Factors Affecting the Low Interest in Using QRIS As a Non-Cash Payment Method

: In the modern era, advancing technology plays a crucial role in simplifying human activities, with the non-cash payment system being a significant development in digital transactions. The objective of this research is to identify the factors influencing the low interest in the use of QRIS as a non-cash payment method among Micro, Small, and Medium Enterprises (MSME) participants in Pesinggahan Village. The sampling technique used a census with a sample size of 200 and 17 variables. Based on the result and discussion of factor analysis, four factors influencing the low interest of MSME players in Pesinggahan Village in adopting QRIS as a non-cash payment method were identified. These four factors include the trust in the effectiveness of QRIS, the capability factor of MSME players, the ease of QRIS usage factor, and the security and convenience factor for customers.


I. INTRODUCTION
In today's modern age, technology and information are constantly evolving.This is used to facilitate the activities carried out by humans.The level of usefulness of technology is something that always coexists with human survival.Technological developments include social media, digital transactions, communication, information and distance learning (PJJ).One of the technological developments in the aspect of digital transactions is an aspect that cannot be separated in this day and age, especially regarding non-cash payment systems.
Based on Bank Indonesia's Infographic for 2023, there is a 10.5% increase in the value of transactions using digital money.Digital transactions are broadly divided into two types, namely e-money and e-wallet [1].The difference between e-money and ewallet lies in the tools and payment systems used [2].E-money uses a chip system with a card as a payment instrument such as Brizzi and Flazz, while application-based E-wallets such as Dana, Ovo, Go-pay, Shopee-pay and so on.Apart from that, there is an alternative digital transaction issued by Bank Indonesia, namely QRIS.The presence of QRIS is expected to be a more efficient payment process and ensure its security is maintained.
Bank Indonesia targets around 5% of MSMEs in Indonesia to install QRIS as a non-cash transaction [3].From the information provided by the Ministry of Cooperatives and Small and Medium Enterprises (SMEs), until now 60 million SMEs in Indonesia have paired QRIS [4].Data from Bank Indonesia in Bali notes that 612,000 merchants have installed QRIS [5].In addition, the One-Stop Integrated Investment and Service Office noted that there are 73 MSMEs in Klungkung Regency that have paired QRIS [6].After conducting a survey and observation, it can be seen from MSME players in one of the villages of Klungkung Regency, Bali Province, namely Pesinggahan Village, that there are still very many who have not installed QRIS as a non-cash payment transaction.However, there are also several MSME players in Pesinggahan Village who have paired QRIS as a non-cash payment transaction.Based on the data and observations, it can be seen that there are quite a few MSME players in Pesinggahan Village who choose to pair QRIS.
Pesinggahan Village, located in Klungkung Regency, Bali Province is designated as a Tourism Village in accordance with Klungkung Regent Regulation Bali Province Indonesia Number 2 of 2017 because it has a variety of tourist attractions ranging from Goa Lawah Temple, Bukit Tengah, Warung Lesehan Sea Fish Satay, Traditional Salt Making and many more.The establishment of Pesinggahan Village as a Tourism Village means that more tourists will visit Pesinggahan Village.However, there are still few MSME players in Pesinggahan Village who pair QRIS as a non-cash payment.
Studies related to QRIS have been conducted by Setiawan & Mahyuni [7] using the technology acceptance model.The results of this study indicate that some of the obstacles faced by MSMEs in using QRIS include the quality of internet connections, usage costs, and transaction limits.Then, another study conducted by Sudiatmika & Martini [8] discussed the factors that influence the intention to use QRIS by MSME players in Denpasar City using the technology acceptance model approach.The results of this study indicate that perceptions of usefulness, ease of use, level of trust, and social influence have a significant impact on the intention of MSME players in Denpasar City to use QRIS as a means of payment.
This study will examine the factors that influence MSME actors in Pesinggahan Village not using QRIS as a non-cash payment method, using the factor analysis method.Santoso [9] suggests that factor analysis is a method that seeks to find a relationship between a number of variables that are initially independent of each other.With this approach, one or more groups of variables are formed that are fewer in number than the initial variables.Factor analysis aims to reduce complexity by combining a number of variables into fewer factors.These factors contain most of the information contained in the variables used [10].Factor analysis is divided into two, namely exploratory factor analysis and confirmatory factor analysis.Exploratory factor analysis includes the formation of factors that have not been predicted before.Meanwhile, confirmatory factor analysis involves testing hypotheses about the existence of factors that have been proposed previously [10].In this study, the exploratory factor analysis method will be used.

