Electronic Commerce Research and Applications最新文献

Artificial intelligence (AI) chatbot have become increasingly popular as a tool for improving employee productivity over the last few years. In the early stages of AI chatbot development, exploring the impact of AI chatbot service failures on user reusage intention is useful for coordinating human–computer interaction and optimizing AI chatbot service mechanisms. The extant literature on AI service failures focuses on service recovery and anthropomorphism. There is less literature comparing different types of service failures and their effects. The article includes three studies. First, a randomized group experiment was conducted with 120 respondents. The results showed significant differences in the impact of different AI chatbot service failures on user reusage intentions. Second, an online questionnaire was completed by 386 respondents, the results found specific impact mechanisms of service failures on user reusage intentions. Third, an interview survey was conducted with 15 customers using AI chatbots to verify the findings of Study 1 and Study 2. Furthermore determine the boundary conditions for the unsupported hypotheses through meta-inference. The research enriches the literature on relationship marketing and expands the attribution theory of service failures. In addition, which provides theoretical basis and practical support for companies to reduce adverse effects of service failures.

This paper analyzes the manufacturer and retailer’s advertising and pricing strategies within three typical modes of advertising under manufacturer encroachment and derives the optimal advertising mode. We find that if the supply chain members choose to advertise, compared to the scenario without manufacturer encroachment, the manufacturer gets more profit attributed to the expanded demand and advertising efforts, but the retailer gets more profit only if the advertising effectiveness is high. Then, we summarize that when the unit advertising cost is low, the supply chain members do not advertise under manufacturer encroachment. Thirdly, we find that retailer advertising cannot be the optimal advertising mode for the manufacturer. When the unit advertising cost is high and the substitutability level is low, joint advertising is optimal; otherwise, manufacturer advertising is optimal. Finally, we delve into the cost-sharing joint advertising strategy of the supply chain members, and find that within the scenario of cost-sharing joint advertising, the manufacturer can get more profit at the retailer’s expense compared to other advertising modes.

This study proposes a heterogeneous recommendation model that does not rely on data sharing. Previous studies have predominantly focused on nested homogeneous domains that share data. However, this approach encounters issues as it could lead to diminished recommendation performance when there is a scarcity of redundant data within these domains. To overcome these challenges, we propose the HeteLFX model, which extracts and bridges the latent features (LF) of each domain. This model resolves the problems by leveraging the metainformation of domain items to generate an LF. LF is extracted for each domain, and bridges are established based on the relevance of the latent knowledge, thereby enabling heterogeneous recommendations. The efficacy of the HeteLFX model was assessed by comparing it with four other heterogeneous recommendation systems, which are variants of X-Map and NX-Map. The results revealed that the HeteLFX model improved performance by reducing the mean absolute error (MAE) by approximately 0.3, thereby underscoring the superiority of the model. Additionally, HeteLFX reduced the MAE by up to approximately 0.45, depending on the relevance of the data within the domain.

The rise of the sharing economy has transformed traditional housing rental practices, with short-term rental (STR) emerging as a successful model in the accommodation sector. However, information asymmetry and trust issues pose significant challenges within STR platforms, emphasizing the importance of improving user stickiness. This study utilizes a two-stage, multi-method approach to validate the dimensions of perceived justice among guests in STR settings and their impact on user stickiness. The results demonstrate that guests’ perceptions of justice are primarily influenced by dimensions such as distributive, interpersonal, informational, and procedural justice, which in turn positively affect platform stickiness through the mediation of trust and relationship commitment. These findings offer valuable insights for addressing justice concerns and enhancing user stickiness in the STR landscape.

Objective

The problem of low model performance caused by the lack of negative samples in the recommendation method based on implicit feedback information can be solved.

Methods

The implicit feedback recommendation model DAEGAN is constructed based on the conditional generative adversarial network framework. The Denoising Auto-Encoder is used as a generator to capture nonlinear potential factors in the interaction and improve the robustness of model. In this paper, a strong and weak negative sampling strategy is proposed, which combines the visibility of user in time points to mine uninteresting items and acquire strong negative samples, and injects these information into the model by modifying the masking mechanism to solve the problem of missing negative samples.

Results

Experiments on MovieLens 100 K, Amazon Movie and TV, MovieLens 1 M datasets show that the recommendation accuracy of CFGAN based on strong and weak negative sampling and DAEGAN proposed in this paper has been improved.

Limitations

The generation of strong negative samples is based on user interaction records, which cannot solve effectively cold start problems in extremely sparse data.

Conclusions

After DAEGAN application, the strong and weak negative sampling method proposed in this paper has generally higher recommendation accuracy than those mainstream recommendation algorithms. The code is available at https://github.com/nanjingzhuyuxuan/DAEGAN.

