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http://dx.doi.org/10.22156/CS4SMB.2017.7.4.053

Study on Intention and Attitude of Using Artificial Intelligence Technology in Healthcare  

Kim, Jang-Mook (Department of Health Administration, College of Health Science, Dankook University)
Publication Information
Journal of Convergence for Information Technology / v.7, no.4, 2017 , pp. 53-60 More about this Journal
Abstract
The purpose of this study was to identify the factors affecting intention and attitude of artificial intelligence technology(AI) of university students in healthcare using UTAUT model. Participants were 278 college students and the data were collected through self-reported questionnaire from May 15 to June 14, 2016. The collected data were analyzed using PASW Statistics/AMOS 22.0. The results were as follows. The effect of expectation factor, social influence, usefulness of work, anxiety factor had a significant effect on use of AI technology Intention. Factor of expectation effect, social influence, usefulness of work, anxiety factor had a significant effect on use of AI technology. As a result of verifying the significance of the indirect effect, it can be seen that the direct effect of the anxiety factor on the attitude factor is partially mediated by the use intention factor and the intention to use was partially mediated in the direct effect of the usefulness factor of the task on the attitude factor. This result means that it is important to increase the expectation factors, social effects, and perceived usefulness through accurate information based on facts and to reduce vague anxiety in order to increase the positive intention and attitude of university students' use of AI technology.
Keywords
Artificial Intelligence Technology(AI); Healthcare; UTAUT Model; Intention; Attitude;
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1 K. Y. Lee & J. H. Kim. (2016). Artificial Intelligence Technology Trends and IBM Watson References in the Medical Field. Korean Medical Education Review, 18(2), 51-57. DOI : 10.17496/kmer.2016.18.2.51   DOI
2 S. G. Lee. (2005). An Empirical Study on Mobile Technology Adoption based on the Technology Acceptance Model and Theory of Planned Behavior. Information Systems Review, 7(2), 61-84.
3 V. Venkatesh. (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. DOI : 10.1287/isre.11.4.342.11872   DOI
4 V. Venkatesh et al. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 27(3), 425-478.   DOI
5 F. D. Davis, R. Bagozzi & R. Warshaw. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management science, 35(8), 982-1003.   DOI
6 I. Ajzen. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 20(2), 179-211.
7 S. Taylor & A. Todd. (1995). Understanding information technology usage: A test of competing models. Information systems research, 6(2), 144-176. DOI : 10.1287/isre.6.2.144   DOI
8 F. D. Davis, R. Bagozzi & R. Warshaw. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of applied social psychology, 22(14), 1111-1132.   DOI
9 R. L. Thompson, C. A. Higgins & J. M. Howell. (1991). Personal computing: toward a conceptual model of utilization. MIS quarterly, 15(1), 125-143. DOI : 10.2307/249443   DOI
10 G. C. Moore & I. Benbasat. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information systems research, 2(3), 192-222.   DOI
11 C. Carlsson, J. Carlsson, K. Hyvonen, J. Puhakainen & P. Walden. (2006). Adoption of mobile devices/services-searching for answers with the UTAUT. The 39th Annual Hawaii International Conference on System Sciences (pp. 132a). Kauia : IEEE. DOI : 10.1109/hicss.2006.38   DOI
12 Y. S. Wang, H. H. Lin & L. P. Luarn. (2006). Predicting Consumer Intention to Use Mobile Service. Information Systems Journal, 16(2), 157-179. DOI : 10.1111/j.1365-2575.2006.00213.x   DOI
13 H. Amin. (2007). An Analysis of Mobile Credit Card Usage Intentions. Information Management & Computer Security, 15(4), 260-269. DOI : 10.1108/09685220710817789   DOI
14 S. Y. Morna, J. Peter, M. Goldrick, A. Kathleen & J. D. Keeling. (2003). Using ZMET to Explore Barriers to the Adoption of 3G Mobile Banking Service. International Journal of Retail and Distribution Management, 31(6), 340-348. DOI : 10.1108/09590550310476079   DOI
15 C. A. Chang. (2011). User Acceptance of NFC Mobile Phone Service: An Investigation Based on The UTAUT Model. The Service Industries Journal, 1-15.
16 D. R. Compeau & C. A. Higgins. (1995). Computer self-efficacy: Development of a measure and initial test. MIS quarterly, 19(2), 189-211. DOI : 10.2307/249688   DOI
17 Y. J. Chun. (2016). AI and Future of Healthcare Personnel-Trends, Prospects and Implications. Healthcare Management and Policy Research, 5(2), 106-112.
18 S. G. Lee. (2015). Artificial Intelligence. Research and Policy, which will dominate the Future of Japan. IT Communication & Broadcast Policy, 27(6), 1-7.
19 J. H. Lee et al. (2014). Big-Data Utilization Trend in Healthcare. Journal of Korean Telecommunication, 32(1), 63-75.