Electronic Commerce Research最新文献

Scholars focus on reducing the compression ratio of high-dimensional information decision models and improving model efficiency. Thereby, the data traceability model is optimized. First, the data intelligent supply chain and traceability models in the post-public health crisis era are analyzed. Next, the issues existing in the current information traceability models are discussed. Finally, a new high-dimensional information decision model is designed by optimizing the data traceability model. The feasibility of the optimized model is validated through experiments. The experimental results demonstrate that different frequency factors impact the model’s compression ratio when comparing the optimized model proposed with the traditional model. Furthermore, the optimized model has a lower compression ratio and higher efficiency, as it does not require encoding the entire origin set. To further validate the rationality of the proposed model, experiments are conducted to compare the compression time ratio for data sizes ranging from 100 to 250 Mb. The experimental results show that the optimized model is minimally affected by the file size and has higher efficiency for files of the same size. Therefore, this work provides valuable insights for optimizing information decision models.

Consumer behavior varies across different countries due to their distinct cultural backgrounds. Gaining a comprehensive understanding of this influence can greatly assist restaurant managers in achieving higher business performance. However, academic inquiry into cross-cultural differences in customer preferences for specific restaurant attributes, such as décor, food variety, and reservation, remains scarce, warranting further scholarly investigation. This paper analyses customer preferences for specific restaurant attributes based on aspect-level sentiment analysis of online reviews from Chinese and Western customers. We adopt ordinary least squares regression to analyze the impact of country on customer attention to different restaurant attributes and carry out quantile regression on customer satisfaction to determine the satisfaction variance in different service performance level. The results show that Chinese and Western customers demonstrate divergent levels of attention and satisfaction towards specific attributes. Specifically, Chinese customers exhibit higher interest and satisfaction in non-functional attributes, such as View, while allocating less attention to value-oriented attributes like Portion size of dish. Moreover, the impact of country on customer satisfaction displays heterogeneity, exhibiting a U-shaped variation across performance levels. To elucidate these differences, we delve into unique cultural elements in China, such as Confucian values and face culture, within the framework of Hofstede's cultural dimensions. Our work delves into the specific attribute-level preferences of Western and Chinese consumers, highlighting the heterogeneity of these preference differences at different performance levels. It underscores for managers the importance of considering consumer preference differences in conjunction with their own service performamce levels.

Brand equity co-creation can drive up platform value. In real world, celebrities play a crucial role in influencing brand equity co-creation. However, in metaverse celebrity-infused entertainment activities, there is a research gap in the impact of celebrities on brand equity co-create intention, which is crucial for marketing. Through a moderated mediation model, we examine the impact of celebrity virtual image and celebrity real-life influence on experience co-creation (parasocial relationship, presence) and further analyze the impact of experience co-creation on brand equity co-create intention. Additionally, we explore the moderating effect of celebrity real-life influence. Our study provides theoretical contributions by linking celebrity features to brand equity co-create intention from a virtual experiential-dominant perspective. We also find that celebrity real-life influence has a virtual-reality continuum and the characteristic of implicit priming moderating effect. We suggest that new media marketing practitioners recognize that metaverse celebrity virtual image branding and endorsement can be cost-effective while ensuring effectiveness.

One-fifth of sales are made on price promotion. Price promotion is like winning a bonus reward. Despite the potential negative effect of price promotion on perceived quality, previous research predominantly indicates a positive effect of price promotion on sales. However, our analysis indicates that price promotion can hurt sales under certain circumstances. We examine more than ten million transactions on an online travel booking agency. The effect of price promotion on sales largely depends on online review valence and decision context. The positive effect of price promotion is attenuated when online review valence signals poor quality and price promotion is normally not expected. Price promotion is only a risk signal of poor quality when it is incongruent with the decision context and online reviews signal poor quality. Price promotion is a preferred sales strategy particularly when customer feedback is unfavorable. Meanwhile, our analysis indicates that launching price promotion in such circumstances hurts sales. The findings of this study show that heuristics and word of mouth together can influence people to make irrational decisions.

Recognizing the challenges identified in the vast literature exploring the intellectual landscape of Online Reputation Management (ORM) in the realm of e-commerce, this study performs a quantitative bibliometric analysis, specifically a co-citation analysis using CiteSpace software, to find thematic clusters in a sample of 1136 papers containing 48,385 cited references. This is the first co-citation analysis of ORM literature that cluster the intellectual structure and identifies both the intellectual turning points and burst papers. The results reveal 14 distinct co-citation clusters, each representing a unique thematic structure. An in-depth analysis further characterizes the clusters, ranging from the impact of online reputation on the hospitality industry to frameworks explaining trust formation in e-commerce. Additionally, the study identifies intellectual turning points by assessing betweenness centrality, highlighting four seminal papers that have strongly influenced the field. Furthermore, burst detection analysis uncovers the temporal dynamics of research trends, showcasing the enduring influence of certain clusters and the transient nature of burst patterns. The novelty and importance of the results from the detailed burst detection analysis lie in identifying a significant evolution in research focus over time. Initially, research was concentrated on foundational studies and understanding customer behavior. It then shifted towards practical applications in specific industries, particularly in hospitality and online reviews. In recent years, the emphasis has been on integrating ORM into broader business strategies, especially within e-commerce and the collaborative economy. This research not only contributes to a deeper understanding of ORM, but also serves as a valuable guide for researchers, practitioners, and policymakers in the evolving landscape of online reputation in e-commerce.

