The Impact of Mobile Banking on ROA of Islamic Banking Which was Listed on The IDX in 2015-2019
Chika Dwi Wijayati1*, Tieka Trikartika Gustyana2
1 Faculty of Economics and Business, Telkom University, Bandung, Indonesia
*Corresponding Author: [email protected]
Accepted: 15 February 2021 | Published: 1 March 2021
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Abstract: The presence of information technology is one of the factors that can facilitate the banking industry sector, which is commonly known as financial technology, especially Mobile banking. Besides providing convenience to customers, mobile banking also able to provide benefits for companies that are expected to increase the profitability of Islamic banking. This study was aimed to analyze of the Influences of Mobile banking on the ROA of Islamic Banking which was listed on the IDX in 2015-2019. To Analyzed these influences, the method used in this study is the quantitative method by using mobile banking as the independent variable, size and credit risk as control variables, and ROA as dependent variables. The population in this study are Islamic banks which are listed on the IDX in 2015-2019, and the sample used is all members of the population. Data were analyzed using panel data regression analysis techniques. The results indicate that mobile banking, size, and NPF had significant effect simultaneously on the ROA of Islamic Banking which was listed on the IDX in 2015-2019.
While partially indicates that mobile Banking, size, and credit risk had no significant effect on the ROA of Islamic Banking which was listed on the IDX in 2015-2019. In this study, it is hoped that Islamic banking can evenly use mobile banking for all its customers by increasing customer confidence in the security system so that there can be an increase in efficiency in mobile banking services, banks also need to pay attention to company size and NPF level in order to affect the ROA of Islamic banking.
Keywords: Mobile banking, ROA, Islamic Banking, Size, NPF
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1. Introduction
The development of information technology is one of the aspects that plays an important role and has implications for the long-term competitiveness and profitability of a company. Today's technology provides an advantage in being able to improve its business strategy, operations and services. One of the institutions that is influenced by technological innovation is a financial institution [1]. Supported by an increase in the number of internet users and cellphone users every year [2], Mobile banking is one of the most popular digital tool choices today to make people's lives easier, by providing efficient time and place in making transactions [3]. With its commitment to implementing interest-free banking, it has received a good response from the Indonesian people [4]. The intense competition between Islamic banks and conventional banks requires Islamic banks to produce better performance [5].
One of the factors that can influence the growth of banking assets is ROA (Return On Asset),
compare profitability and credit balances. So, banks can maximize profitability by minimizing credit risk by improving policies when making loans [6]. In this study, credit risk is measured by the value of non-performing financing (NPF) of banks.
2. Literature Review
Islamic Banking Principles
In carrying out activities, Islamic banks are collecting funds (funding) and channelling funds (financing), using remuneration in the form of rewards based on the principles of Islamic law.
Business activities in Islamic bank financial institutions do not use the interest system, because in Islamic law the use of interest is usury, which is a prohibition in Islam [4].
Mobile Banking
Mobile banking is a service or product offered by banks and non-banks to customers to make transactions using a cellphone or smartphone or tablet [7]. Cellular technology, which continues to increase today, makes mobile banking users able to use its services anytime and anywhere. In fact, currently banking has presented a new paradigm by presenting services and products as well as new points [8].
Financial performance
Bank performance is a description of the company's work performance or the company's work ability which is supported by management in its operational activities [9].
Financial ratios are a study that looks at the comparison between the amounts contained in the financial statements by using formulas that are considered representative to be applied. [10].
Return on Assets (ROA)
ROA is the ratio of the returns on assets used by the company. ROA is used to see the level of effectiveness of the company's overall operations. The bigger the ratio the better, because the company shows that it can utilize its assets effectively to generate profits. Conversely, low ratio indicates model investment as a total asset cannot increase profit [11].
Mathematically, ROA can be formulated as follows:
Return on Asset (ROA) = x 100% (1)
Size
Company size is a representation of the size and size of a company in terms of equity, total assets, and total sales in one period [12]. Large banking companies will generate higher profits than small banks. In this study, size is used as a measuring tool for assets. Size is the logarithm of total assets [6].
Credit Risk
Credit risk is the risk of failure by debtors and / or other parties to fulfill their obligations to the bank [13]. Credit risk can be calculated using the ratio of non-performing loans to total loans [14]. Credit risk is proxied as NPF which is measured using the following formula:
NPF = Non performing loans / total loans. (2)
3. Methodology
Sample and Population
Population is a general area in which there are objects / subjects with certain qualities and characteristics that the researcher has chosen to study and research, and conclusions are made [15]. Samples are used as an effort that researchers can make to get a part of the population [16]. The sample used in this study was all parts of the population, namely using census sampling. If all members of the population are part of the sample, with a population of less than 30 people, it is called saturated sampling or census sampling [15]. So the sample in this study is a Sharia bank listed on the IDX for the 2015-2019 period which has been published.
