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Exploring the Impact of Linguistic Signals Transmission on Patients’ Health Consultation Choice: Web Mining of Online Reviews

Author

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  • Adnan Muhammad Shah

    (Department of Management Sciences, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad 44320, Pakistan
    Charles E. Schmidt College of Science, Florida Atlantic University, Boca Raton, FL 33431-0991, USA)

  • Mudassar Ali

    (School of Management, Harbin Institute of Technology, Harbin 150001, China)

  • Abdul Qayyum

    (Faculty of Management Science, Riphah International University, Rawalpindi 46000, Pakistan)

  • Abida Begum

    (School of Marxism, Northeast Forestry University, Harbin 150040, China)

  • Heesup Han

    (College of Hospitality and Tourism Management, Sejong University, 98 Gunja-Dong, Gwanjin-Gu, Seoul 143-747, Korea)

  • Antonio Ariza-Montes

    (Social Matters Research Group, Universidad Loyola Andalucía, C/Escritor Castilla Aguayo, 4, 14004 Córdoba, Spain)

  • Luis Araya-Castillo

    (Facultad de Economía y Negocios, Universidad Andrés Bello, Santiago de Chile 7591538, Chile)

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 mediated 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 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.

Suggested Citation

  • Adnan Muhammad Shah & Mudassar Ali & Abdul Qayyum & Abida Begum & Heesup Han & Antonio Ariza-Montes & Luis Araya-Castillo, 2021. "Exploring the Impact of Linguistic Signals Transmission on Patients’ Health Consultation Choice: Web Mining of Online Reviews," IJERPH, MDPI, vol. 18(19), pages 1-21, September.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:19:p:9969-:d:640654
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    References listed on IDEAS

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    1. Dongxiao Gu & Xuejie Yang & Xingguo Li & Hemant K. Jain & Changyong Liang, 2018. "Understanding the Role of Mobile Internet-Based Health Services on Patient Satisfaction and Word-of-Mouth," IJERPH, MDPI, vol. 15(9), pages 1-23, September.
    2. Davide Proserpio & Georgios Zervas, 2017. "Online Reputation Management: Estimating the Impact of Management Responses on Consumer Reviews," Marketing Science, INFORMS, vol. 36(5), pages 645-665, September.
    3. Kaushik, Kapil & Mishra, Rajhans & Rana, Nripendra P. & Dwivedi, Yogesh K., 2018. "Exploring reviews and review sequences on e-commerce platform: A study of helpful reviews on Amazon.in," Journal of Retailing and Consumer Services, Elsevier, vol. 45(C), pages 21-32.
    4. Fang, Bin & Ye, Qiang & Kucukusta, Deniz & Law, Rob, 2016. "Analysis of the perceived value of online tourism reviews: Influence of readability and reviewer characteristics," Tourism Management, Elsevier, vol. 52(C), pages 498-506.
    5. Yubo Chen & Jinhong Xie, 2008. "Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix," Management Science, INFORMS, vol. 54(3), pages 477-491, March.
    6. Chris Forman & Anindya Ghose & Batia Wiesenfeld, 2008. "Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets," Information Systems Research, INFORMS, vol. 19(3), pages 291-313, September.
    7. Maria Magdalena Bujnowska-Fedak & Paulina Węgierek, 2020. "The Impact of Online Health Information on Patient Health Behaviours and Making Decisions Concerning Health," IJERPH, MDPI, vol. 17(3), pages 1-13, January.
    8. Adnan Muhammad Shah & Rizwan Ali Naqvi & Ok-Ran Jeong, 2021. "The Impact of Signals Transmission on Patients’ Choice through E-Consultation Websites: An Econometric Analysis of Secondary Datasets," IJERPH, MDPI, vol. 18(10), pages 1-21, May.
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    Cited by:

    1. Tingting Zhang & Qin Chen & William Yu Chung Wang & Yuhan Wei, 2022. "Understanding Physicians’ Motivation to Provide Healthcare Service Online in the Digital Age," IJERPH, MDPI, vol. 19(22), pages 1-11, November.
    2. Muhammad Khalilur Rahman & Noor Raihani Zainol & Noorshella Che Nawi & Ataul Karim Patwary & Wan Farha Wan Zulkifli & Md Mahmudul Haque, 2023. "Halal Healthcare Services: Patients’ Satisfaction and Word of Mouth Lesson from Islamic-Friendly Hospitals," Sustainability, MDPI, vol. 15(2), pages 1-17, January.

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