Factor Affecting Digital Payments Using QRIS On Merchant During Covid-19: Case Study in Indonesia Provinces
Ajisatrio Anggadipati1*, Yunieta Anny Nainggolan1
1 School of Business and Management, Institut Teknologi Bandung, Bandung, Indonesia
*Corresponding Author: [email protected]
Accepted: 15 September 2022 | Published: 1 October 2022 DOI:https://doi.org/10.55057/ijbtm.2022.4.3.21
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Abstract: The COVID-19 pandemic has changed the behaviour of the society, especially the payment system continues to innovate with the rapid presence of e-commerce and the increasing number of fintech. QRIS emerged as one of the innovations from Bank Indonesia by combining all QR payment system service providers implemented on MSMEs or merchants to make transactions more efficient and faster without having to carry cash to shop for daily needs. The purpose of this study is to examine what factors affect the digital payment using QRIS on merchants in all provinces of Indonesia with a macroeconomic approach. This study uses a multiple linear regression method. The results of this research found that the factors affecting digital payment using QRIS on merchant in Indonesia provinces during COVID-19 are financial inclusion, level of education, and individuals using the internet to find information about goods and services. The findings of this research have several significant ramifications of implications for the business practices and academic knowledge in the further research in this area.
Keywords: Digital Payments, QRIS, Covid-19
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1. Introduction
The number of cases with COVID-19 rose in almost every country across the world in 2020, countries have implemented strict restrictions such as school holidays, working from home, quarantine for locations with a high number of patients, and, most importantly, lockdown to contain the COVID-19 pandemic dispersed where days of lockdown vary per country (Atalan, 2020). The social distancing strategies commonly referred to as “lockdown” exhibited incidence trends that significantly lowered the transmission of the COVID-19 virus in Spain and Italy before and after their respective national lockdowns (Saez et al., 2020). Yet simultaneously, governments around the world were forced to function in a state of extreme uncertainty and make tough trade-offs due to the health, economic, and social issues it posed (OECD, 2020). Based on the World Bank (2020) annual Gross Domestic Product (GDP) growth data, the global economy has shrunk by 3.293% in 2020 and is described as the worst economic decline caused by the recession in most countries.
Amid the current COVID-19 pandemic, the G20 Indonesia presidency emphasized the importance of digital financial inclusion and MSMEs financing to reduce inequality by encouraging digitalization that has an impact on increasing productivity, as well as an inclusive and sustainable economy (Bank Indonesia, 2022b).
The emerging COVID-19 pandemic has significant impact towards physical, social, national economy, and real sector (Djalante et al., 2020; IMF, 2021; Kementerian Keuangan Republik Indonesia, 2021; Prasad et al., 2015; Shafi et al., 2020). Unexpected occasion such as natural disaster can make less prepared to face adversity (Jones & Tanner, 2017). Understanding the vulnerabilities that come from risk exposure and lack of access to necessary resources is the first step in constructing financial resilience (Kass-Hanna et al., 2022; Danielle Moore et al., 2019; Salignac et al., 2019). The ability to create resilience in the face of economic vulnerability is thought to be provided by having access to resources such as well-designed financial services (Kass-Hanna et al., 2022).
Several government policies to prevent the pandemic such as social distancing has major impact on business sector, for instance, the MSMEs which has struggled to survive and be resilient with the available capacity. One of the impacts on MSMEs is that they are unable to carry out activities to improve their ability to meet credit obligations. This condition can increase Non-Performing Loan (NPL) significantly for MSMEs that may affect banking sector health (Kementerian Keuangan Republik Indonesia, 2021). MSMEs need support to survive and continue to contribute to the economy (ILO, 2020). Moreover, previous research argued micro enterprises operating in the informal sector of the emerging countries inclined to be prone to disaster effects and have a high failure rate (Prasad et al., 2015).
Financial resilience is being promoted through multiple approaches such as increasing the effectiveness of national financial inclusion policies. The current initiatives by involving access to and use of a wide variety of financial services in addition to just providing bank accounts such as money transfer services, loans, insurance, payment, and investment product (OECD/INFE, 2015, 2019; Stolper & Walter, 2017). Another point of view in global, the growing replacement of cash with cashless payment had been implemented. Other action such as market restraints and national legislative prohibitions that forbid usage of cash also supporting it (Passas, 2018; Shamraev, 2019).
In the G20 Indonesia presidency international seminar on digital financial inclusion, Deputy Governor of the Bank of Indonesia, Doni P. Joewono, discussed the technological advancement, digitization of financial goods and services, and online business operations might help MSMEs sustain revenue and business continuity amid the COVID-19 epidemic (Bank Indonesia, 2022a).
By seeing this case, digital economy opportunities are greatly visible for Indonesia. In 2020, Indonesia is dominated by the millennials and gen Z which can be seen in the Figure Error! No text of specified style in document..1. The millennials and Gen Z can influence to shape the world of commerce for long-term success where these generations wanting to experience modern and efficient payment methods that are almost digital (globalpayment, 2022).
