Electronic Commerce Research and Applications最新文献

Advancements in information and communication technologies (ICT) have transformed the financial industry into “Fintech” and mobile banking. The COVID-19 pandemic has accelerated the adoption of mobile commerce services, with privacy and security concerns arising among consumers. This study adapted the privacy calculus theory to investigate the factors influencing consumers' willingness to provide personal information in mobile banking. It also examines the moderating role of e-lifestyle in the relationship between perceived benefits and risks and the intention to use mobile banking. The results show that perceived benefits outweigh perceived risks, motivating users to provide personal data and use mobile banking. E-lifestyle clusters also moderate consumer attitudes towards mobile banking adoption, with different effects for Mobile Service Enthusiasts and Neutrals. Financial institutions can enhance user trust and adoption by understanding these factors and tailoring mobile banking experiences to users' preferences and lifestyles.

With the development of online platforms, live streaming has become an influential option for manufacturers' sales models. Manufacturers can choose to sell their products on their own or through live streaming platforms. Manufacturers' choices are influenced by their customer base choices. Customer groups have different valuations of products and different levels of patience (waiting costs). In this paper, we construct a two-period game model to analyze the impact of product quality on the sales patterns of manufacturers considering strategic customer behavior. We find that when product quality is relatively low, the manufacturer chooses the live streaming platform to sell the product in the early stage and chooses to sell the product directly in the later stage. When the quality of the product is relatively high, the manufacturer chooses to sell the product directly in both periods. Selling products through live streaming platforms leads to an upgrade in product pricing and may result in higher product pricing in the later period than in the earlier period. Furthermore, we analyze the impact of the manufacturer's livestreaming behavior on the firm's endogenous choice of product quality. We find that a manufacturer's short-term (one period) live streaming reduces product quality, while long-term (two periods) live streaming improves product quality.

Differential pricing in e-commerce has sparked discussions about price discrimination. However, due to the difficulty in measuring consumers’ satisfaction with different prices for the same product, it is hard to quantitatively assess the impact of differential pricing on the interests of merchants and consumers. In this paper, the differential pricing in e-commerce is evaluated from the perspective of utility. For empirical study, a differential pricing strategy is implemented based on reinforcement learning, which can set individualized prices according to different consumption patterns. Utility theory is introduced to quantitatively evaluate the impact of differential pricing on the interests of consumers from two aspects, namely consumer utility and consumer surplus. A win–win situation for merchants and consumers is observed. After a merchant who adopts the proposed differential pricing strategy joins the market competition, not only will it earn more profit, but the utility and surplus of consumers will also increase. The investigation can provide a reference for market supervisors to implement algorithmic regulation.

The study investigates the determinants of trust in sellers and products and purchase intention in the social commerce (s-commerce) context by considering the moderating effects of trust disposition and perceived price fairness. The data were collected from 416 individuals who have followed at least one seller on Instagram and analysed using the Partial Least Squares (PLS) approach. The findings revealed that review quantity, review quality, perceived symmetric product information, and responsiveness positively influence trust in seller. The direct influence of review quality on trust in products was confirmed. Trust disposition negatively moderates the impacts of review quality on trust in sellers and responsiveness on trust in products. Furthermore, perceived price fairness positively moderates the influence of trust in sellers and products on purchase intention. The findings extend the literature on s-commerce in several ways. The findings enable s-commerce sellers to formulate effective marketing strategies and boost purchase intention.

Response modeling, a key to successful direct marketing, has become increasingly prevalent in recent years. However, it practically suffers from the difficulty of class imbalance, i.e., the number of responding (target) customers is often much smaller than that of the non-responding customers. This issue would result in a response model that is biased to the majority class, leading to the low prediction accuracy on the responding customers. In this study, we develop an Ensemble Learning with Dynamic Weighting (ELDW) approach to address the above problem. The proposed ELDW includes two stages. In the first stage, all the minority class instances are combined with different majority class instances to form a number of training subsets, and a base classifiers is trained in each subset. In the second stage, the results of the base classifiers are dynamically integrated considering two factors. The first factor is the cross entropy of neighbors in each subset, and the second factor is the feature similarity to the minority class instances. In order to evaluate the performance of ELDW, we conduct experimental studies on 10 imbalanced benchmark datasets. The results show that compared with other state-of-the-art imbalance classification algorithms, ELDW achieves higher accuracy on the minority class. Last, we apply the ELDW to a direct marketing activity of an insurance company to identify the target customers under a limited budget.

