IDEAS home Printed from https://ideas.repec.org/p/net/wpaper/1306.html
   My bibliography  Save this paper

Competing for Influencers in a Social Network

Author

Listed:
  • Zsolt Katona

    (Haas School of Business, UC Berkeley)

Abstract

This paper studies the competition between firms for influencers in a network. Firms spend effort to convince influencers to recommend their products. The analysis identifies the offensive and defensive roles of spending on influencers. The value of an influencer only depends on the in-degree distribution of the influence network. Influencers who exclusively cover a high number of consumers are more valuable to firms than those who mostly cover consumers also covered by other influencers. Firm profits are highest when there are many consumers with a very low or with very high in-degree. Consumers with an intermediate level of in-degree contribute negatively to profits and high in-degree consumers increase profits when market competition is not intense. Prices are generally lower when consumers are covered by many influencers, however, firms are not always worse off with lower prices. The nature of consumer response to recommendations makes an important difference. When first impressions dominate, firm profits for dense networks are higher, but when recommendations have a cumulative influence profits are reduced as the network becomes dense.

Suggested Citation

  • Zsolt Katona, 2013. "Competing for Influencers in a Social Network," Working Papers 13-06, NET Institute.
  • Handle: RePEc:net:wpaper:1306
    as

    Download full text from publisher

    File URL: http://www.netinst.org/Katona_13-06.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Raghuram Iyengar & Christophe Van den Bulte & Thomas W. Valente, 2011. "Opinion Leadership and Social Contagion in New Product Diffusion," Marketing Science, INFORMS, vol. 30(2), pages 195-212, 03-04.
    2. Varian, Hal R, 1980. "A Model of Sales," American Economic Review, American Economic Association, vol. 70(4), pages 651-659, September.
    3. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    4. Yuxin Chen & Ganesh Iyer, 2002. "Research Note Consumer Addressability and Customized Pricing," Marketing Science, INFORMS, vol. 21(2), pages 197-208, November.
    5. Tingting He & Dmitri Kuksov & Chakravarthi Narasimhan, 2012. "Intraconnectivity and Interconnectivity: When Value Creation May Reduce Profits," Marketing Science, INFORMS, vol. 31(4), pages 587-602, July.
    6. Yuxin Chen & Yogesh V. Joshi & Jagmohan S. Raju & Z. John Zhang, 2009. "A Theory of Combative Advertising," Marketing Science, INFORMS, vol. 28(1), pages 1-19, 01-02.
    7. Ganesh Iyer & David Soberman & J. Miguel Villas-Boas, 2005. "The Targeting of Advertising," Marketing Science, INFORMS, vol. 24(3), pages 461-476, May.
    8. Narasimhan, Chakravarthi, 1988. "Competitive Promotional Strategies," The Journal of Business, University of Chicago Press, vol. 61(4), pages 427-449, October.
    9. Peter Zubcsek & Miklos Sarvary, 2011. "Advertising to a social network," Quantitative Marketing and Economics (QME), Springer, vol. 9(1), pages 71-107, March.
    10. Nair, Harikesh S. & Manchanda, Puneet & Bhatia, Tulikaa, 2006. "Asymmetric Peer Effects in Physician Prescription Behavior: The Role of Opinion Leaders," Research Papers 1970, Stanford University, Graduate School of Business.
    11. Hema Yoganarasimhan, 2012. "Impact of social network structure on content propagation: A study using YouTube data," Quantitative Marketing and Economics (QME), Springer, vol. 10(1), pages 111-150, March.
    12. Juanjuan Zhang, 2011. "The Perils of Behavior-Based Personalization," Marketing Science, INFORMS, vol. 30(1), pages 170-186, 01-02.
    13. Yuxin Chen & Chakravarthi Narasimhan & Z. John Zhang, 2001. "Individual Marketing with Imperfect Targetability," Marketing Science, INFORMS, vol. 20(1), pages 23-41, November.
    14. Catherine Tucker, 2008. "Identifying Formal and Informal Influence in Technology Adoption with Network Externalities," Management Science, INFORMS, vol. 54(12), pages 2024-2038, December.
