Shah A.M. , Ali M. , Qayyum A. , Begum A. , Han H. , ARIZA MONTES, JOSÉ ANTONIO, Araya-Castillo L.
No
Int. J. Environ. Res. Public Health
Article
Científica
4.614
0.814
21/09/2021
000708204100001
2-s2.0-85115390674
Background: Patients face difficulties identifying appropriate physicians owing to the sizeable quantity and uneven quality of information in physician rating websites. Therefore, an increasing dependence of consumers on online platforms as a source of information for decision-making has given rise to the need for further research into the quality of information in the form of online physician reviews (OPRs). Methods: Drawing on the signaling theory, this study develops a theoretical model to examine how linguistic signals (affective signals and informative signals) in physician rating websites affect consumers’ decision making. The hypotheses are tested using 5521 physicians’ six-month data drawn from two leading health rating platforms in the U.S (i.e., Healthgrades.com and Vitals.com) during the COVID-19 pandemic. A sentic computing-based sentiment analysis framework is used to implicitly analyze patients’ opinions regarding their treatment choice. Results: The results indicate that negative sentiment, review readability, review depth, review spelling, and information helpfulness play a significant role in inducing patients’ decision-making. The influence of negative sentiment, review depth on patients’ treatment choice was indirectly medi-ated by information helpfulness. Conclusions: This paper is a first step toward the understanding of the linguistic characteristics of information relating to the patient experience, particularly the emerg-ing field of online health behavior and signaling theory. It is also the first effort to our knowledge that employs sentic computing-based sentiment analysis in this context and provides implications for practice. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Consumer decision-making; COVID-19; Online review helpfulness; Physician rating websites; Sentiment analysis; Signaling theory