The mediating role of behavioral intention on factors influencing user behavior in the E-government state financial application system at the Indonesian Ministry of Finance

Meiranto, Wahyu; Farlyagiza, Fortunella; Faisal, Faisal; Yuyetta, Etna Nur Afri; Puspitasari, Elen Article The mediating role of behavioral intention on factors influencing user behavior in the E-government state financial application system at the Indonesian Ministry of Finance Cogent Business & Management Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Meiranto, Wahyu; Farlyagiza, Fortunella; Faisal, Faisal; Yuyetta, Etna Nur Afri; Puspitasari, Elen (2024) : The mediating role of behavioral intention on factors influencing user behavior in the E-government state financial application system at the Indonesian Ministry of Finance, Cogent Business & Management, ISSN 2331-1975, Taylor & Francis, Abingdon, Vol. 11, Iss. 1, pp. 1-17, https://doi.org/10.1080/23311975.2024.2373341 This Version is available at: https://hdl.handle.net/10419/326395 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Business & Management ISSN: 2331-1975 (Online) Journal homepage: www.tandfonline.com/journals/oabm20 The mediating role of behavioral intention on factors influencing user behavior in the E- government state financial application system at the Indonesian Ministry of Finance Wahyu Meiranto, Fortunella Farlyagiza, Faisal Faisal, Etna Nur Afri Yuyetta & Elen Puspitasari To cite this article: Wahyu Meiranto, Fortunella Farlyagiza, Faisal Faisal, Etna Nur Afri Yuyetta & Elen Puspitasari (2024) The mediating role of behavioral intention on factors influencing user behavior in the E-government state financial application system at the Indonesian Ministry of Finance, Cogent Business & Management, 11:1, 2373341, DOI: 10.1080/23311975.2024.2373341 To link to this article: https://doi.org/10.1080/23311975.2024.2373341 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group Published online: 05 Jul 2024. Submit your article to this journal Article views: 2388 View related articles View Crossmark data Citing articles: 5 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oabm20 InformatIon & technology management | research artIcle Cogent Business & ManageMent 2024, VoL. 11, no. 1, 2373341 The mediating role of behavioral intention on factors influencing user behavior in the E-government state financial application system at the Indonesian Ministry of Finance Wahyu meirantoa , fortunella farlyagizab, faisal faisala , etna nur afri yuyettaa and elen Puspitasaric aDepartment of accounting, Faculty of economics and Business, universitas Diponegoro, semarang, indonesia; bDepartment of Finance and Business, Vocational school, universitas Diponegoro, semarang, indonesia; cDepartment of accounting, Faculty of economics and Business, universitas stikubank, semarang, indonesia ABSTRACT saKtI is a treasury information system and financial report preparation newly implemented by the ministry of finance in 2022. the piloting phase has been conducted before official implementation to identify various challenges and the development of the application. the implementation of saKtI heavily depends on the intention behavior and operator behavior to use the system. therefore, the Unified theory of acceptance and Use of technology 2 (UtaUt2) framework is used to examine the factors influencing behavioral intention and user behavior of the saKtI system. the respondents involved in the study were 271 saKtI users at the state treasury service office. hypothesis testing was conducted on the partial least squares-structural model equation (Pls-sem) with smartPls 3.29 professional software. the research results provide empirical evidence that social influence and hedonic motivation do not affect the behavioral intention to use saKtI. however, performance expectancy, effort expectancy, facilitating conditions, and habit impact the behavioral intention to use saKtI. facilitating conditions, habits, and behavioral intention influences the use of saKtI. Behavioral intention to use saKtI mediates the relationship between facilitating conditions and habit with the behavior of using the saKtI system, with partial complementary mediation. the practical implication is that KPPn needs to provide special technical guidance for saKtI operators to make the system easier for them to use. enhancing security in the saKtI application and improving internet capacity are important considerations to maximize the use of saKtI. 