Keywords

Behaviour intention; Facility condition; Perceived ease of use; Perceived value; Usage of siskeudes.

Background

The village financial system application (siskeudes) is developed by the Financial and Development Supervisory Agency (BPKP) to improve the quality of village financial governance. The village financial management application features are made user-friendly and straightforward to make it easier for users to operate (Kementerian Dalam Negeri & BPKP, 2017). All villages in Indonesia have used the siskeudes application since 2015. The goal is to make village financial reporting transparent and accountable. According to Camilleri (2019a), the findings of many government agencies, both at the central and regional levels using information and communication technology (ICT) to provide public services. Applications made by the government aim to increase the efficiency and effectiveness of work (Saxena & Janssen, 2017; Talukder, 2019).

Based on the preliminary observations at the Kenteng village, Semarang Regency, it is known that those who operate the siskeudes are the village treasurer and the village secretary. Many obstacles include the input data that cannot be edited, the transition to using the online system, and real-time data. Furthermore, the addition of the existing menu in the application often makes users confused. Moreover, there is rarely intensive training from related parties. Finally, users only learn on their own from the module with assistance from village facilitators. The results of this study are also in line with research (Mahmud et al., 2020), which explains that there are still technical and non-technical obstacles in implementing siskeudes.Based on the preliminary observations at the Kenteng village, Semarang Regency, it is known that those who operate the siskeudes are the village treasurer and the village secretary. Many obstacles include the input data that cannot be edited, the transition to using the online system, and real-time data. Furthermore, the addition of the existing menu in the application often makes users confused. Moreover, there is rarely intensive training from related parties. Finally, users only learn on their own from the module with assistance from village facilitators. The results of this study are also in line with research (Mahmud et al., 2020), which explains that there are still technical and non-technical obstacles in implementing siskeudes.

However, the use of ICT in the siskeudes application still has many obstacles, including facilitating conditions, perceived ease of use, and perceived usefulness (Camilleri, 2019a ). Research conducted by Saxena & Janssen (2017) provides recommendations for further researchers to examine the effect of perceived value on using e-government. In addition, performance expectancy, effort expectancy, social influence, and facilitating conditions affect user behaviour systems (Saxena & Janssen, 2017); Kalinić et al., 2020). These studies are by the Unified Theory of Acceptance and Use of Technology (UTAUT) (Robbins, 1964 ; Lee et al., 2003), a technology acceptance model that synthesizes elements in the technology acceptance model that has existed before. This study will analyze the variable conditions of the facilitating conditions, performance expectancy, perceived ease of use of the system, and perceived value.

Facility conditions are the extent to which an individual believes that the organizational and technical infrastructure exists to support the use of the system ( Alshibly et al., 2016; Mansoori et al., 2021; Kalinić et al., 2020); Saxena & Janssen, 2017). Individuals have different perceptions about the functionality of the facilities they use and other issues, including the provision of ongoing training and development, user support and access to technology (Lee & Porumbescu, 2019; Venkatesh et al., 2012). The results showed a relationship between individual facility conditions and intention to use the technology moderated by the experience variable. Experience can moderate the relationship between particular facility conditions and their behavioural intention to use technology.

Performance expectancy effects using and accepting a technology system (Saxena & Janssen, 2017). Performance expectancy is the extent to which a person believes that if they use a particular design or technology, there will be a resulting increase in job performance (Venkatesh et al., 2012). Those individuals believe that using existing technological systems will help them achieve gains in job performance. Performance expectancy also mediates the relationship between the use of m-government (Talukder, 2019). Individual perceptions of the use of technology (perceived ease of use) will affect the acceptance of the technology they face. Individuals often believe that they will benefit from its convenience (Scott et al., 2016 ). Furthermore, perceived value can also determine the behavioural intention and usage behaviour in using technology (Venkatesh et al., 2012; Camilleri, 2019a).

This research is necessary to evaluate the use and acceptance of siskeudes. The objective of this study is to determine the factor affecting the intention to use siskeudes. The novelty of this research is the use of perceived value variables where the values believed by users can evaluate the behaviour of using the system. These values are perceived epistemic value, perceived functional value, and perceived contextual value. This research contributes to the implementation of siskeudes in Central Java and the constraints and obstacles. Furthermore, it contributes to the testing of UTAUT theory which integrates the behaviour of using the system. One of them is that individual experience can mediate and moderate behaviour.

