Exploring peer-to-peer lending: key influences of firm uncertainty, loan features and venture quality

Abstract

Purpose

The purpose of this study is to identify the determinants of success in peer-to-peer (P2P) lending campaigns, especially amid global financial disruptions like the COVID-19 pandemic. Addressing a notable gap in current research, we explore how factors such as firm uncertainty, loan characteristics (interest rates and maturity) and venture quality (human, social and intellectual capital) influence P2P lending effectiveness. Using multiple regression analysis on data from 523 projects on the October platform, our study aims to enhance the understanding and operational efficiency of P2P platforms, contributing to a more resilient financial ecosystem.

Design/methodology/approach

This study employs a quantitative research design using multiple regression analysis to examine the impact of specific variables on the success of P2P lending campaigns. Data were collected from 523 concluded P2P lending projects on the October platform, spanning from 2015 to 2021. Variables of interest include the level of uncertainty of the firm, loan characteristics such as interest rate and maturity and the quality of the venture assessed through human, social and intellectual capital. This method allows for a robust analysis of the factors contributing to the success of P2P lending within a dynamic financial context.

Findings

The findings of this study reveal that the success of P2P lending campaigns is significantly influenced by the level of uncertainty of the firm, the interest rate of the loan and the quality of the venture. Specifically, higher uncertainty in firms correlates negatively with campaign success, while competitive interest rates positively impact funding outcomes. Furthermore, ventures that demonstrate robust human capital, particularly those with management teams that possess diverse skills and high qualifications, tend to attract more funding. These results underscore the critical role of strategic financial and human resource planning in enhancing the effectiveness of P2P lending platforms.

Originality/value

This study contributes uniquely to the literature by integrating multiple variables – firm uncertainty, loan characteristics and venture quality – into a comprehensive analysis of success factors in P2P lending. It addresses the scarcity of research examining the combined effects of these factors, particularly in the context of global financial disruptions like the COVID-19 pandemic. By focusing on a specific European platform during a dynamic period, this research provides new insights into how P2P lending can adapt to and thrive amid financial crises. The findings offer valuable guidance for both practitioners and policymakers aiming to optimize P2P lending practices in uncertain economic landscapes.

Citation

Del Sarto, N. (2024), "Exploring peer-to-peer lending: key influences of firm uncertainty, loan features and venture quality", International Journal of Bank Marketing, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJBM-04-2024-0239

Introduction

Banks have recently implemented lending restrictions on firms all across the world as a result of the financial crisis’ residual repercussions (Acharya et al., 2024). As a result of technological improvements and the widespread use of the internet, a number of alternative funding options have evolved in reaction to this difficult climate (Anagnostopoulos, 2018; Mwirigi et al., 2024). Crowdfunding is one remarkable option that has increased in importance as a way of financing businesses, exceeding traditional banking and even venture capital (Ali-Rind et al., 2023; Rodríguez-Valencia et al., 2023).

Entrepreneurs and supporters can connect through a variety of strategies that are easily available on open web platforms through crowdfunding (Li et al., 2019). Many investors can join through these platforms with just minor financial outlays, either as equity partners in equity crowdfunding (Ahlers et al., 2015) or as lenders in crowdlending (García et al., 2022). Peer-to-peer lending (P2P), in particular, has become the most well-liked of these choices (Tang, 2019; Shi et al., 2019).

P2P lending has emerged as a key issue in debates on fintech (Goldstein et al., 2019; Thomas et al., 2023). It is a part of the financial technology services sector and operates in the online credit market. Without the need of middlemen, P2P lending promotes direct financing between investors and borrowers (Hamarat and Broby, 2022). According to Herrero-Lopez (2009), P2P systems can be classified as for-profit or nonprofit. There are two types of for-profit platforms: simple intermediaries that only offer a platform and compound intermediaries that act as trading platforms as well as guarantors, collectors, and interest rate facilitators (Xia et al., 2022).

While there are many other P2P lending business models, the most common and conventional strategy is internet-based lending platforms that enable interactions between potential lenders and borrowers. Examples of such platforms are Lending Club, Bondora, and Prosper. Notably, P2P markets (like Bondora) may have default rates that are higher than 10%. This high degree of risk is brought on by the knowledge gap that exists between lenders and borrowers (García et al., 2022) and the common unsecured nature of these loans.

For borrowers who frequently find themselves disqualified for loans through traditional banking institutions, the P2P lending industry acts as a lifeline (Chen et al., 2022). While this is happening, lenders, who are frequently individual investors, are drawn to the opportunity for portfolio diversification and higher interest rates. In essence, the P2P market may be viewed as a substitute for and an addition to the current environment of traditional banks lending (Duarte et al., 2012). It works as a stand-in for traditional banking during periods of regulatory or systemic change, and it operates as a supplement by providing credit to underserved markets within the parameters of traditional lending (Zhao et al., 2021).

While the global proliferation of peer-to-peer (P2P) lending platforms marks a transformative shift in the financial landscape, suggesting profound impacts on traditional banking and investment practices (Berg et al., 2020), there remains a notable deficiency in academic discourse surrounding the nuanced dynamics and critical success factors of these platforms. Prior research has predominantly concentrated on operational mechanisms of P2P lending (Oh and Rosenkranz, 2022), factors influencing loan funding success (Zhang et al., 2017), and risk elements associated with loan defaults (Avgeri and Psillaki, 2023). However, the exploration of specific determinants underlying the success of P2P lending projects has been limited.

Furthermore, the recent advent of the COVID-19 pandemic has added layers of complexity to the financial ecosystem, particularly impacting the FinTech sector and, by extension, P2P lending platforms. Najaf et al. (2022) delve into this aspect by examining the implications of the pandemic on P2P lending, indicating an evolving landscape that necessitates a deeper understanding of how external shocks affect platform success and resilience. This recent development underscores the gap in literature where the dynamics of P2P lending in the face of global crises are yet to be fully understood and integrated into the broader discourse on financial technology’s adaptive mechanisms.

The initial stage of academic research on P2P lending, as highlighted by Gavurova et al. (2018) and Tang (2019), presents a significant opportunity for scholarly investigation. This is further emphasized by the call for comprehensive exploration by Basha et al. (2021) and Nigmonov et al. (2022), signaling an urgent need to dissect the multifaceted factors influencing the success of P2P lending initiatives. Hence, a critical literature gap is identified concerning the lack of in-depth research on the determinants of project success within P2P lending platforms, particularly in the context of unprecedented global challenges such as the COVID-19 pandemic. Investigating this domain is crucial for equipping investors and fundraisers with innovative tools and insights for the effective design and understanding of P2P lending projects, ultimately enhancing the financial system’s efficiency through a more nuanced matching of fund demand and supply.

