Ilomata International Journal of Tax & Accounting P-ISSN: 2714-9838; E-ISSN: 2714-9846 Volume 5, Issue 4, October 2024 Page No. 1-12 1 | Ilomata International Journal of Tax & Accounting https://www.ilomata.org/index.php/ijjm Factors Influencing Interest in Using Financial Technology QRIS Among Accounting Lecturers in Bandar Lampung-Indonesia Evi Yuniarti1, Kamelia Septiani2, Rusmianto3, Lihan Rini Puspo Wijaya4, Umarudin Kurniawan5 12345Politeknik Negeri Lampung, Indonesia Correspondent: kameliaseptiani13@gmail.com2 Received : August 25, 2024 Accepted : October 5, 2024 Published : October 31, 2024 Citation: Yuniarti, E., Septiani, K., Rusmianto., Wijaya, ;, R, P., Kurniawan, U. (2024). Factors Influencing Interest in Using Financial Technology QRIS Among Accounting Lecturers in Bandar Lampung-Indonesia. Ilomata International Journal of Tax and Accounting, 5(4), 1-12. https://doi.org/10.61194/ijtc.v5i4.1243 ABSTRACT: This study aims to analyze the effect of perceived promotional attractiveness, ease of use, and risk of use on interest in using QRIS (Quick Response Code Indonesian Standard) financial technology. Quantitative methods are used with primary data obtained through questionnaires. Data analysis was performed using SmartPLS to test measurement and structural models. The results showed that the perception of promotional attractiveness, ease of use, and risk of use did not have a significant effect on interest in using QRIS. The t-count value is smaller than the t-table and the p-values are greater than 0.05. The research model has a predictive relevance value of 3.99% which indicates that this model lacks predictive relevance. The f- squared value of each variable shows a weak effect. The conclusion of this study is that perceived promotional attractiveness, ease of use, and risk of use do not affect interest in using QRIS financial technology. These findings make an important contribution in understanding the factors that influence QRIS usage interest and can be used as a basis for furtheir research by adding other variables such as perceived satisfaction and system reputation to get better results. Keywords: QRIS, Promotional attractiveness, Ease of use, Risk of use, Interest in use. This is an open access article under the CC-BY 4.0 license INTRODUCTION The Industrial Revolution 4.0 has greatly influenced technological advancements, particularly in Indonesia's banking sector (Mawardani & Dwijayanti, 2021). While this revolution provides opportunities for enhanced interaction, it also brings potential disruptions to various aspects of daily life (Yuniarti et al., 2020). A notable change is the increased adoption of internet and digital financial technology by Indonesians, highlighted by the launch of the National Non-Cash Movement (GNNT) by Bank Indonesia in 2014 (Risma & Sri, 2021). Bank Indonesia reported that electronic money transactions reached IDR 35.10 trillion in December 2021, marking a 58.60% growth compared to the previous year (Nadhilah et al., 2021). Innovations in the financial services sector, like Financial Technology (Fintech), have made financial services more accessible https://www.ilomata.org/index.php/ijjm mailto:kameliaseptiani13@gmail.com https://doi.org/10.61194/ijtc.v5i4.1243 Qris: Awareness and Understanding the Key Drivers Yuniarti, Septiani, Rusmianto., Wijaya, and Kurniawan 2 | Ilomata International Journal of Tax & Accounting https://www.ilomata.org/index.php/ijjm to the public through non-cash payment systems available on personal devices (Aisyah et al., 2023; Setiawan & Mahyuni, 2020). One aspect of Fintech is the development of non-cash instruments like e-money and e-wallets, which are becoming increasingly popular with various options such as GoPay, OVO, LinkAja, and DANA. To facilitate, accelerate, and secure transactions using QR codes, Bank Indonesia introduced the Quick Response Code Indonesian Standard (QRIS) on August 17, 2019 (Hu & Li, 2023; Nizar, 2017; Saputri, 2020). Despite the growing adoption of QRIS, several challenges still limit its widespread use, including difficulties with top-ups, application errors, and poor signal quality (Akhyar & Sicily, 2023). In Bandar Lampung City, QRIS is still not widely used, with only 12.43% of the population utilizing it in 2023 (Lampung Provincial Statistics Agency, 2023). Concerns about usage risks and personal data security are major obstacles to increasing interest in QRIS. This research explores factors that influence user interest in QRIS, such as perceived benefits, ease of use, and associated risks (Jaya, 2020). The study aims to address the research gap by providing a deeper understanding of user perceptions of QRIS and how these factors impact the decision to use the technology, particularly among accounting lecturers (Hamdan et al., 2024; Kristyawan Wihayanto Putra, 2025; Permana & Aryowiloto, 2024). Based on the background provided, this study aims to examine the impact of