Enabling re-commerce business models in secondhand fashion retail: logistics challenges and resource demands Open Access

The secondhand fashion retail sector has been reshaped by digital transformation, driven by technological innovation and shifting consumer preferences (Bae et al., 2022). Traditional thrift stores are increasingly transitioning to digital channels, making secondhand fashion more accessible through online platforms. While e-commerce broadly involves online retail of new products, re-commerce specifically refers to the buying and selling of secondhand products online (Agrawal et al., 2023). Initially driven by general online marketplaces such as eBay, re-commerce has evolved into dedicated platforms and mobile apps designed to offer simplicity, convenience and consumer engagement (Weinswig, 2017). This digital shift has expanded the global reach of secondhand fashion, making it more attractive and accessible to customers across diverse markets (Calvo-Porral et al., 2024a). The fashion re-commerce sector has grown rapidly, reaching $99 billion in global revenue in 2024 and is predicted to surge to $448 billion by 2029 (Statista, 2024).

Despite the rapid evolution of digital platforms and growing consumer interest in buying secondhand fashion online, the efficiency and scalability of fashion re-commerce business models remain challenging. The primary source of these challenges lies in the complexity of logistics, which introduces additional operational steps and infrastructure costs in re-commerce that extend beyond physical secondhand retail (Liu et al., 2023). Furthermore, managing a single inventory that supports both physical and online sales is particularly challenging because two systems often require distinct logistics flows and service systems (Guercini et al., 2018). Additionally, a substantial manual effort is required to make products appealing on digital platforms. Each item is unique, requiring individual listing processes that are time-consuming and resource-intensive (Charnley et al., 2022). These operational hurdles pose significant barriers to efficiency and scalability, highlighting the need for strategic resource allocation to streamline the process. A few scholars emphasize the role of technology in addressing these complex challenges in re-commerce operations (Bae et al., 2022; Charnley et al., 2022; Jain et al., 2022). For example, advanced technologies such as automation can streamline backend logistics operations, reducing labor intensity and improving consistency (Charnley et al., 2022). In addition, data-driven tools such as artificial intelligence (AI), Internet of Things (IoT) and blockchain can facilitate product tracking, build customer trust and enable more personalized customer experiences (Amanina, 2025; Bae et al., 2022). However, technological solutions in fashion re-commerce are not yet being fully leveraged to their potential (Charnley et al., 2022).

The existing literature on secondhand fashion has predominantly examined physical markets and consumer buying behavior (Herziger and Shmuely, 2024; Hur, 2020; Lichy et al., 2023; Sepe et al., 2025). Recent research on online resale has largely concentrated on consumer attitudes toward online purchases (Calvo-Porral et al., 2024b; Evans et al., 2022; Liu and Wang, 2024; Murtas and Pedeliento, 2024) and their interaction within peer-to-peer (P2P) marketplaces (Dekhili et al., 2025; Gu et al., 2023; Xie et al., 2025), leaving a notable gap in the understanding of business-to-consumer (B2C) re-commerce business models and their operational strategies. Although fashion re-commerce is becoming increasingly integrated into mainstream B2C retail, the lack of standardized operations and well-established business model practices (Liu and Wang, 2024) makes it challenging to grasp their operational dynamics. This study aims to fill this gap by addressing two research questions: (1) What are the logistical challenges in enabling re-commerce? (2) What critical resources are required to achieve efficient and scalable recommerce operations?

The resource-based view (RBV) is employed as a suitable strategic framework to explore how firms configure and deploy resources to navigate logistical and operational challenges. In RBV, a firm’s resources encompass a wide range of tangible and intangible assets, including processes, capabilities and knowledge that support the execution of strategies aimed at enhancing the firm’s efficiency, performance and competitive advantage (Chaudhuri et al., 2022; Varadarajan, 2020). RBV explains how combining internal and external resources can strengthen performance and foster relational competitive capabilities, allowing firms to leverage partner relationships to extend their resource base (Chaudhuri et al., 2022), particularly in operating under resource constraints (Ren et al., 2023). Such insights are valuable within the emerging domain of re-commerce, helping firms understand how to recombine existing resources, access new ones or manage to work in partnerships under resource constraints. This perspective helps to understand how internal and external resources are configured to overcome logistical challenges in re-commerce business models, supporting efficiency and scalability in a rapidly evolving market.

The paper contributes to the growing research field of e-commerce and emerging technologies in fashion retail by exploring how technological and logistics innovations facilitate the innovation of re-commerce business models. It deepens insight into the ways technology underpins the ongoing evolution of re-commerce within the fashion sector. In doing so, the paper contributes to understanding how digital advances are redefining the boundaries of secondhand fashion retail beyond traditional store formats. The remainder of this article is structured as follows: first, a literature review is presented; second, the research methodology is explained in detail. The results section presents a comprehensive analysis of the data, answering the two research questions. Finally, the article concludes with a discussion of key findings, implications for both research and practice, and future research directions.

Secondhand fashion plays a pivotal role in the transition toward a circular economy by promoting the reuse of products and the decoupling of economic growth from resource consumption (Hultberg and Pal, 2023; Stal and Corvellec, 2018; Wang et al., 2025). Historically rooted in charity shops, flea markets and thrift stores (Yeap et al., 2022), and once limited to niche consumer segments (Wang et al., 2025), this sector has recently evolved into a mainstream business (Persson and Hinton, 2023). The shifting market dynamics, such as the growing consumer shift toward sustainability (Yang et al., 2024) and the preferences for affordable clothing (Balsyte, 2024) have enhanced the commercial potential of secondhand fashion retail (Hellström and Olsson, 2024). Today, the secondhand fashion industry represents a scalable circular business model (CBM) with the highest potential of moving the fashion industry toward a circular economy (Hultberg, 2024). However, the economic viability of secondhand fashion business models heavily depends on how effectively returned items are collected, sorted and redistributed, emphasizing the vital role of reverse logistics, as discussed in the following section.

