Leveraging Machine Learning to Analyze Influencer Credibility’s Impact on Brand Admiration and Consumer Purchase Intent in Social Media Marketing

Abstract

This study harnesses the power of machine learning to unravel the intricate dynamics of influencer credibility in shaping brand admiration and consumer purchase intent within the realm of social media marketing. A survey of 423 consumers, analyzed using JASP software and advanced structural equation modeling (SEM), provides a data-driven lens into how credibility dimensions—experience, trustworthiness, attractiveness, and conformity—influence consumer perceptions and actions. Findings highlight the pivotal roles of experience and trustworthiness in fostering brand admiration, while attractiveness yields inconclusive results. Conformity emerges as a subtle yet significant factor in driving purchase intent. Notably, brand admiration serves as a critical intermediary, bridging the gap between influencer credibility and consumer purchase decisions. This research underscores the transformative potential of machine learning in decoding consumer behavior, offering fresh insights for marketers aiming to optimize influencer-driven campaigns in the ever-evolving digital landscape.