perceived benefits, ease of use, and perceived risk on accounting lecturers' interest in using QRIS electronic money (Lesi, 2020; Santoso et al., 2021). The research questions include whetheir perceptions of promotional attractiveness, ease of use, and risk influence lecturers' interest in using QRIS (Komalawati & Ariftio, 2021; Nugroho, 2023). This study is expected to contribute to the field of accounting and taxation through the digitalization of accounting, and also serve as a reference for future research on the benefits and ease of QRIS transactions (Khofifa, 2023; Li et al., 2022). By investigating the factors that affect user interest in QRIS, this research aims to offer new insights that can help boost the adoption of digital financial technology in Indonesia (Martini et al., 2022; Rjoub et al., 2023). METHOD This research method includes several stages that are comprehensively detailed, from sampling techniques to data analysis. The following is a detailed explanation of each of these stages: Population and Research Sample The population in this study were accounting lecturers at universities in Bandar Lampung City. There are 9 universities with a total population of 155 lecturers recorded in the university database (PDDikti). This population consists of lecturers spread across various universities such as Lampung State Polytechnic, Bandar Lampung University, Lampung University, Technocratic University of Indonesia, Malahayati University, Sang Bumi Ruwa Jurai University, Mitra Indonesia University, Raden Intan Lampung State Islamic University, and Darmajaya Informatics and Business Institute. The sample for this research was selected using a combination of probability sampling and purposive sampling techniques. Probability sampling was employed because the population size https://www.ilomata.org/index.php/ijjm Qris: Awareness and Understanding the Key Drivers Yuniarti, Septiani, Rusmianto., Wijaya, and Kurniawan 3 | Ilomata International Journal of Tax & Accounting https://www.ilomata.org/index.php/ijjm was known, while purposive sampling was chosen due to specific considerations aligning with the research criteria. Purposive sampling was selected because there are often constraints that make random sampling difficult, so it is used to ensure that the sample criteria closely match the research objectives. According to the Slovin formula, with a 10% margin of error, the calculated sample size is 61 respondents. Types and Methods of Data Collection The type of data used in this study is primary data. This primary data was obtained directly from respondents through distributing questionnaires. The questionnaire used contains questions related to perceived ease of use, perceived risk of use, and perceived promotional attractiveness of interest in using Financial Technology QRIS. Primary data is measured using a Likert scale which is commonly used to measure attitudes and opinions. Data Collection Methods The data collection method was carried out using a questionnaire distributed online. This questionnaire is closed with each question measured using a Likert scale of 1-5. This scale is used to measure respondents' attitudes, opinions, and perceptions about the social phenomenon under study. Variable Operationalization This study involveis several variableis, both dependent and independent. The dependent variable is interest in using Financial Technology QRIS, while the independent variables consist of perceived promotional attractiveness, perceived ease of use, and perceived risk of use. Each variable is operationalized through several indicators measured in the questionnaire. Data Analysis and Hypothesis Testing For data analysis, this study employs the Partial Least Squares (PLS) method using SmartPLS software. The PLS method was chosen because it can estimate path models that involve latent constructs with multiple indicators, and it aids in obtaining latent variable values for predictive purposes. PLS is also considered a robust analysis method, as it does not require the assumption of normal data distribution and can estimate linear relationship between the constructs being studied. Model Measurement (Outer Model) Outer model evaluation is carried out by twisting convergent and discriminant validity. Convergent validity is twisted by looking at the loading factor value and Average Variance Extracted (AVEI) for each variable. The loading factor value must be more than 0.70 for confirmatory research and more than 0.60 for exploratory research. The VEI should also be more than 0.50 for both types of research. Discriminant validity is twisted by comparing the square root of their AVEI with their correlation between latent constructs, where the square root of their AVEI must be greater than their correlation between latent constructs. Structural Model (Inner Model) Inner model analysis aims to predict their relationship between latent variables. Evaluation of their structural