Search Results (41)

E-commerce platforms offering regional fresh produce often face a trade-off between logistics costs and product quality. Due to limited use of cold chain logistics, consumers frequently receive damaged goods, resulting in negative post-purchase experiences. This study examines how logistics service encounters, as reflected in consumer reviews, influence subsequent purchase behaviour, and how the alignment between review images and text moderates this relationship. We analyse sales and review data from 694 fruit products on Tmall between February and April 2024. Latent Dirichlet Allocation (LDA) is applied to extract logistics-related review content. At the same time, image–text similarity is assessed using the Chinese-CLIP model. Regression analysis reveals that positive logistics service encounters significantly enhance purchase intention. However, high image–text similarity weakens this positive effect, suggesting that overly repetitive content may reduce informational value for prospective buyers. These findings advance understanding of consumer behaviour in online fresh produce markets by highlighting the interactive effects of logistics experiences and user-generated content. The results offer practical implications for improving logistics services, enhancing content diversity in review systems, and increasing consumer trust in e-commerce environments. Full article

Consumer satisfaction critically determines the operational sustainability of fresh food e-commerce platforms, yet integrated investigations combining multi-source data remain scarce. This study develops a theory–data fusion framework to identify key satisfaction drivers in China’s fresh e-commerce sector. Utilizing Python-based crawlers, we extracted 1252 online reviews of Aksu apples from a certain fresh produce e-commerce platform alongside 509 validated questionnaires. Through systematic literature synthesis, three core dimensions—perceived value (price–performance balance), platform experience (interface usability), and perceived quality (freshness assurance)—were operationalized into measurable indicators. The final structural equation model reveals that perceived value, platform experience, and perceived quality all have significant positive impacts on consumer satisfaction. This study pioneers a methodological paradigm integrating computational text mining (Octopus Collector + SPSS Pro) with traditional psychometric scales, achieving superior model fit (RMSEA = 0.023, CFI = 0.981). These findings empower platforms to implement a precision strategy. The validated framework provides a theoretical basis for omnichannel consumer research while addressing the data-source bias prevalent in prior studies. Full article

As globalization deepens and the digital economy rapidly develops, cross-border e-commerce, especially live-streaming e-commerce, has emerged as a significant driver of international trade growth. However, the highly unpredictable sales demand in this sector and external factors such as the COVID-19 pandemic and Brexit have posed significant challenges in accurately forecasting sales within the UK live-streaming e-commerce market. To address these challenges, we propose a novel sales forecasting framework utilizing the Temporal Fusion Transformer (TFT) model. Our multimodal approach integrates diverse time series data, including historical sales, key opinion leader (KOL) influence, and seasonal patterns. The Temporal Fusion Transformer (TFT) model demonstrated consistently lower Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Squared Error (MSE) across all forecasting horizons compared to other machine learning approaches, including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Gated Recurrent Unit(GPU)-accelerated architectures. Furthermore, it exhibited significantly superior performance over traditional time-series methods such as the Autoregressive Integrated Moving Average (ARIMA) model. This research proposes a phased framework for short-term, medium-term, and long-term forecasting, providing a fresh perspective for product forecasting studies and offering significant theoretical support for cross-border e-commerce enterprises in product life cycle management. Full article

Visual selective attention is a cognitive process by which humans efficiently process critical visual information. It reflects the user’s authentic visual thinking and can be applied by designers in age-friendly design to enhance the user experience of elderly users, meeting their visual needs and attention characteristics. This has significant implications for the age-friendly design of fresh e-commerce product interfaces. This paper explores age-friendly design for fresh e-commerce product interfaces based on the theory of visual selective attention. Experimental data indicate that the optimized interface significantly enhances the user experience for elderly users, with task completion time reduced by 39.62% and satisfaction increased by 60%. First, qualitative research is conducted to uncover the visual selective attention mechanisms of elderly users. Combining this with the framework of fresh e-commerce products, an age-friendly design model is established, including page layout, brand colors, font size, and focal styles. Second, using eye-tracking, descriptive analysis, and correlation coefficient analysis, a comparative analysis of the visual selection behaviors of elderly and young users is conducted, yielding characteristics and principles for age-friendly interactive interface design. Finally, the feasibility and effectiveness of the proposed method are validated through design practice and evaluation. This research provides new insights and methods for the age-friendly design of fresh e-commerce product interfaces. It holds practical significance and value for constructing an elderly perspective in fresh e-commerce and expanding the private traffic of elderly users. Full article