Figure Error! No text of specified style in document..1: Population Classification Source: William H. Frey analysis of Census Bureau Population Estimates (25 June,2020)
Furthermore, in ASEAN countries, Indonesia has the biggest e-commerce platform adoption compared to others as shown in the Figure Error! No text of specified style in document..2 (Ho, 2021).
Figure Error! No text of specified style in document..2: Usage of E-commerce before, during and after COVID-19
Source: research.hktdc.com
Number of fintech in Indonesia are increasing rapidly during the COVID-19 as it reached about more than 300 financial technology in 2020 (DSResearch, 2020; OJK, 2020). Digital technologies are at the forefront of development which can provide unique opportunities for countries to accelerate economic growth and connect people to services and jobs (World Bank, 2022). Despite the fact that 1.7 billion adults in the worldwide do not have a bank account, fintech is assisting in increasing the number of people who can access financial services (Appaya, 2021). Beyond mobile payments, fintech has also demonstrated potential in fields such as government to person payment and cross-border remittances.
According to the survey research which has been conducted by Bank Indonesia in 2021, 20%
of Indonesia MSMEs reduced the impact of the pandemic by digitalizing their business and utilizing online marketing channels, meanwhile according to the supplies-side statistics, the use of cashless transactions, such as debit cards and electronic money, has accelerated which
10.88%
27.94%
25.87%
21.88%
11.56%
1.87% Post Gen Z
Born 2013 and later
Est. current age: up to 7 years Gen Z
Born 1997 - 2012
Est. current age: up to 8-23 years Millenials
Born 1981 - 1996
Est. current age: 40 -55 years Gen X
Born: 1965- 1980
Est. current age: 40 -55 years Baby Boomers
Born: 1946-1964
Est. current age: 56-74 years Pre-Boomers
Born before 1945 Est. current age: 75+ years
1 1 1 1 1 1
2.1
1.9 2
1.8 1.6
1.4
1.7 1.5 1.6
1.2 1.4
1.2
Indonesia Malaysia Philippines Singapore Thailand Vietnam Before During After
has increased 237% on the previous year to Rp 27.7 trillion (Bank Indonesia, 2022a). Even so, the increase of digital payments usage is varying in each Indonesia provinces. For instance, QRIS payment on merchant which is launched since 1st January 2020.
It is claimed that QRIS can be a game changer during the COVID-19. QRIS is claimed may increase financial inclusion in urban and even in the rural area. QRIS usage able to make transaction more efficient and quickly by using various payment system that support QRIS without hassling bringing cash shopping for necessities every day.
However, QRIS is a new technology innovation and people still need to adapt and learn during the process of technological change. Although it is purposely to ease society during the COVID-19 pandemic, only several people may utilize such technology especially person with higher education (Aurazo & Vega, 2021; Wozniak, 1987). New technology may need other training to utilize where it can be frustrating and time-consuming. This suggest that level of education may have a factor influencing the use of QRIS in Indonesia.
The research intends to define the factor affecting the digital payments using QRIS on merchant during COVID-19 macroeconomically that happened in the provinces of Indonesia.
2. Literature Review
Financial Literacy
Financial literacy is an understanding of a prominent financial concept and ability to manage personal finance by means of financial planning with the awareness of economic situations (Remund, 2010). Financial literacy is more than just knowledge, but also includes the abilities, motivation, and self-assurance to put that knowledge and understanding to use in a variety of financial situations, to enhance one one’s own and other’s financial security, and enable participation in the economy (OECD, 2013).
Financial literacy is important for the society when selecting and utilizing the financial product and service based on needs to avoid other unreliable financial product and services (OJK, 2017). Person considered financially literate if they able to demonstrate their understanding (Danna Moore, 2003). To improve financial literacy, previous research show evidence that financial education program able to cause positive treatment effects on financial knowledge and behaviours (Kaiser et al., 2022).
Digital Finance
Digital finance refers to the delivery of financial services using mobile phones, personal computers, the internet, or cards connected to a reliable digital system (Manyika et al., 2016).
Although there is no universally accepted definition of digital finance, it is generally understood to include all products, services, technology, and/or infrastructure that enable individuals and businesses to access payments, savings, and credit facilities via the internet (online) without visiting a bank branch or interacting directly with a financial service provider (Gomber et al., 2017; Ozili, 2018).
The research conducted by Luo et al. (2022) that the main driver of financial efficiency improvement in China is based on the technological advance and digital finance significantly contributing to the improvement of financial efficiency. In addition, the research argued that growing the breadth of coverage and depth of acceptance of digital banking are fundamental elements for promoting financial efficiency.
Digital Payment System
In general, payment system refers the mechanisms to the entire combination of instruments, intermediaries, rules, procedures, processes, and inter-bank funds transfer system that facilitate the circulation of money in a country or currency area (Bank for International Settlements, 2001; Gogoski, 2012).
