Understanding the effectiveness of influencer recommendation information on social media platforms

Abstract Existing social media influencer marketing research mainly explores how to enhance followers purchase intentions through the mechanisms of improving the follower–influencer relationship, however leaves research gaps regarding how to design effective influencer recommendation information (IRI) to stimulate consumer purchase behaviors. To address the gaps, a research model was established based on the information adoption model. This study collected 2276 influencer recommendation posts from typical Chinese social media influencer marketing platform WeChat Shopping Circle. Text‐mining methods were used to develop measurements from influencer posts, and survey data were collected from 20 participants from a large Chinese university to validate measurements. Using negative binomial regression, the empirical study found that the number of consumers who purchase recommended products is determined by the usefulness of IRI, whereby being further affected by content expertise, content novelty, and platform endorsement to influencers. The type of recommended products moderates the relationship between these influencing factors and information usefulness. This study makes contributions by revealing the informational influence mechanism of IRI and identifying what factors can exert effective informational influence for different types of products. It also offers practical implications for the design of influencer recommendation strategies.