IDEAS home Printed from https://ideas.repec.org/p/hig/wpaper/19man2014.html

The Determinants Of Online Merchant’s Price Premium: Evidence From Russia

Author

Listed:
  • Evgeny A. Antipov

    (National Research University Higher School of Economics)

Abstract

Some Internet stores manage to charge prices that are significantly higher than market averages, therefore, obtaining some sort of price premium. This paper is dedicated to building a model that can be used to explain and predict a typical price premium that an Internet store charges for a specific product based on the information about the characteristics of the store and the features of the market for this product. Such models can provide support for pricing and assortment decisions: in particular, they allow detecting products that a store is likely to sell with the highest or the lowest markup based on price premia that are charged by stores with similar characteristics on similar markets

Suggested Citation

  • Evgeny A. Antipov, 2014. "The Determinants Of Online Merchant’s Price Premium: Evidence From Russia," HSE Working papers WP BRP 19/MAN/2014, National Research University Higher School of Economics.
    • Handle: RePEc:hig:wpaper:19man2014
    as

    Download full text from publisher

    File URL: http://www.hse.ru/data/2014/03/24/1318168807/19MAN2014.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Varian, Hal R, 1981. "A Model of Sales: Errata," American Economic Review, American Economic Association, vol. 71(3), pages 517-517, June.
    2. Eric K. Clemons & Il-Horn Hann & Lorin M. Hitt, 2002. "Price Dispersion and Differentiation in Online Travel: An Empirical Investigation," Management Science, INFORMS, vol. 48(4), pages 534-549, April.
    3. Kern, H., 2010. "From the Soviet Bloc to the European Union. The Economic and Social Transformation of Central and Eastern Europe since 1973, Ivan T. Berend, Cambridge University Press, Cambridge, MA, USA, 2009, 299 p," European Review, Cambridge University Press, vol. 18(2), pages 273-275, May.
    4. Glenn Ellison & Sara Fisher Ellison, 2009. "Search, Obfuscation, and Price Elasticities on the Internet," Econometrica, Econometric Society, vol. 77(2), pages 427-452, March.
    Full references (including those not matched with items on IDEAS)

    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. Michael R. Baye & John Morgan, 2009. "Brand and Price Advertising in Online Markets," Management Science, INFORMS, vol. 55(7), pages 1139-1151, July.
    2. Evgeny A. Antipov, 2014. "An HLM-model of online price premia," Economics Bulletin, AccessEcon, vol. 34(2), pages 892-900.
    3. Bachis, Enrico & Piga, Claudio A., 2011. "Low-cost airlines and online price dispersion," International Journal of Industrial Organization, Elsevier, vol. 29(6), pages 655-667.
    4. Nelson Granados & Alok Gupta & Robert J. Kauffman, 2010. "Research Commentary---Information Transparency in Business-to-Consumer Markets: Concepts, Framework, and Research Agenda," Information Systems Research, INFORMS, vol. 21(2), pages 207-226, June.
    5. Thomas A. Weber & Zhiqiang (Eric) Zheng, 2007. "A Model of Search Intermediaries and Paid Referrals," Information Systems Research, INFORMS, vol. 18(4), pages 414-436, December.
    6. Morgan, John & Ong, David & Zhong, Zemin (Zachary), 2018. "Location still matters: Evidence from an online shopping field experiment," Journal of Economic Behavior & Organization, Elsevier, vol. 146(C), pages 43-54.
    7. Edgardo Arturo Ayala Gaytán, 2009. "Social network externalities and price dispersion in online markets," Ensayos Revista de Economia, Universidad Autonoma de Nuevo Leon, Facultad de Economia, vol. 0(2), pages 1-28, November.
    8. Sengupta Anirban & Wiggins Steven N., 2012. "Comparing Price Dispersion on and off the Internet Using Airline Transaction Data," Review of Network Economics, De Gruyter, vol. 11(1), pages 1-38, March.
    9. Øystein Foros & Mai Nguyen-Ones & Frode Steen, 2021. "The Effects of a Day off from Retail Price Competition: Evidence on Consumer Behavior and Firm Performance in Gasoline Retailing," International Journal of the Economics of Business, Taylor & Francis Journals, vol. 28(1), pages 49-87, January.
    10. Ya-Ling Chiu & Jiangze Du & Jying-Nan Wang, 2022. "The Effects of Price Dispersion on Sales in the Automobile Industry: A Dynamic Panel Analysis," SAGE Open, , vol. 12(3), pages 21582440221, August.
    11. Hämäläinen, Saara, 2018. "Competitive search obfuscation," Journal of Economic Dynamics and Control, Elsevier, vol. 97(C), pages 38-63.
    12. Böheim, René & Hackl, Franz & Hölzl-Leitner, Michael, 2021. "The impact of price adjustment costs on price dispersion in e-commerce," International Journal of Industrial Organization, Elsevier, vol. 77(C).
    13. Dirk Bergemann & Benjamin Brooks & Stephen Morris, 2021. "Search, Information, and Prices," Journal of Political Economy, University of Chicago Press, vol. 129(8), pages 2275-2319.
    14. Glenn Ellison & Sara Fisher Ellison, 2005. "Lessons About Markets from the Internet," Journal of Economic Perspectives, American Economic Association, vol. 19(2), pages 139-158, Spring.
    15. Chioveanu, Ioana, 2008. "Advertising, brand loyalty and pricing," Games and Economic Behavior, Elsevier, vol. 64(1), pages 68-80, September.
    16. Haynes, Michelle & Thompson, Steve, 2008. "Price, price dispersion and number of sellers at a low entry cost shopbot," International Journal of Industrial Organization, Elsevier, vol. 26(2), pages 459-472, March.
    17. Fiona Scott Morton, 2006. "Consumer Benefit from Use of the Internet," NBER Chapters, in: Innovation Policy and the Economy, Volume 6, pages 67-90, National Bureau of Economic Research, Inc.
    18. Kenneth Gillingham, Hao Deng, Ryan Wiser, Naim Darghouth, Gregory Nemet, Galen Barbose, Varun Rai, and Changgui Dong, 2016. "Deconstructing Solar Photovoltaic Pricing," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).
    19. Gao, Fujuan & Fenoaltea, Enrico Maria & Zhang, Yi-Cheng, 2023. "Market failure in a new model of platform design with partially informed consumers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 619(C).
    20. Ioana Chioveanu & Jidong Zhou, 2013. "Price Competition with Consumer Confusion," Management Science, INFORMS, vol. 59(11), pages 2450-2469, November.

    More about this item

    Keywords

    ;
    ;
    ;

    JEL classification:

    • L81 - Industrial Organization - - Industry Studies: Services - - - Retail and Wholesale Trade; e-Commerce

    Statistics

    Access and download statistics

    Corrections

    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:hig:wpaper:19man2014. See general information about how to correct material in RePEc.

    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: Shamil Abdulaev or Shamil Abdulaev (email available below). General contact details of provider: https://edirc.repec.org/data/hsecoru.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.