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H4: Credit record keeps an optimistic impact on lenders’ decisions to provide lending that will be in common so you’re able to MSEs’ standards

H4: Credit record keeps an optimistic impact on lenders’ decisions to provide lending that will be in common so you’re able to MSEs’ standards

In the context of digital credit, this basis try influenced by multiple points, along with social network, monetary functions, and exposure perception using its 9 indicators since the proxies. Therefore, if prospective dealers believe that possible borrowers meet the “trust” sign, then they would-be experienced to own investors to give on exact Colorado title loans locations same matter since recommended from the MSEs.

Hstep 1: Sites use circumstances for businesses has a confident affect lenders’ behavior to incorporate lendings which can be equivalent to the requirements of the new MSEs.

H2: Status running a business points features an optimistic impact on the fresh lender’s choice to include a lending that is in keeping to your MSEs’ specifications.

H3: Possession at work funding keeps an optimistic affect brand new lender’s decision to include a lending that is in accordance with the requires of the MSEs.

H5: Financing application keeps an optimistic influence on brand new lender’s decision so you can give a credit that’s in accordance to your demands out of this new MSEs.

H6: Financing fees system have an optimistic influence on the latest lender’s decision to add a lending that’s in common on the MSEs’ requirement.

H7: Completeness off credit needs file enjoys a positive influence on new lender’s choice to include a lending which is in accordance so you’re able to the brand new MSEs’ requirements.

H8: Credit need features an optimistic influence on the new lender’s decision in order to bring a lending that is in accordance to help you MSEs’ means.

H9: Being compatible out of financing size and you will team you desire provides a confident perception on lenders’ decisions to add lending that is in accordance in order to the requirements of MSEs.

3.1. Sort of Collecting Data

The analysis spends second study and you will priple physique and you can procedure to have getting ready a survey regarding the activities that influence fintech to invest in MSEs. Everything try gathered off books knowledge one another record posts, publication chapters, procedures, past research although some. Meanwhile, number 1 information is needed to obtain empirical analysis away from MSEs in the elements one influence them from inside the getting credit because of fintech credit predicated on its criteria.

Number 1 research has been obtained in the form of an on-line survey throughout the within the five provinces in Indonesia: Jakarta, West Coffee, Main Java, East Coffees and Yogyakarta. Paid survey testing put non-possibilities testing which have purposive testing approach toward 500 MSEs opening fintech. Because of the shipment of questionnaires to all respondents, there are 345 MSEs who had been happy to complete brand new questionnaire and you can which obtained fintech lendings. However, simply 103 respondents offered complete responses for example just investigation offered by the her or him is good for additional data.

3.2. Research and Variable

Data which was compiled, modified, and assessed quantitatively according to research by the logistic regression design. Created adjustable (Y) try constructed into the a digital manner from the a question: does the fresh financing gotten from fintech meet the respondent’s standard or perhaps not? Contained in this framework, the fresh subjectively compatible answer was given a get of one (1), in addition to other got a score out-of zero (0). The possibility varying will be hypothetically influenced by several details since the displayed inside Desk 2.

Note: *p-well worth 0.05). This means that the fresh design works with this new observational analysis, which is right for next analysis.

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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