1. Introduction In line with the development of the times and the era of Industry 4.0, where companies integrate automation with cyber technology, information technology has rapidly grown. computers are no longer just tools for processing transactions; they have evolved into integrated systems within a company. the Industrial revolution 4.0 in the government is marked by the implementation of the e-government system, an electronic-based system that utilizes information and communication technology to enhance efficiency, effectiveness, and accountability in the pursuit of good governance. the Indonesian government has issued Presidential regulation number 95 of 2018 concerning the electronic-Based government system (sPBe), regulating governance, management, information and communication technology audits, organizers, acceleration, and monitoring and evaluation of the sPBe. the state financial application system at the Institutional level known as saKtI is an information system that modernizes the implementation of state financial management functions from the perspective of budget users by integrating various application systems in state financial management. saKtI is an information system newly © 2024 the author(s). Published by informa uK Limited, trading as taylor & Francis group CONTACT Wahyu Meiranto [email protected] Department of accounting, Faculty of economics and Business, universitas Diponegoro, Jalan Prof. Dr Moeljono trastotenojo, tembalang, semarang, Central Java, 50275, indonesia. https://doi.org/10.1080/23311975.2024.2373341 this is an open access article distributed under the terms of the Creative Commons attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. the terms on which this article has been published allow the posting of the accepted Manuscript in a repository by the author(s) or with their consent. ARTICLE HISTORY received 25 february 2024 revised 6 June 2024 accepted 22 June 2024 KEYWORDS e-government financial application; accounting information system; behavioral intention; unified theory of acceptance and use of technology REVIEWING EDITOR anbazhagan neelamegam, alagappa University, India SUBJECTS finance; Business, management and accounting; Information technology 2 W. meIranto etal. launched by the ministry of finance on January 27, 2022, complementing the modernization of the general treasurer of the state (BUn) financial management through the implementation of the state treasury and Budget system as known sPan. the function of managing state finances, from the stage of preparation to financial accountability, starting from the Work Unit level (satker) up to the state ministry/Institutions (K/l), is carried out in one system through saKtI, which is a continuation of the implementation of the Integrated financial management Integration system (IfmIs). the development of saKtI goes through several stages namely, the feasibility study stage, needs analysis, application design, and application development (piloting stage) (www.djpb.kemenkeu.go.id). Previous research has been conducted from the piloting stage to the implementation of saKtI by the ministry of finance. according to rahman et al. (2023), saKtI provides convenience for the semarang state treasury office (KPPn 1) to carry out treasury and financial management processes, but improvements are needed to fully utilize the application. Information quality, system quality, and perceived ease of use affect user satisfaction, but access speed, available features in the system, and user training need to be enhanced (rahayuningtyas, 2022). saKtI facilitates the process of preparing financial reports due to single entry, integration of data between modules, real-time data presentation, and automatic generation of financial report components (Pambudi et al., 2022). the advantages of saKtI include a centralized database, high-security level, ease of application installation, and better application performance consistency. the future challenges for the use of saKtI are human resource readiness, internet capacity, and continuous training (nasution & nasution, 2022). Positive individual perception as well as technology and system are factors influencing user satisfaction in the piloting stage (sutiono & taufiqurahman, 2020). amriani and Iskandar (2019) stated that empirically using the Delone and mclean model approach, the implementation of saKtI has not been successful in the piloting stage, and provided recommendations for future research using the UtaUt framework. several studies using the UtaUt framework to test its influence on the acceptance and adoption of e-government systems, which are mandatory systems for government and public sector institutions, are described by ofosu-ampong et al. (2023), explaining two UtaUt variables which are performance expectation and hedonic motivation influencing behavioral intention. Dbesan et al. (2023) indicate that the UtaUt2 framework influences behavioral intention, while according to addy et al. (2022), performance expectation and social influence affect behavioral intention. Price value and habit do not affect doctors’ behavioral intention to use e-consultation during the coVID-19 pandemic (Dash & sahoo, 2021b), while the UtaUt2 framework influences patients’ behavioral intention to use e-consultation (Dash & sahoo, 2021a). Based on amriani and Iskandar (2019) who recommended for future research using the UtaUt framework, this study will use the UtaUt2 framework. Using the UtaUt2 framework is expected to provide empirical evidence for theoretical and applicative development, especially recommendations for the ministry of finance, and factors influencing the behavioral intention of operators to use saKtI. Price value, which is one of the variables in UtaUt2, is not used in this study, because saKtI does not impose costs on operators using it. hedonic motivation is one of the