The Recipe for the Perfect Review?An Investigation into the Determinants of Review Helpfulness

Online product reviews, originally intended to reduce consumers’ pre-purchase search and evaluation costs, have become so numerous that they are now themselves a source for information overload. To help consumers find high-quality reviews faster, review rankings based on consumers’ evaluations of their helpfulness were introduced. But many reviews are never evaluated and never ranked. Moreover, current helpfulness-based systems provide little or no advice to reviewers on how to write more helpful reviews. Average review quality and consumer search costs could be much improved if these issues were solved. This requires identifying the determinants of review helpfulness, which we carry out based on an adaption of Wang and Strong’s well-known data quality framework. Our empirical analysis shows that review helpfulness is influenced not only by single-review features but also by contextual factors expressing review value relative to all available reviews. Reviews for experiential goods differ systematically from reviews for utilitarian goods. Our findings, based on 27,104 reviews from Amazon.com across six product categories, form the basis for estimating preliminary helpfulness scores for unrated reviews and for developing interactive, personalized review writing support tools.

We analyze determinants of review helpfulness in online retailing based on Wang and Strong’s (1996) data quality framework. Helpful reviews consist of 9 % of adjectives, display high product feature entropy, and present opinions that differ from previous reviews for the product in question. Other helpfulness determinants depend on whether experiential or utilitarian products are reviewed. Our research points e-shop providers towards two major improvements in their review systems.