User Preferences for the QRIS Payment System in Banda Aceh Municipality, Aceh, Indonesia
Nawar Ashfiah1, Aliasuddin1*, Rustam Effendi1, Jumadil Saputra2, Thanawit Bunsit3
1Faculty of Economics and Business, Universitas Syiah Kuala, Banda Aceh, Indonesia
2Faculty of Business, Economics, and Social Development, Universiti Malaysia Terengganu, Malaysia
3Thaksin University, Thailand
*Corresponding Author: [email protected]
Received: 15 April 2023 | Accepted: 10 June 2023 | Published: 30 June 2023
DOI:https://doi.org/10.55057/ijaref.2023.5.2.15
_________________________________________________________________________________________
Abstract: This study analyzes user preferences for the Indonesian Standard Quick Response Code (QRIS) in facilitating the payment system in the city of Banda Aceh. The total sample of this study is 190 respondents, and binary logistic regression is utilized to estimate the model in this study. The results show that age and income significantly affected user preferences for the Indonesian standard quick response code (QRIS). Recommendations to the people of Banda Aceh who pay bills using a digital wallet for various information or knowledge about paying bills through a digital wallet. It is suggested that future researchers examine the use of QRIS from the entrepreneur's point of view so that it can complement the results of this study. Bank Indonesia encourages entrepreneurs to use QRIS to increase public interest in QRIS.
Keywords: preferences, QRIS, payment systems, digital payments
___________________________________________________________________________
1. Introduction
Advances in technology and information can also help grow the digital economy and accelerate a country's financial inclusion. One form of application of information technology with digital economic growth is the presence of non-cash or digital-based transaction payments (Sihaloho et al., 2020). According to Bank Indonesia (2020), payment systems can be divided into two, namely, cash payments (cash-based) and non-cash (cashless). Payment in cash is payment using currency (notes and coins). Meanwhile, non-cash payments use ATM cards, credit cards, checking accounts, credit notes, debit notes, and electronic money (card- based and server-based) (Bank Indonesia, 2020).
A server-based non-cash payment system is part of electronic money with a type of payment that provides a means of payment for purchasing goods or services via the Internet. The digital Wallet is Currently the most popular non-cash payment system (Bank Indonesia, 2020). A digital wallet is a part of electronic money, a payment instrument, and storing funds.
There are many digital wallets, such as OVO, GoPay, ShopeePay, Funds, etc. Due to the many types of digital wallets, business actors must provide several types of QR code services and several types of digital wallets the organizers provide so that each digital wallet application can scan them. This also makes it difficult for consumers if the application used by consumers is not the same as the QR code available at the place of business (Saputri, 2020).
To make it easier for businesses and consumers, Bank Indonesia is working with the Indonesian Payment System Association (ASPI) to develop QRIS to make it easier for people to transact using digital wallets (Saputri, 2020). QRIS is an acronym for Quick Response Code Indonesian Standard. This national QR code standard facilitates digital payments through server-based electronic money such as Digital Wallet or mobile banking (Bank Indonesia, 2020). The presence of QRIS aims to launch a secure server-based (digital) payment system, can boost government efficiency, and accelerate digital financial inclusion.
Various factors influence people's choice to use digital payments: age, income, and lifestyle (Sutanto et al., 2021). In their research, Sutanto et al. (2021) showed that the level of use of digital payments is more dominant among young people because many teenagers and people at a young age prefer to use more straightforward and superficial things. The presence of QRIS helps traders not to be easily fooled by the circulation of counterfeit money, can reduce the risk of money theft, and can also support the government in developing a digital economy in Aceh Province.
QRIS users continue to experience an increase. The growth in the number of QRIS merchants is driven by increasing public awareness of utilizing various digital-based payment systems.
As many as 69,740 QRIS merchants are already available in Aceh Province, indicating that the infrastructure for QRIS transactions is relatively available in Banda Aceh City. Even though there are already many QRIS merchants in Aceh, many consumers still need cash payments. This can happen because many people still don't know anything about the digital payment system, which has many benefits. Community preferences are very important because data shows an increase in the use of digital payments. However, there is no empirical evidence regarding this, so this research is very important to be carried out in the city of Banda Aceh. This research is one of the empirical pieces of evidence about QRIS users' preferences in facilitating the Banda Aceh City payment system.