variables that will be used in this study, although saKtI is a mandatory system of the ministry of finance, researchers will explore whether user operators happily use it or see it as an obligation to fulfill. manrai etal. (2021) found empirical evidence that behavioral intention is able to mediate facilitating conditions and habits with behavior using mobile payment services, which is a supporting transaction banking service system for customers. the intention of behavior as a mediating variable in the acceptance of the mandatory system of the ministry of finance will be tested in this study. the research questions arising are (1) whether the UtaUt2 framework influences behavioral intention and the use of saKtI, and (2) whether behavioral intention is able to mediate the relationship between facilitating conditions and habits with behavior usage. 2.Theoretical framework and hypothesis development 2.1. State-level institution application system (SAKTI) minister of finance regulation of the republic of Indonesia number 171/PmK.05/2021 regarding the Implementation of the saKtI system states that saKtI is a system that integrates the processes of planning and budgeting, implementation, as well as accountability for the state revenue and expenditure cogent BUsIness & management 3 budget in government institutions. It is part of the national financial management system. the state treasury and Budget system (sPan) is an integrated system covering all budget management processes, including budget preparation, budget document management, supplier management, procurement commitment management, payment management, state revenue management, cash management, accounting, and reporting. saKtI consists of (1) administration module, (2) Budgeting module, (3) commitment module, (4) treasurer module, (5) Payment module, (6) Inventory module, (7) fixed asset module, (8) receivables module, and (9) accounting and reporting. saKtI is used by the Budget section (Ba) of state ministries/Institutions, the general treasurer’s Budget section (Ba BUn) with User access rights, the general treasurer, and other units granted User access rights (muhtaromin, 2018; nasution & nasution, 2022; nugroho & lestyowati, 2020). rahayuningtyas (2022) provides empirical evidence that system quality, information quality, and perceived usefulness influence saKtI user satisfaction. nugroho and lestyowati (2020), using the PIeces framework (Performance, Information, economics, control, efficiency, and services), provide empirical evidence that control plays a crucial role in user satisfaction, while performance is the least influential factor. sutiono and taufiqurahman (2020) provide empirical evidence that individual perception and technology/system are two factors influencing user satisfaction with saKtI during the piloting phase. amriani and Iskandar (2019) provide empirical evidence that system quality affects user satisfaction with saKtI, while information quality and user satisfaction affect net benefits. according to muhtaromin (2018), saKtI users experience satisfaction with several advantages, namely real-time data, integrated applications, single database, and integrated information. Perceived usefulness and perceived ease of use influence the acceptance of saKtI in the piloting stage (Prabowo, 2017). 2.2. Unified theory of acceptance and use of technology (UTAUT) UtaUt is a framework developed by Venkatesh etal. (2003) based on eight models: (1) theory of reason action (tra), (2) theory of Planned Behavior (tPB), (3) technology acceptance models (tam), (4) model of Pc Utilization (mPcU), (5) Diffusion of Innovation theory (DoI), (6) motivation models, (7) social cognitive theory, and (8) combined tam-tPB. It includes four main variables influencing behavioral intention: performance expectancy, effort expectancy, social influence, and facilitating conditions. Venkatesh et al. (2012) extended UtaUt to UtaUt2 by adding three variables influencing the behavioral intention to use information technology: habit, hedonic motivation, and price value. ofosu-ampong et al. (2023) found empirical evidence that hedonic motivation and technology readiness are the strongest factors influencing the behavioral intention to use the government digital census. Dbesan etal. (2023) used the UtaUt2 framework to test its influence on doctors’ behavioral intention to use blockchain technology in government hospitals, where performance expectancy, effort expectancy, social influence, facilitating conditions, and price value affect it. alkhwaldi et al. (2022) stated that performance expectancy, social influence, facilitating conditions, and technological task fit influence the behavioral intention to use the human resource Information system (hrIs) in public sector companies in Jordan. addy etal. (2022) explained that the UtaUt2 model can provide strong and good predictions for electronic procurement acceptance, where performance expectancy, social influence, and habit are variables influencing the behavioral intention to adopt electronic procurement. Dash and sahoo (2021a) stated that UtaUt2 can influence patient adoption of digital health consultation services, and similarly, Dash