The reverse logistics (RL) process in secondhand business models begins when the consumer donates or resells their pre-owned items (Shirvanimoghaddam et al., 2020). Charities usually collect most of the reusable textiles, followed by private collectors and fashion retailers (Zhuravleva and Aminoff, 2021). Collected textiles are transported to sorting centers, where textiles are sorted into various grades to be reused in either domestic or global markets (Paras et al., 2018). Unlike in traditional logistics, RL must handle diverse product conditions, varying quality and unpredictable return volumes, thus demanding specific resources, expertise and infrastructure. Effective RL focuses on maximizing the value of returned goods while minimizing the total RL costs (Bouzon and Govindan, 2015).

Efficiently managing RL remains a significant challenge in the secondhand fashion industry. These challenges include the uncertainty of supply (Zhuravleva and Aminoff, 2021), low quality and value of returned items (Fani et al., 2025), the lack of collection infrastructure, the heterogeneity of raw materials that complicates handling, labor-intensive sorting (Pal, 2017), lead time issues (Fani et al., 2025) and the absence of comparable technologies to improve process efficiency (Zhuravleva and Aminoff, 2021). Thus, the profitability of RL depends on several factors, such as RL costs, volume of recovered materials, resale price, and innovation in technologies that could contribute to lowering the RL costs (Bouzon and Govindan, 2015). Strategies such as well-planned take-back systems, facility location decisions, product and process design, resource sharing and collaboration among RL actors are highlighted as possible pathways for higher value creation through RL (Haq et al., 2023; Pal, 2017). While literature has largely overlooked the RL challenges associated with online resale, a few studies bring relevant insights. Chen et al. (2018) point to the difficulties associated with handling unique secondhand items, where subjective product descriptions and inconsistent quality in online resale can complicate the process and impact seller credibility. Charnley et al. (2022) highlight that online resale involves additional steps that require significant effort to make products appealing to buyers, which makes the process resource-intensive and costly. Drawing from the e-commerce literature, where the focus is on the sale of new products, logistics is consistently identified as both a critical cost driver and a determinant of service quality (Ghezzi et al., 2012; Yu et al., 2017).

While RL underpins the operational foundation of secondhand fashion, emerging digital business model innovations are transforming the way logistics and operational processes are organized and managed within an online environment. These digital innovations are driving a shift from traditional, manual operations toward technology-enabled re-commerce business models, as discussed in the following section.

While business model literature focuses on how value is created, delivered and captured (Osterwalder and Pigneur, 2010), business model innovation implies how firms redesign these mechanisms by reconfiguring the existing resource base or developing new resources (van Eechoud and Ganzaroli, 2023), thereby changing the way its value network creates, delivers and captures value (Bocken et al., 2014). In recent years, digital transformation has reshaped how companies drive business model innovation and redefine the value creation mechanisms (Matarazzo et al., 2021; Warner and Wäger, 2019). This digital evolution and the growing acceptance of e-commerce have accelerated digital innovation across secondhand business models (Bae et al., 2022). Meanwhile, consumer interest in buying and selling secondhand fashion has surged amid the rising cost of living, as it provides both an opportunity to earn extra income through the resale of used clothes and a more affordable shopping choice (Balsyte, 2024). These factors have driven the rise of re-commerce, initially dominated by C2C marketplaces and later embraced by traditional thrift retailers to expand their reach and competitiveness in the digital market (Godinho Filho et al., 2024).

Literature outlines different CBM typologies reflecting how companies engage in fashion re-commerce. Lüdeke-Freund et al. (2019) identify three categories of reuse-based business models, distinguished by the nature of interaction between actors. The first category involves retailer-driven B2C models for selling secondhand clothing in-store or online (e.g. ThreadUp, RealReal). The second category is the C2C model, where online platforms (e.g. Depop, Vinted) facilitate direct exchanges between consumers (D’Adamo et al., 2022; Ramkumar et al., 2021). The third category represents a hybrid approach, where companies such as Sellpy collect and sell secondhand fashion online, while sharing profit with the original seller. In contrast, Yrjölä et al. (2021) propose three typologies of secondhand business models, classified by the degree of control, revenue streams and seller selection and support mechanisms. First, the “connector” model relies on the self-service concept (e.g. OfferUp), where buyers and sellers play a critical role in managing transactions, payments and deliveries independently (Price, 2019). Second, the “supporter” model (e.g. Poshmark) includes partial control mechanisms to enhance trust and safety and offers additional functions such as payment and delivery support. Finally, the “controller” model (e.g. ThreadUp) offers a more responsible and central role in collection, sorting and reselling (EMF, 2021).

