Factors Affecting Patients’Attitude and Behavioral Intention of Using Hospital Online Services in Shanghai, China
Article Sidebar
Main Article Content
บทคัดย่อ
This study conducts a survey at three hospitals in Shanghai, China, to explore the factors influencing patients' attitudes and behavioral intentions toward using hospital online registration systems. A five-point Likert scale was used to develop the questionnaire, which was then validated by three experts through an IOC test. Prior to the formal survey, 50 samples were tested for internal consistency and reliability. A total of 500 valid questionnaires were distributed, with data analyzed using SPSS for descriptive statistics and AMOS for confirmatory factor analysis (CFA) and structural equation modeling (SEM). The CFA results confirmed the adequacy of the factor structure and the model’s fit. Based on theoretical frameworks, the study introduces attitude, perceived usefulness, and satisfaction as mediating variables to examine the relationships between independent variables (such as perceived usefulness, social influence, and promotion conditions) and the dependent variable (behavioral intention). The findings show that satisfaction, social influence, and promotion conditions significantly impact behavioral intention, while perceived usefulness and perceived ease of use significantly affect attitude. Additionally, attitude was found to have a direct impact on behavioral intention.
The results highlight that satisfaction, social influence, promotion conditions, perceived usefulness, perceived ease of use, and attitude significantly influence patients' behavioral intention to use the online registration system. Specifically, satisfaction, social influence, and promotion conditions directly influence behavioral intention, while perceived usefulness and perceived ease of use indirectly affect behavioral intention through their impact on attitude. Attitude also has a direct influence on behavioral intention. These findings provide important insights for promoting and developing digital health services, particularly in enhancing patients' acceptance and usage of online registration systems. Hospitals can improve adoption rates by focusing on increasing patient satisfaction, optimizing system usability, and leveraging social influence. The study also offers practical recommendations for policymakers and technology developers to facilitate the widespread adoption of digital health services. Future research could extend these findings to other regions and further investigate additional factors such as privacy protection and security.
Article Details
เอกสารอ้างอิง
Al-Mamary, Y. H., & Shamsuddin, A. (2015). Factors affecting enterprise resource planning systems implementation in the public sector. Journal of Enterprise Information Management, 28 (3), 328-354.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50 (2), 179-211.
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103 (3), 411-423.
Anderson, E. W., Fornell, C., & Mazvancheryl, S. K. (2004). Customer satisfaction and shareholder value. Journal of Marketing, 68 (4), 172-185.
Anderson, E. W., & Srinivasan, S. S. (2003). E-satisfaction and e-loyalty: A contingency framework. Psychology & Marketing, 20 (2), 123-138.
Babin, B. J., Boles, J. S., & Griffin, M. (2008). The impact of customer participation in service delivery on perceptions of service quality: An empirical study. Journal of Retailing, 84 (2), 123-135.
Bell, J. (2010). Doing your research project: A guide for first-time researchers (5th ed.). McGraw-Hill Education.
Biernacki, P., & Waldorf, D. (1981). Snowball sampling: Problems and techniques of chain referral sampling. Sociological Methods & Research, 10 (2), 141-163.
Bryman, A. (2012). Social research methods (4th ed.). Oxford University Press.
Chen, L. D., & Tan, J. (2004). Technology adaptation in e-commerce: Key determinants of virtual stores acceptance. European Management Journal, 22 (1), 74-86.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. 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.
Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17 (4), 460-469.
Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6 (2), 144-176.
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.
