ESSEC METALAB

RESEARCH

THE EFFECT OF REVIEW IMAGES ON REVIEW HELPFULNESS: A CONTINGENCY APPROACH

[ARTICLE] This paper analyzes Amazon reviews to determine how review images impact review helpfulness, finding that images are most effective in increasing helpfulness when included in extremely positive reviews, by high-reputation reviewers, or for hedonic/experience products..

by Raoul V. Kübler, Lina Welke (ESSEC Business School), Lara Lobschat,  Hugo van der Meij

Online retailing is still dominated by information asymmetries, as it often remains difficult for consumers to fully judge the quality of a product online. Reviews written by customers help to reduce this asymmetry. Helpful reviews have thus become an important tool to drive online sales. Beside textual information, reviews nowadays also often include images that can further help consumers to better judge products or services. While online retailers need to invest substantial resources in hosting and incentivizing review images, it remains unclear under which conditions review images drive (or reduce) review helpfulness and how review image content affects review helpfulness. We rely on a set of more than 97,000 reviews from Amazon to investigate the contingencies under which review images increase review helpfulness. Furthermore, we rely on more than 6,000 images in our data set to explore how review image content (i.e., image focus and context fit) drives review helpfulness. Our results show that online retailers should especially motivate consumers to include images in a review when the overall rating is extremely positive, when the reviewer has a high reputation, and when the review addresses a hedonic or experience product. Our image content analysis further shows that images help to increase helpfulness when they show the product in application. This effect is especially strong in the case of longer reviews.

[Please read the research paper here]

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