The digitalization of the secondhand business models requires the adoption of innovative, technology-driven resources and strategies to enhance value creation in an online environment (Baah et al., 2024; Bae et al., 2022). While still emerging, literature outlines a few technological advancements that have improved both customer, logistics and operational interfaces. For instance, the use of deep-learning-based automated filtering systems that screen out malicious posts creates a safer platform and trust (Bae et al., 2022). Moreover, product taxonomy tools and algorithm-based personal recommendations (Charnley et al., 2022), virtual product visualization tools, such as avatar fitting (Bae et al., 2022), present promising tools for personalized online shopping. Additionally, there is a growing use of blockchain, QR codes and RFID tags to generate and track product data, which supports both supply chain integrity and consumer confidence (Charnley et al., 2022). In particular, blockchain technology offers a reliable method for tracing product origins, helping reduce counterfeiting, verifying authenticity and improving trust (Jain et al., 2022; Kim et al., 2021).

Digital transformation extends beyond the adoption of technology to represent a strategic shift that reconfigures organizational resources, processes and value creation mechanisms (Verhoef et al., 2021). In the context of fashion re-commerce, it enables firms to embed digital resources and capabilities that enhance logistics efficiency, traceability, customer interaction and competitiveness (Jain et al., 2022; Leão and da Silva, 2021). Simultaneously, platform literature highlights how digital platforms enable CBMs to function as intermediaries that orchestrate interactions and facilitate value co-creation among multiple stakeholders, including consumers, logistics partners and resale businesses (Blackburn et al., 2023; Juge et al., 2022). Such platforms lower operational costs, enable network effects and resource complementarity by connecting distributed actors and support data-driven decision-making (Tian et al., 2024). From an RBV perspective, these digital and platform capabilities reshape the resource landscape and allow firms to extend, access and share resources beyond organizational boundaries, to achieve competitive advantage and circular value creation (Blackburn et al., 2023). The integration of digital transformation and platform perspectives provides a theoretical understanding of how technology drives resource reconfiguration and innovation in fashion re-commerce business models.

Given the limited availability of prior literature on fashion re-commerce and the specific resource configurations that underpin it, a qualitative research design was adopted. This approach is particularly well-suited for exploring a complex and under-researched phenomenon (Lim, 2025), thus enabling a deeper understanding of the logistics challenges and resource demands that shape reverse supply chains in fashion re-commerce operations.

To collect rich, in-depth empirical data, a semi-structured interview method was employed. This approach allows flexibility in exploring emerging themes while maintaining consistency across core topics (Qu and Dumay, 2011). It also enables the inclusion of diverse perspectives from key actors involved in fashion re-commerce, thereby contributing to a more comprehensive understanding of re-commerce logistics. The interview guide was carefully developed by identifying gaps in the existing literature and aligning the questions with the RBV framework. The interview guide primarily included “what” and “how” questions designed to elicit in-depth insights from participants. These questions covered the logistical and operational challenges firms face, how they respond to these challenges by mobilizing various resources, such as infrastructure, technologies, partnerships and capabilities, and how the availability of these resources shapes their business model. This approach ensured that the interview questions effectively captured insights into the resources and capabilities relevant to the study’s objectives, while also addressing logistics challenges that have been underexplored in prior research.

Interview participants were selected through purposive sampling to ensure relevance and richness of information. The primary inclusion criterion was that the organization operated a B2C fashion re-commerce platform within the Nordic region. This geographical focus was chosen to balance heterogeneity with contextual coherence. The Nordic countries, Sweden, Denmark, Norway and Finland, share comparable socioeconomic conditions and regulatory frameworks, particularly concerning textile reuse. The annual consumption of new clothing and textiles in the Nordics amounts to approximately 352,450 tons, of which about one-third is collected for reuse or recycling (Nordic Innovation, 2023). Online shopping of secondhand fashion is increasingly popular in the Nordics, with nearly a quarter of Finnish online shoppers, over half of Swedish shoppers, and around eight in ten Danish shoppers purchasing pre-owned items from online marketplaces (Statistica, 2023). By 2024, 27% of consumers in the region reported buying secondhand fashion frequently, a trend driven by both the growing emphasis on circularity and increasing economic pressures (Balsyte, 2024). Correspondingly, Nordic online resale platforms such as Tori generated a revenue of EUR 36.1 million in 2022, compared to EUR 9.6 million in 2020 (Statistica, 2023), and Tradera recorded a 26% increase in revenue between 2021 and 2023 (Balsyte, 2024). These figures lend empirical support to the relevance of focusing on this region for studying fashion re-commerce, while shared characteristics among these countries provide a consistent and meaningful context for analyzing fashion re-commerce operations.

In total, 15 interviews were conducted, each representing a different firm headquartered across four countries: Sweden (10), Finland (2), Denmark (1) and Norway (1). Although the sample is not evenly distributed geographically, most firms operate re-commerce activities across borders, engaging in collection, sorting or online resale activities throughout the Nordic region. Consequently, despite their physical location, the participating companies share a comparable business landscape and strategic focus within the broader Nordic circular ecosystem. Interviews, each lasting about 45–60 min, were recorded with permission and later transcribed in full. Additionally, information from company websites was reviewed, such as process flow information and videos, to gain additional insights into the logistics workflows. Furthermore, site visits were conducted where possible (Companies B, C and J) to gain more insights into operational processes. Table 1 provides an overview of the participating organizations of the study.

Data saturation was reached when the final three interviews produced no additional themes or major new insights related to operational and logistical challenges, resource mobilization or re-commerce models. Saturation was assessed iteratively during data collection and analysis, while coding revealed recurring patterns across cases, and the emergent classification of operating models had stabilized. Accordingly, the sample size of 15 was judged adequate to provide both depth and sufficient cross-case diversity for the study’s aims.

