IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i20p13293-d942991.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/20/13293/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/20/13293/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Weitzman, Martin L, 1979. "Optimal Search for the Best Alternative," Econometrica, Econometric Society, vol. 47(3), pages 641-654, May.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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).
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.
    2. Dong‐Hui Yang & Ke‐Hui Zhu & Ruo‐Nan Wang, 2024. "Forecasting healthcare service volumes with machine learning algorithms," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 2358-2377, September.
    3. Jiexiang Jin & Mi Hyun Ryu, 2024. "Sustainable Healthcare in China: Analysis of User Satisfaction, Reuse Intention, and Electronic Word-of-Mouth for Online Health Service Platforms," Sustainability, MDPI, vol. 16(17), pages 1-18, September.
    4. Xiaodan Yu & Hongyang Wang & Zhenjiao Chen, 2024. "The Role of User-Generated Content in the Sustainable Development of Online Healthcare Communities: Exploring the Moderating Influence of Signals," Sustainability, MDPI, vol. 16(9), pages 1-21, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rafael P. Greminger, 2022. "Heterogeneous Position Effects and the Power of Rankings," Papers 2210.16408, arXiv.org, revised Dec 2023.
    2. Gibbard, Peter, 2023. "Search with two stages of information acquisition: A structural econometric model of online purchases," Information Economics and Policy, Elsevier, vol. 65(C).
    3. Giovanni Compiani & Gregory Lewis & Sida Peng & Peichun Wang, 2024. "Online Search and Optimal Product Rankings: An Empirical Framework," Marketing Science, INFORMS, vol. 43(3), pages 615-636, May.
    4. Peter Gibbard, 2022. "A Model of Search with Two Stages of Information Acquisition and Additive Learning," Management Science, INFORMS, vol. 68(2), pages 1212-1217, February.
    5. Leon Yang Chu & Hamid Nazerzadeh & Heng Zhang, 2020. "Position Ranking and Auctions for Online Marketplaces," Management Science, INFORMS, vol. 66(8), pages 3617-3634, August.
    6. Raluca Ursu & Stephan Seiler & Elisabeth Honka, 2023. "The Sequential Search Model: A Framework for Empirical Research," CESifo Working Paper Series 10264, CESifo.
    7. Honka, Elisabeth & Seiler, Stephan & Ursu, Raluca, 2024. "Consumer search: What can we learn from pre-purchase data?," Journal of Retailing, Elsevier, vol. 100(1), pages 114-129.
    8. Hana Choi & Carl F. Mela, 2019. "Monetizing Online Marketplaces," Marketing Science, INFORMS, vol. 38(6), pages 948-972, November.
    9. 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.
    10. Raluca M. Ursu & Qingliang Wang & Pradeep K. Chintagunta, 2020. "Search Duration," Marketing Science, INFORMS, vol. 39(5), pages 849-871, September.
    11. 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.
    12. Yuxin Chen & Song Yao, 2017. "Sequential Search with Refinement: Model and Application with Click-Stream Data," Management Science, INFORMS, vol. 63(12), pages 4345-4365, December.
    13. Navid Mojir & K. Sudhir, 2014. "Price Search Across Time and Across Stores," Cowles Foundation Discussion Papers 1942R, Cowles Foundation for Research in Economics, Yale University, revised Jul 2019.
    14. Ilya Morozov & Stephan Seiler & Xiaojing Dong & Liwen Hou, 2021. "Estimation of Preference Heterogeneity in Markets with Costly Search," Marketing Science, INFORMS, vol. 40(5), pages 871-899, September.
    15. Chris Gu & Yike Wang, 2022. "Consumer Online Search with Partially Revealed Information," Management Science, INFORMS, vol. 68(6), pages 4215-4235, June.
    16. Avery Haviv, 2022. "Consumer Search, Price Promotions, and Counter-Cyclic Pricing," Marketing Science, INFORMS, vol. 41(2), pages 294-314, March.
    17. Gu, Chris & Wang, Yike, 2022. "Consumer online search with partially revealed information," LSE Research Online Documents on Economics 109871, London School of Economics and Political Science, LSE Library.
    18. Dan Yavorsky & Elisabeth Honka & Keith Chen, 2021. "Consumer search in the U.S. auto industry: The role of dealership visits," Quantitative Marketing and Economics (QME), Springer, vol. 19(1), pages 1-52, March.
    19. Robert Donnelly & Ayush Kanodia & Ilya Morozov, 2024. "Welfare Effects of Personalized Rankings," Marketing Science, INFORMS, vol. 43(1), pages 92-113, January.
    20. Xinyu Cao & Yuting Zhu, 2024. "The Power of Commitment in Group Search," Marketing Science, INFORMS, vol. 43(1), pages 213-228, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:20:p:13293-:d:942991. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.