The widespread application of robotic technology across social contexts has underscored the potential of anthropomorphic features in enhancing social attributes and user acceptance, drawing increasing academic attention. Despite its emergence as a prominent research topic in human-robot interaction (HRI), existing studies lack systematic bibliometric reviews. To address this gap, we conducted a comprehensive review of 1,082 academic publications from the Web of Science Core Collection. By constructing knowledge maps that include collaboration networks, co-citation clustering, and keyword co-occurrence analysis, we identified the developmental trajectory and key themes in anthropomorphism research within HRI. The analysis revealed three core components: application scenarios in HRI, anthropomorphism theory and psychological effects, social behavior and emotional interaction. Keyword clustering further identified four major research hotspots: system design, emotional interaction, user adoption and experience, and assistive interaction techniques. This study provides a comprehensive depiction of the knowledge landscape of anthropomorphic research in HRI, offering valuable references for further research direction.

This study examines consumer readiness to adopt palmprint recognition payment technology in Chinese retail settings. Using data from 530 participants, the study investigates factors such as performance expectancy, effort expectancy, social influence, facilitating conditions, personal innovativeness, coupon availability, and perceived sensitivity of palmprint information. Partial least squares structural equation modeling (PLS-SEM) reveals that all factors except perceived sensitivity positively influence behavioral intention. Perceived sensitivity negatively moderates the effects of performance expectancy and coupon availability, underscoring privacy concerns. A multigroup analysis shows that social influence strongly affects experienced users, while coupon availability drives intention among less experienced users. These findings offer practical insights for retailers and contribute to the literature on biometric payment adoption, emphasizing the need to balance technological benefits with consumer privacy concerns.

Generative artificial intelligence (GAI) advancements have ignited new expectations for artificial intelligence (AI)-enabled educational transformations. Based on the theory of planned behavior (TPB), this study combines structural equation modeling and interviews to analyze the influencing factors of Chinese university students’ GAI technology usage intention. Regarding AI literacy, students’ cognitive literacy in AI ethics scored the highest (M = 5.740), while AI awareness literacy scored the lowest (M = 4.578). Students’ attitudes toward GAI significantly and positively influenced their usage intention, with the combined TPB framework and AI literacy explaining 59.3% of the variance. AI literacy and subjective norms positively influenced students’ attitudes toward GAI technology and perceived behavioral control, and attitude mediated the impact of AI literacy and subjective norms on GAI usage intention. Further, the interviews provide new insights for university management and educational leadership regarding the construction of an educational ecosystem under the application of GAI technology.

The rise of live streaming e-commerce (LSC) platforms has transformed consumer interactions. However, research on virtual streamers remains limited. This study applies the stimulus-organism-response framework and flow theory to explore the factors shaping purchase intention in LSC. The study analyzes survey data from 577 virtual streamer viewers using partial least squares structural equation modeling. The findings show that interactivity, entertainment, social presence, and telepresence significantly influence flow experience. Additionally, animacy, vividness, attractiveness, intelligence, and flow experience enhance trust. Moreover, flow experience and trust predict continuous watching intention, which positively affects purchase intention. Multigroup analysis reveals gender differences, with intelligence significantly influencing trust among female viewers but not among males. The study’s findings offer insights into the roles of live scene characteristics, virtual streamer characteristics, and viewers’ psychological perceptions in LSC.

The emergence of educational video platforms has led to microlearning resources becoming increasingly mainstream. These platforms offer unique ecosystems and resource designs that better cater to the needs of learners. In this study, we examined the technology acceptance model (TAM) and task-technology fit (TTF) theory and conducted an empirical analysis of user satisfaction with new online learning spaces. We learned that perceived usefulness, perceived ease of use, and task-technology fit had significantly impacted user satisfaction, with these three factors collectively contributing to 78.2% of the variance in user satisfaction. Additionally, user satisfaction and task-technology fit significantly influenced the continuance intentions of users toward using these spaces, with both factors contributing to 66.7% of the variance in continuance intention. Overall, our findings revealed that the future development of new online learning spaces should consider the task requirements of learners and improve the platforms accordingly.