CC BY
4
UDC 332; DOI 10.18551/rjoas.2022-12.09
ANALYSIS OF THE SOCIO-ECONOMIC EFFECT AND PERFORMANCE EXPECTANCY ON THE USE OF FINANCIAL TECHNOLOGY APPLICATIONS
Kussujaniyatun Sri*, Sujatmika, Harilaksana Dwi, Hartati Anis Siti
Yogyakarta "Veteran" National Development University, Yogyakarta, Indonesia E-mail: sri.kussujaniatun@upnyk.ac.id
ABSTRACT
This study aims to analyze the socio-economic dimensions and performance expected by customers in using Kredit Adil Cooperative application fintech, Sakti.link. This research was carried out at the Kredit Adil Cooperative in Dlingo, Bantul, the only cooperative that has used fintech and serves areas spread across Bantul. Data was collected by survey method on 200 respondents with purposive sampling technique. The data analysis technique used Structural Equation Modeling (SEM) PLS. The results show that the socio-economic effect that affect customers using fintech in cooperatives are income, occupation, the distance (space) of the customer's domicile with financial institutions, and education (education). The variable of performance expectancy that affect the use of fintech include cost efficiency, residual income, ability to disburse loans/liquidity (ability to disburse loans), time efficiency (time efficiency), and environmentally friendly (paperless). The use of fintech is seen based on transaction traffic, the number of users, and changes in the structure of outstanding loans and bad loans.
KEY WORDS
Socio-economics, performance expectations, financial technology.
The existence of the Covid-19 Pandemic and the implementation of Physical Distancing which aims to break the spread of the Covid-19 chain and stay away from all forms of association, maintain distance between humans, and avoid various gatherings that involve large numbers of people. This situation makes all sectors of human life uncertain, including the economic and social sectors in society. As a result, internet users in Indonesia continue to grow from year to year. This was revealed by the General Chairperson of the Indonesian Internet Service Providers Association (APJII) Muhammad Arif. He said that now approximately 77 percent of Indonesia's population is already using the internet. This growth is fantastic, before the pandemic the figure was only 175 million. While the latest data from APJII, in 2022 internet users in Indonesia will reach around 210 million. This means that there will be an additional 35 million internet users in Indonesia. This large number of internet users allows people to take advantage of innovative products and digital instructions [1].
The development of the growth rate of internet-based technology that is currently happening, has caused a complete change in various aspects of life today. Utilization of technology can provide many convenience facilities in every business activity with quick access. Technological developments have given rise to various new things as a form of utilization, including in the financial sector. The emergence of digital financial services or better known as financial technology (fintech) is a tangible manifestation of technological developments [2]. One of the technological developments that have become the dynamics of business development today is Financial Technology (Fintech). Fintech can assist MSME actors in providing convenience and efficiency in terms of technology-based financial management including digitizing financial reports, payment technology and online-based loans. The application of Fintech in MSMEs also has several challenges including infrastructure, legislation, limitations. Fintech began to emerge because the public could not be served by the traditional financial industry such as banking where banks were bound by strict regulations and the limitations of the banking industry in serving the public in certain areas. Therefore, the public is looking for alternative funding other than traditional financial
industry services that are more democratic and transparent as well as cost-efficient financial services and reach the wider community [3]. With the digitization of the financial sector, financial institutions are competing to improve financial technology innovation and increase efficiency and market share. Previously, it was known that lending to business actors could only be done through banks and had to go through a complex and time-consuming process. Fintech provides easy access to borrowers from the MSME sector. One of them is online loans, where MSME owners only need to attach the required documents online [4].
The Fair Credit Cooperative in Dlingo is a financial service institution that is professionally managed with cooperative principles as a reference point for members and the community to utilize their money in a healthy and safe manner as a source of financing for economic activities, improving education in an effort to improve the welfare of members and the community. The mission of this cooperative is to strengthen the organization of credit cooperatives, improve the welfare of members and the community, assist the community in increasing family income, assist members and the community in efforts to improve formal and non-formal education, help the community to create businesses and develop businesses, create human resources for management , competent supervisors and managers, creating quality human resources for members, instilling an entrepreneurial spirit for members and the community, applying technology, information and communication in an effort to improve services for members and the community, realizing the welfare of members and the community. The objectives of this Fair Credit Cooperative are the realization of a society with a decent level of education, the realization of the potential level of human resources of potential and innovative members, the creation of competitive management and management human resources. The Fair Credit Cooperative itself is the only cooperative in Bantul that has implemented Fintech in the process of its activities. Cooperative Fair also manages village markets which are expected to be able to alleviate poverty for members and the surrounding community.
