Designing marketing content for social commerce to drive consumer purchase behaviors: A perspective from speech act theory

With the emergence of content-driven social commerce, designing marketing content that better stimulates consumer purchase behaviors has become increasingly essential. However, it remains unclear what and how linguistic features of marketing content in emerging social commerce influence consumer purchase behaviors. Drawing on speech act theory, this paper proposes a multi-level research model to conceptualize the linguistic features of content from the aspects of word usage (micro level), within-content argument development (macro level), and between-content linguistic mimicry (meta level), and investigate their impact on consumer purchase behaviors. With a unique dataset that includes 44,256 textual posts from JD WeChat shopping circle (a content-driven social commerce platform), this paper combines text mining methods with a series of regression analyses to test the research model. The empirical analyses find that the number of customers who make a purchase increases 1) at the micro-level due to self-referencing and detailing, 2) at the macro-level due to argument structuring, and 3) at the meta-level due to linguistic style matching, while linguistic content matching negatively affects the number of customers. These findings reveal how content creators strategically use language to design marketing content that encourages consumer purchase behaviors in emerging social commerce. This study has important theoretical contributions and practical implications.