Electronic Commerce Research and Applications最新文献

Offline payment solutions exist today to enable customers to avail themselves of the privileges of the cashless economy. Financial inclusion is visualized as a key factor in encouraging digital transactions in rural and semi-urban regions. Recently, the Government of India, along with the Reserve Bank of India, announced a framework that allows offline payments with finite transaction limits. This aims at providing access to formal financial services in an affordable manner to deepen financial inclusion. However, the remote population rarely access their digital bank accounts due to intermittent network connectivity issues. Hence, the majority of these accounts are in a dormant state, which demeans the impact of financial inclusion. The aim of this research is to design and develop a sustainable, low-cost, highly efficient offline payment solution using a LoRa-enabled IoT ecosystem to connect with Space Information Network (SIN). A novel offline payment system is designed with features of a) recharge and b) payment with offline wallet technology, which can be availed of in the absence of Internet or telecommunication services. The proposed solution, LIO-PAY, allows merchants and end users across the ecosystem to run seamless transactions. We developed a proof of concept and tested it in a lab setup with volunteers to analyze the usability of the application for initiating transactions. We examined 46 customer-initiated transactions and found that 16 (around 34.8%) failed due to merchant data entry errors, likely typos. In a separate analysis of these transactions, 4 customers (approximately 11.5%) incorrectly entered their PIN. The average time to complete a transaction was 35.42 s. Bringing satellite payments to life faces hurdles but holds immense promise for the unbanked. Getting the green light from banking authorities and the satellite network provider is no easy feat. Even if approved, launching a whole new satellite network just for low-value transactions might be too expensive. A more realistic approach could be using existing telecom satellites to bounce transaction data to banks. Regulatory approval is needed from both banking authorities and the satellite network provider. Hence, we simulated the SIN environment with seven LEO (Low Earth Orbit) satellites using the Matlab tool. These simulations revealed a transmission time range of 2 min to 1 h, indicating a reasonable timeframe for delivering information to a ground station via SIN. Despite terrestrial networks expanding their reach, satellite-based solutions hold promise for areas with no or weak internet connectivity. This becomes even more attractive if launch costs go down or if banking authorities help subsidize the service to bring financial inclusion to remote regions. Thus, we hope this approach may help researchers and policymakers visualize the possibilities of bridging the gap in financial inclusion.

In the ever-expanding digital library of scholarly articles, navigating through the vast amount of available research papers to find relevant work poses a significant challenge to researchers. Addressing this issue, we introduce the Graph Topic-aware Recommender (GTR), an innovative end-to-end deep neural model tailored for scholarly recommendation systems. Unlike traditional methods that primarily rely on Collaborative Filtering, Content-Based Filtering, and Graph-Based approaches with limited consideration of the intricate citing logic within scientific documents, GTR captures the nuanced relationships and citing topics inherent in scholarly networks. By leveraging an advanced neural topic modeling technique, GTR transfers item-to-user recommendation into an item-to-item framework, facilitating a more accurate and contextually relevant paper recommendation process. Our study leverages the contextual richness of scholarly networks through Graph Neural Networks (GNNs), addressing the overlooked aspect of differentiated semantic relationships within these networks. The model stands out by effectively mining relation topics and conducting differentiated representations to minimize information redundancy, which enables GTR to adaptively infer latent citation topics, enhancing the model’s explainability and recommendation accuracy. Besides, the optimization function of GTR incorporates a novel component pooling module, designed to encode the sub-graph information of samples without traditional message passing, thereby improving the model’s efficiency and scalability. Through comprehensive experiments on multiple real-world scholarly datasets, GTR demonstrates superior performance over existing state-of-the-art models, offering both high accuracy and explainability in its recommendations.

