IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v387y2008i19p4889-4895.html
   My bibliography  Save this article

Earning potential in multilevel marketing enterprises

Author

Listed:
  • Legara, Erika Fille
  • Monterola, Christopher
  • Juanico, Dranreb Earl
  • Litong-Palima, Marisciel
  • Saloma, Caesar

Abstract

Government regulators and other concerned citizens warily view multilevel marketing enterprises (MLM) because of their close operational resemblance to exploitative pyramid schemes. We analyze two types of MLM network architectures — the unilevel and binary, in terms of growth behavior and earning potential among members. We show that network growth decelerates after reaching a size threshold, contrary to claims of unrestricted growth by MLM recruiters. We have also found that the earning potential in binary MLM’s obey the Pareto “80–20” rule, implying an earning opportunity that is strongly biased against the most recent members. On the other hand, unilevel MLM’s do not exhibit the Pareto earning distribution and earning potential is independent of member position in the network. Our analytical results agree well with field data taken from real-world MLM’s in the Philippines. Our analysis is generally valid and can be applied to other MLM architectures.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:phsmap:v:387:y:2008:i:19:p:4889-4895
    DOI: 10.1016/j.physa.2008.04.009
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437108003701
    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.2008.04.009?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. 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.
    2. Barabási, Albert-László & Albert, Réka & Jeong, Hawoong, 1999. "Mean-field theory for scale-free random networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 272(1), pages 173-187.
    3. Juanico, Dranreb Earl & Monterola, Christopher & Saloma, Caesar, 2003. "Allelomimesis as a generic clustering mechanism for interacting agents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 320(C), pages 590-600.
    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. Ioana-Mădălina Purcaru & Ana-Maria Urdea & Cristinel Petrişor Constantin & Gabriel Brătucu, 2022. "Building Long-Term Business Sustainability: The Influence of Experiential Marketing on Sales Representatives’ Loyalty to Multi-Level Marketing Systems," Sustainability, MDPI, vol. 14(15), pages 1-16, August.
    2. Bäckman, Claes & Hanspal, Tobin, 2018. "Participation and Losses in Multi-Level Marketing: Evidence from an FTC Settlement," Working Papers 2018:13, Lund University, Department of Economics, revised 22 Aug 2019.
    3. Pierpaolo Andriani & Bill McKelvey, 2009. "Perspective ---From Gaussian to Paretian Thinking: Causes and Implications of Power Laws in Organizations," Organization Science, INFORMS, vol. 20(6), pages 1053-1071, December.

    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. Liang, Wei & Shi, Yuming & Huang, Qiuling, 2014. "Modeling the Chinese language as an evolving network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 268-276.
    2. Yan Qiang & Bo Pei & Weili Wu & Juanjuan Zhao & Xiaolong Zhang & Yue Li & Lidong Wu, 2014. "Improvement of path analysis algorithm in social networks based on HBase," Journal of Combinatorial Optimization, Springer, vol. 28(3), pages 588-599, October.
    3. Stephanie Rend'on de la Torre & Jaan Kalda & Robert Kitt & Juri Engelbrecht, 2016. "On the topologic structure of economic complex networks: Empirical evidence from large scale payment network of Estonia," Papers 1602.04352, arXiv.org.
    4. Gabrielle Demange, 2012. "On the influence of a ranking system," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 39(2), pages 431-455, July.
    5. Hang-Hyun Jo & Jeoung-Yoo Kim, 2012. "Competitive Targeted Marketing," ISER Discussion Paper 0834, Institute of Social and Economic Research, Osaka University.
    6. Tsao, J.Y. & Boyack, K.W. & Coltrin, M.E. & Turnley, J.G. & Gauster, W.B., 2008. "Galileo's stream: A framework for understanding knowledge production," Research Policy, Elsevier, vol. 37(2), pages 330-352, March.
    7. Pier Paolo Saviotti, 2011. "Knowledge, Complexity and Networks," Chapters, in: Cristiano Antonelli (ed.), Handbook on the Economic Complexity of Technological Change, chapter 6, Edward Elgar Publishing.
    8. Duan, Shuyu & Wen, Tao & Jiang, Wen, 2019. "A new information dimension of complex network based on Rényi entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 529-542.
    9. Chung-Yuan Huang & Chuen-Tsai Sun & Hsun-Cheng Lin, 2005. "Influence of Local Information on Social Simulations in Small-World Network Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 8(4), pages 1-8.
    10. Sun, Bingbin & Yao, Jialing & Xi, Lifeng, 2019. "Eigentime identities of fractal sailboat networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 338-349.
    11. Yang, Xu-Hua & Lou, Shun-Li & Chen, Guang & Chen, Sheng-Yong & Huang, Wei, 2013. "Scale-free networks via attaching to random neighbors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(17), pages 3531-3536.
    12. Colizza, Vittoria & Flammini, Alessandro & Maritan, Amos & Vespignani, Alessandro, 2005. "Characterization and modeling of protein–protein interaction networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 352(1), pages 1-27.
    13. Wouter Vermeer & Otto Koppius & Peter Vervest, 2018. "The Radiation-Transmission-Reception (RTR) model of propagation: Implications for the effectiveness of network interventions," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-21, December.
    14. Haider, Sajjad & Mariotti, Francesca, 2016. "The orchestration of alliance portfolios: The role of alliance portfolio capability," Scandinavian Journal of Management, Elsevier, vol. 32(3), pages 127-141.
    15. Lotfi, Nastaran & Rodrigues, Francisco A., 2022. "On the effect of memory on the Prisoner’s Dilemma game in correlated networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    16. Laurienti, Paul J. & Joyce, Karen E. & Telesford, Qawi K. & Burdette, Jonathan H. & Hayasaka, Satoru, 2011. "Universal fractal scaling of self-organized networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(20), pages 3608-3613.
    17. L. Jarina Banu & P. Balasubramaniam, 2014. "Synchronisation of discrete-time complex networks with randomly occurring uncertainties, nonlinearities and time-delays," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(7), pages 1427-1450, July.
    18. Chen, Qinghua & Shi, Dinghua, 2004. "The modeling of scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 335(1), pages 240-248.
    19. Csárdi, Gábor & Strandburg, Katherine J. & Zalányi, László & Tobochnik, Jan & Érdi, Péter, 2007. "Modeling innovation by a kinetic description of the patent citation system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 374(2), pages 783-793.
    20. Wang, Yi & Cao, Jinde & Jin, Zhen & Zhang, Haifeng & Sun, Gui-Quan, 2013. "Impact of media coverage on epidemic spreading in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 5824-5835.

    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:387:y:2008:i:19:p:4889-4895. 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.