Electronic Commerce Research最新文献

Abstract

Despite the rapid advancement of online shopping, the challenge of asymmetric information, particularly within the realm of agricultural products, poses a hurdle to sustaining consistent customer engagement in e-commerce. Drawing upon governance theory and the cue congruence effect, the research aims to construct a comprehensive theoretical framework that encompasses platforms, providers, and customers. The primary objective is to empirically examine how platform governance can facilitate the presentation of valuable systems through signaling, review clues, and their significant moderating effects. Through an analysis of transaction data from China’s online rice market, employing both two-stage least square and three-stage least square models, the study reveals that robust signals and reputation can lead to increased prices and sales for high-quality products. Additionally, the study identifies the presence of congruence: prices appear to amplify the influence of signaling and reviews, while congruence between video displays and signals yields a negative impact. This research offers theoretical insights for future study and practical recommendations for policymakers and e-commence participants.

Currently, small and medium-sized enterprises (SMEs) have been the mainstay of technological innovation. Given the paucity of financial support, equity crowdfunding has become a popular way for SMEs to obtain project funds in recent years. Based on all successful crowdfunding projects on Seedrs, an equity crowdfunding platform, we delved into the impact of the content not displayed on project’s presentation page, particularly past fundraising experiences, on fundraising performance. Additionally, we empirically explored the influence of three dimensions, namely whether there was past fundraising experience, the number of past fundraising experience(s), and the amount of the last fundraising, on the fundraising ratio and the number of investors. The research results indicate that (1) the fundraising ratio is not significantly related to whether there was past fundraising experience and the number of past fundraising experience(s), but positively related to the amount of the last fundraising; (2) the number of investors is not significantly related to whether there was past fundraising experience, but positively related to the number of past fundraising experience(s) and negatively related to the amount of the last fundraising. Theoretically, we explored the relations between the information not displayed and fundraising performance from the perspective of past fundraising experience, supplementing and enriching the related research. Practically, we revealed the information searching behaviors of investors and guided the crowdfunding platforms in the information display.

This study empirically analyzes the impacts of e-commerce adoption by Chinese agri-food companies on their financial performance, with a nationwide dataset of Chinese agri-food companies. Results show that, on average, the adoption of e-commerce can significantly improve the financial performance of Chinese agri-food companies by 20%. The impacts of e-commerce adoption on financial performance are heterogeneous regarding enterprise scale, industry type, enterprise ownership, enterprise location, and enterprise maturity. In light of these findings, this study discusses the suggestions to encourage e-commerce adoption and improve the financial performance of companies from the perspectives of business operators and policymakers.

The proliferation of mobile devices has made mobile shopping extremely popular among the digital natives. Due to the multitasking nature of mobile devices, users are often disturbed by various interruptions, such as incoming calls, SMSs and APP notifications. Prior studies suggest that interruptions likely affect users’ shopping experience and decision-making, but they find inconclusive results regarding how interruptions influence users’ behavior. Based on the flow theory and prior research on human–computer interaction, this study focuses on the interplay between the design of interruptions and human–computer interaction mode, and examines the effects of interruptions on users’ decision efficiency in a mobile shopping context. Three laboratory experiments were conducted to verify the research hypotheses. The results show that interruptions improve users’ decision efficiency and satisfaction when their content is irrelevant to users’ shopping task. When the content of interruptions is relevant to users’ shopping task, the interruptions reduce users’ decision efficiency. Furthermore, the interruptions reduce (improve) users’ decision efficiency when users browsed the mobile interface in a horizontal (vertical) manner. In general, this study makes one of the early attempts to investigate the effects of interruptions on users’ mobile shopping behavior and provides practical guidance for practitioners to design effective mobile marketing campaigns.

Recently, global environmental and political changes have heightened the risk of supply chain disruptions. To bolster the risk resilience of the supply chain, this paper delves into its robustness structure using complex network theory. Firstly, a network of automotive suppliers is established to analyze its structural characteristics. Notably, the network degree distribution manifests a power-law distribution, highlighting an intricate interplay of mutual cooperation within cohesive communities. Subsequently, the paper performs simulation analyses under scenarios involving both random and deliberate attacks on the automotive supply chain network. The focus is on assessing the network efficiency, the size of the largest connected subgraph, and the variations in three distinct module structures. The results indicate that the automotive supply network exhibits good robustness under random attacks but relatively weak robustness under deliberate attacks. Additionally, a hub-like structure demonstrates significant robustness in the automotive supply chain network, showing optimal risk resistance even in the scenario of deliberate attacks. Therefore, when constructing network connections in the automotive supply chain, it is advisable to focus on establishing hub-like connections with upstream and downstream partners, while also preventing both internal and external risks of deliberate attacks, thereby enhancing the overall robustness of the entire supply chain network. These findings provide insights into risk mitigation in terms of network construction for automotive supply chain management and optimization.

