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Pure and hybrid crowds in crowdfunding markets

Author

Listed:
  • Liang Chen

    (West Texas A&M University)

  • Zihong Huang

    (Yuanshihui Inc.)

  • De Liu

    (University of Minnesota, 3-163 Carlson School of Management)

Abstract

Background Crowdfunding has risen rapidly as a way of raising funds to support projects such as art projects, charity projects, and new ventures. It is very important to understand how crowds in the crowdfunding market are organized to carry out various activities. This study documents and compares two crowd designs for crowdfunding, namely pure crowds, where all crowd members participate as equals, and hybrid crowds, where crowd members are led by an expert investor. The hybrid design is rarely studied in the crowdfunding literature despite its large presence in equity crowdfunding. Methods We examine industry practices from various countries in terms of crowd designs, review relevant literature on this topic, and develop a conceptual framework for choosing between pure and hybrid crowds. Results We identify several inefficiencies of pure crowds in crowdfunding platforms and discuss the advantages of hybrid crowds. We then develop a conceptual framework that illustrates the factors for choosing between pure and hybrid crowds. Finally, we discuss the issue of how to manage and regulate lead investors in hybrid crowds. Conclusions Pure crowds have several shortcomings that could be mitigated by a hybrid crowd design, especially when the proposed project suffers from greater risks, a high degree of information asymmetry, concerns about information leakage, and a high cost of managing the crowds. But for the hybrid crowd to work well, one must carefully design mechanisms for lead investor selection, compensation, and discipline. Our study contributes to the crowdfunding literature and to crowdfunding practice in multiple ways.

Suggested Citation

  • Liang Chen & Zihong Huang & De Liu, 2016. "Pure and hybrid crowds in crowdfunding markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 2(1), pages 1-18, December.
  • Handle: RePEc:spr:fininn:v:2:y:2016:i:1:d:10.1186_s40854-016-0038-5
    DOI: 10.1186/s40854-016-0038-5
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    References listed on IDEAS

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    3. Yaokuang Li & Li Ling & Daru Zhang & Juan Wu, 2021. "Lead investors and information disclosure: A test of signaling theory by fuzzy‐set qualitative comparative analysis approach," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 42(4), pages 836-849, June.
    4. Astebro, Thomas B. & Lovo, Stefano & Fernandez Sierra, Manuel & Vulkan, Nir, 2017. "Herding in Equity Crowdfunding," HEC Research Papers Series 1245, HEC Paris, revised 04 Jun 2018.
    5. Yuze Li & Shangrong Jiang & Xuerong Li & Shouyang Wang, 2022. "Hybrid data decomposition-based deep learning for Bitcoin prediction and algorithm trading," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-24, December.
    6. Yanhong Guo & Shuai Jiang & Wenjun Zhou & Chunyu Luo & Hui Xiong, 2021. "A predictive indicator using lender composition for loan evaluation in P2P lending," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-24, December.

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