Online product reviews are a pervasive form of public feedback, yet research on their perceived helpfulness has focused almost exclusively on overall sentiment. Drawing on feedback literature, we propose that review structure—the sequencing of positive and negative content across the evaluative message—shapes helpfulness above and beyond aggregate valence. We conducted an exploratory study by analyzing 195,675 Amazon reviews using growth curve modeling to capture each review’s opening tone and valence trajectory. Results show that the most effective structures depend on product ratings. For highly rated products, reviews that grow increasingly positive are most helpful, while those that turn negative are least. For average-rated products, progressively negative trajectories enhance helpfulness, whereas reviews that start negative and grow positive are least effective. For low-rated products, reviews are judged most helpful when they open constructively before introducing criticism. These findings advance theories of online reviews and feedback by showing that how evaluative information is organized matters as much as what is said.
Luan et al. (Wed,) studied this question.