Data analysis followed a two-stage process to ensure transparency and reliability. First, interview data were coded around two core elements: (1) logistics and operational challenges and (2) resources mobilized to address them. Coding was conducted manually by two researchers, with regular cross-checks to ensure consistency. Differences in interpretation were discussed until full agreement was reached, enhancing coding reliability. This analysis discovered key logistics challenges and, most importantly, the pivotal role of technology as a cross-cutting enabler influencing resource configuration and operational performance.

In the second stage, a cross-case comparison was conducted using inductive reasoning, which is particularly suitable for analyzing rich, textual data to identify patterns or trends leading to generalization through systematic interpretation (Yuwono and Rachmawati, 2023). The data classification was guided by three key dimensions: (1) control of logistics (the extent to which firms manage logistics internally or through partners), (2) ownership of the re-commerce platform (whether owned or outsourced) and (3) the degree of technological integration (ranging from own digital interfaces to purchased solutions). This multidimensional framework provided insight into how variations in resource configurations and technological capabilities influence firms’ operational approaches. This analysis led to the classification of four distinct operating models, reflecting different ways firms organize and manage re-commerce activities, depending on the availability of resources. The robustness of the findings was ensured through this systematic coding, cross-case comparisons, iterative analysis until data saturation, and the inclusion of multiple organizational perspectives to enhance credibility and consistency.

To ensure the quality and rigor of this study, several established criteria for qualitative research were applied. Credibility was enhanced through purposive sampling to select knowledgeable participants directly involved in fashion re-commerce operations, ensuring that the data collected were both relevant and rich in detail. To promote dependability and transparency, the interview guide was carefully developed based on identified gaps in the literature. All interviews were audio-recorded and transcribed to maintain accuracy and minimize researcher bias. Triangulation was achieved through the integration of multiple data sources and perspectives. Besides interviews, data from company websites, published process videos and direct site visits (Companies B, C and J) were analyzed to validate and enrich the findings. The inclusion of diverse organizational types – fashion brands, charities, independent retailers and logistics or technology service providers – further strengthened the robustness of the analysis by capturing a broad range of practices and viewpoints within the fashion re-commerce ecosystem. Internal validity was supported by maintaining consistency in the interview process and conducting iterative analysis to ensure that the themes accurately reflected participants’ insights. External validity was addressed by focusing on the Nordic region, which, while contextually coherent, includes a variety of organizational types, enhancing the transferability of the findings.

The results section is structured around three key themes that emerged from empirical data. First, the analysis identifies core logistics and operational challenges that firms face in managing fashion re-commerce. Second, the findings highlight the emerging role of technology as a vital resource in addressing these challenges, with firms increasingly turning to automation and AI-powered digital tools. Third, the results investigate how the availability of logistics and digital resources influences firms’ choice of operating models, ranging from fully integrated solutions to collaborative partnerships. Together, these themes highlight the dynamic interplay between operational challenges, technological innovation and strategic resource configurations in shaping the development of fashion re-commerce.

The operational and logistical execution of fashion re-commerce presents a distinct set of challenges. While significantly different from those of physical resale, these challenges stem primarily from the heterogeneity of secondhand garments, the need for item-level handling, and the complexities involved in ensuring quality, transparency and efficiency across digital platforms. The following section outlines key challenges encountered across five critical operational areas: collection, sorting, photography, item listing and digital platform management.

Collection: A significant challenge in the collection phase is the necessity to acquire high-quality, high-value garments that can generate sufficient revenue to offset the operational costs of online sales. Unlike physical retail, where garments of varying quality and price points can be accommodated, online resale demands a minimum pricing threshold to ensure profitability. Several firms have already instituted a minimum sales price for online listings, often guided by their break-even analysis. This issue is further complicated due to increasing competition from P2P apps and social media marketplaces that allow individuals to sell directly to each other, effectively capturing a significant portion of high-quality secondhand garments and reducing the available supply for B2C re-commerce firms.

Sorting: The sorting process is hindered by the absence of a standardized quality grading system across the industry. While in physical retail environments, customers can assess garment condition directly, online resale platforms rely heavily on accurate quality assessments to manage customer expectations. Without such standards, there is an increased risk of customer dissatisfaction, returns and reputational damage. Firms emphasize the need for a universally accepted grading scale, such as “Grade A” to “Grade C,” which can serve as a reliable description for garment condition, reducing ambiguity in product listings and aiding customer decision-making. Such a system would improve operational consistency and customer trust in the re-commerce space.

Photography: Product photography presents both technical and resource-related challenges in fashion re-commerce. Accurately conveying the true condition of secondhand garments through images is crucial, as customers cannot physically inspect items before purchasing. This necessitates high-quality, standardized photography setups capable of capturing wear, texture, color and garment details. Moreover, the process is resource-intensive; it typically requires professional-grade equipment and consistent lighting conditions. To maintain operational efficiency, many firms aim to standardize photography workflows to minimize reliance on skilled photographers, but setting up such systems demands upfront investment in infrastructure.

Item description and listing: Each secondhand garment is unique, making item-level data creation a critical yet labor-intensive task. Unlike traditional e-commerce, where one description can be reused for multiple identical garments, re-commerce platforms must generate individual listings with detailed and accurate descriptions for each garment. While this step is essential to prevent customer dissatisfaction, challenges arise particularly when garment labels are missing, making it difficult to identify materials, size or brand. The process demands both time and specialized knowledge to ensure descriptions are accurate, comprehensive and aligned with consumer expectations.

