Generative AI-based shopping assistants leveraging large language models (LLMs) offer new ways to interact with product information in online shops. They can deliver highly personalized product descriptions based on consumer explicit input and implicit behavior. They are especially useful for tasks that involve processing and comparing a lot of product information. First applications in this field have been made available recently, e.g., Zalando provides an AI shopping assistant for their customers. The new technology offers interesting opportunities. At the same time, however, it is not clear what impact it has on customers' cognitive processes and decision-making behavior. Currently, there is a lack of knowledge about how GenAI-based shopping assistants affect consumers in comparison to traditional online shopping of e-commerce online shops. In this project, we perform a large scale experimental study to investigate the impact of hyperpersonaliization in GenAI-based shopping assistants on online consumers.