Exploring the Impact of Linguistic Signals Transmission on Patients' Health Consultation Choice: Web Mining of Online Reviews.
COVID-19
consumer decision-making
online review helpfulness
physician rating websites
sentiment analysis
signaling theory
Journal
International journal of environmental research and public health
ISSN: 1660-4601
Titre abrégé: Int J Environ Res Public Health
Pays: Switzerland
ID NLM: 101238455
Informations de publication
Date de publication:
22 Sep 2021
22 Sep 2021
Historique:
received:
08
07
2021
revised:
13
09
2021
accepted:
14
09
2021
entrez:
13
10
2021
pubmed:
14
10
2021
medline:
21
10
2021
Statut:
epublish
Résumé
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). 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. 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 mediated by information helpfulness. This paper is a first step toward the understanding of the linguistic characteristics of information relating to the patient experience, particularly the emerging 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.
Sections du résumé
BACKGROUND
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
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
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 mediated by information helpfulness.
CONCLUSIONS
CONCLUSIONS
This paper is a first step toward the understanding of the linguistic characteristics of information relating to the patient experience, particularly the emerging 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.
Identifiants
pubmed: 34639266
pii: ijerph18199969
doi: 10.3390/ijerph18199969
pmc: PMC8507958
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Références
J Med Internet Res. 2020 Feb 3;22(2):e13830
pubmed: 32012063
Int J Environ Res Public Health. 2021 May 13;18(10):
pubmed: 34068291
BMC Med Inform Decis Mak. 2016 Nov 26;16(1):151
pubmed: 27888834
J Med Internet Res. 2019 May 02;21(5):e12522
pubmed: 31045507
J Med Internet Res. 2019 Feb 08;21(2):e11129
pubmed: 30735144
J Med Internet Res. 2018 Apr 16;20(4):e126
pubmed: 29661747
J Med Internet Res. 2013 Aug 28;15(8):e187
pubmed: 23985220
J Med Internet Res. 2018 Aug 16;20(8):e254
pubmed: 30115610
J Med Internet Res. 2019 May 13;21(5):e12891
pubmed: 31094342
J Med Internet Res. 2011 Nov 02;13(4):e81
pubmed: 22047810
Int J Environ Res Public Health. 2020 Jan 31;17(3):
pubmed: 32023828
Int J Environ Res Public Health. 2018 Sep 10;15(9):
pubmed: 30201921
J Med Internet Res. 2018 Jul 25;20(7):e243
pubmed: 30045831
Int J Med Inform. 2018 Aug;116:33-45
pubmed: 29887233
Behav Res Methods. 2008 Aug;40(3):879-91
pubmed: 18697684
J Biomed Inform. 2019 Oct;98:103272
pubmed: 31479747
Int J Tuberc Lung Dis. 2014 Feb;18(2):168-73, i-iv
pubmed: 24429308
IEEE J Biomed Health Inform. 2020 Aug;24(8):2146-2156
pubmed: 31995507
J Med Internet Res. 2016 Jun 16;18(6):e129
pubmed: 27311623
J Med Internet Res. 2017 Apr 24;19(4):e130
pubmed: 28438725
JAMA. 2014 Feb 19;311(7):734-5
pubmed: 24549555
J Med Internet Res. 2015 Jan 12;17(1):e15
pubmed: 25582914
Psychol Methods. 2002 Dec;7(4):422-45
pubmed: 12530702
Interact J Med Res. 2016 Jun 17;5(2):e19
pubmed: 27317159
Int J Med Inform. 2021 May;149:104434
pubmed: 33667929
JMIR Med Inform. 2019 Dec 2;7(4):e16185
pubmed: 31789597