Search Results (12)

Most current second-hand housing sales, contract signing, and other processes require the participation of intermediaries. However, suppose the intermediary refuses to disclose all information to the parties involved in the transactions. In that case, this traditional model can lead to weak supervision and punishment, adverse selection, moral hazards, and weak contract enforcement. Blockchain technology can not only secure the information intermediaries share, encouraging them to disclose information, but can also generate irreversible records of housing transactions for data traceability. Therefore, this study aims to develop a framework based on blockchain technology for the trading of second-hand housing. In this study, a second-hand housing online trading framework (SHHOTF) based on smart contract development is proposed for the second-hand housing business process, aiming to promote second-hand housing transactions. The contributions of this study lie in (1) determining the framework requirements, (2) proposing the functional module of a framework based on the blockchain and designing a complete business process, (3) developing an architecture for integrating blockchain and second-hand housing transaction processes, and developing technical components that support the framework functions, and (4) demonstrating the use case in Britain, analyzing the effectiveness and innovation of the framework. Furthermore, the framework demonstrated a 24% increase in transaction speed compared to the traditional Ethereum public network. The proposed process is highly adaptable within the current second-hand housing domain, and the developed framework can serve as a reference for introducing blockchain technology into other industries or application scenarios. Full article

At the intersection of the circular economy and artificial intelligence (AI), high-value secondhand trading faces a “triple decision dilemma” of cognitive overload, trust risk, and emotional attachment. To address the limits of traditional human-centered theories, this study develops and empirically tests a novel framework of Algorithmic Empowerment. Drawing on data from 1396 users of Chinese secondhand luxury platforms and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM), the findings reveal that users’ empowerment perception arises from three dimensions—Algorithmic Connectivity (AC), Human–Agent Symbiotic Trust (HAST), and Algorithmic Value Alignment (AVA). This perceived empowerment affects participation willingness through two parallel pathways: the social pathway, where algorithmic curation shapes social norms and recognition, and the cognitive pathway, where AI enhances decision fluency and reduces cognitive friction. The results confirm the dual mediating effects of these mechanisms. This study advances understanding of human–AI collaboration in sustainable consumption by conceptualizing empowerment as the bridge linking algorithmic functions to user engagement, and provides actionable implications for designing AI systems that both enhance efficiency and foster user trust and identification. Full article

Over 24 billion items of used clothing are traded annually from high- to low-income countries in a sector worth more than 4.9 billion dollars. Imported second-hand clothes are the primary source of garments for many of the world’s poorest people. The sustainability of this system and its contribution to the circular economy is explored. Results map the structure of used clothing networks. UN Comtrade data is analyzed to trace the major exporters and importers, and trade and NGO reports are explored to consider the environmental and economic impacts. Second-hand clothing imports have a negative effect on local clothing industries and cause environmental harm in developing countries. The article further explores how the second-hand clothing trade intersects with the circular economy and emerging patterns of clothing re-sale, such as the Vinted peer-to-peer retail platform. Rather than contributing to economic or environmental sustainability or a true circular economy, the second-hand clothing sector is facilitating the expanding consumption of fast fashion in developed economies, stifling industrial development and causing environmental damage via a form of ‘waste colonialism’ particularly in sub-Saharan Africa. Anti-poverty organizations like Oxfam play a contradictory role as they benefit from the trade, which is part of a wider fashion system that is unsustainable. Full article

Driven by the increasing awareness of environmental protection and the demand for personalized fashion, China’s second-hand clothing market is developing rapidly. Chinese consumers have begun to accept second-hand clothing, and online platforms such as Xianyu and Zhier have promoted the widespread trading of second-hand clothing. This study explored the key factors influencing customer satisfaction in China’s sustainable second-hand clothing market. Using a mixed research approach, factors such as pricing strategy, product quality, brand image, customer service, market environment and promotions were identified. The conclusion of grounded theory is that price, product quality, brand reputation, customer service quality, economic environment and platform promotions have a strong impact on customer satisfaction. The Kano model highlights the sensitivity of customer service quality, economic environment and promotions in improving satisfaction. Price is crucial, confirming the price sensitivity of customers. Brand reputation and product quality significantly increase satisfaction. Customer satisfaction significantly affects the amount of sustainable recycling. This study improves the theoretical framework and research hypotheses, provides valuable insights for future research and practical applications and contributes to the sustainable development of the second-hand clothing market. Full article

