| Summary: | In the rapidly evolving technological landscape, social media platforms have transformed, embracing virtual communities that reshape online commerce dynamics. Anchored in the Elaboration Likelihood Model (ELM), this research employs a dual-pronged methodology encompassing both qualitative and quantitative approaches. Qualitative methods involved semi-structured interviews with four marketing experts from diverse countries, which are Iceland, France, China, and the UK. Insights gleaned into their perceptions of past, current, and future trends in general social media shopping underscore the function of virtual community features. Engagements in sharing or commenting, or the need users manifest to browse through virtual communities on social media platforms, bolsters user retention, sustains attention, and amplifies the likelihood of future purchases. Intriguing content attracts and help prompting users to explore unrealized life facets. Virtual community generally functions as a searching engine, fulfilling as a significant reference for purchase decision, while comments below also hold significance. Additionally, virtual communities enable users to acquire social currency, granting them conversational material. Quantitative analysis applied a 5-point Likert scale questionnaire with 211 effective collections to regressively examine persuasion routes, specifically within the realm of Chinese social media marketing. Compelling factors influencing purchase decisions of users in "Little Red Book" were identified. Within the central route, persuasive arguments wielded significant positive impact. Conversely, within the peripheral route, attributes like detailed information, purchase links inserted in the posts, user interactions of high frequency, and positive community attitudes exerted significant positive effects. However, high-quality multimedia, high quantity of images or tags, celebrity endorsements, and engaging themes yielded unexpectedly non-significant negative effects. Innovatively diverging from ...
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