and sahoo (2021b) provide empirical evidence that the UtaUt2 framework can influence doctors’ adoption of e-consultation, where price value and habit have no influence. Kirat rai et al. (2020) explained that the UtaUt framework can influence attitudes and behavioral intentions to use government-to-government (g2g) systems. rinjany (2020) explained that effort expectancy and facilitating conditions influence the individual behavioral intention to use e-government. Wiafe et al. (2019) explained that the UtaUt model can contribute to the use of information systems in the maritime industry (Inttra), where performance expectancy and facilitating conditions influence individual behavioral intention. Wu and Wu (2019) provided empirical evidence that performance expectancy, effort expectancy, and facilitating conditions influence individuals to continue using library information systems in the context of public services. naranjo-Zolotov et al. (2019) provided empirical evidence that performance expectancy and facilitating conditions influence the individual behavioral intention to use 4 W. meIranto etal. e-participation systems. mansoori et al. (2018) stated that performance expectancy, effort expectancy, and facilitating conditions affect citizens’ behavioral intention to use e-government systems. saxena and Janssen (2017) provided empirical evidence that the UtaUt framework can influence individual behavioral intention to use open government data in India. 2.2.1. Performance expectancy (PE) Performance expectancy is defined as the level of an individual’s belief that using information technology will help them gain benefits in their job performance (Venkatesh et al., 2003). Performance expectation is defined as the extent to which the use of digital census tools enables officials to perform their tasks effectively (ofosu-ampong et al., 2023). In the context of saKtI usage, performance expectation refers to the level of belief that operators are capable of effectively, efficiently, and timely completing treasury tasks and preparing financial reports for work units. this is consistent with findings by ofosu-ampong etal. (2023), Dbesan etal. (2023), alkhwaldi etal. (2022), addy etal. (2022), sanjeev etal. (2021), Wiafe etal. (2019), naranjo-Zolotov et al. (2019), mansoori et al. (2018) who found empirical evidence that performance expectation influences behavioral intention to use e-government. H1. Performance expectancy positively influences individuals’ behavioral intention to use saKtI 2.2.2. Effort expectancy (EE) effort expectancy is defined the level of ease an individual perceives in using a particular information technology system (Venkatesh etal., 2003). effort expectation reflects users’ perception of how easy it is to use the human resources Information system (hrIs) in human resources management (hrm) (alkhwaldi etal., 2022). saKtI users perceive the system to be easy to understand and use, thus requiring minimal effort to learn. this is consistent with the findings of Dbesan etal. (2023), which state that effort expectation influences doctors’ behavioral intention to use blockchain technology in healthcare, as well as with rinjany (2020), Wu and Wu (2019), and mansoori etal. (2018), who found empirical evidence that effort expectation influences individuals’ behavioral intention to use e-government. H2. effort expectancy positively influences individuals’ behavioral intention to use saKtI 2.2.3. Social influence (SI) social influence is the extent to which an individual perceives that important others believe they should use a new system (Venkatesh et al., 2003). the effort of saKtI users to learn the system is through peer-to-peer learning provided by colleagues, as well as through training conducted by the workplace. this is consistent with the research conducted Dbesan et al. (2023), which state that social influence affects doctors’ behavioral intention to use blockchain technology in healthcare services. alkhwaldi et al. (2022) and addy et al. (2022) provide empirical evidence that social influence affects the behavioral intention to use e-government. Dash and sahoo (2021a) and Dash and sahoo (2021b) give empirical evidence that social influence affects the behavioral intention of doctors and patients to use e-consultation. While, saxena and Janssen (2017) explaining that respondents’ perceptions of family, coworkers, and superiors influence their behavioral intention to use open government data (ogD). H3. social influence positively influences individuals’ behavioral intention to use saKtI 2.2.4. Facilitating conditions (FC) facilitating conditions are the level of belief or the extent to which an individual believes that organizational and technical infrastructure can support a new system (Venkatesh et al., 2003). facilitating conditions can be defined as someone having easy access to electronic resources, such as computers, smartphones, internet connection, chat rooms, and other supportive conditions (naranjo-Zolotov et al., 2019). facilitating conditions such as computers, laptops, smartphones, internet, and dedicated staff are able to run the Jakarta smart city (Jsc) application program (rinjany, 