Modeling Consumer Adoption Intention of an AI-Powered Health Chatbot in Taiwan: An Empirical Perspective

doi:10.23940/ijpe.22.05.p4.338349

Abstract

Abstract: The research of mobile health application interventions has attracted much interest from practitioners and scholars. There is a relative paucity of research investigating the factors influencing consumers to use a health chatbot for weight and health management. This study utilized the extended unified theory of acceptance and use of technology (UTAUT2) model as the theoretical basis and extended it with personal innovativeness and network externality to investigate the predominant factors affecting one’s intention to use a health chatbot. Materials and Methods: An online survey was carried out for people aged≥20 years throughout Taiwan from 23 November to 30 December, 2019. Structural equation modeling (SEM) was used to test the hypotheses. Results: In total, 415 responses were analyzed. Our proposed model explained 87.1% of the variance in behavioral intention. Among eight factors, five of the factors were found to be the significant predictors of intention to use a health chatbot. Gender and experience were seen to exert a moderating effect on some of the relationships hypothesized in our research model, whereas education, chronic conditions, BMI, and age did not play a significant role. This study provides academics, health professionals, and practitioners with insights into the factors influencing the acceptance and use of a health chatbot. In the future, researchers could extend the model to investigate the effects of user intention on actual use behavior.

Key words: mobile health, UTAUT2 model, chatbot, network externality, personal innovativeness

Chin-Yuan Huang, Ming-Chin Yang, I-Ming Chen, and Wen-Chang Hsu. Modeling Consumer Adoption Intention of an AI-Powered Health Chatbot in Taiwan: An Empirical Perspective [J]. Int J Performability Eng, 2022, 18(5): 338-349.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

