AI-enhanced neuromarketing and social media communication: Evidence from PLS-SEM analysis in an academic context

Artificial intelligence (AI) is reshaping neuromarketing by enabling the real-time analysis of neurometric, biometric, and psychometric data to optimize consumer engagement. This study investigates how AI-enhanced neuromarketing influences social media marketing strategies, using a structural equation modeling (PLS-SEM) approach to assess relationships between neuromarketing knowledge, application, activities, and social media communication. Data were collected through a survey of 416 Romanian university students and professors with practical exposure to neuromarketing tools in educational environments. The results confirm that neuromarketing knowledge significantly improves practical application (β = 0.726, p < 0.001), which in turn enhances both marketing activities (β = 0.555, p < 0.001) and social media communication effectiveness (β = 0.633, p < 0.001). AI was found to amplify these effects through predictive analytics, real-time consumer data processing, and automated content optimization. Ethical considerations—such as privacy risks and algorithmic bias—are acknowledged, and the academic sample limits generalizability to commercial contexts. Future research should explore cross-industry applications, diverse cultural settings, and longitudinal impacts to strengthen external validity.