An Investigation on Behavioral Intention toward Usage of Personal Health Assistant Service and Technology Among Patients in Bangkok, Thailand
Article Sidebar
Main Article Content
Abstract
Purpose: The study aims to investigate the determinants of behavioral intention toward the usage behavior of personal health assistant services and technology for hypertension patients of a private hospital in Bangkok. Eight variables conform to the conceptual framework, including perceived usefulness, perceived ease of use, attitude toward using, customer satisfaction, social influencing, facilitating condition, behavioral intention, and usage behavior. Research design, data, and methodology: The data were collected from 500 participants. The sampling techniques used were purposive, stratified random, and convenience samplings. Before collecting the data, the index of item objective congruence (IOC) and Cronbach’s Alpha coefficient value (pilot testing) of 50 samples were applied. The main statistical approaches involve confirmatory factor analysis (CFA) and structural equation modeling (SEM). Results: Perceived usefulness has a significant influence on attitude toward use. Attitude toward use has a significant influence on customer satisfaction and behavioral intention. Social influence and facilitating conditions significantly influence behavioral intention. In addition, behavioral intention significantly influences use behavior. On the other hand, perceived ease of use does not significantly influence attitudes toward using personal healthcare assistant services. Conclusions: Healthcare service providers can enhance the purchase intention of digital healthcare technology, which could remarkably benefit patients by tracking and monitoring their health conditions.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
The submitting author warrants that the submission is original and that she/he is the author of the submission together with the named co-authors; to the extend the submission incorporates text passages, figures, data, or other material from the work of others, the submitting author has obtained any necessary permission.
Articles in this journal are published under the Creative Commons Attribution License (CC-BY What does this mean?). This is to get more legal certainty about what readers can do with published articles, and thus a wider dissemination and archiving, which in turn makes publishing with this journal more valuable for you, the authors.
References
Abate, F. R. (1999). The Oxford American dictionary of current English (1st ed.). Oxford University.
Aggelidis, V. P., & Chatzoglou, P. D. (2009). Using a modified technology acceptance model in hospitals. International Journal of Medical Informatics, 78(2), 115-126.
Ajzen, I., & Fishbein, M. (1980). Understanding Attitudes and Prediction Social Behavior. Prentice-Hall.
Alam, M. Z., Hoque, M. R., Hu, W., & Barua, Z. (2020). Factors influencing the adoption of mHealth services in a developing country: a patient-centric study. International Journal of Information Management, 50, 128-143, https://doi.org/10.1016/j.ijinfomgt.2019.04.016
Al-Rahmi, W. M., Yahaya, N., Aldraiweesh, A. A., Alamri, M. M., Aljarboa, N. A., Alturki, U., & Aljeraiwi, A. A. (2019). Integrating Technology Acceptance Model with Innovation Diffusion Theory: An Empirical Investigation on Students’ Intention to Use E-Learning Systems. IEEE Access, 7, 26797- 26809. https://doi.org/10.1109/ACCESS.2019.2899368
Alraja, M. N. (2015). User Acceptance of Information Technology: A field study of an e-mail system Adoption from the Individual students’ Perspective. Mediterranean Journal of Social Sciences, 6(6), 1-19.
Baron, A., & Banaji, M. (2006). The Development of Implicit Attitudes. Evidence of Race Evaluations From Ages 6 and 10 and Adulthood. Psychological science, 17(1), 53-8. https://doi.org/10.1111/j.1467-9280.2005.01664.x
Barsky, J., & Nash, L. (2003). Customer satisfaction: Applying concepts to industry- wide measures. The Cornell Hotel and Restaurant Administration Quarterly, 44(4), 173-183.
Barua, Z., & Barua, A. (2021). Acceptance and usage of mHealth technologies amid COVID-19 pandemic in a developing country: the UTAUT combined with situational constraint and health consciousness. Journal of Enabling Technologies, 15(1), 1-22. https://doi.org/10.1108/JET-08-2020-0030
Boontarig, W., Chutimaskul, W., Chongsuphajaisiddhi, V., & Papasratorn, B. (2012). Factors influencing the Thai elderly intention to use smartphone for e-Health services [Paper presentation] IEEE symposium on humanities, science and engineering research.