Digital or e-payment Digital payment systems can support entrepreneurs in connecting with banks, employees, suppliers, and new markets for their goods and services by making financial transactions with them safer, convenient, and affordable by minimizing travel time and costs, these technologies help accelerate business registration and payments for business licenses and permits (Klapper, 2017).
QRIS
Quick Response Code Indonesian Standard or known as QRIS is the use of QR Code to unify multiple QR kinds from various electronic digital payment system service providers or known in Indonesian as Pelayanan Jasa Sistem Pembayaran (PJSP). QRIS was created by the payment system industry in collaboration with Bank Indonesia and Indonesia Payment System Association (ASPI) to make the QR code transaction process easier, quicker, and more secure.
QRIS must be implemented by all payment system service providers who will accept QR Code payments (Bank Indonesia, 2020). Bank Indonesia (2020b) Indonesia officially implemented QRIS as a means of payment for digital transaction nationally starting on January 1st, 2020.
QRIS can also be used in e-banking application (Bank Indonesia, 2020; Chohan et al., 2022).
Previous Research
Based on the previous study, numerous macroeconomic aspects must be considered in the factors influencing digital payments (Frączek & Urbanek, 2021). The following factors should be considered:
• The level of social standards and educational achievement, which promotes societal acceptance of contemporary technology tendencies
• Financial literacy enabling online payment operation for QRIS payment
• Digitalization is supported by usage of trends for contemporary technology in daily life
• Financial inclusion is made possible by electronic channels that facilitate digital payments
3. Research Methodology
Population and Sample
The population and sample used in this research are specifically chosen for this investigation.
The target population of this research is Indonesia provinces in 2020. According to (BPS, 2021), the size of population in Indonesia at 2020 was 270.20 million people. The research uses clustered sampling technique because the research use sample from large population and the population consists of groups of elements called clusters. The researched form clusters area in form of Indonesia provinces which size is 34 with the alpha 0.05. The ideal sample size using alpha 0.05 is 32 which is fulfilling the criteria conducting the research (Israel, 2003).
Data Collection
The data will be collected based on secondary data. The research data for analysis from the following sources/reports/databases:
• Digital payment using QRIS on merchant [DQM] – Bank Indonesia database (data from 2020)
• Gross Domestic Regional Product [GRDP] – Badan Pusat Statistik (data from 2020)
• Mean years of schooling [MYS] – Badan Pusat Statistik (data from 2020)
• Financial Literacy [FL] – Survei Nasional Literasi dan Inklusi Keuangan 2019 conducted by OJK
• Individual using mobile devices [MD] – Badan Pusat Statistik (data from 2020)
• Individual using the internet for finding information about goods and services [IGS] – Badan Pusat Statistik (data from 2020)
• Financial Inclusion [DFI] – Survei Nasional Literasi dan Inklusi Keuangan 2019 conducted by OJK
4. Data Analysis
The researcher employed cross-sectional data to conduct the research where cross-sectional data is gathered across set of sample units in a particular period (Hill et al., 2018). The statistical data analysis will be using regression analysis to find out the relation between dependent variable and independent variable. The data processing of this research will use IBM SPSS Statistic Subscription version 26 to analyse and test findings for the variables being investigated. After conducting the data processing, the result will be presented in the form of tables, graphs, and pictures to ease of understanding and interpreting the results of data processing.
Descriptive Statistics Analysis
The following is the descriptive statistics of the data by using SPSS version 26.0 program:
Table Error! No text of specified style in document..1 Descriptive Statistics Analysis
Descriptive Statistics
N Minimum Maximum Mean Std. Deviation
DQM 34 8.6928 14.0212 11.1078 1.3407
GRDP 34 1.9110 2.4132 2.1597 0.1064
MYS 34 -0.5135 -0.0227 -0.2668 0.1625
FL 34 9.4697 12.0441 10.4832 0.5474
MD 34 -1.2794 -0.5249 -0.9738 0.1726
IGS 34 -0.0388 -0.0039 -0.0134 0.0067
FI 34 -2.3497 -0.9548 -1.7331 0.2957
Valid N (listwise) 34
Classical Assumption
The classical assumption is tested before forming the regression model to provide a BLUE estimation (Best, Linear, Unbiased, Estimator).
4.2.1 Normality
Table Error! No text of specified style in document..2 Normality Test Result One-Sample Kolmogorov-Smirnov Test
Unstandardized Residual
N 34
Normal Parametersa,b Mean 0.0000000
Std. Deviation 0.89240009
Most Extreme Differences Absolute 0.122
Positive 0.122
Negative -0.103
Test Statistic 0.122
Asymp. Sig. (2-tailed)c .200d
a. Test distribution is Normal.
b. Calculated from data.
c. Lilliefors Significance Correction.
Based on the result as shown in the Table Error! No text of specified style in document..2, the obtained value of the significance normality test using the Kolmogorov-Smirnov test is 0.20 Considering that p-value exceeded than alpha (0.200 > 0.05), thus the residual data is concluded normally distributed and fulfil the normality assumption.
