Son, JaebongNegahban, ArashLee, YoungsuConnolly, JamesChiang, Dalen2020-01-042020-01-042020-01-07978-0-9981331-3-3http://hdl.handle.net/10125/64249With the growth of online shopping coupled with mobile technology, user-generated product reviews have become an important source of information for product diagnosticity. A significant academic endeavor has been made to comprehend what information factors of reviews help prospective customers better diagnose products. One such factor is review depth that is estimated by the number of a review’s words. We propose review breadth as an additional factor based on a review’s number of topics—the more review breadth, the more diverse information. By conducting the statistical and predictive analyses, we demonstrate that review breadth reliably measures a review’s information. This study makes academic and practical contributions. For academic researchers, review breadth is worth considering as a factor to estimate a review’s information over and above review depth. Based on the two information factors of review breadth and review depth, practitioners can recommend more helpful product reviews to their prospective customers.10 pagesengAttribution-NonCommercial-NoDerivatives 4.0 InternationalFirm and User Generated Content in the Digital Economy: Key Players, Management and Impactproduct reviewsreview breadthreview depthreview helpfulnesstopic modelingConference Paper10.24251/HICSS.2020.507