FinTechs have gained an important place in the financial sector with their dynamic and customer-oriented structures and innovative approaches and business models. In recent years, the competition between actors in the financial sector has intensified. In a rapidly developing sector, where competition is intense and challenging, efficiency is a very important performance indicator for fintechs. This study aims to measure and examine the efficiency performance of FinTech companies. Data Envelopment Analysis was used for efficiency measurement. By establishing a two-stage data envelopment analysis model, "profitability", "marketability" and "overall process" efficiencies of fintechs were measured and analyzed. Efficiency of FinTechs operating in eight different sub-sectors was measured for the period of 2010–2019. The results show that, fintechs’ profitability efficiency performances are higher than their marketability efficiency performances. Besides, efficiency performance of the fintech companies differs on the basis of sub-sectors. This paper is a pioneer study in efficiency measurement of FinTechs.

The quality of recommender systems (RS) is typically measured by their predictive accuracy. There is an emerging understanding that RS must provide not just accuracy, but also usefulness and enhanced user engagement, where diversity, novelty, and serendipity have been identified as the most common quality features to improve the RS beyond accuracy measures. This research investigates how diversity, novelty and serendipity of the recommended items as well as user’s prosumer behavior affect user engagement dynamically. We formulate a dynamic panel data model using the data collected from NetEase Cloud Music, one of China’s largest music streaming platforms. The findings indicate that both novelty and serendipity of the recommended items have positive impact on user engagement while a more diversified recommendation list could hurt user engagement. Our findings also suggest being a prosumer who also creates videos instead of a pure consumer of music videos will make the user more engaged with the platform in the long run. In addition, our findings clarify the relationship between prosumer behavior and the impact of diversity, novelty and serendipity on user engagement. Being a prosumer alters the effect of diversity on user engagement from negative to positive. Also, creators are drawn to unpopular and unexpected videos as they serve as a source of inspiration for their creative endeavors. The findings of this study have substantial implications for music streaming platforms and other social media and e-commerce platforms to leverage long-term customer engagement through the improvement of recommender systems. For example, a targeted 90-2-20 rule can be implemented to balance the diversity, novelty and serendipity of the recommended items, which prioritizes the selection of 90% of recommended items from the user’s top 2 preferred genres, the remaining 10% from unrecommended genres, and includes 20% of unpopular items within each genre. To encourage the users to create contents, various means can be applied by the platforms such as bestowing a creator badge, offering reward cashback and subscription discounts.

Based on e-commerce live broadcast data, this paper, with the support of big data technology, aims to explore the impact of Internet celebrities on consumers' impulse purchase behavior, analyzing the relevant factors. According to big data technology, this paper carries out e-commerce live broadcast big data processing and constructs the Internet celebrity marketing model. This paper, with the support of the model, analyzes the impact of Internet celebrities on consumers' impulse purchase behavior. Through the data collected, this paper, from both positive and negative aspects, analyzes the impact of Internet celebrities on consumers. Judging by the experimental research, the data mining approaches proposed here can play a certain effect in the analysis of the impact of Internet celebrities on consumer impulse purchase behavior. According to the experimental analysis of mathematical statistics, Internet celebrity consumption has become one of the important consumption forms at present.

In the contemporary arena of e-commerce strategies, companies are increasingly drawn to the use of referral program incentives to prompt existing customers to recruit new ones. However, the existing knowledge falls short of unraveling the intricate dynamics governing the sharing of diverse rewards in company-consumer and consumer-consumer relationships. This study bridges this gap by unveiling a nuanced connection between the effectiveness of referral rewards and the interplay of both the recipient’s propensity to refer during the referral stage and the recipient’s inclination to accept during the acceptance stage. Through three scenario-based experiments, we explore the influence of the consumer-company relationship on individuals’ willingness to engage in referrals during the referral stage, identifying two pivotal psychological mechanisms: economic and social motivation. Our findings underscore those selfish incentives, primarily benefiting the sender, outperform prosocial incentives, particularly within exchange norms, yet reveal the reputational advantages associated with prosocial referral rewards in communal norms. Shifting the focus to the acceptance stage, we scrutinize the relationship between the referrer and the recipient, discovering that sender-benefiting rewards may undermine a recipient’s acceptance due to negative motivational inferences, yet this effect can be moderated by relationship norms. Our findings offer a comprehensive understanding of the multifaceted role played by referral rewards in shaping consumer behavior within social e-commerce, providing valuable guidance for companies seeking to optimize their referral strategies by aligning rewards with relationship norms to enhance overall effectiveness.

Abstract

The emergence of artificial intelligence technologies, such as recommendation agents, presents new challenges and opportunities for marketing. Recommendation agents assist consumers in their online grocery shopping decisions by analyzing data on preferences and behaviors. This research highlights that while recommendation agents can reduce choice overload and make purchase decisions easier for consumers, they are also associated with higher uncertainty in decision-making. Three experimental studies confirmed that purchases aided by recommendation agents are perceived as more uncertain and reduced perceptions of control over the choices explain this outcome. Furthermore, lower choice satisfaction and purchase intentions are confirmed as consequences of perceived uncertainty. Personal characteristics such as risk aversion and maximization tendencies are considered boundary conditions for these effects.