Trust Calibration in the Era of Generative AI: Unravelling AI-Generated eWOM Adoption and Purchase Intention through Multi-Group PLS-SEM

The present study examined the factors influencing AI-generated electronic word-of-mouth adoption and purchase intention by integrating Trust Calibration into the Theory of Reasoned Action. The findings demonstrate that AI-eWOM Disclosure Transparency, Algorithmic Amplification, Consumer Trust in AI, Subjective Norms, and Perceived Diagnosticity positively influence Trust Calibration, whereas Perceived Manipulation has a negative effect. Trust Calibration significantly enhances AI-generated eWOM Adoption, which subsequently increases consumers' Purchase Intention, while Perceived Diagnosticity also exerts a direct positive influence on Purchase Intention. The study contributes to theory by extending the Theory of Reasoned Action and integrating Trust Calibration Theory into AI-enabled consumer behaviour research. It also advances the literature on AI-generated eWOM and social commerce by proposing and empirically validating a comprehensive research framework. From a practical perspective, the findings highlight the importance of transparency, ethical AI deployment, trustworthy algorithms, and responsible AI-generated content in strengthening consumer trust and encouraging AI adoption. As generative AI continues to transform digital commerce, developing transparent, reliable, and consumer-centric AI systems will become increasingly important for sustaining consumer confidence and supporting responsible innovation.

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