H4: Borrowing background have a positive influence on lenders’ decisions to provide financing that are in common so you can MSEs’ standards


H4: Borrowing background have a positive influence on lenders’ decisions to provide financing that are in common so you can MSEs’ standards

Relating to digital lending, it basis try determined by multiple affairs, in addition to social media, economic qualities, and you will chance effect having its 9 signs given that proxies. Ergo, in the event the potential people go to website accept that potential individuals meet the “trust” indicator, they was thought for buyers so you’re able to lend regarding the same number since the suggested because of the MSEs.

Hstep 1: Internet sites use things for companies have an optimistic influence on lenders’ choices to add lendings that are comparable to the requirements of this new MSEs.

Hdos: Updates in business products features a positive impact on the new lender’s choice to add a lending that is in keeping towards the MSEs’ requisite.

H3: Control where you work capital keeps a positive effect on brand new lender’s decision to provide a financing which is in accordance towards the requires of one’s MSEs.

H5: Financing application features an optimistic impact on the brand new lender’s decision in order to bring a lending which is in keeping towards the need out-of the MSEs.

H6: Financing installment program enjoys an optimistic influence on brand new lender’s decision to add a financing that is in common to the MSEs’ needs.

H7: Completeness away from borrowing requirements document features an optimistic impact on the new lender’s choice to incorporate a financing which is in accordance in order to the brand new MSEs’ requisite.

H8: Borrowing from the bank need has actually a confident impact on the newest lender’s decision to help you promote a lending which is in accordance to MSEs’ demands.

H9: Being compatible from financing dimensions and providers you need have an optimistic effect into lenders’ behavior to incorporate lending that’s in keeping so you’re able to the needs of MSEs.

step 3.step one. Style of Event Research

The study spends secondary data and you may priple physical stature and you can material to possess making preparations a questionnaire towards points you to dictate fintech to invest in MSEs. Everything is actually obtained out of books studies each other log stuff, publication sections, legal proceeding, prior look and others. Meanwhile, no. 1 information is necessary to get empirical studies regarding MSEs about elements you to definitely influence her or him when you look at the getting borrowing through fintech lending considering their requirement.

Number one data has been obtained as an on-line survey during within the five provinces from inside the Indonesia: Jakarta, West Coffees, Main Coffee, East Coffee and you may Yogyakarta. Paid survey sampling made use of non-probability testing having purposive testing method into the five-hundred MSEs opening fintech. Because of the shipment out-of forms to all the respondents, there were 345 MSEs have been willing to fill in the brand new survey and exactly who gotten fintech lendings. Yet not, simply 103 participants provided done responses and therefore simply study provided from the him or her was appropriate for additional study.

step 3.dos. Investigation and Variable

Investigation which was collected, edited, right after which assessed quantitatively in accordance with the logistic regression model. Created varying (Y) are constructed during the a binary fashion from the a question: really does the fresh new credit received of fintech meet the respondent’s criterion otherwise not? Inside framework, the new subjectively compatible respond to got a get of one (1), and also the other was given a get from zero (0). The possibility changeable will be hypothetically determined by numerous variables because shown in Table 2.

Note: *p-well worth 0.05). This is why the brand new model works with new observational studies, and is suitable for next investigation.

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.