In the ever-changing world of business, gaining valuable insights from customer perspectives is crucial. Consumer evaluations are crucial performance indicators for businesses seeking to enhance their impact. Cyberspace is expanding with an increasing volume of reviews, making it challenging to extract relevant information for desired products. This research explores sentiment analysis for Amazon product reviews in the domain of communication technology, utilizing four publicly available datasets. Sentiment analysis is frequently employed to support E-Commerce platforms in monitoring customer feedback on their products and striving to understand customer needs and preferences. Acknowledging that solely relying on user reviews is insufficient to achieve the best performance, we enhance our approach by incorporating additional context from product titles and headlines for a more comprehensive understanding of the learning algorithm. This paper utilizes three distinct embedding methods, including TF-IDF, Word2Vec, and FastText. FastText outperformed other embeddings when stacked with XGBoost and CatBoost, resulting in the FastXCatStack model. This model achieved accuracy scores of 0.93, 0.93, and 0.94 on mobile electronics, major appliances, and personal care appliances datasets respectively, and linear SVM showed an accuracy score of 0.91 on software reviews when combined with FastText. This research study also provides a comprehensive analysis of deep learning-based models, including approaches like LSTM, GRU, and convolutional neural networks as well as transformer-based models such as BERT, RoBERTa, and XLNET. In the concluding phase, interpretability modeling was applied using Local Interpretable Model-Agnostic Explanations and Latent Dirichlet Allocation to gain deeper insights into the model’s decision-making process.

Knowledge sharing in the digital platform ecosystem is important for the entire system to remain innovative and thrive. However, the research on the knowledge-sharing behaviour and optimal strategy of the platform owner and third-party developer in the platform ecosystem is lacking. This study employs a differential game model to explore the optimal knowledge-sharing effort and revenue levels for each platform owner and third-party developer and the optimal revenue for the whole ecosystem in three scenarios: a Nash noncooperative game, a Stackelberg game, and a cooperative game. The results show that the knowledge decay, the cost and revenue of knowledge sharing, and the knowledge sharing ability of participants have important impacts on the knowledge sharing behaviour of participants. Platform owners can improve the knowledge-sharing efforts of the third-party developers in the form of subsidy, while improving the optimal returns for both parties and the whole system. In the cooperative game scenario, the payoff of the participants is strictly better than that in the non-cooperative game scenarios. Finally, based on these conclusions, this paper presents the optimal knowledge sharing strategy for all participants in the platform ecosystem.

Product mapping or product matching is the field of research dedicated to solving the problem of identifying which product listings (including names, descriptions, specifications, images, and other information) from different e-shops refer to the same product. The problem belongs among important data integration tasks processing data originating from different sources and with different structures. In our previous work, we created basic ProMapEn and ProMapCz datasets for product mapping in English and Czech. The main advantage of the ProMap datasets compared to existing product mapping datasets is that they contain different types of non-matches based on the similarity of the two products. In this paper, we extend the previous two datasets into a completely new collection of datasets for generalized product mapping in the Czech and English languages. We publish those datasets freely for other researchers in the area of product mapping on e-commerce. The main contributions are the extension of the ProMap datasets by adding a new class of non-matching products, the introduction of new ProMapMulti datasets of product pairs from multiple English e-shops, and the introduction of ProMapTransl datasets, obtained by translating the Czech datasets to English and vice versa. Moreover, we provide a very detailed analysis of these datasets with several experiments based on neural network techniques comparing different text preprocessing methods, and similarity computation methods. We also compare the differences among several product categories and evaluate state-of-the-art product mapping methods on these datasets. We also include generalised entity matching techniques and compare their behaviour on product mapping datasets which belong to this area. Finally, we include an appendix with a number of other basic experiments, such as an analysis of feature importances.

In a dynamically changing environment of post-pandemic, uncooperative investment of multiple tourism product suppliers’ service efforts and non-synergistic bundled pricing of multiple complementary tourism products lead to poor consumer experiences and satisfactions and low operational performance of tourism omnichannel. This study takes a game-theoretical approach to explore and understand the business dynamics regarding the investment of service efforts and bundled pricing mechanism of multiple suppliers’ complementary products in a tourism omnichannel, evolving rapidly in the tourism industry with still rare research literature. A generic tourism omnichannel structure with multiple complementary tourism product suppliers is conceptualized and formulated into game-theoretical models to investigate the optimal operational decisions on the service efforts investment and bundled pricing approach considering centralized, decentralized and cooperative decision scenarios. The derivation and comparison of the optimal decisions and outcomes for these models have shown that the cooperative strategy regarding the investment of service efforts and the bundled pricing of complementary products creates better operational performance than those of the decentralized ones in a tourism omnichannel. The findings of the numerical and sensitivity analyses offer valuable strategic insights to tourism omnichannel practitioners. The tourism omnichannel study also provides a better theoretical foundation for future tourism omnichannel research.

Internet of Things (IoT) technological firms have the potential to enter new product domains, which poses a significant impact on both themselves and established incumbents. This study investigates whether an IoT technological firm (entrant) enters a new product area. If so, does the entrant make its technology available and do the incumbents adopt this technology? We develop game-theoretical models in three scenarios: the base scenario with only an incumbent, the competitive scenario where an incumbent and an entrant compete, and the co-opetitive scenario where an incumbent and an entrant form a co-opetition relationship. Our results suggest that an IoT technological firm enters a new product area only when the IoT functionality embedding cost falls within a certain range. The entrant shares its technology when the payment rate is not particularly low. The lower the product substitution rate and/or the higher the IoT functionality embedding cost, the more willing the entrant is to share. The incumbent adopts the entrant’s IoT technology only when the payment rate is not too high and is more reluctant to adopt the technology as the IoT functionality embedding cost goes up. Furthermore, we consider an extension where the IoT technological firm enters multiple product areas simultaneously. We find that the entrant is always willing to enter more product areas. And the more product areas the entrant enters, the more eager the incumbents are to use the entrant’s technology.