Contents
Title
Trust, Perceived Empathy, and Adoption Intentions Toward AI-Driven mHealth Diagnostic Platforms: A Consumer Behavior Perspective Among Generation Z and Millennials
Author(s)
Claudia-Maria MIU
Classification JEL
M31.
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 < 0.001), ARS = 0.423 (p < 0.001), GoF = 0.463 (large effect), SPR = RSCR = SSR = NLBCDR = 1.000. Ten of twelve hypotheses were supported. TAI and HAC mediated the relationships between antecedents and behavioral outcomes, with TAI → AI (β = 0.426, p < 0.001) and SI → HAC (β = 0.472, p < 0.001) emerging as the strongest structural paths. The model explained 64.1% of variance in adoption intention and 42.2% in WSHD. These findings extend TAM and trust theory to AI-mediated mHealth contexts and offer actionable insights for digital health marketers targeting Generation Z and Millennial consumers.
Keywords
mHealth, artificial intelligence, trust in AI, adoption intention, consumer health behavior.
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