(1) Background: This study aimed to investigate the influence of seller trust on the purchase intention of consumers of used golf clubs in the context of increasing C2C transactions and further explore the interaction effect of regulatory focus. (2) Methods: Data were collected from 200 participants who had experience purchasing golf clubs through the Carrot Market platform, employing a 2 × 2 experimental design. An independent samples t-test was utilized to examine the effect of seller trust on purchase intention, followed by a two-way analysis of variance to assess the interaction effect of regulatory focus. (3) Results: The results revealed a significant difference in purchase intention based on seller trust, with higher purchase intentions observed when seller trust was high compared to when it was low. Additionally, the interaction effect of regulatory focus was found to be significant in the impact of seller trust on golf club purchase intention. Specifically, when seller trust was high, no significant differences were observed among control focus groups. However, when seller trust was low, promotion-focused consumers exhibited higher purchase intentions than prevention-focused consumers. (4) Conclusions: These findings underscore the importance of seller trust in the context of an expanding market for online second-hand trading platforms. Full article

The sale of second-hand vehicles is a popular trade worldwide, and vehicle fraud is currently a common issue, mainly because buyers can lack a complete view of the historical transactions related to their new acquisition. This work presents a distributed architecture for stakeholders to register transactions over a vehicle’s life cycle in a blockchain network. The architecture involves a non-fungible token (NFT) linked to a physical motorized vehicle after a tokenization process, which denote as the NFT-Vehicle. The NFT-Vehicle is a hierarchical smart contract designed using an object-oriented paradigm and a modified version of the ERC721 standard. Every stakeholder engages with the NFT-Vehicle through distinct methods embedded within a smart contract. These methods represent internal protocols meticulously formulated and validated based on a finite-state machine (FSM) model. We implemented our design as a proof of concept using a platform based on Ethereum and a smart contract in the Solidity programming language. We carried out two types of proof: (a) validations, following the FSM model to ensure that the smart contract remained in a consistent state, and (b) proofs, to achieve certainty regarding the amount of ETH that could be spent in the life cycle of a vehicle. The results of the tests showed that the total transaction cost for each car throughout its life cycle did not represent an excessive cost considering the advantages that the system could offer to prevent fraud. Full article

This study aimed to determine the appropriate fashion design elements for fashion product innovation to achieve continuous product value and promote sustainable fashion. We focused on five fashion design elements: classical, transformative, patterned, multimaterial, and decorative. Using an apparel auction market dataset, we verified how the five design elements contributed to the value continuation of apparel products across five categories. We statistically confirmed the design elements that positively affect the continuous value of different apparel products per fashion product categories. This study can help designers select elements that sustain product value, providing them with direct references for recommendations. Full article

Second-hand trading platforms are helpful to the recycling of resources. It is important to accelerate the construction of second-hand trading platforms and improve people’s willingness to buy second-hand goods. However, due to the uncertainty of second-hand goods, it is difficult to establish the trust between users and complete second-hand transactions. Nowadays, more and more platforms use community-based governance mechanisms to promote relationships between users. Taking the second-hand trading platform Xianyu as an example, this study explores the influence of three specific community-based mechanisms (interest group, feedback mechanism and dispute resolution mechanism) on trust and transaction intention from three dimensions of relational governance. This study compares the different effect between consumers and prosumers. Based on 721 valid questionnaires, a structural equation model was used to analyze the data. The results show that interest group, feedback mechanism and dispute resolution mechanism all have significant positive effects on trust in sellers and platforms. In addition, the impact of a dispute resolution mechanism on trust in sellers and platforms is higher for prosumers than for consumers. This study extends the previous research on community-based governance, contributes to the design of second-hand trading platforms and promotes more users to participate in recycling economy. Full article

With the development of internet-related technology, more and more consumers are trading on second-hand platforms. However, due to the uncertainty of second-hand products, there are a lot of transaction disputes on the platform. It is important to efficiently solve the transaction disputes on the platform. Based on the comment data on social media, this paper systematically studies the public opinion towards the online dispute resolution of Xianyu, China’s largest second-hand goods-trading platform, using word-frequency analysis, theme analysis and sentiment analysis. The study found that online disputes mainly focus on the return and refund issues between buyers and sellers. Crowdsourcing online dispute resolution cannot completely solve all problems, and the platform is still needed to help solve disputes. Consumers are concerned about the convenience of the process operation and the fairness of the results of the crowdsourcing online dispute resolution, as well as the contact channels of customer service. The platform should prevent these situations in advance and avoid disputes from the source. Moreover, the platform should perfect the design of public review system and mechanism, broaden the appeal channels of platform customer service, and improve the platform dispute resolution mechanism. It is of great significance for the development of a second-hand goods-trading platform and the application of an online dispute resolution mechanism to mine the service evaluation and satisfaction factors of consumers on the online dispute resolution mechanism. Full article