2. Literature Review
The payment system in Indonesia continues to develop rapidly, from cash payment systems to non-cash payment systems (Bank Indonesia, 2020). Digital payments are payments that use two forms of payment, namely, using a computer network and digitally. The instrument used in digital payments is electronic money (Sihaloho et al., 2020). Digital payment systems consist of several main elements, such as applications for money transfers, network infrastructure, and the policies and rules that control how these various systems work. QR code payments are non-cash transactions made by scanning the QR code provided by business actors and transferring funds quickly and safely (Azzahroo & Estiningrum, 2021).
The Quick Response Code Indonesia Standard (QRIS) is a Quick Response Code payment (QR Code) developed by Bank Indonesia and the Indonesian Payment System Association (ASPI) to facilitate various kinds of digital payments via server-based electronic money such as digital wallets or mobile banking (Tarantang et al., 2019). Transactions using QRIS require various devices to support the transaction process, including smartphones that can scan QR codes, sufficient internet quota, payment applications, and balances available in payment applications (Bank Indonesia, 2020).
The presence of QRIS can influence people's preferences in choosing to make non-cash payments, namely using QRIS or cash. In general, preference is consumers’ reaction to showing their satisfaction with an item or service used. Preference theory is commonly used
to measure the level of satisfaction of consumers. Consumer preference is a process that underlies consumers in choosing an item based on taste (Mankiw, 2019). Most people will prefer a payment system that has more advantages. Besides being able to be used by various digital wallets, QRIS payments are also safer and more efficient because they save time and effort and are quickly done. The benefits offered by QRIS can affect consumer perceptions.
These perceptions create tendencies and generate interest or preference (Permadi & Wilandari, 2021).
Age and income influence the choice of using QRIS for digital payment. Age affects a person in taking action. The older enough, the more mature in thinking. Age also affects a person in buying goods and services for consumption activities. It has become commonplace for young people to shop without using cash. They use electronic money more often (Katon et al., 2020).
According to Hanum (2017), Income has a close relationship with consumption, the higher a person's income, the greater his consumption, and vice versa.
Risma et al. (2021) researched IAIN Tulungagung students' preferences in using QRIS as payment technology. The model used in this research is the Unified Theory of Acceptance and Use of Technology (UTAUT) Model. The model describes the factors influencing individual acceptance of information technology: performance expectations, business expectations, social influences, and facilitating conditions. The results of this study indicate that performance expectations, namely public perceptions of using QRIS and conditions that facilitate the use of QRIS, influence interest in using QRIS as payment technology.
Expectations of effort and social influence, namely the influence of the people around, are not significant predictors influencing the intention to use QRIS.
Research conducted by Oktoviana (2020) shows that simultaneously all perception factors influence consumer preferences for using QRIS as a means of digital payment transactions.
Meanwhile, based on the results of individual testing, the perceived benefits of using QRIS significantly influence the intention to use QRIS. Meanwhile, other perception factors obtained results that did not significantly affect consumer interest in using QRIS. Permadi (2021) also conducted the same research, which showed that out of 154 respondents, the majority felt the convenience and benefits of QRIS. However, even though it is accessible and valuable, respondents still use QRIS carefully because it is considered risky. Siregar (2021) also researched the determinants of interest in using QRIS. He said that knowledge and risk had a positive and insignificant effect. In comparison, the benefits and convenience positively and significantly impact the interest in using QRIS.
Sihaloho et al. (2020) reviewed the application of QRIS to MSMEs in Medan City by explaining the roles, constraints, and income of MSMEs with QRIS. The results of the interviews obtained, most informants said that QRIS influenced the development of MSMEs.
The existence of QRIS greatly supports merchant transaction processes and maintains income security. The increase in the daily income of MSME traders is due to the presence of QRIS.
The increase in income is around 5-10% in a day for some traders. In addition, some traders experience increased income on Saturdays and Sundays when using QRIS. This increase in revenue will increase financial inclusion for MSME traders and the country's digital economy.
Research conducted by Husna (2020) regarding the analysis of the determinants of Muslim society towards preferences for charity payment methods through the QRIS code shows that trust, competitiveness, and satisfaction influence payment preferences using the QRIS code.
The social factor variable, namely the influence of the surrounding people, did not affect the preference for using QRIS.
