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Exploring the Impact of Review and Service-related Signals on Online Physician Review Helpfulness: A Multi-Methods Approach

By adnan shah, Xiangbin Yan, Salim Khan and Syed Jamal Shah

Abstract

Health consumers often seek the most-helpful reviews to minimize information overload problems. Currently, there is scarce research available that looks into a physician review helpfulness (RH). Through econometric and text analytics, this study examines physician reviews from Healthgrades.com, in terms of review and service-related signals about the entire physician population in the U.S. The findings of this study extend the earlier work in healthcare by suggesting that illness severity moderates the impact of review and service-related signals on perceived RH. Regarding review-related signals, review depth and readability positively influence RH, whereas sentiment (emotions) expressed in online review negatively affect the perceived RH. Regarding service-related signals, service quality and popularity have a significant positive impact on RH. Moreover, service quality and popularity exert a more significant positive effect on perceived RH for serious diseases than of mild illnesses. These results contribute to a better interpretation of the critical role of different online information signals on the perceived RH

Topics: Physician reviews, review helpfulness, information overload, sentiment analysis, text mining
Publisher: AIS Electronic Library (AISeL)
Year: 2020
OAI identifier: oai:aisel.aisnet.org:pacis2020-1119
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