Research data
The data used in this study are derived from secondary data in the form of financial reports published by Islamic Banks consisting of financial balance sheets, profit and loss statements, and financial ratio calculations, as well as other reliable sources either in written form or in relevant digital formats. and relates to the object under study.
Data analysis
a. Descriptive Statistical Analysis
Table 1: Descriptive Statistic
Source: processed by researchers
b. Classic assumption test 1) Normality Test
Table 2: Normality Test
Source: processed by researchers
The results of the normality test in this study, the Asymp. sig. (2-tailed) in the unstandardized residual column shows a number 0.53 greater than 0.05, which means that the normality test accepts H0, i.e. data is normally distributed.
2) Autokorelation Test
Table 3: Autokorelation Test
Source: processed by researchers
The Durbin-Watson value shows a value of 1.215, meaning that the Durbin-Watson value is in an area where there is no decision. So, to overcome this it is necessary to run a test. The Run Test can be compared using the following guidelines (Ghozali, 2018):
H0: residual (res_1) random (Random) H1: the residual (res_1) is not random
Table 4: Run test
Source: processed by researchers
Run Test, shows the value in Asymp. Sig (2-tailed) is 1,000. This value is above 0.05, meaning that the null hypothesis is accepted, so the residuals are random and there is no autocorrelation.
3) Mulicollinearity Test
Table 5: Mulicollinearity Test
Source: processed by researchers
Based on the results of the tests, the VIF value generated from each independent variable does not show a value above 10. This means that there is no multicollinearity or multicollinearity in the regression model used by the author.
4) Heterocedasticity Test
Table 5: Heterokedastisity Test
Source: processed by researchers
The test results show that the significance probability value for each independent variable is not below 0.05. That is, the regression model does not occur heteroscedasticity.
c. Multiple Linear Regression Analysis
Table 6: Multiple Linear Regression Analysis
Source: processed by researchers
Based on the output generated from the test processing using SPSS, the equation can be obtained:
ROA = 0.031+ 0.019 MOBILEBANKING - 0.001 SIZE - 0.171 NPF
The formula above can produce the following analysis:
1) A constant value of 0.031 explains that in the Mobile banking variable, Size, Credit Risk (NPF) if the value is 0 then the ROA value is 0.031. companies can have an ROA of 0.031.
2) The mobile banking coefficient value is 0.019 in positive form. This means that for each additional use of mobile banking services, the company's ROA will increase by 0.019, assuming the other variables are constant.
3) The value of the Size Coefficient is -0.001 in negative form. This means that for each increase in size of 1, the company's ROA will decrease by 0.001. Assuming the other variables are constant.
4) The NPF coefficient value is -0.171 in negative form. This means that for each increase in NPF of 1, the company's ROA will decrease by 0.171. Assuming the other variables are constant.
d. F Statistical test
Based on the test results, it shows that the resulting Fcount value is 3.831 greater than the Ftable, which is 3.049 and has a probability value of 0.046 (p <0.05). So it can be seen that
e. T Statistical Test
The t statistical test can explain how the influence of each independent variable partially on the dependent variable. The test results show the following results
1) The mobile banking variable shows the Sig. (probability) of 0.517> 0.05. This means that the effect of mobile banking is not significant.
2) The variable Size shows the Sig value. (probability) of 0.773> 0.05. These results indicate that the effect of size on ROA is not significant.
3) The third hypothesis proposed by the author is that NPF has an effect on ROA. Based on the results obtained, namely Sig. (probability) NPF 0.129> 0.05. That is, the test results show that the effect of NPF is not significant.
4. Conclusion
1) There is no significant influence on the mobile banking variable on the ROA of Islamic banking listed on the IDX for the 2015-2019 period.
2) There is no significant influence on the size variable on the ROA of Islamic banking listed on the IDX for the 2015-2019 period.
3) There is no significant effect on the NPF variable on the ROA of Islamic banking listed on the IDX for the 2015-2019 period.
4) There is a simultaneous positive and significant impact on the variables of mobile banking, size, and credit risk on the ROA of sharia banks listed on the IDX for the 2015-2019 period.
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