Literature Review

Unified Theory of Acceptance and Use of Technology (UTAUT)

The Unified Theory of Acceptance and Use of Technology (UTAUT) is an integrated and systematic model that aims to explain the user's interest in using an information technology system and the behaviour of its users (Venkatesh et al., 2012). UTAUT is a technology acceptance that synthesizes elements in the technology acceptance model before, namely Theory Reasoned Action (TRA). Furthermore, develop the Theory Acceptance Model (TAM), Motivation Model (MM), Theory of Planned Behavior (TPB), Combined TAM and TPB, Model of PC Utilization (MPTU), Innovation Diffusion Theory (IDT) and Social Cognitive Theory (SCT) to get a balanced view on technology acceptance.

Seven constructs have been shown to have a significant effect on behavioural intention and usage behaviour. From this theory, four constructs play a direct influence on user acceptance and usage behaviour. The four constructs are performance expectancy, effort expectancy, social impact, and facilitating conditions. It is known that attitude toward using technology, self-efficacy, and anxiety do not directly affect intention. In this theory, it is also explained that there is a moderating factor, namely experience. The following is a picture of the explanation of the UTAUT model (Figure 1 & Figure 2).

Conceptual Framework and Hypothesis Development

A relevant literature review is highly recommended in determining the conceptual framework of a theory in a study. This study using various theories, including the technology acceptance model (Davis et al., 1989), theory of planned behaviour (Ajzen, 1991), and unified theory of acceptance and use of technology (Venkatesh et al., 2003)

. Furthermore, the research has been proven valid in communication and technology research which contains investigations of individual readiness to use technology in different contexts (Venkatesh et al., 2012).

 

Behaviour intention to use technology

TAM explains that the behavioural intention of individuals to use technology will be determined by their attitude towards the use of technology, which in turn will determine the usefulness and perceived ease of use of information systems (Davis et al., 1989). Individual behavioural intentions are determined by the individual's attitude towards the behaviour and by the normative pressures experienced by the individual. Normative pressure or subjective norms directly affect individual behavioural intentions because others can influence individuals to use technology (Camilleri & Camilleri, 2017a; Ajzen, 1991; Venkatesh et al., 2003). They integrated elements of different theoretical models and empirically validated them in their UTAUT model. They explore how individuals accept and use technology in their workplace environment. Individual performance expectancy, effort expectations, social influence, and facilitation conditions are the four primary constructs influencing their intention to use technology. They state that behavioural intentions have a significant positive effect on technology use. User experience moderates the relationship between behavioural intentions and use (Park et al., 2012; Venkatesh et al., 2003).

Performance expectancy

Performance expectancy describes how users believe the technology will support them. This construct is related to perceived usefulness with extrinsic motivation because it emphasizes utilitarian values (Davis et al., 1989). The relationship between users’ performance expectancy and their intention to use technology will be moderated by experience Venkatesh et al., 2003. These demographic variables can affect the adoption of technology adoption (Camilleri, 2019b ).

Facility condition

The users of technology depend on its function and the structural features of the environment, such as training, support, and access to technology (Hassan & Lee, 2019). Facility condition constructs as the extent to which an individual believes that the organizational and technical infrastructure exists to support the use of the system (Venkatesh et al., 2003). Older people may face more difficulty in adopting new technologies (Camilleri, 2017a). In addition, there are differences between genders. Therefore, age, gender and experience may moderate the relationship between facilitating conditions and behavioural intentions.

Perceived ease of use

Perceived ease of use, TAM measures the extent to which an individual feels that the technology is user-friendly and effort-free. Individuals' perceptions of technology use (PEoU) will influence their technology acceptance (Davis et al., 1989). Frequently, individuals believe that they will benefit if they use easy-to-use technology (Scott et al., 2016 ). Similarly, citizens' perceived ease of use is a significant predictor of their intention to use e-government services. Age, gender, and experience influence the causal relationship between individuals' effort expectations and their behavioural intention to use technology (Venkatesh et al., 2012).

Perceived value

Individual's perceived value can determine the behavioural intention and usage behaviour in using technology (Camilleri, 2019a; Woodside & Quaddus, 2015 ; Mansoori et al., 2021). The value of beliefs that individuals have can determine the behaviour and acceptance of individual technology. So they have a good perception of the technology system used.

