The Longer the Better? The Interplay Between Review Length and Line of Argumentation in Online Consumer Reviews

TL;DR

This study investigates how the line of argumentation in online reviews influences the relationship between review length and perceived helpfulness, challenging the assumption that longer reviews are always more helpful.

Contribution

The paper introduces a novel analysis of argumentation lines at sentence level and demonstrates their moderating effect on review helpfulness, using advanced NLP techniques.

Findings

Argumentation line frequency moderates review length and helpfulness.

Longer reviews are not always more helpful, depending on argumentation.

Insights can improve customer feedback systems on retail platforms.

Abstract

Review helpfulness serves as focal point in understanding customers' purchase decision-making process on online retailer platforms. An overwhelming majority of previous works find longer reviews to be more helpful than short reviews. In this paper, we propose that longer reviews should not be assumed to be uniformly more helpful; instead, we argue that the effect depends on the line of argumentation in the review text. To test this idea, we use a large dataset of customer reviews from Amazon in combination with a state-of-the-art approach from natural language processing that allows us to study argumentation lines at sentence level. Our empirical analysis suggests that the frequency of argumentation changes moderates the effect of review length on helpfulness. Altogether, we disprove the prevailing narrative that longer reviews are uniformly perceived as more helpful. Our findings allow retailer platforms to improve their customer feedback systems and to feature more useful product reviews.