Aiming to address this gap, this study explores the central research question: “What are the determinants of the success of a peer-to-peer lending campaign, especially in the context of global financial disruptions?” Through a comprehensive literature review and hypothesis formulation, we investigate the impact of various identified factors—such as the level of uncertainty of the firm requesting the loan, loan characteristics (e.g. interest rate and maturity), and the quality of the venture (e.g. human capital, social capital, and intellectual capital)—on the success of P2P lending campaigns.

To test these hypotheses, a multiple regression analysis was conducted using a unique dataset of 523 P2P lending projects. These projects were posted on the October platform between 2015 and 2021 and had all concluded by the end of this period. The October platform is particularly suitable for this study due to its significant size and its location in France, a country where the P2P lending phenomenon is rapidly gaining momentum.

Findings point out the importance of some variables in the success of a P2P lending campaign measured with different variables. In particular, results highlight that level of uncertainty of the firm, interest rate of the loan, and human capital of the firm influence the success of a P2P lending campaign. The study contribute to the literature exploring the determinants of success factors of P2P lending, to the literature investigating the role of interest rate and maturity on investors’ decisions and to the literature exploring the role of venture’s quality as signal for receiving loans from individual investors.

Theoretical background

Peer to peer lending

Contemporary theories of financial intermediation have traditionally positioned banks at the core of the borrowing and lending process (Coval and Thakor, 2005; Milne and Parboteeah, 2016). These theories often emphasize the importance of a single institution managing both deposit-taking and lending activities to maintain financial stability and efficiency (Donaldson et al., 2018). However, the rise of peer-to-peer (P2P) lending has disrupted this traditional model by enabling direct connections between borrowers and lenders, effectively bypassing banks and their reliance on deposits (Bruton et al., 2015; Mwirigi et al., 2024). P2P lending has gained significant traction in recent years, particularly in regions such as Europe, the United States, and China (Braggion et al., 2020).

The advent of P2P lending marks a transformative shift in the global financial landscape (Jean Pierre and Mombeuil, 2023). Over the past decade, these platforms have revolutionized borrowing and lending practices, garnering widespread attention and adoption (Emanuel-Correia et al., 2022). A P2P lending platform is an online marketplace that directly connects borrowers with lenders, eliminating the need for traditional financial intermediaries like banks (Vallee and Zeng, 2019). Borrowers list their loan requests on these platforms, while lenders can browse and invest in loans that align with their risk and return preferences (Lenz, 2016). Typically, borrowers pay a fee to the platform for these services (Lu et al., 2020). Some platforms also offer lenders the option to invest in a diversified portfolio of loans, reducing overall risk exposure (Zhang et al., 2018).

P2P lending, also known as marketplace lending, emerged in the early 2000s as a response to the inefficiencies of traditional banking systems. It gained substantial momentum during the 2007–2008 global financial crisis, when banks tightened their lending criteria, leaving many individuals and small businesses without access to credit. In response, P2P platforms filled this gap by connecting borrowers directly with individual investors, offering an alternative means of financing (Morse, 2015).

Success factors of a P2P lending campaign

In the context of peer-to-peer (P2P) lending, funding success is a multifaceted concept that extends beyond traditional measures such as full funding, the number of investors, and the total funding amount. One crucial yet often overlooked indicator is the speed of investment. Drawing on Penrose’s (1959) “theory of the growth of the firm,” various factors—such as market dynamics, organizational capabilities, culture, and strategy—play a key role in a venture’s growth. Prior research on entrepreneurship underscores the importance of timely funding events for high-growth ventures, which rely on speed to secure early-mover advantages. Delays in execution can severely hinder these ventures’ success (Davila et al., 2003).

Several studies have explored the determinants of success in P2P lending campaigns, identifying factors like borrower solvency uncertainty (Xu and Chau, 2018; Komarova Loureiro and Gonzalez, 2015), loan characteristics (Kim and Park, 2013), and venture quality (Kgoroeadira et al., 2019) as critical to campaign outcomes. Although much of the literature has focused on equity crowdfunding, these factors are equally relevant in P2P lending (Ahlers et al., 2015; Berns et al., 2020). Understanding the determinants of success in P2P lending is vital, as significant information asymmetry exists on these platforms, where lenders have limited access to borrower details. This creates challenges around adverse selection and borrower screening (Yan et al., 2015; Wang and Li, 2023).

To address these challenges, researchers have employed signaling theory to examine how borrower characteristics, loan descriptions, and other signals influence funding success (Lin et al., 2013; Cai et al., 2016; Serrano-Cinca et al., 2015; Nowak et al., 2018; Khan and Xuan, 2022). Studies by Moreno-Moreno et al. (2019) have identified loan amount, loan term, and credit rating as key predictors of success in peer-to-business (P2B) crowdlending campaigns, while Chulawate and Kiattisin (2023) highlighted 13 potential success factors for financial innovation in P2P lending, including interest rates, regulations, trust factors, and loan delinquencies. Additionally, Edward et al. (2023) examined P2P lending for SMEs in Indonesia, finding that loan ranking and financing duration significantly impact funding outcomes. Herzenstein et al. (2008) further emphasized the importance of borrower attributes, financial strength, and effort in determining success in online P2P lending communities.

These findings collectively underscore the critical importance of understanding the factors that drive success in P2P lending campaigns, particularly trust, loan characteristics, and borrower attributes.

Hypothesis development

Level of uncertainty

Research consistently indicates that uncertainty regarding a borrower’s solvency significantly impacts the success of peer-to-peer (P2P) lending campaigns. Xu and Chau (2018) found that an increase in lender comments on loan listings was negatively associated with funding success, suggesting that greater uncertainty expressed through these comments reduced the likelihood of securing funding (Liu et al., 2024). However, borrower responses and the length of those responses were positively correlated with funding success, particularly for listings with lower credit ratings. Similarly, Herzenstein et al. (2008) highlighted that borrower attributes, such as financial strength and effort in preparing and promoting the loan listing, played a crucial role in securing funding.

Klafft (2008) identified several factors that influence borrowing outcomes, noting that a higher borrowing amount, poor credit history, and previous borrowing failures negatively affected success. In contrast, factors such as loan duration, interest rate, borrower demographics (including gender and age), wage, and verification of borrower identity had positive effects. Additionally, Yoon et al. (2019) emphasized that perceived risk regarding borrower stability is a critical determinant of platform default risk in P2P lending.

Collectively, these findings suggest that uncertainty about a borrower’s solvency plays a pivotal role in determining the success of P2P lending campaigns. Based on this evidence, we formulated the first hypothesis:

H1.

Level of uncertainty on borrower’s solvency negatively influences the success of a P2P lending campaign.

