 Are longer reviews always more helpful? Disentangling the interplay between review length and line of argumentationBernhard Lutz, Nicolas Pröllochs and Dirk Neumann Journal of Business Research, 2022, vol. 144, issue C, 888-901 Abstract: 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. Keywords: Consumer reviews; Argumentation patterns; Online word-of-mouth; Machine learning; Natural language processing; Data-driven decision-making (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jbrese:v:144:y:2022:i:c:p:888-901 DOI: 10.1016/j.jbusres.2022.02.010 Access Statistics for this article Journal of Business Research is currently edited by A. G. Woodside More articles in Journal of Business Research from Elsevier |
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