IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v503y2018icp582-590.html
   My bibliography  Save this article

Exploring the participate propensity in cyberspace collective actions: The 5‰ rule

Author

Listed:
  • Lu, Peng
  • Wang, Zheng
  • Nie, Shizhao
  • Pujia, Wangmo
  • Lu, Pengfei
  • Chen, Baosheng

Abstract

The Internet and Big data have become indispensable in people’s daily life. The whole participation spectrum of cyberspace collective actions contains four stages, such as Access, Browse, Participate, and Offline. There exist three transition probabilities within four stages. This paper focuses on the ratio (second transition probability) between numbers of browse and participate, which is defined as the participate propensity PBP. In the real world, amounts of browse (millions) and participation (tens of thousands) are huge, and they take on irregular distributions. However, it is discovered in this paper that this participate propensity is stable and takes on the regularity of 5‰, i.e. the participation propensity is slightly under 5‰ for most times, while sometimes it is slightly over 5‰. The empirical big data of 310 online collective actions are collected from a famous BBS (Tianya.cn) in China. This 5‰ rule not only holds true for the total 310 cases, but also for the yearly, quarterly, and other subgroups. Furthermore, we check distributive traits of PBP. It is not normally distributed as it has longer right tails. It is verified by the empirical big data that the propensity follows the lognormal distribution, which is relatively robust in the total and subgroups of 310 cyberspace collective actions. Given the distributive regularity of the participate propensity PBP, its probability density function can be obtained. Combined with the known amount of browsers, big data prediction of participation would be possible.

Suggested Citation

  • Lu, Peng & Wang, Zheng & Nie, Shizhao & Pujia, Wangmo & Lu, Pengfei & Chen, Baosheng, 2018. "Exploring the participate propensity in cyberspace collective actions: The 5‰ rule," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 582-590.
    • Handle: RePEc:eee:phsmap:v:503:y:2018:i:c:p:582-590
    DOI: 10.1016/j.physa.2018.02.152
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037843711830222X
    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.2018.02.152?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. Granovetter, Mark & Soong, Roland, 1986. "Threshold models of interpersonal effects in consumer demand," Journal of Economic Behavior & Organization, Elsevier, vol. 7(1), pages 83-99, March.
    2. Damon M. Centola, 2013. "Homophily, networks, and critical mass: Solving the start-up problem in large group collective action," Rationality and Society, , vol. 25(1), pages 3-40, February.
    3. DavidP. Myatt & Chris Wallace, 2009. "Evolution, Teamwork and Collective Action: Production Targets in the Private Provision of Public Goods," Economic Journal, Royal Economic Society, vol. 119(534), pages 61-90, January.
    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. Lu, Peng, 2016. "Predicting peak of participants in collective action," Applied Mathematics and Computation, Elsevier, vol. 274(C), pages 318-330.
    2. Lu, Peng, 2015. "Imitating winner or sympathizing loser? Quadratic effects on cooperative behavior in prisoners’ dilemma games," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 327-337.
    3. Jun Honda, 2015. "Games with the Total Bandwagon Property," Department of Economics Working Papers wuwp197, Vienna University of Economics and Business, Department of Economics.
    4. Norman Braun, 1995. "Individual Thresholds and Social Diffusion," Rationality and Society, , vol. 7(2), pages 167-182, April.
    5. Bonache, Adrien, 2008. "Les ventes de produits innovants à la mode sont-elles chaotiques? Le cas des ventes de Game Boy au Japon [Are innovative and fashion goods sales chaotic? The case of Game Boy sales in Japan]," MPRA Paper 12964, University Library of Munich, Germany.
    6. Robin Cowan & William Cowan & G.M. Peter Swann, 2004. "Waves in consumption with interdependence among consumers," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 37(1), pages 149-177, February.
    7. Olivier Mesly & David W. Shanafelt & Nicolas Huck, 2021. "Dysfunctional Markets: A Spray of Prey Perspective," Journal of Economic Issues, Taylor & Francis Journals, vol. 55(3), pages 797-819, July.
    8. K. Kesteloot, 1992. "Multimarket cooperation with scope effects in demand," Journal of Economics, Springer, vol. 55(3), pages 245-264, October.
    9. Jacobs Martin, 2016. "Accounting for Changing Tastes: Approaches to Explaining Unstable Individual Preferences," Review of Economics, De Gruyter, vol. 67(2), pages 121-183, August.
    10. Michal Grajek, 2003. "Estimating Network Effects and Compatibility in Mobile Telecommunications," CIG Working Papers SP II 2003-26, Wissenschaftszentrum Berlin (WZB), Research Unit: Competition and Innovation (CIG).
    11. Gavrel, Frédéric & Rebiere, Therese, 2015. "On the Equilibrium and Welfare Consequences of 'Keeping up with the Joneses'," IZA Discussion Papers 9056, Institute of Labor Economics (IZA).
    12. Conlisk, John & Gong, Jyh-Chyi & Tong, Ching H., 2000. "Imitation and the dynamics of norms," Mathematical Social Sciences, Elsevier, vol. 40(2), pages 197-213, September.
    13. Bonache, Adrien & Moris, Karen, 2009. "Nonlinear and chaotic patterns in Japanese video game console sales and consequences for management control," MPRA Paper 18196, University Library of Munich, Germany.
    14. Janssen, Marco A. & Jager, Wander, 2001. "Fashions, habits and changing preferences: Simulation of psychological factors affecting market dynamics," Journal of Economic Psychology, Elsevier, vol. 22(6), pages 745-772, December.
    15. Alessandro Di Stefano & Marialisa Scatà & Aurelio La Corte & Pietro Liò & Emanuele Catania & Ermanno Guardo & Salvatore Pagano, 2015. "Quantifying the Role of Homophily in Human Cooperation Using Multiplex Evolutionary Game Theory," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-21, October.
    16. Guseo, Renato & Guidolin, Mariangela, 2010. "Cellular Automata with network incubation in information technology diffusion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(12), pages 2422-2433.
    17. Elgaaied-Gambier, Leila & Monnot, Elisa & Reniou, Fanny, 2018. "Using descriptive norm appeals effectively to promote green behavior," Journal of Business Research, Elsevier, vol. 82(C), pages 179-191.
    18. Murat Somer, 2001. "Cascades of Ethnic Polarization: Lessons from Yugoslavia," The ANNALS of the American Academy of Political and Social Science, , vol. 573(1), pages 127-151, January.
    19. René van den Brink & Agnieszka Rusinowska & Frank Steffen, 2009. "Measuring Power and Satisfaction in Societies with Opinion Leaders: Dictator and Opinion Leader Properties," Tinbergen Institute Discussion Papers 09-052/1, Tinbergen Institute.
    20. Ángel L. Martín‐Román & Jaime Cuéllar‐Martín & Alfonso Moral, 2020. "Labor supply and the business cycle: The “bandwagon worker effect”," Papers in Regional Science, Wiley Blackwell, vol. 99(6), pages 1607-1642, December.

    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:503:y:2018:i:c:p:582-590. 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.