With the acceleration of fresh food e-commerce development, cold chain logistics for agricultural products has increasingly become a research hotspot. However, limited by the number of orders accepted by enterprises, many cold chain transportation vehicles for agricultural products struggle to reach a full load. This undoubtedly increases transportation costs for agricultural product cold chain logistics enterprises. In order to reduce the cost of transportation and increase the profit of enterprises, this paper will adopt the strategy of building enterprise coalition based on cooperative game theory. By increasing the loading rate of transportation vehicles, it will increase the profit of enterprises. First, utilizing the minimization of overall dissatisfaction among players in profit allocation after coalition participation as the objective function, a model of the equal division contribution values of the trapezoidal fuzzy number will be constructed, which will be used as the profit allocation model for the players. Then, the solution of the model will be provided, and the properties including symmetry are analyzed. Second, by improving the loading rate of cold chain transport vehicles as the key and combining various parameters in the transportation stage of agricultural product cold chain logistics, the coalition profit will be calculated. Finally, using the solution of the equal division contribution value of the trapezoidal fuzzy number in the cooperative game as the allocation strategy, the obtained profit will be distributed to each enterprise participating in the coalition. The results show that when dealing with cooperative profit allocation problems in similar scenarios, the solutions of the equal division contribution value of the trapezoidal fuzzy number are highly reliable and adaptable. The method presented in this paper can not only increase the profit of enterprises but also minimize the overall dissatisfaction of all enterprises with the allocation result. Full article

While microparticles can be removed by a filtration step at a drinking water treatment plant (DWTP), engineered nanoparticles (ENPs), which are widely used in industry, commerce and households, pose a major problem due to their special properties, e.g., size, reactivity and polarity. In addition, many ENPs exhibit toxic potential, which makes their presence in drinking water undesirable. Therefore, this study investigated the removal of ENPs in the laboratory and at a pilot-scale DWTP. Eight ENPs were synthesized and tested for stability in different types of water. Only three of them were stable in natural water: cetyltrimethylammonium bromide-coated gold (CTAB/AuNPs), polyvinylpyrrolidone-stabilized gold and silver nanoparticles (PVP/AuNPs, PVP/AgNPs). Their retention on quartz sand, silica gel and fresh anthracite was low, but CTAB/AuNPs could be retained on fresh river sand and thus should not overcome riverbank filtration, while PVP/AuNPs and PVP/AgNPs showed no retention and may be present in raw water. During ozonation, PVP/AuNPs remained stable while PVP/AgNPs were partially degraded. The advanced oxidation process (AOP) was less effective than ozone. PVP/AgNPs were almost completely retained on the pilot plant anthracite sand filter coated with manganese(IV) oxide and ferrihydrite from raw water treatment. PVP/AuNPs passed the filter with no retention. In contrast to PVP/AuNPs, PVP/AgNPs and CTAB/AuNPs were also retained on activated carbon. The integration of a flocculation step with iron(III) salts can improve ENP removal, with PVP/AuNPs requiring higher flocculant doses than PVP/AgNPs. PVP/AuNPs, in particular, are well-suited for testing the effectiveness of water treatment. Further data on the occurrence of stable ENPs in raw water and their behavior during water treatment are needed to perform a risk assessment and derive the measures. Full article

With the rapid development of e-commerce in China, “live broadcast + e-commerce” has become an emerging mode of the e-commerce industry. The speedy growth of the digital economy has also provided a favorable external environment for fresh agricultural products to ride on the e-commerce express train. As a result, more and more suppliers of fresh agricultural products choose to sell their products on e-commerce platforms and conduct live streaming. This paper utilizes the e-commerce supply chain field survey data, takes tomato as the specific analyzed variety, and studies the revenue distribution pattern of each subject in the supply chain. The purpose of this paper is to explore the optimization strategy of the revenue distribution of an agricultural e-commerce supply chain, and the results show that the agricultural e-commerce supply chain effectively promotes the matching of supply and demand of medium- and high-end agricultural products, realizes the quality premium of medium- and high-end agricultural products, and significantly improves the overall revenue level of the supply chain. The field survey found that under the supply chain model of “farmers + cooperatives + e-commerce platform”, farmers, cooperatives, and e-commerce platforms gained $2865, $3098, and $1111, respectively, for operating one mu of tomatoes in a year. According to the traditional Shapley value method, from the point of view of the contribution to the cooperative income, part of the income of the farmers and cooperatives should be compensated to the e-commerce platform, and the specific results are as follows: the income of the farmers should be reduced from $2865 to $2729, the income of the cooperatives should be reduced from $3099 to $2955, and the income of the e-commerce platform should be increased from $1111 to $1390. According to the Shapley value method based on the risk coefficient correction, from the perspective of risk compensation, part of the earnings of the e-commerce platform should be compensated to farmers and cooperatives, so as to establish a more equitable and reasonable pattern of earnings distribution, with the specific results: the earnings of farmers should be increased to $3205, the earnings of cooperatives should be increased to $3148, and the earnings of the e-commerce platform should be reduced to $722. Full article