4.2.2 Heteroscedasticity
Figure Error! No text of specified style in document..3 Heteroscedasticity Plot
By observing the Figure Error! No text of specified style in document..3 Heteroscedasticity Plot, it demonstrates that the dots are distributed randomly and do not form a pattern. In addition, there are distributed points within the dots between -2 and 2. Given that there is no evidence of heteroscedasticity in the regression model, it can be used for further investigation.
4.2.3 Multicollinearity Test
The goal of multicollinearity test is to find investigate whether the independent in the model are correlated. In a good model, there should be no correlation between the independent variables. By using SPSS version 26.0, the output of the VIF value for each independent variable produced the following results:
Table Error! No text of specified style in document..3 Multicollinearity Test Coefficientsa
Model
Collinearity Statistics
Tolerance VIF
1 GRDP 0.580 1.724
MYS 0.423 2.362
FL 0.448 2.233
MD 0.584 1.713
IGS 0.433 2.310
FI 0.493 2.030
a. Dependent Variable: DQM
The result shows that the independent variables in the model have no multicollinearity.
4.2.4 Autocorrelation
Table Error! No text of specified style in document..4 Model Summary Model Summaryb
R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson
.782a 0.612 0.525 0.92371081 1.912
a. Predictors: (Constant), FI, MD, GRDP, FL, IGS, MYS b. Dependent Variable: DQM
Based on the Durbin-Watson test on the Table Error! No text of specified style in document..4, the result is 1.912 where it is nearly 2 that indicates there is no autocorrelation within the model.
Therefore, the multiple regression can be conducted.
4.2.5 Pearson Product Moment Correlation Analysis
The purpose of the person product moment correlation analysis is to discover and verify the hypothesis of a relationship between two or more variables when the data for the variables are in the form of intervals or ratios and the data sources for each variable are the same (Sugiyono, 2011). Based on the previous Table Error! No text of specified style in document..4 , the correlation coefficient (R) is 0.782. The value means a strong relationship between the independent variable and the dependent variable with coefficient intervals between 0.6 – 0.799 in the Guilford criteria.
4.2.6 Coefficient of Determination Analysis (Goodness of Fit)
When forecasting the result of an event, the coefficient of determination is used as a statistical measurement that examines the differences in one variable affects to the other variable.
(Sugiyono,2011). The previous section in the Table Error! No text of specified style in document..4
has the result of the R value of 0.782. The coefficient of determination analysis can be calculated which is the R Square with the value of 61.2%. Thus, the interpretation of the model is the digital payment using QRIS on merchant [DQM] is influenced by Gross Regional Domestic Product [GRDP], mean years of school [MYS], Financial Literacy [FL], Mobile Devices [MD], individual using the internet for finding information about goods and services [IGS], and Financial Inclusion [FI] by 61.2% whereas the other 38.8% influenced by other factors that is not investigated within the research.
4.3 Multiple Linear Regression
The operationalization of this research variable will consist of dependent and independent variables. The number of variables and the model of the multiple linear regression is following the previous research conducted by Frączek and Urbanek (2021) to find other factors that may influence the use of digital payment of QRIS on merchant.
DQM(X) = β0 + β1GRDP + β2MYS + β3FL + β4MD + β5IGS + β6FI + ε
DQM = digital payment using QRIS on merchant GRDP = gross regional domestic product per capita MYS = mean years of school
FL = financial literacy
MD = individual using mobile devices to access the internet
IGS = individual using the internet for finding information about goods and services FI = financial inclusion
ε = error term
The β0, β1, β2, β3, β4, β5, and β6 are the regression coefficients that will be used to interpret the multiple linear regression results. The coefficients are as follows:
Table Error! No text of specified style in document..5 Table of Coefficients Coefficientsa
Model
Unstandardized Coefficients Standardized Coefficients
t Sig.
