Relating to digital financing, it basis try dependent on several activities, and social media, economic qualities, and you may risk effect having its 9 symptoms as the proxies. For this reason, in the event that prospective buyers believe that possible consumers meet the “trust” sign, chances are they might be thought having traders in order to lend on the same number as suggested by MSEs.
H1: Websites use issues for enterprises possess an optimistic effect on lenders’ behavior to incorporate lendings that are equivalent to the requirements of the MSEs.
Hdos: Position in operation issues possess a confident impact on the fresh lender’s decision to incorporate a financing which is in accordance into the MSEs’ demands.
H3: Ownership at your workplace financial support has a positive effect on the latest lender’s choice to add a credit which is in common into the requires of your own MSEs.
H5: Mortgage utilization has actually a positive impact on the new lender’s choice to help you offer a credit that is in common toward demands out-of new MSEs.
H6: Mortgage repayment program possess a positive effect on the newest lender’s choice to include a financing that is in keeping to your MSEs’ requirement.
H7: Completeness away from borrowing from the bank needs file has a positive affect this new lender’s decision to incorporate a financing which is in keeping so you can the latest MSEs’ specifications.
H8: Borrowing from the bank cause features a positive impact on the fresh lender’s choice to help you provide a credit which is in common so you’re able to MSEs’ demands.
H9: Being compatible out-of loan dimensions and you may providers need have an optimistic impact toward lenders’ behavior to incorporate lending that’s in common so you’re able to the needs of MSEs.
step 3.step 1. Variety of Gathering Investigation
The study uses secondary research and priple frame and you can question to own making preparations a survey towards products one influence fintech to invest in MSEs. All the info try accumulated out-of books training each other record content, book chapters, proceedings, past search although some. At the same time, primary data is needed to obtain empirical study regarding MSEs on the elements you to definitely dictate him or her within the obtaining borrowing owing to fintech financing based on the demands.
Primary study could have been compiled in the shape of an internet questionnaire while in the during the five provinces within the Indonesia: Jakarta, Western Java, Central Coffees, East Coffees and you may Yogyakarta. Online survey sampling put non-possibilities testing with purposive sampling techniques to the five hundred MSEs opening fintech. Of the shipments out-of surveys to all respondents, there have been 345 MSEs who were ready to fill out the brand new questionnaire and you may whom obtained fintech lendings. But not, simply 103 respondents provided done responses meaning that only analysis offered by the them are valid for additional analysis.
step 3.dos. Analysis and you will Variable
Research which had been built-up, modified, immediately after which assessed quantitatively based on the logistic regression model. Mainly based adjustable (Y) was built inside the a binary trend of the a question: does the fresh credit obtained away from fintech meet up with the respondent’s expectations or maybe not? In this framework, the latest subjectively appropriate respond to was given a get of one (1), and also the other received a score from zero (0). The possibility adjustable will then be hypothetically determined by multiple details since the presented in the Dining table dos.
Note: *p-really worth 0.05). Thus this new model works with the observational data, that is right for further 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 Idaho car loan and title 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.