model is done by looking at the R-Square value for each endogenous latent variable, which shows the predictive power of the structural model. An R-Squared value of 0.67 indicates strong strength, 0.33 indicates moderate strength, and 0.19 indicates weak strength. In addition, the inner model analysis also involves measuring effect size (f²) and predictive relevance (Q²) to assess the quality of the prediction model. Overall, the research method was carefully designed to ensure the validity and reliability of the research results. The use of PLS methods and appropriate https://www.ilomata.org/index.php/ijjm Qris: Awareness and Understanding the Key Drivers Yuniarti, Septiani, Rusmianto., Wijaya, and Kurniawan 4 | Ilomata International Journal of Tax & Accounting https://www.ilomata.org/index.php/ijjm sampling techniques is expected to provide an accurate picture of the perceptions of accounting lecturers in Bandar Lampung towards the use of QRIS Financial Technology. RESULT AND DISCUSSION Validity Teist There are 2 validity tests in PLS-SEM, namely Convergent Validity and Discriminant Validity which are seen based on the value of loading factor, Average Variance Extracted (AVEI) values and Cross Loading value (Abdillah & Hartono, 2015). Validity Convergent - Loading Factor Convergent validity has 2 test criteria, namely looking at the loading factor value and the Average Variance Extracted (AVEI) value. Question items are considered to have passed the Convergent Validity test if the loading factor value must be greater than 0.70 and the Average Variance Extracted (AVEI) value must be greater than 0.50 (Irwandi & Yuniarti, 2023). The results of the loading factor in this study area in Table 1. Table 1. Hasil Uji Validity Convergent - Loading Factor Variable Indicator Loading Factor P-Value Information Perception of Promotional Appeal X1_1 0.802 <0.001 VALID X1_2 0.822 <0.001 VALID X1_3 0.782 <0.001 VALID X1_4 0.750 <0.001 VALID X1_5 0.818 <0.001 VALID Perception Ease of Use X2_1 0.861 <0.001 VALID X2_2 0.812 <0.001 VALID X2_3 0.796 <0.001 VALID X2_4 0.841 <0.001 VALID X2_5 0.844 <0.001 VALID X2_6 0.850 <0.001 VALID Perception of Usage Risk X3_1 0.777 <0.001 VALID X3_2 0.771 <0.001 VALID X3_3 0.762 <0.001 VALID X3_4 0.778 <0.001 VALID X3_5 0.776 <0.001 VALID Interest in Using Financial Technology QRIS Y1_1 0.905 <0.001 VALID Y1_2 0.951 <0.001 VALID Y1_3 0.885 <0.001 VALID Y1_4 0.708 <0.001 VALID Y1_5 0.776 <0.001 VALID Y1_6 0.844 <0.001 VALID Y1_7 0.862 <0.001 VALID Source: SmartPLS data processing, 2024 https://www.ilomata.org/index.php/ijjm Qris: Awareness and Understanding the Key Drivers Yuniarti, Septiani, Rusmianto., Wijaya, and Kurniawan 5 | Ilomata International Journal of Tax & Accounting https://www.ilomata.org/index.php/ijjm Based on Table 2. Each question of the variables of perception of promotional attractiveness, perception of ease of use and perception of risk of use has a valid loading factor value. This is due to the fact that the loading factor value on each question item has a value of more than 0.70. Validity Convergent - Average Variance Extracted (AVEI) Table 2. Hasil Uji Validity Convergent - Average Variance Extracted Variable AVE Information Perception of Promotional Appeal 0.633 VALID Perception of Ease of Use 0.696 VALID Perception of Usage Risk 0.597 VALID Interest in Using Financial Technology QRIS 0.724 VALID Source: SmartPLS data processing, 2024 Based on the table above, it can be concluded that all variables in this study have AVEI values of more than 0.50 (Yuniarti et al., 2023). Discriminant Validity - Cross Loading Discriminant validity is used to ensure that each latent variable or indicator in the study has a unique value and is not intertwined with other variables or indicators. The model is considered good if the loading factor value is the largest compared to the cross loading value for each variable. Table 3. Hasil Uji Discriminant Validity - Cross Loading X1 X2 X3 And X1_1 0.802 0.150 0.097 0.161 X1_2 0.822 0.145 0.115 0.200 X1_3 0.782 0.264 0.224 0.073 X1_4 0.750 0.145 0.286 0.083 X1_5 0.818 0.309 0.246 0.261 X2_1 0.157 0.861 0.373 0.109 X2_2 0.299 0.812 0.406 0.176 X2_3 0.295 0.796 0.253 0.133 X2_4 0.217 0.841 0.190 0.124 X2_5 0.193 0.844 0.266 0.122 X2_6 0.127 0.850 0.281 0.149 X3_1 0.154 0.152 0.777 0.185 X3_2 0.231 0.345 0.771 0.212 X3_3 0.201 0.392 0.762 0.089 X3_4 0.081 0.231 0.778 0.176 X3_5 0.214 0.318 0.776 0.198 Source : SmartPLS data processing, 2024 https://www.ilomata.org/index.php/ijjm Qris: Awareness and Understanding the Key Drivers Yuniarti, Septiani, Rusmianto., Wijaya, and Kurniawan 6 | Ilomata International Journal of Tax & Accounting https://www.ilomata.org/index.php/ijjm Based on Table 3. the results show that the cross loading value can be concluded that there is no problem with the discriminant validity, because all indicators have a greater correlation coefficient with their latent variables. Reliability Teist