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The Impact of Internet Referral Services on a Supply Chain

Author

Listed:
  • Anindya Ghose

    (Stern School of Business, New York University, 40 West Fourth Street, New York, New York 10012)

  • Tridas Mukhopadhyay

    (Tepper School of Business, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213)

  • Uday Rajan

    (Ross School of Business, University of Michigan, 701 Tappan Street, Ann Arbor, Michigan 48109)

Abstract

In many industries, Internet referral services, hosted either by independent third-party infomediaries or by manufacturers, serve as digitally enabled lead generators in electronic markets, directing consumer traffic to downstream retailers in a distribution network. This reshapes the extended enterprise from the traditional network of upstream manufacturers and downstream retailers to include midstream third-party and manufacturer-owned referral services in the supply chain. We model competition between retailers in a supply chain with such digitally enabled institutions and consider their impact on the optimal contracts among the manufacturer, referral intermediary, and the retailers. Offline, retailers face a higher customer discovery cost. In return, they can engage in price discrimination based on consumer valuations. Online, they save on the discovery costs but lose the ability to identify consumer valuations. This critical trade-off drives firms' equilibrium strategies. We derive the optimal contracts for different entities in the supply chain and highlight how these contracts change with the entry of independent and manufacturer-owned referral services. The establishment of a referral service is a strategic decision by the manufacturer. It leads to diversion of supply chain profit from a third-party infomediary to the manufacturer. Further, it enables the manufacturer to respond to an infomediary, by giving itself greater flexibility in setting the unit wholesale fee to the profit-maximizing level. Both third-party and manufacturer-sponsored referral services play a critical role in enabling retailers to discriminate across consumers' different valuations. Retailers use online referral services to screen out low-valuation consumers and sell only to high-valuation consumers in the online channel. Our model thus endogenously derives a correlation between consumer valuation and online purchase behavior. Finally, we show that under some circumstances, it is too costly for the manufacturer to eliminate the referral infomediary.

Suggested Citation

  • Anindya Ghose & Tridas Mukhopadhyay & Uday Rajan, 2007. "The Impact of Internet Referral Services on a Supply Chain," Information Systems Research, INFORMS, vol. 18(3), pages 300-319, September.
  • Handle: RePEc:inm:orisre:v:18:y:2007:i:3:p:300-319
    DOI: 10.1287/isre.1070.0130
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    Cited by:

    1. Anindya Ghose & Yuliang Yao, 2011. "Using Transaction Prices to Re-Examine Price Dispersion in Electronic Markets," Information Systems Research, INFORMS, vol. 22(2), pages 269-288, June.
    2. Cai, Gangshu (George) & Chen, Ying-Ju, 2011. "In-Store Referrals on the Internet," Journal of Retailing, Elsevier, vol. 87(4), pages 563-578.
    3. Xin (Shane) Wang & Feng Mai & Roger H. L. Chiang, 2014. "Database Submission ---Market Dynamics and User-Generated Content About Tablet Computers," Marketing Science, INFORMS, vol. 33(3), pages 449-458, May.
    4. Sagit Bar-Gill & Shachar Reichman, 2015. "When Online Engagement Gets in the Way of Offline Sales - A Natural Experiment," Working Papers 15-13, NET Institute.
    5. Feiyan Han & Herui Wang & Hongyu Lv & Bo Li, 2022. "Research on Manufacturers’ Referral Strategy Considering Store Brand Retailers and Traditional Retailers," Mathematics, MDPI, vol. 10(18), pages 1-23, September.
    6. Wu, Kewen & Vassileva, Julita & Noorian, Zeinab & Zhao, Yuxiang, 2015. "How do you feel when you see a list of prices? the interplay among price dispersion, perceived risk and initial trust in Chinese C2C market," Journal of Retailing and Consumer Services, Elsevier, vol. 25(C), pages 36-46.
    7. Yucai Wu & Jiguang Wang & Lu Chen, 2021. "Optimization and Decision of Supply Chain Considering Negative Spillover Effect and Service Competition," Sustainability, MDPI, vol. 13(4), pages 1-20, February.
    8. Yuangao Chen & Shuiqing Yang & Zhoujing Wang, 2016. "Service cooperation and marketing strategies of infomediary and online retailer with eWOM effect," Information Technology and Management, Springer, vol. 17(2), pages 109-118, June.
    9. Yang Liu & Juan Feng & Xiuwu Liao, 2017. "When Online Reviews Meet Sales Volume Information: Is More or Accurate Information Always Better?," Information Systems Research, INFORMS, vol. 28(4), pages 723-743, December.
    10. Anindita Chakravarty & Rajdeep Grewal & V. Sambamurthy, 2013. "Information Technology Competencies, Organizational Agility, and Firm Performance: Enabling and Facilitating Roles," Information Systems Research, INFORMS, vol. 24(4), pages 976-997, December.
    11. Wang, Yulan & Wallace, Stein W. & Shen, Bin & Choi, Tsan-Ming, 2015. "Service supply chain management: A review of operational models," European Journal of Operational Research, Elsevier, vol. 247(3), pages 685-698.
    12. Amit Mehra & Ram Bala & Ramesh Sankaranarayanan, 2012. "Competitive Behavior-Based Price Discrimination for Software Upgrades," Information Systems Research, INFORMS, vol. 23(1), pages 60-74, March.
    13. Jianqiang Zhang & Zhuping Liu & Raghunath Singh Rao, 2018. "Flirting with the enemy: online competitor referral and entry-deterrence," Quantitative Marketing and Economics (QME), Springer, vol. 16(2), pages 209-249, June.
    14. Yanjie Wu & Sujuan Wang, 2021. "Sustainable Market Entry Strategy under a Supply Chain Environment," Sustainability, MDPI, vol. 13(6), pages 1-15, March.
    15. Ryang Suk Lee & Sheng Yen Lee, 2023. "Analysis of Relationship between Service Quality of Food and Beverage and Customer Behaviors for Sustainable Golf Course Management," Sustainability, MDPI, vol. 15(14), pages 1-19, July.

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