Budu, K. W. A., Yinping, M., & Mireku, K. K. (2018). Investigating The Effect of Behavioral Intention on E-learning Systems Usage: Empirical Study on Tertiary Education Institutions in Ghana. Mediterranean Journal of Social Sciences, 9(3), 201- 216. https://doi.org/10.2478/mjss-2018-0062
Chan, F. K. Y., Thong, J. Y. L., Venkatesh, V., Brown, S. A., Hu, P. J. H., & Tam, K. Y. (2010). Modeling citizen satisfaction with mandatory adoption of an E-Government technology. Journal of the Association for Information Systems, 11(10), 519-549. https://aisel.aisnet.org/jais/vol11/iss10/2
Childers, T. L., Carr, C. L., Peck, J., & Carson, S. (2001). Hedonic and utilitarian motivations for online retail shopping behavior. Journal of Retailing, 77(4), 511-535.
Choi, N. H., & Kim, Y. S. (2011). The roles of hotel identification on customer-related behavior. Nankai Business Review International, 2(3), 240-256.
Cimperman, M., Brencic, M. M., & Trkman, P. (2016). Analyzing older users’ home telehealth services acceptance behavior – applying an extended UTAUT model. International Journal of Medical Informatics, 90, 22-31.
Dabholkar, P. A., & Bagozzi, R. P. (2002). An attitudinal model of technology-based self-service: moderating effects of consumer traits and situational factors. Journal of the Academy of Marketing Science, 30(3), 184-201.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 318-339.
Davis, F. D., Bagozzi, R., & Warshaw, P. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35(8), 982-1003. https://doi.org/10.1287/mnsc.35.8.982
Delegated. (2020). Virtual Assistant Statistics You Need to Pay Attention To in 2020. https://www.delegated.com/blog/virtual-assistant-statistics
Didyasarin, H., Vongurai, R., & Inthawadee, S. (2017). The factors impact attitude toward using and customer satisfaction with elderly health care mobile application services: a case study of people in Bangkok metropolitan, Thailand. AU-GSB E-JOURNAL, 10(1), 167-176. http://www.assumptionjournal.au.edu/index.php/AU-GSB/article/view/2870
Egan, T. M., Yang, B., & Bartlett, K. R. (2004). The effects of organizational learning culture and job satisfaction on motivation to transfer learning and turnover intention. Human Resource Development Quarterly, 15(3), 279-301. https://doi.org/10.1002/hrdq.1104
Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research (1st ed.). Addison-Wesley.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312
Ghauri, P., & Gronhaug, K. (2005). Research Methods in Business Studie (4th ed.). FT/Prentice Hall.
Hair, J., Hult, T., Ringle, C., & Sarstedt, M. (2014). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (3rd ed.). Sage Publications, Inc.
Hair, J. F., Babin, B., Money, A. H., & Samouel, P. (2003). Essential of business research methods (3rd ed.). John Wiley & Sons.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate Data Analysis (6th ed.). Pearson Education.
Hambleton, R. K., Swaminathan, H., Algina, J., & Coulson, D. B. (1978). Criterion-referenced testing and measurement: A review of technical issues and developments. Review of Educational Research, 48(1), 1–47. https://doi.org/10.2307/1169908
Haslam, S. A., McGarty, C., & Turner, J. C. (1996). Salient group memberships and persuasion: The role of social identity in the validation of beliefs. In J. L. Nye & A. M. Brower (Eds.), What's social about social cognition? Research on socially shared cognition in small groups (pp. 29–56). Sage Publications. Inc. https://doi.org/10.4135/9781483327648.n2
Holden, R. J., & Karsh, B. T. (2010). Methodological Review: The Technology Acceptance Model: Its past and its future in health care. Journal of Biomedical Informatics, 43(1), 159–172. https://doi.org/10.1016/j. jbi.2009.07.002 PMID:19615467
Kamaghe, J., Luhanga, E., & Kisangiri, M. (2020). The challenges of adopting M-learning assistive technologies for visually impaired learners in higher learning institution in Tanzania. International Journal of Emerging Technologies in Learning, 15(1), 140-151. https://doi.org/10.3991/ijet.v15i01.11453
Kandampully, J., & Suhartanto, D. (2000). Customer loyalty in hotel industry: the role of customer satisfaction and image. International Journal of Contemporary Hospitality Management, 12(6), 346-351.
Kleijnen, M., Wetzels, M., & de Ruyter, K. (2004). Consumer acceptance of wireless finance. Journal of Financial Services Marketing, 8(3), 206-217.
Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). The Guilford Press.