Research Hypotheses

H1: Behavioral intention to use siskeudes has a significant effect on the usage of siskeudes

H1a: User experience moderate effect between behavioural intentions and the usage of siskeudes

H2: Performance expectancy has a significant effect on behavioural intention to use siskeudes

H2a:User experience moderate effect between Performance expectancy and behavioural intention to use siskeudes

H3: Facility condition has a significant effect on usage of siskeudes

H4: Facility condition has a significant impact on behavioural intentions to use siskeudes

H4a: User experience moderate effect between facility conditions and behavioral intention to use siskeudes

H5: Perceived ease of use has a significant effect on behavioural intentions to use siskeudes

H5a: User experience moderate effect between perceived ease of use and behavioural intention to use siskeudes

H6: Perceived value has a significant effect on the usage of siskeudes

Methodology

Research Design

This research is a quantitative method that emphasizes analysis of numerical data or numbers obtained by statistical methods and is carried out in inferential research or to test hypotheses. The significance of the relationship between the variables studied is obtained. By using the cluster sampling technique, the villages in Karisidenan Pati, Banyumas, Kedu, Surakarta, Semarang, and Tegal were determined. Each residency will be taken by one district that has good performance. Based on the results of the district performance in 2019, Banjarnegara Regency (Karisidenan Banyumas), Kudus Regency (Kariseidenan Pati), Klaten Regency (Karisidenan Surakarta), Semarang Regency (Karisidenan Semarang) and Tegal Regency (Karisidenan Tegal) were selected. The samples in this study were the village head, village treasurer, village secretary, and siskeudes admin consisting of 137 people.

Research Variable

The research variables consisted of independent variables, moderating variables, intervening variables, and dependent variables. The independent variables consist of performance expectancy (PERFEXP), perceived ease of use (PEOUSE), condition of facilities (FACCON), and perceived value (PERVAL). Performance Expectancy is individuals who believe that technology will support job performance with indicators of perceived usefulness, extrinsic motivation, job-fit, relative advantage, and outcome expectations (Venkatesh et al., 2003).The condition of the facility is that the individual believes that the organizational and technical infrastructure exists to support the use of siskeudes, with indicators necessary to use resources, essential to use, and compatible (Venkatesh et al., 2003; Kalinić et al., 2020). Perceived ease of use means that individuals feel that the technology is user-friendly and easy to use, with indicators of learning how to use, clear and understandable, easy to use, and skilful (Mansoori et al., 2021). Perceived values are individuals who believe that self-values can support the acceptance and use of siskeudes with indicators of self-esteem, belief-future change, and belief-future prosperity. The moderating variable is experience (EXPER). Experience is the time an individual uses the siskeudes application (Venkatesh et al., 2012). Behavioural intention (BEHAVINT) is an intervening variable, namely the behavioural tendency of individual siskeudes users with indicators likely to continue, habit, and happy to use (Saxena & Janssen, 2017). The dependent variable, namely usage behaviour (SISKEUD), accepts and uses technology in their work environment with regularly using, informative, and flexible use indicators (Venkatesh et al., 2003).

Data Collection and Analysis Methods

Data collection methods used are questionnaires and documentation. Questionnaires were used to explore using siskeudes, while documentation was used to obtain data on the number of villages in the district. We sent a google-form questionnaire to the respondent by Whatsapp. The data that has been collected is then processed. It tested the research hypothesis using structural equation modelling (SEM) with the WarpPLS approach to explaining research variables' relationship and interdependence. This model was developed as an alternative for situations where the theoretical basis for the model's design is weak or has not been found. Some indicators do not meet the reflective measurement model, so it is formative. PLS is a powerful analytical method because it does not require many assumptions, and the sample size can be small or large. Besides being used as a confirmation of theory (hypothesis testing), PLS can also be used to build relationships for which there is no theoretical basis or testing propositions.

Results

Questionnaire Validity and Reliability Test

Test the validity and reliability of the research questionnaire is a step that must be done before testing the hypothesis. This pre-hypothesis test is known as convergent validity. Convergent validity is part of the measurement model in SEM-PLS, usually referred to as the outer model. The criteria used to determine whether the questionnaire is valid or not refers to the previous description that the factor loading is more significant than 0.05 to 0.05. 0.06 is considered sufficient as a criterion for meeting convergent validity.