References

1. Statista. Smartphones - Statistics & Facts; Available from: https://www.statista.com/topics/840/smartphones/, accessed on May 2021.
2. Gagnon M.P., Ngangue P., Payne-Gagnon, J., and Desmartis, M. M-health Adoption by Healthcare Professionals: A Systematic Review. Journal of the American Medical Informatics Association, vol. 23, no. 1, pp. 212-220, 2016.
3. Snyder G.Next-generation “Smart” Medtech Devices: Preparing for an Increasingly Intelligent Future.Deloitte Center for Health Solutions, pp. 2-3, 2015.
4. Nilsen W., Kumar S., Shar A., Varoquiers C., Wiley T., Riley W.T., Pavel M., andAtienza A.A.Advancing the Science of Mhealth. Journal of health communication, vol. 17, no. sup1, pp. 5-10, 2012.
5. Byambasuren O., Beller E., andGlasziou P.Current Knowledge and Adoption of Mobile Health Apps among Australian General Practitioners: Survey Study. JMIR mHealth and uHealth, vol. 7, no. 6, pp. e13199, 2019.
6. EASO. Obesity Facts & Figures: World Health Organisation Fact sheet; Available from: http://easo.org/education-portal/obesity-facts-figures/, 2013, accessed on May 2022.
7. Prevention C.C.f.D.C.a. The Health Effects of Overweight and Obesity; Available from: https://www.cdc.gov/healthyweight/effects/index.html, accessed on May 2022.
8. Piette J.D., List J., Rana G.K., Townsend W., Striplin D., andHeisler M.Mobile Health Devices as Tools for Worldwide Cardiovascular Risk Reduction and Disease Management. Circulation, vol. 132, no. 21, pp. 2012-2027, 2015.
9. Donaldson E.L., Fallows S., andMorris M.A Text Message Based Weight Management Intervention for Overweight Adults. Journal of Human Nutrition and Dietetics, vol. 27, pp. 90-97, 2014.
10. Lim S., Xue L., Yen C.C., Chang L., Chan H.C., Tai B.C., Duh H.B.L., and Choolani, M. A Study on Singaporean Women’s Acceptance of Using Mobile Phones to Seek Health Information. International journal of medical informatics, vol. 80, no. 12, pp. e189-e202, 2011.
11. Fadhil A.Can a Chatbot Determine My Diet? Addressing Challenges of Chatbot Application for Meal Recommendation.arXiv preprint arXiv:1802.09100, 2018.
12. Buch V., Varughese G., andMaruthappu M.Artificial Intelligence in Diabetes Care. Diabetic medicine: a journal of the British Diabetic Association, vol. 35, no. 4, pp. 495-497, 2018.
13. Huang, C.Y. and Yang, M.C.Empirical Investigation of Factors Influencing Consumer Intention to Use an Artificial Intelligence-powered Mobile Application for Weight Loss and Health Management. Telemedicine and e-Health, vol. 26, no. 10, pp. 1240-1251, 2020.
14. Huang C.Y., Yang M.C., Huang C.Y., Chen Y.J., Wu M.L., andChen K.W.A Chatbot-supported Smart Wireless Interactive Healthcare System for Weight Control and Health Promotion. In2018 IEEE international conference on industrial engineering and engineering management (IEEM), IEEE, pp. 1791-1795, 2018.
15. Venkatesh V., Morris M.G., Davis G.B., andDavis F.D.User Acceptance of Information Technology: Toward a Unified View.MIS quarterly, pp. 425-478, 2003.
16. Venkatesh V., Thong J.Y., andXu X.Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology.MIS quarterly, pp. 157-178, 2012.
17. Midgley, D.F. and Dowling, G.R.Innovativeness: The Concept and Its Measurement. Journal of consumer research, vol. 4, no. 4, pp. 229-242, 1978.
18. Rogers E.M.,Diffusion of Innovations, New York: The Free Press, 1995.
19. Agarwal, R. and Prasad, J.A Conceptual and Operational Definition of Personal Innovativeness in the Domain of Information Technology. Information systems research, vol. 9, no. 2, pp. 204-215, 1998.
20. Park, J.S. and Kim, H.S.Impacts of Individual Innovativeness on the Acceptance of IT-based Innovations in Health Care Fields. Healthcare informatics research, vol. 16, no. 4, pp. 290-298, 2010.
21. Katz, M.L. and Shapiro, C.Network Externalities, Competition, and Compatibility. The American economic review, vol. 75, no. 3, pp. 424-440, 1985.
22. Gao, L. and Bai, X.An Empirical Study on Continuance Intention of Mobile Social Networking Services: Integrating the IS Success Model, Network Externalities and Flow Theory.Asia Pacific Journal of Marketing and Logistics, 2014.
23. Chiu C.M., Cheng H.L., Huang H.Y., andChen C.F.Exploring Individuals’ Subjective Well-being and Loyalty Towards Social Network Sites from the Perspective of Network Externalities: The Facebook Case. International Journal of Information Management, vol. 33, no. 3, pp. 539-552, 2013.
24. Huang C.Y., Yang M.C., Huang C.Y., Chiu P.S., Liu Z.S., andChang R.I.Design and Implementation of a Dynamic Healthcare System for Weight Management and Health Promotion. In2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), IEEE, pp. 2386-2390, 2017.
25. Carver, C.S. and Scheier, M.F.Control Theory: A Useful Conceptual Framework for Personality-social, Clinical, and Health Psychology. Psychological bulletin, vol. 92, no. 1, pp. 111, 1982.
26. Curtis K.E., Lahiri S., andBrown K.E.Targeting Parents for Childhood Weight Management: Development of a Theory-driven and User-centered Healthy Eating App. JMIR mHealth and uHealth, vol. 3, no. 2, pp. e3857, 2015.
