Determinants of the helpfulness of specialty coffee reviews on Amazon

Abstract

With the increasing volume of online reviews on platforms like Amazon, customers must put considerable effort into isolating useful information from irrelevant or ambiguous content. One solution implemented by e-commerce sites to address this challenge is product review systems, where customers can vote on whether they find a review helpful, thereby reducing uncertainty in purchase decisions. Therefore, it is crucial to understand what makes a review helpful and how to enhance customer access to these valuable reviews. This study aims to analyze the content characteristics of online reviews that influence their perceived helpfulness in the context of specialty coffee products sold on Amazon. A content analysis was conducted on 2369 reviews to identify elements that contribute to their informational value. Subsequently, a zero-inflated negative binomial regression model was applied to test the hypotheses, addressing issues of overdispersion and an excess of zeros in the response variable. The findings suggest that aspects such as multimedia format, review depth, and the inclusion of extrinsic product attributes (price and quality) are key factors that enhance review helpfulness. On one hand, the use of images and videos helps consumers visualize the product and understand its features, while detailed and extensive reviews provide more comprehensive information. Moreover, factors like price and quality allow consumers to assess whether the product meets their needs and preferences. These findings are crucial for developing more effective marketing strategies in the coffee industry by providing a more precise understanding of the attributes most valued by consumers.