Empirical Investigation of Factors Influencing Consumer Intention to Use an Artificial Intelligence-Powered Mobile Application for Weight Loss and Health Management.

Abstract

Background: Research into interventions based on mobile health (m-Health) applications (apps) has attracted considerable attention among researchers; however, most previous studies have focused on research-led apps and their effectiveness when applied to overweight/obese adults. There remains a paucity of research on the attitudes of typical consumers toward the adoption of m-Health apps for weight management. This study adopted the tenets of the extended unified theory of acceptance and use of technology 2 (UTAUT2) as the theoretical foundation in developing a model that integrates personal innovativeness (PI) and network externality (NE) in seeking to identify the factors with the most pronounced effect on one's intention to use an artificial intelligence-powered weight loss and health management app. Materials and Methods: An online survey was conducted for Taiwanese participants aged ≥21 years from May 23 to June 30, 2018. Hypotheses were tested using structural equation modeling. Results: In the analysis of 458 responses, the proposed research model explained 75.5% of variance in behavioral intention (BI). Habit was the independent variable with the strongest performance in predicting user intention, followed by PI, NE, and performance expectancy (PE). Social influence weakly affects user intention through PE. In multi-group analysis, education was shown to exert a moderating influence on some of the relationships hypothesized in the model. Conclusions: The empirically validated model in this study provides insights into the primary determinants of user intention toward the adoption of m-Health app for weight loss and health management. The theoretical and practical implications are relevant to researchers seeking to extend the applicability of the UTAUT2 model to health apps as well as practitioners seeking to promote the adoption of m-Health apps. In the future, researchers could extend the model to assess the effects of BI on actual use behavior.