The live streaming e-commerce industry is developing rapidly, significantly influencing consumer decision-making processes. Unlike previous studies focusing solely on singular outcomes, this research concurrently examines impulse buying and repurchase intentions. It constructs a theoretical model based on the heuristic-systematic model to validate the effectiveness of two types of marketing signals (product-related signals and interactive signals) in live streaming. Additionally, this study introduces the uniqueness of streamers, products, and performance as moderating variables, aiming to elucidate the mechanisms through which different marketing signals in live streaming affect consumer cognition, impulse buying, and repurchase intentions. Through the collection and analysis of 468 survey responses, this research reveals that viewer-streamer interactive signals and viewer-viewer interactive signals positively impact the narrowing of psychological distance. Simultaneously, product-related signals positively influence the enhancing of consumer perceived value. Furthermore, three interesting moderating effects are identified. By integrating signal theory and the heuristic-systematic model, this study provides a clear depiction of the pathway through which consumers process marketing signals in live streaming, offering theoretical support and practical insights for streamers, platforms, and businesses.

Online reviews help consumers make purchasing decisions on e-commerce platforms by addressing uncertainties in online shopping. In the context of information overload, the value of review helpfulness has become increasingly evident. This study aims to explore the effects of discrete emotions on helpfulness by categorizing Ekman’s six basic emotions based on arousal and valence, and extracting their probabilities from Amazon reviews using BERT (Bidirectional Encoder Representations from Transformers). The results indicate high arousal or negative emotions adversely influence review usefulness. Furthermore, anger, disgust, and fear, characterized by high arousal and displeasure, significantly affect the perceived helpfulness of the reviews. Additionally, we identify product involvement as a significant moderator, suggesting that consumers’ investment in products influences their interpretation of reviews’ emotional content. This study contributes to understanding the relationship between discrete emotions, product involvement, and review helpfulness in e-commerce, offering insights for online retailers and platform developers to enhance consumer decision-making.

Conventional wisdom demonstrates that the retailer sharing information voluntarily can prevent encroachment (i.e., information strategy) under the reselling agreement. Considering the prevailing emerging agency selling agreement, we reexamine the effectiveness of the platform’s information strategy when the manufacturer and platform jointly participate in selling format decisions, and further analyze the strategic role of selling format in preventing encroachment. We show that adjusting selling format decision (i.e., sales strategy) can also be an anti-encroachment tool which may be even better than information strategy for the platform. Interestingly, adopting sales strategy and information strategy synthetically (i.e., synthetical strategy) may produce a synergy area to prevent encroachment, outperforming the two strategies used alone. Surprisingly, selling format can also serve as a strategic tool for the manufacturer to promote encroachment. Specifically, if the manufacturer changes from reselling to agency selling, information strategy’s anti-encroachment effectiveness may be eliminated, thus encouraging encroachment. Moreover, switching to agency selling may induce the platform to change information sharing decision, resulting in promoting encroachment.

Many e-commerce websites use product recommendation systems. With the growing awareness of the environmental impact of our consumption, these recommender systems, well-known for encouraging purchases and consumption, are being challenged. In recent literature, it has been suggested that recommender systems should balance the exploitation of existing preferences with the exploration of sustainable items, i.e., to make sustainable alternatives more accessible to consumers and promote sustainable consumption habits. In this paper, we therefore analyse how to increase the presence of sustainable products in recommendation lists, without overly decreasing their accuracy. More precisely, we test three in-processing and four post-processing strategies using an offline experimental design. The post-processing strategies 1 (relevance scores’ adjustment) and 3.1. (incremental list formation with calibration) manage to offer interesting accuracy-sustainability trade-offs on our datasets. For instance, by applying post-processing strategy 3.1 to a content-based recommendation algorithm, a gain of up to 20% can be achieved for the sustainability metric without any loss of accuracy. Greater sustainability improvements can be achieved if a loss of accuracy is tolerated. For practitioners, i.e., e-commerce platforms, this means that they could continue to offer relevant recommendations while promoting a more sustainable consumption.

