| Título |
Exploring the impact of linguistic signals transmission on patients’ health consultation choice: web mining of online reviews |
| Autores |
Shah A.M. , Ali M. , Qayyum A. , Begum A. , Han H. , ARIZA MONTES, JOSÉ ANTONIO, Araya-Castillo L. |
| Publicación externa |
No |
| Medio |
International Journal of Environmental Research and Public Health |
| Alcance |
Article |
| Naturaleza |
Científica |
| Cuartil JCR |
1 |
| Cuartil SJR |
2 |
| Impacto JCR |
4.614 |
| Impacto SJR |
0.814 |
| Web |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115390674&doi=10.3390%2fijerph18199969&partnerID=40&md5=b87d94d03fff247b0a4c3a56181344bd |
| Fecha de publicacion |
21/09/2021 |
| ISI |
000708204100001 |
| Scopus Id |
2-s2.0-85115390674 |
| DOI |
10.3390/ijerph18199969 |
| Abstract |
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. |
| Palabras clave |
Consumer decision-making; COVID-19; Online review helpfulness; Physician rating websites; Sentiment analysis; Signaling theory |
| Miembros de la Universidad Loyola |
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