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This paper aims to evaluate how the type of streamer (human vs. AI) influences consumer desire in live-streaming commerce through socio-cognitive and motivational mechanisms. Building on the CASA framework, Self-Determination Theory (SDT), and the Model of Goal-Directed Behavior (MGB), this study examines whether human and AI streamers differentially generate perceived empathy, which then activates intrinsic and extrinsic motivations leading to consumer desire. This paper also aims to test whether personal involvement and skepticism moderate the empathy–motivation pathway. A between-subjects experimental design was used to randomly assign participants to view identical livestreaming scripts presented by either a human or a humanoid AI streamer. Data from all 277 Chinese livestreaming participants were analyzed using PLS-SEM. The key findings show that (i) perceived empathy is significantly higher for human streamers than AI streamers, (ii) perceived empathy significantly boosts intrinsic motivation and consumer desire, with a smaller effect on extrinsic motivation, and (iii) intrinsic motivation, but not extrinsic motivation, is a significant predictor of consumer desire. Additionally, personal involvement strengthens the link between empathy and intrinsic motivation, while skepticism weakens the motivational pathways—especially in AI-based interactions. This research reveals that empathy is the main psychological link between streamer identity and consumer desire, and that intrinsic motivation is the dominant mechanism driving consumer engagement in live commerce, grounded in warmth and relatedness. The findings also indicate that emotional authenticity and the fulfillment of psychological needs remain crucial determinants of consumer desire, even in AI-mediated retail. Practical implications include enhancing empathetic communication and balancing the use of human and AI streamers in digital commerce.

The e-commerce experience has been transformed by artificial intelligence, which has also opened new avenues for customer communication through chatbots. This study aims to examine how chatbot characteristics influence continuance intention in e-commerce. Specifically, it explores how interactivity, compatibility, information quality, and service quality affect users' perceptions of chatbot usefulness and enjoyment. A quantitative approach using Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed for data analysis. The main findings reveal that (i) information quality and service quality positively influence both perceived usefulness and perceived enjoyment of chatbots, which in turn enhance continuance intention; (ii) compatibility positively affects perceived enjoyment but not perceived usefulness; and (iii) interactivity moderates the relationship between information quality and both perceived usefulness and enjoyment. This study extends the Technology Acceptance Model by incorporating service-related and interactive factors, contributing to the growing body of knowledge on consumer behavior and technology adoption in e-commerce. The findings offer valuable insights for chatbot designers, managers, and customer service practitioners.

Technological advancements in automation, artificial intelligence (AI), and robotics have completely changed the service industries, including the hospitality sector. This study aims to investigate the fundamental elements that impact the acceptance (or rejection) of artificial intelligence assistants' services. This research employed semi-structured interviews to gather insights from both individuals who use AI services in hotels and those who do not. Based on Behavioral Reasoning Theory, this study is empirically tested by interviews with intended respondents. This study examines the "reasons for" and "reasons against" using AI assistant services in the hotel. Technology anxiety, privacy concern, prior experience, and task complexity are "reasons against," while performance expectancy, effort expectancy, social influence, facilitating conditions, humanness, social presence, perceived interaction enjoyment, cuteness, and trust are "reasons for." This study found that "reasons against" negatively affect attitude and adoption intention, while "reasons for" positively affect them. Behavioral Reasoning Theory is used for the first time to evaluate Bangladeshi hotel consumers' AI service assistant adoption intentions. As a new technology, AI assistants provide a better understanding of user usage. Furthermore, this study offers essential facts regarding the utilization of technology in the hospitality sector.