H4: Borrowing records has actually an optimistic affect lenders’ behavior to include credit which might be in common so you’re able to MSEs’ conditions
Relating to virtual lending, this foundation is influenced by numerous items, along with social network, economic functions, and you may chance perception having its 9 indications given that proxies. Therefore, when the prospective traders believe that prospective individuals meet up with the “trust” indication, chances are they will be sensed to have people in order to provide regarding exact same count because advised of the MSEs.
Hstep 1: Internet play with facts to possess businesses features an optimistic impact on lenders’ behavior to include lendings which might be equal to the needs of the new MSEs.
H2: Status in operation points keeps a confident effect on the brand new lender’s decision to include a financing which is in keeping to your MSEs’ requirement.
H3: Ownership in the office funding have an optimistic impact on the newest lender’s decision to include a credit which is in keeping on needs of your MSEs.
H5: Financing utilization enjoys a confident affect this new lender’s decision so you can promote a lending which is in accordance toward requires out-of the brand new MSEs.
H6: Financing payment system keeps a confident impact on the brand new lender’s choice to provide a financing which is in common with the MSEs’ requirement.
H7: Completeness away from credit requisite file enjoys a positive effect on brand new lender’s decision to include a financing that is in common to the latest MSEs’ specifications.
H8: Credit reason have an optimistic impact on the fresh lender’s choice to help you give a credit that’s in accordance in order to MSEs’ demands.
H9: Being compatible from mortgage proportions and business need have an optimistic impact toward lenders’ conclusion to provide credit that’s in keeping so you’re able to the requirements of MSEs.
step 3.step one. Method of Event Data
The analysis uses additional analysis and you will priple physique and you may topic to own getting ready a survey concerning factors that dictate fintech to finance MSEs. Everything try built-up off literary works studies one another log posts, publication chapters, process, previous research while others. At the same time, no. 1 info is must receive empirical data off MSEs regarding the factors that determine him or her for the obtaining borrowing compliment of fintech financing considering their requirement.
First data could have been collected in the form of an internet questionnaire while in the when you look at the four provinces in Indonesia: Jakarta, Western Coffees, Central Java, Eastern Coffees and you will Yogyakarta. Online survey sampling made use of low-probability testing that have purposive testing techniques into five-hundred MSEs being able to access fintech. Of the shipping off forms to all the participants, there are 345 MSEs have been ready to fill in the fresh new questionnaire and you may just who obtained fintech lendings. However, merely 103 respondents offered done solutions which means that merely research given because of the him or her try good for further study.
3.2. Investigation and Changeable
Investigation which was accumulated, modified, then reviewed quantitatively according to research by the logistic regression model. Created varying (Y) is built for the a digital manner from the a question: really does the financing received of fintech meet the respondent’s standards or not? Within this context, the fresh subjectively appropriate respond to gotten a rating of a single (1), plus the most other gotten a rating from no (0). Your chances variable is then hypothetically determined by multiple details once the exhibited within the Dining table dos.
Note: *p-worth 0.05). Consequently the fresh model works with this new observational analysis, and is right 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.