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Internal or External Word-of-Mouth (WOM), Why Do Patients Choose Doctors on Online Medical Services (OMSs) Single Platform in China?

Author

Listed:
  • Jiang Shen

    (College of Management and Economy, Tianjin University, Tianjin 300072, China)

  • Bang An

    (College of Management and Economy, Tianjin University, Tianjin 300072, China)

  • Man Xu

    (Business School, Nankai University, Tianjin 300071, China)

  • Dan Gan

    (School of Economics and Management, Hebei University of Technology, Tianjin 300071, China)

  • Ting Pan

    (College of Management and Economy, Tianjin University, Tianjin 300072, China)

Abstract

(1) Background: Word-of-mouth (WOM) can influence patients’ choice of doctors in online medical services (OMSs). Previous studies have explored the relationship between internal WOM in online healthcare communities (OHCs) and patients’ choice of doctors. There is a lack of research on external WOM and position ranking in OMSs. (2) Methods: We develop an empirical model based on the data of 4435 doctors from a leading online healthcare community in China. We discuss the influence of internal and external WOM on patients’ choice of doctors in OMSs, exploring the interaction between internal and external WOM and the moderation of doctor position ranking. (3) Results: Both internal and external WOM had a positive impact on patients’ choice of doctors; there was a significant positive interaction between internal and third-party generated WOM, but the interaction between internal and relative-generated WOM, and the interaction between internal and doctor-generated WOM were both nonsignificant. The position ranking of doctors significantly enhanced the impact of internal WOM, whereas it weakened the impact of doctor recommendations on patients’ choice of doctors. (4) The results emphasize the importance of the research on external WOM in OMSs, and suggest that the moderation of internal WOM may be related to the credibility and accessibility of external WOM, and the impact of doctor position ranking can be explained by information search costs.

Suggested Citation

  • Jiang Shen & Bang An & Man Xu & Dan Gan & Ting Pan, 2022. "Internal or External Word-of-Mouth (WOM), Why Do Patients Choose Doctors on Online Medical Services (OMSs) Single Platform in China?," IJERPH, MDPI, vol. 19(20), pages 1-14, October.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:20:p:13293-:d:942991
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    References listed on IDEAS

    as
    1. Raluca M. Ursu, 2018. "The Power of Rankings: Quantifying the Effect of Rankings on Online Consumer Search and Purchase Decisions," Marketing Science, INFORMS, vol. 37(4), pages 530-552, August.
    2. Anindya Ghose & Avi Goldfarb & Sang Pil Han, 2013. "How Is the Mobile Internet Different? Search Costs and Local Activities," Information Systems Research, INFORMS, vol. 24(3), pages 613-631, September.
    3. Gong, Yingli & Wang, Hongwei & Xia, Qiangwei & Zheng, Lijuan & Shi, Yunxiang, 2021. "Factors that determine a Patient's willingness to physician selection in online healthcare communities: A trust theory perspective," Technology in Society, Elsevier, vol. 64(C).
    4. Qihua Liu & Xiaoyu Zhang & Liyi Zhang & Yang Zhao, 2019. "The interaction effects of information cascades, word of mouth and recommendation systems on online reading behavior: an empirical investigation," Electronic Commerce Research, Springer, vol. 19(3), pages 521-547, September.
    5. Manqi (Maggie) Li & Yan Huang & Amitabh Sinha, 2020. "Data-Driven Promotion Planning for Paid Mobile Applications," Information Systems Research, INFORMS, vol. 31(3), pages 1007-1029, September.
    6. Jun B. Kim & Paulo Albuquerque & Bart J. Bronnenberg, 2010. "Online Demand Under Limited Consumer Search," Marketing Science, INFORMS, vol. 29(6), pages 1001-1023, 11-12.
    7. Tat Y. Chan & Young-Hoon Park, 2015. "Consumer Search Activities and the Value of Ad Positions in Sponsored Search Advertising," Marketing Science, INFORMS, vol. 34(4), pages 606-623, July.
    8. Chensang Ye & Cong Cao & Jinjing Yang & Xiuyan Shao, 2022. "Explore How Online Healthcare Can Influence Willingness to Seek Offline Care," IJERPH, MDPI, vol. 19(13), pages 1-20, June.
    9. J. Myles Shaver, 2019. "Interpreting Interactions in Linear Fixed-Effect Regression Models: When Fixed-Effect Estimates Are No Longer Within-Effects," Strategy Science, INFORMS, vol. 4(1), pages 25-40, March.
    10. Adnan Muhammad Shah & Wazir Muhammad & Kangyoon Lee & Rizwan Ali Naqvi, 2021. "Examining Different Factors in Web-Based Patients’ Decision-Making Process: Systematic Review on Digital Platforms for Clinical Decision Support System," IJERPH, MDPI, vol. 18(21), pages 1-23, October.
    11. Weitzman, Martin L, 1979. "Optimal Search for the Best Alternative," Econometrica, Econometric Society, vol. 47(3), pages 641-654, May.
    12. Yan Zhang, 2014. "Beyond quality and accessibility: Source selection in consumer health information searching," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(5), pages 911-927, May.
    13. Zhenling Jiang & Tat Chan & Hai Che & Youwei Wang, 2021. "Consumer Search and Purchase: An Empirical Investigation of Retargeting Based on Consumer Online Behaviors," Marketing Science, INFORMS, vol. 40(2), pages 219-240, March.
    14. Yang, Yefei & Zhang, Xiaofei & Lee, Peter K.C., 2019. "Improving the effectiveness of online healthcare platforms: An empirical study with multi-period patient-doctor consultation data," International Journal of Production Economics, Elsevier, vol. 207(C), pages 70-80.
    15. 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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    1. 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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