The Authenticity Gap: Comparing the Effects of Human and AI-Generated Virtual Influencers on Consumer Trust and Purchase Intention
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This study investigates whether consumer trust, perceived authenticity, and purchase intention are impacted by the type of social media influencer—human or AI-generated virtual. A sample of N = 200 university-aged social media users (ages 18–21) participated in a quantitative, between-subjects study. An Instagram-style advertisement for a fictional sportswear product ("VeloRun" running shoes) with either a human influencer (n = 100) or an AI-generated virtual influencer (n = 100) was shown to participants at random. Using validated 5-point Likert scales, consumer trust, perceived authenticity, and purchase intention were assessed. Human influencers produced noticeably higher levels of customer trust, according to independent-samples t-tests (M = 3. 68 vs. Perceived authenticity (M = 3. 75 vs. M = 3. 12, p < . 001). M = 3. 05, p < . 001), and purchase intention (M = 3. 52 vs. M = 3. 08, p < . 001) in contrast to virtual influencers created by AI. According to multiple regression analysis, perceived authenticity (eta =. 341, p < . 001) and trust (eta =. 384, p < . 001) are strong positive predictors of purchase intention, explaining 48. 2 percent of the variance in consumer purchasing intent. Additionally, the association between influencer type and purchase intention is significantly mediated by consumer trust, according to a 5,000-sample bootstrap mediation analysis (indirect effect ab = -0. 215, 95 percent CI [-0. 328, -0. 118]). Virtual influencers can help marketers maximize new esthetic appeal and operational control, but real consumer purchase intention still depends on closing the "authenticity gap" through increased visual realism, relational transparency, and strategic message design.
Keywords
Artificial Intelligence, Consumer Behavior, Consumer Trust, Influencer Marketing, Perceived Authenticity, Purchase Intention, Social Media Marketing, Virtual Influencers