ARTIFICIAL INTELLIGENCE AND INFLUENCER MARKETING: MAPPING THE FIELD OF KNOWLEDGE

Abstract

Purpose: This review aims to map the current body of knowledge on AI in influencer marketing by outlining the theoretical and methodological backgrounds and contexts and identifying key areas for further exploration in this field. Method: A systematic literature review of 188 articles published on the Scopus database on AI and influencer marketing was conducted to identify recent research trends in the field. The review was performed based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol guidelines and the TCM organizing framework encompassing theory, context, and methodology analyses. Findings: The domain of AI in influencer marketing was found to be in its infancy stage; however, the number of articles published on AI and influencer marketing has been exponentially growing since 2017. The main term used by the authors to name the influencers generated using AI was found to be ‘virtual influencer’ and is constantly gaining its position. The most followed theories were found to be the Source Credibility Theory, Mind Perception Theory, Parasocial Interaction Theory, Construal Level Theory, and Trust Transfer Theory. Five main research contexts were identified: sender-focused, source-focused, message-focused, receiver-focused, and context-focused. The research methods used in the field of AI in influencer marketing have been identified as three principal approaches: quantitative, experimental, and qualitative. Contributions/implications: This paper is among the first review papers mapping the innovative and complex domain of AI and its intersections with influencer marketing. By mapping the existing research scope, it identifies the main theoretical, methodological, and contextual backgrounds of the domain.