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Information Quality of Online Reviews in the Presence of Potentially Fake Reviews

Author

Listed:
  • Wonho Song

    (Chung-Ang University)

  • Sangkon Park

    (Korea Culture & Tourism Institute)

  • Doojin Ryu

    (Sungkyunkwan University)

Abstract

Online reviews are important in the evaluation of product quality. This paper seeks to assess information quality of online reviews using the TripAdvisor data for Korean hotels. We first estimate the review model developed by Dai, Jin, Lee, and Luca (2012) and show that high-quality reviews contain most of the information for the quality of hotels. Second, we assess the degree of distortions caused by fake reviews through numerical experiments and show that the distortions of fake reviews are serious. Third, we compare the simple average and weighted average aggregation methods. Weighted average method is better than simple average in finding the quality of hotels but it is more vulnerable to fake reviews. Fourth, we suggest excluding low-quality reviews to deal with fake reviews and show that the benefit of avoiding serious distortions from potentially fake reviews is greater than the cost of losing information from low-quality reviews.

Suggested Citation

  • Wonho Song & Sangkon Park & Doojin Ryu, 2017. "Information Quality of Online Reviews in the Presence of Potentially Fake Reviews," Korean Economic Review, Korean Economic Association, vol. 33, pages 5-34.
    • Handle: RePEc:kea:keappr:ker-20170630-33-1-01
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    References listed on IDEAS

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    1. Dina Mayzlin & Yaniv Dover & Judith Chevalier, 2014. "Promotional Reviews: An Empirical Investigation of Online Review Manipulation," American Economic Review, American Economic Association, vol. 104(8), pages 2421-2455, August.
    2. Jacob Goeree & Thomas Palfrey & Brian Rogers, 2006. "Social learning with private and common values," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 28(2), pages 245-264, June.
    3. Liu, Zhiwei & Park, Sangwon, 2015. "What makes a useful online review? Implication for travel product websites," Tourism Management, Elsevier, vol. 47(C), pages 140-151.
    4. Davide Crapis & Bar Ifrach & Costis Maglaras & Marco Scarsini, 2017. "Monopoly Pricing in the Presence of Social Learning," Management Science, INFORMS, vol. 63(11), pages 3586-3608, November.
    5. Celen, Bogachan & Kariv, Shachar, 2004. "Observational learning under imperfect information," Games and Economic Behavior, Elsevier, vol. 47(1), pages 72-86, April.
    6. Kim, Myung-Ja & Chung, Namho & Lee, Choong-Ki, 2011. "The effect of perceived trust on electronic commerce: Shopping online for tourism products and services in South Korea," Tourism Management, Elsevier, vol. 32(2), pages 256-265.
    7. Sparks, Beverley A. & Browning, Victoria, 2011. "The impact of online reviews on hotel booking intentions and perception of trust," Tourism Management, Elsevier, vol. 32(6), pages 1310-1323.
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    Cited by:

    1. Josef Zelenka & Tracy Azubuike & Martina Pásková, 2021. "Trust Model for Online Reviews of Tourism Services and Evaluation of Destinations," Administrative Sciences, MDPI, vol. 11(2), pages 1-21, March.

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    More about this item

    Keywords

    Online Review; Fake Review; Rating; Aggregation; Numerical Experimentation; Tourism Management;
    All these keywords.

    JEL classification:

    • D70 - Microeconomics - - Analysis of Collective Decision-Making - - - General
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software
    • M37 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Advertising

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