Trust in AI-Driven Decision Support Systems: Modeling the Dual Effects on User Innovation and Technostress

Trust in AI-Driven Decision Support Systems: Modeling the Dual Effects on User Innovation and Technostress | 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 Trust in AI-Driven Decision Support Systems: Modeling the Dual Effects on User Innovation and Technostress Qijie Ruan, Xiaorui Han, Huinan Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9362915/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract When employees trust AI-driven decision support systems (AI-DSS), does that trust help or hurt them? This study argues it does both at the same time. Drawing on Cognitive Appraisal Theory, Social Exchange Theory, and Technology Threat Avoidance Theory, we propose that trust in AI-DSS operates as a double-edged sword: it can free employees to think more creatively and generate new ideas (the empowerment pathway), yet it can also make them overly reliant on AI recommendations, creating stress and anxiety when the system changes or fails (the dependency pathway). Which effect dominates depends on two boundary conditions: employees with higher AI Literacy gain more from trust (innovation benefits), while stronger Organizational AI Support reduces the stress costs of dependency for everyone. We tested this model with a two-wave survey of 452 knowledge workers who use AI-DSS regularly. Results confirm both pathways: trust in AI-DSS significantly boosts user innovation behavior through perceived empowerment (indirect effect = 0.249), while simultaneously increasing technostress through perceived dependency (indirect effect = 0.175). AI Literacy amplifies the innovation benefit only above a critical threshold (scale score = 2.81), meaning that about one in four employees currently receives no innovation benefit from their trust. Organizational support, by contrast, reduces stress costs unconditionally, regardless of support level. A non-linear analysis further reveals an optimal trust zone: moderate-to-high trust maximizes net benefit, whereas excessive trust accelerates stress costs faster than it grows innovation returns. To estimate these effects robustly from Likert-scale survey data, we develop a Neural-Augmented Bayesian SEM (NB-SEM) framework that reduces estimation error by up to 51% compared with standard structural equation modeling. These findings suggest that the goal of AI-DSS governance should shift from simply building more trust to cultivating the right level of trust, supported by targeted AI literacy training and proactive organizational support structures. Business and commerce/Business and management Social science/Business and management Business and commerce/Information systems and information technology Physical sciences/Mathematics and computing Biological sciences/Psychology Social science/Psychology Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 13 May, 2026 Reviewers agreed at journal 03 May, 2026 Reviewers invited by journal 30 Apr, 2026 Editor assigned by journal 30 Apr, 2026 Editor invited by journal 23 Apr, 2026 Submission checks completed at journal 15 Apr, 2026 First submitted to journal 15 Apr, 2026 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. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9362915","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":634605720,"identity":"296a7ae5-a9bf-4b6e-802d-f76c8b3e6f91","order_by":0,"name":"Qijie Ruan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYDADfvbmgw8SKmzk2NjbDxCnRbLnWLLBhzNpxnw8ZxKI02JwI8dMcmbbocR5Eg4GeFWaz0h+9riyzS4PqMVAmoftQHqbBEMCw4+KbTi1yNxIMzc825ZcLHnmWYExD8+d3DbpxgOMPWdu49QiIZFgJtnYxpzYdzx5QzKPxLPcNpkDCcyMbfi0pH8DaqlPbDiQYHCYx+BwOptEggEBLTkgWw4nTjiRYtg4I+FwAmEtPG/KJBvOHU+cCQxkhg8H0gzbgIF8EK9f2NO3STaUVSf2szcf/5H4z0Zevr394IMfFbi1YAcHSFQ/CkbBKBgFowANAAD8G14D8LNokQAAAABJRU5ErkJggg==","orcid":"","institution":"Chuzhou University","correspondingAuthor":true,"prefix":"","firstName":"Qijie","middleName":"","lastName":"Ruan","suffix":""},{"id":634605721,"identity":"d692b7ee-1cc8-4c65-ad91-97cbc71b628e","order_by":1,"name":"Xiaorui Han","email":"","orcid":"","institution":"Hebei Finance University","correspondingAuthor":false,"prefix":"","firstName":"Xiaorui","middleName":"","lastName":"Han","suffix":""},{"id":634605722,"identity":"2ae78d5c-31fd-45b8-82e8-bc1ff0d83fca","order_by":2,"name":"Huinan Liu","email":"","orcid":"","institution":"School of Economics and Management of Shanghai Zhongqiao Vocational and Technical University","correspondingAuthor":false,"prefix":"","firstName":"Huinan","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2026-04-09 04:09:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9362915/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9362915/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108806232,"identity":"b3f19536-af67-40cc-8b9a-88175871d65c","added_by":"auto","created_at":"2026-05-08 15:28:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4716601,"visible":true,"origin":"","legend":"","description":"","filename":"ScientificReportsTitletoseehere3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9362915/v1_covered_57bdc47b-0692-4692-a0d8-0d84fda4cd58.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Trust in AI-Driven Decision Support Systems: Modeling the Dual Effects on User Innovation and Technostress","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9362915/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9362915/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"When employees trust AI-driven decision support systems (AI-DSS), does that trust help or hurt them? This study argues it does both at the same time. Drawing on Cognitive Appraisal Theory, Social Exchange Theory, and Technology Threat Avoidance Theory, we propose that trust in AI-DSS operates as a double-edged sword: it can free employees to think more creatively and generate new ideas (the empowerment pathway), yet it can also make them overly reliant on AI recommendations, creating stress and anxiety when the system changes or fails (the dependency pathway). Which effect dominates depends on two boundary conditions: employees with higher AI Literacy gain more from trust (innovation benefits), while stronger Organizational AI Support reduces the stress costs of dependency for everyone. We tested this model with a two-wave survey of 452 knowledge workers who use AI-DSS regularly. Results confirm both pathways: trust in AI-DSS significantly boosts user innovation behavior through perceived empowerment (indirect effect = 0.249), while simultaneously increasing technostress through perceived dependency (indirect effect = 0.175). AI Literacy amplifies the innovation benefit only above a critical threshold (scale score = 2.81), meaning that about one in four employees currently receives no innovation benefit from their trust. Organizational support, by contrast, reduces stress costs unconditionally, regardless of support level. A non-linear analysis further reveals an optimal trust zone: moderate-to-high trust maximizes net benefit, whereas excessive trust accelerates stress costs faster than it grows innovation returns. To estimate these effects robustly from Likert-scale survey data, we develop a Neural-Augmented Bayesian SEM (NB-SEM) framework that reduces estimation error by up to 51% compared with standard structural equation modeling. 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