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The life-cycle influence mechanism of the determinants of financing performance: an empirical study of a Chinese crowdfunding platform

Author

Listed:
  • Yue Chen

    (Guangdong University of Technology
    University of Science and Technology Beijing)

  • Wentao Zhang

    (Harbin Institute of Technology)

  • Xiangbin Yan

    (University of Science and Technology Beijing)

  • Jiahua Jin

    (University of Science and Technology Beijing)

Abstract

Employing a unique data set that incorporates development events for 243 reward-based projects on one of the biggest crowdfunding platforms in China, JingDong Crowdfunding, this paper examines two vital aspects related to the financing performance of crowdfunding projects: the influencing factors and the change laws of the effects of the determinants. The econometric analysis results of individual fixed effects models with instrumental variables reveal that the fundraiser’s online social capital, number of description images and project popularity, as well as the herding effects that exist in the financing process, have significant positive impacts on financing performance, while the funding goal has negative impact. More importantly, the data highlights the life-cycle features of each determinant’s influence in the financing process. Campaign popularity and herding effects both have progressively increasing influences on fundraising performance, while online social capital of the fundraiser and the number of description images exercise declining effects over it. One notable finding is that the funding goal’s effect on the fundraising outcome shifts from decreasing positive in the growth stage towards increasing negative in the stable and maturity stages. The findings are of great guiding significance for improving campaign performance and development of the crowdfunding industry.

Suggested Citation

  • Yue Chen & Wentao Zhang & Xiangbin Yan & Jiahua Jin, 2020. "The life-cycle influence mechanism of the determinants of financing performance: an empirical study of a Chinese crowdfunding platform," Review of Managerial Science, Springer, vol. 14(1), pages 287-309, February.
    • Handle: RePEc:spr:rvmgts:v:14:y:2020:i:1:d:10.1007_s11846-018-0295-y
    DOI: 10.1007/s11846-018-0295-y
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    Cited by:

    1. Samuel Ribeiro-Navarrete & Juan Piñeiro-Chousa & M. Ángeles López-Cabarcos & Daniel Palacios-Marqués, 2022. "Crowdlending: mapping the core literature and research frontiers," Review of Managerial Science, Springer, vol. 16(8), pages 2381-2411, November.
    2. Iris Maria Oliveira Sousa & Fabíola Kaczam & Luciano Luiz Dalazen & Wenner Glaucio Lopes Lucena & Wesley Vieira Silva & Claudimar Pereira Veiga, 2024. "The dynamics of the life cycle theory and organizational culture: a systematic literature review," SN Business & Economics, Springer, vol. 4(1), pages 1-26, January.
    3. Zhao, Liang & Shneor, Rotem & Sun, Zhe, 2022. "Skin in the game: Self-funding and reward crowdfunding success," Business Horizons, Elsevier, vol. 65(1), pages 89-100.
    4. Markus Weinmann & Abhay Nath Mishra & Lena Franziska Kaiser & Jan vom Brocke, 2023. "The Attraction Effect in Crowdfunding," Information Systems Research, INFORMS, vol. 34(3), pages 1276-1295, September.
    5. Xin Tang & Haibing Lu & Wei Huang & Shulin Liu, 2023. "Investment decisions and pricing strategies of crowdfunding players: In a two-sided crowdfunding market," Electronic Commerce Research, Springer, vol. 23(2), pages 1209-1240, June.
    6. René Vásquez-Ordóñez, Luis & Lassala, Carlos & Ulrich, Klaus & Ribeiro-Navarrete, Samuel, 2023. "Efficiency factors in the financing of renewable energy projects through crowdlending," Journal of Business Research, Elsevier, vol. 155(PB).
    7. Shneor, Rotem & Zhao, Liang & Fabian Michael Goedecke, Jann, 2023. "On relationship types, their strength, and reward crowdfunding backer behavior," Journal of Business Research, Elsevier, vol. 154(C).

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    More about this item

    Keywords

    Reward-based crowdfunding; Financing performance; Life-cycle theory; Panel data;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • G02 - Financial Economics - - General - - - Behavioral Finance: Underlying Principles

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