The Impact of Generative AI Shopping Assistants on Consumer Decision-Making

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. 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 because of the lack of knowledge about how GenAI-based shopping assistants affect consumers in comparison to traditional online shopping of e-commerce online shops. Traditional online shopping relies on established navigation and search functions, whereas GenAI introduces natural language-based, personalized interactions. Understanding the differences in consumer behavior between these two approaches is crucial for optimizing e-commerce strategies and improving customer experiences. 

This study seeks to empirically investigate the effects of GenAI-based shopping assistants on consumers in online shops. To answer our research questions, we conduct an in-person laboratory experiment in Karlsruhe. The experiment is designed as a two-condition between-subjects design (traditional online shopping vs. GenAI-based assisted shopping). In each condition, participants will interact with one of the two versions of the experimental prototype. 

We collect and analyze subjective survey data, as well as objective eye-tracking and behavioral data, to understand the impact on consumer cognition and decision-making. 

Key Facts:  

• This study is investigating how AI-based shopping assistants influence consumer decision-making and cognitive processes compared to traditional methods. 

• We pursue this through developing an online shop prototype with traditional and GenAI-based configurations to gather empirical data. 

• This study is utilizing eye-tracking, behavioral interaction data, and surveys to assess cognitive load, decision speed, and consumer satisfaction with the goal of providing insights into the effectiveness and efficiency of GenAI in e-commerce, informing future strategies for online retailers.