From Virtual Persona to Purchase: The Trust Engagement Pathway of AI-Generated Influencers in Sustainable Textile Marketing

Abstract

While AI-generated social media influencers can provide consistency, scalability, and creative control to the textile industry, there is a question about their credibility when they make sustainability-related statements. The current research investigates how the effectiveness of AI-influencers affects consumers' trust in brands, sustainable brand engagement, and purchase intention in the textile industry in Pakistan. The model combines the Stimulus-Organism-Response model and source credibility and signaling models. To finalize and validate the reporting framework, a deliberately generated data set of 250 potential social-media consumers was analyzed with an explicitly simulated approach in PLS-SEM with 5,000 bootstrapped replications. All HTMT ratios were below .85 and the measurement model had acceptable reliability (Cronbach's alpha = .854-.884) and convergent validity (AVE = .683-.718). AI-influencer effectiveness was a strong predictor of consumer trust, sustainable engagement, and purchase intention (beta = .497, .251, and .181, respectively). The trust variable (beta = .425) and the variable of sustainable engagement (beta = .176) to purchase intention were significant, while the variable of sustainable engagement was the strongest predictor of purchase intention (beta = .346). Indirect effects via trust and engagement and the serial pathway of trust-engagement also showed significant results. The results confirm that AI-powered endorsers have the greatest impact when they are transparent and credible, fostering trust and driving sustainability awareness to genuine engagement with the brand. This is a simulated numerical dataset, and thus the results illustrate an analytically coherent paper structure; these should be replaced or verified with real field data prior to the actual publication of the dataset.