Reselling or online marketplace are the two common selling modes for e-tailers in online retailing, and in each selling mode, logistics service is an important strategy that is provided by e-tailers, manufacturers, or third-party logistics (abbreviated as 3PL) companies. We formulate a multi-stage game to sequentially investigate the optimal selling mode and the equilibrium of service format selection and find that under the identical logistics service costs for all service providers, the e-tailer is willing to choose reselling when the fraction of the manufacturer’s revenue sharing as a commission rate is low or choose online marketplace when the fraction is medium–low. When the fraction is medium to high, the selling mode choice is influenced by the fraction and the logistics service’s cost sensitivity to demand, and interestingly the e-tailer does not always choose online marketplace with a high fraction of revenue sharing. Further, we find the logistics service undertaken by the e-tailer is the equilibrium when the e-tailer chooses reselling, or online marketplace with a medium to high fraction, while the manufacturer is the equilibrium when the e-tailer chooses online marketplace with a medium–low fraction. Moreover, we identify the “win–win” conditions by examining the consistency in choosing the selling mode between the e-tailer and the manufacturer. Finally, the e-tailer and manufacturer would outsource the logistics service to a 3PL company with a lower service cost regardless of the selling mode. Last, the optimal selling mode and the consistency are robust under each extended discussion.

Consumer innovation, which is the participation of consumers (or users) in product research and development, has become increasingly popular in recent years. Simultaneously, large technological advances on the internet have provided a convenient channel for consumers to contribute creative ideas in co-creation activity, providing an opportunity to rebuild our business ecosystem. We focus on how the platform impacts the process of consumer innovation. In this paper, we consider a setting in which a platform enterprise, a producer, and a group of consumers jointly design and produce a co-created product. In the co-creation process, the platform collects consumers’ ideas, sets three different innovation standards (i.e., practicality-oriented innovation, novelty-oriented innovation, and mixed innovation), and then sells the integrated idea to the producer. We demonstrate that each of the three innovation standards has its own scope of application, and no one standard is strictly dominant. Furthermore, a consumer is more willing to purchase the product with the increase in the effort exerted by himself when participating in a co-creation activity. Finally, we find that government subsidies for the platform enterprise are not always high-efficient in terms of increasing social welfare.

Electronic commerce (EC) is becoming a popular platform for online retail shopping and online payment, and it has become a new phenomenon of business transactions and innovation. In view of EC importance in global business transactions, surprisingly, there is no proper and appropriate system of performance measurement. Even if an EC firm were to measure performance, typically, the focus is on financial results using the traditional accounting system (AS) of measurement. The aim of this paper is to identify the metrics and indicators needed for EC performance measurement as the AS is inappropriate for many of the EC activities such as traffic flow, reliability of security of online payments, use of social-media data, etc. We first use a systematic analysis of selected articles to help us identify, understand and select metrics and indicators that are applicable for EC performance measurement. The Delphi method is then undertaken to conduct a questionnaire survey to gauge consensus of experts’ opinions with respect to (wrt) the use of those metrics and indicators selected. We found the respondents largely agreed that the metrics and indicators proposed by us are necessary or very necessary for use in EC performance measurement, although the experts consider three of the metrics proposed − traffic sources, emails subscribers and customer lifetime value be replaced with three accounting indicators (net income, return on investment and break-even points). The data is robust as it is verified by statistical tests. This study has both academic and practical implications; for the former, the metrics and indicators proposed meet the theoretical mathematical measurement system, and for the latter, the metrics and indicators proposed allows operation and strategic managers to design and develop an organization’s PMS.

People prefer managing diverse activities within a “Super App,” driving the need for additional services to monopolize users’ time continually. In advancing this emerging theme, we present a model integrating synergy and brand extension theories to deepen the understanding, from the user’s perspective, of how an established super app (LINE) influences the launch of a new service (LINE Shopping). Focusing on their unique synergy, a phenomenon absent in single-purpose apps, we introduce the novel concept of expected service synergy. We establish five enabler-synergy linkages and one synergy-use linkage, empirically supporting all the hypotheses with survey data from 814 LINE users. Finally, enablers associated with LINE (perceived external prestige and brand competence), LINE Shopping (complementarity and compatibility), and their relationship (perceived fit) significantly contribute to expected service synergy. This synergy is crucial in motivating users’ willingness to utilize LINE Shopping. Our findings offer actionable guidelines for super app practitioners and researchers.