II. METHODS
The subjects used are MSME actors in Pesinggahan Village who do not pair QRIS as a non-cash payment method.This research will focus on the factors that influence the low interest of MSME actors in Pesinggahan Village in using QRIS as a non-cash payment method.The research population in this study are all MSME players in Pesinggahan Village who do not pair QRIS as a non-cash payment method.In this study, the number of samples taken from a population of 200 respondents.The sampling technique used the census method for all MSME actors in Pesinggahan Village who did not install QRIS with data on MSME actors obtained from the Pesinggahan Village Office.This study uses a quantitative approach as a research method.The purpose of this method is to understand the factors that influence the low interest in using QRIS as a non-cash payment method.The implementation of the research was carried out in the time span from November 2023 to January 2024.The variables used are presented in Table 1.

Table 1. Research Variables
Variables Statement Variables Statement Using QRIS requires more time to make transactions.
The level of trust that by using QRIS customers feel uncomfortable in transactions.The chance of transaction errors through QRIS is high than cash payments.QRIS usage has no positive impact on community engagement and participation in their business.Feels that QRIS is difficult for customers to use.
There is no socialization conducted to MSME players regarding the installation of QRIS.Feel that QRIS is not safe to use, both in terms of data security and transaction security.
Unwillingness to move from conventional to digital.
Feel that QRIS does not allow them to track transactions more easily and access sales revenue reports.
No significant demand from customers for QRIS payments.
Difficult to learn how to use QRIS.
Administrative requirements for installation that are difficult to complete.Difficult to get technical assistance if experiencing problems or difficulties in using QRIS.
Lack of skills in using electronic devices.
Level of trust that transactions using QRIS are not safe from the risk of fraud or unauthorized actions.
Not tech-savvy.
Level of trust that transactions using QRIS are not safe from the risk of fraud or unauthorized actions.
The data analysis technique followed the following steps: 1. Designing a research framework including determining research variables, making research questionnaires.2. Conducting questionnaire feasibility tests with validity and reliability tests in pre-reconnaissance activities by distributing questionnaires to 30 respondents.3. Collecting data in the field.4. Checking the feasibility of data to be analyzed by factor analysis.There are 3 tests carried out to provide the feasibility of data used in factor analysis, namely the Bartlett test of sphericity, the Measure of sampling of adequacy (MSA) test and the Kaiser-Meyer-Olkin (KMO) test. 5. Perform exploratory factor analysis with the first stage being factor extraction, which is reducing the initial number of variables to a small number of factors.6. Eliminate variables that have a communality value <0.5.7. Performing factor rotation.8. Determining the membership of a variable in a particular factor group in the study, we see the largest correlation value between the variable and the associated factor.9. Performing factor interpretation.

III. RESULT AND DISCUSSION A. Data Eligibility Test
Before conducting factor analysis, researchers must verify that the data matrix has sufficient correlation.For this test, there is an approach with three assumption tests, namely the Kaiser-Meyer-Olkin (KMO) test, Bartlett test, and MSA (Hair et al., 2019).The KMO test and Bartlett test are shown in Table 2. .000Sumber: Data Diolah (2024)

KMO Test
The KMO criterion that is sufficient for factor analysis is a value that exceeds 0.5.Based on Table 2, it can be seen that the KMO value reached 0.867.This figure exceeds the required 0.5 threshold, indicating that the data has qualified for factor analysis.