Digital platform operations: Re-commerce requires systems that can handle diverse and unpredictable inventory inputs and facilitate seamless customer experiences while accommodating variable quality and pricing structures. Existing e-commerce platforms are often ill-suited to the operational complexities of fashion re-commerce. These platforms typically lack the necessary backend integration to support reverse logistics, dynamic inventory management for non-standardized garments and digital storefront configurations. Furthermore, payment structures such as commission models are not supported to account for one-off items and varying margins. Developing or customizing such platforms involves substantial investment in technology infrastructure, as well as ongoing support for continued digital operations.

Most challenges in fashion re-commerce operations arise from the labor-intensive and time-consuming nature of handling unique, secondhand garments, which place significant demands on human resources. Efficient management of these operations relies heavily on the availability and competence of human resources. Firms A and C emphasize the need for specific IT skills to operate and maintain online resale platforms, in contrast to in-store resale. Moreover, product-specific knowledge and skills are essential for effectively sorting and grading garments for online resale, particularly when catering to a diverse online consumer base, and handling specific tasks such as photography, editing, and composing detailed product descriptions. Given the uniqueness of each garment, Firms E and C underscore the importance of attention to detail in assessing each item’s condition and presentation, ensuring the product condition is accurately conveyed in the final product listing.

Brand equity emerged as a significant intangible resource, as emphasized by Firms B and C. In particular, when re-commerce activities are conducted on behalf of well-established fashion brands through outsourcing to a service provider, it becomes essential to recruit personnel who are well-versed in the brand’s identity and values. For instance, Firm H, which provides end-to-end re-commerce services for over 35 fashion brands, highlights the need for employees with specific knowledge and competencies related to each brand.

To overcome such challenges, firms emphasized the importance of investing in process standardization. For example, Firms C and L have implemented automated photo studios equipped with professional-grade cameras and lighting systems to streamline product photography, thereby eliminating the need for skilled photographers. Similarly, Firm D introduced an assembly line featuring specialized machinery to semi-automate its sorting operations. Additionally, collaboration and networking were identified as critical enablers for accessing complementary resources. Firm J, for example, collaborates with web developers to design add-on solutions that enhance processing speed. Firm E benefits from a distinctive integration with logistics providers, facilitating smoother shipping operations.

Technology has emerged as a critical resource that enables efficiency and scalability of fashion re-commerce operations, as consistently emphasized by the firms participating in this study. The following section elaborates on the technology-based resources envisioned by re-commerce firms in addressing ongoing and emerging challenges.

Technology has emerged as a critical resource in overcoming core logistical and operational challenges. In particular, recent developments in process automation and the integration of artificial intelligence (AI) and machine learning (ML) have proven to be transformative across re-commerce operations. For instance, Firm I developed a dedicated mobile application that facilitates the intake and quality assessment of garments directly from individual consumers. The app facilitates uploading images, enabling the firm to conduct a preliminary assessment to determine whether the garment meets the criteria for online resale. This pre-screening mechanism not only enhances the efficiency of the sorting process but also provides consumers with immediate feedback regarding the resale potential of their items. Additionally, the app integrates logistical support by allowing users to download pre-paid shipping labels, thereby facilitating free and convenient shipping.

To minimize the time-consuming and labor-intensive process of photographing items, some firms have adopted innovative technological solutions. For instance, Firm D utilizes a mobile application combined with a dedicated software tool that allows employees to capture product images using their smartphones. The integrated software facilitates batch processing of photos to enhance image quality by removing backgrounds, correcting lighting and adjusting contrast, thus eliminating manual editing. Similarly, Firm E uses integrated software that produces consistent, high-quality images at scale. These digital tools not only save time but also enable firms to maintain visual consistency and professionalism in their online listings.

An AI-powered image recognition tool facilitates Firm I to automate the generation of product descriptions and dynamic pricing for online listings. This tool automatically scans garments, produces high-quality product images while identifying key attributes such as garment type, color, condition and brand. By analyzing this visual data, the system can determine accurate and competitive prices for each item. This integrated approach streamlines the listing process, enhances consistency and improves overall efficiency in fashion e-commerce.

Similarly, Firm O developed a standalone pricing API that functions as an intelligent pricing agent. It leverages both historical pricing data collected over several years and real-time data from various global fashion re-commerce platforms to generate accurate, data-driven price recommendations, helping to standardize and optimize pricing across listings. The solution operates independently and can be integrated into different systems, providing flexibility and scalability for resale businesses. This automation significantly reduces manual effort and accelerates listing speed, a critical factor in scaling re-commerce operations. Moreover, this tool is capable of determining accurate pricing based on image data, ensuring consistency in product pricing and shortening the time from garment intake to sale.

Advanced technology and AI-driven solutions also play a critical role in linking and streamlining back-end and front-end operations. For instance, Firm E uses an NFC (Near Field Communication) chip to track garment status throughout the process, and Firm I uses RFID tags with QR codes with integrated data. These tags are reusable, and data can be assigned and unassigned. Another advancement is the seamless integration of inventory data with Point of Sale (POS), enabling real-time visibility and coordination across inventory and sales activities, both physical and online. This integration supports data-driven decisions, such as real-time inventory updates, reservation-based shopping for consumers and reallocating garments to stores where they are most likely to achieve higher turnover rates in the shortest possible time. These solutions enhance operational efficiency, minimize stock imbalances and contribute to smarter retail decision-making.

