From Algorithmic Recommendations to GAI Recommendations: Consumers’ Intention to Switch Shopping Recommendation Channels

The introduction of Generative Artificial Intelligence (GAI) in the marketing field has brought about a revolution in recommendation methods. Compared to traditional algorithmic recommendation lists, Generative Shopping Recommendation Assistant (GSRA) is expected to provide a more interactive and personalized shopping experience. However, many consumers are still unsure whether to continue using familiar algorithmic recommendations or turn to GSRA. To address this issue, this study employs a mixed-methods design. Initially, qualitative interviews and service-switching literature are used to identify antecedents specific to GSRA adoption. Then, drawing on the Push-Pull-Mooring (PPM) model and Diffusion of innovation theory (DIT), a research framework is developed and tested with survey data from 499 consumers using PLS-SEM. Necessary Condition Analysis further reveals that personalization and trait innovativeness are threshold conditions for switching intentions. By conceptualizing recommendation channel migration, this study enriches consumer switching literature and offers actionable implications for AI service marketing on how to attract and retain consumers.