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Crowd-Driven Competitive Intelligence: Understanding the Relationship Between Local Market Competition and Online Rating Distributions

Author

Listed:
  • Dominik Gutt

    (Paderborn University)

  • Philipp Herrmann

    (Consultant)

  • Mohammad S. Rahman

    (Purdue University)

Abstract

In this paper, we analyze how changes in local market structure affect the properties of a market’s mean rating distribution. To this end, we combine demographic, socioeconomic, and Yelp restaurant review data for 372 isolated markets in the United States. Our empirical estimates demonstrate that an increase in overall competition – measured as total number of businesses in a market – leads to a broader range and to a decrease in the average of a market’s mean rating distribution. The implication is that a larger market has proportionately more lower rated restaurants, whereas higher rated restaurants have relatively fewer comparable substitutes and face less competition in such a market. These effects are particularly pronounced when the analysis is limited to specific cuisine types where vertical differentiation is more natural or when we control for city-specific unobserved heterogeneity. Our findings highlight that practitioners and scholars using online mean ratings of businesses from disparate markets should account for the local market structure to judiciously analyze the relative market power of a business.

Suggested Citation

  • Dominik Gutt & Philipp Herrmann & Mohammad S. Rahman, 2018. "Crowd-Driven Competitive Intelligence: Understanding the Relationship Between Local Market Competition and Online Rating Distributions," Working Papers Dissertations 41, Paderborn University, Faculty of Business Administration and Economics.
  • Handle: RePEc:pdn:dispap:41
    as

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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. Erik Brynjolfsson & Yu (Jeffrey) Hu & Mohammad S. Rahman, 2009. "Battle of the Retail Channels: How Product Selection and Geography Drive Cross-Channel Competition," Management Science, INFORMS, vol. 55(11), pages 1755-1765, November.
    3. Edward L. Glaeser & Hyunjin Kim & Michael Luca, 2019. "Nowcasting the Local Economy: Using Yelp Data to Measure Economic Activity," NBER Chapters, in: Big Data for Twenty-First-Century Economic Statistics, pages 249-273, National Bureau of Economic Research, Inc.
    4. Luigi Guiso & Paola Sapienza & Luigi Zingales, 2009. "Does Local Financial Development Matter?," Springer Books, in: Damiano Bruno Silipo (ed.), The Banks and the Italian Economy, chapter 0, pages 31-66, Springer.
    5. Xinxin Li & Lorin M. Hitt, 2008. "Self-Selection and Information Role of Online Product Reviews," Information Systems Research, INFORMS, vol. 19(4), pages 456-474, December.
    6. Michael Anderson & Jeremy Magruder, 2012. "Learning from the Crowd: Regression Discontinuity Estimates of the Effects of an Online Review Database," Economic Journal, Royal Economic Society, vol. 122(563), pages 957-989, September.
    7. Steven Berry & Joel Waldfogel, 2010. "Product Quality And Market Size," Journal of Industrial Economics, Wiley Blackwell, vol. 58(1), pages 1-31, March.
    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. Butler, Alexander W. & Cornaggia, Jess, 2011. "Does access to external finance improve productivity? Evidence from a natural experiment," Journal of Financial Economics, Elsevier, vol. 99(1), pages 184-203, January.
    10. Balázs Kovács & Glenn R. Carroll & David W. Lehman, 2014. "Authenticity and Consumer Value Ratings: Empirical Tests from the Restaurant Domain," Organization Science, INFORMS, vol. 25(2), pages 458-478, April.
    11. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, April.
    12. Xianghua Lu & Sulin Ba & Lihua Huang & Yue Feng, 2013. "Promotional Marketing or Word-of-Mouth? Evidence from Online Restaurant Reviews," Information Systems Research, INFORMS, vol. 24(3), pages 596-612, September.
    13. Andrews,Donald W. K. & Stock,James H. (ed.), 2005. "Identification and Inference for Econometric Models," Cambridge Books, Cambridge University Press, number 9780521844413, September.
    14. Robert Zeithammer & Raphael Thomadsen, 2013. "Vertical Differentiation with Variety-Seeking Consumers," Management Science, INFORMS, vol. 59(2), pages 390-401, August.
    15. Shaked, Avner & Sutton, John, 1987. "Product Differentiation and Industrial Structure," Journal of Industrial Economics, Wiley Blackwell, vol. 36(2), pages 131-146, December.
    16. Marcelo Olivares & Gérard P. Cachon, 2009. "Competing Retailers and Inventory: An Empirical Investigation of General Motors' Dealerships in Isolated U.S. Markets," Management Science, INFORMS, vol. 55(9), pages 1586-1604, September.
    17. 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.
    18. Chris Forman & Anindya Ghose & Batia Wiesenfeld, 2008. "Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets," Information Systems Research, INFORMS, vol. 19(3), pages 291-313, September.
    19. Chevalier, Judith A, 1995. "Capital Structure and Product-Market Competition: Empirical Evidence from the Supermarket Industry," American Economic Review, American Economic Association, vol. 85(3), pages 415-435, June.
    20. Bresnahan, Timothy F & Reiss, Peter C, 1991. "Entry and Competition in Concentrated Markets," Journal of Political Economy, University of Chicago Press, vol. 99(5), pages 977-1009, October.
    21. Neven, D. & Thisse, J-F., 1989. "On Quality And Variety Competition," LIDAM Discussion Papers CORE 1989020, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    22. Michael Luca & Georgios Zervas, 2016. "Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud," Management Science, INFORMS, vol. 62(12), pages 3412-3427, December.
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    More about this item

    Keywords

    Local Market Competition; Online Ratings; Online Offline Interplay; Geographic;
    All these keywords.

    JEL classification:

    • D43 - Microeconomics - - Market Structure, Pricing, and Design - - - Oligopoly and Other Forms of Market Imperfection
    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • L11 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Production, Pricing, and Market Structure; Size Distribution of Firms
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

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