AI and ML also facilitate determining the dynamic pricing of secondhand items based on real market data. For instance, Firm O aggregates data from over 500 million secondhand products from re-commerce platforms worldwide, including price variations from initial listing to final sale, and product attributes (condition, color, size, region) to develop an AI-powered pricing agent. Similarly, Firm I utilizes ML algorithms to analyze historical sales data, demand patterns and market trends to recommend optimal pricing. Such tools enhance the competitiveness of listings and help maximize revenue. Moreover, when product owners or sellers receive a commission upon sale, AI can automatically determine and allocate payouts based on pre-agreed terms, reducing manual work.

These findings demonstrate that fashion re-commerce business models increasingly integrate automation and AI-driven tools to enhance efficiency, competitiveness and long-term success. However, firms with limited financial resources often postpone adoption, waiting for these technologies to mature and become more accessible. As re-commerce continues to grow, the use of AI, ML and automation will remain a critical enabler, shaping the feasibility and performance of emerging business models within the evolving fashion re-commerce ecosystem.

The analysis shows that firms adopt different operating models based on the availability and configuration of their logistical and digital resources. Four distinct models have emerged, each reflecting strategic choices shaped by varying levels of logistics capacity, technological capability, brand positioning and reliance on external partnerships. These choices illustrate how firms align their operational structures with both internal and external resources to achieve operational efficiency and scalability while overcoming key challenges.

• (1)

  Direct re-commerce model

In the direct re-commerce model, firms operate in-house re-commerce platforms, based on three scenarios: (i) develop own platforms (Firms D, E), (ii) adopt freely available e-commerce platforms such as Shopify (Firms A, H, L), or (iii) add a resale function to their existing e-commerce platform (Firms B, F). This direct re-commerce model allows firms to maintain full control over their logistics process and customer interface. Firms manage every aspect of the process from collecting used items, sorting, photographing, product listing, pricing and last-mile delivery. These operations are often vertically integrated and optimized for control, meaning they are resource-intensive but aligned with strategic goals around circularity and brand positioning. While operationally demanding, this model provides high autonomy and the ability to retain the brand’s identity and customer relationship throughout the resale journey.

The direct re-commerce model tends to be adopted by firms with strong internal resources across both digital and operational domains. These businesses typically have access to in-house e-commerce expertise, warehouse and process infrastructure, and the organizational routines needed to manage product intake, sorting and resale. Some firms (Firms D, M) adopt a store-integrated resale model, incorporating re-commerce within their physical retail environments to create seamless omnichannel experiences. A key intangible resource here is brand strength, which allows these firms to attract customers to their own resale platform without relying on third-party visibility. The ability to control every stage of the resale process is valuable, but it also demands significant investment and coordination, making this model feasible only for firms with established systems and sufficient operational maturity.

• (2)

  Platform integration model

In the platform integration model, firms (C, G, J, N) sell secondhand fashion through an established third-party resale platform that caters B2C transactions, such as Tradera. In this model, the platform provides visibility, customer access and transaction management, while the secondhand resale firm handles all logistics operations, including collection, inventory, order fulfillment, and returns. Thus, the platform serves as a facilitator, but not as an intermediary in ownership or logistics. This model is often chosen by firms that want to minimize upfront technology investment while reaching a wider audience. However, since logistics remain the business’s responsibility, operational complexity can still be significant.

The platform integration model is more common among firms that have sufficient logistical resources but lack the digital reach or technical capability to build and maintain their own platform. These firms strategically leverage the visibility, customer base and transactional tools of an established resale platform while retaining control over physical operations. The critical resources in this model include logistics coordination and product flow management, combined with the agility to adapt to platform-specific requirements. Although these firms sacrifice some control over customer interaction, they benefit from lower barriers to market entry and reduced technological overhead.

• (3)

  Outsourced re-commerce service model

The outsourced re-commerce service model involves a more comprehensive partnership. In this model, firms collaborate with a specialized re-commerce service provider (Firms E, H) that offers both an online resale platform and end-to-end logistics solutions. The firm’s role is limited to collecting secondhand garments, often through take-back schemes or return programs, and forwarding them to the service provider. The service provider handles the entire process, including sorting, pricing, listing, photographing, inventory management, selling and shipping. This model is best described as B2B2C because the brand works with a business partner to ultimately reach the consumer. This model is driven by firms with limited internal logistics capacity or strategic intent to outsource operational complexity.

The outsourced re-commerce service model is typically chosen by firms with strong brand equity and consumer trust but lack the operational infrastructure or resource commitment necessary to handle resale internally. In this model, brand identity is the firm’s primary intangible asset, and it is strategically paired with the tangible and organizational resources of an external re-commerce service provider. From an RBV perspective, this approach relies on relational resources and external capabilities, meaning competitive advantage is dependent on partner selection and relational quality rather than internal resources. While this model enables rapid market entry and lowers operational complexity, it limits control over branding and customer data. While this model reduces operational burden, it limits the firm’s ability to control service levels, customer experience and resale value capture. This is particularly attractive to fashion brands that want to offer resale options without building internal infrastructure or disrupting core operations.

• (4)

  White-label technology model

The white-label technology model centers around technology solutions offered to secondhand reselling firms rather than end-to-end services. In this case, firms purchase or license a resale platform, either as a complete, ready-to-use solution or as modular tools that integrate into an existing re-commerce environment. For instance, some tech providers (Firms I, O) offer automated solutions for fashion re-commerce, including photography, product tagging or description generation, and streamline listing processes. Unlike the outsourced model, logistics in this model are entirely managed by the reselling firm. The tech provider delivers digital tools but does not intervene in physical operations. This model offers high flexibility and can be tailored to suit the specific challenges of secondhand inventory management.

