서비스 이용자의 지각된 위험이 AI추천시스템 이용의도에 미치는 영향
The effect of service user’s perceived risk on intention to use AI recommendation system
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This study examines the intention to use, and the willingness to pay for an AI treatment recommendation system according to the disease severity and risk attitude of medical service users. To test the effects of disease severity and risk attitude on the medical service users’ intention to use AI treatment recommendation system, we compared the users’ intention and willingness to pay for the treatment on the assumption that they had mild disease such as a cold or severe disease such as a cancer. We also measured their risk-taking attitudes and categorized them into high-and low-risk-taking groups. Based on the results, respondents had a higher intention to use and willingness to pay for the AI treatment recommendation system for severe diseases than for mild ones. Furthermore, respondents with high risk-taking attitudes were more willing to use the AI treatment recommendation system than those with low risk-taking attitudes. The risk-taking attitude moderated the effect of disease severity on the intention to use and willingness to pay, as in the case of severe disease , the respondents had a strong intention to use it regardless of their risk-taking attitude, while in the case of mild diseases, the intention differed between the low and the high risk-taking groups.
Abstract
This study examines the intention to use, and the willingness to pay for an AI treatment recommendation system according to the disease severity and risk attitude of medical service users. To test the effects of disease severity and risk attitude on the medical service users’ intention to use AI treatment recommendation system, we compared the users’ intention and willingness to pay for the treatment on the assumption that they had mild disease such as a cold or severe disease such as a cancer. We also measured their risk-taking attitudes and categorized them into high-and low-risk-taking groups. Based on the results, respondents had a higher intention to use and willingness to pay for the AI treatment recommendation system for severe diseases than for mild ones. Furthermore, respondents with high risk-taking attitudes were more willing to use the AI treatment recommendation system than those with low risk-taking attitudes. The risk-taking attitude moderated the effect of disease severity on the intention to use and willingness to pay, as in the case of severe disease , the respondents had a strong intention to use it regardless of their risk-taking attitude, while in the case of mild diseases, the intention differed between the low and the high risk-taking groups.
참고문헌 (27)
- Ajzen, I, "The Theory of Planned Behavior", Organizational Behavior and Human Decision Process, 50(2), pp.179~211 (1991)
- Davis, F. D., "Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology", MIS Quarterly, 13(3), pp.319~339 (1989)
- Dholakia, U. M., "An Investigation of the Relationship Between Perceived Risk and Product Involvement", Advances in Consumer Research, 24(1), pp.159~167 (1997)
- Dohmen, T., "Are Risk Aversion and Impatience Related to Cognitive Ability", American Economic Review, 100(3), pp.1238~1260 (2010)
- Esteva, A., "Dermatologist-level Classification of Skin Cancer with Deep Neural Networks", Nature, 542, pp.115~118 (2017)
- Featherman, M. S., "Predicting e-Services Adoption : A perceived Risk Facets Perspective", International Journal of Human-Computer Studies, 59(4), pp.451~474 (2003)
- Featherman, M. S., "The Intangibility of e-Services: Effects on Artificiality, Perceived Risk, and Adoption", Proceedings of the 37th Hawaii International Conference on System Sciences, pp.177~187 (2004)
- Gerke, S., "Ethical and Legal Challenges of Artificial Intelligence-Driven Healthcare", Artificial Intelligence in Healthcare, 6, pp.295~336 (2020)
- Ha, S., "Consumer e-shopping acceptance : antecedents in a technology acceptance model", Journal of Business Research, 62(5), pp.565~571 (2009)
- Nicolin Hainc, "The Bright, Artificial Intelligence-Augmented Future of Neuroimaging Reading", Frontiers in Neurology, 8 (2017), Frontiers Media SA[DOI]
- Herzenstein, M., "Adoption of New and Really New Products : The Effects of Self-Regulation Systems and Risk Salience", Journal of Marketing Research, 44(2), pp.251~260 (2007)
- Higgins., E. T., "Advances in Experimental Social Psychology", pp.1~46 (1998), Academic Press
- Holden, R. J., "Methodological Review : The Technology acceptance model : Its past and its future in health care", Journal of Biomedical Informatics, 43(1), pp.159~172 (2010)
- Kahneman, D., "Prospect Theory : An Analysis of Decision Under Risk", Econometrica, 47(2), pp.263~291 (1979)
- Kowatsch, T., "Critical Privacy Factors of Internet of Things Services: An Empirical Investigation with Domain Experts", Lecture Notes in Business Information Processing, 129 (2012)[DOI]
- Li, G., "Luxury Fashion Brand Consumers in China : Perceived Value, Fashion Lifestyle, and Willingness to Pay", Journal of Business Research, 65(10), pp.1516~1522 (2012)
- Li, Y. H., "Applying Theory of Perceived Risk and Technology Acceptance Model in the Online Shopping Channel. World Academy of Science", Engineering and Technology, 3(5), pp.919~925 (2009)
- Liobikienė, G., "Theory of Planned Behavior Approach to Understand the Green Purchasing Behavior in the EU : A Cross Cultural Study", Ecological Economy, 125, pp.38~46 (2016)
- Liu, X., "An Empirical Study of Product Differences in Consumers’ E-Commerce Adoption Behavior", Electronic Commerce Research and Applications, 2(3), pp.229~239 (2003)
- Lopes, B. A., "Consumer Perceptions and Willingness to Pay for Intrinsically Motivated Online Content", Journal of Management Information Systems, 23(2), pp.203~231 (2006)
- Miller, D. D, "Artificial Intelligence in Medical Practice : The Question to the Answer", The American Journal of Medicine, 131(2), pp.129~133 (2018)
- Pavlou, P. A., "Consumer Acceptance of Electronic Commerce : Integrating Trust and Risk with the Technology Acceptance Model", International Journal of Electronic Commerce, 7(3), pp.101~134 (2003)
- Park, Y., "Acceptance and adoption of the innovative use of smartphone", Industrial Management & Data Systems, 107(9), pp.1349~1365 (2007)
- Rajpurkar, P., "CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning" (2017)
- Venkatesh, V., "A Theoretical Extension of the Technology Acceptance Model : Four Longitudinal Field Studies", Management Science, 46(2), pp.186~204 (2000)
- Wu, S. I., "A Model of Green Consumption Behavior Constructed by the Theory of Planned Behavior", International Journal of Marketing Studies, 6(5), pp.119~132 (2014)
- Yunhi, K., "Intention to Pay Conventional-hotel Prices at a Green Hotel-A Modification of the Theory of Planned Behavior", Journal of Sustainable Tourism, 18(8), pp.997~1014 (2010)