Electronic Commerce Research and Applications最新文献_第2页

The thrust and drag forces affecting norm violation in live streaming eCommerce

IF 5.9 3区管理学 Q1 BUSINESS

Electronic Commerce Research and Applications

Pub Date : 2025-01-17 DOI: 10.1016/j.elerap.2025.101478

Yu-Ting Chang-Chien , Kuang-Ting Cheng , Jack Shih-Chieh Hsu , Hsieh-Hong Huang

Norm violation, in the form of buyers failing to confirm and pay for orders lodged, increases costs for sellers in the live streaming online transaction context in which most sellers are small brands or customer-to-customer sellers. Attempting to violate norm causes cognitive dissonance and consumers may alter their cognition or avoid behavior to reduce negative feeling caused by inner conflict. The former may be done by rationalizing the focal behavior with neutralization technique (thrust force) and the latter is to avoid behavior through maintaining strong cognition formed by commitment (drag force). In addition, we proposed potential measures for the inhibition and enhancement of such thrust and drag forces, respectively. Furthermore, we explored whether the magnitude of these forces is contingent on buyers’ gender. After collecting survey data from 331 buyers, we found that neutralization strengthens, and commitment weakens norm-violating intention. Furthermore, the impact of neutralization was greater for male buyers, and the impact of normative commitment was greater for female buyers. In addition, the four proposed measures (including descriptive norm, community participation, policy communication, and sanction policy) can effectively reduce neutralization and increase commitment.

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引用次数: 0

A multi-level sparse attentive fusion network integrating hard and soft information for firm-level loan default prediction

IF 5.9 3区管理学 Q1 BUSINESS

Electronic Commerce Research and Applications

Pub Date : 2025-01-15 DOI: 10.1016/j.elerap.2025.101479

Jingling Ma , Junqiao Gong , Gang Wang , Xuan Zhang

Firm-level loan default prediction (FLDP) deserves much attention from both academic and industry. Even a small improvement in the accuracy of FLDP could lead to significant savings by reducing credit risk. While previous studies have utilized deep learning models for FLDP task, they failed to well handle the intra-type ambiguity and inter-type interaction simultaneously facing with combined hard and soft information, thus remaining an area of ongoing development. By this perspective, we seek to design a novel Multi-level Sparse Attention (MLSA) based deep learning fusion framework for FLDP, aiming to fully capture default signals conveyed from both hard and soft information. First, multiple types of information are extracted grounded in 5P theory and LAPP theory, ensuring the sufficiency and rationality of the features. Second, Sparse Attentive MLP (SA-MLP) and Sparse Attentive GRU (SA-GRU) module are proposed to handle the intra-type ambiguity embedded in hard and soft information separately. Further, the Attentive Fusion (AF) module including Differential Enhancive module and Common Selective module is proposed to explore inter-type interaction among hard and soft information. Last, we adopt the focal loss function to mitigate the adverse effects of imbalanced data. The proposed MLSA informs future FLDP research about how to fully exploit the value of hard and soft information by considering their intra-type ambiguity and inter-type interaction. Empirical evaluation of the MLSA on a real-world dataset demonstrates its outperformance of state-of-the-art benchmarks in the FLDP task. Our results also contribute to the growing literature on this topic by highlighting the roles of hard and soft information and improving interpretability.

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引用次数: 0

Revenue-based personalized product recommendation considering stochastic purchase probability

IF 5.9 3区管理学 Q1 BUSINESS

Electronic Commerce Research and Applications

Pub Date : 2025-01-13 DOI: 10.1016/j.elerap.2025.101477

Chao Huang, Xi Zhang, Yifan Zhang, Qinghao Hu

Recommender systems(RS) play a critical role in e-commerce platforms by providing personalized and relevant product suggestions to customers, thereby enhancing their shopping experience and increasing platform revenue. Existing RSs focus on improving accuracy or maximizing user purchase probability when generating recommendations. However, a sole emphasis on accuracy does not ensure the optimization of platform revenue, and recommendations that maximize user purchase probability can also fail to simulate the real purchase behavior of users, which shows strong uncertainty due to external factors. To address these issues, we propose a two-stage personalized product recommendation method based on stochastic purchase probability (PRSPP). In the first stage, we follow previous studies which prioritize user preferences during the recommendation process. A taxonomy-based approach is employed to estimate user preferences at the category level and select candidate products for each user. Subsequently, considering the impact of factors such as product price, sales and category similarity on user utility, a logistic regression model is employed to quantify user preferences for these candidate products and further estimate user purchase probability for them. In the second stage, we aim to optimize the recommendation from the perspective of platform operators, considering user purchases are subject to diverse external factors and exhibit strong uncertainty. We treat purchase probability as a random variable, and a stochastic optimization model with the objective of maximizing platform revenue is formulated. Furthermore, we apply the Sample Average Approximation (SAA) approach to solve the model. Finally, we conduct experiments on Amazon public dataset, and the results present advantages of PRSPP in improving both recommendation accuracy and platform revenue.