The white-label technology model aligns with businesses that are looking to scale their resale operations efficiently while maintaining operational independence. These firms may have existing logistics capabilities but require support in specific technical areas such as photography, automated cataloging or user interface enhancement. Their resource configuration reflects a need for tailored technological augmentation rather than full operational outsourcing. By adopting digital tools, these firms can optimize full or parts of the resale process while keeping control of logistics or customer service, enabling a scalable yet flexible approach.

In summary, the choice of operational models in secondhand fashion resale is not arbitrary but is deeply influenced by the firm’s internal resource landscape. The RBV helps explain these configurations by illustrating how firms draw on combinations of logistics infrastructure, technology, brand equity and partner networks to design an operating model that fits their capabilities and strategic priorities, as shown in Table 2.

This study explored the digital business model innovation of the secondhand fashion sector, with a specific focus on key challenges and how technology facilitates more efficient and scalable re-commerce activities. One of the central findings in this study is the distinct set of challenges that differentiate it from traditional e-commerce operations. Unlike conventional e-commerce platforms, which are built around standardized, mass-produced items, re-commerce encounters unique difficulties because each garment is one-of-a-kind, demanding a considerable effort to showcase each item online (Charnley et al., 2022). Tasks such as photographing, describing, pricing, and listing each item add to operational complexity and restrict scalability. Additionally, high-quality images and clear descriptions are vital for building consumer trust and increasing sales (Llach et al., 2023), yet automating such activities remains challenging due to product heterogeneity. These insights underline the necessity for specialized systems and technological solutions specifically tailored for fashion re-commerce.

The study highlights that technological advancements, such as AI, ML, data-driven image recognition and traceability tools (RFID, QR codes), facilitate overcoming logistical challenges and improving process efficiency and traceability. AI-powered tools are increasingly being used to generate product descriptions from images, derive optimal pricing based on market data and automate commission calculations, reducing logistics and operational barriers for firms lacking in-house expertise. The modular and adaptive nature of these digital tools allows firms to selectively integrate technological solutions that align with their strategic goals and resource configurations. As noted by Bae et al. (2022), such digital innovations are redefining how secondhand fashion is bought, sold, and managed in the re-commerce landscape. Importantly, technological solutions offer the potential not only to overcome challenges but also to capture emerging opportunities that can accelerate the growth and scaling (Charnley et al., 2022). By integrating emerging technologies such as blockchain, AI and ML-powered tools for scalable logistics solutions, firms reshape how value is captured and delivered in circular fashion ecosystems. Therefore, the role of technology in re-commerce extends beyond operational efficiency by opening new pathways for business model innovation and value creation. This aligns with emerging literature on digital transformation, which suggests that technologies such as AI, data analytics and platform integration not only enhance efficiency but also open new pathways for value creation and differentiation in digitally enabled circular models (Juge et al., 2022; Tian et al., 2024). Hence, we propose:

This study also emphasizes that a firm’s ability to innovate and scale re-commerce business models is largely shaped by its access to and configuration of both internal and external resources. While existing CBM typologies focused primarily on the degree of involvement of the focal firm (Yrjölä et al., 2021) or the nature of the interaction (Lüdeke-Freund et al., 2019), this study identifies “resources” as a new dimension that determines business model typologies. Internal resources, such as logistics infrastructure and digital tools, alongside access to external resources, such as platforms, logistics partners and technology providers, significantly influence how re-commerce business models are selected and executed. The presence of multiple typologies in this study reflects a strategic diversity in how firms navigate challenges and enhance growth opportunities. This study further identifies that each model represents a distinct strategic response to the opportunities and constraints imposed by the availability and combination of four key resources: logistics assets, technological infrastructure, brand equity and collaborative networks. Therefore, rather than a linear progression, four typologies represent varied organizational responses shaped by differences in a firm’s resources and value creation logics. What emerges is not a hierarchy of models but a spectrum, each with trade-offs in autonomy, complexity, and value appropriation. Based on this analysis, we propose:

Finally, secondhand markets have operated for decades before the digitalization, often without relying on collaboration and the involvement of intermediaries (Hinojo et al., 2022). The direct re-commerce model identified in this study reflects this logic and typically relies on existing e-commerce platforms not tailored for re-commerce. In contrast, this study highlights the critical role of collaboration and resource orchestration in bridging resource gaps to make the re-commerce process resource and cost-efficient. This aligns with the business model innovation literature, for instance, as Bocken et al. (2019) noted, an innovative business model does not only have a company focus, but involves a wider set of stakeholders, necessitating a broader value-network perspective. When a firm operates under resource constraints, collaborative resource sharing, both internally and externally, can assist a firm to gain a competitive advantage (Ren et al., 2023). This study highlights that external re-commerce service providers and white-label technology companies are positioning themselves as resource hubs, offering scalable solutions and technological tools that evolve with industry needs. Their competitive edge increasingly lies in innovative technologies that optimize logistics and resale, from product assessment to increased consumer experience. Established fashion brands opted for partnerships with logistics and platform service providers to reduce the financial and resource needs for in-house re-commerce platforms (Charnley et al., 2022). This innovative approach highlights the network effects in resource orchestration (Sandberg, 2023): it is no longer solely dependent on internal resources or branding, but also on the ability to access, integrate and capitalize on logistical and technological resources in a collaborative network. Thus, we propose.

