The Dynamic Evolution Mechanism of Consumer Alienation Behavior in AIGC Marketing: Empirical Analysis Based on the Technical Acceptance Model and Information Overload Theory

The extensive application of artificial intelligence-generated content (AIGC) has significantly improved the efficiency of content marketing but intensified the complexity and dynamics of consumer behavior alienation. Based on the technical acceptance model and information overload theory, this study observes the behavior log data of e-commerce, short video, and social platforms, integrates relevant literature research results, and constructs a dynamic evolution mechanism model of consumer alienation behavior. The study finds that: (1) perceived usefulness and perceived ease of use indirectly aggravate the level of information overload by improving the acceptance of personalized recommendations; (2) information overload is the core intermediary path driving the alienation of consumer behavior; and (3) the degree of content homogenization significantly strengthens the positive effect of recommendation acceptance on information overload. This study expands beyond the traditional static analysis framework, reveals the non-linear evolution law of “technology acceptance-cognitive overload-behavioral resistance,” and proposes the hierarchical dual-path intervention strategy of dynamic threshold early warning and algorithm transparency, thereby providing a theoretical and practical basis for the balance between AIGC technology efficiency and consumer subjectivity.