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Bjering, Einar; Havro, Lars Jaakko (2014)
Publisher: Institutt for industriell √łkonomi og teknologiledelse
Languages: English
Types: Master thesis
Subjects:
This paper tests a previously proposed model for assessing consumer generated online reviews effect on sales, the review impact continuum. Product category is found to play an important role as a moderating factor of several properties concerning user generated online reviews - including its impact on sales. The authors introduce a novel method for product category classification using natural language processing (NLP), and by applying this method show that reviews are more influential for subjectively evaluated products than objectively evaluated products. In addition, with a selection of hit products and random products for each category, it demonstrates that product popularity has a moderating impact on the effect of online reviews on sales, and that reviews tend to be more influential for the less popular products. Additionally, the study mirrors previous research on the perceived helpfulness of reviews and finds that consumers seem to rate longer reviews as more helpful, and that the effect is stronger for reviews on objectively evaluated products. Information verifying the validity of the review is found to affect helpfulness positively as well.The study is based on review data from Amazon, including an unprecedented 1.1 million unique reviews from more than 4,600 products in 30 different product categories, collected during the spring of 2014.
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