Unboxing Expectations through AI‑Driven Analysis of Fashion Reviews to Understand Consumer Satisfaction and Reduce Product Returns

Unboxing Expectations through AI‑Driven Analysis of Fashion Reviews to Understand Consumer Satisfaction and Reduce Product Returns | 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 Unboxing Expectations through AI‑Driven Analysis of Fashion Reviews to Understand Consumer Satisfaction and Reduce Product Returns Marie Das, Pieter Fivez, Els Du Bois, Ingrid Moons, Dirk Van Rooy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9106024/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract The fashion industry faces deeply interlinked sustainability challenges that span environmental, economic, social, and technological dimensions. In fashion e‑commerce, short garment lifespans and high product‑return rates are particularly problematic. Returns signal misalignments between consumer expectations and experiences while generating substantial environmental and economic waste. This study investigates how experiential clothing attributes shape consumer (dis)satisfaction and return behaviour by analysing a large corpus of ASOS product reviews (n = 44.408). Using large language models, we analyse the presence, frequency, and sentiment of satisfaction attributes derived from Niinimäki’s Satisfaction Attributes for Clothing Longevity (SACL) and refine which attributes can be reliably detected in real consumer language at scale. The results show that consumer satisfaction is primarily driven by immediate, observable attributes, especially size, physical reactions, colour, and perceived value, while long‑term and use‑phase attributes remain largely absent from review data. Moreover, consumers describe product experiences in holistic and intertwined ways, complicating attempts to separate satisfaction attributes into clean analytical categories presented in theoretical frameworks. Based on these insights, the study proposes a three‑level set of design strategies that enhance review content (Level 1), translate this content, using AI, into structured insights (Level 2), and present these insights on product pages (Level 3) to reduce consumer uncertainty and return rates. Additionally, it also provides brands with actionable input for designing longer‑lasting garments. Overall, the findings demonstrate how AI‑driven review analysis can support more accurate consumer expectations and contribute to longer garment lifespans. Fashion e-commerce Online reviews Consumer satisfaction Product returns Large language models Sustainable Consumption Full Text Additional Declarations No competing interests reported. Supplementary Files ESM1.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 05 May, 2026 Reviewers agreed at journal 19 Apr, 2026 Reviewers invited by journal 27 Mar, 2026 Editor assigned by journal 27 Mar, 2026 Submission checks completed at journal 25 Mar, 2026 First submitted to journal 25 Mar, 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-9106024","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":613373408,"identity":"6758b69c-af1a-472f-b190-9781283139f5","order_by":0,"name":"Marie 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