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Dealing with negative reviews on physician-rating websites: An experimental test of how physicians can prevent reputational damage via effective response strategies

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  • Arendt, Florian
  • Forrai, Michaela
  • Findl, Oliver

Abstract

•Patients are increasingly turning to physician-rating websites and write reviews.•Negative reviews can elicit reputational damage for physicians.•A web-based experiment was used to test the effectiveness of response strategies.•Physicians can prevent reputational damage by using effective response strategies.•We provide evidence-based action recommendations for physicians.

Suggested Citation

  • Arendt, Florian & Forrai, Michaela & Findl, Oliver, 2020. "Dealing with negative reviews on physician-rating websites: An experimental test of how physicians can prevent reputational damage via effective response strategies," Social Science & Medicine, Elsevier, vol. 266(C).
    • Handle: RePEc:eee:socmed:v:266:y:2020:i:c:s0277953620306419
    DOI: 10.1016/j.socscimed.2020.113422
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    References listed on IDEAS

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    1. Arendt, Florian & Scherr, Sebastian & Pasek, Josh & Jamieson, Patrick E. & Romer, Daniel, 2019. "Investigating harmful and helpful effects of watching season 2 of 13 Reasons Why: Results of a two-wave U.S. panel survey," Social Science & Medicine, Elsevier, vol. 232(C), pages 489-498.
    2. Menon, Alka V., 2017. "Do online reviews diminish physician authority? The case of cosmetic surgery in the U.S," Social Science & Medicine, Elsevier, vol. 181(C), pages 1-8.
    3. Arendt, Florian & Haim, Mario & Scherr, Sebastian, 2020. "Investigating Google's suicide-prevention efforts in celebrity suicides using agent-based testing: A cross-national study in four European countries," Social Science & Medicine, Elsevier, vol. 262(C).
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    Cited by:

    1. Reilley, Jacob & Pflueger, Dane & Huber, Christian, 2024. "A typology of evaluative health platforms: Commercial interests and their implications for patient voice," Social Science & Medicine, Elsevier, vol. 350(C).
    2. Golmohammadi, Davood & Zhao, Lingyu & Dreyfus, David, 2023. "Using machine learning techniques to reduce uncertainty for outpatient appointment scheduling practices in outpatient clinics," Omega, Elsevier, vol. 120(C).

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