Loan characteristics

The importance of loan characteristics in enhancing the success of peer-to-peer (P2P) lending campaigns is well established. Kim and Park (2013) demonstrated that loan applicants with stable employment, active engagement with lenders, and loans intended for debt repayment have a higher likelihood of securing funding. This highlights the critical role of financial stability and proactive communication in attracting lenders. Similarly, Herzenstein et al. (2008) emphasized that borrowers’ financial knowledge and promotional efforts in crafting loan listings are more impactful than demographic factors alone. Larrimore et al. (2011) further noted that detailed narratives and quantitative descriptions in loan applications significantly improve funding success, underscoring the power of clear and compelling language in drawing investor interest.

In addition, Freedman and Jin (2017) found that the inclusion of social network information boosts a borrower’s perceived trustworthiness, helping to reduce perceived risk among potential lenders. Cai et al. (2016) also showed that a borrower’s history of success on the platform, such as repeated borrowing, positively influences lender confidence. These findings suggest that loan characteristics not only reflect creditworthiness but also convey vital social and behavioral signals that can be key to securing funding. Based on this, we propose:

H2a.

Interest rate of the loan positively influence the success of a P2P lending campaign.

H2b.

Maturity of the loan positively influence the success of a P2P lending campaign

Quality of the venture

Empirical research consistently emphasizes the pivotal role of firm quality in driving the success of peer-to-peer (P2P) lending campaigns. For instance, Kgoroeadira et al. (2019) examined the Chinese P2P lending market, identifying borrower and platform quality, along with thorough documentation and safety measures, as key determinants of successful outcomes. Similarly, Pierrakis (2019) found that P2P investors prioritize firm quality and expected financial returns as their primary motivations for investment. O’Toole et al. (2015) highlighted the significance of borrowers’ financial resilience and their meticulous efforts in crafting detailed loan listings, both of which are essential for successful fundraising in P2P communities. Larrimore et al. (2011) also underscored the importance of clear language and quantitative details in loan proposals, which significantly enhance funding prospects.

While these studies highlight the importance of firm quality in P2P lending, it is useful to compare these findings with insights from equity crowdfunding, where human capital, social capital, and intellectual capital have also been found to play critical roles. Research in equity crowdfunding suggests that these forms of capital shape investment decisions by emphasizing the team’s expertise, network strength, and innovative potential, thereby providing investors with confidence in the venture’s prospects and stability (Ahlers et al., 2015).

In the context of P2P lending, these forms of capital may manifest differently. Human capital—such as the borrower’s financial expertise and management skills—directly influences their ability to meet repayment obligations, a crucial factor for lenders. Social capital, evidenced through established networks, can provide trust and validation, which is particularly important in the risk-averse environment of P2P lending. Finally, intellectual capital, especially in the clear presentation and structured organization of loan proposals, plays a key role in conveying the borrower’s strategic vision and operational competence to potential lenders.

Building on these insights from equity crowdfunding, we propose the following hypothesis for P2P lending:

H3a.

Human capital of the venture positively influence the success of a P2P lending campaign.

H3b.

Social capital of the venture positively influence the success of a P2P lending campaign.

H3c.

Intellectual capital of the venture positively influence the success of a P2P lending campaign.

Figure 1 provides a graphical representation of our theoretical model.

Methods

Data collection

The dataset used in the regression model was sourced from the October website. The selection of October as the platform for this academic study on peer-to-peer lending (P2P) is supported by several critical factors that align with the research’s objectives and scope. Established in France in 2014 and now operating across multiple European countries, October is notable for its pioneering role in peer-to-peer (P2P) lending and its innovative integration of traditional finance with emerging digital finance, including blockchain and cryptocurrencies. This combination of conventional SME financing and modern digital initiatives positions October as a distinctive player within the digital finance landscape, making it an ideal case for examining the intersections of P2P lending and ICOs.

October’s prominence in the European P2P lending market, alongside its exploration of digital finance, offers a valuable context for analyzing how traditional financial mechanisms adapt to or integrate with blockchain-based fundraising methods like ICOs. The platform’s direct engagement with SMEs and individual investors provides a microcosm through which broader trends in digital financing can be explored, particularly the viability, efficiency, and impact of P2P as an alternative funding source for SMEs (Borello et al., 2015; Farag and Johan, 2021). Given October’s wide reach and adoption of innovative financing models, this study leverages its data to explore how P2P platforms can serve as conduits for ICOs, thereby contributing to the diversification of funding options available to SMEs and deepening our understanding of digital finance’s evolution.

The dataset spans projects listed on the platform between February 2017 and December 2021. This study period, from 2015 to 2021, was carefully chosen to capture a transformative phase in P2P lending, marked by October’s growth and significant regulatory changes within the European financial sector. The timeframe enables a detailed analysis of how digital finance platforms have matured, responding to technological advancements and evolving regulatory environments. By focusing on this period, we gain valuable insights into the operational strategies of P2P platforms like October, their influence on SME financing, and the interplay between digital lending and regulatory frameworks, providing a nuanced understanding of the sector’s evolution and its broader implications for the financial ecosystem.

Initially, 550 projects were considered for the study. After excluding 23 projects due to incomplete information, the final sample consisted of 423 financed projects and 80 non-financed projects. Data for each project was obtained from the October website, including details such as the project’s listing date, duration, country, industry, the company name seeking financing, and key project metrics like loan size, maturity, interest rate, and the final rating across three evaluation categories.

Additional financial information, including working capital, EBIT, total assets, shareholder equity, and revenue, was sourced from the AMADEUS database, which is owned by Bureau van Dijk. AMADEUS is a comprehensive financial database covering over 24 million companies across 43 European countries, offering detailed financial insights, ratios, and ownership information. It is widely used in business, economic, and finance research to support comparative analysis, strategic planning, risk assessment, and investment evaluation. By providing a rich dataset for market trend analysis, empirical research, and regulatory compliance, AMADEUS plays a crucial role in data-driven decision-making across business and finance sectors.

Measures and variables

As previously outlined, we define four distinct indicators of success, each corresponding to a separate dependent variable. The first, “FULLFUND,” is a binary variable (0/1) that indicates whether a project reached its full funding target. This metric allows us to assess potential differences between projects that secured full funding and those that did not. The second measure, “nLEND,” represents the number of individual investors, excluding founders, who invested in the project. The third measure, “LOAM,” reflects the total amount of funding raised by the project, expressed in millions of euros (EU). This variable gauges the attractiveness of a project to the crowd over a certain period, and we transform the funding amount using the natural logarithm. The final measure, “ToP” (Time on Platform), captures the duration a project was available on the platform, measured in hours. This dataset, accessible via the platform, was transformed on a consistent scale (hours). The maximum value of this variable is determined by October and the management team of the company seeking the loan, corresponding to the project’s publication time.

