Team 24: Yu Ru Liu Fu (637634yl), Lara Oliveira Simões Alves Serra (703496lo), Fabian Pusceddu (788425fp), Olivier Reintjes (585065or)
The production and consumption of fast fashion is cheap. However, there is a significant environmental cost to be paid. Tons of textile waste, thousands of litres of water consumed, and not to mention the numerous landfills of unwanted clothes across the globe (Long & Nasiry, 2022). Although fashion trends continue to change, their consequences on the environment are definitely here to stay.
Consumers now also have the responsibility to be aware of their purchasing habits since the Internet and social media have made information widely accessible. This can be seen reflected in the increasing sales of clothes via second-hand channels like Vinted and Marktplaats (Ministry of Infrastructure and Water Management, 2024). Such businesses built on sustainable practices can leverage this current movement and demand to capture new customers who want to purchase more sustainably.
Our innovation aims to combat this problem by adding GenAI to Vinted’s platform. Vinted is a major consumer-to-consumer (C2C) marketplace for second-hand fashion. It generates revenue by collecting transaction fees from buyers, while offering free listings to sellers. To improve on this model, Vinted can integrate a personal AI sales assistant. This is aimed at improving both the buyer and seller experience. This increases the listing process speed for sellers and enhances the shopping experience for the buyer by acting as a virtual stylist. With this innovation, Vinted transforms from a simple resale platform into a smart and interactive shopping experience that will boost engagement, loyalty, and transaction volume.
When building the prototype on the seller side, automation tools such as image recognition, AI-generated product descriptions, and dynamic pricing recommendations reduce the hassle of listing items, encouraging more users to sell rather than throw away unwanted clothing. On the buyer side, intelligent discovery and outfit completion recommendations transform the platform into a personalized styling ecosystem that promotes reuse and creative combination of existing garments. Together, these innovations help extend the lifecycle of clothing and make sustainable fashion more accessible and appealing to a broader audience, aligning the profitability of Vinted with positive environmental impact.
Prototype:
Vinted’s use of GenAI improves customer satisfaction, market competitiveness, and operational efficiency. GenAI increases scalability and decreases friction by automating price calibration, photo enhancement, and listing creation (Trabucchi & Buganza, 2025). In line with Expectation-Confirmation Theory, which highlights that satisfaction is dependent on verified expectations, personalized recommendations and standardized descriptions boost user trust and purchase confidence (Brill et al., 2019). GenAI also strengthens Vinted’s position in the market by boosting seller participation and generating network effects that attract more customers (Mikalef et al., 2021; Parker et al., 2016). Additionally, data-driven insights enhance pricing and demand forecasting (Babina et al., 2024). However, there are challenges, including the possibility of data bias (Ferrara et al., 2023), privacy concerns (Ethical Considerations in Driven E-Commerce, 2025), and the environmental effects of AI energy use (von Zahn et al., 2022; Vergallo et al., 2025).
References:
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Brill, T. M., Munoz, L., & Miller, R. J. (2019). Siri, Alexa, and other digital assistants: A study of customer satisfaction with artificial intelligence applications. Journal of Marketing Management, 35(15–16), 1401–1436. https://doi.org/10.1080/0267257X.2019.1687571
Ethical Considerations in AI-Driven E-Commerce Solutions: Balancing Personalization and Privacy. (2025). ResearchGate. https://www.researchgate.net/publication/389250000_Ethical_Considerations_in_AI-Driven_E-Commerce_Solutions_Balancing_Personalization_and_Privacy
Ferrara, E., Qamar, M., & Keegan, B. (2023). Algorithmic bias and fairness in online recommendation systems. Journal of Information Science, 49(4), 553–567. https://doi.org/10.1177/01655515221103100
Global Fashion Agenda. (2024, February 8). Pulse of the Fashion Industry 2019. https://globalfashionagenda.org/resource/pulse-of-the-fashion-industry-2019/
Long, X., & Nasiry, J. (2022). Sustainability in the fast fashion industry. Manufacturing & Service Operations Management, 24(3), 1276–1293. https://doi.org/10.1287/msom.2021.1054
Mikalef, P., Krogstie, J., Pappas, I. O., & Pavlou, P. A. (2021). Investigating the effects of big data analytics capabilities on firm performance: The mediating roles of dynamic capabilities. Information & Management, 58(3), 103414. https://doi.org/10.1016/j.im.2020.103414
Ministry of Infrastructure and Water Management. (2024). Policy Programme for Circular Textile 2025–2030. https://www.government.nl/binaries/government/documenten/reports/2024/12/31/policy-programme-for-circular-textile-2025-2030/Policy+Programme+for+Circular+Textile+2025-2030.pdf
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Vergallo, M., Fakhfakh, M., & Zanetti, D. (2025). Carbon-aware AI scheduling: Balancing performance and sustainability. arXiv preprint arXiv:2506.10990. https://arxiv.org/abs/2506.10990
von Zahn, J., Herden, M., & Becker, J. (2022). The cost of fairness in algorithmic decision-making. Business & Information Systems Engineering, 64(4), 389–401. https://doi.org/10.1007/s12599-021-00716-w
