{"id":1199,"date":"2026-06-16T12:32:39","date_gmt":"2026-06-16T09:32:39","guid":{"rendered":"https:\/\/www.etimm.ase.ro\/?p=1199"},"modified":"2026-06-16T12:46:40","modified_gmt":"2026-06-16T09:46:40","slug":"trust-perceived-empathy-and-adoption-intentions-toward-ai-driven-mhealth-diagnostic-platforms-a-consumer-behavior-perspective-among-generation-z-and-millennials","status":"publish","type":"post","link":"https:\/\/www.etimm.ase.ro\/?p=1199","title":{"rendered":"Trust, Perceived Empathy, and Adoption Intentions Toward AI-Driven mHealth Diagnostic Platforms: A Consumer Behavior Perspective Among Generation Z and Millennials"},"content":{"rendered":"<h4>\n<span style=\"font-size:12px;\"><span style=\"font-family: georgia,serif;\"><strong>Title<\/strong><\/span><\/span><br \/>\n<\/h4>\n<p style=\"text-align: justify;\">\n<span style=\"font-size:12px;\"><span style=\"font-family: georgia,serif;\"><meta name=\"citation_title\" content=\"Trust, Perceived Empathy, and Adoption Intentions Toward AI-Driven mHealth Diagnostic Platforms: A Consumer Behavior Perspective Among Generation Z and Millennials\"\/>Trust, Perceived Empathy, and Adoption Intentions Toward AI-Driven mHealth Diagnostic Platforms: A Consumer Behavior Perspective Among Generation Z and Millennials<\/span><\/span>\n<\/p>\n<h4>\n<span style=\"font-size:12px;\"><span style=\"font-family: georgia,serif;\"><strong>Author(s)<\/strong><\/span><\/span><br \/>\n<\/h4>\n<p style=\"text-align: justify;\">\n<span style=\"font-size:12px;\"><span style=\"font-family: georgia,serif;\"><meta name=\"citation_author\" content=\"Claudia-Maria MIU\"\/>Claudia-Maria MIU<\/span><\/span>\n<\/p>\n<p>\n<span style=\"font-family:georgia,serif;\"><span style=\"font-size:12px;\"><meta name=\"citation_publication_date\" content=\"2026\/06\/16\"\/><meta name=\"citation_journal_title\" content=\"Journal of Emerging Trends in Marketing and Management\"\/><meta name=\"citation_volume\" content=\"1\"\/><meta name=\"citation_issue\" content=\"2\"\/><meta name=\"citation_firstpage\" content=\"14\"\/><meta name=\"citation_lastpage\" content=\"24\"\/><meta name=\"citation_place_published\" content=\"Bucharest, Romania\"\/><meta name=\"citation_publisher\" content=\"The Bucharest University of Economic Studies Publishing House\"\/><meta name=\"citation_issn\" content=\"2537-5865\"\/><\/span><\/span>\n<\/p>\n<h4>\n<span style=\"font-family:georgia,serif;\"><span style=\"font-size:12px;\"><strong>Classification JEL <\/strong><\/span><\/span><br \/>\n<\/h4>\n<p style=\"text-align: justify;\">\n<span style=\"font-family:georgia,serif;\"><span style=\"font-size:12px;\"><meta name=\"citation_classification_jel\" content=\"M31.\"\/>M31.<\/span><\/span>\n<\/p>\n<h4>\n<span style=\"font-family:georgia,serif;\"><span style=\"font-size:12px;\"><strong>Abstract<\/strong><\/span><\/span><br \/>\n<\/h4>\n<p style=\"text-align: justify;\">\n<span style=\"font-family:georgia,serif;\"><span style=\"font-size:12px;\"><meta name=\"citation_abstract\" content=\"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\u201342 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 \u2192 AI (\u03b2 = 0.426, p < 0.001) and SI \u2192 HAC (\u03b2 = 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.\"\/>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\u201342 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 \u2192 AI (\u03b2 = 0.426, p < 0.001) and SI \u2192 HAC (\u03b2 = 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.<\/span><\/span>\n<\/p>\n<h4>\n<span style=\"font-family:georgia,serif;\"><span style=\"font-size:12px;\"><strong>Keywords<\/strong><\/span><\/span><br \/>\n<\/h4>\n<p style=\"text-align: justify;\">\n<span style=\"font-family:georgia,serif;\"><span style=\"font-size:12px;\"><meta name=\"citation_Keywords\" content=\"mHealth, artificial intelligence, trust in AI, adoption intention, consumer health behavior.\"\/>mHealth, artificial intelligence, trust in AI, adoption intention, consumer health behavior.<\/span><\/span>\n<\/p>\n<h4>\n<span style=\"font-family:georgia,serif;\"><span style=\"font-size:12px;\"><strong>Full Text: <meta name=\"citation_pdf_url\" content=\"https:\/\/www.etimm.ase.ro\/RePEc\/aes\/jetimm\/2026\/JETIMM_V02_2026_186.pdf\"\/><a href=\"https:\/\/www.etimm.ase.ro\/RePEc\/aes\/jetimm\/2026\/JETIMM_V02_2026_186.pdf\" target=\"_blank\"><span style=\"color: rgb(0, 0, 205);\">download pdf<\/span><\/a><\/strong><\/span><\/span><br \/>\n<\/h4>\n<p>\n<span style=\"font-family:georgia,serif;\"><span style=\"font-size:12px;\"><strong>Cite: <a href=\"https:\/\/www.etimm.ase.ro\/RePEc\/aes\/jetimm\/2026\/JETIMM_V02_2026_186.ris\" target=\"_blank\"><span style=\"color: rgb(255, 0, 0);\">download citation<\/span><\/a> (for EndNote, Reference Manager, ProCite)<\/strong><\/span><\/span>\n<\/p>\n<p>\n&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>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 &hellip;<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_ef_editorial_meta_date_first-draft-date":"","_ef_editorial_meta_paragraph_assignment":"","_ef_editorial_meta_checkbox_needs-photo":"","_ef_editorial_meta_number_word-count":"","footnotes":""},"categories":[33],"tags":[],"coauthors":[18],"class_list":["post-1199","post","type-post","status-publish","format-standard","hentry","category-jetimm_2026"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.etimm.ase.ro\/index.php?rest_route=\/wp\/v2\/posts\/1199","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.etimm.ase.ro\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.etimm.ase.ro\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.etimm.ase.ro\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.etimm.ase.ro\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1199"}],"version-history":[{"count":2,"href":"https:\/\/www.etimm.ase.ro\/index.php?rest_route=\/wp\/v2\/posts\/1199\/revisions"}],"predecessor-version":[{"id":1207,"href":"https:\/\/www.etimm.ase.ro\/index.php?rest_route=\/wp\/v2\/posts\/1199\/revisions\/1207"}],"wp:attachment":[{"href":"https:\/\/www.etimm.ase.ro\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1199"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.etimm.ase.ro\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1199"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.etimm.ase.ro\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1199"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.etimm.ase.ro\/index.php?rest_route=%2Fwp%2Fv2%2Fcoauthors&post=1199"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}