Topic diversity and review usefulness: A text-based analysis

While scholars and managers have long queried how a review message can be useful, the e-word-of-mouth (eWOM) literature yields inconclusive findings. Our study approaches this classic research question from the novel angle of diversity and investigate how topic diversity of online reviews affect review usefulness. Specifically, topic diversity manifests in both topic variety, the extent to which different topics are discussed in a review message, and content distribution across topics, the extent to which content is distributed across different topics. We leverage the aspect-based topic modeling technique to uncover the extent of different topics discussed in individual review messages as the nested subunits and operationalize topic variety and content distribution as two main unit-level predictors of review usefulness. This text-based approach helps analyze about 200k restaurant reviews from Yelp.com and generates six different topics relating to restaurant reviews. Results reveal that (1) on average, topic variety positively affected review usefulness, while content distribution across topics did not; (2) however, when topic variety is high (low), the impact of content distribution on review usefulness is significant and positive (significant and negative). Our study contributes to the e-WOM literature by offering new knowledge regarding topic diversity and review usefulness.