Harder, Better, Faster, Stronger: The Adoption and Use of AI-based m-Health Applications
(A2021-93433)

AUTHORS

Vincent FAVARIN, Université Toulouse Capitole; Lars Meyer-Waarden, Toulouse School of Management Research -CNRS University Toulouse 1 Capitole; Julien Cloarec, Université Laval

ABSTRACT

Thanks to technological progress, m-Health apps using AI is increasing and it allows data monitoring through the personalization of results. To better understand the impact on consumer behavior, we identify new concepts and their impact on well-being, intention to adopt and actual use. We use a multi-method approach, simultaneously using text mining and structural equation modeling through two studies. A first one (N = 129) on the intention to adopt an Artificial Intelligence generating health's recommendations, and a second one (N = 3 496) on the score obtained by m-Health apps on app stores from a database of nearly 200 000 comments. Our results highlight three main topics that impact adoption intent and score: personalization, automation and connectivity of the user environment. This paper contributes to the emerging literature studying the impact of AI on consumer behavior and to the one on Personalization-Privacy Paradox, using an innovative methodological approach.