Electronic Commerce Research and Applications最新文献

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.

Extended reality (XR) has attracted the attention of both scholars and practitioners, and the literature addressing XR in the e-commerce context has expanded. Based on the reviewed literature, the current study applied a systematic literature review method to investigate XR use in e-commerce at the individual-consumer level. It aimed to holistically explain how XR use affects individual consumers in B2C e-commerce. The screening of 71 selected peer-reviewed journal articles that had been published since 2000 enabled the current research to identify which factors affect XR use in B2C e-commerce for individual consumers. Specifically, this study analyzed the effects of stimuli on consumers’ cognitive, emotional, attitudinal, and behavioral responses to XR-based e-commerce and the interacting effects among technological, consumer, and product-related factors. Based on the literature review’s findings, an integrated framework is proposed to explain how XR influences individual e-commerce consumers, and four avenues for future research are recommended. This study provides scholars with a holistic understanding of how XR affects consumers in B2C e-commerce at the individual level. It also provides e-commerce practitioners with useful guidelines concerning consumer experience management in XR-based online shopping.

This study explores the effect of user-generated content (UGC) on consumer behavior and investigates how different types of UGC affect consumer purchase intention. The research model was constructed based on media richness theory (MRT), dual coding theory (DCT), and construal level theory (CLT), with the social media and e-commerce platform, Xiaohongshu, serving as the research object. Through five experiments, this study investigates the effect of graphic and short video UGC on consumer purchase intention and explores the mediating effects of perceived psychological distance and perceived value as well as the moderating roles of different UGC creators and experiential disclosure. The findings reveal that short video UGC generates a stronger purchase intention than graphic UGC, and that perceived value acts as a mediator between the type of UGC and purchase intention. Perceived psychological distance mediates the type of UGC and perceived value. Additionally, the type of UGC creator and disclosure of experiential information influence consumer purchase decisions across different contexts. These findings enrich our understanding of the conditions under which the type of UGC affects purchase intention. Our research contributes theoretically to our understanding of how UGC influences consumer purchase intention and, practically, by offering insights for e-commerce development.

Social e-commerce platforms need to undertake the two core tasks of recommending potential social friends and preferred consumption items for users. However, the use of traditional one-dimensional information is no longer able to accurately make personalized recommendations. Early scholars have confirmed that users’ social and consumption behaviors do not exist independently: users with the same interests are more likely to become friends, and there is a high probability of similar consumption behaviors among friends. In this paper, we propose a joint friend and item recommendation model based on multidimensional feature reciprocal interaction (MFRI). Which is based on the user’s social friends and item preference information, extracts the shallow and deep features of the user’s social and consumption behaviors, and utilizes the reciprocity between unusual behaviors to achieve mutual enhancement. The reciprocity between shallow and deep features in similar behaviors is also explored based on the attention mechanism, and the model is trained by a joint loss function. We conducted experiments on real datasets, and the results confirm the effectiveness and robustness of MFRI for potential friend and preference item recommendations.

The anticipatory shipping practiced by online retailers plays an important role in improving customer satisfaction. However, online retailers face a new challenge in anticipatory shipping: they are required to ship a significant amount of products due to a surge of demand during the large e-commerce promotion, which dramatically aggravates the pressure on logistics distribution and reduces logistics efficiency. Therefore, making anticipatory shipping decisions to meet the suddenly increased demand has become an urgent problem for online retailers. Our research addresses this challenge by establishing a new anticipatory shipping system. We propose three cost-sensitive anticipatory shipping models, including cost-sensitive logistic regression (CSLR), cost-sensitive LightGBM (CS-LightGBM), and cost-sensitive CatBoost (CS-CatBoost). Their loss functions are constructed according to the cost of the anticipatory shipping system. Furthermore, we propose two new evaluation criteria to assess the effectiveness of the anticipatory shipping system. It intuitively demonstrates the cost differences after adopting the anticipatory shipping system. Moreover, we explore the real large promotion customer behavior data containing nearly three million samples. Our results find that the proposed cost-sensitive based forecasting models significantly outperform reference forecasting models. Our experimental evaluation concludes that forecasting AUC is more instructive to operational strategy than accuracy. Additionally, our empirical findings suggest that the anticipatory shipping system should be preferentially applied to high-value products. Conversely, low-value products should not choose anticipatory shipping to control logistics costs during surges.