Bartlett Test
Based on Table 2 shows that the p-value of 0.000 < 0,005, then it is rejected.This means that the correlation matrix is not an identity matrix, so there is a correlation between variables.

MSA Test
The results of the sampling adequacy measurement are shown in Table 3.Based on Table 3, it is shown that all variables have intercorrelations greater than 0.5, so it can be said that these variables can be predicted by factor analysis.

B. Factor Extraction
The next step is to perform factor extraction, which includes determining the number of factors to be generated.Factor extraction is done by Principal Component Analysis (PCA) which aims to summarize the data into a minimum number of factors for prediction.In factor analysis, eigenvalues or characteristic values are a measure of the extent to which variability in the data can be explained by latent factors.When the eigenvalue is more than 1, this means that the latent factor is able to explain more variability than the variability contained in one original variable.The results of factor extraction are shown in Table 4.Given that the purpose of factor analysis is data reduction, the number of factors formed must be less than the number of initial variables.Furthermore, factor extraction was carried out on the number of loadings squared using Principal Component Analysis (PCA), resulting in 4 extracted factors.Keep in mind that loadings squared is the correlation/relationship between the original variable and the factor.The squared loadings value shows the percentage of variance in the original variable explained by a factor.Given that only factors that have an eigenvalue of more than 1 will be considered significant, the formation of 4 factors has met these criteria, namely 1,107 with a cumulative variance explained by 4 factors of 59.465%.
The cumulative percentage value in factor analysis illustrates the extent to which the amount of variability in the data can be explained by the latent factors extracted.If the cumulative percentage is low, below 60% this indicates that the latent factors identified may not explain most of the variation in the data.Some reasons that may lead to a low cumulative percentage value include: a. Irrelevant Variables There are variables in the analysis that are not relevant or do not contribute significantly to the latent factors identified.

b. Insufficient or Too Many Factors
If you limit the number of factors extracted to too few, the researcher will not extract most of the variation in the data.Conversely, if the number of factors is too large, some factors may not be significant.
In factor extraction, the communality value is obtained, which is the total amount of variance shared by the original variable with all other variables in the factor analysis.The recommended communality value to maintain is > (more than) 0.5 so that variables with a communality value below 0.5 will be excluded from the analysis.With this, the researcher will check the communality value of each variable and perform elimination for communality values less than 0.5.In each variable elimination, the researcher will check the number of factors formed as well as the cumulative percentage value explained from several factors formed.The communality value of the extraction results is shown in Table 5.It can be seen in Table 5 that the variables 1,2,11 and 12, have a communality value of less than 0.5.The communality value that is recommended to be maintained is greater than 0.5, in this case it means that variables with a communality value below 0.5 will be excluded from the analysis.The variable 12 that will be excluded is the variable with the smallest communality value, namely the variable 12 so that to get valid results the variable 12 will be removed from the analysis first and factor analysis will be carried out once again by eliminating the variables 12 shown in Table 6.

Table 6. Communality Value II
Variable Communality 0,478 0,583 0,519 0,657 0,738 0,659 0,660 0,772 0,822 0,809 0,339 0,564 0,527 0,562 0,714 0,549 Source: Data Processed (2024) Seen in Table 6 that the variable 1 and 11, has a communality value of less than 0.5.The variable that will be excluded is the variable 11 with the smallest communality value, namely the variable so that to get valid results the variable 11 will be removed from the analysis first and factor analysis will be carried out once again by eliminating the variable 11 shown in Table 7.It can be seen in Table 7 that the variable 1 has a communality value of less than 0.5, which is 0.485.So to get valid results, the variables 1 will be removed from the analysis first and factor analysis will be carried out once again by eliminating the variables 1 shown in Table 8.Table 8 shows that the communality value of all variables is more than 0.5.After eliminating 3 variables, the extraction of the formed factors can be explained in table 9. Table 9 shows that the amount of diversity of the 14 variables explained by the four factors formed by the Principal Component Analysis (PCA) method is 67.4% of the total diversity, which means that about 32.6% of the diversity is not explained by the four factors.In social research, at least 60% of the total diversity is explained by the factors formed, so forming four factors is considered sufficient with 67.4% of the total diversity explained.
Furthermore, the results of the component matrix before factor rotation are shown in Table 10.