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引用次数: 0

Insurance policy and pricing decisions in online food delivery market with consumer ratings

IF 5.9 3区管理学 Q1 BUSINESS

Electronic Commerce Research and Applications

Pub Date : 2025-01-08 DOI: 10.1016/j.elerap.2024.101474

Minjian Liu , Shaofu Du , Tengfei Nie , Yangguang Zhu , Qi Dong

Recent advances in mobile technology and the rise of aggregator apps have led many consumers to purchase products through online food delivery platforms, and the effects of COVID-19 have accelerated the trend. Consumers often observe other consumers’ ratings before purchasing to reduce the product’s taste and the delivery service uncertainties. However, the information accuracy of the ratings can be affected by the seller’s marketing strategies, such as the pricing and delivery insurance policy. Considering consumers’ ratings, firstly, we develop two-period models to examine how consumer uncertainty and different delivery insurance policies impact the seller and consumers. We then determine the seller’s optimal insurance policy and pricing decision. We find that the probability of delayed delivery has a nonmonotonic effect on the product’s optimal price. Counterintuitively, our analysis shows that a Free Insurance (FI) policy allows the seller to benefit from a greater delayed delivery probability by influencing consumer ratings. We show that FI is not always beneficial for the seller and consumers; it may reduce the seller’s profit and the total consumer surplus simultaneously. Surprisingly, the seller can always charge a lower price yet earn more profit by adopting a Paid Insurance (PI) policy. Furthermore, when the seller strategically chooses its price and insurance policy (e.g., No Insurance, FI, or PI), it can achieve a win-win situation; and the seller should always support insurance, i.e., adopt FI or offer PI, even though neither FI nor PI dominates under all conditions.

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引用次数: 0

Collaboration or Encroachment? The Content Provider’s Strategic Content Provision Strategy

IF 5.9 3区管理学 Q1 BUSINESS

Electronic Commerce Research and Applications

Pub Date : 2025-01-01 DOI: 10.1016/j.elerap.2024.101470

Siyu Du, Xu Wang

With the rapid growth of the streaming industry, an increasing number of Super Content Providers (SCPs), such as Disney, Warner Bros., and NBC Universal, have launched their own streaming platforms, entering the downstream streaming market. However, some content providers, like Sony, still collaborate with Netflix. Therefore, this paper employs game theory to investigate the optimal content provision strategy for SCPs: collaboration or encroachment. We derive some interesting findings. First, contrary to intuition, we find that whether under the collaboration or encroachment strategy, improving the quality of a player’s own film library (the SCP or the incumbent platform) does not always result in higher profit for that player (first hurts then benefits the player). Second, for both the platform and the SCP, an increase in the scale of their film libraries does not automatically lead to higher profits. Third, when the revenue share is positively correlated with the scale of the film library, a small-scale library strengthens the motivation to encroach; conversely, when the library scale does not influence the revenue share, a large-scale library increases the motivation to encroach. Lastly, while the encroachment strategy can be a win–win situation for the incumbent platform and the SCP, it hurts consumer surplus. In contrast, the collaboration strategy can achieve a win–win–win situation under some conditions. Our study helps to explain the market practices and provides valuable guidelines for SCPs’ content provision strategies.

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引用次数: 0

E-servicescape and customer equity based customer loyalty model for digital services

IF 5.9 3区管理学 Q1 BUSINESS

Electronic Commerce Research and Applications

Pub Date : 2025-01-01 DOI: 10.1016/j.elerap.2024.101475

Feyza Nur Ozkan, Ahmet Sekerkaya

Customer loyalty is a significant metric due to its positive effects on market share and business performance. Understanding the antecedents of customer loyalty is vital for companies to gain and maintain competitive advantage. However, the models developed to explain customer loyalty in offline services are not directly applicable to digital services due to differences in their nature. Hence, this study aimed to provide an e-servicescape and customer equity-based customer loyalty model for digital services and test this model in different digital service contexts. The research scope includes online video streaming, online banking, online marketplace, and online grocery services. Four separate questionnaire forms were created depending on the types of digital services and applied to the people representing sample characteristics. A total of 600 valid data were obtained through face-to-face interviews. A structural equation modeling approach was employed to test the model, and the multi-group analyses were performed to evaluate the differences in effects for different digital services contexts. We found that e-servicescape and customer equity are significant determinants of customer loyalty in digital services. The effects of e-servicescape on customer equity drivers and customer equity drivers’ effects on customer loyalty differ according to digital service type.

