KİŞİSEL SAĞLIK TEKNOLOJİLERİNİ KULLANIM NİYETİNİ ETKİLEYEN FAKTÖRLER

Yıl 2018, Cilt: 11 Sayı: 22, 155 - 170, 01.12.2018

Semra Caliskan Aysegul Toker V. Aslihan Nasir

Öz

Sağlık endüstrisi, teknoloji ve bilimdeki gelişmelerle önemli bir dönüşüm yaşamaktadır. Sağlık alanındaki bilgi teknolojileri yeniliklerinin; hastanın güçlenmesi, kişisel sağlık yönetimi ve sağlık motivasyonu gibi olası etkileri, Kişisel Sağlık Teknolojileri’ni kullanma niyetini etkileyen temel faktörler hakkında merak uyandırmaktadır. Bu çalışma, Yeniliklerin Yayılımı Teorisi ve Teknoloji Kabul Modeli ışığında, kabul sonrası dönem için tüketicinin yenilikleri kullanma niyetinin öncüllerini sağlık teknolojileri kapsamında incelemektedir. Tüketici davranışı bağlamında, araştırmamız yeni teknolojilerin kullanma niyetinin anlaşılmasına katkıda bulunmaktadır. Algılanan yenilik özeliklerinin yanı sıra, çalışmamızda sağlık motivasyonu ve gizlilik kaygısı gibi bağlamsal faktörler de incelenmiştir. AMOS 24 uygulaması üzerinden Yapısal Eşitlik Modellemesi analiz yöntemi ile 520 katılımcıdan toplanan anket verileri analiz edilmiştir. Analizlerin sonuçlarına göre, algılanan göreceli fayda kullanım niyetinin en güçlü pozitif belirleyicisi olarak tespit edilmiştir. Öte yandan sağlık bilgisi gizliliği kaygısı kullanım niyetinin en güçlü negatif öncülü olarak bulunmuştur. Araştırma modelindeki ilişkiler üzerindeki göreceli avantajın aracılık, bireysel yenilikçiliğin moderasyon ve sağlık motivasyonunun moderasyon etkileri bu çalışma kapsamında analiz edilmiş ve sonuçları irdelenmiştir

Anahtar Kelimeler

Teknoloji kabulü ve kullanımı, kabul sonrası tüketici davranışı, bireysel yenilikçilik, sağlık bilgisi gizlilik endişesi, kişisel sağlık teknolojileri

DETERMINANTS OF CONSUMERS’ PERSONAL HEALTH TECHNOLOGY USAGE INTENTIONS1

Öz

Healthcare industry experiences a tremendous transformation with the proliferation of technology and science. The possible effects of this transformation such as patient empowerment, self-health management, and health promotion make us curious about the underlying factors that influence intention to use healthcare innovations. This research investigates the determinants of consumer intention to use innovations for the post-adoption period, particularly Personal Health Technologies PHTs , from the perspective of diffusion of innovation and technology acceptance and use literature. This research contributes to the understanding of important phenomena, namely intention to use innovations, in a consumer behavior context enriched with health-related constructs. 520 completed questionnaires were included in our empirical study. The primary method of analysis was Structural Equation Modeling SEM conducted through AMOS 24. We found perceived relative advantage as the strongest positive determinant of usage intention, whereas we delineated health information privacy concern as the strongest negative determinant of usage intention. The mediation effect of relative advantage and the moderation effects of personal innovativeness and health motivation on the relationships of research model were also analyzed

Anahtar Kelimeler

Technology acceptance and usage, post-adoption consumer behavior, personal innovativeness, health information privacy concern, personal health technologies