3. Research Method
This research was conducted in the city of Banda Aceh. Banda Aceh City was chosen as the research location because Banda Aceh City is the capital of Aceh Province. The city of Banda Aceh is also the economic center of the Province of Aceh, so Banda Aceh can be an appropriate representative for this study. This research uses quantitative methods. The data source in this study came from primary data obtained directly through questionnaires distributed to research subjects in the city of Banda Aceh. The sample in this study was obtained through a simple random sampling technique, where every member of society has the same opportunity to be selected as a sample. To determine the number of samples, this study used the Slovin formula. The sample used in this study was 190 respondents in Banda Aceh City.
This study uses a binary logistic regression model approach. Binary logistic regression is an analytical technique intended to explain the influence of one or several categorical independent variables or a combination of both with the dependent variable, which is also categorical (Saputri, 2020). This study uses the dependent variable, namely QRIS user preferences (QR), and the independent variables, namely education (Edu), income (Inc), age (Age), and gender (Gender). Binary logistic regression estimation is part of a non-linear approach that uses the maximum likelihood (MLE) method, so the model is estimated as follows:
𝑄𝑅 = 𝛽1+ 𝛽2𝐸𝑑𝑢 + 𝛽3𝑌 + 𝛽4𝐴𝑔𝑒 + 𝛽5𝐺𝑒𝑛𝑑𝑒𝑟+∈ (1)
where QR is the preference of QRIS users as a response variable that states people's preferences in using QRIS as a means of digital payment as measured by a dummy variable, 1 if the respondent uses QRIS and 0 otherwise; β_j are regression coefficients for j from 1 to 5; Edu is education level in term of years of schooling; Y is total income; Age is the age of respondent in years; Gender is a dummy variable, 1 male and 0 otherwise; and ∈ is residuals.
4. Findings and Discussion
4.1 Matrix Classification
The classification matrix will show the strength of the estimate from a logistic regression model for the assumptions that might occur in the preference for selecting public bill payment instruments. The classification matrix is presented in Table 1.
Table 1: Matrix Classification
Observed Predicated Percentage
Correct
0 1
PK Community Preferences Prefer not to Use QRIS 13 38 25,5 Community Preferences Choosing to Use QRIS 6 133 95,7
Overall Percentage 76,8
Sources: Estimated Results, 2023.
Table 1 shows that the classification matrix test shows that this logistic regression equation model can predict people's choice to use QRIS based on people's preferences in Banda Aceh Municipality by 76.8 percent. This result indicates that people who choose to use QRIS are relatively more dominant, and many are already aware of the benefits of using QRIS.
4.2 Overall Fit of Model
A model can be assessed by comparing the Log Likelihood at the beginning with the Log Likelihood value at the end. If the Log Likelihood value gets smaller, it indicates that the model is moving toward an equilibrium point, which means it is stable and can be used for analysis.
Table 2: Overall Fit of the Model
Log Likelihood Value
Beginning 221.039
Ending 191.557
Sources: Estimates Results, 2023.
Table 2 shows the overall model fit test. There is a decrease in the likelihood value between before the dependent variable is entered and the dependent variable has been entered. This decrease proves that the regression model is better, or it can be stated that the model is hypothesized to fit the data. This model is stable and can be used as an analytical model in this study.
4.3 Omnibus Test for the Coefficients
The model coefficient omnibus test is used to see the overall results of the regular model. The omnibus test can be explained in Table 3.
Table 3: Omnibus Coefficient of the Model
Chi-square Degree of Freedom
Probability
Step 1
Step 29.482 4 0,000
Block 29.482 4 0,000
Model 29.482 4 0,000
Sources: Estimated Results, 2023.
Table 3 shows that the calculated Chi-square value is 29.482 > critical value at the degree of freedom 6, which is 9.492 or a model significance value of 0.000 where the value is less than 5 percent. So, this shows that the addition of the independent variable has a real influence, which means that this model is appropriate to use or the model is said to be fit.
4.4 Testing for Reliability
The regression model feasibility test can be assessed using Hosmer and Lemeshow's Goodness of Fit Test. This test is used to determine whether the model fits the data. Table 4 is a table from the Hosmer and Lemeshow Test.
Table 4: Hosmer and Lemeshow Test
Step Chi-Square Degree of Freedom Probability
1 4,774 8 0,781
Sources: Estimated Results, 2023.
Based on Table 4 of the Hosmer and Lemeshow test the Chi-square value obtained is 4.774 with a significance level of 0.781. The significance level obtained in the Hosmer and Lemeshow Test model is more remarkable than 0.05 percent. This explains that there is no significant difference between the model and the data, so the regression model is feasible and can estimate the observed value.
