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The Signaling Effect of Critics: Do Professionals outweigh Word-of-Mouth? Evidence from the Video Game Industry

Author

Listed:
  • Daniel Kaimann

    (University of Paderborn)

  • Joe Cox

    (Portsmouth Business School)

Abstract

Experience goods are characterized by information asymmetry and a lack of ex ante knowledge of product quality, such that reliable external signals of product quality are likely to be highly valued. Two potentially credible sources of such information are reviews from professional critics with expert reputations, as well as ‘word-of-mouth’ reviews from other consumers. This paper makes a direct comparison between the relative influence of both critic and user reviews on the sales of video games software. In order to empirically estimate and separate the effects of the two signals, we analyze a sample of 1,480 video games and their sales figures between 2004 and 2010. We find clear evidence to suggest that reviews from professional critics have a significantly positive influence on sales that outweighs word-of-mouth reviews. Consequently, we support the hypothesis that professional critics adopt the role of an influencer whereas word-of-mouth opinion acts merely as a predictor of sales.

Suggested Citation

  • Daniel Kaimann & Joe Cox, 2014. "The Signaling Effect of Critics: Do Professionals outweigh Word-of-Mouth? Evidence from the Video Game Industry," Working Papers Dissertations 10, Paderborn University, Faculty of Business Administration and Economics.
    • Handle: RePEc:pdn:dispap:10
    as

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    File URL: http://groups.uni-paderborn.de/wp-wiwi/RePEc/pdf/dispap/DP10.pdf
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    References listed on IDEAS

    as
    1. Ravid, S Abraham, 1999. "Information, Blockbusters, and Stars: A Study of the Film Industry," The Journal of Business, University of Chicago Press, vol. 72(4), pages 463-492, October.
    2. Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2007. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Working Papers 07-36, NET Institute.
    3. Chrysanthos Dellarocas, 2003. "The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms," Management Science, INFORMS, vol. 49(10), pages 1407-1424, October.
    4. Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2011. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Management Science, INFORMS, vol. 57(8), pages 1485-1509, August.
    5. Yubo Chen & Jinhong Xie, 2008. "Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix," Management Science, INFORMS, vol. 54(3), pages 477-491, March.
    6. Nelson, Phillip, 1970. "Information and Consumer Behavior," Journal of Political Economy, University of Chicago Press, vol. 78(2), pages 311-329, March-Apr.
    7. Arthur De Vany & W. David Walls, 2002. "Does Hollywood Make Too Many R-Rated Movies? Risk, Stochastic Dominance, and the Illusion of Expectation," The Journal of Business, University of Chicago Press, vol. 75(3), pages 425-452, July.
    8. Dellarocas, Chrysanthos, 2003. "The Digitization of Word-of-mouth: Promise and Challenges of Online Feedback Mechanisms," Working papers 4296-03, Massachusetts Institute of Technology (MIT), Sloan School of Management.
    9. Peter Boatwright & Suman Basuroy & Wagner Kamakura, 2007. "Reviewing the reviewers: The impact of individual film critics on box office performance," Quantitative Marketing and Economics (QME), Springer, vol. 5(4), pages 401-425, December.
    10. Pradeep K. Chintagunta & Shyam Gopinath & Sriram Venkataraman, 2010. "The Effects of Online User Reviews on Movie Box Office Performance: Accounting for Sequential Rollout and Aggregation Across Local Markets," Marketing Science, INFORMS, vol. 29(5), pages 944-957, 09-10.
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    Cited by:

    1. Tom Hamami, 2019. "Network Effects, Bargaining Power, and Product Review Bias: Theory and Evidence," Journal of Industrial Economics, Wiley Blackwell, vol. 67(2), pages 372-407, June.

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    More about this item

    Keywords

    Signaling Theory; Information Asymmetry; Critics; Word-of-Mouth; Video Game Industry;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • L14 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Transactional Relationships; Contracts and Reputation
    • L82 - Industrial Organization - - Industry Studies: Services - - - Entertainment; Media

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