C. Faktor Rotation
Oblique rotation is used considering that this research is social research, so it is very unlikely that the factors formed are not correlated with each other.After applying several oblique rotation methods, the researcher settled on direct oblimin rotation because it provides results that are considered quite reasonable for each variable [10].The results of the direct oblimin rotation are shown in Table 11.In the factor solution with the direct oblimin rotation method, it has not produced a simple structure because it does not produce a single high loadings for each variable on only one factor.Interpretation will be easy if high loadings are only for one factor, but in this case there are several variables that have high loadings with more than one factor.[10] proposed guidelines for identifying cross-loadings based on two principles, namely: Table 12 shows that all cross-loadings are negligible (factors 3 and 4).To achieve a simple interpretation, the variables will be considered to have no cross-loadings and are strong representative variables of each factor.On this occasion, the purpose of factor rotation is to obtain a simpler and easier to interpret factor solution.So, from the results of identifying cross-loadings I, it can be taken that variable 5 correlates with factor 3 by 0.819 and correlates with factor 4 by 0.464 so that variable 5 has a significant load on factor 3.

D. Interpretation of Results
After the factors are formed, each variable in one factor is sorted from the variable with the highest loadings to the variable with the lowest loadings shown in Table 13.Based on Table 13, the results of factor extraction show that the initial 17 variables were reduced to four factors.The first factor explains four significant variables with a variance percentage value of 37.907%.The second factor explains four significant variables with a percentage variance of 12.907%.The third factor explains three significant variables with a percentage variance of 9.856%.The fourth factor explains three significant variables with a percentage variance of 7.174%.
The factors identified as influencing the lack of interest of MSME actors in using QRIS as a non-cash payment method, especially MSME actors in Pesinggahan Village, are the factor of trust in the effectiveness of QRIS, the ability factor of MSME actors, the ease of use of QRIS and the factor of customer safety and comfort.The results of these factors are in line with the results of research conducted by [8] regarding the factors influencing the intention of Denpasar City MSME players to use QRIS with a technology acceptance model approach.The results of this study state that perceived usefulness, convenience, trust and social influence have a significant effect on the intention to use QRIS merchants by Denpasar City MSME players.

CONCLUSIONS
From the results and discussion that have been described, it can be concluded that: 1) The factors identified as influencing the lack of interest of MSME actors in using QRIS as a non-cash payment method, especially MSME actors in Pesinggahan Village, are the ability factor of MSME actors, the confidence factor in the effectiveness of QRIS, the safety and comfort factor and the ease of use of QRIS; 2) The ability factor of MSME actors consists of a lack of ability to use electronic devices, unwillingness to move from conventional to digital, not being technologically literate and administrative requirements for installation that are difficult to complete; 3) The QRIS effectiveness trust factor consists of the level of trust that their personal and business data will not be properly protected in the use of QRIS, the level of trust that with the use of QRIS customers feel uncomfortable in transactions, the level of trust that transactions using QRIS are not safe from the risk of fraud or unauthorized actions and the absence of significant demand from customers for payment through QRIS; and 4) The customer security and convenience factor consists of feeling that QRIS is difficult for customers to use, feeling that QRIS is not safe to use, both in terms of data security and transaction security, the chance of transaction errors through QRIS is high compared to cash payments, and feeling that QRIS does not allow them to transactions with more mud.

1 .
Comparing variances, not loadings; 2. Comparing the ratio of two variances.The results of identifying the value of cross-loadings are shown in Table 12.Tabel 12. Nilai Cross-Loading 0,216 3,116 Source: Data Processed (2024)

Table 2 . KMO Test and Bartlett Test KMO Test and Bartlett Test
Kaiser-Meyer-Olkin Measure of Sampling Adequancy .867