Relating to virtual financing, so it basis was dependent on numerous items, and social networking, economic attributes, and you will risk feeling having its 9 indications due to the fact proxies. Hence, if prospective dealers believe that potential borrowers meet the “trust” signal, chances are they was sensed having people so you can give on the same amount due to the fact advised because of the MSEs.
Hstep 1: Web sites play with facts to have enterprises has a positive affect lenders’ conclusion to include lendings which can be equivalent to the needs of the latest MSEs.
H2: Reputation in business activities keeps an optimistic effect on new lender’s choice to incorporate a credit that’s in keeping to your MSEs’ criteria.
H3: Ownership at the office financial support keeps an optimistic effect on new lender’s decision to provide a financing that is in common to the means of your MSEs.
H5: Loan use has a positive influence on this new lender’s choice in order to promote a credit which is in accordance to your need from the fresh new MSEs.
H6: Mortgage repayment system enjoys a positive influence on the fresh lender’s choice to add a credit that’s in common with the MSEs’ specifications.
H7: Completeness away from borrowing requirements document features an optimistic impact on this new lender’s choice to provide a lending that is in common so you can the fresh new MSEs’ demands.
H8: Credit cause possess a confident impact on the brand new lender’s choice so you can promote a financing that’s in common in order to MSEs’ means.
H9: Compatibility regarding loan dimensions and you may team you prefer enjoys an optimistic impact to your lenders’ conclusion to add lending that is in keeping to help you the needs of MSEs.
step 3.1. Form of Event Study
The analysis spends secondary study and priple frame and you may issue getting making preparations a survey concerning facts one to determine fintech to finance MSEs. The information are built-up regarding literature knowledge each other record content, guide chapters, proceedings, early in the day browse while others. At the same time, number 1 info is necessary to receive empirical study from MSEs about elements that dictate her or him from inside the getting credit courtesy fintech lending predicated on the demands.
No. 1 investigation could have been accumulated in the form of an online questionnaire during in the five provinces in Indonesia: Jakarta, Western Coffees, Main Java, East Coffee and you can Yogyakarta. Paid survey sampling made use of low-opportunities testing having purposive you can find out more sampling method toward five-hundred MSEs opening fintech. Because of the delivery out-of surveys to all respondents, there were 345 MSEs who were willing to submit the brand new questionnaire and whom gotten fintech lendings. Yet not, merely 103 respondents provided complete solutions for example merely studies considering by the them try legitimate for further studies.
step three.2. Research and you can Varying
Analysis that has been compiled, edited, and then analyzed quantitatively according to research by the logistic regression design. Created changeable (Y) is constructed inside a binary trend because of the a question: really does the newest financing acquired from fintech meet the respondent’s standard or perhaps not? Inside context, the new subjectively appropriate answer was given a score of a single (1), together with almost every other got a rating from zero (0). The possibility varying will then be hypothetically determined by multiple parameters because the displayed inside Desk dos.
Note: *p-value 0.05). This means that the fresh new design is compatible with the brand new observational study, in fact it is right for subsequent studies.
The first interesting thing to note is that the internet use activity (X1) has a negative effect on the probability gaining expected loan size (see Table 2). This implies that the frequency of using internet to shop online can actually reduce an opportunity for MSEs to obtain fintech loans. It is possible as fintech lenders recognize that such consumptive behavior of MSEs could reduce their ability to secure loan repayment. Secondly, borrowers’ position in business (X2) is not significant statistically at = 10%. However, regression coefficient of the variable has a positive sign, indicating that being the owner of SME provides a greater opportunity to obtain fintech loans that are equivalent to their needs. Conversely, if a business person is not the owner of an SME then it becomes difficult to obtain a fintech loan. The result is similar to Stefanie & Rainer (2010) who found that information concerning personal characteristics, such as professional status was an important consideration for investors in fintech lending. Unlike traditional financial institutions, fintech lending is not a direct lender but an agent that acts as a liaison between the investors and the borrowers. It means that the availability of information about personal qualifications is important for investors to minimize the risk of online-based lending. A research by Ding et al. (2019) on 178, 000 online lending lists in China, also revealed that the reputation of the borrower is the main signal in making fintech lending decisions.