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引用次数: 0

From efficiency to equity: A multi-user paradigm in mobile route optimization 从效率到公平：移动路线优化中的多用户范例

IF 5.9 3区管理学 Q1 BUSINESS

Electronic Commerce Research and Applications

Pub Date : 2024-11-01 DOI: 10.1016/j.elerap.2024.101459

Pengzhan Guo , Keli Xiao

This study addresses the challenge of optimizing vehicle mobility in urban environments, which is significant for the advancement of smart city initiatives and spatial data analysis. We introduce a novel mobile recommendation system designed for multi-user scenarios, aiming to achieve a balance between effectiveness and fairness. The system prioritizes maximizing the profitability of vehicle service providers while ensuring an equitable distribution of recommended routes among users. Our approach features a redefined objective function that integrates a fairness criterion alongside path quality optimization. We further propose PSA-DLMA (Parallel Simulated Annealing with Deep Learning-Guided Move Adaptation), a stochastic path search method that leverages deep learning to guide move and strategy selection, alongside a dynamic termination mechanism and a parallel processing strategy. We validate our methodology using recent yellow taxi data from New York City and its surroundings, conducting comprehensive experiments to assess the performance of the system. The results demonstrate the superiority of PSA-DLMA over existing state-of-the-art solutions, offering significant contributions to improving urban vehicle mobility within the smart city framework.

本研究解决了优化城市环境中车辆移动性的难题，这对推进智慧城市计划和空间数据分析意义重大。我们介绍了一种为多用户场景设计的新型移动推荐系统，旨在实现有效性和公平性之间的平衡。该系统优先考虑车辆服务提供商的利润最大化，同时确保推荐路线在用户之间的公平分配。我们的方法采用了重新定义的目标函数，将公平性标准与路径质量优化相结合。我们进一步提出了 PSA-DLMA（深度学习指导移动适应的并行模拟退火），这是一种随机路径搜索方法，利用深度学习指导移动和策略选择，同时还采用了动态终止机制和并行处理策略。我们利用纽约市及其周边地区最近的黄色出租车数据验证了我们的方法，并进行了全面的实验来评估系统的性能。结果表明，PSA-DLMA 优于现有的先进解决方案，为改善智慧城市框架内的城市车辆流动性做出了重大贡献。

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引用次数: 0

Subjective variability of the “just-right feeling”: Effectiveness of social media advertising design 恰到好处的感觉 "的主观可变性：社交媒体广告设计的有效性

IF 5.9 3区管理学 Q1 BUSINESS

Electronic Commerce Research and Applications

Pub Date : 2024-11-01 DOI: 10.1016/j.elerap.2024.101466

Ya Wang , Shuilong Wu , Jiajun Zhao , Yongna Yuan

With the rapid advancement of AI and algorithmic technologies, social media platforms have gained the ability to identify consumer personality traits. However, developing compelling advertising strategies for individuals with different self-construals remains challenging. Based on construal level theory, this research investigates the interactions between shot scale, advertising appeals, and self-construal on social media. The results of the four empirical studies indicated that independent individuals prefer advertisements with long-shot images and desirability appeals. In contrast, interdependent individuals favor advertisements with close-up images and feasibility appeals. Furthermore, the findings reveal that long-shot images match with desirability appeals or close-up images paired with feasibility appeals significantly increase the click-through rate and foster more positive advertising attitudes. The above findings are central to the feeling right during information processing, which plays a crucial role in advertising acceptance. Therefore, this research constructs a new framework for personalized advertisement design in social media and offers a practical guide for businesses seeking to optimize their advertising strategies.

随着人工智能和算法技术的飞速发展，社交媒体平台已经具备了识别消费者个性特征的能力。然而，针对不同自我构架的个体制定有吸引力的广告策略仍具有挑战性。基于构念水平理论，本研究探讨了社交媒体上的拍摄尺度、广告诉求和自我构念之间的相互作用。四项实证研究的结果表明，独立个体更喜欢长镜头图片和欲望诉求的广告。相比之下，相互依存的个体更喜欢特写图片和可行性诉求的广告。此外，研究结果还显示，长镜头图片与可取性诉求相匹配，或特写图片与可行性诉求相匹配，都能显著提高广告的点击率，培养更积极的广告态度。上述发现的核心是信息处理过程中的正确感觉，这对广告接受度起着至关重要的作用。因此，本研究构建了社交媒体个性化广告设计的新框架，为企业优化广告策略提供了实用指南。