Online reviews and management responses are important user-generated content (UGC) and marketer-generated content (MGC) in social media platforms, which directly reflect the customer’s and merchant’s feedback on a specific consumption and may further affect customer satisfaction. However, as the targeted feedback to UGC, the impact of MGC and the total impact between UGC and MGC are still understudied. In this work, we proposed a systematic analysis procedure for unravelling the impacts of online catering UGC and MGC on customer satisfaction in-depth. By studying the collected 130,412 review data from a leading online catering review website in China, we empirically extracted several new UGC topics using a topic model, thereby expanding the customer value factor set from previous literature; we also quantitatively analyzed their effectiveness in explaining customer satisfaction. We further integrated the UGC and MGC, and causally elaborated upon the usefulness of targeted responses in customer relationship management through difference-in-difference (DID) analysis. The findings validated that the effectiveness of management response on customer future satisfaction is relatively limited compared to the impact brought by the rating variables in UGC, but targeted management responses can perform more effectively in improving customer satisfaction. In addition, moderation roles based on the consumption price and the subjective emotional tendency of customers revealed in the textual comments were also investigated. Thus, this study helped to form a comprehensive understanding of customer satisfaction by contributing to a better quantification of customer value factors and understanding of the influence mechanism for customer satisfaction through the integration of UGC and MGC. Several managerial implications for optimizing customer relationship management in online catering markets were also provided.

Transaction efficiency is critical to the success of e-commerce platforms. We propose a Reverse Double Auction Mechanism (RDouMech), an innovative auction-based algorithm, to revolutionize the transaction process in e-commerce operations. It incorporates unique features of reverse auction, multi-bids, and multi-constraints (i.e., market clearing, individual rationality, and incentive compatibility). This operational innovation facilitates market clearing by precisely matching buyers with sellers, leading to a balance between demand and supply, improved matching rates between buyers and sellers, and a significant increase in transaction prices on the platform. We further validate our algorithm using proprietary data from an online automobile transaction platform. Our results show that RDouMech raises the transaction revenues by 19.73% and doubles social welfare compared to the human expert-assisted artificial intelligence (AI) algorithm that is currently employed by the platform.

This study investigates the impact of macro-level dynamics, such as the COVID-19 pandemic, on customer point-usage behavior in e-commerce, focusing on how global crises alter consumer spending and loyalty program engagement. Using big data from SPK, an F&B holding company, and employing binary logit and panel regression models, this study highlights significant shifts in post-pandemic customer behavior. The findings reveal that both customers with low and high usage levels decreased their spending, transaction frequency, and point accumulation. However, the former showed an increased propensity to use points for purchases, with heightened redemption rates and average points used per transaction, whereas the latter exhibited a decline in these areas. These results underscore the importance of understanding customer-specific responses to economic crises for designing effective loyalty program strategies in e-commerce, and offer valuable insights for the development of promotional strategies and theories in this domain.

Online car-hailing service is practicing the concept of sharing, bringing new energy and convenience to the transportation market. However, with its rapid development, service issues have become increasingly prominent, and improving service quality is the key to the sustainable growth of the online ride-hailing industry. Microblog, as a major online platform for gathering public views, have become one of the significant media for publishing and disseminating information nowadays, which also contains numerous views of online taxi users on online taxi services. Aiming at the deficiency of online car-hailing service under microblog public opinion, this paper integrates Service Profit Chain (SPC) and Process Chain Network (PCN) from the perspective of service management. Firstly, a crawler program is used to collect the Internet Word of Mouth data related to online taxi. Then, a BERT text classification model is constructed to roughly classify the data, and the accuracy of this model is tested to 94.54 %. Afterwards, layer segmentation is implemented based on SPC and CatBoost. Besides, a series of problems in the current online taxi service, such as poor attitude of drivers, unsafe driving, and inefficient communication with the customer service staff, have been identified by using sentiment analysis and frequent mining methods. Finally, PCN analysis is used to optimize the online taxi service, proposing strategies such as setting a reservation reminder function, building a dynamic feedback mechanism, and establishing a contact point for lost and found items. The new SPC-PCN online taxi service system constructed in this paper is vital for service quality optimization, and can also promote the sustainable growth of the online taxi market.