Revisiting the elaboration likelihood model in the context of a virtual influencer: A comparison between high- and low-involvement products

Abstract

While existing research on the elaboration likelihood model (ELM) has primarily focused on human influencers, the literature on virtual influencers (also known as digital humans or AI influencers) remains limited. To address this research gap, this study investigates the factors influencing consumer attitudes toward ads promoted by virtual influencers. Extending the ELM framework, we examine perceived expertise and attractiveness as antecedents, and explore consumer online-to-offline dynamics as consequences. Using a multigroup structural equation model, the study compares high- and low-involvement product ads by analyzing questionnaires (n = 406). A novelty lies in our findings that, contrary to existing ELM research, perceived attractiveness significantly influences attitudes toward the ads across both high- and low-involvement levels. We attribute this result to a shift of consumer attention towards virtual influencers, driven by their attention-grabbing aesthetic appeals. Our study extends the ELM framework and contributes to the literature on digital marketing, advertising, and human-computer interaction.