The paper aims to analyze the consumer joint choice behavior on fresh food purchase channels and terminal delivery services during major public health events, with the purpose of revealing the underlying influencing factors and behavioral characteristics. First, based on random utility maximization theory, the cross-nested logit model is formulated, which takes into account the influence of socioeconomic attribute factors, service attribute factors, risk perception attribute factors and trust perception attribute factors. Second, a questionnaire survey is conducted, and the obtained data are used to estimate the model parameters and perform an elasticity analysis of the utility variables. The parameter estimation results demonstrate that in the context of major public health events, consumers consider adjusting their attitudes toward e-commerce platforms first when the utility variables are altered, and fresh food purchase channels are easily replaced for consumers who choose unmanned equipment home delivery. The elasticity analysis results suggest that consumers are more willing to buy fresh food through community group-buying channels, are more sensitive to the convenience of the purchase process and are less concerned with delivery time. Although person-to-person contact increases the risk of infection, consumers still prefer attended terminal delivery services. Furthermore, consumers least agree with the effectiveness of body temperature detection methods in public places but feel that an effective way to increase consumer trust in enterprises is to strengthen personnel protection measures. Full article

Different refund policies offered by e-commerce platforms provide diverse options for consumers and are crucial for enhancing after-sales service. This study constructs a refund and ordering decision model based on three typical refund policies: both basic refund and refund guarantee option (‘Policy I’), basic refund only (‘Policy II’), and refund guarantee option only (‘Policy III’). We examine scenarios where demand is influenced by price, refund policies, and stochastic factors, and returns are affected by refund policies, aiming to determine the optimal refund and ordering decisions for fresh produce e-commerce platforms. Our results indicate that, under the same parameters, the platform achieves the maximum order quantity and highest expected profit with Policy I. The return rate under Policy I is always higher than under Policy III, but not consistently higher than under Policy II. Additionally, as the sensitivity of demand to the refund policy increases, both the order quantity and basic refund price rise, while the refund guarantee option price decreases. Conversely, as the sensitivity of returns to the refund policy increases, the opposite occurs. Although market demand uncertainty does not impact the basic refund or refund guarantee option prices, the platform must increase order quantities to manage market volatility. Full article

Logistics service quality (LSQ) plays a vital role in providing excellent customer experience, particularly in e-commerce. Using mobile devices for food and fresh product orders is very common, but delivering these products is very challenging. In this study, we aim to evaluate the factors influencing logistics service quality (LSQ) in the context of fresh product e-commerce. The relevant literature was reviewed and a preliminary survey among field experts was conducted to establish the proposed LSQ scale. A qualitative study was carried out on fresh product e-commerce customers. A survey involving 222 participants was analyzed, and an LSQ evaluation scale was formed and evaluated scientifically and empirically. Research results showed that reliability, convenience, freshness, and personnel contact quality are the four key dimensions of the LSQ scale in the e-commerce platform for fresh and perishable items. The results of the study can help the managers of e-commerce companies to understand the LSQ criteria that determine customer satisfaction and consequently make the appropriate LSQ improvements. Full article

Given the widespread adoption of fresh food e-commerce, this study aimed to explore the service quality attributes influencing utilitarian value, hedonic value, and impulsive buying behavior. A survey was conducted with 263 participants who had experience in purchasing fresh food online. Their responses were analyzed to test hypotheses using structural equation modeling. The findings reveal significant influences of information quality, ease of use, and problem resolution on utilitarian value. Additionally, ease of use, problem resolution, and trendiness were found to impact hedonic value. Problem resolution was a quality factor that affected both practical value and hedonic value, and its influence was found to be greater than that of other service quality factors. Hedonic value was also found to significantly affect impulsive buying behavior; however, utilitarian value did not exhibit a significant impact on impulsive buying behavior. The results provide useful theoretical and managerial implications in the field of fresh food e-commerce business. Full article

