FROM INNOVATION ADOPTION TO SERVICE RE-INTENTION VIA ELECTRONIC WORD OF MOUTH: A REGRESSION STUDY IN DIGITAL HEALTH
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Abstract
This study examines whether electronic word of mouth (eWOM) serves as the behavioral bridge that links innovation adoption to service re-intention in digital health. Using survey data from 400 users, Partial Least Squares regression was applied to test the proposed model. The findings show strong support for the pathway: innovation adoption encourages positive eWOM, which in turn strengthens intentions to reuse digital health services. Adoption also exerts a substantial indirect effect on re-intention, highlighting the mediating role of eWOM. These results extend continuance models by introducing social proof as a key mechanism between initial adoption and sustained use. From a managerial perspective, the study underscores the need to engineer shareable service quality, facilitate effortless feedback, and ensure rapid recovery to mitigate negative eWOM. Overall, managing eWOM emerges as a pivotal strategy for transforming initial uptake into long-term, trust-based engagement with digital health services.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
บทความที่ได้รับการตีพิมพ์เป็นลิขสิทธิ์ของคณะวิทยาการจัดการ มหาวิทยาลัยราชภัฏอุดรธานี
ข้อความที่ปรากฏในบทความแต่ละเรื่องในวารสารวิชาการเล่มนี้ ไม่ใช่ความคิดเห็นและความรับผิดชอบของผู้จัดทำ บรรณาธิการ กองบรรณาธิการ และคณะวิทยาการจัดการ มหาวิทยาลัยราชภัฏอุดรธานี ความรับผิดชอบด้านเนื้อหาและการตรวจร่างบทความแต่ละเรื่องเป็นความคิดเห็นของผู้เขียนบทความแต่ละท่าน
References
Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370.
Cheung, C. M. K., & Thadani, D. R. (2012). The impact of electronic word-of-mouth communication: A literature analysis and integrative model. Decision Support Systems, 54(1), 461–470.
Estrela, M., Semedo, G. M., Roque, F., Ferreira, P. L., & Herdeiro, M. T. (2023). Sociodemographic determinants of digital health literacy: A systematic review and meta-analysis. International Journal of Medical Informatics, 177, Article 105124.
Godes, D., & Mayzlin, D. (2004). Using online conversations to study word-of-mouth communication. Marketing Science, 23(4), 545–560.
Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004). Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the Internet? Journal of Interactive Marketing, 18(1), 38–52.
Pavlou, P. A., & Gefen, D. (2004). Building effective online marketplaces with institution-based trust. Information Systems Research, 15(1), 37–59.
Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
Shaver, J. (2022). The state of telehealth before and after the COVID-19 pandemic. Primary Care: Clinics in Office Practice, 49(4), 517–530.
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.
Yang, S., Chen, X., & Li, H. (2025). Understanding inequalities in mHealth utilization: A systematic review and meta-analysis. Journal of Medical Internet Research, 27, Article e71349.
Zeithaml, V. A., Parasuraman, A., & Malhotra, A. (2002). Service quality delivery through web sites: A critical review of extant knowledge. Journal of the Academy of Marketing Science, 30(4), 362–375.
Zhao, Y., Ni, Q., & Zhou, R. (2018). What factors influence mobile health service adoption? A meta-analysis and the moderating role of age. International Journal of Information Management, 43, 342–350.
