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Matching donations based on social capital in Internet crowdfunding can promote cooperation

Author

Listed:
  • Cheng, Fei
  • Chen, Tong
  • Chen, Qiao

Abstract

Social capital is an important but often overlooked intangible resource. Unlike other ways of matching donations, we try to match the social capital contributed by individuals in Internet crowdfunding to promote cooperation, and at the same time publish the material capital along with social capital to stimulate people’s enthusiasm for donations. Evolutionary results show that making full use of social capital attracts more public donations on the Internet. Through the analysis of marginal cost, it can be found that the organization can achieve higher level of cooperation at lower cost. Furthermore, there is an optimal threshold for publishing donation lists on the Internet to achieve better cooperation. We also find that network structure, historical information and the existence of altruists have influences on cooperation. Our research helps organizers effectively solve the dilemma of insufficient public participation in public utilities and high construction cost of public goods.

Suggested Citation

  • Cheng, Fei & Chen, Tong & Chen, Qiao, 2019. "Matching donations based on social capital in Internet crowdfunding can promote cooperation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 531(C).
    • Handle: RePEc:eee:phsmap:v:531:y:2019:i:c:s0378437119310258
    DOI: 10.1016/j.physa.2019.121766
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    Citations

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    Cited by:

    1. Lee, Hsuan-Wei & Cleveland, Colin & Szolnoki, Attila, 2021. "Small fraction of selective cooperators can elevate general wellbeing significantly," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).
    2. Szolnoki, Attila & Chen, Xiaojie, 2020. "Blocking defector invasion by focusing on the most successful partner," Applied Mathematics and Computation, Elsevier, vol. 385(C).
    3. Szolnoki, Attila & Chen, Xiaojie, 2020. "Gradual learning supports cooperation in spatial prisoner’s dilemma game," Chaos, Solitons & Fractals, Elsevier, vol. 130(C).
    4. Lee, Hsuan-Wei & Cleveland, Colin & Szolnoki, Attila, 2022. "Mercenary punishment in structured populations," Applied Mathematics and Computation, Elsevier, vol. 417(C).
    5. Szolnoki, Attila & Chen, Xiaojie, 2020. "Strategy dependent learning activity in cyclic dominant systems," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    6. Szolnoki, Attila & Chen, Xiaojie, 2022. "Tactical cooperation of defectors in a multi-stage public goods game," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).

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