4.5 Estimated Results
This research uses binary logistic regression analysis to analyze the preferences of public bill payment instruments in Banda Aceh Municipality. The method used is Maximum Likelihood (MLE). Estimation of the logistic regression model is used to model the relationship between the dependent and independent variables. The independent variable can be significant if the sig value <0.05, which means that each variable will have a partially significant effect on the dependent variable in the model. The results of the binary logistic regression analysis are shown in Table 5.
Table 5: Estimated Logistic Model
Variable Coefficient Sig. Exp (B)
Step 1 Gender -.648 .080 .523
Age -.082 .000 .921
Income .000 .004 1.000
Education .126 .097 1.134
Intercept 1.111 .296 3.036
Sources: Estimated Results, 2023.
Table 5 explains that the variables of age and income significantly influence people's preferences in using QRIS as a means of digital payment in Banda Aceh Municipality. The age variable has a significant value or P-Value of 0.000 <0.05 with a coefficient of -0.082.
This shows that age has a negative and significant effect on user preferences of the Indonesian Standard Quick Response Code in facilitating the payment system in Banda Aceh City. The odd ratio value of the age variable of 0.921 states that if the community's age increases by one year, the interest in using the Quick Response Code Indonesia Standard (QRIS) in Banda Aceh City will decrease by 0.942 times. This research is consistent with previous research, which explains that young people are more dominant in using digital wallets (Sulistyowati et al., 2020).
The significance value or p-value (sig) for the income variable is 0.004 <0.05 percent, with a coefficient of 0.000, meaning that income has a positive and significant relationship to the Quick Response Code Indonesia Standard user preferences in facilitating the Banda Aceh City payment system. The income variable’s odd ratio value is 1,000, with income expressed in rupiah units. This shows that if people's income increases by one rupiah, then people's interest in using QRIS will increase by one-fold. People with high incomes are one time more likely to transact using QRIS than those with low incomes.
5. Conclusion
Age and income variables can affect people's preferences in using QRIS for digital payment in Banda Aceh City. Young people dominate using digital payments in Banda Aceh City, so the older an individual is, the more reluctant they are to use digital payments. It's the same as age; the more the number of family members, the less they use digital payments. As for the income variable, the higher the income, the higher the interest in making digital payments.
The people of Banda Aceh City who choose to use QRIS are more than those who do not vote, with an average of 73.2. This result means that the people of Banda Aceh Municipality already understand digital payments to those of Banda Aceh Municipality who make payment transactions by scanning QR codes or QRIS so that they can share information and knowledge about payment transactions using QRIS.
To increase the use of QRIS in Aceh, the government and Bank Indonesia can conduct broader outreach to the people of Aceh, especially in the City of Banda Aceh, regarding the convenience and benefits of transacting using QRIS. This research shows that various factors influence the use of digital payments. However, this research in the future will be further developed and adapted to the existing possibilities.
References
Ambarini, L. (2015). Ekonomi Moneter (1 ed.). Bogor: Penerbit IN Media.
Azzahroo, R. A., & Estiningrum, S. D. (2021). Preferensi Mahasiswa dalam Menggunakan Quick Response Code Indonesia Standard (QRIS) sebagai Teknologi Pembayaran.
Jurnal Manajemen Motivasi, 17(1), 10–17.
Badan Pusat Statistik. (2022). Statistik Daerah Provinsi Aceh 2021. September. BPS Kota Banda Aceh. Aceh.
Bank Indonesia. (2020). QR Code Indonesian Standard (QRIS). https://www.bi.go.id. 14 April 2022 (20:18).
Bank Indonesia. (2022). Statistik Ekonomi dan Keuangan Indonesia. https://www.bi.go.id. 14 April 2022 (20:30)
Daulay, D. I., Alfiyanna, G., Anggraeni, I., Sitohang, R. A., & Simatupang, T. (2020). Faktor Penentu Penggunaan Dompet Digital pada Konsumen di Daerah Jabodetabek.
Indonesia Business Review, 3(1), 76–90.
Hanum, N. (2017). Analisis Pengaruh Pendapatan Terhadap Perilaku Konsumsi Mahasiswa Universitas Samudra di Kota Langsa. Jurnal Samudra Ekonomika, 1(2), 107–116.
Husna, Z. (2020). Analisis Faktor-Faktor Penentu Masyarakat Muslim Terhadap Preferensi Metode Pembayaran Infaq Dan Shadaqah Melalui Kode Qris (Studi Kasus Di Yayasan Masjid Jami’ Kota Malang). Skripsi. Fakultas Ekonomi dan Bisnis, Universitas Brawijaya. Malang.