2020). mansoori etal. (2018) stated cogent BUsIness & management 5 that adequate facilitating conditions can encourage the use of e-government services. facilitating conditions influence the behavioral intention to use blockchain healthcare services (Dbesan et al., 2023), as well as human resource systems alkhwaldi et al. (2022). according to Dash and sahoo (2021a) and Dash and sahoo (2021b), facilitating conditions influence the behavioral intention of doctors and patients to use e-consultation. adequate facilitating conditions are able to influence the use of the Inntra information system (Wiafe et al., 2019). supportive facilities also contribute to increased usage of mobile banking in malaysia (ahmad & yahaya, 2022). according to mohd thas thaker etal. (2021), technical support and basic infrastructure needs can support the usage of internet banking. manrai et al. (2021) explained that with a little basic training, rural women’s habits indirectly increase their usage of digital payment systems through their behavioral intentions. Based on evidence of manrai et al. (2021), this research will test whether behavioral intention becomes a mediating variable in the context of saKtI usage. H4a. facilitating conditions positively influence individuals’ behavioral intention to use saKtI H4b. facilitating conditions positively influence the behavior of using saKtI H4c. Behavioral intention to use saKtI mediates the relationship between facilitating conditions and behavior of using saKtI 2.2.5. Habit (H) habit is the extent to which someone tends to perform behavior automatically due to learning (limayem et al., 2007). hsu et al. (2019) explained that habit is a perception construct that can be influenced by the environment and experiences that may be unconscious. the behavioral intention of saKtI users arises from experiences using systems prior to saKtI as well as other software, as well as the ability to adopt information technology. according to ahmad and yahaya (2022), the behavioral intention of asnaf to use mobile banking is influenced by previous experiences using Internet banking and other financial applications. habit also influences the behavioral intention of sharia bank consumers to use m-banking (Iqbal et al., 2022), as well as internet banking (mohd thas thaker et al., 2021). the habits of rural women bring about their behavioral intention to use digital payment systems (manrai et al., 2021). consumers of Islamic banks who are accustomed to adopting information technology systems will use m-banking in their financial transaction activities (Iqbal et al., 2022), and consumers accustomed to digital systems will use ride-hailing applications for their daily activities (chakraborty etal., 2021). Çera etal. (2020) and owusu Kwateng et al. (2019) state that someone who is accustomed to digital systems will use m-banking in every financial transaction activity. manrai et al. (2021) found empirical evidence that the behavioral intention to use digital payment systems mediates the relationship between habit and usage behavior. like facilitating conditions, the behavioral intention to use saKtI will be tested as a variable mediating the relationship between habit and usage behavior. H5a. habit positively influences individuals’ behavioral intention to use saKtI H5b. habit positively influences the behavior of using saKtI H5c. Behavioral intention to use saKtI mediates the relationship between habit and behavior of using saKtI 2.2.6. Hedonic motivation (HM) hedonic motivation is the pleasure and comfort that arises when someone uses information technology (Venkatesh et al., 2012). When saKtI users feel comfortable and happy with the system, they will intend to use saKtI. this state aligns with ofosu-ampong etal. (2023), explaining that the pleasure and comfort of using the government’s digital census system influence users’ behavioral intention to use the system. 6 W. meIranto etal. alkhwaldi (2023), chakraborty etal. (2021), addy etal. (2018) found empirical evidence that comfort and happiness when using information systems influence users’ behavioral intention to use the information system. H6. hedonic motivation has a positive impact on individuals’ behavioral intention to use saKtI 2.2.7. Behavioral intention (BI) and behavior (B) Behavioral intention is the willingness and effort of an individual to engage in underlying behavior (Upadhyay etal., 2022). When saKtI users intend to use it, they will behave sustainably. this is supported by ofosu-ampong etal. (2023), Dash and sahoo (2021a) and Dash and sahoo (2021b), who explain that the behavioral intention of e-government users influences their usage behavior of the system. H7. the individual’s behavioral intention to use saKtI has a positive impact on the individual’s behavior of using saKtI figure 1 is a research framework illustrating the relationship between independent and dependent variables as explained in the hypothesis development above. 