Trust, Perceived Empathy, and Adoption Intentions Toward AI-Driven mHealth Diagnostic Platforms: A Consumer Behavior Perspective Among Generation Z and Millennials

Claudia-Maria Miu ()
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Claudia-Maria Miu: The Bucharest University of Economic Studies

Journal of Emerging Trends in Marketing and Management, 2026, vol. 1, issue 2, 14-24

Abstract: The proliferation of artificial intelligence (AI)-powered mobile health (mHealth) diagnostic applications has introduced new dynamics in consumer health behavior, particularly among digitally native populations such as Generation Z and Millennials. Despite growing adoption rates, critical questions remain regarding the psychological and contextual factors that drive-or inhibit the adoption and continued use of AI-driven diagnostic tools. This study proposes and empirically tests a comprehensive structural equation model integrating constructs from the Technology Acceptance Model (TAM) and trust theory to explain adoption intention and willingness to share health data (WSHD) among young digital health consumers. Drawing on a sample of N = 400 respondents aged 18–42 from Romania, the model examines four exogenous predictors-perceived AI accuracy (PAA), perceived ease of use (PEOU), social influence (SI), and perceived data privacy risk (PDPR)-channeled through two mediating constructs: trust in AI (TAI) and health anxiety/consciousness (HAC). The model encompasses 12 hypothesized structural paths analyzed via WarpPLS 8.0. Model fit indices confirmed good fit: APC = 0.253 (p

Keywords: mHealth; artificial intelligence; trust in AI; adoption intention; consumer health behavior. (search for similar items in EconPapers)
JEL-codes: M31 (search for similar items in EconPapers)
Date: 2026
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