Based on the results, the factor loading greater equals 0.05, and each is significant (p <0.001). The indicator of the facility condition, perceived ease of use, performance expectancy, perceived value, behavioural intention and usage of siskeudes variables has a factor loading greater equal to 0.05, and each is significant (p<0.001). Thus, the research questionnaire consisting of these six variables met convergent validity. The following is a table of instrument validity (Table 1).

Tabel 1 Result of Validity Test
	FACC	PEOU	PERF	PERV	BEHAV	SISKE	Type	SEs	P value
FACCON1	0.818	-0.041	-0.515	0.185	0.193	-0.008	Reflect	0.071	<0.001
FACCON2	0.888	0.426	-0.115	0.008	0.06	-0.074	Reflect	0.07	<0.001
FACCON3	0.78	-0.38	0.438	-0.168	-0.313	0.073	Reflect	0.071	<0.001
FACCON4	0.848	-0.056	0.214	-0.032	0.039	0.018	Reflect	0.07	<0.001
PEoUse1	-0.129	0.936	-0.148	-0.02	0.101	-0.048	Reflect	0.069	<0.001
PEoUse2	0.119	0.906	0.027	0.077	0.082	-0.01	Reflect	0.069	<0.001
PEoUse3	0.075	0.938	0.033	0.013	-0.034	-0.024	Reflect	0.069	<0.001
PEoUse4	-0.062	0.929	0.089	-0.068	-0.147	0.082	Reflect	0.069	<0.001
PERFEXP	-0.035	0.219	0.88	-0.205	0.059	0.016	Reflect	0.07	<0.001
PERFEXP	-0.02	-0.209	0.905	0.063	0.029	0.003	Reflect	0.069	<0.001
PERFEXP	0.121	-0.129	0.948	0.024	0.017	-0.035	Reflect	0.069	<0.001
PERFEXP	-0.073	0.132	0.907	0.111	-0.104	0.018	Reflect	0.069	<0.001
PERVAL1	0.18	-0.255	0.419	0.775	-0.089	-0.012	Reflect	0.071	<0.001
PERVAL2	-0.059	0.109	-0.039	0.857	-0.008	0.003	Reflect	0.07	<0.001
PERVAL3	0.117	-0.003	0.079	0.886	-0.111	0.027	Reflect	0.07	<0.001
PERVAL4	0.006	-0.217	0.005	0.872	0.079	0.01	Reflect	0.07	<0.001
PERVAL5	-0.224	-0.063	-0.186	0.783	0.198	-0.094	Reflect	0.071	<0.001
PERVAL6	-0.031	0.471	-0.301	0.735	-0.069	0.064	Reflect	0.072	<0.001
BEHAVIN	-0.246	0.219	-0.108	-0.016	0.738	-0.159	Reflect	0.072	<0.001
BEHAVIN	-0.044	-0.024	0.119	-0.121	0.828	0.011	Reflect	0.07	<0.001
BEHAVIN	0.296	-0.192	-0.026	0.152	0.737	0.146	Reflect	0.072	<0.001
SISKEUD	-0.101	-0.067	-0.016	0.075	0.189	0.663	Reflect	0.073	<0.001
SISKEUD	-0.007	0.29	0.043	0.005	-0.334	0.781	Reflect	0.071	<0.001
SISKEUD	0.09	-0.228	-0.029	-0.067	0.17	0.799	Reflect	0.071	<0.001

The next step is to examine the consistency level of the measuring instrument, namely the instrument reliability test. This measurement is carried out using one-shot, namely the measurement is only done once, then the results are compared with other questions or measure the correlation between the answers to the questions. The test is done by looking at the Cronbach Alpha value. The instrument is reliable if the Cronbach Alpha value is > 0.60. the results of instrument reliability testing are shown in Table 2 as follows.

