Influencer marketing and the growth of affiliates: The effects of language features on engagement behavior

Abstract

Although most major brands are utilizing affiliate marketing programs, potential drivers of engagement with influencer affiliate marketing content have yet to be explored. To address this gap, the authors apply the Elaboration Likelihood Model to propose that linguistic characteristics of the text within influencers’ affiliate marketing posts motivate either peripheral or central route processing, which in turn impacts behavioral interactions with the content. To empirically test these relationships, text mining and natural language processing are used to construct a large dataset of influencers’ affiliate marketing posts from their Instagram feeds. The analysis reveals certain linguistic styles can enhance engagement, while others negatively impact these behaviors. In addition to advancing understanding of influencer affiliate marketing and social media engagement, the findings offer important insights for both brands and influencers participating in affiliate marketing.