BWL XI: Paper in Journal of Business Research
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BWL XI: Paper in Journal of Business Research

A new research paper has been accepted for publication in Journal of Business Research. The paper uses machine learning to examine the effects of argumentation patterns in customer reviews on helpfulness.

Title: Are Longer Reviews Always More Helpful? Disentangling the Interplay Between Review Length and Argumentation Complexity

Authors: Bernhard Lutz (University of Freiburg), Nicolas Pröllochs, Dirk Neumann (University of Freiburg)


An overwhelming majority of previous works find longer product reviews to be more helpful than short reviews. In this paper, we build upon information overload theory and propose that longer reviews should not be assumed to be uniformly more helpful; instead, we argue that the effect depends on the complexity of the line of argumentation. To test this idea, we implement state-of-the-art machine learning methods that allow us to study the line of argumentation in reviews at the sentence-level. Our empirical analysis based on a dataset of Amazon customer reviews suggests that line of argumentation and review length are closely intertwined such that longer reviews with frequent changes between positive and negative arguments are perceived as less helpful. Our work has important implications for marketing professionals and retailer platforms that can utilize our results to optimize their customer feedback systems, enhance reviewer guidelines, and include more useful product reviews.