Table 2 Result of Reliability Test
No.	Variable	Cronbach’s Alpha	Conclusion
1.	FACCON	0.854	Reliabel
2.	PEOUSE	0.946	Reliabel
3.	PERFEXP	0.931	Reliabel
4.	PERVAL	0.901	Reliabel
5.	BEHAVIORAL INTENTION	0.652	Reliabel
6.	SISKEUD	0.608	Reliabel

Based on the table above, it can be seen that the Cronbach alpha value for all variables shows the number >0.60. Thus, this research instrument meets the reliable criteria to be used as a variable measurement tool,

Hypothesis Test

Model fit and quality indices

Testing the research hypothesis use structural equation modelling through WarpPLS. In summary, the fit and quality indices model is presented in Table 3.

Table 3 Model Fit and Quality Indices
No.	Model Fit and Quality Indices	Fit Criteria	Result	Conclusion
1.	Average path coefficient (APC)	P = 0.003	0.205 P=0.003	Acceptable
2.	Average R-squared (ARS)	P < 0.001	0.441 P<0.001	Acceptable
3.	Average adjusted R-squared (AARS)	P < 0.001	0.421 P<0.001	Acceptable
4.	Average block VIF (AVIF)	Acceptable if <= 5, ideally <= 3.3	2.121	Ideal
5.	Average full collinearity VIF (AFVIF)	Acceptable if <= 5, ideally <= 3.3	2.188	Ideal
6.	Tenenhaus GoF (GoF)	small >= 0.1, medium >= 0.25, large >= 0.36	0.556	Large, Acceptable
7.	Sympson's paradox ratio (SPR)	Acceptable if >= 0.7, ideally = 1	0.889	Acceptable
8.	R-squared contribution  ratio (RSCR)	Acceptable if >= 0.9, ideally = 1	0.995	Acceptable
9.	Statistical suppression ratio (SSR)	Acceptable if >= 0.7	1.000	Acceptable
10.	Nonlinear bivariate  causality direction ratio (NLBCDR)	Acceptable if >= 0.7	1.000	Acceptable

From the output results in the table above, it can be seen that the model has a good fit which means that there is no problem with multicollinearity between indicators and between variables. There is no causality problem in the model. The conclusion is that the model from this study fits with the data to continue the next test. The following is an image of the research model and the results obtained based on data processing using the WarpPLS 6.0 program (Figure 3).

Hypothesis testing in WarpPLS analysis uses a t-test. The decision-making rules for hypothesis testing are carried out as if the p-value is 0.10 (alpha 10%). It is said to be significantly weakened if the p-value is 0.05 (alpha 5%). It is said to be significant. If the p-value is 0.01 (alpha 1 %), it is said to be very significant. Based on data processing, the output path coefficients and p values are obtained in the Table 4.

Table 4 Direct Effect
VAR	FACCON		PEOUSE		PERFEXP		PERVAL		BEHAVINT
	Path	p-value	Path	p-value	Path	p-value	Path	p-value	Path	p-value
BEHA	0.120	0.076	0.269	<0.001	0.333	<0.001	0.111	0.092
SISKE	0.113	0.089	-0.015	0.431	0.141	0.046	0.266	<0.001	0.474	<0.001

Furthermore, the indirect effect of facility condition, perceived ease of use, performance expectancy, the perceived value was also tested on the use of siskeudes. The analysis table as follows (Table 5).

Table 5 Indirect Effect
VARIABLE	FACCON		PEOUSE		PERFEXP		PERVAL
	Path	p-value	Path	p-value	Path	p-value	Path	p-value
SISKEUDES	0.057	0.172	0.127	0.016	0.128	0.004	0.053	0.189

Furthermore, the experience variable is also tested as a moderating variable that strengthens the relationship between facility condition, perceived ease of use, performance expectancy, the perceived value on the behavioural intention to use siskeudes. The analysis table is as follows (Table 6).

Table 6 Total Effect
VARIABLE	EXPER*BEHA		EXPER*PEOUSE		EXPER*PERFE		EXPER*FACCO
	Path	p-value	Path	p-value	Path	p-value	Path	p-value
BEHAVINT			0.014	0.436	-0.016	0.427	-0.091	0.140
SISKEUDES	-0.186	0.012	-0.142	0.044	0.205	0.006	0.011	-0.189

Based on Tables 4 and 5, the results of hypothesis testing can be summarized as follows (Table 7).

