How consumer attention shapes personalized experiences in generative AI products: A configurational perspective

As generative artificial intelligence (GenAI) reshapes consumer–product interactions, understanding how consumer attention drives personalized experiences has become increasingly vital. This study examines how distinct attention configurations shape consumer satisfaction, offering new insights into AI‐enabled product personalization. Using consumer reviews from leading GenAI applications, including ChatGPT, Copilot, and Gemini, we combine semantic analysis powered by large language models (LLMs) with configurational analysis to identify cognitive, emotional, and habitual attention patterns and their effects on consumer experience. The results show that hedonic motivation and habitual use are primary drivers of high satisfaction, while performance expectancy and effort expectancy exert complementary influence within specific configurations. Negative outcomes arise from misalignments between performance expectations and perceived price value, highlighting the importance of aligning experiential value with consumer expectations. By introducing consumer attention configurations as a marketing‐oriented mechanism for personalization, this study proposes an experiential co‐creation framework that enhances GenAI product design. The findings contribute to AI‐driven service innovation research and offer actionable guidance for organizations seeking to develop emotionally engaging AI products that cultivate sustained consumer loyalty.