Karmawan, I. G. M. (2017). Konsep Utilitas/Daya Utility.
https://sis.binus.ac.id/2017/01/14/konsep-utilitasdaya-guna-utility/. 20 Juni 2022 (21:02)
Kuncoro, M. (2013). Metode Riset Untuk Bisnis dan Ekonomi (1 ed.). Jakarta: Penerbit Erlangga.
Mankiw, G. N. (2019). Pengantar Ekonomi Mikro (7 ed.). Jakarta: Penerbit Salemba Empat.
Ningsih, H. A., Sasmita, E. M., & Sari, B. (2021). Pengaruh Persepsi Manfaat, Persepsi Kemudahan Penggunaan, Dan Persepsi Risiko Terhadap Keputusan Menggunakan Uang Elektronik (QRIS) Pada Mahasiswa. Jurnal IKRA-ITH Ekonomika, 4(1), 1–9.
Permadi, Y. A., & Wilandari, A. (2021). Preferences of Using Quick Response Code Indonesian Standard (QRIS) Among Students as a Means of Digital Payment. Journal of Enterprise and Development, 03(01), 31–41.
Purba, B., SN, A., Purba, E., & Sitorus, S. (2021). Ekonomi Demografi (1 ed.). Medan: Yayasan Kita Menulis.
Putri, D. A. (2022). Pengujian Usability Aplikasi pada E-Wallet dengan Menggunakan Metode System Usabitity Scale. Smatika Jurnal, 12(01), 114–122.
Rahmatika, U., & Fajar, M. A. (2019). Faktor-Faktor Yang Mempengaruhi Minat Penggunaan Electronic Money: Integrasi Model Tam-Tpb Dengan Perceived Risk. Jurnal Nominal, 7(2), 274–284.
Saputri, O. B. (2020). Preferensi konsumen dalam menggunakan quick response code indonesia standard (qris) sebagai alat pembayaran digital. Kinerja, 17(2), 237–247.
Sihaloho, J. E., Ramadani, A., & Rahmayanti, S. (2020). Implementasi Sistem Pembayaran Quick Response Indonesia Standard Universitas Sumatera Utara (1)(2)(3). Jurnal
Manajemen Bisnis, 17(2), 287–297.
Siregar, D. S. (2021). Determinan Minat Menggunakan Quick Response Code Indonesia Standard (QRIS). Skripsi. Fakultas Ekonomi dan Bisnis Islam, Institut Agama Islam Negeri Padang Sidempuan. Padangsidempuan.
Sugiyono. (2018). Metode Penelitian Pendidikan (1 ed.). Bandung: Alfabeta.
Sulistyowati, R., Paais, L. S., & Rina, R. (2020). Persepsi Konsumen Terhadap Penggunaan Dompet Digital. Jurnal Ekonomi, Manajemen dan Akuntansi, 4(1), 17–34.
Sutanto, W. L., Kiswati Zaini, O., & Irawan, A. W. (2021). Analisis Faktor-Faktor Keputusan Mahasiswa/I Pengguna Dompet Digital (Studi Kasus Pada Mahasiswa/I Fakultas Ekonomi Universitas Pakuan). Jurnal Online Mahasiswa, 5(3).
Taluke, D., Lakat, R. S. M., & Sembel, A. (2019). Analisis Preferensi Masyarakat Dalam Pengelolaan Ekosistem Mangrove Di Pesisir Pantai Kecamatan Loloda Kabupaten Halmahera Barat. Jurnal Spasial, 6(2), 531–540.
Tarantang, J., Awwaliyah, A., Astuti, M., & Munawaroh, M. (2019). Perkembangan Sistem Pembayaran Digital Pada Era Revolusi Industri 4.0 Di Indonesia. Jurnal Al Qardh, 4(1), 60–75.
Tejada, J. J., & Punzalan, J. R. B. (2012). On the Misuse of Slovin’s Formula. The Philippine Statistician, 61(1), 129–136.
Yanti, Z., & Murtala. (2019). Pengaruh Pendapatan, Jumlah Anggota Keluarga Dan Tingkat Pendidikan Terhadap Konsumsi Rumah Tangga Di Kecamatan Muara Dua Kota Lhokseumawe. Jurnal Ekonomika Indonesia, VIII(02), 72–81.
