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How do reputation, structure design and FinTech ecosystem affect the net cash inflow of P2P lending platforms? Evidence from China

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  • Xueru Chen

    (Zhejiang University)

  • Xiaoji Hu

    (Renming University of China)

  • Shenglin Ben

    (Zhejiang University)

Abstract

Based on daily data of 749 active online P2P lending platforms in Chinese market, this study explores the key factors affecting the net cash inflow rate of the platform which is vital for its operation and survival from the perspective of reputation, structure design and FinTech ecosystem. Internal governance issues of P2P lending platforms are further discussed according to the model results. A positive U-shaped relationship has been found between the platform duration and its net cash inflow rate which proves the role of reputation in the long-term development of P2P lending platforms. In addition, we demonstrate that both capital and operational structure design of the platform (e.g. shareholders background, credit assignment, trusteeship and guarantee) have a significant impact on the platform’s net cash inflow rate. The cash flow level of the platform has also been affected by the regional FinTech ecosystem. Platforms in a medium-developing ecosystem may have the highest net cash inflow rate, while a backward ecosystem will lower the cash flows of the platforms located in this area on average. Some suggestions on cash flow management and internal governance of P2P lending platforms for both platform founders and governments are put forward in the end of the study.

Suggested Citation

  • Xueru Chen & Xiaoji Hu & Shenglin Ben, 2021. "How do reputation, structure design and FinTech ecosystem affect the net cash inflow of P2P lending platforms? Evidence from China," Electronic Commerce Research, Springer, vol. 21(4), pages 1055-1082, December.
    • Handle: RePEc:spr:elcore:v:21:y:2021:i:4:d:10.1007_s10660-020-09400-9
    DOI: 10.1007/s10660-020-09400-9
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    2. Marta Gancarczyk & Óscar Rodil-Marzábal, 2022. "Fintech framing financial ecologies: Conceptual and policy-related implications," Journal of Entrepreneurship, Management and Innovation, Fundacja Upowszechniająca Wiedzę i Naukę "Cognitione", vol. 18(4), pages 7-44.
    3. Yeh, Jen-Yin & Chiu, Hsin-Yu & Huang, Jhih-Huei, 2024. "Predicting failure of P2P lending platforms through machine learning: The case in China," Finance Research Letters, Elsevier, vol. 59(C).
    4. Aditya Halim Perdana Kusuma Putra, 2024. "Learning from the Past Bridging Digital and Physical Markets: A Guidelines for Future Research Agenda of Online-to-Offline (O2O) Marketing Strategy," International Review of Management and Marketing, Econjournals, vol. 14(3), pages 82-96, May.
    5. Jen-Yin Yeh & Hsin-Yu Chiu & Jhih-Huei Huang, 2023. "Predicting Failure of P2P Lending Platforms through Machine Learning: The Case in China," Papers 2311.14577, arXiv.org.
    6. Evangelos Katsamakas & J. Manuel Sanchez-Cartas, 2024. "A computational model of the effects of borrower default on the stability of P2P lending platforms," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 14(3), pages 597-618, September.

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    Keywords

    P2P lending; Cash flow; Reputation; Structure design; Ecosystem;
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