Proceedings of the 12th International Conference on Opportunities and Challenges in Management, Economics and Accounting
Year: 2024
DOI:
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Generative AI and the Evolution of Virtual Influencers: A Marketing Perspective
Tara Ghanizadeh, Nima Taraghi, Enrico Scarso and Kathrin Kirchner
ABSTRACT:
Virtual influencers (VIs) are digital avatars that look like real people. They primarily promote fashion, beauty, and lifestyle products through marketing or social campaigns. These influencers are active on platforms such as Instagram; for example, ‘Lil Miquela’ has 2.5 million followers. Some scholars have found that while VIs effectively build brand awareness and improve brand image, they struggle to influence purchase intention significantly. VIs provide novel advertising experiences, though consumers often have difficulty distinguishing them from real humans. However, VIs led to higher perceived novelty in ads, especially when well-aligned with the product category, making brand-product fit critical for success. Other scholars have compared human-like VIs (HVIs) with anime-like ones, revealing that HVIs generate greater message credibility and positive attitudes. Generative AI has further revolutionized marketing through VIs, providing brands with enhanced control and cost-efficiency compared to human influencers. Despite these advantages, challenges related to trust and authenticity persist. Recent studies show that parasocial interaction and source credibility are essential for consumer acceptance, especially among Gen Z. Although VIs’ brand alignment can boost purchase intention, their perceived lack of warmth limits their effectiveness in specific contexts, such as live-streaming. Given the scarcity of related studies, our research reviewed current literature and case studies, providing valuable insights for practitioners and academics while addressing critical ethical and trust-related challenges.
keywords: Consumer Acceptance, Human Influencers, Literature Review, Social Media Marketing, Virtual Influencers Marketing