Blockchain technology (BCT) enables the automated execution of smart contracts in peer-to-peer (P2P) energy trading. BCT-based P2P platforms allow the sharing, exchange and trade of energy among consumers or prosumers as peers, fostering the decarbonization, decentralization and digitalization of the energy industry. On the other hand, BCT-based P2P energy trading relies on the collection, storage and processing of a large amount of user data, posing interdisciplinary challenges, including user anonymity, privacy, the governance of BCT systems and the role of energy market players. First, this paper seeks to review the state of the art of European data protection law and regulations by focusing on BCT compliance with the General Data Protection Regulation (GDPR) of 2018. Second, it explores both the potentials and the challenges of BCT-based P2P energy trading from a legal–economic perspective. To do so, the paper adopts an interdisciplinary approach which intertwines both law and economics, by reviewing the recent literature on BCT and P2P energy trading. Findings have revealed that the deployment of BCT-based P2P energy trading is still in its pilot stage because of technology immaturity, data protection uncertainty, incomplete disintermediation and the lack of both user awareness and collaboration among market players. Drawing on the review, the paper also proposes a selection of solutions to foster the implementation of BCT-based P2P energy trading. Full article

As the mobile Internet improves by leaps and bounds, the model of traditional offline used car trading has gradually lost the ability to live up to the needs of consumers, and online used car trading platforms have emerged as the times require. Second-hand car price assessment is the premise of second-hand car trading, and a reasonable price can reflect the objective, fair, and true nature of the second-hand car market. In order to standardize the evaluation standards of used car prices and improve the accuracy of used car price forecasts, the linear correlation between vehicle parameters, vehicle conditions, and transaction factors and used car price was comprehensively investigated, grey relational analysis was applied to filter the feature variables of factors affecting used car price, the traditional BP neural network was also optimized by combining the particle swarm optimization algorithm, and a used car price prediction method based on PSO-GRA-BPNN was proposed. The results show that only the correlation coefficient of new car price, engine power, and used car price is greater than 0.6, which has a certain linear correlation. The correlation between new car price, displacement, mileage, gearbox type, fuel consumption, and registration time on used car prices is greater than 0.7, and the impact of other indicators on used car prices is negligible. Compared with the traditional BPNN model and the multiple linear regression, random forest, and support vector machine regression models proposed by other researchers, the MAPE of the PSO-GRA-BPNN model proposed in this paper is 3.936%, which is 30.041% smaller than the error of the other three models. The MAE of the PSO-GRA-BPNN model is 0.475, which is a maximum reduction of 0.622 compared to the other three models. R can reach up to 0.998, and R² can reach 0.984. Although the longest training time is 94.153 s, the overall prediction effect is significantly better than other used car price prediction models, providing a new idea and method for used car evaluation. Full article

Global economic growth, demographic explosion, digitization, increased mobility, and greater demand for heating and cooling due to climate change in different world areas are the main drivers for the surge in energy demand. The increase in energy demand is the basis of economic challenges for power companies alongside several socio-economic problems in communities, such as energy poverty, defined as the insufficient coverage of energy needs, especially in the residential sector. Two main strategies are considered to meet this increased demand. The first strategy focuses on new sustainable and eco-friendly modes of power generation, such as renewable energy resources and distributed energy resources. The second strategy is demand-side oriented rather than the supply side. Demand-side management, demand response (DR), and energy efficiency (EE) programs fall under this category. On the other hand, the decentralization and digitization of the energy sector convoyed by the emersion of new technologies such as blockchain, Internet of Things (IoT), and Artificial Intelligence (AI), opened the door to new solutions for the energy demand dilemma. Among these technologies, blockchain has proved itself as a decentralized trading platform between untrusted peers without the involvement of a trusted third party. This newly introduced Peer-to-Peer (P2P) trading model can be used to create a new demand load control model. In this article, the concept of an energy cap and trade demand-side management (DSM) model is introduced and simulated. The introduced DSM model is based on the concept of capping consumers’ monthly energy consumption and rewarding consumers who do not exceed this cap with energy tradeable credits that can be traded using blockchain-based Peer-to-Peer (P2P) energy trading. A model based on 200 households is used to simulate the proposed DSM model and prove that this model can be beneficial to both energy companies and consumers. Full article