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H4: Borrowing from the bank background possess a confident effect on lenders’ behavior to provide financing which can be in common to MSEs’ requirements

H4: Borrowing from the bank background possess a confident effect on lenders’ behavior to provide financing which can be in common to MSEs’ requirements

In the context of digital lending, this factor try dependent on multiple factors, as well as social media, economic services, and you can risk effect having its 9 evidence because proxies. Thus, in the event the potential investors believe that potential individuals meet the “trust” indication, then they could well be considered having people to help you lend in the same amount since recommended because of the MSEs.

Hstep one: Internet explore items to possess organizations possess an optimistic influence on lenders’ choices to include lendings that will be comparable to the requirements of new MSEs.

H2: Updates operating affairs enjoys an optimistic impact on the fresh lender’s choice to incorporate a financing that is in common on the MSEs’ demands.

H3: Ownership in the office financial support has a confident impact on the fresh lender’s choice to incorporate a lending which is in keeping with the needs of your own MSEs.

H5: Financing application have an optimistic impact on the newest lender’s decision in order to promote a lending that’s in keeping to your demands regarding the MSEs.

H6: Mortgage cost system provides a confident affect the lender’s decision to incorporate a financing that’s in common towards MSEs’ requisite.

H7: Completeness out-of credit requirements document keeps a confident impact on the fresh new lender’s choice to add a lending which is in common so you can the fresh MSEs’ demands.

H8: Borrowing reason has actually an optimistic impact on the brand new lender’s decision so you can bring a lending which is in common so you’re able to MSEs’ means.

H9: Being compatible out-of loan size and you can team you need have a confident perception with the lenders’ decisions to include credit which is in common in order to the requirements of MSEs.

3.step 1. Types of Meeting Investigation

The study uses additional research and you can priple body type and you may point to have planning a questionnaire concerning the points one to influence fintech to invest in MSEs. All the info was compiled away from literature studies each other record content, book sections, legal proceeding, earlier lookup although some. Meanwhile, top data is wanted to see empirical investigation off MSEs regarding the the standards you to dictate her or him for the obtaining borrowing from the bank using fintech credit predicated on the needs.

First data has been accumulated in the form of an internet questionnaire while in the when you look at the five provinces inside Indonesia: Jakarta, Western Coffees, Main Coffee, Eastern Coffees and you can Yogyakarta. Online survey testing used low-opportunities sampling having purposive sampling strategy toward five-hundred MSEs being able to access fintech. By distribution from questionnaires to any or all respondents, there had been 345 MSEs who have been happy to fill out the newest questionnaire and you will exactly who acquired fintech lendings. Although not, only 103 participants provided done solutions and therefore simply investigation given by them try valid for further data.

step 3.2. Study and you may Adjustable

Study that has been compiled, edited, following analyzed quantitatively based on the logistic regression model. Established adjustable (Y) is created in a digital manner of the a question: do this new lending received out-of fintech meet with the respondent’s standards or perhaps not? Within this perspective, the newest subjectively appropriate respond to received a score of just one (1), in addition to most other was given a rating of zero (0). Your chances varying will then be hypothetically dependent on numerous variables once the demonstrated into the Dining table dos.

Note: *p-worth 0.05). This means that the latest model works with new observational data, which will be right for further study.

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.

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