The proliferation of large language models and systems for generating images, videos, and intelligent dialogue has transformed how consumers encounter marketing content within e-commerce. Despite advantages such as speed, scalability, and personalization, the implications of using generative AI-generated content for brand trust and purchase intention have not been consistently reported. This study aims to elucidate the positive and negative mechanisms through which generative content influences consumer behavior and to propose a conceptually sound model suitable for empirical testing. The research methodology involves an analytical review and critical synthesis of studies published primarily between 2024 and 2026. The theoretical framework incorporates the Stimulus-Organism-Response (S-O-R) model, source credibility, persuasion knowledge, and both cognitive and affective trust. Based on the evidence, factors such as information quality, content relevance, perceived utility, and interactivity can positively influence purchase intention by enhancing credibility and cognitive trust. Conversely, the perception of deception, informational errors, ambiguity regarding the content's origin, and a reduction in perceived authenticity may undermine affective trust. The impact of labeling generative content depends on the product type, the consumer's level of involvement, and the method of disclosure. This paper does not present new statistical results or field data; instead, it offers hypotheses, proposed measurement instruments, and a factorial experimental design for future empirical testing. The practical implication of this research is that the effectiveness of generative content is not solely a function of content-generation capabilities; rather, it depends on the alignment between quality, responsible transparency, human oversight, and consumer expectations.