This study examines how ride-sharing platforms (e.g., Uber) influence the online reviews of a home-sharing platform (e.g., Airbnb) based on a natural experiment conducted in the city of Austin, Texas, in the United States. A causal relationship between ride-sharing platforms and the valence and helpfulness of online reviews on a home-sharing platform is evaluated through propensity score matching and difference-in-differences estimation. The empirical evidence shows that the valence and helpfulness of online reviews on Airbnb increased during the period when Uber/Lyft was withdrawn from the market. Furthermore, there are asymmetric interaction effects depending on the location of the property. Specifically, the effect of ride-sharing platforms on review valence is stronger for properties located in less accessible areas, whereas the effect on review helpfulness is more pronounced for properties in more accessible locations.

Social gamification design has been widely used in various industries to enhance user engagement. Although social gamification design can help to shape user behavior to some extent, this design mechanism has significant negative effects on users. Few studies have explored the relationship between social gamification and user fatigue. To fill this research gap, we constructed an empirical research model based on the transactional theory of stress and coping and explored the impact of social gamification on users’ psychological stress (reputation maintenance concern and fear of missing out) and fatigue, as well as the moderating effects of player type (achievers and socializers) in this process. To test our research hypotheses, we collected information from 450 users via a questionnaire. The empirical results reveal that social gamification competition and interactivity are positively associated with reputation maintenance concern, which is positively related to fear of missing out and user fatigue. Additionally, our research determined that being an achiever positively moderates the relationships between competition, reputation maintenance concern, and fear of missing out, while being a socializer positively moderates the relationships between interactivity, reputation maintenance concern, and fear of missing out. Our results are helpful in understanding the negative effects of social gamification design and contribute to the literature on social gamification and user fatigue.

Recently, live streaming sales have played an important role in promoting the fresh product industry. Producers (e.g., smallholders, cooperatives, family farms) can either directly build their own live rooms or use third-party streamers’ live rooms to sell fresh products, thereby forming two common live streaming sales modes. The former is called as self-broadcasting and the latter is called as streamer cooperation. Therefore, we build a theoretical model to investigate the performance of the two live streaming sales modes, and explore the monopolistic producer’s preference on them. We incorporate key characteristics of fresh products and live streaming into our model simultaneously, such as random output, endogenous decision of influence level and the resulting potential market size (i.e., fan base). Several extended cases are also considered to check the impact of other factors on the producer’s mode preference. The results show that when the probability of a good harvest is small (or large), the producer always prefers cooperating with a third-party streamer (or self-broadcasting). Conversely, when the probability of a good harvest is medium, the producer’s preference on the two live streaming sales modes depends on the magnitude of the relative efficiency of influence acquisition. We also find that the influence level in the streamer cooperation mode is not always higher than in the self-broadcasting mode, but the selling price is always higher. Additionally, the conclusions of several extended cases have confirmed the applicability of our main findings.

We report on two studies that examine how social sentiment influences information asymmetry in digital currency markets. We also assess whether cryptocurrency can be an investment vehicle, as opposed to only an instrument for asset speculation. Using a dataset on transactions from an exchange in South Korea and sentiment from Korean social media in 2018, we conducted a study of different trading behavior under two cryptocurrency trading market microstructures: a bid-ask spread dealer’s market and a continuous trading buy-sell, immediate trade execution market. Our results highlight the impacts of positive and negative trader social sentiment valences on the effects of information asymmetry on observed trading patterns. This includes the spillover effect of volatile social sentiment, and the leverage effect when a cryptocurrency’s value falls or rises in response to changing sentiment. Our results highlight how volatility arises in parallel with investors’ reactions to public information and trade transaction volume. If the price falls beyond participants’ expectations, a negative shock from new information may lead to more volatile social sentiment than a positive shock of the same size does. Our work supports the roles of investment and speculation for cryptocurrency by identifying an immediate impulse and a longer-term effect of economic variables in the presence of sentiment-led information asymmetry. We contribute new knowledge to the interdisciplinary literature on the financial economics of digital currency for a technological innovation that has matured earlier than many observers may realize.

Consumers’ participation in brand value co-creation has played an important role in brand building since the rise of social media. There is little research that investigates brand value co-creation behaviors and their antecedents in social media-based brand communities (SMBBCs). This research proposes and tests an integrated model of brand value co-creation behaviors on the basis of the uses and gratifications and attachment theories. This study recruited 932 valid brand community members on Weibo, one of the most well-known social media platforms in China. Structural equation modeling was used to test the research model. The research findings confirm that brand community members’ motivations positively influence common identity-based and bond-based attachments, which in turn affect knowledge sharing behavior and brand advocacy. This research provides theoretical and practical contributions for SMBBC managers.