B Std. Error Beta
(Constant) 33.256 6.202 5.362 0.000
GRDP 0.160 0.386 0.065 0.415 0.681
MYS -7.756 2.323 -0.615 -3.338 0.002
FL 2.137 1.392 0.275 1.536 0.136
MD -25.987 31.417 -0.130 -0.827 0.415
IGS 2.421 0.827 0.534 2.929 0.007
FI 4.301 1.410 0.521 3.051 0.005
a. Dependent Variable: ln DQM
As shown in the Table Error! No text of specified style in document..5, the constants and regression coefficient in the regression model can be shown as follows:
DQM(X) = 33.256 + 0.160 GRDP – 7.756 MYS + 2.137 FL – 25.987 MD + 2.421 IGS + 4.301 FI + ε
The following is the interpretation of the above equation:
β0 = If GRDP, MYS, FL, MD, IGS, and FI are equal to zero, thus the DQM will be worth 33.256 units;
β1 = If GRDP increases by one unit, thus the GRDP will increase by 0.160 units β2 = If MYS increases by one unit, thus the MYS will decrease by 7.756 units β3 = If FL increases by one unit, thus the FL will increase by 2.137 units β4 = If MD increases by one unit, thus the MD will decrease by 25.987 units β5 = If IGS increases by one unit, thus the IGS will increase by 2.421 units β6 = If FI increases by one unit, thus the FI will increase by 4.301 units
4.4 Simultaneous Hypothesis Testing
Simultaneous hypothesis test aims to determine whether all the components of the independent variable factor have a significant or insignificant influence on the dependent variable. Below is the proposed hypothesis:
H0: The independent variables have no significant effect on the dependent variable within the model
H1: The independent variables have significant effect on the dependent variable within the mode
The criteria that should be fulfilled the simultaneous hypothesis are if p-value (Sig.) ≤ alpha 0.05, reject H0 and accept H1. Otherwise, accept H0 and reject H1 if p-value (Sig.) > alpha 0.05. Following are the result of simultaneous hypothesis testing (F-Test) through analysis of variances (ANOVA) by using SPSS version 26.0:
Table Error! No text of specified style in document..6 Hypothesis Testing (F-Test)
ANOVAa
Sum of
Squares df
Mean
Square F Sig.
Regression 36.280 6 6.047 7.087 <.001b
Residual 23.038 27 0.853
Total 59.318 33
a. Dependent Variable: DQM
b. Predictors: (Constant), FI, MD, GRDP, FL, IGS, MYS
In accordance with the Error! Reference source not found. , it represents the F value for 7.087 with the p-value (Sig.) of <.001b which means the H0 is rejected because it less than alpha 0.05. Meanwhile, the H1 is accepted that explained the independent variables have significant effect on the dependent variable.
4.5 Partial Hypothesis Testing
The partial hypothesis testing (t-test) is to test the influence of between the dependent variable and each independent variable. The partial hypothesis testing criteria are if the p-value (Sig.) is ≤ alpha 0.05, reject H0 and accept H1. Otherwise, if the p-value (Sig.) > alpha 0.05, accept H0 and reject H1. The following is the result of partial hypothesis testing:
Table Error! No text of specified style in document..7 Partial Hypothesis Testing (t-test) Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t Sig.
B Std. Error Beta
(Constant) 33.256 6.202 5.362 0.000
GRDP 0.160 0.386 0.065 0.415 0.681
MYS -7.756 2.323 -0.615 -3.338 0.002
FL 2.137 1.392 0.275 1.536 0.136
MD -25.987 31.417 -0.130 -0.827 0.415
IGS 2.421 0.827 0.534 2.929 0.007
FI 4.301 1.410 0.521 3.051 0.005
a. Dependent Variable: DQM
4.5.1 Partial Hypothesis Testing Testing of Gross Regional Domestic Product [GRDP] on Digital Payment Using QRIS on Merchant [DQM]
H0: Gross Regional Domestic Product [GRDP] has no significant effect on digital payment using QRIS on merchant [DQM]
H1: Gross Regional Domestic Product [GRDP] has significant effect on digital payment using QRIS on merchant [DQM]
The p-value (sig. value) of the gross regional domestic product [GRDP] as independent variable is 0.681. Since the p-value is over 0.05, thus H0 is accepted and H1 is rejected which means there is insufficient evidence that gross regional product [GRDP] has significant effect on digital payment using QRIS merchant [DQM].
4.5.2 Partial Hypothesis Testing of Mean Years of School [MYS] on Digital Payment Using QRIS on Merchant [DQM]
H0: Mean years of school [MYS] has no significant effect on digital payment using QRIS on merchant [DQM]
H1: Mean years of school [MYS] has significant effect on digital payment using QRIS on merchant [DQM]
The p-value (sig. value) of the mean years of school [MYS] as independent variable is 0.002 below alpha 0.05. Hence, H1 is accepted and H0 is rejected which means there is evidence that mean years of school [MYS] has significant effect on digital payment using QRIS merchant [DQM].
4.5.3 Partial Hypothesis Testing of Financial Literacy [FL] on Digital Payment Using QRIS on Merchant [DQM]
H0: Financial Literacy [FL] has no significant effect on digital payment using QRIS on merchant [DQM]
H1: Financial Literacy [FL] has significant effect on digital payment using QRIS on merchant [DQM]
The p-value (sig. value) of the financial literacy [FL] as independent variable is 0.136 above alpha 0.05. Thus, H0 is accepted and reject H1 which means there is insufficient evidence that financial literacy [FL] has significant effect on digital payment using QRIS merchant [DQM].
4.5.4 Partial Hypothesis Testing of Individual Using Mobile Devices [MD] to Access the Internet on Digital Payment Using QRIS on Merchant [DQM]
H0: Individual using mobile devices to access the internet [MD] has no significant effect on digital payment using QRIS on merchant [DQM]
H1: Individual using mobile devices to access the internet [MD] has significant effect on digital payment using QRIS on merchant [DQM]
The p-value (Sig.) of the individual using mobile devices to access the internet [MD] as independent variable is 0.415 above alpha 0.05, thus H0 is accepted and reject H1 which means there is insufficient evidence that individual using mobile devices to access the internet [MD]
has significant effect on digital payment using QRIS on merchant [DQM].
