The Singular Effect of Expert Short Recommendation Lists

Recommendation lists (RLs), such as the 10 best restaurants to try or the 30 top films to watch, are widespread in the marketplace. They comprise several impersonalized items recommended to consumers by either a human expert (e.g., journalist, blogger) or a data‐aggregation process, that is, an aggregator (e.g., consumer ratings). We investigate the effects of RL length (number of items) and RL source (expert vs. aggregator) on consumer perception, behavior, purchase intention and satisfaction. Experts signal more refined tastes than aggregators, which summarize ordinary people's reviews, and short RLs signal a more diversified set of items than long RLs. This leads to an advantage for short expert RLs, which we describe as the " expert‐shortlist effect ." Across four studies ( N = 1100) in different product categories, we demonstrate that (1) consumers perceive short expert RLs as more content‐driven than popularity‐driven and associate them with greater diversity, (2) they spend more time learning about items in short human RLs, are less likely to select the first recommended item and opt for more expensive items, and (3) their purchase intention and satisfaction are moderated by their understanding of the recommendations (i.e., mentalizing). These results offer direct practical implications for recommendation lists.