Trait-Driven Neuroadaptive Interfaces in Smart Hospitality: A DEMATEL-Guided Agent-Based Simulation Approach

Trait-Driven Neuroadaptive Interfaces in Smart Hospitality: A DEMATEL-Guided Agent-Based Simulation Approach | 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 Research Article Trait-Driven Neuroadaptive Interfaces in Smart Hospitality: A DEMATEL-Guided Agent-Based Simulation Approach Majid Heidari This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8050980/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 This study introduces a trait-driven neuroadaptive interface model for smart hospitality platforms, grounded in the premise that effective personalization must align with user traits rather than only inferred preferences. This paper integrates the Decision-Making Trial and Evaluation Laboratory (DEMATEL) causal mapping and agent-based simulation approach, drawing on Dual-Process Theory and Affective Computing, to model how emotional reactivity, cognitive load tolerance, and fairness sensitivity influence adaptive interface needs. DEMATEL assesses which characteristics serve as systems drivers, steering the establishment of static, personalized, and fairness-aware interfaces in the context of a simulation involving 500 simulated cognitively differentiated agents. Behavioral outcomes—trust, satisfaction, and cognitive load—were analyzed in relation to trait–interface congruence. The results suggest that alignment can improve the user experience, particularly when agents are emotionally reactive or fairness-sensitive and interfaces change in tone or embed ethical transparency. Misalignment, conversely, leads to overload, confusion, or disengagement. The model embeds trait causality into system logic, reflecting a transition from general personalization to ethically responsive, psychologically aligned participatory design. The framework provides a replicable process for trait-based adaptation, generating implications for AI developers, tourism interface designers, and human–AI researchers interested in fairness-aware personalization. The proposed model advances personalization beyond preference matching by offering a computational and ethical rationale for interfaces that adapt to neuro-cognitive variability, and the framework can be extended to other adaptive systems such as healthcare or education. Hospitality and Tourism Neuroadaptive Interface Design Trait-Based Personalization Agent-Based Simulation DEMATEL Smart Hospitality Fairness-Aware AI Full Text Additional Declarations The authors declare no competing interests. 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. 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-8050980","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":541180353,"identity":"9de93023-d6a1-47e9-85fa-15ef61153065","order_by":0,"name":"Majid Heidari","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFElEQVRIie3Rv2uDQBTA8SfCubwkq8Ehf0HhScFS2ib/iofQSdpAwaVgBOGyCFlb6B+j3ODmP9DF0iFLh3bLUGhOiGQ5Q8cM953OH597JwKYTGcZAoQMgDmZulALsLu71C9OESytrCN4JEMGD5u74YEcH+nJRTaq2jaZP46n2+0nJrcPCweYt1ymM3AmpY4E5TiisImuhRf7OTb3T+pgzHsh6WcDBwtKDFwubGJebOUjIXnREST1ZcPkasfFiti0/lDkryfp4tQU4EISc8FXpOyJzQeJxEs3bGpiGPuvb03EC2nlN0gyErZNWlIX/s8ueabZum6/v5I5X29k9Y6/6d1mUrXaMZrh6v90Me37JpPJZPpPe0ozTu8s51nMAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-5619-2289","institution":"University of Salerno","correspondingAuthor":true,"prefix":"","firstName":"Majid","middleName":"","lastName":"Heidari","suffix":""}],"badges":[],"createdAt":"2025-11-06 19:32:44","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8050980/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8050980/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95528839,"identity":"8920d944-a3ac-4672-b369-5b6e531f8522","added_by":"auto","created_at":"2025-11-10 10:16:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":739532,"visible":true,"origin":"","legend":"","description":"","filename":"BlindedManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8050980/v1_covered_f66e94b5-fe61-4706-9fc1-40b96eec33b4.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eTrait-Driven Neuroadaptive Interfaces in Smart Hospitality: A DEMATEL-Guided Agent-Based Simulation Approach\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Salerno","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"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},"keywords":"Neuroadaptive Interface Design, Trait-Based Personalization, Agent-Based Simulation, DEMATEL, Smart Hospitality, Fairness-Aware AI","lastPublishedDoi":"10.21203/rs.3.rs-8050980/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8050980/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study introduces a trait-driven neuroadaptive interface model for smart hospitality platforms, grounded in the premise that effective personalization must align with user traits rather than only inferred preferences. 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