Data Capture Triggers Privacy-Value Trade-offs in Consumer Adoption of Smart vs Mechanical Watches: Experimental Evidence from a Randomized SEM Study in Shanghai

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Abstract Smartwatches enable systematic data capture absent in mechanical watches, yet how this activates privacy-value trade-offs in adoption remains unclear. We conducted a 1:1 randomized field survey (N = 1,002 Shanghai adults) to isolate these mechanisms, integrating S–O–R with privacy calculus and UTAUT2 elements. Stimuli included performance, effort, ecosystem fit, social/fashion cues, and privacy signals (smartwatches only), shaping two paths: Value & Empowerment (functional, hedonic, social value; health empowerment) and Privacy Appraisals (perceived risk; control). A placebo privacy block in the mechanical group enabled causal comparison, with configural to partial-scalar invariance confirmed.Multi-group structural equation modeling (SEM) showed value paths generalize across categories, explaining substantial variance in intentions (smartwatches: R² = 0.57 purchase, 0.50 upgrade; mechanical: similar). Privacy calculus activates only with data flows—control promotes (+), risk inhibits (−) intention in smartwatches, but paths are null in mechanicals (placebo yields no model fit gains). Category-specific effects: ecosystem fit dominates smartwatches, social/fashion cues mechanicals. Vignette-based privacy-signal manipulation, bootstrapped indirects, and response-surface analyses validate paths.Findings demonstrate privacy mechanisms are contingent on device telemetry, providing experimental evidence for replicable frameworks in IoT adoption research.
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Data Capture Triggers Privacy-Value Trade-offs in Consumer Adoption of Smart vs Mechanical Watches: Experimental Evidence from a Randomized SEM Study in Shanghai | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Data Capture Triggers Privacy-Value Trade-offs in Consumer Adoption of Smart vs Mechanical Watches: Experimental Evidence from a Randomized SEM Study in Shanghai Xiaoqiang Fu, Oyyappan Duraipandi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8040702/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Smartwatches enable systematic data capture absent in mechanical watches, yet how this activates privacy-value trade-offs in adoption remains unclear. We conducted a 1:1 randomized field survey (N = 1,002 Shanghai adults) to isolate these mechanisms, integrating S–O–R with privacy calculus and UTAUT2 elements. Stimuli included performance, effort, ecosystem fit, social/fashion cues, and privacy signals (smartwatches only), shaping two paths: Value & Empowerment (functional, hedonic, social value; health empowerment) and Privacy Appraisals (perceived risk; control). A placebo privacy block in the mechanical group enabled causal comparison, with configural to partial-scalar invariance confirmed.Multi-group structural equation modeling (SEM) showed value paths generalize across categories, explaining substantial variance in intentions (smartwatches: R² = 0.57 purchase, 0.50 upgrade; mechanical: similar). Privacy calculus activates only with data flows—control promotes (+), risk inhibits (−) intention in smartwatches, but paths are null in mechanicals (placebo yields no model fit gains). Category-specific effects: ecosystem fit dominates smartwatches, social/fashion cues mechanicals. Vignette-based privacy-signal manipulation, bootstrapped indirects, and response-surface analyses validate paths.Findings demonstrate privacy mechanisms are contingent on device telemetry, providing experimental evidence for replicable frameworks in IoT adoption research. Physical sciences/Mathematics and computing Biological sciences/Psychology Social science/Psychology Smartwatches Mechanical watches Datafication S–O–R Privacy calculus UTAUT2 Usable data control Ecosystem fit Multi-group SEM Measurement invariance Shanghai Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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Ecosystem fit, Multi-group SEM, Measurement invariance, Shanghai","lastPublishedDoi":"10.21203/rs.3.rs-8040702/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8040702/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSmartwatches enable systematic data capture absent in mechanical watches, yet how this activates privacy-value trade-offs in adoption remains unclear. We conducted a 1:1 randomized field survey (N\u0026thinsp;=\u0026thinsp;1,002 Shanghai adults) to isolate these mechanisms, integrating S\u0026ndash;O\u0026ndash;R with privacy calculus and UTAUT2 elements. Stimuli included performance, effort, ecosystem fit, social/fashion cues, and privacy signals (smartwatches only), shaping two paths: Value \u0026amp; Empowerment (functional, hedonic, social value; health empowerment) and Privacy Appraisals (perceived risk; control). A placebo privacy block in the mechanical group enabled causal comparison, with configural to partial-scalar invariance confirmed.Multi-group structural equation modeling (SEM) showed value paths generalize across categories, explaining substantial variance in intentions (smartwatches: R\u0026sup2; = 0.57 purchase, 0.50 upgrade; mechanical: similar). Privacy calculus activates only with data flows\u0026mdash;control promotes (+), risk inhibits (\u0026minus;) intention in smartwatches, but paths are null in mechanicals (placebo yields no model fit gains). Category-specific effects: ecosystem fit dominates smartwatches, social/fashion cues mechanicals. Vignette-based privacy-signal manipulation, bootstrapped indirects, and response-surface analyses validate paths.Findings demonstrate privacy mechanisms are contingent on device telemetry, providing experimental evidence for replicable frameworks in IoT adoption research.\u003c/p\u003e","manuscriptTitle":"Data Capture Triggers Privacy-Value Trade-offs in Consumer Adoption of Smart vs Mechanical Watches: Experimental Evidence from a Randomized SEM Study in Shanghai","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-28 15:58:40","doi":"10.21203/rs.3.rs-8040702/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2a7a3f13-015d-4cbc-9c98-da43570d4426","owner":[],"postedDate":"November 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":58703243,"name":"Physical sciences/Mathematics and computing"},{"id":58703244,"name":"Biological sciences/Psychology"},{"id":58703245,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-01-28T04:39:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-28 15:58:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8040702","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8040702","identity":"rs-8040702","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}

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