    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. Zhang, Jianqiang & He, Xiuli, 2019. "Targeted advertising by asymmetric firms," Omega, Elsevier, vol. 89(C), pages 136-150.
    2. Peter Landry, 2021. "Keywords, limited consideration, and organic product listings," Quantitative Marketing and Economics (QME), Springer, vol. 19(3), pages 505-566, December.
    3. Lin, Yuanfang & Pazgal, Amit & Soberman, David A., 2021. "Who is the winner in an industry of innovation?," International Journal of Research in Marketing, Elsevier, vol. 38(1), pages 50-69.
    4. Fay, Scott, 2008. "Selling an opaque product through an intermediary: The case of disguising one's product," Journal of Retailing, Elsevier, vol. 84(1), pages 59-75.
    5. Qingliang Wang & Fred Miao & Giri Kumar Tayi & En Xie, 2019. "What makes online content viral? The contingent effects of hub users versus non–hub users on social media platforms," Journal of the Academy of Marketing Science, Springer, vol. 47(6), pages 1005-1026, November.
    6. Kaifu Zhang & Zsolt Katona, 2012. "Contextual Advertising," Marketing Science, INFORMS, vol. 31(6), pages 980-994, November.
    7. Sarah Gelper & Ralf van der Lans & Gerrit van Bruggen, 2021. "Competition for Attention in Online Social Networks: Implications for Seeding Strategies," Management Science, INFORMS, vol. 67(2), pages 1026-1047, February.
    8. Il-Horn Hann & Kai-Lung Hui & Sang-Yong Tom Lee & Ivan P.L. Png, 2005. "Sales and Promotions: A More General Model," Industrial Organization 0508014, University Library of Munich, Germany.
    9. Bernard Caillaud & Romain De Nijs, 2014. "Strategic Loyalty Reward in Dynamic Price Discrimination," Marketing Science, INFORMS, vol. 33(5), pages 725-742, September.
    10. Rosa Branca Esteves, 2009. "Customer Poaching And Advertising," Journal of Industrial Economics, Wiley Blackwell, vol. 57(1), pages 112-146, March.
    11. Il-Horn Hann & Kai-Lung Hui & Sang-Yong Tom Lee & Ivan Png, 2005. "Consumer Privacy and Marketing Avoidance," Industrial Organization 0503009, University Library of Munich, Germany.
    12. Il-Horn Hann & Kai-Lung Hui & Sang-Yong T. Lee & Ivan P. L. Png, 2008. "Consumer Privacy and Marketing Avoidance: A Static Model," Management Science, INFORMS, vol. 54(6), pages 1094-1103, June.
    13. Galeotti, Andrea & Moraga-González, José Luis, 2008. "Segmentation, advertising and prices," International Journal of Industrial Organization, Elsevier, vol. 26(5), pages 1106-1119, September.
    14. Flavio Pino, 2022. "The microeconomics of data – a survey," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 49(3), pages 635-665, September.
    15. Ganesh Iyer & David Soberman & J. Miguel Villas-Boas, 2005. "The Targeting of Advertising," Marketing Science, INFORMS, vol. 24(3), pages 461-476, May.
    16. Dmitri Kuksov & Ashutosh Prasad & Mohammad Zia, 2017. "In-Store Advertising by Competitors," Marketing Science, INFORMS, vol. 36(3), pages 402-425, May.
    17. Vineet Kumar & K. Sudhir, 2019. "Can Friends Seed More Buzz and Adoption"," Cowles Foundation Discussion Papers 2178, Cowles Foundation for Research in Economics, Yale University.
    18. Sumitro Banerjee & Alex P. Thevaranjan, 2013. "How to deal with unprofitable customers? A salesforce compensation perspective," ESMT Research Working Papers ESMT-13-05, ESMT European School of Management and Technology.
    19. Kutsal Dogan & Ernan Haruvy & Ram Rao, 2010. "Who should practice price discrimination using rebates in an asymmetric duopoly?," Quantitative Marketing and Economics (QME), Springer, vol. 8(1), pages 61-90, March.
    20. Wen, Zhong, 2014. "Mixed pricing in oligopoly with limited monopoly," Economics Letters, Elsevier, vol. 125(1), pages 87-92.

    More about this item

    Keywords

    Social Networks; Influencers; Competition;
    All these keywords.

    JEL classification:

    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:net:wpaper:1306. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: http://www.NETinst.org/ .

    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: Nicholas Economides (email available below). General contact details of provider: http://www.NETinst.org/ .

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.