Relating to digital lending, this grounds are influenced by multiple facts, also social network, financial qualities, and you may chance perception having its nine signs as the proxies. Therefore, when the prospective traders believe that possible consumers meet the “trust” sign, they would be experienced to possess dealers to help you lend from the same count as recommended by the MSEs.
H1: Internet sites fool around with issues getting businesses have an optimistic affect lenders’ decisions to incorporate lendings that are equivalent to the needs of this new MSEs.
H2: Position in business things provides an optimistic affect the lender’s decision to provide a credit that’s in common into MSEs’ requisite.
H3: Possession in the office money has a confident impact on new lender’s choice to include a financing that’s in common into need of the MSEs.
H5: Loan usage features an optimistic effect on brand new lender’s decision in order to render a financing which is in accordance for the needs out-of the new MSEs.
H6: Loan fees system have a positive affect the brand new lender’s choice to include a lending which is in keeping towards MSEs’ requirements.
H7: Completeness of borrowing requisite document possess a positive influence on the latest lender’s decision to provide a financing which is in common to help you brand new MSEs’ specifications.
H8: Credit reason have an optimistic affect the fresh lender’s choice to help you render a lending that is in accordance to MSEs’ requires.
H9: Compatibility away from loan size and you can organization you want features an optimistic impact to your lenders’ conclusion to include lending which is in keeping to help you the requirements of MSEs.
step three.step 1. Types of Collecting Research
The study spends secondary studies and you will priple physical stature and you will point to have preparing a questionnaire concerning items one influence fintech to finance MSEs. Every piece of information is actually accumulated off literary works education each other record articles, guide sections, process, earlier in the day search while others. At the same time, top information is superb website to read needed to receive empirical data regarding MSEs throughout the elements you to definitely influence him or her inside getting credit through fintech credit considering its requirements.
No. 1 investigation has been gathered in the shape of an on-line questionnaire through the for the four provinces for the Indonesia: Jakarta, Western Coffees, Central Java, Eastern Coffees and you may Yogyakarta. Paid survey testing made use of non-possibilities sampling having purposive testing techniques toward 500 MSEs accessing fintech. Of the shipping off questionnaires to any or all respondents, there had been 345 MSEs who had been willing to complete this new questionnaire and you can who acquired fintech lendings. not, merely 103 respondents provided over solutions which means only studies provided from the them try legitimate for additional data.
step three.2. Analysis and Changeable
Investigation which had been built-up, edited, and then assessed quantitatively based on the logistic regression model. Mainly based changeable (Y) is actually created into the a digital trend by a question: do the newest financing acquired out-of fintech meet the respondent’s requirement or not? Within context, the fresh new subjectively compatible respond to obtained a score of 1 (1), therefore the other received a rating regarding zero (0). Your chances changeable will then be hypothetically dependent on multiple variables just like the exhibited inside Table dos.
Note: *p-worth 0.05). Thus the latest design is compatible with the newest observational investigation, which will be suitable for then 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.