Reliability test has the aim of making the accuracy of the consistency of the instrument to measure a construct, this test is carried out using the composite reliability value and Croncbach alpha. Table 4. Reliability Test Results Variable Composite reliability Cronbach alpha Information Perception of Promotional Appeal 0.896 0.864 Reliable Perception of Ease of Use 0.932 0.913 Reliable Perception of Usage Risk 0.881 0.836 Reliable Interest in Using Financial Technology QRIS 0.948 0.938 Reliable Source: SmartPLS data processing, 2024 Based on the table above, the reliability test results show that the Cronbach's Alpha and Composite Reliability values for all constructs exceed 0.70. This indicates that the constructs have meet the reliability criteria and have high reliability. With the previous data, it can be concluded that this study has good convergent validity, discriminant validity, and composite reliability. The analysis and evaluation of the measurement model in the PLS-SEM Algorithm calculate results are as follows: Figure 3. PLS-SEIM Algorithm Outer Model Equation https://www.ilomata.org/index.php/ijjm Qris: Awareness and Understanding the Key Drivers Yuniarti, Septiani, Rusmianto., Wijaya, and Kurniawan 7 | Ilomata International Journal of Tax & Accounting https://www.ilomata.org/index.php/ijjm Structural Model Analysis (Inner Model) Coefficient of Determination (R-Squared) In assessing the structural model, R-Square is used to determine how much the independent variables are able to explain the dependent variable. The greater their-Square value, the greater the influence of exogenous latent variables on endogenous variables. Their-Squared value obtained of 0.093 indicates that the independent variables (promotional attractiveness, ease of use, and risk of use) only explain 9.3% of the variance in the dependent variable (interest in using Financial Technology QRIS). This means that 90.7% of the variability in interest in using QRIS is influenced by other variables outside this research model, which indicates that the model is weak. Predictive Relevance (Q-Squared) The next step in measuring the structural model is with Predictive Relevance (Q-Square). Predictive Relevance (Q-Square) evaluates how well the model can predict its value and parameter estimates. The model is considered to have predictive relevance if the Q-Squared value> 0, while if the Q-Squared value ≤ 0, the model is considered to lack predictive relevance. Table 5. Q-Squared Test Results SSO SSEI Q² (=1-SSEI/SSO) X1_Daya Withdraw Promotion 305.0000 305.0000 X2_Keimudahan Usagei 366.0000 366.0000 X3_Risiko Usagei 305.0000 305.0000 Y_Minat Use of Fintech QRIS 427.0000 409.9533 0.0399 Source: SmartPLS data processing, 2024 The Q-Squared result is 0.0399 if presented at 3.99%, which means that the research model can be said to lack predictive relevance. F-Squared The f-squared value shows the effect of endogenous variables on exogenous variables. The f- squared criteria are: 0.02≤ f ≤ 0.15 = weak effect, 0.15 ≤ f ≤ 0.35 = medium effect, f ≥ 0.35 = large effect. Based on Table 6, each variable has a weak effect size. Table 6. F-Squared Test Results Relationship F2 Criterion Promotional Appeal to Interest in Using QR IS Finteich 0.032 Weak EIsai of Use for Interest in Using QRIS Fintech 0.003 Weak Usage Risks to Interest in Using QR IS Fintech 0.028 Weak Source: SmartPLS data processing, 2024 https://www.ilomata.org/index.php/ijjm Qris: Awareness and Understanding the Key Drivers Yuniarti, Septiani, Rusmianto., Wijaya, and Kurniawan 8 | Ilomata International Journal of Tax & Accounting https://www.ilomata.org/index.php/ijjm Normed Fit Index (NFI) The last step in this study is to analyze the fitt model that shows how good or good the author's research model is, which can be seen through the value of the Normed Fit Index. the NFI yield value is 0.6787 if rounded to 0.68 or 68%. This shows that the researcher's research model is 68% fit or the fit is good. The analysis and evaluation of the measurement model in the PLS-SEM Algorithm calculate results are as follows: Figure 4. PLS-SEIM Algorithm Inner Model Equation Hypothesis Test The way to seiei the results of hypothesis testing is done by comparing t-tables and t-counts. The value of the t-calculation is seen through the results of bootstrapping testing using SmartPLS software. The hypothesis test in this study will use the value of t-Statistic, so this study uses a two- tailed hypothesis test so that the value of t-Statistic must be > 1.65 and the value of p values must be < 0.05. If the value of t-calculate> t-table, then H0 is rejected and Ha is accepted and vice versa. Table 7. Hasil Uji Path Coefficients Original Sample (O) Sample Mean (M) Standard Deviation (STDEV) T Statistics (|O/STDEV|) P Values Information X1 > Y 0.178 0.1662 0.1807 0.9880 0.3236 Rejected X2 > Y 0.056 0.0680 0.1648 0.3445 0.7306 Rejected X3 > Y 0.175 0.2115 0.1479 1.1853 0.2365 Rejected