In the context of virtual credit, so it basis was determined by multiple points, along with social media, financial attributes, and you will exposure effect using its nine evidence as the proxies. Therefore, if possible investors believe that prospective consumers meet with the “trust” signal, then they is felt getting people so you’re able to provide about same count just like the advised because of the MSEs.
Hstep one: Internet use points to own companies has a confident effect on lenders’ decisions to incorporate lendings that are comparable to the requirements of the MSEs.
H2: Standing in business factors enjoys a confident impact on the lender’s decision to include a credit that is in common with the MSEs’ needs.
H3: Possession at work investment has a positive influence on the newest lender’s decision to include a lending which is in keeping toward needs of the MSEs.
H5: Mortgage application provides a confident impact on the newest lender’s choice to help you give a financing that’s in common towards need regarding this new MSEs.
H6: Financing payment system keeps an optimistic impact on new lender’s decision to include a credit that is in accordance to your MSEs’ requisite.
H7: Completeness from credit criteria document has a confident effect on the fresh lender’s choice to provide a financing that is in common to help you brand new MSEs’ demands.
H8: Borrowing from the bank cause have an optimistic affect the fresh lender’s choice to help you give a lending that’s in keeping to help you MSEs’ needs.
H9: Being compatible out of loan dimensions and you will providers you need enjoys a confident perception on the lenders’ conclusion to incorporate lending that is in accordance so you’re able to the needs of MSEs.
step three.1. Method of Gathering Study
The analysis uses additional data and you can priple frame and you may material having making preparations a questionnaire concerning the items one to influence fintech to finance MSEs. All the information is collected off books education each other record articles, guide chapters, legal proceeding, earlier in the day search although some. Meanwhile, top data is wanted to get empirical analysis out-of MSEs from the elements you to dictate her or him into the getting borrowing as a result of fintech financing based on its demands.
Top studies might have been built-up by means of an internet questionnaire while in the within the four provinces inside Indonesia: Jakarta, Western Coffees, Central Coffees, Eastern Java and you can Yogyakarta. Online survey testing used low-opportunities sampling that have purposive testing strategy towards five hundred MSEs opening fintech. Of the shipping regarding surveys to all the participants, there were 345 MSEs who had been prepared to submit the fresh new questionnaire and you can whom received fintech lendings. Yet not, simply 103 participants click here now offered complete solutions which means that simply study provided by the her or him is appropriate for additional research.
step three.dos. Study and you may Variable
Research which was accumulated, edited, after which assessed quantitatively in line with the logistic regression design. Founded adjustable (Y) try constructed in a digital fashion of the a question: do the new financing acquired from fintech meet the respondent’s expectations or not? In this perspective, new subjectively appropriate address obtained a get of a single (1), plus the other gotten a score from zero (0). The probability variable is then hypothetically determined by several details because showed into the Table dos.
Note: *p-well worth 0.05). Consequently the new model works with this new observational study, that is right for after that data.
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.