Uncovering the paths to helpful reviews using fuzzy-set qualitative comparative analysis

Researchers have found evidence that helpful product reviews written by other consumers have the potential to alter consumers’ purchase decision and influence overall sales. In the quest to find what makes a review helpful, prior studies have documented volume, valence, argument quality, and source certainty as determinants of helpful reviews. However, these studies used regression analysis and found significant effects of each of the determinants regardless of other variables. Taking a different perspective, the present study uncovers the “causal recipe” (combination of antecedent conditions) of helpfulness review by applying a fuzzy-set qualitative comparative analysis. Congruent with elaboration likelihood model, this study finds that high argument quality and high source certainty, together in a review, do not make a review helpful, and consumers use heuristics (peripheral cues) when reading a long review. Negative as well as long reviews are found to be helpful. Real consumer reviews collected from Amazon.com are used for this study. The study contributes to the literature by uncovering different paths (path signifies configuration of variables) that lead to helpful reviews by using a fs-QCA technique on real Amazon.com reviews and attends to the call of using sophisticated techniques in exploring new online data. © Macmillan Publishers Ltd 2017.