Influential Factors of Usage Behavior of Potential Hypertension Patients to Use Personal Health Assistant Service and Technology in a Private Hospital in Bangkok

Authors

  • Nitisatn Wisarnses

DOI:

https://doi.org/10.14456/shserj.2023.49
CITATION
DOI: 10.14456/shserj.2023.49
Published: 2023-12-13

Keywords:

Customer Satisfaction, Social Influence, Facilitating Condition, Behavioral Intention, Usage Behavior

Abstract

Purpose: The study aims to investigate the determinants of behavioral intention toward using personal health assistant services and technology for potential hypertension patients in a private hospital in Bangkok. The developed conceptual framework contains perceived usefulness, perceived ease of use, attitude toward using, customer satisfaction, social influence, facilitating condition, behavioral intention, and usage behavior. Research design, data, and methodology: 500 participants involved in this study, applying purposive, stratified random, and convenience samplings. To assess content validity and reliability test, the index of item objective congruence (IOC) and Cronbach’s Alpha coefficient value (pilot testing) of 50 samples were conducted. The research applied statistical method, using confirmatory factor analysis (CFA) and structural equation modeling (SEM). Results: Perceived ease of use significantly influences attitudes toward using. Attitude toward use has a significant influence on customer satisfaction but has no significant influence on behavioral intention. Social influence and facilitating conditions significantly influence behavioral intention. Furthermore, behavioral intention significantly influences usage behavior. Nevertheless, perceived usefulness has no significant influence on attitude toward use. Conclusions: The leading enterprises in healthcare industry should push effort more than even to redefine health technology by moving to the value-based care model for patients, considering significant factors enhancing usage behavior.

Author Biography

Nitisatn Wisarnses

Ph.D. in Technology Education and Management, Graduate School of Business and Advanced Technology Management, Assumption University of Thailand.

References

Ahadzadeh, A. S., Sharif, S. P., Ong, F. S., & Khong, K. W. (2015). Integrating health belief model and technology acceptance model: an investigation of health-related internet use. Journal of Medical Internet Research, 17(2), 45. https://doi.org/10.2196/jmir.3564

Alalwan, A. A., Dwivedi, Y. K., & Rana, N. P. (2017). Factors influencing adoption of mobile banking by Jordanian bank customers: extending UTAUT2 with trust. International Journal of Information Management, 37(3), 99-110.

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

Andaleeb, S. S., & Conway, C. (2006). Customer satisfaction in the restaurant industry: an examination of the transaction-specific model. Journal of Services Marketing, 20(1), 3-11.

Ardies, J., De Maeyer, S., & Gijbels, D. (2013). Reconstructing the pupil’s attitude towards technology-survey. Design and Technology Education: An International Journal, 18(1), 8-19.

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

Bollen, K. (1989). Structural equations with latent variables (1st ed.). John Wiley & Sons.

Brown, S. A. (1992). Total Quality Service: How Organizations Use It to Create a Competitive Advantage (1st ed.). Prentice Hall Canada Inc.

Burns, N., & Grove, S. K. (1993). The practice of nursing research conduct, critique, and utilization (2nd ed.). WB Saunders Company.

Cho, H., & Fiorito, S. S. (2009). Acceptance of online customization for apparel shopping. International Journal of Retail & Distribution Management, 37(5), 389-407.

Cho, H., Chung, S., & Filippova, A. (2015). Perceptions of Social Norms Surrounding Digital Piracy: The Effect of Social Projection and Communication Exposure on Injunctive and Descriptive Social Norms. Computers in Human Behavior, 48, 506-515. https://doi.org/10.1016/j.chb.2015.02. 018

Choi, N. H., & Kim, Y. S. (2011). The roles of hotel identification on customer-related behavior. Nankai Business Review International, 2(3), 240-256.

Cialdini, R. B. (2003). Crafting Normative Messages to Protect the Environment. Current Directions in Psychological Science, 12(4), 105-109. https://doi.org/10.1111/1467-8721.01242

Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297-334

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.

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

Dünnebeil, S., Sunyaev, A., Blohm, I., Leimeister, J., & Krcmar, H. (2012). Determinants of physicians’ technology acceptance for e-health in ambulatory care. International Journal of Medical Informatic, 81(11), 746-760.

Fan, X., & Sivo, S. A. (2007). Sensitivity of fit indices to model misspecification and model types. Multivariate Behavioral Research, 42(3), 509-529.

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), 3950. https://doi.org/10.2307/3151312

Gehrt, K. C., & Yan, R. N. (2004). Situational, consumer, and retailer factors affecting internet, catalog, and store shopping. International Journal of Retail & Distribution Management, 32(1), 5-18. https://doi.org/10.1108/09590550410515515

Giovanis, A., Assimakopoulos, C., & Sarmaniotis, C. (2019). Adoption of mobile self-service retail banking technologies: the role of technology, social, channel and personal factors. International Journal of Retail & Distribution Management, 47(9), 894-914. https://doi.org/10.1108/IJRDM-05- 2018-0089

Global Data. (2020, May 20). Thailand’s focus to promote use of technology in healthcare will have significant impact. Global Data. https://www.globaldata.com/thailands-focus-to-promote -use-of-technology-in-healthcare-will-have-significant-impact -says-globaldata/

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.

Hair, J., Hult, T., Ringle, C., & Sarstedt, M. (2014). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (3rd ed.). Sage Publications.