3. Research methodology 3.1. Data collection the population in this study consists of users and operators of saKtI at the level of the national treasury office (KPPn) work units. respondents involved in the sample were selected using the purposive sampling method (gray et al., 2007; ranjit, 2012; sugiyono, 2013) with the criteria that (1) respondents are employees of KPPn type a1 semarang II, (2) respondents are users and operators of saKtI at KPPn type a1 semarang II. the selection of KPPn semarang II as the sampling location is because it had the highest value for budget implementation realization compared to other KPPns in 2022. the research sample size is determined based on hair, Black, etal. (2019), stating that the minimum research sample size can be obtained by multiplying the number of question indicators in the research by 5 or 10. the research data was obtained from respondents who filled out a 1–5 likert scale questionnaire in google forms (appendix) via the s.id link. the informed written consent has been obtained in this study for the respondents who are involved as research samples. ethical approval was obtained from ethics committee of faculty of Business and economics, Universitas Diponegoro with reg number 29/Un.7f2.6.2/aK/V, and head of KPPn a1 semarang II with the letter number s-467/KPn.1402. research data were collected during the period of september 2023, and 271 out of a total of 486 respondents who were given the questionnaire provided complete answers. the response rate for this study was 55,76%, which consists of 150 males and 121 females, with 178 individuals aged between 21–40 years and 93 individuals aged between 40–59 years. there are 3 types of operators, namely payment operators consisting of 96 people, budget operators consisting of 100 people, and commitment Figure 1. Research model. cogent BUsIness & management 7 operators consisting of 75 people. regarding saKtI usage experience, 55 respondents had less than 1 year of experience, 105 had 1–2 years, and 111 had 3–5 years of experience. table 1 summarizes the demographic characteristics of the respondents involved in this study. 3.2. Data analysis method Data analysis in this study used the partial least squares structural equation modeling (Pls-sem) method analyzed with smartPls 3.2.9 professional software. Pls-sem is used in this study because it has stronger statistical power compared to cB-sem, allowing for certain relationships to be more significant (hair et al., 2022). non-response bias testing was conducted because there were respondents who answered late using an independent sample t-test. Descriptive analysis presented minimum and maximum values, mean, and standard deviation in this study. hair et al. (2022) explained that there are two evaluations, (1) evaluation for the measurement model and (2) evaluation for the structural model. the evaluation for the measurement model consists of four steps, indicator reliability, internal consistency reliability, convergent validity, and discriminant validity. evaluation for the structural model also consists of four steps, assessing the collinearity of the structural model, assessing the explanatory power of a model, assessing the predictive capability of a model, and evaluating the significance and relevance of the relationships in the structural model. 4. Analysis and result 4.1. Non-response bias test there were 30 respondents out of the 271 who answered the questionnaire late. therefore, a non-response bias test was conducted on the responses of those who answered late to investigate whether there were differences in responses between those who answered and returned the questionnaire on time. If the significance value (2-tailed) > 0.05, then there is no difference in responses between respondents who answered on time and those who did not. table 2, concerning the non-response bias test, shows significance values (2-tailed) for respondents who answered on time and those who answered late, 0.495 and 0.362, respectively. the test results indicate no difference in responses among respondents, allowing the late responses to be utilized as research data for hypothesis testing. 4.2. Descriptive statistics Descriptive analysis was conducted to determine the minimum, maximum, mean, and standard deviation of the variables used in the study. the average value of performance expectation falls within a high range at 17.04, approaching the maximum value of 20. other variables, such as effort expectation, social influence, facilitating conditions, habit, and hedonic motivation, have mean values within a moderate range above the midpoint. similarly, for behavioral intention and behavior variables, the mean values are within a moderate range, above the midpoint. the standard deviation values for each variable are below the mean, indicating that respondents’ answers fall within the range of the mean, with no abnormal data. table 3 shows descriptive statistic results. Table 1. Respondent demographic. Respondent demographic Category total Percentage gender Male 150 55,35% Female 121 44.,65% age 21–40 178 63,69% 40–59 93 34,31% operators Payments operators 96 35,8% Budgeting operators 100 36,6% Commitment operators 75 27,6% experience <1 year 55 20.29% 1–2 year 105 38.74% 3–5 year 111 40.97% 14 W. meIranto etal. 7. Limitation and future research the limitations of this study include the low response rate of respondents to