The development of fintech in the Fair Credit Cooperative began in 2007 in collaboration with Rabobank and received a grant worth Rp. 254 million for the capacity building system, all transactions are still carried out face-to-face. From 2012 to 2017, the village cash account (rkd) has used multiuser, which means that all reports to the Fair Credit Cooperative have been carried out in a systematic manner. Started in 2017, the Fair Credit Cooperative designed the sakti.link application. The sakti.link application can effectively be used by management and cooperative members starting on October 13, 2018. The sakti.link application can be used to view the balances of each deposit member and borrower, pay or make transactions such as credit, electricity, transfers between banks with a 24-hour server.
Socio-economic conditions are a position that is socially regulated and places a person in a certain position in society. Socio-economic conditions of the community can be seen through three aspects, including education, employment and income. Education plays an important role in efforts to create quality human beings. The higher a person's education level, the easier it will be to get a job with a high income, and vice versa, the lower a person's education level, it will be difficult to find a job which will have an impact on his socio-economic conditions [5]. The distance between cooperatives and customers is also a variable in this study.
Variable Performance Expectancy means the extent to which if individuals use the system, the system can increase its performance [6]. In this study, the variables used in the measurement include dividends, cost efficiency, time efficiency, liquidity, and environmental friendliness.
METHODS OF RESEARCH
This research was conducted in the working area of Cooperative Kredit Adil, Dlingo, Bantul. The type of research used is quantitative research.
In the PLS-SEM model, latent variables and measured variables are needed. Latent variable is defined as an abstract concept, in this case such as people's behavior, attitudes, roles and motivations. This latent variable can only be observed indirectly and imperfectly
through its effect on the observed variable. SEM has two (2) types of latent variables, namely exogenous and endogenous where exogenous variables always appear as independent variables in all equations in the model. While the endogenous variable is the dependent variable in at least one equation in the model, although in all the remaining equations the variable is the independent variable.
In this study, the structural model consists of two exogenous (free) latent variables, namely socioeconomic (SE) with four (4) observed variables and performance expectancy (PE) with three (3) observed variables, and one endogenous (bound) latent variable, namely the use of fintech with five (5) variables observed. Thus the total latent variables are 3 and the total number of indicator variables is 12.
Table 1 - The model path diagram of the relationship between latent
Latent Variable Indicator Variables
Socio-Economic (X1 ) is the condition of the Kredit Adil X1.1 Income
Cooperative customers in terms of their social and X1.2 Education Level
economic conditions X1.3 Job
X1.4 Distance with Cooperative
Performance Expectancy (X2) is the expectation of X2.1 Time efficiency,
Kredit Adil Cooperative customers towards fintech- X2.2 Cost efficiency,
based applications, where customers hope that they X2.3 Liquidity,
will be able to provide positive benefits when used X2.4 Dividen,
X2.5 Environmentally friendly.
Use of Fintech (Y) is the level of use of fintech by Y1.1 Transaction traffic (traffic),
Cooperative Fair customers Y1.2 Number of users,
Y1.3 Bad loans and outstanding loans.
The population in this study were credit customers at three offices of the Kredit Adil Cooperative, Bantul. The population in this study are members of the Kredit Adil Cooperative which currently has a total of 400 members who have used sakti.link. In the Chi Square test the SEM model is very sensitive to the number of samples, so the sample of this study will refer to [7], namely the Maximum Likelihood Estimation (MLE) technique. A good number of samples according to MLE is in the range of 100-200 samples. In this study, the sample taken amounted to 200 respondents which was taken by purposive sampling technique (purposed sample), namely only users of fintech-based applications.
The unit of analysis in this study covers a very wide area, so in collecting the necessary data, the researcher made a questionnaire distributed to the three offices of the Kredit Adil Cooperative in Bantul, to be distributed to customers who came to take care of the administration. The questionnaire uses a Likert scale with a value of 1: Strongly Disagree to 5 (Strongly Agree). The analytical method used in this study is the PLSS structural equation modeling (SEM) analysis method. SEM PLS is a multivariate analysis technique that combines factor analysis and path analysis, and multiple indicators can be used to simultaneously test and estimate the relationship between extrinsic and intrinsic variables. The stages of analysis of the SEMPLS model in this study include: (A) Evaluation is often referred to as an external model or measurement model that relates all manifest variables or indicators to latent variables. ) Evaluation of the internal model (internal model) or structural model (structural model). That is, all latent variables are interrelated based on the path model of the relationship diagram that is built. In this model, everyone uses reflexive and formative models.
