The Mitigation Mechanism of AIGC-based Personalized Marketing Strategies on the Consumer Privacy Paradox
Personalized marketing, while enhancing consumer experience, intensifies privacy concerns, a contradiction known as the "privacy paradox." The emergence of Generative Artificial Intelligence (AIGC) offers a novel pathway to resolve this paradox. This paper explores how AIGC reshapes marketing interaction paradigms to alleviate consumer privacy concerns while delivering highly personalized services. Integrating Service-Dominant Logic (S-D Logic) and Privacy Calculus Theory, this study constructs a theoretical analysis framework. The analysis proposes that AIGC primarily functions through three core mechanisms: the trust reconstruction mechanism from data extraction to transparent value exchange, the privacy sensitivity reduction mechanism from push-based personalization to contextualized intelligent symbiosis, and the perceived control enhancement mechanism from algorithmic black box to controllable generation processes. Concurrently, the paper examines potential risks associated with AIGC applications, such as algorithmic bias and sophisticated fraud, and proposes adaptive pathways for building a "responsible and empathetic" AIGC marketing system. This research provides theoretical foundations and practical guidance for balancing marketing efficacy and privacy protection in the AI era.
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