Electronic Commerce Research最新文献

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.

The recent developments in forms of online shopping have been shaped by emerging information technologies, and the sources of online shopping safety have been accompanied by numerous changes. Based on the database of the 2022 Chinese Internet Safety Satisfaction Survey, this paper explored the relationship between consumers’ livestreaming shopping usage frequency and their online shopping safety satisfaction, then focusing on the moderating effect of consumers’ opinion leader acceptance, finally providing a further analysis based on the cultural theory of risk. The study finds that: (1) Consumers’ livestreaming shopping usage frequency positively affects consumers’ online shopping safety satisfaction. (2) Consumers’ opinion leader acceptance plays a significant positive moderating role in the relationship between consumers’ livestreaming shopping usage frequency and their online shopping safety satisfaction. (3) Based on the cultural theory of risk, the moderating effect of consumers’ opinion leader acceptance becomes stronger for consumers whose educational level is lower (technical school and junior college) or occupational status is less relevant to livestreaming shopping (non-employed by the livestreaming shopping industry such as students, doctors, jobless, etc.).

This study aims to explore the significant factors driving food consumption through three e-commerce modes: Business-to-Consumer, Online-to-Offline Food Delivery Service, and Click & Collect in developed Western 98countries. A total of 1,461 samples were collected through online surveys in New Zealand, the United Kingdom, and Denmark. Descriptive analysis and ordered logistic regression were employed for data analyses. Overall, consumers’ food consumption frequencies with e-commerce were found to be significantly influenced by several socio-demographics, e-commerce food choice motives, innovation-adoption characteristics and e-service quality attributes.

Bitcoin has gradually gained acceptance as a payment method that, unlike electronic payments in dollars or euros, passes through the international trading system with zero or lower fees. Moreover, Bitcoin and e-commerce have become increasingly intertwined in recent years as cryptocurrencies gain mainstream acceptance. In this paper, we analyze Bitcoin price evolution from September 2014 until July 2023, factors that influence price volatility and assess its future volatility using Autoregressive Conditional Heteroskedasticity (ARCH) models that predict the volatility of financial returns to conceive strategies for merchants that accept Bitcoin as a payment option. The Generalized ARCH model (GARCH) extends the model to capture more persistent volatility patterns. Further, we estimate symmetric and asymmetric GARCH (1,1)-type models with normal and non-normal innovations. The best proved to be EGARCH (1,1) with t-distribution innovation. To assist merchants in making decisions regarding Bitcoin adoption, two concepts are relevant: the EGARCH model and VaR. EGARCH model is used to forecast the volatility of the financial asset, while VaR is a widely used risk management tool that estimates the potential loss in value of a portfolio over a defined period. For a merchant holding Bitcoin, VaR assists in understanding the maximum expected loss over a certain time frame with a certain level of confidence (like 95% or 99%). The results show that a VaR coverage of 0.044 at a 5% probability level suggests that there is 95% confidence that the maximum loss will not exceed 4.4% of the investment value.

Social networking websites usage is becoming popular these days among individuals and organizations. Several organizations and researchers started investigating how social networking websites can be used as a potential tool to innovate and improve the sales of products. However, in the hustle of using social networking sites, the users knowingly or unknowingly expose their personal data to unintended users. The literature identifies the need for privacy scores of a social networking website so that the users can easily identify the level of disclosure of their personal information on the website. Quantifying privacy on social networking websites is a new and trending area of research. We propose a novel approach to calculate the privacy score of a user on a social networking website. The calculated privacy score of the user takes into account the user’s personal profile attributes and settings along with the network characteristics of the social network.

Bitcoin is one of the most well-known cryptocurrencies worldwide. Recently, as the COVID-19 pandemic raged globally, a new wave of price volatility and interest in Bitcoin was witnessed. Identifying the roles played by different information sources in the emergence and diffusion of content through Internet resources can reveal the influential factors affecting cryptocurrencies’ value. This study aims to reveal the forces behind cryptocurrencies’ monetary value—the market price movements on major exchanges before, during, and post the March 2020, COVID-19 market crash. The daily prices of the two largest cryptocurrencies, Bitcoin and Ether, were obtained from CoinDesk. By integrating Google Trends data, we found that Google searches increase when the number of tweets on COVID-19 soars, with a one-period lag (one day). Furthermore, search trends have a significant impact on cryptocurrencies’ future returns such that increased (decreased) searches for a negative event indicate lower (higher) future cryptocurrency prices.

This paper studies e-grocery order fulfillment policies by leveraging both customer and e-grocery-based data. Through the utilization of historical purchase data, product popularity trends, and delivery patterns, allocation strategies are informed to optimize performance metrics such as fill rate, carbon emissions, and cost per order. The study aims to conduct a sensitivity analysis to identify key drivers influencing these performance metrics. The results highlight that fulfillment policies optimized with the utilization of the mentioned data metrics demonstrate superior performance compared to policies not informed by data. These findings underscore the critical role of integrating data-driven models in e-grocery order fulfillment. Based on the outcomes, a grocery allocation policy, considering both proximity and product availability, emerges as promising for simultaneous improvements in several performance metrics. The study recommends that e-grocery companies leverage customer data to design and optimize delivery-oriented policies and strategies. To ensure adaptability to new trends or changes in delivery patterns, continual evaluation and improvement of e-grocery fulfillment policies are emphasized.