Electronic Commerce Research and Applications最新文献

As an important means to optimize organizational profitability, recommendation systems have been widely applied on e-commerce platforms in recent years. Their goal is to identify products of interest from which users have not browsed. To achieve this, prior work often relies on negative sampling strategies to guide the learning of user and product representations. In these strategies, products that users have not browsed are treated as negative labeled samples (products that users dislike). However, the negative sampling strategy fundamentally contradicts the goal of recommendation systems. With the number of products further increases, more “positive but not been browsed” products will be treated as negative labeled samples, leading to the introduction of noisy supervision signals during model training and thereby affecting recommendation performance. This paper proposes a Graph-based Bootstrapped Latent Recommendation model, dubbed GBLR. GBLR is a self-supervised framework that is trained using only positive user–product pairs. It utilizes a graph convolutional network to aggregate local neighborhood features of users and products, bootstrapping latent contrastive views. Subsequently, a symmetric cosine similarity loss function aligns the contrastive views of positive user-product pairs, guiding the model to learn consistent representations of users and products. With this self-supervised approach, the model can effectively learn the user and product representations in the absence of negative labeled samples. Experiments on three public datasets show that the proposed GBLR can effectively complete the recommendation task and outperforms the state-of-the-art baseline models. In the era of e-commerce, the innovative research on recommendation methods conducted in this work can optimize platform operations, enhance user experience and merchant revenue, thereby achieving a win–win situation for all parties involved, and holds significant practical value.

When not effectively addressed, ambivalence in customers can lead to various negative outcomes, such as decreased loyalty or purchase intent. Despite this, current research has yet to account for approaches that govern ambivalent customer behavior. To close this gap, we introduce a novel research conceptualization that considers a customer’s engagement with technology as an interaction with instrumental, symbolic, identity, and aesthetic cues. Building on the results of a hermeneutic literature review and construction of a pathway model, we demonstrate the potential of a cue-based design approach for Customer Facing Technologys (CFTs). The cue-based design emphasizes the reinforcing or oppositional interactions of signals surrounding the engagement with technology, making it possible to address ambivalence. Ultimately, we propose reconsidering the way CFTs are currently investigated in research to help explain deviations in customer behavior.

This research seeks to investigate how interactions on social media influence the collaborative creation of value in promoting healthy consumption. To gain a more profound understanding of the process of social media communication and its impact on generating shared value, we employed the Stimuli-Organism-Response (SOR) model to formulate our hypotheses. Our study drew upon a survey conducted across five countries and regions (Canada, Malaysia, Taiwan, the United Kingdom, and the United States). By utilising Structural Equation Modelling (SEM), we tested these hypotheses. The results of our research indicate that social media communication significantly enhances the quality of relationships within online communities, subsequently influencing the creation of value in the context of healthy consumption. The findings show individuals who maintain good relationships within their social media networks and believe they can obtain not only important information but also are more likely to share information about their own experiences regarding healthy consumption. We conclude the paper by discussing the theoretical and practical implications of our findings.

Supply chains play a decisive role in the development of e-commerce. Although site selection problem for e-commerce warehouses is an important aspect of supply chain management, this topic has received insufficient research attention. This paper fills this research gap by proposing an evaluation framework for the e-commerce warehouse site selection. This framework incorporates consumer repurchase intention (CRI) as a key factor in the evaluation to analyze its impact. This study also employs the matter-element evaluation model to classify the levels of alternatives, and then adopts the probabilistic linguistic terms set to optimize the research results. The feasibility and effectiveness of the proposed evaluation framework is validated through a case study of e-commerce warehouse site selection in China. The results of the case study demonstrate the performance of the proposed framework and highlight the significant impact of CRI on the evaluation outcomes. The proposed framework makes full use of the evaluation information and significantly improves the differences between adjacent alternatives, which can enhance the credibility and accuracy of decision results to provide a more reliable alternative ranking for decision makers.