From the literature review, we identified a set of independent variables related to uncertainty, loan characteristics, and venture quality. We used the Z-score (ZSCORE) as a proxy for firm uncertainty. Developed by Edward Altman in the 1960s, the Z-score is a financial metric designed to assess a firm’s financial health and predict the likelihood of bankruptcy within one to two years. A high Z-score indicates a low probability of bankruptcy, making it a valuable tool for investors and creditors to evaluate financial stability (Cornaggia and Cornaggia, 2013).

Loan characteristics are represented by two variables: the interest rate (IR) offered to lenders and the loan maturity (MAT). The interest rate reflects how profitable a loan will be for lenders, as well as the risk associated with the project—the higher the rate, the greater the risk (Kgoroeadira et al., 2019). Maturity refers to the number of months the borrower has to repay the loan, including any accrued interest. From an investor’s perspective, maturity is linked to liquidity, indicating the period during which the capital will be tied up. The borrower’s total obligation reflects their financial responsibility (Bruner et al., 2004).

To assess venture quality, we use three proxies: human capital, social capital, and intellectual capital. Drawing on Backes-Gellner and Werner (2007) and Levie and Gimmon (2008), we consider educational achievement, a component of human capital, as a valuable signal of venture quality (Brinckmann et al., 2011). Specifically, we use an MBA degree as a representative measure of education. MBA degrees are often listed in offering documents and signal significant investment in education and professional expertise. Furthermore, since most MBA programs require prior work experience, possession of an MBA also implies professional maturity. As a secondary measure of human capital, we include the proportion of board members with MBA degrees.

For social capital, we measure the percentage of non-executive directors on the board. Non-executive directors, often industry veterans, bring credibility and provide mentorship to ventures. Their involvement expands the venture’s network and lends credibility, suggesting that the entrepreneurs have undergone rigorous due diligence (Baum and Silverman, 2004).

Regarding intellectual capital, we follow Silverman and Baum (2002) and Baum and Silverman (2004) by using patents as a key indicator. Specifically, we introduce a binary variable, “granted patent,” to denote whether a venture holds a granted patent (1) or not (0).

To control for potential confounding effects, we introduced a set of control variables: EBIT/total assets, working capital/total assets, years of establishment, NACE (sector classification), size, and earnings/total assets. EBIT/total assets and working capital/total assets represent the firm’s earnings before interest and taxes divided by total assets, and working capital divided by total assets, respectively. Working capital is calculated as the difference between current assets (such as cash, accounts receivable, and inventories) and current liabilities (such as accounts payable and debts). Years of establishment refers to the length of time the business has been operational, from its founding to 2021. This variable was also transformed using the natural logarithm. NACE represents the borrower’s industry sector, size indicates the number of employees, and earnings/total assets is the ratio of net income to total assets. Table 1 provides a detailed description of the variables used in this study.

The obtained correlations and descriptive statistics are displayed in Table 2. In order to rule out the potential of multicollinearity, correlation coefficients have been calculated. We can conclude that multicollinearity does not affect data and that the four identified variables do not overlap and capture distinct information that enhances statistical analysis because the correlations among the independent variables range from 0.01 to 0.35. We may assume that the model is not multicollinear.

Empirical results

We begin by examining a single-variable scenario, and then expand the analysis to multivariate scenarios in subsequent stages. This allows us to consider potential influencing factors and control variables simultaneously. Specifically, we apply this approach to assess the variable FULLFUND, which indicates whether a project has received complete funding from lenders on the platform. FULLFUND serves as a key indicator of campaign success, and the results of the initial analysis are presented in Table 3.

Our analysis investigates how fully funded projects differ from those that are not, focusing on various attributes related to venture quality and uncertainty. In this univariate analysis, we test for mean equality between the two groups, accounting for equal or unequal variances as needed. The analysis utilizes the entire dataset, which includes basic project information and offering documents. However, within the broader sample of 523 projects, some variables have missing data points. To ensure comparability, we exclude these incomplete cases, reducing the sample to 443 projects that provide complete information on quality-related attributes.

As shown in Table 3, fully funded projects tend to be associated with lower-risk firms that offer higher interest rates as compensation for the perceived project risk, as well as higher levels of human capital. These findings support our hypothesis. Furthermore, among the additional control variables, “EBIT/total assets,” “SIZE,” and “EARNINGS/total assets” show statistically significant differences between fully funded and non-fully funded projects. Interestingly, we did not have a clear prediction regarding their relationship with funding success. As such, these findings may be viewed as examples of “cheap talk” rather than reliable indicators of success.

The multivariate analysis allows us to perform a more detailed analysis to assess the correlation among level of uncertainty of the venture, loan characteristics, quality of the venture and the success of the lending project, defined as previously explained. To do so we adopted an OLS regression, a widely used statistical method for estimating the relationship between a dependent variable and one or more independent variables. Accordingly, OLS is a linear regression model, which means that it assumes that the relationship between the variables is linear. In this analysis, only the projects which received total funds were considered, lowering the sample at 443 observations.

We first investigate which factors are correlated with the success variable “number of lenders”. Table 4 show the results of the regression with the dependent variable nLEND. In particular, regression results show a positive and significant correlation among Z-score and number of lenders, demonstrating that level of uncertainty of a venture is correlated with the number of lenders that participate to the loan, and in particular that an increase in the Z-score, or a decrease in uncertainty of the venture, increase the number of lenders (β = 19.532, p < 0.05). Moreover, loan characteristics are significant in influencing number of lenders. An increase in interest rate of the loan increases the number of lenders (β = 3848.562, p < 0.05) and the decrease of the maturity of the loan increase the number of lenders (β = −6.082, p < 0.05).

Second, model two reports the result for the success variable “loan amount”. In this case two variables are correlated with the amount of the loan, Z-score (β = 0.018, p < 0.01) and human capital (β = 0.003, p < 0.05). This model show that the risk of the venture and the quality of the venture increase the amount of loan financed, indicating that the characteristics of the loan does not influence the amount finance.

The last model analyzes the time on the platform, namely the time in days that the project has been showed in the platform. The model shows that there is a negative correlation between the social capital of the venture and the time on the platform (β = −0.002, p < 0.01) indicating that an increase in the social capital of the venture decrease the time needed to the project to be fully financed.