This study contributes to the expanding field of fashion re-commerce by providing valuable insights into how businesses can effectively manage and scale re-commerce operations. By examining the operational realities of online fashion resale, the study identifies four distinct re-commerce operating models that firms adopt in the growing secondhand market. Using the RBV as an analytical lens, the three propositions illustrate that the development of effective re-commerce business models depends on the interplay between technological innovation, resource configuration and collaboration. Emerging technologies such as AI and data-driven systems (proposition 1) act as catalysts that enhance firms’ resource bases and operational capabilities. However, their effectiveness is contingent on how internal and external resources are configured to support specific business model typologies (proposition 2). Furthermore, collaborative networks (proposition 3) serve as the mechanism through which these technological and resource-based capabilities are orchestrated and scaled across the re-commerce ecosystem. These interrelated dynamics highlight that innovation in fashion re-commerce is not driven by technology alone but by the synergistic alignment of technological, organizational and relational resources.

Overall, the findings of this study offer a framework to understand how firms can leverage internal and external resources to navigate the complexities of fashion re-commerce, providing strategic guidance for both emerging and established players in the secondhand fashion sector. This study aims to develop theoretical insights rather than statistically generalizable results; therefore, the findings are generalizable at an analytical level. The inclusion of firms representing diverse business model typologies enhances the contextual richness of the analysis and strengthens the transferability of insights to other firms undergoing similar digital and circular transitions.

This study makes several important theoretical contributions that deepen the understanding of resource-based perspectives in the context of digital and circular business model transformations. While RBV has long been used to explain the competitive advantage through the possession and deployment of resources, its application in digitally mediated and collaborative business environments remains limited. First, by situating RBV within the emerging context of fashion re-commerce, this study advances theoretical discussions on how firms acquire, configure, and leverage resources in increasingly networked and technology-driven systems. It extends the RBV by illustrating how digital technologies and collaborative partnerships reconfigure traditional firm boundaries. It shows that re-commerce as a viable business model depends not only on the ownership of internal resources but also on the ability to access, integrate and orchestrate external resources through collaborative networks. This perspective broadens the RBV from a firm-centric view toward a more ecosystem-oriented understanding of resource advantage.

Secondly, the study introduces the concept that digitalization and resource sharing jointly drive digital business model innovation in fashion re-commerce. It positions technological capabilities, such as AI-driven systems, data analytics and platform technologies, as both strategic and enabling resources that support innovation and scalability. This contributes to the integration of RBV with the digital business model innovation literature by demonstrating how digital tools not only enhance operational efficiency but also transform the very foundations of value creation and capture. Finally, the study enriches the fashion re-commerce literature by identifying four distinct business model typologies and establishing resources as a central dimension shaping these typologies. This extends prior fashion re-commerce CBM typologies, which largely emphasize the degree of firm involvement or the nature of stakeholder interactions. By highlighting resource configuration as a differentiating factor, the study provides a more comprehensive theoretical lens for understanding the strategic diversity of re-commerce models.

Building on the RBV, this study provides practical guidance for managers navigating the digital transformation of fashion re-commerce. From a practical standpoint, the study emphasizes that firms can adopt a resource-integrative approach that combines internal strengths and external resources. Collaborations with platform providers, logistics partners and technology firms allow companies to bridge resource gaps, reduce capital intensity and accelerate market entry. The integration of digital technologies, particularly AI and data-driven systems, further enables automation in product handling, pricing and quality evaluation, thereby improving operational efficiency and customer trust. These findings highlight that resource orchestration is not only a theoretical construct but a strategic tool for building scalable and resilient re-commerce systems. Moreover, managers should recognize that re-commerce is not merely a sustainability initiative but a strategic extension of the firm’s business model. By aligning digital investments, logistics infrastructure, and collaboration, firms can simultaneously enhance economic, environmental and customer value creation. This integrated approach reflects that innovative re-commerce business models depend on a firm’s ability to access, integrate and reconfigure both tangible and intangible resources across collaborative networks.

From a societal perspective, the study highlights the broader sustainability implications of digital re-commerce. By extending product lifecycles, improving resource efficiency and promoting reuse, these business models contribute to waste reduction and carbon footprint minimization. Furthermore, the accessibility of shared technological and logistical resources democratizes participation, enabling smaller firms and consumers to engage in circular practices. This contributes to a more inclusive and sustainable fashion ecosystem, where economic value creation aligns with societal and environmental well-being.

This study is based on qualitative data collected from a limited number of firms, primarily within specific regional and market contexts. As such, the findings may not fully capture the diversity of re-commerce practices globally, especially in regions with different regulatory environments, consumer behaviors or levels of digital infrastructure. Additionally, the absence of quantitative performance metrics, such as cost efficiency, return rates or environmental impact, limits the ability to link resource configurations to firm performance. Future research could build on this framework by integrating quantitative indicators and including a wider range of stakeholders and complementary cases to enhance understanding of the re-commerce landscape. Additionally, comparative studies across different market segments, such as luxury fashion vs mass market fashion, could offer valuable insights into how segmentation and brand positioning influence the viability of re-commerce models, resource requirements, and operational strategies. Such comparisons may also shed light on how factors such as consumer trust, brand loyalty, and resale uptake vary across segments. Future studies should also examine how technologies such as AI, ML, automation and predictive analytics impact labor dynamics, sustainability performance, and firms’ ability to sustain a competitive advantage in an increasingly digital and circular economy. Furthermore, propositions developed in this study provide a foundation for future empirical testing and theoretical refinement in digitally enabled circular business models.