Kaynakça

• Agarwal, R., and Prasad, J. (1998). A Conceptual and Operational Definition of Personal Innovativeness in The Domain of Information Technology. Information Systems Research, 9(2): 204-215.
• Ajzen, I. (1985). From Intentions to Actions: A Theory of Planned Behavior. In Julius Kuhl and Jürgen Beckmann (Eds.), Action Control: From Cognition to Behavior. Berlin: Springer-Verlag. 11-39.
• Arning, K., and Ziefle, M. (2009). Different Perspectives on Technology Acceptance: The Role of Technology Type and Age. Symposium of the Austrian HCI and Usability Engineering Group, Berlin, Heidelberg. November 20-41.
• Bansal, G., and Gefen, D. (2010). The Impact of Personal Dispositions on Information Sensitivity, Privacy Concern and Trust in Disclosing Health Information Online. Decision Support Systems, 49(2): 138-150.
• Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, And User Acceptance of Information Technology. MIS Quarterly, 13(3): 319–340.
• Davis, F. D., Bagozzi, R. P., and Warshaw, P. R. (1992). Extrinsic and Intrinsic Motivation to Use Computers in The Workplace. Journal of Applied Social Psychology, 22(14): 1111-1132.
• Fishbein, M., and Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Reading, PA:Addison-Wesley.
• Fox, N. J. (2017). Personal Health Technologies, Micropolitics and Resistance: A New Materialist Analysis. Health, 21(2): 136-153.
• Gao, Y., Li, H., and Luo, Y. (2015). An Empirical Study of Wearable Technology Acceptance in Healthcare. Industrial Management & Data Systems, 115(9): 1704-1723.
• Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., and Tatham, R.L. (2010). Multivariate Data Analysis (7th ed.). Upper Saddle River, NJ: Prentice Hall.
• Jayanti, R. K., and Burns, A. C. (1998). The Antecedents of Preventive Health Care Behavior: An Empirical Study. Journal of the Academy of Marketing Science, 26(1): 6.
• Kahn, J. S., Aulakh, V., and Bosworth, A. (2009). What It Takes: Characteristics of the Ideal Personal Health Record. Health Affairs, 28(2): 369-376.
• Karahanna, E., Straub, D. W., and Chervany, N. L. (1999). Information Technology Adoption across Time: A Cross-Sectional Comparison of Pre-Adoption and Post-Adoption Beliefs. MIS Quarterly, 183-213.
• Lee, M. C. (2009). Factors Influencing the Adoption of Internet Banking: An Integration of TAM and TPB with Perceived Risk and Perceived Benefit. Electronic Commerce Research and Applications, 8(3): 130-141.
• Luo, X., Li, H., Zhang, J., and Shim, J. P. (2010). Examining Multi-Dimensional Trust and Multi-Faceted Risk in Initial Acceptance of Emerging Technologies: An Empirical Study of Mobile Banking Services. Decision Support Systems, 49(2): 222-234.
• Liu, L. S., Shih, P. C., and Hayes, G. R. (2011, February). Barriers to the Adoption and Use of Personal Health Record Systems. Proceedings of the 2011 iConference. ACM. 363-370.
• Moore, G. C., and Benbasat, I. (1991). Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation. Information Systems Research, 2(3): 192-222.
• Moorman, C. (1990). The Effect of Stimulus and Consumer Characteristics on the Utilization of Nutrition Information. Journal of Consumer Research, 17(3): 362-374.
• Moorman, C., and Matulich, E. (1993). A Model of Consumers’ Preventive Health Behaviors: The Role of Health Motivation and Health Ability. Journal of Consumer Research, 20(2): 208-228.
• Pavlou, P. A. (2003). Consumer Acceptance of Electronic Commerce: Integrating Trust and Risk with the Technology Acceptance Model. International Journal of Electronic Commerce, 7(3): 101-134.
• Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., and Podsakoff, N. P. (2003). Common Method Biases in Behavioral Research: A Critical Review of the Literature and Recommended Remedies. Journal of Applied Psychology, 88(5): 879-903.
• Rogers, E. M. (1983). Diffusion of Innovation (3rd ed.). New York, NY: Free Press.
• Tang, P. C., Black, W., and Young, C. Y. (2006). Proposed Criteria for Reimbursing Evisits: Content Analysis of Secure Patient Messages in a Personal Health Record System. In AMIA Annual Symposium Proceedings American Medical Informatics Association. 2006, 764.
• Wu, J. H., and Wang, S. C. (2005). What Drives Mobile Commerce?: An Empirical Evaluation of the Revised Technology Acceptance Model. Information & Management, 42(5): 719-729.
• Venkatesh, V., and Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision sciences, 39(2), 273-315.
• Venkatesh, V. (2000). Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into The Technology Acceptance Model. Information Systems Research, 11(4): 342-365.
• Venkatesh, V., Morris, M. G., Davis, G. B., and Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3): 425-478.
• Venkatesh, V., Thong, J. Y., and 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.
• Yamin, C. K., Emani, S., Williams, D. H., Lipsitz, S. R., Karson, A. S., Wald, J. S., and Bates, D. W. (2011). The Digital Divide in Adoption and Use of a Personal Health Record. Archives of Internal Medicine, 171(6): 568-574.