Informational and Affective Interactions in Live-Streaming Commerce: User Engagement and Its Relationship with Trust-Related Expressions Based on Multiple Public Data So

Abstract

             Live-streaming commerce has emerged as an important format of online shopping, integrating product demonstrations, real-time communication, entertainment, and transactional functions. Streamers not only provide product-related information but also engage viewers through emotional expression, interpersonal communication, and relationship-building strategies. Although informational and affective interactions may influence consumer responses through different mechanisms, user engagement, streamer trust, and purchase intention represent distinct constructs that should not be treated as interchangeable.
           This study aims to examine the characteristics of informational and affective interactions in live-streaming commerce, investigate their relationships with observable user engagement and trust-related expressions, and explore the potential association between trust-related expressions and purchase-intention expressions. A multiple-source public data analysis approach is proposed, combining literature-based evidence synthesis, public-text coding, thematic analysis, descriptive statistics, and appropriate statistical modelling. The planned data sources include publicly accessible livestream content, user comments, industry reports, and published academic research. Artificial intelligence (AI) will be used to assist with literature retrieval, text classification, and data organization, while the researcher will be responsible for source verification, coding decisions, and validation of analytical results.
           Existing research provides an initial empirical foundation. Luo, Xu, and Zheng (2024) analysed real-world data from the Dongfang Zhenxuan livestreaming channel on Douyin, distinguishing informational and affective topics and examining their relationships with different dimensions of user engagement. Tian et al. (2023) investigated the antecedents of streamer trust and its relationship with consumer purchasing behavior using a cognition–affect–conation framework. Liu and Zhang (2024) examined the relationships among interactive elements, social presence, trust, and purchase intention using questionnaire data from 326 live-streaming commerce consumers. These studies provide relevant evidence for examining interaction types, user engagement, and trust-related variables. However, their findings cannot substitute for the original cross-platform comment data that this study intends to collect.
            This study conceptualizes trust-related expressions in public comments as observable textual indicators rather than direct measures of consumers’ underlying psychological trust. Similarly, purchase-intention expressions are distinguished from actual purchasing behavior. The proposed research is expected to establish a reproducible framework for classifying livestream interactions and to provide a more nuanced understanding of the relationships among information provision, affective engagement, and consumer responses. As the collection of original cross-platform data has not yet been completed, this paper does not report uncalculated sample sizes, statistical coefficients, or significance tests as empirical findings.