4.5.5 Partial Hypothesis Testing of Proportion of Individual Using Mobile Devices to Access the Internet for Finding Information about Goods and Services [IGS] on Digital Payment Using QRIS on Merchant [DQM]
H0: Individual using mobile devices to access the internet for finding information about goods and services [IGS] has no significant effect on digital payment using QRIS on merchant [DQM]
H1: Individual using mobile devices to access the internet for finding information about goods and services [IGS] has significant effect on digital payment using QRIS on merchant [DQM]
The p-value (Sig.) of the individual using mobile devices to access the internet for finding information about goods and services [IGS] as independent variable is 0.007 below alpha 0.05.
Therefore, reject H0 and accept H1 whereas individuals using mobile devices to access the internet for finding information about goods and services [IGS] has significant effect on digital payment using QRIS on merchant [DQM].
4.5.6 Partial Hypothesis Testing of Financial Inclusion [FI] on Digital Payment Using QRIS on Merchant [DQM]
H0: Financial Inclusion has no significant effect on digital payment using QRIS on merchant [DQM]
H1: Financial Inclusion [FI] has significant effect on digital payment using QRIS on merchant [DQM]
The p-value (Sig.) of the financial inclusion [FI] as independent variables is 0.005 below alpha 0.05. Thus, reject H0 and accept H1 whereas financial inclusion [FI] has significant effect on digital payment using QRIS on merchant [DQM].
5. Conclusion and Recommendation
The first main factor of this research is whether financial inclusion have effect to the use of QRIS payment on merchant in Indonesia provinces during the COVID-19 pandemic. Previous study confirmed that there is relationship between financial inclusion and digital payment.
Based, on the obtained result by using the multiple linear regression, it confirmed that financial inclusion is statistically significant on the use of QRIS as digital payment on merchant in Indonesia provinces during the COVID-19 pandemic with positive coefficient
The second main factor of this research is whether education factor have effect towards the implementation of QRIS payment on merchant during the COVID-19 pandemic. The obtained result shows that level of education have significant relationship on the use of QRIS as digital payment on merchant in Indonesia provinces during the COVID-19 pandemic. But, there is negative correlation because of the coefficient.
This purposely to define other factors affect the use of QRIS payment on merchant during the COVID-19 pandemic. By using the macroeconomic approach, it is found that other factors affect the digital payment using QRIS on merchant is the individual using mobile devices to access the internet for finding information about goods and services which has significant relationship with positive coefficient. The other factor such as gross regional domestic product, financial literacy, and mobile device show insufficient evidence of effect on the digital payment using QRIS on merchant.
This research only focuses on digital payment using QRIS where it could be useful the society and policy maker to acknowledge the macroeconomic factor influencing QRIS.. Banks, payment system, policy makers need to realize that digital payment using QRIS on merchant during the COVID-19 in 2020 are affected by level of education, financial inclusion and individual using mobile devices to access the internet for finding information about goods and services. Increasing the effectiveness of national financial inclusion can also encourage financial resilience through digital payment using QRIS on merchant (Kouladoum et al., 2022).
Also, favourable consumer buying behaviours regarding the use of contemporary technology
in daily life by an appropriate level of internet use for finding information about goods and services have effect on the digital payment using QRIS on merchant. The findings of this research have several significant ramifications of implications for the business practices and academic knowledge in the further research for the development of digital payment using QRIS on merchant.
This research only identifies factor that affects the digital payment using QRIS on merchant in macroeconomically in Indonesia provinces. Additionally, the criteria for the matching sample could be different and irrelevant in the future. More empirically based research is also required to comprehend the relationships between financial inclusion and digital payments using QRIS on merchant. The findings of this research by using macro-scale study can be regarded as preliminary and further research are needed to investigate in this area.
References
Appaya, S. (2021). On Fintech and Financial Inclusion. Blogs.Worldbank.Org.
https://blogs.worldbank.org/psd/fintech-and-financial-inclusion
Atalan, A. (2020). Is the lockdown important to prevent the COVID-19 pandemic? Effects on psychology, environment and economy-perspective. Annals of Medicine and Surgery, 56(June), 38–42. https://doi.org/10.1016/j.amsu.2020.06.010
Aurazo, J., & Vega, M. (2021). Why people use digital payments: Evidence from micro data in Peru. Latin American Journal of Central Banking, 2(4), 100044.
https://doi.org/10.1016/j.latcb.2021.100044
Bank for International Settlements. (2001). Committee on Payment and Settlement Systems Core Principles for Systemically Important Payment Systems (Issue January).
www.bis.org
Bank Indonesia. (2020). QR Code Indonesian Standard (QRIS).
https://www.bi.go.id/QRIS/default.aspx
Bank Indonesia. (2022a). DIGITALISATION INCREASING ECONOMIC PRODUCTIVITY.