RESULTS AND DISCUSSION
Based on the results of the validity and reliability test, the cross loading value of each indicator can be said to be valid and reliable in measuring each of the variables, namely; income, education, employment, distance from cooperatives, cost efficiency, time efficiency, liquidity, dividen, environmental friendliness, transaction traffic, number of users, bad credit and outstanding credit.
Table 2 - Path Coefficients Model Results Using Bootstrapping
Variable Path Coefficient T-Table T-Statistics P-Values
Socio-Economic > Usage Fintech 0.412 1.962 5,395 0.000
Performance Expectancy > Use of Fintech 0.388 1.962 4,645 0.000
Based on the table above, it shows that after bootstrapping, socio-economic variables and performance expectancy have a significant relationship with the use of fintech as indicated by the socio-economic t-statistics value of 5.395 and performance expectancy of 4,645 which is greater than t-table 1.962, the p-values each variable 0.000 <0.05, and a positive path coefficient value.
Table 3 - R-Square Values on Dependent Variables (Bound)
Exogenous Latent Variables R-Square (R2)
Use of Fintech 0.632
Based on table 3, the use of fintech can be explained by 63.2% by socio-economic factors and performance expectancy and the rest by other variables not examined in this study.
Table 4 - Goodness of Fit (GoF)-index
AverageCommunalities Index AverageR-Square GoF Information
Socio-Economic 0.353 0.37 Big GoF
Performance Expectancy 0.458 0.632 0.42 Big GoF
Use of Fintech 0.376 0.38 Big GoF
Table 4 shows the inner model of this study has a GoF with an average value of 0.39 (Big GoF), it can be concluded that the feasibility level of the research model is 39% and has a high level of feasibility.
The Influence of Socio-Economic Factors on the Use of Fintech in Cooperatives. The results of the analysis show that socio-economic factors consisting of income variables, education level factors, occupations, and distance factors between customers and Kredit Adil Cooperatives have a significant and positive effect on the use of fintech in Kredit Adil Cooperatives. Members with higher jobs, incomes and education are bold in trying and daring to use new technologies. With the use of the Sakti.link application, of course, all transaction processes can be more easily accessed, saving more time because it can be accessed anywhere and anytime. The distance between cooperative members and cooperatives also has a positive effect, which means that the closer cooperatives are to the community, more members will register and use fintech.
The Effect of Performance Expectancy on the Use of Fintech in Cooperatives. The results of the analysis show that performance expectancy which includes time efficiency, cost efficiency, ability to disburse loans (liquidity), remaining operating income/dividends, and being environmentally friendly affects customers positively and significantly in the use of fintech applications. Customers use the application because it has benefits for customers, for example in purchasing credit, paying for electricity and water, transferring to other banks, saving time and saving costs, which in fact Kredit Adil Cooperative customers are spread across Bantul. With this application, customers and cooperative will be helped in financial services. In addition, the level of liquidity (the ability of the application to withdraw funds) affects customers using it. [8] stated that the advantages of fintech which are relatively higher than conventional services such as time efficiency, cost efficiency, liquidity for example in loans made by customers affect customers to use fintech compared to conventional services. With the ease of liquidity, customers use the fintech. With the use of this fintech, the remaining operating results can increase. The more often and more members make transactions such as topping up credit, paying electricity bills, the customer/member fees for transactions will increase the cooperative's profits. Kredit Adil
Cooperative customer members totaling 400 people will continue to increase according to the agreed target in every regular meeting of members. Thus, if this fintech-based fintech is used, then the remaining operating results can be increased. In addition, fintech is used because it is more environmentally friendly, uses less paper (paperless), so it can reduce waste. Thus, fintech implements the principle of ecological responsibility.
CONCLUSION
The results show that socio-economic aspects form the variables that influence customers using fintech in cooperatives, namely income, employment (occupation), the distance (space) of the customer's domicile to financial institutions, and education. cost (cost efficiency), residual income (residual income), ability to disburse loans/liquidity (ability to disburse loans), time efficiency (time efficiency), and environmentally friendly (paperless). The use of fintech is seen based on transaction traffic, the number of users, and changes in the structure of outstanding loans and bad loans.
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