In the context of “Internet + recycling”, remanufacturing enterprises seek cooperation with recycling platforms to develop online recycling channels. Given that many manufacturers have capital constraints in the recycling and production process, we consider bank and recycling platform to provide financial assistance to the manufacturer. This paper constructs a Stackelberg game model under bank financing and recycling platform financing based on two forms of online recycling cooperation: entrusted recycling and direct recycling. Through the comparison of equilibrium profits, the participants’ financing and recycling channel preferences are explored. The results show that the recycling platform is willing to provide financing service for the manufacturer with a larger capital gap, but there are feasibility conditions for the setting of its loan interest rate. The manufacturer with less initial capital tends to choose the financing scheme with a relatively low interest rate, while the reduction of capital gap increases the propensity for bank financing. The lower platform commission prompts the manufacturer to implement direct recycling, and the increase in cost saving rate of remanufacturing and platform interest rate makes the superiority of direct recycling even more significant. However, entrusted recycling is always more profitable for the recycling platform regardless of the financing scheme. Furthermore, differentiated product sales and portfolio financing can have a certain impact on the manufacturer’s financing and recycling decisions.

Live streaming in e-commerce often faces challenges like counterfeit products and high return rates. To mitigate this issue, retailers can adopt blockchain technology for product traceability to enhance consumer trust, i.e., blockchain-traceable products may be sold in live streaming. Our paper presents three analytical models under various return policies to explore how live streaming and blockchain technology interact in e-commerce. Retailers can invest in live streaming with influencers to attract consumers and in blockchain technology to reduce product returns, benefiting from a demand-enhancing effect and a price-enhancing effect, respectively. The retailer’s investment strategy between live streaming and blockchain technology depends on the interactions of these two effects, moderated by the retailer’s budget type, the market commission level of live streaming, and the traceability cost of blockchain technology. For budget-constrained retailers, live streaming and blockchain technology act as substitutes. When the commission level is low (high) and the traceability cost is high (low), the demand-enhancing (price-enhancing) effect dominates the price-enhancing (demand-enhancing) effect, thus the retailer invests in live streaming (blockchain technology) as a substitute for blockchain technology (live streaming). Conversely, for budget-unconstrained retailers, they may act as complements, prompting investment in both when commission levels and traceability costs are low. The retailer does not invest in either when both the commission level and traceability cost are high. We extend our results to a full refund policy to show the robustness of our result and find that it reduces the retailer’s incentive to invest in live streaming or blockchain technology.

Nowadays, consumers often sequentially use multiple screens to watch media content, so that firms advertise on TV, websites, and apps at the same time period to reach consumers widely. However, sequential multiscreeners may repeated exposure to the same advertisement and develop negative attitudes. Utilizing real-world data, this paper employs a zero-inflated negative binomial (ZINB) model to predict consumer sequential multiscreen viewing frequency. Moreover, we quantify the net impacts of significant predictors on the sequential multiscreen viewing frequency and find that media factors (such as number of viewing devices, internet access, PC screen size, cellphone ownership, and device concentration ratio) have equivalent impacts as audience factors (including user demographics and past viewing behaviors) and the new created variable device concentration ratio has the largest impact. The accurate prediction and quantified net impacts can guide firms allocate their advertising budget more efficiently across multiple screens and avoid consumer overexposure to the same ad content.

Live commerce is a novel form of social commerce characterized by real-time interaction and synchronous visualization. Drawing upon affordance theory and the affective-cognitive framework, we propose a comprehensive research model that integrates three key dimensions of affordances of live commerce technology, namely utilitarian (visibility), social (social presence, interactivity, self-presentation), and hedonic affordances (entertainment), together with consumers’ cognitive and affective states (perceived diagnosticity and psychological distance). Furthermore, we explore age as an important contingency factor in understanding consumers’ purchase intention in live commerce. We collect data from 353 consumers who have live shopping experiences in China and analyze the research model with the structural equation model approach. Results indicate that visibility, social presence, and interactivity positively contribute to perceived diagnosticity, whereas entertainment and the three social affordances reduce psychological distance. Notably, the influence of perceived diagnosticity on purchase intention is more pronounced among older consumers, whereas the impact of psychological distance on purchase intention is stronger among younger consumers. This study not only advances the understanding of affordance theory within live commerce contexts but also offers practical insights for streamers and live commerce platforms.