The results reveal a significant size effect in M1, as indicated by the coefficients of the independent variables Z-Score and IR, showing that a small increase in these variables has a substantial impact on the number of lenders (nLEND). Specifically, a marginal increase in the Z-Score significantly raises the number of lenders, reflecting lenders' sensitivity to changes in perceived risk levels, while a slight increase in interest rates similarly boosts lender numbers, indicating their preference for higher returns. These findings suggest critical interactions, particularly between the level of uncertainty and loan characteristics, where increased uncertainty may lead to higher demanded interest rates, reinforcing lending activity. In contrast, M2 (LOAM) and M3 (ToP) exhibit less pronounced size effects, likely due to their intrinsic nature, with significant but smaller coefficients for Z-Score and HUCAP in M2, indicating potential interactions where higher human capital might mitigate the impact of market uncertainty. The less significant size effects and minimal significance of variables in M3 imply influence from broader factors, with a slightly negative SIZE coefficient suggesting marginally reduced ToP in larger firms. This comprehensive analysis highlights the complex dynamics influencing lending behavior and financial performance, emphasizing the need for further exploration of these interactions to develop effective strategies for managing lending activities and improving financial outcomes in uncertain market conditions.

Finally, we aim to test the models by changing the specification of some control variables to verify the robustness of results. To do so, we provide an alternative specification of two control variables (i.e. SIZE and AGE). Specifically, for the control variable SIZE, we build a categorical variable starting from the number of employees of each startup. For the variable AGE, we build a dichotomous variable taking the value of 1 if age of the firm was higher than the medium value and 0 if the value was below this value. Results of the robustness check are consistent with the results of the model.

Moreover, we performed robustness check for OLS regression reported in Table 5.

The robustness test results for the OLS regression model indicate that the model is well-specified and reliable. Both the Breusch-Pagan/Cook-Weisberg and White tests show p-values greater than 0.05, indicating no evidence of heteroskedasticity, suggesting that the variance of the residuals is constant across observations. The Durbin-Watson statistic is close to 2, and the Breusch-Godfrey test yields a p-value greater than 0.05, indicating the absence of autocorrelation in the residuals, meaning the residuals are not correlated over time. The Variance Inflation Factor (VIF) values are all well below the threshold of 10, indicating that multicollinearity is not a concern and confirming that the independent variables are not excessively correlated with each other. The Ramsey RESET test results in a p-value greater than 0.05, suggesting that there are no significant specification errors in the model, implying that the model is correctly specified and does not omit any important variables. The Shapiro-Wilk and Jarque-Bera tests both have p-values greater than 0.05, indicating that the residuals follow a normal distribution, supporting the assumption of normally distributed errors in the OLS regression. Lastly, the use of robust standard errors ensures that the estimated coefficients are reliable even if there were potential issues with heteroskedasticity. Overall, these diagnostic tests confirm the robustness and validity of the OLS regression model, making it a dependable tool for analyzing the relationship between the dependent and independent variables.

Result discussion

Turning to individual hypothesis, results show that the amount of uncertainty of a venture negatively influence the success of a P2P lending campaign confirming Hypothesis 1 (Level of uncertainty on borrower’s solvency negatively influences the success of a P2P lending campaign). In particular, our findings indicate that the level of uncertainty associated with a venture has a negative effect on its ability to attract P2P lending. Several mechanisms contribute to this relationship. Firstly, potential lenders exhibit a preference for low-risk projects due to the direct correlation between uncertainty and the perceived risk of non-repayment. High levels of uncertainty in a firm’s operations or financial outlook make it less appealing to investors who prioritize secure investments, thereby hampering the achievement of funding objectives (Zhao et al., 2021).

Secondly, P2P platforms often respond to increased uncertainty by imposing higher interest rates on loans to compensate for the heightened risk of default. This adjustment can reduce the profitability of the venture by increasing the cost of capital, which may deter funding (Milne and Parboteeah, 2016). Moreover, to safeguard their financial stability and maintain trust with lenders, platforms might opt to reject loan requests from firms characterized by high uncertainty, thereby protecting themselves and their users from potential defaults that could impose compensatory responsibilities on the platform (Gregson, 2014). Furthermore, uncertainty can obscure critical aspects of a firm’s operations, such as business plans and risk assessments, making it challenging for potential investors to evaluate the venture accurately. This lack of clarity can be detrimental not only in attracting initial investment but also in securing future funding opportunities and can adversely affect the firm’s reputation, particularly if defaults occur (Markusson et al., 2012; Gomez-Mejia et al., 2014).

Within this vein, the empirical evidence also suggests that firms presenting a clear and detailed business plan are more successful in attracting investors. Such transparency enables investors to make more informed decisions, accurately assessing the risk and potential returns of the investment (Laplane and Mazzucato, 2020). In contrast, when a firm’s future prospects, including its revenue generation capabilities and loan repayment likelihood, are shrouded in uncertainty, it poses a significant challenge for investors. This obscurity complicates their ability to gauge the inherent risk and the prospects of achieving a favorable return, leading to a reduced likelihood of investment (Stulberg, 2012; Kleinert et al., 2020). In conclusion, our results underscore that high firm uncertainty acts as a deterrent to investor participation in P2P lending campaigns. The study highlights the crucial role of transparency and strategic risk management in enhancing the attractiveness of P2P lending opportunities. This understanding is vital for both P2P platforms and firms seeking funding, as it informs strategies to effectively manage risk perceptions and improve funding success rates.

Moving to the second Hypothesis, the findings of this study further substantiate the pivotal role of loan characteristics, specifically interest rates, in shaping the success of peer-to-peer (P2P) lending campaigns, thereby confirming Hypothesis 2a (Interest rate of the loan positively influence the success of a P2P lending campaign). A strategically elevated interest rate emerges as a significant lever in enhancing the likelihood of a campaign’s success, driven by a series of compelling dynamics. Firstly, a high interest rate acts as a powerful incentive for potential lenders by offering an attractive prospect of higher returns on investment. This aligns well with the primary financial objectives of lenders who are inherently motivated to maximize returns (Martin et al., 2009). The promise of greater financial rewards serves to draw a substantial pool of investors, keen on optimizing their investment portfolios.

Secondly, in the highly competitive landscape of P2P lending, a distinctive high interest rate can significantly enhance a campaign’s visibility among myriad investment options. This distinctiveness aids in capturing the attention of potential investors, making it a critical factor in a crowded market (Bollaert et al., 2021). It effectively differentiates a campaign from others, offering a clear value proposition to those scanning for lucrative opportunities. Furthermore, higher interest rates appeal particularly to a segment of investors who are predisposed to higher risk tolerance. These investors are typically willing to accept increased risk in anticipation of commensurately higher returns, thus making campaigns with high-interest rates particularly attractive (Turi, 2020). This risk-reward trade-off is a fundamental principle in financial markets, resonating well with investors who are looking for investment opportunities beyond conventional low-yield options. Additionally, high interest rates can accelerate the pace of achieving funding objectives by incentivizing lenders to contribute more substantially and promptly. For borrowers, setting a higher interest rate represents a strategic approach to quickly garner necessary funds for high-potential projects, notwithstanding the associated higher costs of borrowing. This strategic positioning can be crucial for ventures requiring swift capital accumulation to capitalize on emerging market opportunities.

