Most research on recommendation sources focuses on what happens before consumers make a purchase. This dissertation asks a different question: How do recommender types (AI vs. human) and product framing (utilitarian vs. hedonic) shape how consumers feel after the experience? Using Expectancy-Disconfirmation Theory (EDT) as a framework, this dissertation explores the influence of the variable, post-consumption satisfaction. It also examines whether consumer expertise moderates these effects.
A 2x2 between-subjects experiment (N = 500) tested the impact of recommendation source and product framing using a common stimulus—a jazz clip—framed as either utilitarian or hedonic, and recommended by either an AI or a human. Results revealed that consumer expertise was the most consistent and powerful predictor of post-consumption satisfaction across all conditions. The type of recommender and product framing had little to no direct effect. Of the anticipated effects, only a modest increase in satisfaction for AI-recommended utilitarian products, approached statistical significance.
Contrary to expectations, the post-consumption experience is less about who recommended the product and more about how confident the consumer feels in the category. These findings challenge assumptions about matching recommender type to product type and suggest a shift toward tailoring recommendation strategies based on consumer expertise.