the provided questionnaires and the delayed responses collected beyond the researcher’s specified deadline. future research should focus on maximizing the distribution and collection of questionnaires, with the assistance of an intermediary or liaison appointed to facilitate communication between respondents and researchers. further development of additional variables that can be integrated into the UtaUt2 framework is still possible in future research. concepts such as the D&m model and task technology fit (ttf) model can be integrated with UtaUt2. Ethical approval statement the study was approved by ethics committee of Business and economics faculty, Universitas Diponegoro with reg number 29/Un.7f2.6.2/aK/V, and KPPn a1 semarang II with the letter number s-467/KPn.1402. Authors’ contributions conceptualization: Wahyu meiranto, fortunella farlyagiza. Data collection: Wahyu meiranto, fortunella farlyagiza, etna nur yuyetta, elen Puspitasari. formal analysis: Wahyu meiranto, fortunella farlyagiza, etna nur yuyetta. methodology: Wahyu meiranto, faisal faisal, etna nur yuyetta. Project administration: elen Puspitasari. software: fortunella farlyagiza. Validation: Wahyu meiranto, faisal faisal. Writing – original draft: Wahyu meiranto. Writing – review and editing: faisal faisal, elen Puspitasari. all authors agree to be accountable for all aspects of the work. Disclosure statement no potential conflict of interest was reported by the authors. Informed consent Informed written consent is applicable to tis research. Funding this research did not receive any research grants or funding from other sources. About the authors Wahyu Meiranto is a lecturer at the accounting Department, faculty of Business and economics, Universitas Diponegoro, Indonesia. he has published paper in international journals with interest research in accounting information system, behavioral accounting. Fortunella Farlyagiza is an alumni of accounting and Business Department, school of Vocation, Universitas Diponegoro, Indonesia. she has expertise in accounting and taxation. Faisal Faisal is lecturer and Professor at the accounting Department, faculty of Business and economics, Universitas Diponegoro, Indonesia. he has published many papers in International journals with interest research in green accounting, corporate responsibility, sustainability and corporate governance. he is now Dean of faculty of Business and economics. Etna Nur Afri Yuyetta is a lecturer at the accounting Department, faculty of Business and economics, Universitas Diponegoro, Indonesia. she has published paper in international journals with interest research in accounting, capital market, corporate governance. Elen Puspitasari is a lecturer at the accounting Department, faculty of Business and economy, Universitas stikubank, Indonesia. she has published paper in international journals with interest research in accounting, finance, and capital market. she is now Vice rector III. cogent BUsIness & management 15 ORCID Wahyu meiranto http://orcid.org/0009-0004-7594-429X faisal faisal http://orcid.org/0000-0002-4847-0306 etna nur afri yuyetta http://orcid.org/0000-0001-6467-984X elen Puspitasari http://orcid.org/0000-0001-5076-0102 Data availability statement the datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. 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My productivity can increase by using saKti. effort expectancy 1. i will not have difficulty learning how to use the saKti application. 2. i feel comfortable doing my job using the saKti application. 3. it is easy for me to become proficient in using the saKti application. 4. overall, i find saKti easy to use. social influence 1. People i respect suggest that i use saKti. 2. People important to me think that i should use saKti. 3. People in my environment who use the saKti application have higher prestige than those who do not. 4. i use the saKti application for work because many people also use it. Facilitating condition 1. i have the necessary resources (computer/internet) to use the saKti application. 2. i have the necessary knowledge to use the saKti application. 3. saKti is compatible with other systems that i use. 4. saKti has guidelines for its usage. Hedonic motivation 1. using the saKti application is enjoyable for me. 2. using the saKti application is very entertaining for me. 3. using the saKti application is very rewarding for me. 4. using the saKti application is very interesting and enhances my prestige. Habits 1. using the saKti application is a habit for every operator at the DJPb agency level. 2. i feel satisfied when using the saKti application. 3. every operator at the DJPb agency level should use the saKti application. 4. using the saKti application is something i do without thinking for my job. Behavioral intention 1. i intend to continue utilizing saKti in my future work. 2. i will use saKti in my daily life. 3. Considering that i have a computer capable of accessing the internet, i will continue to use the saKti application. use behavior 1. sometimes, i use saKti for my work. 2. i often use the saKti application to perform daily tasks. 3. i always use the saKti application in all of my work.