Ratings lead you to the product, reviews help you clinch it? The mediating role of online review sentiments on product sales

By Nan Hu, Noi Sian Koh, Srinivas K. Reddy. Decision Support Systems, 2014

426 citations recorded by the source corpus.

It is generally assumed that ratings are a numeric representation of text sentiments and their valences are consistent. This however may not always be true. Using a panel of data on over 4000 books from Amazon.com, we develop a multiple equation model to examine the inter-relationships between ratings, sentiments, and sales. We find that ratings do not have a significant direct impact on sales but have an indirect impact through sentiments. Sentiments, however, have a direct significant impact on sales. Our findings also indicate that the two most accessible types of reviews - most helpful and most recent - play a significant role in determining sales. This suggests that information that is easily accessible and cognitive effort-reducing heuristics play a role in online purchase decisions. This study advances our understanding on the inter-relationship between ratings, sentiments, and sales and sheds insight on the relevance of ratings and sentiments over a sequential decision making process.

Subfields: Artificial Intelligence, Information Systems and Management, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/fa58e991af24c9f2e6531d148c2f8fc4ac6729e9

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Citations in the corpus

Predicting the “helpfulness” of online consumer reviews (2017, background): “Hu, Koh, and Reddy (2014) develop model containing multiple equations in order to determine the interrelationship between rating, sentiments and sales.”
Mining customer requirements from online reviews: A product improvement perspective (2016, background): “The literature has used a variety of measures to measure the performance of online reviews: purchase intentions [49], readership [50], and sales revenue [10, 44, 51].”