Limitations

Like most studies that use secondary data, our data were not specifically collected for small business study; hence we have some limitations. First, ideally, any analysis of small business funding ought to include those applicants who chose not to apply for credit - even though they could potentially qualify for credit (Hanley and Germa, 2006).

Second, the data do not contain borrower demographic variables and human capital variables. Previous studies (see for example Coleman, 2000; Cressy, 1996; Burke et al, 2000) have found these to be important in influencing credit access and the cost of credit. Our study is therefore not immune to omitted variable bias.

Third, majority of our data (in terms of loan volumes) comes from the period 2008; the nearest crisis and post-crisis period may not be very representative, but it stimulated an innovative ways of interrelation between lenders and borrowers. Hence, as part of future research, it would be interesting to see whether factors driving the likelihood of attaining small business funds in this market vary in more cheerful – post crisis market conditions (Cowling et al, 2012).

Overall, in terms of our finding, we interpret two of our key findings with caution. First, is the interpretation of the variable Bid_count; which we use to measure the level of information asymmetries reduced by the intelligence of the crowd. In this study, we simply use the count (number) of lenders that extend credit as an indicator of some form of due diligence coming through. It is possible that other effects may actually be at play. For, instance, herding behavior may be a possible explanation; where lenders gravitate to loan requests based on other features that we are unaware of and hence did not investigate here. For instance, there are blogs that lenders use to share information and communicate about the loan requests, which were not available to the researches due to regulatory issues; hence they were unavailable in our data. Since, P2P context continues to evolve, some of the data has since been made public to anyone who registers and joins the site as a lender. Therefore one area of future research would be to look closer at the collective intelligence of the crowd and ascertain more precisely what forces are at play use to overcome problems lending under asymmetric information.

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number of cases (N =12, 526); we have created this variable as a binary indicator – showing whether the small business borrower tells a story or not. A better measure would have been to disentangle and look at the actual contents of the story. Moreover, borrowers also had private conversations with lenders in the form of Questions/Answers; where lenders could ask borrowers any questions based on their loan request. Similarly, these were not made public due to regulatory constraints. Hence they are unavailable for our data. However, some of these restrictions have since been lifted such that any lender, that registers on the platform they can have access to full disclosure of any conversation that potential borrowers may have had with prospective lenders. This would make an interesting extension of the research. Finally, since this market is new and continues to grow, there will be more opportunity to track the data and see how the return evolves over time.

Research dissemination

In terms of disseminating our research to a wider academic community, we have developed two empirical papers from the three chapters, which have been disseminated through academic conferences as follows:

 Paper titled: “New technology same old story: factors driving credit allocation for small business loan on commercial Peer-to-Peer lending websites” accepted at the 6th Annual conference academy of innovation and entrepreneurship (AIE, 2013), Oxford University, UK

 Presenter at policy conference: “New technology same old story: factors driving credit allocation for small business loan on commercial Peer-to-Peer lending websites” 11th Annual International Conference on Finance, Athens, Greece (2013)

 Presenter at policy conference, “What’s in store for tomorrow’s SME finance: the case of Crowd Funding”, Strategies to Overcome Poverty and Inequality: Towards Carnegie 3 , University of Cape Town, South Africa (2012)

 Presenter paper titled: Research methods in entrepreneurship: “what determines the endowment of quality entrepreneurs in an economy”,19th European Doctoral Programme Association in Management and Business Administration Seminar, Soreze, France (2010)

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Table 7-1: Summary of findings

This table shows the developed hypotheses, predicted relationships and what we found as determinants of: credit allocation, the cost of credit and loan default for P2P small business loans.

Hypothesis Predicted

relationship

Found relationship Factors driving credit approval

H1a small business borrowers, who own their homes, demonstrate better credit risk (relative to those that rent) and therefore are more likely to be extended credit by prospective lenders

+ +

H2a small business borrowers, with high credit ratings, demonstrate better credit risk and therefore are more likely to be extended credit by prospective lenders

+ +

H3a small business borrowers who have successfully paid back a previous loan are more likely to be extended credit

+ +

H4a existing firms are more likely to be funded (relative to new business start- ups) in the P2Plending context

+ Variable

insignificant H5 the likelihood of a partially funded loan receiving additional credit will

increase with the total number of already extended offers

+ +

H6a small business borrowers who use text elaborations are more likely to be funded in the P2P lending context

+ Variable

insignificant H7a small business borrowers who have previous failures are less likely to

access funds in the P2P lending context

- -

H8a small business borrowers who post pictures are likely to get funded by lenders in the P2P lending context

+ +

Factors driving cost of credit

H1b small business borrowers, who own their homes, demonstrate better credit risk (relative to those that rent) and therefore are more likely to pay lower interest rates

- Variable

insignificant

H2b small business borrowers, with high credit ratings, demonstrate better credit risk and therefore are more likely to pay lower interest rates

- -

H3b small business borrowers who have successfully paid back a previous loan are more likely to pay lower interest rates

- Variable

insignificant H4b existing firms are more likely to pay lower interest rates (relative to new

business start-ups) in the P2Plending context

- Variable

insignificant H6b small business borrowers who use text elaborations are more likely to be

pay cheaper interest rates in the P2P lending context

- Variable

insignificant H7b small business borrowers who have previous failures are more likely to pay

higher interest rates

+ Variable

insignificant H8b small business owners who post pictures are likely to pay lower interest

rates in the P2P lending context

- -

Factors driving default activity

H1c small business borrowers, who own their homes, demonstrate better credit risk (relative to those that rent) and therefore are less likely to default

- Variable

insignificant H2c small business borrowers, with high credit ratings, demonstrate better

credit risk and therefore are less likely to default

- -

H3c small business borrowers who have successfully paid back a previous loan are less likely to default

- -

H4c existing firms are less likely to default (relative to new business start-ups) in the P2P the lending context

- Variable

insignificant H6c small business borrowers who use text elaborations are more (less) likely

to be default

+/- Variable insignificant H7c small business borrowers who have previous failures are more likely to

default

+ Variable

insignificant H8c small business owners who post pictures are more (less) likely to default +/- -

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