<![CDATA[User-Generated Content (UGC) has become a central element of digital brand ecosystems, shaping how consumers evaluate products, form attitudes, and make purchase decisions. Despite its importance, existing studies often focus on a single content modality such as text or images resulting in incomplete explanations of how UGC influences consumer perceptions. This research addresses that gap by examining the multimodal characteristics of UGC, the problems arising from fragmented analytical approaches, and the need for a comprehensive model capable of capturing the combined effects of emotional tone, visual vividness, and social engagement intensity. The study aims to explain how these multimodal UGC features shape brand perception and purchase intention, and to provide a more integrated understanding of digital consumer behavior. A quantitative, explanatory methodology was proposed, combining UGC scraping from multiple platforms, multimodal feature extraction, and consumer surveys analyzed using Structural Equation Modeling. The findings show that UGC valence, vividness, and engagement intensity significantly influence brand perception, with valence emerging as the strongest driver. Brand perception subsequently demonstrates a robust mediating effect on purchase intention, confirming the theoretical expectation that consumers’ internal evaluations translate UGC exposure into behavioral outcomes. Comparisons with state-of-the-art research indicate that the proposed multimodal approach provides stronger explanatory power than single-modality models, advancing the conceptual and methodological understanding of UGC effects. The study concludes that UGC exerts both direct and indirect influence on consumer decisions through affective and cognitive pathways, offering substantive implications for digital marketing strategies while also highlighting opportunities for extending multimodal analysis through cross-cultural or longitudinal research designs.]]>
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