AI vs. Traditional Influencers: Consumer Perception Research

During my MSc in Marketing and Consumer Behaviour, I conducted quantitative research examining the emerging phenomenon of AI influencers compared to traditional human influencers.

The study explored critical dimensions of consumer perception, focusing on self-congruency, authenticity, trustworthiness, and purchase intention. Using virtual influencer Lil Miquela and established content creator Emma Chamberlain as case studies, the research provided valuable insights into how consumers interact with and respond to AI-powered social media personalities.

Through rigorous statistical analysis using SPSS, including paired t-tests, Pearson's correlations, Wilcoxon signed rank tests, and Spearman's rho, the study revealed significant differences in consumer responses between AI and human influencers. The research involved 70 female participants from the UK, ensuring robust and reliable findings.

Key findings demonstrated that AI influencers consistently scored lower across all measured metrics, highlighting important implications for brands considering partnerships with virtual influencers. The research contributes to the growing body of knowledge surrounding AI in marketing and provides practical insights for industry professionals navigating this evolving landscape.

This dissertation showcases:

• Advanced quantitative research methods
• Statistical analysis using SPSS
• Consumer behaviour analysis
• Digital marketing trends
• AI marketing applications