IDEAS home Printed from
   My bibliography  Save this article

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


  • Wonho Song

    (Chung-Ang University)

  • Sangkon Park

    (Korea Culture & Tourism Institute)

  • Doojin Ryu

    (Sungkyunkwan University)


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

    Download full text from publisher

    File URL:
    Download Restriction: no

    References listed on IDEAS

    1. 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.
    2. 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.
    3. Celen, Bogachan & Kariv, Shachar, 2004. "Observational learning under imperfect information," Games and Economic Behavior, Elsevier, vol. 47(1), pages 72-86, April.
    4. 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.
    5. 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.
    Full references (including those not matched with items on IDEAS)


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

    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, Open Access Journal, vol. 11(2), pages 1-21, March.

    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. Wu, Laurie & Shen, Han & Fan, Alei & Mattila, Anna S., 2017. "The impact of language style on consumers′ reactions to online reviews," Tourism Management, Elsevier, vol. 59(C), pages 590-596.
    2. Book, Laura A. & Tanford, Sarah & Chang, Wen, 2018. "Customer reviews are not always informative: The impact of effortful versus heuristic processing," Journal of Retailing and Consumer Services, Elsevier, vol. 41(C), pages 272-280.
    3. Guo, Yue & Barnes, Stuart J. & Jia, Qiong, 2017. "Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation," Tourism Management, Elsevier, vol. 59(C), pages 467-483.
    4. Sunyoung Hlee & Hanna Lee & Chulmo Koo, 2018. "Hospitality and Tourism Online Review Research: A Systematic Analysis and Heuristic-Systematic Model," Sustainability, MDPI, Open Access Journal, vol. 10(4), pages 1-27, April.
    5. Ert, Eyal & Fleischer, Aliza & Magen, Nathan, 2016. "Trust and reputation in the sharing economy: The role of personal photos in Airbnb," Tourism Management, Elsevier, vol. 55(C), pages 62-73.
    6. Jacob K. Goeree & Thomas R. Palfrey & Brian W. Rogers & Richard D. McKelvey, 2007. "Self-Correcting Information Cascades," Review of Economic Studies, Oxford University Press, vol. 74(3), pages 733-762.
    7. Ilan Lobel & Evan Sadler, 2016. "Preferences, Homophily, and Social Learning," Operations Research, INFORMS, vol. 64(3), pages 564-584, June.
    8. Pera, Rebecca & Viglia, Giampaolo & Grazzini, Laura & Dalli, Daniele, 2019. "When empathy prevents negative reviewing behavior," Annals of Tourism Research, Elsevier, vol. 75(C), pages 265-278.
    9. Sergio M. Fernández-Miguélez & Miguel Díaz-Puche & Juan A. Campos-Soria & Federico Galán-Valdivieso, 2020. "The Impact of Social Media on Restaurant Corporations’ Financial Performance," Sustainability, MDPI, Open Access Journal, vol. 12(4), pages 1-14, February.
    10. Yi Luo & Xiaowei Xu, 2019. "Predicting the Helpfulness of Online Restaurant Reviews Using Different Machine Learning Algorithms: A Case Study of Yelp," Sustainability, MDPI, Open Access Journal, vol. 11(19), pages 1-17, September.
    11. Babajide Abubakr Muritala & Maria-Victoria Sánchez-Rebull & Ana-Beatriz Hernández-Lara, 2020. "A Bibliometric Analysis of Online Reviews Research in Tourism and Hospitality," Sustainability, MDPI, Open Access Journal, vol. 12(23), pages 1-18, November.
    12. Zhang, Ziqiong & Zhang, Zili & Yang, Yang, 2016. "The power of expert identity: How website-recognized expert reviews influence travelers' online rating behavior," Tourism Management, Elsevier, vol. 55(C), pages 15-24.
    13. Liang, Sai & Schuckert, Markus & Law, Rob & Chen, Chih-Chien, 2017. "Be a “Superhost”: The importance of badge systems for peer-to-peer rental accommodations," Tourism Management, Elsevier, vol. 60(C), pages 454-465.
    14. Bikhchandani, Sushil & Hirshleifer, David & Tamuz, Omer & Welch, Ivo, 2021. "Information Cascades and Social Learning," MPRA Paper 107927, University Library of Munich, Germany.
    15. Brzozowska-Woś Magdalena & Schivinski Bruno, 2019. "The Effect of Online Reviews on Consumer-Based Brand Equity: Case-Study of the Polish Restaurant Sector," Journal of Management and Business Administration. Central Europe, Sciendo, vol. 27(3), pages 2-27, September.
    16. Berné Manero, Carmen & Ciobanu, Andreea V. & Pedraja Iglesias, Marta, 2020. "The electronic word of mouth as a context variable in the hotel management decision-making process," Cuadernos de Gestión, Universidad del País Vasco - Instituto de Economía Aplicada a la Empresa (IEAE).
    17. Saeideh Sharifi fard & Ezhar Tamam & Md Salleh Hj Hassan & Moniza Waheed & Zeinab Zaremohzzabieh, 2016. "Factors affecting Malaysian university students’ purchase intention in social networking sites," Cogent Business & Management, Taylor & Francis Journals, vol. 3(1), pages 1182612-118, December.
    18. Amrei Lahno & Marta Serra-Garcia, 2015. "Peer effects in risk taking: Envy or conformity?," Journal of Risk and Uncertainty, Springer, vol. 50(1), pages 73-95, February.
    19. Lahno, Amrei M. & Serra-Garcia, Marta, 2012. "Peer Effects in Risk Taking," Discussion Papers in Economics 14309, University of Munich, Department of Economics.
    20. Inmaculada Rabadán-Martín & Francisco Aguado-Correa & Nuria Padilla-Garrido, 2020. "Online reputation of 4- and 5-star hotels," Tourism and Hospitality Management, University of Rijeka, Faculty of Tourism and Hospitality Management, vol. 26(1), pages 157-172, June.

    More about this item


    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


    Access and download statistics


    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:kea:keappr:ker-20170630-33-1-01. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: .

    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: KEA (email available below). General contact details of provider: .

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.