In the evolving landscape of food e-commerce live streaming, the profusion of textual data, marked by an excess of promotional vernacular and unstructured formats, presents a formidable challenge for event extraction. Addressing these hurdles, we introduce a tailored ontology-based method alongside FMLEE (Food Marketing Live Event Extraction), a joint event extraction algorithm. This approach simplifies the event identification process through meticulous segmentation and the development of an ontology comprising 5 event categories and 19 argument roles. By integrating context-aware embeddings derived from pre-trained language models and applying an adversarial learning tactic, our methodology not only bolsters the robustness of our model but also significantly refines its accuracy in discerning relevant events within the scarce-resource milieu of food live streaming promotions. The effectiveness of the FMLEE model is validated by its achievement of an F1 score of 73.05%, with the inclusion of adversarial learning contributing to a 2.61% enhancement in performance. This evidences our novel contribution to the domain, offering robust technical support for the optimal exploitation of information within the sphere of food live streaming promotions. Simultaneously, this aids in the investigation of innovative applications for consumer engagement within marketing strategies and the smart regulation of marketing activities.

In this paper, we introduce the optimization of hybrid delivery by vehicle and drones. Hybrid delivery by vehicle and drones is advantageous for improving O2O last-mile delivery efficiency. However, vehicle stop selection and drone delivery sortie arrangement are challenging in the optimization of hybrid delivery by vehicle and drones because cyclic sorties and forward sorties jointly influence the total makespan of the delivery. To minimize the delivery makespan, an MILP model for hybrid delivery is formulated considering both the cyclic sorties and forward sorties of drones. In the proposed model, the intertwined influences of the flight times of cyclic and forward sorties and their further effects on total delivery makespan are depicted. Additionally, we introduce an ACO based on set covering for the selection of vehicle stops and a customized HGSADC for the arrangement of drone sorties. A study based on a simulated case and experiments based on 40 generated instances at different scales are explored to assess the optimization of hybrid delivery by vehicle and drones.

A signaling game model is constructed in the framework of a supply chain system consisting of a financially constrained supplier and an e-commerce platform acting as marketplace and a creditor simultaneously. The primary concern of this work is to investigate how the e-commerce platform, which has superior information about market demand, indirectly communicates this information to the financially constrained supplier through its marketing efforts. Under both separating and pooling equilibria, the decisions and profits of each member of the supply chain are firstly considered. Furthermore, using an “intuitive criterion”, the e-commerce platform’s dominant strategy and its influencing factors are analyzed. The results show that when there is a significant difference in the marginal marketing costs, the platform tends to choose the separating equilibrium. When the difference in marginal marketing costs is relatively slight, the e-commerce platform’s dominant strategy depends on the capital of the supplier and the level of market demand fluctuations. Specifically, the e-commerce platform will select the pooling equilibrium when market demand fluctuations are minimal, but the platform will select the separating equilibrium when market demand fluctuations are substantially large. If the supplier’s capital is increased within a certain range, the e-commerce platform is more motivated to select the pooling equilibrium. In addition, the platform usage fee ratio, the platform loan interest rate and the supplier’s prior beliefs are important factors influencing the platform’s decision.

Derivative videos generated based on copyright videos are a common type of user-generated content (UGC) on today’s video platforms. Given the role of derivative videos in resolving consumers’ uncertainty about the quality of paid copyright videos, and their potential to generate advertising revenue, we develop a game-theoretic model to investigate a video platform’s optimal joint-decisions on pricing and advertising to maximize the utilization of derivative videos. We find that including advertisements into derivative videos to derive advertising revenue is not always advantageous for platforms. Under certain conditions, it is more beneficial for platforms to exclude advertisements from derivative videos. The presence of a derivative video may not affect the optimal price of the copyright video. Furthermore, a higher consumer initial quality expectation for the copyright video does not necessarily lead to a greater platform profit in the presence of a derivative video.

Live-streaming commerce differs from traditional e-commerce by bringing consumers and sellers closer together, enhancing the perception of para-social interaction through real-time interactivity. Based on para-social interaction theory, this study proposed two types of para-social interaction: mentor-oriented and partner-oriented. And explored the impact of para-social interaction and product type on purchase intention, mediated by trust. Additionally, this research examined the moderating influence of individual construal level, in combination with the construal level theory. The results of three scenario experiments showed that mentor-oriented para-social interaction promotes purchase intention more than partner-oriented para-social interaction when purchasing utilitarian products. However, partner-oriented para-social interaction promotes purchase intention more than mentor-oriented para-social interaction in the purchase decision of hedonic products. In addition, the construal level moderates the matching impact of para-social interaction and products type on purchase intention. Studying from para-social interaction type perspective, this paper provided insights into the research of consumption behavior in the field of live-streaming commerce and marketing.