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Information Systems and e-Business Management

, Volume 16, Issue 4, pp 817–829 | Cite as

To compete or to take over? An economic analysis of new sellers on e-commerce marketplaces

Original Article

Abstract

The rise of e-commerce has inspired entrepreneurs to start their businesses online. E-commerce platforms, such as eBay, Amazon Marketplace, and Taobao, feature low barriers of entry to their sellers, as there is very low cost involved in entering an online marketplace and becoming a seller. In the meantime, we observe that many sellers choose to incur a much higher cost and enter the marketplace by taking over an existing seller’s account. We identify an interesting problem faced by entrants to online marketplaces: should they enter as new or should they take over another seller? Entrants’ decisions are complicated by several factors, including the reputation of the existing seller, the entrant’s capability, and the information asymmetry between the entrant and the incumbent. We develop an economic model to study the interactions between an entrant and an incumbent in an e-commerce marketplace. We discover that reputation plays an important role in the entrant’s competition-takeover decision. Our findings carry useful insights for e-commerce platforms in understanding the impact of entrants on the existing community of sellers.

Keywords

Online reputation Competition Electronic commerce Analytical modeling Economics of IS 

References

  1. Ba S, Pavlou PA (2002) Evidence of the effect of trust building technology in electronic markets: price premiums and buyer behavior. MIS Q 26(3):243–268CrossRefGoogle Scholar
  2. Chen Y (2001) On vertical mergers and their competitive effects. RAND J Econ 32(4):667–685CrossRefGoogle Scholar
  3. Chen J, Fan M, Li M (2016) Advertising versus brokerage model for online trading platforms. MIS Q 40(3):575–596CrossRefGoogle Scholar
  4. Dewan S, Hsu V (2004) Adverse selection in electronic markets: evidence from online stamp auctions. J Ind Econ 52(4):497–516CrossRefGoogle Scholar
  5. Ghose A (2009) Internet exchanges for used goods: an empirical analysis of trade patterns and adverse selection. MIS Q 33(2):263–291CrossRefGoogle Scholar
  6. Holsapple CW, Sasidharan S (2005) The dynamics of trust in B2C e-commerce: a research model and agenda. IseB 3(4):377–403CrossRefGoogle Scholar
  7. Jin GZ, Kato A (2006) Price, quality, and reputation: evidence from an online field experiment. RAND J Econ 37(4):983–1004CrossRefGoogle Scholar
  8. Kim DJ (2012) An investigation of the effect of online consumer trust on expectation, satisfaction, and post-expectation. IseB 10(2):219–240CrossRefGoogle Scholar
  9. Malmendier U, Tate G (2008) Who makes acquisitions? CEO overconfidence and the market’s reaction. J Financ Econ 89(1):20–43CrossRefGoogle Scholar
  10. Saloner G (1987) Predation, mergers, and incomplete information. RAND J Econ 18(2):165–186CrossRefGoogle Scholar
  11. Scott JE (2004) Measuring dimensions of perceived e-business risks. IseB 2(1):31–55CrossRefGoogle Scholar
  12. Soper S (2015) Amazon woos eBay’s once-loyal merchants with sales growth. Bloomberg April 6Google Scholar
  13. Stevens L (2016) Survey shows rapid growth in online shopping. The Wall Street Journal June 8Google Scholar
  14. Tadelis S (2002) The market for reputations as an incentive mechanism. J Polit Econ 110(4):854–882CrossRefGoogle Scholar
  15. Wang Y, Wong DS, Lin K-J, Varadharajan V (2008) Evaluating transaction trust and risk levels in peer-to-peer e-commerce environments. IseB 6(1):25–48CrossRefGoogle Scholar
  16. Xu H, Chen J, Whinston AB (2016) Identity management and tradable reputation. MIS Q (Forthcoming)Google Scholar
  17. You L, Sikora R (2014) Performance of online reputation mechanisms under the influence of different types of biases. IseB 12(3):417–442CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.School of Business and ManagementThe Hong Kong University of Science and TechnologyClearwater Bay, KowloonHong Kong

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