Misreporting is prevalent in supply chain characterized by asymmetric information, and its impact on the supply chain is substantial and cannot be overlooked. In order to explore the impact of fresh supplier’s misreporting decisions on fresh supply chain, this paper takes the fresh supply chain with a single fresh supplier and a single e-commerce enterprise as the research object, and constructs five Stackelberg game models based on the differences of supply chain information transparency and power structure. Particularly, the effect of fresh-keeping level on the after-sales rate and market demand of the product is incorporated into the model, and the following conclusions are drawn by solving and analyzing the decision results of the different models: (1) When the supplier has the decision advantage, it will not choose to misreport. But when it loses the decision advantage, it will produce the misreporting behavior. Supplier misreporting is detrimental to the retailer and the supply chain; specifically, it can lead to lower fresh-keeping level and higher after-sales rates. (2) In the decentralized decision-making model, it is more beneficial for the supply chain that the supplier has the leading right of decision-making. In the absence of misreporting, the leader’s profit is always higher than that of the follower. When there is misreporting, even if the retailer is the dominant player, its profit is still lower than the manufacturer’s. (3) Both supply chain profit and fresh-keeping level are positively correlated with the coefficient of consumer perception of freshness and the coefficient of sensitivity to fresh-keeping technology, and are more significant under the centralized decision-making model. Furthermore, in response to supplier misreporting behavior, this paper achieves coordination in the fresh supply chain by designing a joint contract and confirms the effectiveness of this contract through an arithmetic analysis. Full article

Given the swift expansion of fresh e-commerce, the front warehouse mode can respond quickly and ensure the quality of fresh products. However, the complexity of the supply chain structure under front warehouse mode poses a challenge in reducing logistics costs and improving distribution efficiency while meeting consumers’ immediate delivery demands. Therefore, this paper studies the vehicle routing problem of two-echelon fresh e-commerce under front warehouse mode. Considering trans-shipment time constraints between the two echelons and the characteristics of terminal distribution, this paper initially models the vehicle routing problem for front warehouses as a two-echelon multi-trip capacitated vehicle routing problem with time windows. A mixed-integer linear programming model is subsequently established. To solve the model, a hybrid genetic algorithm integrated with neighborhood search is developed. Matrix coding is employed to merge vehicle selection and route assignment decisions. Simultaneously, neighborhood search is applied to enhance the search capability of algorithms, thereby improving the quality of solutions. Furthermore, the effectiveness and efficiency of the model and algorithm are verified through experiments of varying scales. Finally, comparative strategies and sensitivity analysis highlight the advantages of multi-trip strategies and provide insights into the optimal vehicle capacity limit. Full article

To reduce the losses caused by insufficient preservation efforts during transportation, the preservation effort level has been the focus of research. In the fierce competition of online sales, it is particularly important to reduce the cost of damaged goods by improving the level of preservation efforts. Therefore, according to Stackelberg game theory, this article establishes five decision-making models and incorporates the damage rate and preservation effort level into the research. Finally, this article coordinates the online shipping supply chain (SC) through a joint contract. After comparing and analyzing the model results, research has found that: (1) in centralized model, the level of preservation effort reaches its optimal level and the system benefit is maximized; (2) under third-party logistics (TPL) leading decision-making, the different bearers of cargo damage costs will not affect the profits of both parties and the system; (3) among the four decentralized models, the level of preservation efforts and system profit are highest when the decision is led by online store and TPL bears the cost of damaged goods; and (4) under a given sharing ratio, when the logistics service quotation satisfies a certain range of condition, the online shopping SC can achieve Pareto improvement. This paper studies the differences and reasons for decision models in the supply and demand relationship between online stores and TPL, which provides fresh product e-commerce decision-makers with a theoretical basis. Full article

In the context of an evolving socio-economic landscape and rising living standards, the online market for fresh products, encompassing fruits, vegetables, meats, dairy, and eggs, has seen substantial growth, necessitating sophisticated logistics for e-commerce home delivery. This study tackles the distinct challenges of fresh product delivery, which demand rigorous adherence to climate conditions and product mix during transport, significantly influencing the operational strategies and scheduling of delivery platforms. To address these challenges, a comprehensive mathematical model was developed to optimize fresh food home delivery scheduling, focusing on reducing spoilage rates and accommodating the dynamic impact of environmental temperature changes. The model posits assumptions of a consistent and ample supply of fresh goods, standard initial quality loss, and efficient porter assignment for multi-category order combinations. It introduces three objective functions, targeting the minimization of fresh food loss, maximization of customer satisfaction, and reduction of distributor costs. The efficacy of the model and its genetic-algorithm-based solution method was assessed through numerical analysis and case studies, illustrating that the model enhances delivery efficiency and service quality across varying temperature conditions. This substantiates the critical role of environmental temperature management in optimizing fresh food delivery, offering a robust framework for advancing logistical operations in the perishable goods sector and ensuring quality and efficiency in fresh food e-commerce delivery. Full article