Electronic Commerce Research最新文献

With the advent of reform and an open economic policy, the tourism domain has undergone accelerated growth paralleled by an enhancement in people’s standard of living. The diversity in tourism offerings corresponds to the evolving preferences of consumers, and the tourism industry has transformed into a foundational component supporting the national economy. The expansion of tourism can exert a significant influence on the Gross Domestic Product (GDP) of an entire region, subsequently fostering the development of the regional economy, population, and cultural aspects. The economic status and influence of tourism in society can be seen. Today, consumers have focused on pursuing tourism brands. This study aims to examine the influence of multimedia technology on branding within the tourism industry and its implications for driving growth. Data was collected from 520 participants who are Chinese tourists, and through SPSS software the data was analyzed. The validity of the constructs is measured by a Convergent validity test, hypothesis testing is employed to understand the complex relationships between the variables. The experimental results showed that the number of visitors to tourism brand websites increased four times since they were spread by Multimedia Technology (MT), which changed from 119 to 500; the website click-through rate also nearly doubled over the same period, and even the highest website click-through rate was 98.87%. This showed that the user’s interest in tourism brands also increased exponentially. The novelty of the study focuses on multimedia technology and its implications for branding and development. By merging quantitative data analysis with qualitative observations, it presents tactics for implementing multimedia technology in the field of tourism. The study offers a point of reference for devising design and communication strategies for tourism brands that incorporate MT. It not only provides theoretical support for the design and communication facets of tourism brands but also guides the trajectory for their future development.

We investigate the effect of online grocery shopping (OGS) business operators’ responses to negative online customer reviews (NOCRs) on prospective shoppers’ attitudes toward OGS. Previous research indicates online customer reviews (OCRs) affect purchasing decisions and trust. We consider the occurrence of NOCRs as a service failure and appropriate managerial responses (apology, compensation) as service recovery efforts. Equity theory suggests managerial responses affect perceived justice and generate trust, enhancing intention to use. A scenario-based experiment was conducted to examine the impact of managerial responses on prospective shoppers’ perceived justice, trust, and intention to use. The results revealed that an apology has a more significant effect on perceived justice than on compensation, influencing trust and intention to use. We defined NOCRs as service failures exposed to prospective shoppers and evaluated the effect of managerial responses on perceived justice. We provide practical implications for OGS by suggesting appropriate responses to NOCRs.

Many online businesses strive to optimize last-mile delivery cost by implementing delivery modes aimed at cutting costs through automation or collaboration. Convincing consumers to adopt cost-efficient last-mile delivery modes is imperative to successfully achieve this goal. Yet, evidence on consumers’ attitude towards different delivery modes is scarce and causes of differences in attitude are currently not well understood. Based on qualitative interviews with e-food industry experts, we identify potential drivers of consumers’ attitude towards last-mile delivery modes. We test these drivers experimentally, finding privacy concerns to fully mediate differences in attitude of in-home and in-garage delivery when compared to timed in-person delivery. In addition, we find evidence indicating the presence of privacy calculus in delivery mode choice. Next to reducing customer risk, providing added customer value could therefore be a viable approach to encourage consumer adoption of more cost-efficient last-mile delivery.

Recommender systems are becoming increasingly indispensable for e-commerce. To achieve interpretable recommendation, review topic-based recommendation is an important research area that aims to infer users’ ratings over their unrated items using existing reviews and corresponding ratings simultaneously. However, combining latent factors and review topics can also introduce uncertainties in the model learning process, both in inference and sampling. To address this challenge, we propose a new model called type-2 fuzzy review topic-based recommendation (T2FR). T2 fuzzy membership functions are introduced to represent the topic parameter uncertainties, and a strategy of dual sampling is developed to deal with the topic T2 membership functions and further used for uncertainty handling. Abundant experiments on data collected from real-world e-commerce platforms demonstrate the effectiveness of the proposed model compared with baseline methods in terms of rating prediction accuracy with interpretation. Our proposed T2FR can substantially benefit both e-commerce platforms and consumers.

Consumers often view online reviews as trustworthy sources of information in Consumer-to-Consumer (C2C) sharing economy, these online reviews create a word-of-mouth (WOM). This paper focuses on four pricing strategies: fixed pricing based on registration fee, dynamic pricing based on registration fee, fixed pricing based on transaction fee and dynamic pricing based on transaction fee, to judge the impact of WOM. Consider a C2C sharing economy market composed of consumers, suppliers, and a platform, construct a game theory model, and obtain the equilibrium solution using backward induction. We find that a moderate WOM always makes the fixed pricing strategy attract more demand. However, the dynamic pricing strategy consistently yields higher profit. Furthermore, if total demand and profit are pursued by the platform at the same time, WOM production makes the dominant strategy shift to fixed pricing strategy based on transaction fee and dynamic pricing strategy based on transaction fee.