27. Ware P., Dorai M., Ross H.J., Cafazzo J.A., Laporte A., Boodoo C., andSeto E.Patient Adherence to a Mobile Phone-based Heart Failure Telemonitoring Program: A Longitudinal Mixed-methods Study. JMIR mHealth and uHealth, vol. 7, no. 2, pp. e13259, 2019.
28. Yuan S., Ma W., Kanthawala S., andPeng W.Keep Using My Health Apps: Discover Users’ Perception of Health and Fitness Apps with the UTAUT2 Model. Telemedicine and e-Health, vol. 21, no. 9, pp. 735-741, 2015.
29. Sun Y., Wang N., Guo X., andPeng Z.Understanding the Acceptance of Mobile Health Services: A Comparison and Integration of Alternative Models. Journal of electronic commerce research, vol. 14, no. 2, pp. 183, 2013.
30. Oliveira T., Thomas M., Baptista G., andCampos F.Mobile Payment: Understanding the Determinants of Customer Adoption and Intention to Recommend the Technology. Computers in human behavior, vol. 61, pp. 404-414, 2016.
31. Bettiga D., Lamberti L., andLettieri E.Individuals’ Adoption of Smart Technologies for Preventive Health Care: A Structural Equation Modeling Approach. Health care management science, vol. 23, no. 2, pp. 203-214, 2020.
32. Lu J.Are Personal Innovativeness and Social Influence Critical to Continue with Mobile Commerce?Internet research, 2014.
33. Lu J., Yao J.E., andYu C.S.Personal Innovativeness, Social Influences and Adoption of Wireless Internet Services via Mobile Technology. The Journal of Strategic Information Systems, vol. 14, no. 3, pp. 245-268, 2005.
34. Economides N.The Economics of Networks. International journal of industrial organization, vol. 14, no. 6, pp.673-699, 1996.
35. Hsu, C.L. and Lin, J.C.C. An Empirical Examination of Consumer Adoption of Internet of Things Services: Network Externalities and Concern for Information Privacy Perspectives.Computers in Human Behavior, vol. 62, pp. 516-527, 2016.
36. Sledgianowski, D. and Kulviwat, S.Using Social Network Sites: The Effects of Playfulness, Critical Mass and Trust in a Hedonic Context. Journal of computer information systems, vol. 49, no. 4, pp. 74-83, 2009.
37. Huang, C.Y. and Kao, Y.S.UTAUT2 Based Predictions of Factors Influencing the Technology Acceptance of Phablets by DNP.Mathematical Problems in Engineering, 2015.
38. We Are Social.Digital in 2018 in Eastern Asia; Available from: https://www.slideshare.net/wearesocial/digital-in-2018-in-eastern-asia-86866557, 2018, accessed on May 2022.
39. Cheung, G.W. and Lau, R.S.Testing Mediation and Suppression Effects of Latent Variables: Bootstrapping with Structural Equation Models. Organizational research methods, vol. 11, no. 2, pp. 296-325, 2008.
40. Anderson, J.C. and Gerbing, D.W.Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach. Psychological bulletin, vol. 103, no. 3, pp. 411, 1988.
41. Hu, L.T. and Bentler, P.M.Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria versus New Alternatives. Structural equation modeling: a multidisciplinary journal, vol. 6, no. 1, pp. 1-55, 1999.
42. Bollen K.A.Structural equations with latent variables. vol. 210, John Wiley & Sons, 1989.
43. Hair J.F., Black W.C., Babin B.J., Anderson R.E., andTatham R.L.Multivariate Data Analysis. vol. 5, no. 3, 1998.
44. Bentler, P.M. and Bonett, D.G.Significance Tests and Goodness of Fit in the Analysis of Covariance Structures. Psychological bulletin, vol. 88, no. 3, pp. 588, 1980.
45. Podsakoff, P.M. and Organ, D.W.Self-reports in Organizational Research: Problems and Prospects. Journal of management, vol. 12, no. 4, pp. 531-544, 1986.
46. Stevens J.P.Applied multivariate statistics for the social sciences. Routledge, 2012.
47. Sarstedt, M. and E. Mooi, A Concise Guide to Market Research - The Process, Data, and Methods Using IBM SPSS Statistics: Springer, 2014.
48. Gaskin, J. and Lim, J.Multigroup Analysis, Amos Plugin.Gaskination’s StatWiki, 2018.
49. Slade E.L., Dwivedi Y.K., Piercy N.C., andWilliams M.D.Modeling Consumers’ Adoption Intentions of Remote Mobile Payments in the United Kingdom: Extending Utaut with Innovativeness, Risk, and Trust. Psychology & Marketing, vol. 32, no. 8, pp. 860-873, 2015.
50. Huang, C.Y. and Yang, M.C.Empirical Investigation of Factors Influencing Consumer Intention to Use an Artificial Intelligence-powered Mobile Application for Weight Loss and Health Management. Telemedicine and e-Health, vol. 26, no. 10, pp. 1240-1251, 2020.
51. Ghalandari K.The Effect of Performance Expectancy, Effort Expectancy, Social Influence and Facilitating Conditions on Acceptance of E-banking Services in Iran: The Moderating Role of Age and Gender. Middle-East Journal of Scientific Research, vol. 12, no. 6, pp. 801-807, 2012.
52. Zarehmohzzabieh Z., Samah B.A., Omar S.Z., Bolong J., andShaffril H.A.M. Fishermen’s Acceptance and Use of Information and Communication Technology Integration in Malaysia. Exploring the Moderating Effect of Age and Experience. Journal of Applied Sciences, vol. 14, no. 9, pp. 1-10, 2014.
53. Riaz, A. and Adnan, A.Acceptance of Tablet Apps for E-learning by Senior Users: A Test of Unified Theory of Acceptance and Use of Technology, 2016.
54. Kim, S. and Noh, M.J.Determinants Influencing Consumers' Trust and Trust Performance of Social Commerce and Moderating Effect of Experience. Information technology journal, Vol. 11, no. 10, pp. 1369, 2012.