Review reader segmentation based on the heterogeneous impacts of review and reviewer attributes on review helpfulness: A study involving ZIP code data

This study addresses gaps in online consumer review research. First, drawing on information overload and configuration theories, the authors posit that reviewer characteristics provide a context within which review readers consider the content of the review. Second, the authors employ a finite mixture model that uncovers distinct online reader segments based on their heterogeneous use of review and reviewer characteristics in determining review helpfulness. Third, the authors use demographic ZIP code-level data to provide more nuanced segment descriptions. Thus, businesses can use the study's approach to gain a better understanding of how their customer segments evaluate the helpfulness of reviews.