Success factors for peer-to-peer lending for SMEs: Evidence from Indonesia

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Sharia fintech lending grew up at the teenage stage and has successfully taken a strategic place in the Indonesian loan market. Adopting the economics of information and signaling theory, this paper investigates the probability of successful crowdfunding. Using cross-section data, this study analyzes 1,153 funded projects on Ammana.id platform, a well-known Indonesia’s sharia P2P lending. This study runs OLS regressions to examine the effect of loan information (ranking, estimated profit shares, and financing duration) on the amount of crowded funding. This finding support both theories, that the information about the loan is a signal in determining the success of project funding. Ranking and duration of financing significantly affect the success of the P2P sharia lending platform, nevertheless profit share estimation is not significant. Loans that operated in short, tend to raise more funding, and vice versa. Loan ranking can provide the lender with instant information about the borrowers’ condition. Lenders tend to avoid low rankings loans due to the potential failure of loan payments. This study also found a surprising result that the coefficient of profit sharing is positive for Islamic funding but insignificant. This result shows that material gain is not the main issue for investors, but the elements of trust and justice are nobler according to Islamic beliefs. This study proves that loan information as a low-cost signal can be used by investors to make the best decision and reduce adverse selection problems. The findings support the strategic growth of Islamic platforms to build a sustainable Islamic investment and maintain financial stability.

Acknowledgments
Appreciation is given to the General Directorate of Higher Education, Research and Technology, Ministry of Education, Culture, Research and Technology, and the Institute for Research and Community Service of Universitas Islam Nahdlatul Ulama (Unisnu) Jepara, Indonesia.

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    • Table 1. Descriptive statistics
    • Table 2. Classical assumption testing results
    • Table 3. OLS regression results
    • Conceptualization
      Mohammad Yunies Edward, Eko Nur Fuad, Hadi Ismanto, Apriani Dorkas Rambu Atahau, Robiyanto
    • Data curation
      Mohammad Yunies Edward, Eko Nur Fuad, Hadi Ismanto
    • Formal Analysis
      Mohammad Yunies Edward, Apriani Dorkas Rambu Atahau, Robiyanto
    • Funding acquisition
      Mohammad Yunies Edward, Eko Nur Fuad, Hadi Ismanto, Apriani Dorkas Rambu Atahau, Robiyanto
    • Methodology
      Mohammad Yunies Edward, Eko Nur Fuad, Hadi Ismanto
    • Resources
      Mohammad Yunies Edward, Apriani Dorkas Rambu Atahau, Robiyanto
    • Software
      Mohammad Yunies Edward, Eko Nur Fuad, Hadi Ismanto
    • Validation
      Mohammad Yunies Edward, Eko Nur Fuad, Hadi Ismanto, Apriani Dorkas Rambu Atahau, Robiyanto
    • Writing – original draft
      Mohammad Yunies Edward, Eko Nur Fuad, Hadi Ismanto, Apriani Dorkas Rambu Atahau, Robiyanto
    • Writing – review & editing
      Mohammad Yunies Edward, Eko Nur Fuad, Hadi Ismanto
    • Investigation
      Eko Nur Fuad, Hadi Ismanto
    • Project administration
      Eko Nur Fuad, Hadi Ismanto
    • Visualization
      Eko Nur Fuad, Hadi Ismanto
    • Supervision
      Apriani Dorkas Rambu Atahau, Robiyanto