The impact of recommender type and recommendation framing on consumer attitudes and purchase intentions

Abstract

Purpose: In an increasingly personalized marketplace, understanding the dynamics between different types of recommenders and their influence on consumer behavior is necessary for businesses looking to optimize their engagement strategies. Thus, this research aims to analyze the impact of the type of recommender (AI vs. human) and the type of recommendation (material vs. experiential) on consumer' attitude and purchase intention. Design/methodology: We conducted a factorial manipulation approach in our experimental studies to examine the mais effect, serial mediation effects, and moderation effect. Specifically, we conducted three experiments with different scenarios to check the hypothesis. Findings: The results show that AI recommendations generated more positive reviews and greater purchase intention compared to human recommendations (Studies 1a and 1b). However, about experiential framing vs. material, AI recommendations in an experiential context were better evaluated (Study 2). On the other hand, recommendations from a human expert were more preferred in material context. Originality: This study is the first application of model serial mediation (Hayes PROCESS model 6) relating AI recommendations and consumer' evaluations. The research contributes to the Choice Process Theory, in addition to the literature that addresses Artificial Intelligence and its applicability in consumer relations. Managerial implications: This study can guide companies in developing personalized recommendation strategies, adapted to different user profiles, aiming to improve their experience. Research limitations: The data is limited to recommendations made in three different scenarios. It is necessary to test other different products/services.