Table 7 Results of Hypothesis Testing
			Coeficient (β)	P-value	Conclution
Direct Effect
BEHAVINT	<---	FACCON	0.12	0.08 ≤ 0.10	Accepted (weakly significant)
BEHAVINT	<---	PEOUSE	0.27	<0.001	Accepted (highly significant)
BEHAVINT	<---	PERFEXP	0.33	<0.001	Accepted (highly significant)
BEHAVINT	<---	PERVAL	0.11	0.09 ≤ 0.10	Accepted (weakly significant)
SISKEUD	<---	FACCON	0.11	0.09 ≤ 0.10	Accepted (weakly significant)
SISKEUD	<---	PEOUSE	-0.01	0.431	Rejected
SISKEUD	<---	PERFEXP	0.14	0.046 ≤0.05	Accepted (significant)
SISKEUD	<---	PERVAL	0.27	<0.001	Accepted (highly significant)
SISKEUD	<---	BEHAVINT	0.47	<0.001	Accepted (highly significant)
Indirect Effect
SISKEUD <-- BEHAVINT <--- FACCON			0.057	0.172	Rejected
SISKEUD <--- BEHAVINT <--- PEOUSE			0.127	0.016 ≤0.05	Accepted (significant)
SISKEUD <--- BEHAVINT <--- PERFEXP			0.158	0.004 ≤0.05	Accepted (significant)
SISKEUD <--- BEHAVINT <--- FPERVAL			0.053	0.189	Rejected

Based on the summary results of hypothesis testing in Table 7, it can be concluded that behavioural intention significantly affects the use of siskeudes. Experience significantly strengthened the relationship between behavioral intention and siskeudes (β=0.47, p<0.001; =-0.186, p=0.012). So, H1 and H1a were accepted (highly significant). Performance expectancy can affect behavioural intention and experience to strengthen the effect of performance expectancy on the intention to use siskeudes (β=0.33, p<0.001; =0.014, p=0.427). So, H2 was accepted while H2a was rejected. Facility conditions significantly affect the use of siskeudes (β= 0.11, p=0.09≤0.010). So, H3 was accepted with a low significant value because it obtained a p-value of 0.09 < 0.010. Facility condition significantly affects behavioural intention, and there was no significant effect between experience with facility condition and behavioural intention (β= 0.12, p=0.08≤0.010; = -0.091, p=0.140). So, H4 was accepted with a low significant value because the p-value is 0.08 < 0.010 and H4a was rejected. Perceived ease of use had a significant effect on behavioral intention and experience was not able to strengthen this effect (β= 0.27, p < 0.001, = 0.14, p = 0.436). So that H5 was accepted, H5a was rejected. Perceived value significantly affected the Usage Behavior of siskeudes (β= 0.27, p<0.001). That is, H6 was accepted.

In this study, several hypotheses were built to determine the role of the moderating variable experience on the effect of several independent variables such as performance expectancy, perceived ease of use, facility condition on behavioural intention and the impact of behavioural intention on the usage of siskeudes. Statistical analysis relevant to the hypothesis related to EXPER (H1a, H2a, H4a, H5a) showed an insignificant value (p>0.05), although, for H1, the experience can moderate the effect between behavioural intention and siskeudes (R2=0.24, =- 0.186, p=0.012).

Discussion

On the issue of behavioural intention, respondents showed a positive attitude towards the use of siskeudes. The behavioural intention in this study is considered a factor that influences the user attitude towards using technology. This study consistently supports the previous research on tests using UTAUT, showing that there are two direct determinants of why someone uses a system: the intention to use and the condition of the facility (Venkatesh et al., 2003; Camilleri, 2019a ).

Performance expectancy had a significant effect on behavioural intention. Performance expectancy is considered to be the most potent factor influencing someone's intention to use siskeudes. This study is consistent with Talukder et al. (2019). When someone uses a system or technology, they will consider cost and risk factors to achieve the desired performance expectancy. Individuals will prefer to use e-government services to take care of administration rather than traditional methods because e-government has a low cost and risk factor in achieving the desired performance expectancy. In addition, individuals will prioritize e-government systems that are not so complex but useful and easy to use (Alanazi, 2013; Talukder, et al. 2019). Research conducted in Abu Dhabi regarding the use of e-government services and research performed by Technology adoption and innovation of e-government in the Republic of Iran stated that there is a positive and significant influence between performance expectancy and intention to use e-government services (Mansoori, et al., 2021; Munadil, et al., 2015).

