This paper summarized the factors that influence consumers' trust in AI chatbots and divided it into chatbot-related factors (expertise, anthropomorphism, responsiveness, and ease of use), company-related factors (perceived risk, brand trust, human support), and consumer-related factors (privacy concerns). This research attempts to explore the mechanism of human-AI chatbots trust formation and answer the question of how to promote consumers' trust in AI chatbots. The results found that the chatbot-related factors (expertise, responsiveness, and anthropomorphism) positively affect consumers' trust in chatbots. The company-related factor (brand trust) positively affects consumers' trust in chatbots, and perceived risk negatively affect consumers' trust in chatbots. Privacy concerns have a moderating effect on company-related factors. This study helps deepen the understanding of human-AI chatbots communication trust, constructs a basic model of human-AI chatbots trust, and provides insights for e-commerce enterprises to improve chatbots and enhance consumer trust.

Why does the continued use of social commerce platforms fail to promote consumer wellbeing? This study explores the roles of influencers, informational incentives and fear of missing out (FoMO) in the relationships between social commerce platform use and consumer mental health. Data were obtained through questionnaires, as well as constructing a research model. Statistical analysis and path analysis of the structural equation model were performed by the software IBM SPSS and AMOS, and the following results were obtained. (1) Influencer expertise and interactivity, informational incentives and FoMO have a significant impact on consumers’ continued use of social commerce platforms. (2) Materialism has no significant effect on consumer social commerce platform use. (3) FoMO mediates the relationships between informational incentives and continued use of social commerce platforms. (4) Consumers’ continuous use of social commerce platforms has a strong relationship with mental health. (5) Continued use of social commerce platforms can lead to intense social engagement, as well as more severe outcomes such as psychological anxiety and compulsive buying. The findings of the paper have important implications for the development of social business theory and management practice.

Online community marketing and social media influencer marketing have aroused the interest of many researchers and practitioners around the world. Companies building online content communities to implement community marketing and influencer marketing has become a new corporate strategy, especially in the tourism and hotel industries in which experiential products are sold. However, based on the content community, maintaining the sustainable development of a consumer advice network composed of opinion leaders and consumers is a major challenge. This paper selects the travel content community of Qunar.com as the research object to study the role of opinion leaders in the sustainable development of corporate-led consumer advice networks (CANs). Empirical evidence based on network evolution data from 1356 “Hotel Sleep Testers” across 11 years shows that: (1) the creation and provision of information can obviously increase the probability of the relationship construction and increase the number of relationships, thus facilitating the formation of opinion leadership (OL); (2) active participation in interactions and withhigh-quality information brings greater effects; (3) the network structure variables, such as preferential attachment, structural equivalence, and similarity, can also better predict the probability of a potential relationship; and (4) reciprocity in consumer advice networks has no significant impact on the establishment of network relationships.

We investigate the impact of human dynamics on the information propagation in online social networks. First, statistical properties of the human behavior are studied using the data from “Sina Microblog,” which is one of the most popular online social networks in China. We find that human activity patterns are heterogeneous and bursty and are often described by a power-law interevent time distributionP(τ)~τ−α. Second, we proposed an extended Susceptible-Infected (SI) propagation model to incorporate bursty and limited attention. We unveil how bursty human behavior and limited attention affect the information propagation in online social networks. The result in this paper can be useful for optimizing or controlling information propagation in online social networks.

In the period of public health crisis, effective and efficient transmission of crisis information to the public through social media is an important support for achieving social stability and orderly online public engagement. From the perspective of public value management, this study systematically investigated how local government agencies in China used social media to promote public engagement and raise public sentiment during the COVID-19 crisis. Using data captured from the “Wuhan Release” Sina Weibo account, the authors studied the factors that influence public engagement, including information sources, language styles, and media types. Further, it explores the influence of the interactive effects of public value with information sources, language styles, and media types on public engagement and public sentiment. The results show that the consistency of government response content and public value promotes public engagement and raises public sentiment. This research provides enlightenment and ideas for cognition, understanding and governance of public opinion in practice.

Web 2.0时代, 建模和预测在线信息流行度是信息传播中的重要问题. 本文基于社交网络系统信息传播的机制, 通过假设和简化, 提出了分支过程的概率模型, 来描述在线社交网络信息的流行度动力学过程. 对典型在线社交网络系统的信息流行度数据和网络结构数据进行了分析, 统计结果表明信息流行度衰减遵循幂律分布(幂指数为1.8), 微博网络的入度和出度分布也均服从幂律分布(幂指数为1.5). 模型仿真结果发现, 该模型能够再现真实社交网络数据的若干特征, 且信息流行度与网络结构相关. 对模型方程进行求解得到理论预测的结果与仿真分析和实际数据结果相符合.