When and how do Recommendation Lists influence consumers?

Recommendation Lists (RLs), such as “the 15 best candles according to our perfumer”, are widespread on websites, blogs and even newspapers, yet are basic and impersonalized lists suggesting items to consumers. Despite their ubiquity, their usefulness for consumers is questionable. In this article, we investigate whether RLs can influence consumers’ responses (attitude, behaviors), depending on their source (i.e., created by a human or by an algorithm) and size (i.e., number of items recommended). Based on two studies in two different settings and with three distinct categories, we show that the interaction between RL size and the RL source influences consumers’ response. Consumers gave more positive ratings (Study 1), spent more time browsing items, inspected more items, spent more time inspecting each item and accepted higher prices (Study 2) in a small-human RL and in a large-algorithm RL. This effect is stronger when consumers have experience of the category recommended.