Facility conditions in this study had a significant positive effect on the actual use of siskeudes, although the effect was weak. These results are in line with the research of Camilleri, M. A (2019a). The UTAUT model tested by Venkatesh et al., 2003also showed that the condition of the facility had a positive relationship with the actual use of a system. Facility conditions also had a significant positive effect on behavioural intention even though the result was weak. The facility's state will increasingly have a more substantial influence on someone's intention to use a service if the service provider provides a good internet network, mobile network, and e-government infrastructure. In addition, conducting training programs, providing infrastructure for organizations and technology, and providing relevant resources to facilitate e-government are some of the mechanisms a government can use to increase the intention to use e-government services (Talukder et al., 2019). This study is in line with the research of Venkatesh et al., 2012. Respondents showed positive behaviour to use e-government service facilities because of the adequate condition of the facilities. An e-service or e-government service is equally compatible with other respondents' other technologies (Mansoori et al., 2021).

This study tested the influence of perceived ease of use on behavioural intention. In this case, the respondents showed a robust significant positive attitude. This research was conducted in districts in each Karisidenan in Central Java that had good performance in 2019. Easy use of siskeudes can increase village performance. This finding aligns with Lewis and Sambamurthy, who need top management commitment (Lewis & Sambamurthy, 2003). The selection of districts with good performance is appropriate to test how perceived ease of use plays a role in a person's intention to use siskeudes services. Furthermore, some things that make someone intend to use a system are the perceived benefits, ease of use, competence, and integrity possessed by a service offered by the government (Lin, 2011).

Perceived value in this study can affect the use of siskeudes with a robust level of significance. Perceived value is an essential determinant for someone to use siskeudes services. This study supports previous research by Koenig-Lewis et al. (2010) who researched adolescents' mobile banking services. Siskeudes can be efficiently used in record keeping financial data. Siskeudes can create a digital resume professionally and illustrate the achievement of village financial performance. It can also reflect on the work that has been done. However, the data inputted cannot be edited, so transaction input often delays until the end of the month. This means that users feel uncomfortable when the data is inputted incorrectly, resulting in the delivery of incorrect financial information. This finding in line with (Setyawan et al., 2018), siskeudes makes it easier for village officials to budget, implement and report village funds. With this system, the use of village funds can be controlled effectively, efficiently and accountably.

The results showed that compatibility, perceived usefulness, and risk are factors in a users’ behaviour in using technology. These findings are consistent with previous research ( Davis, 1989; Venkatesh and Morris, 2000), which stated that computer literacy and experience are likely moderators for technology-related work. In addition, Fishbein and Ajzen (1975) described previous experience providing additional information for users to assess the consequences of using technology or a system. As a result, users have a more powerful statement to decide whether to strengthen, shape or moderate intentions to use a system.

Conclusion and Implication

This study found performance expectancy and perceived ease of use as the most substantial influence of using the siskeudes application. Meanwhile, facility conditions had a significant positive effect on behavioural intention. Then, behavioural intention and perceived value had a powerful impact on the actual use of siskeudes. The presence of experience strengthens the relationship between behavioural intention and actual use of siskeudes. However, the experience did not affect the relationship between performance expectancy, perceived ease of use, facility condition and behavioural intention. In these findings, the government should increase the intention to use, which ultimately continues with the use of siskeudes.

This research implies that the implementation of siskeudes requires knowledge and understanding of government financial accounting skills. Furthermore, it is necessary to increase the treasurer, village head, village secretary, and siskeudes admin's capability to understand every menu in the siskeudes application. Examples include training on understanding village finances, practical training on village finance application simulations, and understanding taxation. Understanding taxation is essential because, in the application, their must input taxes correctly. All of which will affect the output of village financial reports in the siskeudes application.

Nevertheless, this research has limitations. The limitations of this study are the research population and the geographical location of the research samples, which have differences. The research was only conducted in the best districts in each Karisidenan in Central Java. In addition, the lack of references to the use of e-government in Indonesia is also a challenge when explaining the comparative discussion. This research was conducted to find factors that can increase the intention and use of siskeudes in Central Java, Indonesia. The researcher suggests conducting similar research in other parts of Indonesia, using a more complex research model or adding independent variables, such as public trust in SISKEUDES services, business expectations, and the respondent's education siskeudes services others.

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