Abstract
To address a research gap on how AI can be leveraged to enhance customers’ in-store journeys, this study evaluates the effectiveness of an AI-powered conversational decision bot (mobile messaging app) employing two laboratory experiments in a simulated store. Study 1 revealed that consumers found in-store shopping to be more enjoyable and effortful with a decision bot than without, demonstrating that these bots reinforce consumers’ in-store hedonic and utilitarian experiences. Study 2 evaluated the effectiveness of two types of decision bots (attribute- vs. alternative-based), revealing that consumers’ perceived usefulness and reuse/recommend intentions for the bot were higher when using an attribute- as compared to an alternative-based bot. When shopping in-store, consumers benefit from a decision bot that retrieves attribute-level information for the product category rather than one that focuses on single product alternatives. This study makes important theoretical contributions to constructive decision theory, the Elaboration Likelihood Model, and language-based adaptive intelligence.
