The asymmetry of tourism information makes social media an important information source. Previous research has been conducted on the influence of tourist-generated content on tourism consumption behavior, but few studies have concentrated on the mechanism of tourism information quality on consumers’ travel intention in the social media environment. Adopting the Elaboration Likelihood Model, this paper aims to investigate how the information quality of social media affects consumers’ travel intention rationally and emotionally and the moderation effect of tourists’ prior knowledge. The empirical results indicate that the quality of social media information positively affects travel intention and self-congruity and trust mediates the relationship between the quality of social media information and travel intention. Moreover, this study identified that tourists’ prior knowledge negatively modifies the relationship between information quality and self-congruity in line with the proposed hypotheses. The research explores the influence mechanism of tourist-generated content quality on consumers’ travel intention, which benefits destination management and content marketing.

Artificial intelligence (AI) chatbots, represented by ChatGPT, have shown significant improvement in natural learning and problem analysis and solving, which could trigger a profound change in the education sector and have a far-reaching impact on educational practices. This study aimed to gain insights into the various impacts on users’ experience when using the AI tool ChatGPT in higher education. The study analyzed questionnaire data from 328 college students who used ChatGPT. Employing structural equation modeling, this study examined the technology acceptance model in the higher educational setting to identify factors influencing continued use of ChatGPT. The results show that ChatGPT’s compatibility positively affected users’ perceived ease of use and that efficiency positively affected perceived usefulness. Furthermore, perceived ease of use and perceived usefulness were identified as core factors affecting users’ satisfaction and continued use intentions, with user satisfaction showing a significant positive effect on continued use intention. The findings not only provide new perspectives on human–computer interaction theory in higher education but also further refine the functions of current smart devices. This study will help to promote the continued progress and improvement of educational technology, while also deepening understanding of the relationship between attitude and behavior.

This study examines consumers’ reactions to the communication styles of chatbots during failed service experiences. The current study explores whether the communication style adopted by a chatbot impacts consumer satisfaction and behavior intention and how expectancy violations can moderate these relationships in the service context. A pre-test examined the validity of the stimuli of chatbots that were either task-oriented or social-oriented after consumers encountered service failure. For more information, the experiment was designed to manipulate the AI-based chatbot agent’s process and style of communication and measure the role of expectancy violations. The main experiment results showed that interactions with social-oriented communication style chatbots enhance the level of consumers’ interaction satisfaction and intention of behavior. Respondents experienced a higher perception of warmth when interacting with social-oriented communication style chatbots than task-oriented. Moreover, expectancy violation moderates the mediation of warmth on the relationship between the chatbot’s communication style/type and interaction satisfaction, trust, and intention of patronage. Setting chatbots’ communication styles to be social-oriented can help reduce negative emotions among consumers caused by service failure; specifically, the perception of warmth created by the social-oriented communication style can alleviate negative evaluations of service agents and companies, such as dissatisfaction and loss of interest. Therefore, in managerial practice, the firm should choose the social-oriented communication style chatbot agent to recover the customer relationship after a service failure.

In the context of severe turnover, taking measures to enhance core employee management, prevent the turnover of talents, and improve employees’ sense of belonging and responsibility to the firm can become a non-negligible problem in human resource management. Considering Chinese enterprises as the research background, this study starts with the related theories of organizational support, psychological ownership, and turnover intention to explore the impact of organizational support on psychological ownership and its dimensions (self-efficacy, taking responsibility, a sense of belonging, and self-identification), the effect of each dimension of psychological ownership on turnover intention, and the relationship between organizational support and turnover intention, as well as verifies the mediating role of psychological ownership. The main findings show that (1) perceived organizational support positively affects psychological ownership; (2) psychological ownership negatively affects turnover intention; (3) perceived organizational support negatively influences turnover intention; and (4) psychological ownership mediates the relationship between perceived organizational support and turnover intention. The study results contribute to the relevant literature and guide human resource practice.

Live streamers’ power and attraction influence consumer behavior. This study focuses on streamer-central formed social capital and the relationship between streamers and audiences on live streaming video platforms (LSVP). First, we explored the impact of trust, norm of reciprocity, and network on social capital formation. Second, we investigated the effect of social capital on streamers’ attributes (attractiveness, expertise, and trustworthiness) and on the audience’s social capital formation. The main findings show that trust and network positively affect social capital. Social capital increases the level of streamers’ attractiveness, expertise, and trustworthiness perceived by the audience, which facilitates sustainable development of the LSVP and the streamer. Perceived streamers’ attractiveness negatively affects social capital formation, while perceived expertise positively affects it. To promote social capital development, streamers and operators of LSVPs should continuously emphasize social capital formation. Moreover, LSVPs should provide audiences with novel and interesting content to enable active networking. For sustainable development of LSVPs, when providing live streaming video services, streamers should deliver content that the audience perceives as based on their expertise rather than on their physical attractiveness.

Recent research on chatbots has revealed consumers’ skeptical attitudes toward them in the form of less interaction and inconsistent use intentions. This study investigates how three social attributes of chatbots (perceived warmth, perceived ability, and social presence) affect consumers’ perceived trust in chatbots and their intention to use them. Furthermore, the moderating effect of Implicit theory on perceived trust, willingness to use, and interaction satisfaction was tested. An empirical analysis of a questionnaire survey of 303 consumers indicated that the perceived warmth, competence, and social presence of chatbots positively influence consumers’ trust in chatbots. Moreover, the perceived trust toward chatbots mediates the relationship between the perceived warmth, competence, and social presence of chatbots (IV), consumers’ continuous usage intention, and interaction (DV) satisfaction. Moreover, Implicit theory, as beliefs about the variability of human characteristics (such as intelligence, personality) gradually formed by individuals in interpersonal interactions, can affect individuals’ cognitive processing and behavioral decision-making. Implicit self-theory can be divided into Entity theory and gradient theory based on different beliefs. This article examines the applicability of Implicit self-theory in the field of trust and explores the moderating role of Implicit self-theory. Whether it is the entity theorist or gradient theorist, once they trust a chatbot, they will increase their willingness to use it and improve their interaction satisfaction. It also discusses ways to improve the design of chatbots and maximize their utility, providing theoretical and managerial implications for scholars and practitioners.