Bank Indonesia. https://www.bi.go.id/en/publikasi/ruang-media/news- release/Pages/sp_243422.aspx
Bank Indonesia. (2022b, May 14). INKLUSI KEUANGAN DAN PEMBIAYAAN UMKM
KUNCI DALAM MENDORONG EKONOMI INKLUSIF.
https://www.bi.go.id/id/publikasi/ruang-media/news-release/Pages/sp_2412822.aspx BPS. (2021). Hasil Sensus Penduduk 2020. Badan Pusat Statistik.
https://www.bps.go.id/pressrelease/2021/01/21/1854/hasil-sensus-penduduk-2020.html Chohan, F., Aras, M., Indra, R., Wicaksono, A., & Winardi, F. (2022). Building Customer
Loyalty In Digital Transaction Using QR Code: Quick Response Code Indonesian Standard (QRIS). Journal of Distribution Science, 20(1), 1–11.
https://doi.org/10.15722/jds.20.01.202201.1
Djalante, R., Lassa, J., Setiamarga, D., Sudjatma, A., Indrawan, M., Haryanto, B., Mahfud, C., Sinapoy, M. S., Djalante, S., Rafliana, I., Gunawan, L. A., Surtiari, G. A. K., & Warsilah, H. (2020). Review and analysis of current responses to COVID-19 in Indonesia: Period of January to March 2020. Progress in Disaster Science, 6.
https://doi.org/10.1016/j.pdisas.2020.100091
DSResearch. (2020). Indonesia Fintech Report 2020. 1–33. https://fintechnews.sg/wp- content/uploads/2020/12/Fintech-Indonesia-Report-2020.pdf
Frączek, B., & Urbanek, A. (2021). Financial inclusion as an important factor influencing digital payments in passenger transport: A case study of EU countries. Research in Transportation Business and Management, 41(April 2020).
https://doi.org/10.1016/j.rtbm.2021.100691
Gogoski, R. (2012). Payment Systems in Economy - Present End Future Tendencies. Procedia
- Social and Behavioral Sciences, 44, 436–445.
https://doi.org/10.1016/j.sbspro.2012.05.048
Gomber, P., Koch, J. A., & Siering, M. (2017). Digital Finance and FinTech: current research and future research directions. Journal of Business Economics, 87(5), 537–580.
https://doi.org/10.1007/s11573-017-0852-x
Hill, R. C., Griffiths, W. E., & Lim, G. . (2018). Principles of Econometrice 5th Edition (Vol.
53, Issue 9).
Ho, M. (2021). ASEAN E-commerce: Beyond the Pandemic.
https://research.hktdc.com/en/article/NzY4MzkzMzg1
ILO. (2020). MSME Day 2020: the COVID -19 pandemic and its impact on small business.
55. https://www.ilo.org/safework/countries/africa/algeria/lang--en/index.htm
IMF. (2021). Policy Response to COVID-19. https://www.imf.org/en/Topics/imf-and- covid19/Policy-Responses-to-COVID-19#U
Israel, G. D. (2003). Sampling The Evidence Of Extension Program Impact, Reviewed.
Program Evaluation and Organizational Development, IFAS, University of Florida.
PEOD-5. October. Kish, 1–9. https://edis.ifas.ufl.edu/publication/PD005
Jones, L., & Tanner, T. (2017). ‘Subjective resilience’: using perceptions to quantify household resilience to climate extremes and disasters. Regional Environmental Change, 17(1), 229–
243. https://doi.org/10.1007/s10113-016-0995-2
Kaiser, T., Lusardi, A., Menkhoff, L., & Urban, C. (2022). Financial education affects financial knowledge and downstream behaviors. Journal of Financial Economics, 145(2), 255–
272. https://doi.org/10.1016/j.jfineco.2021.09.022
Kass-Hanna, J., Lyons, A. C., & Liu, F. (2022). Building financial resilience through financial and digital literacy in South Asia and Sub-Saharan Africa. Emerging Markets Review, 51(PA), 100846. https://doi.org/10.1016/j.ememar.2021.100846
Kementerian Keuangan Republik Indonesia. (2021). Merekam Pandemi Covid-19 dan Memahami Kerja Keras Pengawal APBN. Kementerian Keuangan Republik Indonesia.
http://portal.fiskal.kemenkeu.go.id/pustaka/index.php?p=show_detail&id=5710&keywo rds=
Klapper, L. (2017). How digital payments can benefit entrepreneurs. IZA World of Labor, 1–
9. https://doi.org/10.15185/izawol.396
Kouladoum, J., Awal, M., Wirajing, K., & Nchofoung, T. N. (2022). Digital technologies and financial inclusion in Sub-Saharan Africa. Telecommunications Policy, May, 102387.
https://doi.org/10.1016/j.telpol.2022.102387
Luo, D., Luo, M., & Lv, J. (2022). Can Digital Finance Contribute to the Promotion of Financial Sustainability ? A Financial Efficiency Perspective.