The worldwide popularity of blind boxes is growing rapidly. A blind box contains one of a series of products, which is not revealed until after a consumer receives and opens it. This separation of the transaction and fulfillment processes creates a mystery and excitement when purchasing a blind box. Thus, online information provision plays an important role for consumers to understand blind box and generate their willingness to pay. This study investigates the link between the format of online information provision and the market price of a blind box, focusing on a comprehensive examination of the linguistic characteristics of sellers’ product descriptions. This study finds that a U-shaped relationship exists between the subjectivity, diversity, and readability of the blind box product description and its market price. This calls online sellers to use an “extreme-point” approach when crafting their product descriptions. However, the findings suggest that an inverted U-shape relationship between ambiguity and the market price. That is, online sellers should provide the necessary information but in a brief way to keep the mystery of blind box. In addition, the findings suggest that ambiguity of the product description plays the most important role in affecting the blind box price. However, when a blind box is open, ambiguity does not play a significant role, and the impact of diversity is reversed. The findings offer guidelines for online sellers to implement their optimal information provision strategy to enhance the product attractiveness and increase consumers’ willingness to pay and thus their financial performance.

Abstract

In the competitive landscape of mobile food ordering applications (MFOA) in India, the primary focus is enhancing the customer experience to mirror or even exceed their offline meal acquisition experiences. Existing research underscores the pivotal role of a superior online experience in driving business success. Against the backdrop of a dearth of studies addressing online customer experience (OCE), our current research seeks to gain insight into its state and its implications for attitudes and intentions. Specifically, we investigate the impact of OCE on the continued usage intentions (CUI) of new MFOA users. This study not only sheds light on the relationship between OCE and CUI but also presents a fresh configuration of OCE, addressing its varied conceptualization. Furthermore, drawing on data collected from over 400 first-time users of MFOA, our findings reveal that e-satisfaction and e-trust act as full mediators in influencing CUI. Finally, the study also suggests that e-trust mediates the effect of e-satisfaction on the CUI of MFOA users. Our research contributes to our understanding of OCE by specifically highlighting the experiences and outcomes of first-time users of MFOAs. Practitioners should employ strategies including personalized orientation and data gathering, location-based services, in-app messaging, push notifications and gamification techniques to increase OCE and drive CUI.

This paper aims to explore the role of blockchain in recycling when consumers have quality concerns about remanufactured products. Using the game-theoretical method, we analyze recycling and differential pricing strategies in closed-loop supply chains when a third party conducts the recycling activity. We further study the impacts of recycling channels. Our findings demonstrate that blockchain can increase the retail prices of new and remanufactured products and the profit of the third party when the blockchain usage fee is low. Otherwise, the manufacturer and the retailer will benefit from the use of blockchain. Besides, our study shows that when the blockchain usage fee is low, or the degree of quality information disclosure of remanufactured products is low, the application of blockchain can increase social welfare. Finally, we find blockchain can raise the collection rate, which is independent of the recycling channels.

The surge in online grocery shopping amid the COVID-19 pandemic has significantly altered the balance between offline and online purchases, influencing consumer attitudes toward these channels. Given the anticipated continued growth of online grocery shopping in the coming years, research on this topic becomes increasingly crucial for retailers, manufacturers, and consumers. Particularly, brand managers and retailers find themselves uncertain about the implications for their consumer segments and products, including national versus private label brands, organic products, and fair-trade products. Against this backdrop, our study delves into consumer characteristics and purchase behavior to explore the distinctions between offline-only and also-online grocery shoppers. Additionally, we examine whether consumer behavior varies across offline and online channels. Specifically, we analyze extensive household panel data encompassing 4,142,485 purchases and diverse consumer characteristics (such as demographics and attitudes) from an average of 21,428 households spanning the years 2016–2020. It is noteworthy that also-online shoppers, despite their preference for convenience, tend to be younger, reside in larger cities, and exhibit more positive attitudes toward buying local and environmental responsibilities. These consumers, characterized by lower price consciousness and higher brand preferences, display a greater inclination toward national brands online compared to offline. Furthermore, they express more favorable attitudes toward organic and fair-trade products and exhibit relatively higher purchasing of these items. Our extensive empirical analyses reveal that these cross-sectional differences are attributable, in part, to demand-driven factors and, in part, to supply-side effects. Through this research, we provide valuable insights to brand managers, retailers, and researchers, facilitating a better understanding of the evolving retail landscape, particularly within the dynamic realm of online grocery shopping.

This paper proposes a method to improve the identification effect of technical Trajectory by adding ghost edges in the patent citation network, which includes calculating patent technology similarity, constructing ghost edge candidate set, adding the ghost edges by evaluating the utility measures, and using main path analysis to identify four technical trajectories. Taking US e-commerce data technology as an example, we find the following three points. (1) Adding a small amount of ghost edges in the patent citation network helps to increase the accuracy of technical trajectory identification, but adding a large number of ghost edges may cause destructive effects on the network structure and lead to identification bias. The experience value of this case is at most 10%. (2) Different construction methods of ghost edge candidate sets will have an important impact on the result of improving the trajectory recognition. No matter which candidate set is used, there is no deviation in the primary technical trajectory identification. However, there are differences in the subsequent technical trajectory identification. (3) The addition of the ghost edges further improves the network characteristics, especially the technical trajectory differences in subsequent locations which are identified.