Mind the fake reviews! Protecting consumers from deception through persuasion knowledge acquisition

Abstract

A growing body of research has shown that while computers can effectively detect fake reviews, humans are no more accurate than chance. Since consumers strongly trust online reviews, and fake reviews are pervasive, they often make suboptimal choices. However, whether consumers can learn to detect fake reviews and whether this knowledge would help them make better-informed decisions remain open questions. We propose that learning four distinctive features of fake reviews (one-sidedness, exaggeration, personal selling style, and generic descriptions) affects consumers’ trustworthiness in them and their perceived favorability, thus affecting their purchase intentions toward the target product. Five studies support our theoretical model. We also show that one-sidedness is the most discriminating among the four features and that simply activating consumers’ current knowledge is not enough to protect them from fake reviews.