Source: SmartPLS data processing, 2024 The findings of this study offers a detailed understanding of how perceived promotional attractiveness, ease of use, and risk impact the interest in using QRIS Financial Technology, while also comparing these outcomes with relevant theories and previous research. https://www.ilomata.org/index.php/ijjm Qris: Awareness and Understanding the Key Drivers Yuniarti, Septiani, Rusmianto., Wijaya, and Kurniawan 9 | Ilomata International Journal of Tax & Accounting https://www.ilomata.org/index.php/ijjm Firstly, regarding perceived promotional attractiveness, the study reveals that QRIS promotions do not significantly influence user interest. This aligns with the research by (Mulyaningtyas & Ihsan, 2023), which also found that promotion has a positive but insignificant effect. However, this result contrasts with (Mujib & Amin, 2023) research, which identified a significant positive impact of perceived promotion on customer interest in using QRIS in Surabaya. This discrepancy suggests that while promotion is a key factor in capturing consumer attention, QRIS promotions may not be as competitive as other payment methods like Gopay and LinkAja, which offer more appealing and frequent promotions. Therefore, to enhance QRIS's appeial, it is essential to reassess and improve promotional strategies, incorporating more aggressive and creative promotional techniques. (Ghozali, 2021) Second, in terms of perceived ease of use, the results of this study indicate that the perceived ease of use of QRIS does not have a significant influence on user interest. This finding is in line with (Mulyati, 2023) which also states that ease of use has no effect on QRIS user interest. However, this is different from the results of research by Ningsih, Sasmita & Sari (2021) which found that ease of use has a positive and significant effect. These findings indicate that although QRIS is designed to facilitate transactions, users' lack of understanding and experience with this system can be a major inhibiting factor in its adoption. Therefore, it is important to increase education and socialization efforts regarding how to use QRIS, so that users feel more comfortable and confident in using this technology. Third, regarding perceived risk of use, the results of this study indicate that perceived risk does not have a significant effect on interest in using QRIS, in line with Mulyati's research (2023). However, this result is different from the research of (Fadhli & Fachruddin, 2016) which found that risk perception has a negative effect on the use of internet banking. Although the risk perception of QRIS has no effect on user interest in this study, it is still important to maintain and improve security and transparency measures in the QRIS payment system. Security and protection of user data are crucial factors that can affect user trust and convenience. If QRIS can maintain high security standards and address security issues quickly and effectively, then the perception of risk can be minimized, which in turn can increase user interest and adoption. Overall, this study makes a significant contribution in understanding the factors that influence interest in using QRIS and how they differ from previous research findings. The findings underscore the importance of improved promotional strategies, increased user education, as well as reinforcement of high security standards in influencing interest in using QRIS. The practical implications of this research includes the need for QRIS managers to adjust promotional and educational strategies, and ensure system security that can increase user trust and satisfaction. CONCLUSION Based on the test results and discussion, the conclusion of this study shows that the perception of promotional attractiveness, ease of use, and risk of use does not have a significant effect on interest in using QRIS financial technology. QRIS users feel that the promotions offered are still lacking, the ease of use of QRIS is not fully understood even though it has been introduced as an easy and reliable electronic payment standard, and perceptions of usage risk have no effect on user interest. https://www.ilomata.org/index.php/ijjm Qris: Awareness and Understanding the Key Drivers Yuniarti, Septiani, Rusmianto., Wijaya, and Kurniawan 10 | Ilomata International Journal of Tax & Accounting https://www.ilomata.org/index.php/ijjm For future research, it is recommended that researchers add additional variables such as user satisfaction, system reputation, and system quality to get more comprehensive results. In addition, the use of better data collection methods and expansion of the population, sample, and research locations will help obtain more accurate data and produce better research. REFERENCE Abdillah, W., & Hartono, J. (2015). Partial Least Squares (PLS): An alternative to Structural Equation Modeling (SEIM. 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