IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v360y2006i2p493-504.html
   My bibliography  Save this article

Network marketing on a small-world network

Author

Listed:
  • Kim, Beom Jun
  • Jun, Tackseung
  • Kim, Jeong-Yoo
  • Choi, M.Y.

Abstract

We investigate a dynamic model of network marketing in a small-world network structure artificially constructed similarly to the Watts–Strogatz network model. Different from the traditional marketing, consumers can also play the role of the manufacturer's selling agents in network marketing, which is stimulated by the referral fee the manufacturer offers. As the wiring probability α is increased from zero to unity, the network changes from the one-dimensional regular directed network to the star network where all but one player are connected to one consumer. The price p of the product and the referral fee r are used as free parameters to maximize the profit of the manufacturer. It is observed that at α=0 the maximized profit is constant independent of the network size N while at α≠0, it increases linearly with N. This is in parallel to the small-world transition. It is also revealed that while the optimal value of p stays at an almost constant level in a broad range of α, that of r is sensitive to a change in the network structure. The consumer surplus is also studied and discussed.

Suggested Citation

  • Kim, Beom Jun & Jun, Tackseung & Kim, Jeong-Yoo & Choi, M.Y., 2006. "Network marketing on a small-world network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 360(2), pages 493-504.
    • Handle: RePEc:eee:phsmap:v:360:y:2006:i:2:p:493-504
    DOI: 10.1016/j.physa.2005.06.059
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437105006928
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2005.06.059?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jun, Tackseung & Kim, Jeong-Yoo, 2008. "A theory of consumer referral," International Journal of Industrial Organization, Elsevier, vol. 26(3), pages 662-678, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shi, Y.Y. & Wei, Z.X. & Shahbaz, M., 2023. "Analyzing the co-evolutionary dynamics of consumers’ attitudes and green energy technologies based on a triple-helix model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 171(C).
    2. Jun, Tackseung & Kim, Jeong-Yoo, 2008. "A theory of consumer referral," International Journal of Industrial Organization, Elsevier, vol. 26(3), pages 662-678, May.
    3. Hang-Hyun Jo & Jeoung-Yoo Kim, 2012. "Competitive Targeted Marketing," ISER Discussion Paper 0834, Institute of Social and Economic Research, Osaka University.
    4. Legara, Erika Fille & Monterola, Christopher & Juanico, Dranreb Earl & Litong-Palima, Marisciel & Saloma, Caesar, 2008. "Earning potential in multilevel marketing enterprises," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(19), pages 4889-4895.

    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. Arbatskaya Maria & Konishi Hideo, 2014. "Managing Consumer Referrals on a Chain Network," Review of Network Economics, De Gruyter, vol. 13(1), pages 69-94, March.
    2. Hang-Hyun Jo & Jeoung-Yoo Kim, 2012. "Competitive Targeted Marketing," ISER Discussion Paper 0834, Institute of Social and Economic Research, Osaka University.
    3. Atabek Atayev & Maarten Janssen, 2021. "Information Acquisition and Diffusion in Markets," Papers 2109.15288, arXiv.org.
    4. Arbatskaya, Maria & Konishi, Hideo, 2016. "Consumer referrals," International Journal of Industrial Organization, Elsevier, vol. 48(C), pages 34-58.
    5. Atayev, Atabek & Janssen, Maarten C. W., 2021. "Information acquisition and diffusion in markets," ZEW Discussion Papers 21-091, ZEW - Leibniz Centre for European Economic Research.
    6. Janssen, Maarten & Atayev, Atabek, 2019. "Information Acquisition and Diffusion in Markets," CEPR Discussion Papers 14036, C.E.P.R. Discussion Papers.

    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:eee:phsmap:v:360:y:2006:i:2:p:493-504. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.