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.

This study examines how chatbots respond to emotional expressions in consumer inquiries within online Q&A communities, where questions often convey confusion, frustration, or anger. Grounded in Affect-as-Information (AAI) theory, it compares chatbot and human responses across different stages of the consumer journey. Data from Reddit included consumer-generated questions, human replies, and responses from two large language model (LLM) chatbots with varying NLP capabilities. The findings reveal that positive emotions in consumer questions reduce response cognitivity, while negative emotions in post-purchase contexts trigger reverse emotional contagion. Chatbots showed higher emotionality for pre-purchase and greater cognitivity for post-purchase questions, unlike human responders who remained consistent. These insights refine AAI theory by showing how positive emotions can disrupt information exchange and highlight key differences between human and AI responses, informing improved chatbot design.

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.