Hampshire, C. (2017). A mixed-methods empirical exploration of UK consumer perceptions of trust, risk, and usefulness of mobile payments. International Journal of Banking Marketing, 35(3), 1-34.

Hsiao, S. J., & Tseng, H. T. (2020). The Impact of the Moderating Effect of Psychological Health Status on Nurse Healthcare Management Information System Usage Intention. Healthcare, 8(1), 1-28. https://doi.org/10.3390/healthcare8010028

Hülür, G., & Macdonald, B. (2020). Rethinking Social Relationships in old age: Digitalization and the Social Lives of Older Adults. American Psychologist, 75(4), 554-566. https://doi.org/10.1037/amp0000604

Igbaria, M., Iivari, J., & Maragahh, H. (1995). Why do individuals use computer technology? A Finnish case study. Information & Management, 29(5), 227- 238. https://doi.org/10.1016/0378-7206(95)00031-0

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.

Kim, S. H. (2008). Moderating effects of job relevance and experience on mobile wireless technology acceptance: Adoption of a smartphone by individuals. Information & Management, 45(6), 387-393. https://doi.org/10.1016/j.im.2008.05.002

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.

Kotler, P. (2015). Marketing Management (15th ed.). Prentice Hall.

KResearch. (2021, July 14). 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, C. H. (2009). A study of the user attitude, satisfaction and behavioral intention of HR service website with technology acceptance model [Unpublished Master Dissertation]. Shu-Te University, Taiwan.

Lee, C., Tsao, C., & Chang, W. (2015). The relationship between attitude toward using and customer satisfaction with mobile application services: An empirical study from the life insurance industry. Journal of Enterprise Information Management, 28(5), 680-697. https://doi.org/10.1108/JEIM-07-2014-0077

Lee, Y., Kozar, K. A., & Larsen, K. R. (2003). The Technology Acceptance Model: Past, Present, and Future. CAIS, 12(1), 752-780.

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

Maloney, E. K., Lapinski, M. K., & Neuberger, L. (2013). Predicting Land use Voting Behavior: Expanding our Understanding of the Influence of Attitudes and Social Norms. Journal of Applied Social Psychology, 43(12), 2377-2390. https://doi.org/10.1111/jasp.12186

Marakarkandy, B., Yajnik, N., & Dasgupta, C. (2017). Enabling internet banking adoption: An empirical examination with an augmented technology acceptance model (TAM). Journal of Enterprise Information Management, 30(2), 263-294.

Mathieson, K., Peacock, K. M. E., & Chin, W. W. (2001). Extending the Technology Acceptance Model: The Influence of Perceived User Resources. The DATA BASE for Advances in Information Systems, 32(3), 86-112.

Metsärinne, M., & Kallio, M. (2015). How are students’ attitudes related to learning outcomes? International Journal of Technology and Design Education, 26(3), 1-19. https://doi.org/10.1007/s10798-015- 9317-0

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

Nguyen, M., Fujioka, L., Wentlandt, K., Onabajo, N., Wong, I., Bhatia, R. S., Bhattacharyya, O., & Stamenova, V. (2020). Using the technology acceptance model to explore health provider and administrator perceptions of the usefulness and ease of using technology in palliative care. BMC Palliative Care, 19(138), 1-9. https://doi.org/10.1186/s12904-020-00644-8

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.

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.

Rezaei, D., Khosravani, A., & Babakhani, L. (2015). An investigation of effective factors on customers' intentions to use mobile banking. International Journal of Scientific Management and Development, 3(2), 844-852.

Saheb, T. (2020). An empirical investigation of the adoption of mobile health applications: integrating big data and social media services. Health and Technology, 10(5), 1-15. https://doi.org/10.1007/s12553-020-00422-9

Shiferaw, K. B., & Mehari, E. A. (2019). Modeling predictors of acceptance and use of electronic medical record system in a resource limited setting: Using modified UTAUT model. Informatics in Medicine Unlocked, 17(1), 1-9.

Soper, D. S. (2022, May 24). A-priori Sample Size Calculator for Structural Equation Models. Danielsoper. www.danielsoper.com/statcalc/default.aspx

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

Tubaishat, A. (2017). Perceived usefulness and perceived ease of use of electronic health records among nurses: Application of Technology Acceptance Model. Informatics for Health and Social Care, 43(3), 1-11. https://doi.org/10.1080/17538157.2017.1363761

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204.

Venkatesh, V., Morris, M. G., & Ackerman, P. L. (2000). A longitudinal field investigation of gender differences in individual technology adoption decision-making processes. Organizational Behavior and Human Decision Processes, 83(1), 33-60, https://doi.org/10.1006/obhd.2000.2896

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

Vroman, K. G., Arthanat, S., & Lysack, C. (2015). Who Over 65 is Online?’ Older Adults’ Dispositions Toward Information Communication Technology. Computers in Human Behavior, 43, 156-166. https://doi.org/10.1016/j.chb.2014.10.018

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

Downloads

Published

2023-12-13

How to Cite

Wisarnses, N. (2023). Influential Factors of Usage Behavior of Potential Hypertension Patients to Use Personal Health Assistant Service and Technology in a Private Hospital in Bangkok. Scholar: Human Sciences, 15(2), 227-237. https://doi.org/10.14456/shserj.2023.49