As artificial intelligence is increasingly being integrated into daily life, understanding the factors that drive individuals to seek AI-related information becomes more important. This study employs the Planned Risk Information Seeking Model as a theoretical framework to explore AI information-seeking behaviours from the perspective of individual differences. Apart from the effects of individual differences, the findings were generally in line with the theoretical model. However, negative affect negatively predicted information insufficiency and was not significantly related to information seeking intention. Furthermore, examination of the effects of individual differences revealed that I-type epistemic curiosity positively predicted both information insufficiency and information seeking intention, yet D-type epistemic curiosity was not significantly related to information insufficiency and information seeking intention. Moreover, information innovativeness was found to be negatively related to information insufficiency but positively related to information seeking intention. Theoretical and practical implications are discussed.

While much AI adoption research emphasizes technological factors, few studies explore social determinants or consumer purchase intention. This study addresses this gap by examining how social influence affects Chinese consumers’ intention to purchase AI products. Conducted in the rapidly evolving Chinese market, an online survey of 538 respondents was analyzed using social influence theory and the value-based adoption model. Results show that social influence positively impacts perceived usefulness, enjoyment, and ease of use, while negatively affecting perceived cost. In turn, perceived usefulness and enjoyment significantly increase purchase intention, whereas perceived cost reduces it. Moreover, personal innovativeness moderates these relationships: it strengthens the effect of social influence on perceived ease of use and weakens its effect on perceived enjoyment, but shows no moderation effect on perceived usefulness or cost. These findings highlight the importance of integrating social and individual factors when investigating consumer behavior in emerging AI markets.

This study explored the relationships between three types of Internet use (i.e. educational, socialising, and entertainment) and externalising behaviours in early adolescents. Using a representative sample of early adolescents in China (N = 819, Mage = 11.76), the result revealed that the indirect effect of educational Internet use on externalising behaviours was serially mediated by depression and self-control. Meanwhile, entertainment Internet use was found to be indirectly associated with a higher level of externalising behaviour via self-control. Last, socialising Internet use was found to have no effect on externalising behaviours and the two mediators. Theoretical implications for Internet use and externalising behaviours, as well as practical implications for problematic behaviour interventions are discussed.

Algorithmic recommendations, characterized by features including accuracy, familiarity, novelty, and transparency, have been used in TikTok for a long time. However, the impact of these features on users’ privacy calculus and continuance usage intention remains unclear. Survey data from 625 Chinese users was analyzed using the least-squares partial structural equation modelling. Results showed that four features positively affect users’ perceived benefits; perceived familiarity positively affects privacy risk; perceived effectiveness of privacy policy moderates the relationship between perceived accuracy and privacy risk. Moreover, perceived benefits positively influence continuance intention to use, whereas privacy risk has a negative impact. This study shed light on why users are willing to compromise privacy for the benefits originating from features of algorithmic recommendations. Furthermore, this study provided valuable insights for platforms and practitioners concerning the design and development of effective algorithmic recommendations, as well as improving the effectiveness of privacy policy.

Chatbots have been widely adopted to support online customer service and supplement human agents. However, online data transmission may involve privacy issues and arouse users’ privacy concerns. In order to understand the privacy management mechanism when interacting with chatbots and human agents, we designed a cross-national comparative study and conducted online experiments in China and the United States based on Communication Privacy Management (CPM) theory. The results show that privacy concerns and boundary linkage played different mediation roles between agent identity and the intention to disclose as well as the intention to use the service. Information sensitivity had a significant moderating effect on the mechanism. Our research contributes to a better understanding of personal boundary management in the context of human-machine interaction.