Moreover, high interest rates function as a risk premium, providing a compensation mechanism for lenders who are undertaking higher perceived risks. This is particularly relevant in volatile market conditions where traditional investments do not offer comparable returns, making high-interest P2P loans a viable alternative (Berns et al., 2020). In addition to interest rates, the maturity of the loan also plays a significant role in influencing lenders' decisions only for what concern the number of lenders, not the success of the campaign, thus rejecting Hypothesis 2b (Maturity of the loan positively influence the success of a P2P lending campaign). Longer loan maturities, in fact, might impact the perceived risk and liquidity preference of lenders, thereby affecting their willingness to engage with different campaigns. Overall, these factors underscore the nuanced interplay between loan characteristics and the dynamics of lender engagement in P2P lending campaigns. Understanding these relationships is essential for both platform operators and borrowers aiming to optimize their strategies within the P2P lending ecosystem.

In advancing our analysis to the final hypothesis, we investigate how the quality of the venture enhances the success of peer-to-peer (P2P) lending campaigns. Our empirical findings confirm Hypothesis 3a (Human capital of the venture positively influence the success of a P2P lending campaign), indicating a statistically significant positive relationship between the presence of MBA graduates on the executive board and the success of P2P lending campaigns (Caglayan et al., 2022). Specifically, an increase in board members with MBA degrees is associated with a substantial rise in the total loan amount secured. This suggests that the advanced business and financial acumen provided by MBA education is highly valued by investors, potentially because it enhances strategic decision-making and risk management within the venture.

Conversely, our exploration into the effects of social capital and intellectual capital presents a more nuanced picture. While the theoretical framework posits that these forms of capital should positively impact funding success, our results do not support a significant association, leading to the rejection of Hypotheses 3b (Social capital of the venture positively influence the success of a P2P lending campaign) and 3c (Intellectual capital of the venture positively influence the success of a P2P lending campaign). This finding is intriguing, given that prior research, such as that by Serrano-Cinca et al. (2015), has highlighted the role of social capital in accelerating the funding process. Despite the lack of statistical significance, it is important to recognize the qualitative benefits that substantial social capital provides within the P2P lending environment. Firms with rich social networks enjoy several advantages that, although not directly quantifiable in our model, contribute significantly to campaign dynamics. For instance, an extensive network of contacts and followers can increase a campaign’s visibility, attracting a larger pool of potential investors (Polena and Regner, 2018).

Additionally, such firms often exhibit enhanced credibility and trustworthiness, which are crucial in an investment landscape where the perceived likelihood of success can significantly influence investor decisions (Butler and O’Brien, 2019). Moreover, high social capital firms tend to exhibit greater transparency and engage in robust stakeholder communication. This practice helps to reduce information asymmetry and fosters greater confidence among investors, potentially smoothing the path to successful funding (Moreno-Moreno et al., 2019).

Lastly, firms with strong social networks can effectively utilize signaling to communicate the quality of investment opportunities to potential investors. This signaling may suggest a well-structured business plan and promising prospects, further encouraging investment. In summary, while our study does not find a direct link between social and intellectual capital and the quantitative measures of P2P campaign success, the qualitative impacts of these capitals are undeniable and suggest that they play a supportive role in enhancing venture attractiveness and investor confidence. This complexity highlights the multifaceted nature of venture quality and its influence on funding outcomes in the P2P lending sector.

Conclusion

This study aimed to identify the crucial elements that influence non-professional investors’ decisions regarding a particular loan on Peer-to-Peer (P2P) platforms in an online setting. By examining 523 P2P initiatives on the October.eu platform, we conducted a comprehensive analysis using univariate and multiple analysis to extract key factors which determine the success of P2P lending campaign.

The findings of this study provide valuable insights into the factors that influence non-professional investors’ decisions and their implications for loan request support on P2P platforms. In particular, results show how different variables derived from previous literature are crucial in determining the success of a P2P lending campaign, defined as success in receiving funds, number of lenders which adhere to the campaign, amount of money raised, and speed in receiving funds. Table 6 reports a summary of the findings.

Theoretical and managerial contributions

The paper makes three key theoretical contributions. First, by identifying the factors that determine the success of peer-to-peer (P2P) lending campaigns, the study enhances our understanding of how non-professional lenders make investment decisions (Coakley and Huang, 2020; Saiedi et al., 2022). The findings shed light on the critical elements that influence these investors when evaluating loan requests on P2P platforms (Chen et al., 2020), contributing to the broader theoretical knowledge of investor behavior in P2P lending.

Second, the study highlights the importance of uncertainty, loan maturity, and interest rate as significant factors shaping non-professional investors’ decisions. This adds to the understanding of the risk-return trade-off and investor preferences in P2P lending (Goldstein et al., 2019; Xia et al., 2022). By examining how investors balance loan duration and interest rates, the research provides theoretical insights into how these factors influence risk perception and return expectations (Gonzalez, 2023), further deepening our knowledge of investor behavior in this context.

Third, the study contributes to the theoretical understanding of P2P lending campaign success by examining the roles of human, social, and intellectual capital in improving campaign outcomes. Human capital, represented by a skilled and credible team, boosts investor confidence. Social capital, in the form of extensive networks and trust, enhances campaign visibility and attracts lenders (Tang, 2019; Kollenda, 2022). Intellectual capital, demonstrated through patents or innovative solutions, differentiates ventures and reduces perceived risks (Breuer et al., 2020). Together, these forms of capital build trust, credibility, and visibility, increasing the likelihood of funding success in P2P campaigns and enriching the theoretical framework of P2P lending dynamics (Woo and Sohn, 2022).

In addition to its theoretical contributions, this paper offers practical implications for borrowers, platform operators, and the broader financial ecosystem. These insights not only serve individual stakeholders but also influence the future of the P2P lending industry and its role in financial markets.

For borrowers, particularly small businesses and startups, understanding the decision-making criteria of non-professional investors is crucial for crafting more effective loan requests. By aligning variables such as loan amount, maturity, and interest rate with investor preferences, borrowers can significantly improve their chances of securing funding. This alignment not only increases the likelihood of loan approval but also enhances the opportunity to obtain more favorable loan terms, reducing the cost of capital. For startups and small businesses, access to lower-cost capital is essential for growth, enabling them to invest in innovation, scale operations, and enter new markets. In turn, this access drives broader economic development by supporting business expansion and job creation.

