Human Spokesperson Versus Artificial Intelligence Avatar and Authorship Disclosure in Digital Advertising: Effects on Performance Metrics

Virtual influencers generated by artificial intelligence have emerged as a strategic tool in digital advertising, although evidence regarding their actual effectiveness in market campaigns remains limited to laboratory experiments based on self-report measures. This study examines the effect of advertising spokesperson type (human versus AI-generated avatar) and authorship disclosure (stating or not stating the origin of the advertisement) on the performance metrics of an actual campaign on the Meta platform. Through a field experiment with a 2 × 2 factorial design (spokesperson type × authorship disclosure), stratified by gender and age group, 384 advertisements were placed with a fixed budget on Facebook, and performance metrics (reach, video plays, clicks, and messages) were modeled using generalized structural equation modeling (GSEM) with a negative binomial distribution. The results reveal a trade-off: the AI avatar is associated with 25% more video plays, but with 13% less reach and 38% fewer clicks, and with no significant difference in messages. Because impressions were allocated by the platform’s delivery algorithm, these estimates are read as performance differences under standard algorithmic delivery rather than as effects of full random assignment of users to conditions. Authorship disclosure did not show a statistically significant effect on any of the evaluated metrics, nor did its interaction with spokesperson type, suggesting that stating the origin of the advertisement can be implemented without a detectable cost to performance, although for messages the evidence remains inconclusive. The originality of this study lies in providing empirical evidence based on real behavioral data from an advertising campaign, overcoming the limitations of experiments relying on self-report measures that predominate in the literature.