Manyika, J., Lund, S., Singer, M., White, O., & Berry, C. (2016). Digital Finance for All : Powering Inclusive Growth in Emerging Economies. McKinsey Global Institute, September, 1–15. https://www.mckinsey.com/~/media/McKinsey/Featured Insights/Employment and Growth/How digital finance could boost growth in emerging economies/MGI-Digital-Finance-For-All-Executive-summary-September-2016.ashx Moore, Danielle, Niazi, Z., Rouse, R., & Kramer, B. (2019). Building Resilience through
Financial Inclusion: A Review of Existing Evidence and Knowledge Gaps. Ipa Innovations for Poverty Action, January, 1–13. https://www.poverty- action.org/sites/default/files/publications/Building-Resilience-Through-Financial-
Inclusion-January-2019.pdf
Moore, Danna. (2003). Survey of Financial Literacy in Washington State: December 2003, 1–
61. https://doi.org/10.13140/2.1.4729.4722
OECD/INFE. (2015). NATIONAL STRATEGIES FOR FINANCIAL EDUCATION.
http://webarchive.nationalarchives.gov.uk/20110113.
http://webarchive.nationalarchives.gov.uk/20110113104120/http://nationalstrategies.stan dards.dcsf.gov.uk/node/102679
OECD/INFE. (2019). OECD/INFE Report on Financial Education in APEC Economies.
OECD. (2013). Financial Literacy and Inclusion: results of OECD/INPE survey across countries and by gender. Paris: OECD Centre, June, 1–175.
https://www.oecd.org/daf/fin/financial-
education/TrustFund2013_OECD_INFE_Fin_Lit_and_Incl_SurveyResults_by_Country _and_Gender.pdf
OECD. (2020). The territorial impact of COVID-19: Managing the crisis across levels of government. https://www.oecd.org/coronavirus/policy-responses/the-territorial-impact- of-covid-19-managing-the-crisis-across-levels-of-government-d3e314e1/
OJK. (2017). Literasi Keuangan. https://www.ojk.go.id/id/kanal/edukasi-dan-perlindungan- konsumen/Pages/literasi-keuangan.aspx
OJK. (2020). Digital Finance Innovation. Indonesia Financial Service Authority (OJK).
http://www.digifin.org/digital-finance-innovation/
Ozili, P. K. (2018). Impact of digital finance on financial inclusion and stability. Borsa Istanbul Review, 18(4), 329–340. https://doi.org/10.1016/j.bir.2017.12.003
Passas, N. (2018). Report on the debate regarding EU cash payment limitations. Journal of Financial Crime, 25(1), 5–27. https://doi.org/10.1108/JFC-06-2017-0058
Prasad, S., Su, H. C., Altay, N., & Tata, J. (2015). Building disaster-resilient micro enterprises in the developing world. Disasters, 39(3), 447–466. https://doi.org/10.1111/disa.12117 Remund, D. L. (2010). Financial literacy explicated: The case for a clearer definition in an
increasingly complex economy. Journal of Consumer Affairs, 44(2), 276–295.
https://doi.org/10.1111/j.1745-6606.2010.01169.x
Saez, M., Tobias, A., Varga, D., & Barceló, M. A. (2020). Effectiveness of the measures to flatten the epidemic curve of COVID-19. The case of Spain. Science of the Total Environment, 727, 138761. https://doi.org/10.1016/j.scitotenv.2020.138761
Salignac, F., Marjolin, A., Reeve, R., & Muir, K. (2019). Conceptualizing and Measuring Financial Resilience: A Multidimensional Framework. Social Indicators Research, 145(1), 17–38. https://doi.org/10.1007/s11205-019-02100-4
Shafi, M., Liu, J., & Ren, W. (2020). Impact of COVID-19 pandemic on micro, small, and medium-sized Enterprises operating in Pakistan. Research in Globalization, 2.
https://doi.org/10.1016/j.resglo.2020.100018
Shamraev, A. (2019). Legal and regulatory framewo rk of the payment and e-money services in the brics countries. BRICS Law Journal, 6(2), 60–81. https://doi.org/10.21684/2412- 2343-2019-6-2-60-81
Stolper, O. A., & Walter, A. (2017). Financial literacy, financial advice, and financial behavior.
Journal of Business Economics, 87(5), 581–643. https://doi.org/10.1007/s11573-017- 0853-9
Sugiyono. (2011). Metode Penelitian Kuantiatif, Kualitatif, dan R & D. Alfabeta.
World Bank. (2020). GDP growth (annual%).
https://data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG
World Bank. (2022). Digital Development. In International Bank for Reconstruction and Development. https://www.worldbank.org/en/country/vietnam/overview
Wozniak, G. D. (1987). The Adoption of Interrelated Innovations: A Human Capital Approach.
Journal of Human Resources, 22, 101–112.