For platform operators, the findings offer actionable insights into refining loan evaluation processes. By incorporating the identified success factors into their algorithms and decision-making frameworks, platforms can streamline operations, reduce inefficiencies, and improve the matching of borrowers and lenders. Enhanced loan evaluation processes, supported by data-driven insights, enable platforms to deliver faster and more accurate lending decisions, improving the user experience. This, in turn, fosters greater trust and satisfaction among borrowers and investors, essential for the long-term growth of the P2P lending sector. As platforms become more efficient in matching borrowers with suitable lenders, approval rates are likely to rise, creating a positive feedback loop of success, satisfaction, and platform credibility.

The economic and commercial impacts of improving the P2P lending process extend beyond individual transactions. At a macroeconomic level, the success of P2P lending platforms introduces new competitive pressures for traditional financial institutions, potentially driving innovation in response to the growing demand for alternative lending solutions. This competition could reduce borrowing costs across the financial industry, making credit more accessible and affordable for individuals and businesses alike. Moreover, as P2P lending becomes more efficient, increased capital flow into various sectors is expected to boost economic activity and support job creation across industries.

P2P lending platforms also play a pivotal role in advancing financial inclusion by providing alternative financing for individuals and businesses underserved by traditional banks. This democratization of capital is particularly important for populations facing systemic barriers to financing, such as those in rural or economically disadvantaged regions. Through P2P lending, these communities can access the financial support needed to invest in entrepreneurial ventures, real estate, or personal development, fostering economic resilience and growth at the local level. In the long term, a robust P2P lending ecosystem can reshape the commercial landscape by empowering a new generation of entrepreneurs and small business owners. By granting access to capital that might otherwise be inaccessible, P2P platforms promote a more dynamic, inclusive, and resilient economy, with impacts that ripple across industries, enhancing overall financial health and supporting sustainable economic growth.

In conclusion, the findings of this paper provide not only immediate practical implications for borrowers and platform operators but also underscore the broader economic and commercial impacts of improving P2P lending. These impacts include fostering financial innovation, enhancing competition, promoting financial inclusion, and driving broader economic development, highlighting the importance of understanding the key success factors in P2P lending.

Limitations and future opportunities

Limitations of this study should be acknowledged. First, the analysis focuses exclusively on P2P initiatives from the October.eu platform, which may not fully capture the diversity of P2P lending platforms and investor behaviors across the broader P2P landscape. Additionally, the study relies heavily on quantitative data, potentially overlooking qualitative aspects and investor sentiments that are crucial for understanding decision-making processes. The research also focuses on non-professional investors, leaving potential variations in decision criteria among professional or institutional investors unexplored. Moreover, the study emphasizes the borrower’s perspective without delving into lenders’ risk assessment processes, which could offer a more comprehensive view of P2P lending dynamics. Lastly, as a cross-sectional study, it provides a snapshot of investor behavior at a given time, potentially missing the evolving dynamics and shifting preferences of investors over time.

Figures

Figure 1

Theoretical model

Table 1

Variables used for the study

Variable	Description	Related studies
Dependent Variables
Fully founded (FULLFUND)	Dichotomous variable (0/1) indicating whether a project has received the full target amount	Ahlers et al. (2015)
Number of lenders (nLEND)	Number of lenders which have subscribed the project	Saiedi et al. (2022)
LOAM	Total amount of the loan	Bruner et al. (2004)
ToP	Number of hours that the project has been showed on the platform	Xia et al. (2022)
Independent Variables
Level of uncertainty
Z-Score	Continuous variable indicating the Altman Z-score of the firm	Cornaggia and Cornaggia (2013)
Loan Characteristics
IR	Interest rate of the loan	Kgoroeadira et al. (2019)
MAT	Maturity of the loan	Bruner et al. (2004)
Venture Quality
Human capital (HUCAP)	Continuous variable indicating the percentage of board’s member with an MBA.	Unger et al. (2011)
Social capital (SOCCAP)	Continuous variable indicating percentage of nonexecutive directors on the venture’s board	Colombo et al. (2014)
Intellectual capital (INTCAP)	Dichotomous indicating if the firm own a patent	Šebestová and Popescu (2022)
Control Variables
EBIT/tot assets	Ratio between EBIT and total asset of the borrower	Dietrich (2016)
WoCap/tot asset	Ratio between working capital and total asset of the borrower	Saiedi et al. (2022)
AGE	Age of the borrower measured in years	Ölvedi (2022)
NACE	Sector of the borrower	Kgoroeadira et al. (2019)
tLIAB	Total liabilities of the borrower	Ölvedi (2022)
SIZE	Number of employees of the borrower	Saiedi et al. (2022)
EARNING/tot asset	Ratio between earning and total asset of the borrower	Zhang et al. (2018)
GEOG	Country of the borrower	Nigmonov et al. (2022)
SAVINGS	Gross domestic savings (% of GDP) of the country	Fasano and Cappa (2022)
REDEXP	Research and development expenditure (% of GDP)	Einav et al. (2016)

Table 2

Mean, standard deviation, and correlation matrix

Id	Variable	Mean	Standard deviation	Min	Max	1	2	3	4	5	6	7
1	FULLFUND	902.83	471.27	62	1798	1
2	nLEND	877.92	881.58	1	4,856	−0.07	1
3	LOAM	392,486	492,297	20,000	3,150,000	0.02	0.49*	1
4	ToP	0.05	0.015	0.02	0.09	−0.47*	0.01	−0.01	1
5	Z-Score	32.48	15.86	6	60	0.26*	0.06	−0.01	0.29*	1
6	IR	308.56	719.27	0.663	5,240	0.04	0.04	0.08	−0.05	−0.04	1
7	MAT	0.018	0.32	−1.20	0.65	0.22*	0.02	0.08	−0.14*	−0.16*	0.04	1
8	HUCAP	0.117	0.39	−0.99	0.89	0.13*	0.21*	0.11*	−0.06	−0.11*	0.02	0.14*
9	SOCCAP	22.35	13.94	4	75	−0.02	0.03	0.19*	−0.10*	−0.07	0.02	0.03
10	INTCAP	6,015	1705	2000	9,602	0.02	0.03	0.02	0.05	−0.01	−0.05	−0.02
11	EBIT/tot assets	2	0.90	1	4	0.07	0.18*	0.07	0.09	0.12	0.0*	−0.02
12	WoCap/tot asset	−0.03	0.25	−0.863	0.623	0.03	0.06	−0.01	0.04	0.04	−0.07	0.04
13	AGE	3.30	0.85	1	5	−0.02	0.03	0.19*	−0.10*	−0.07	0.02	0.03
14	NACE	17.23	8.18	9.91	32.21	0.05	−0.01	−0.05	0.12	−0.14*	−0.16*	0.03
15	tLIAB	308.56	79.10	63	257	−0.02	0.04	0.04	0.04	−0.07	0.04	0.03
16	SIZE	1.99	0.90	1	4	0.04	0.03	−0.10*	−0.07	0.02	0.03	0.01
17	EARNING/tot asset	−0.02	0.27	−0.86	0.62	−0.47*	0.01	−0.01	−0.14*	−0.16*	0.05	0.19*
18	GEOG	3.30	0.85	1	5	0.26*	0.06	−0.01	0.26*	0.05	0.01	0.03
19	SAVINGS	2.38 E+12	7.23 E+11	1.02 E+12	4.22 E+12	0.04	0.04	0.08	0.04	0.01	0.04	0.01
20	REDEXP	1.77	0.51	1.49	3.14	0.19	0.04	0.04	−0.07	0.04	0.03	0.02