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引用次数: 0

Antecedents of users’ switching intention to Central Bank Digital Currency: A push-pull-mooring model perspective 用户转向央行数字货币意向的前因：推拉移动模型视角

IF 5.9 3区管理学 Q1 BUSINESS

Electronic Commerce Research and Applications

Pub Date : 2024-11-01 DOI: 10.1016/j.elerap.2024.101467

Chen Sha , Tong Che , Tingjie Xu , Zi Yang

Since 2020, the People’s Bank of China has promoted the pilot work on e-CNY payment, but it has not been widely used and popularized. The public still prefers third-party payment as the main payment method. In the incumbent literature, most studies on e-CNY payment focus on the macro perspective and less on users. Based on grounded theory and the push–pull-mooring framework, we investigate users’ intention to switch from third-party payment to e-CNY payment, and identify fee cost, system function overload, convenience, trust, inertia, and external influence as core factors. The theoretical model and research hypotheses are validated using structural equation modeling approach. Results show that system function overload, convenience, trust, and external influence significantly facilitate switching intention, whereas inertia negatively impedes switching intention. The findings of this study enrich the literature on e-CNY and serve as a reference for the promotion of central bank digital currencies around the world.

2020 年以来，中国人民银行推进了电子人民币支付试点工作，但并未得到广泛应用和普及。公众仍倾向于将第三方支付作为主要的支付方式。在现有文献中，关于电子人民币支付的研究大多侧重于宏观视角，对用户的研究较少。基于基础理论和推拉移动框架，我们研究了用户从第三方支付转向电子人民币支付的意向，并将费用成本、系统功能过载、便利性、信任、惰性和外部影响确定为核心因素。采用结构方程模型法验证了理论模型和研究假设。结果表明，系统功能超载、便利性、信任和外部影响显著促进了转换意向，而惰性则消极地阻碍了转换意向。本研究的结论丰富了有关电子人民币的文献，为在全球推广央行数字货币提供了参考。

引用次数: 0

Unveiling the forces driving expert activity: The impact of information environment and peer behavior on expert reviewer engagement behavior 揭示专家活动的驱动力：信息环境和同行行为对专家审稿人参与行为的影响

IF 5.9 3区管理学 Q1 BUSINESS

Electronic Commerce Research and Applications

Pub Date : 2024-11-01 DOI: 10.1016/j.elerap.2024.101463

Zhaoyang Yu , Zili Zhang , Yunzhijun Yu , Ziqiong Zhang

Online platform engagement with customers, especially those with high expertise, is crucial for companies. As these expert customer reviews directly impact a company’s brand image and sales volume, an understanding of expert reviewer engagement behavior (EREB) is critical for companies’ marketplace success. This study explores the factors that influence EREB from two key situational cues: those from the company’s information environment and from peer expert behavior. Data from 144,634 Yelp reviewers and 5,080 restaurants were analyzed. The results reveal that companies with higher aggregate ratings are more likely to encourage EREB. The impact of overall and peer expert rating inconsistency on EREB varies: Overall rating inconsistency has a positive effect, while inconsistency among expert peers has a negative effect. Additionally, EREB exhibits herding and differentiation patterns in response to changes in peer expert engagement density. This results in a U-shaped relationship between EREB and peer expert engagement density, moderated by aggregate rating, overall rating inconsistency, and peer expert rating inconsistency. This study provides practical insights for marketers looking to engage expert customers and expands on the literature on expert customer engagement behavior.

在线平台上的客户参与，尤其是那些具有较高专业知识的客户参与，对公司来说至关重要。由于这些专家客户评论直接影响公司的品牌形象和销售量，因此了解专家评论者的参与行为（EREB）对公司的市场成功至关重要。本研究从两个关键情境线索出发，探讨了影响EREB的因素：来自公司信息环境的线索和来自同行专家行为的线索。研究分析了来自 144,634 位 Yelp 评论者和 5,080 家餐厅的数据。结果显示，综合评分较高的公司更有可能鼓励EREB。总体评分和同行专家评分不一致对EREB的影响各不相同：总体评级不一致会产生积极影响，而同行专家评级不一致则会产生消极影响。此外，随着同行专家参与密度的变化，EREB 呈现出羊群和分化模式。这导致了EREB 与同行专家参与密度之间的 U 型关系，并受到总体评级、总体评级不一致和同行专家评级不一致的调节。这项研究为希望吸引专家客户的营销人员提供了实用的见解，并扩展了有关专家客户参与行为的文献。

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引用次数: 0