Journal of Biomedical Informatics, 44(2), 372–383.

Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.

Venkatesh, V., Thong, J., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. Forthcoming in MIS Quarterly, 36(1), 157–178.

Woodruff, R., & Gardial, S. (1996). Know your customer: New approaches to customer value and satisfaction. Blackwell Business.

Yao, W., Chu, C., & Li, Z. (2011). Leveraging complex event processing for smart hospitals using RFID. Journal of Network and Computer Applications, 34(3), 799–810.

Yazici, H. (2014). An exploratory analysis of hospital perspectives on real time information requirements and perceived benefits of RFID technology for future adoption. International Journal of Information Management, 34(5), 603–621.

Zeithaml, V. (1988). Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. The Journal of Marketing, 52(3), 2–22.