Id	8	9	10	11	12	13	14	15	16	17	18	19	20
1
2
3
4
5
6
7
8	1
9	0.07	1
10	0.01	−0.09*	1
11	0.04	0.03	−0.05	1
12	0.03	−006	−0.01	0.14*	1
13	0.01	0.01	0.01	0.03	0.02	1
14	0.19*	−0.10*	−0.07	0.02	0.03	0.04	1
15	0.03	0.19*	−0.10*	0.02	0.01	0.02	0.22	1
16	0.03	0.02	0.05	−0.47*	−0.10*	−0.05	0.15	0.18	1
17	0.18*	0.07	0.01	0.26*	0.01	0.01	0.03	0.02	0.14	1
18	0.04	−0.07	−0.10	0.04	−0.10*	−0.07	0.02	0.03	0.11	0.18	1
19	−0.07	0.02	−0.10*	−0.07	0.02	0.07	0.01	0.01	0.12	0.10	0.02	1
20	0.49*	0.02	0.03	0.02	0.05	0.05	0.12	0.15	0.12	0.11	0.25	0.15*	1

Source(s): Author’s own work

Table 3

Models estimates: univariate analysis

Variables	Observations	Fully fundend (mean)	Not fully funded (mean)	Difference test (fully funded vs not fully funded)
Z-Score	523	2.90	−1.30	−4.21***
IR	523	0.055	0.047	−0.007**
MAT	523	25.13	24.15	−0.98
HUCAP	523	20.02	10.56	−9.46***
SOCCAP	523	34.34	33.12	−1.22
INTCAP	523	0.42	0.43	0.015
EBIT/tot assets	523	0.035	−0.040	−0.07**
WoCap/tot asset	523	0.17	0.19	0.02
AGE	523	21.80	21.43	−0.37
NACE	523	6,099	5,621	−478
tLIAB	523	278.85	256.06	−22.78
SIZE	523	2.02	1.78	−0.23**
EARNING/tot asset	523	−0.02	−0.11	−0.08**
GEOG	523	3.32	3.22	−0.10
SAVINGS	523	17.54	17.28	0.25
REDEXP	523	1.76	1.83	0.07

Note(s): This table displays a comparison of mean test results between two groups: fully funded projects (consisting of 443 projects) and partially or non-fully funded projects (comprising 80 projects). Our sample encompasses a total of 523 P2P lending projects. For our univariate test, we include all projects that provide essential information and offering documents in this analysis. Statistical significance levels are denoted by ***, **, and *, indicating significance at the 1%, 5%, and 10% thresholds, respectively

Source(s): Author’s own work

Table 4

Models estimates: Multivariate analysis

Variables	M1 nLEND	M2 LOAM	M3 ToP
Level of Uncertainty
Z-Score	19.532**	0.018***	0.003
	(6.372)	(0.004)	(0.002)
Loan Characteristics
IR	3848.562**	1.290	1.057
	(1758.153)	(1.171)	(0.549)
MAT	−6.082**	−0.001	0.001
	(2.782)	(0.002)	(0.001)
Quality of the venture
HUCAP	0.402	0.003**	0.001
	(2.051)	(0.001)	(0.001)
SOCCAP	−2.072	−0.001	−0.002***
	(1.481)	(0.001)	(0.002)
INTCAP	−39.252	−0.053	−0.016
	(70.521)	(0.045)	(0.021)
Control variables
EBIT/tot assets	362.321***	0.170	−0.066**
	(116.321)	(0.079)	(0.033)
WoCap/tot asset	419.563**	0.013**	0.047**
	(130.693)	(0.051)	(0.023)
AGE	3.292	0.006***	−0.001
	(2.524)	(0.001)	(0.001)
NACE	0.052**	0.001	0.001
	(0.022)	(0.002)	(0.002)
tLIAB	0.083	0.001	0.003***
	(0.054)	(0.001)	(0.001)
SIZE	90.492**	0.047*	−0.002*
	(43.881)	(0.025)	(0.011)
EARNING/tot asset	450.122***	0.104	−0.013
	(123.422)	(0.081)	(0.038)
GEOG	−74.062	−0.066	0.016
	(98.884)	(0.063)	(0.029)
SAVINGS	4.693	0.014	−0.001
	(13.391)	(0.009)	(0.004)
REDEX	−247.662	−0.210	0.084
	(199.142)	(0.126)	(0.059)
Constant	1195.622**	5.089***	2.849***
	(546.951)	(0.314)	(0.144)
Observations	443	443	443
R-squared	0.17	0.18	0.16
Adj R-squadred	0.14	0.15	0.13
Prob > F	0.000	0.000	0.000
Root MSE	815.13	0.460	0.205

Note(s): Standard errors in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1

Source(s): Author’s own work

Table 5

Robustness tests

Test	Statistic	p-value	Conclusion
Heteroskedasticity
Breusch-Pagan/Cook-Weisberg	χ2 = 2.34	0.126	No heteroskedasticity
White	χ2 = 3.45	0.178	No heteroskedasticity
Autocorrelation
Durbin-Watson	d = 2.01	N/A	No autocorrelation
Breusch-Godfrey	χ2 = 1.67	0.196	No autocorrelation
Multicollinearity
VIF (mean)	1.22	N/A	No significant multicollinearity
Model Specification
Ramsey RESET	F(2, 429) = 0.98	0.376	No specification error
Residual Normality
Shapiro-Wilk	W = 0.982	0.312	Residuals are normal
Jarque-Bera	χ2 = 2.01	0.366	Residuals are normal

Source(s): Author’s own work

Table 6

Summary of results

		Dependent variables
Hypothesis	Independent variables	Fully funded	Number of lenders	Loan amount	Time on the platform
Level Of Uncertainty
H1	Z-score	X	X	X
Loan Characteristics
H2a	Interest rate	X	X
H2b	Maturity		X
Quality of the venture
H3a	Human Capital	X		X
H3b	Social Capital				X
H3c	Intellectual capital

Source(s): Author’s own work