KResearch. (2021). Growth potential for health tech in Thailand amid rising demand for health services (Current Issue No.3243). https://www.kasikornresearch.com/en/analysis/k-econ/business/Pages/Health-Tech-z3243.aspx
Lee, J. W. (2017). Critical Factors Affecting Consumer Acceptance of Online Health Communication: An Application of Service Quality Models. Journal of Asian Finance, Economics and Business, 4(3), 85-94. https://doi.org/10.13106/jafeb.2017. vol4.no3.85
Leppäniemi, M., Jayawardhena, C., Karjaluoto, H., & Harness, D. (2017). Unlocking behaviors of long-term service consumers: the role of action inertia. Journal of Service Theory and Practice, 27(1), 270-291. https://doi.org/10.1108/JSTP-06-2015-0127
Lin, J. C., & Chang, H. (2011). The role of technology readiness in self‐service technology acceptance. Managing Service Quality: An International Journal, 21(4), 424-444. https://doi.org/10.1108/09604521111146289
McDonald, R. P., & Ho, M. H. (2002). Principles and practice in reporting structural equation analyses. Psychology Methods, 7(1), 64–82.
Moudud-Ul-Huq, S., Sultana Swarna, R., & Sultana, M. (2021). Elderly and middle-aged intention to use m-health services: an empirical evidence from a developing country. Journal of Enabling Technologies, 15(1), 23-39. https://doi.org/10.1108/JET-04-2020-0018
Neslin, S. A., & Shankar, V. (2009). Key Issues in Multichannel Customer Management: Current Knowledge and Future Directions. Journal of Interactive Marketing, 23(1), 70-81. https://doi.org/10.1016/j.intmar.2008.10.005
Nysveen, H., Pedersen, P. E., & Thorbjørnsen, H. (2005). Intentions to use mobile services: antecedents and cross-service comparisons. Journal of the Academy of Marketing Science, 33(3), 330-346.
Pan, S., & Jordan-Marsh, M. (2010). Internet use intention and adoption among Chinese older adults: from the expanded technology acceptance model perspective. Computers in Human Behavior, 26(5), 1111-1119.
Pedroso, R., Zanetello, L., Guimaraes, L., Pettenon, M., Goncalves, V., Scherer, J., Kessler, F., & Pechansky, F. (2016). Confirmatory factor anlaysis (CFA) of the crack use relapse scale (CURS). Archives of Clinical Psychiatry, 43(3), 37-40.
Robinson, L., Jr., Marshall, G. W., & Stamps, M. B. (2005). Sales force use of technology: antecedents to technology acceptance. Journal of Business Research, 58(12), 1623-1631. https://doi.org/10.1016/j.jbusres.2004.07.010.
Soper, D. S. (2022, May 24). A-priori Sample Size Calculator for Structural Equation Models. Danielsoper. www.danielsoper.com/statcalc/default.aspx
Stibe, A. (2015). Advancing Typology of Computer-Supported Influence: Moderation Effects in Socially Influencing Systems. In T. MacTavish & S. Basapur (Eds.), Persuasive Technology (pp. 251–262). Springer.
Sun, Y., Wang, N., Guo, X., & Peng, Z. (2013). Understanding the acceptance of mobile health services: a comparison and integration of alternative models. Journal of Electronic Commerce Research, 14(2), 183.
Taylor, S., & Todd, P. (1995). Assessing IT usage: the role of prior experience. MIS Quarterly, 19(4), 561-570. https://doi.org/10.2307/249633
To, W. M., & Tang, M. N. F. (2019). Computer-based course evaluation: An extended technology acceptance model. Educational Studies, 45(2), 131–144.
Tran, V. D. (2020). Assessing the Effects of Service Quality, Experience Value, Relationship Quality on Behavioral Intentions. Journal of Asian Finance, Economics and Business, 7(3), 167-175. https://doi.org/10.13106/jafeb.2020.vol7.no3.167
Venes, D. (2001). Taber’s cyclopedic medical dictionary (19th ed.). F.A. Davis Company.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: toward a unified view. MIS Quarterly, 27(3), 425-478.
Venkatesh, V., Thong, J. Y., & 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, https://doi.org/10.2307/41410412
Yuan, D., Rahman, M. K., Issa Gazi, M. A., Rahaman, M. A., Hossain, M. M., & Akter, S. (2021). Analyzing of User Attitudes Toward Intention to Use Social Media for Learning. SAGE Open, 11(4), 1-13. https://doi.org/10.1177/21582440211060784
Zeithaml, V. A., Bitner, M. J., & Gremler, D. D. (2006). Service Marketing (4th ed.). McGraw-Hill.
Zhong, K., Feng, D., Yang, M., & Jaruwanakul, T. (2022). Determinants of Attitude, Satisfaction and Behavioral Intention of Online Learning Usage Among Students During COVID-19. AU-GSB E-JOURNAL, 15(2), 49-57. https://doi.org/10.14456/augsbejr.2022.71