1. Introduction

Current technological advances have been widely used in various environments, especially the government in Indonesia. One of the technologies utilised is the Employee Management Information System that has been set in the Decree of the Minister of Home Affairs No. 17 of 2000. Article 1 in the Decree states that the Employee Management Information System, hereinafter abbreviated as SIMPEG, is an integrated totality consisting of processing devices including collectors, procedures, processing employee and software; storage devices include data centres and data banks as well as communication devices that are interrelated, interdependent and mutually determine in order to provide information in the field of employee [1].

Therefore, the Regional Office of the Ministry of Law and Human Rights (Kanwil Kemenkumham) of West Sulawesi applies SIMPEG which is used to process data, information, and management as well as employee administration so that it can support employee performance and can simplify the staff administration process [2].

SIMPEG has been developed web-based with fast installation and implementation, and structurally SIMPEG was developed with a modular method so that it can be adapted to user needs in a short time. The implementation of SIMPEG is the realisation of a management information system that integrates into a computer network that is capable of producing quality information to support employee management decision-making in the agency environment. However, to achieve a system that can contribute to organisational performance, employees must be able to use the system effectively and correctly [3], so that the successes and failures of the systems used to depend heavily on user acceptance of the system [4]. The adoption of new systems is the lack of training using Information Communication Technology (ICT), the quality of the technology itself, and organisational support [5]. Therefore, this study aims to identify behaviour and factors that influence user acceptance of employee management information systems by using an integrated model between HOT-Fit and UTAUT2 models.

The integration of the two models is the HOT-Fit Model from Yusof et al. (2006) [6], and the UTAUT2 model from V.Venkatesh et al. (2012) [7] were conducted to check the use of SIMPEG. The proposed integrated model contributes to the literature review because there is no recent research on the integration of HOT-Fit and UTAUT2 in one model to identify behaviour and factors that influence the acceptance of employee management information systems from a human, technology and organisation perspective. Furthermore, this study analyses the data using the GeSCA, which is a model of the structural equation model (SEM) [8].

2. Literature Review

Evaluation of information systems is crucial to find out whether information systems fail or succeed. Evaluation of this system has been carried out in various fields, such as health by Jiunn-Woei Lian, David C. Yen, Yen-Ting Wang (2013) investigated important aspects that had an influence in making decisions to adopt cloud computing in the Taiwan hospital industry using the TOE (Technology-Organization-Environment) framework and combined with HOT-fit model (Human-Organization-Technology fit) [9]. Furthermore, Noor Azizah K. S. Mohamadali and Jonathan M. Garibaldi (2010) proposed an integrated evaluation model namely UTAUT, DeLone and McLean, TTF to evaluate user responses to software technology in the health sector [10]. Then, Lourent Monalizabeth E., Ahmad Holil NA, Anisah Herdiyanti (2015) who conducted research to evaluate the EMR system used in hospitals using the HOT-Fit model and this study is useful to help understand aspects interrelated aspects between humans, organisations, and technology [11]. In the field of finance, Abdullah M. Baabdullah, Ali Abdallah Alalwan, Nripendra P. Rana, Hatice Kizgin, Pushp Patil (2019) conducted research to combine UTAUT2 and D & M to understand what aspects can influence mobile banking, and how to use the system can contribute both to customer satisfaction and loyalty [12].

Based on previous research, the HOT-Fit model is useful for identifying interrelated aspects between humans, environment, organisation, and technology. UTAUT2 is useful for identifying only responses from users regarding the use of information systems. The UTAUT2 model can also be integrated with other evaluation models to find out the correlation between responses from users and other aspects related to system user acceptance. The integration model will produce a more effective evaluation of information systems so that this study proposes an integrated model between HOT-Fit and UTAUT2 models that are tailored to the case of information system evaluation to be studied to identify behaviours and factors that affect user acceptance of the Employee Management system Information System (SIMPEG).

3. Proposed Integrated Model and Hypothesis Development

Mohamadali and Garibaldi’s research (2010) proposed an integrated evaluation model by combining three IS evaluation models, namely DeLone and McLean (D & M) models, UTAUT models and TTF models. The D & M model that has been developed into a HOT-Fit Model uses the term intention to use or use while the UTAUT2 model uses the term behavioural intention or use. The term is almost the same in describing the dimensions of dependent factors, namely the intention to use a system. However, each model has different independent factors, such as the HOT-Fit Model has human factors, technology organizations affect the use of the system, and the UTAUT2 Model has seven main factors in the form of human factors namely performance expectancy, effort expectancy, social influences, hedonic motivations, price value, and habits affect the behaviour of system usage.

Conformity between human, technological, and organisational factors influences IS successes and failures [4]. However, the UTAUT2 model only examines the relationship of the influence of human factors on system use and has not examined the relationship between technological and organisational factors on system usage. Whereas HOT-Fit has examined the relationship of human, technological, and organisational factors in the use of the system, but this model has not elaborated further the effect of the relationship of the human factor dimension to IS use.

Based on the strengths and weaknesses of the HOT-Fit and UTAUT2 models, independent factors in the two models are combined to produce integrated models that provide a better representation of the determinants of IS use. The integrated model between HOT-Fit and UTAUT2 with modifications that are adjusted to government characteristics can be seen in Figure 1.

Figure 1. Proposed Integrated Model

3.1.  Human

Human is an important factor in the development and application of IS. This study has human factors consisting of several variables explored in Table 2. Social influence variables and price value are not included in human factors because they are adjusted to the characteristics of the government scope that implements all employees must use SIMPEG without any costs to be spent by employees in using the system.

For the effects of gender, previous research shows that gender differences in completing tasks affect performance expectations [15]. Also, efforts were found to be more prominent in women than men [16] whereas previous studies have found that men are more schematic in processing information and relevant details tend to be ignored, while women tend to be piecemeal and detail in processing information so that it will weaken the effects of habits on intention or behaviour [17].

Table 2. Definition of Variable Human Factors

Variables Human Factor
Performance Expectancy (PE)	The extent to which people believe using SIMPEG will provide increased performance in their work [13].
Effort Expectancy (EE)	The level of ease experienced by users when using SIMPEG [13].
Hedonic Motivation (HM)	The extent to which people get pleasure from using technology [14].
Habit (HT)	The extent to which the tendency to conduct causal behaviour is a recurring action in using SIMPEG [14].

Furthermore, it is found that consumer technological innovation is related to gender differences [18]. Using new technology at an early stage, men tend to trace innovation and novelty [19]. In turn, this tendency will increase hedonic motivation in the initial decisions on the use of technology in men. Thus, this study proposes:

H₁. Human factors influence the behavioural intention to use the

system with gender moderation

3.2.  Technology

An information system is one technology that can simplify the process of human work. A good information system is assessed in terms of the quality of information, systems, and services that this research explored in Table 3 [20].

Table 3. Definition of Variable Technology Factors

Variable Technology Factors
System Quality (SQ)	Measuring SIMPEG quality related interfaces, response time, system integration, and system security.
Information Quality (IQ)	The quality of SIMPEG information is measured by the accuracy, relevance, completeness, and accuracy of the information produced.
Service Quality (SQ)	Refer to SIMPEG manager support in providing assistance services and speed in responding to complaints/problems that occur.

Technology has a close relationship with humans because humans as users are in direct contact with the system. Several factors that influence decision making using a new system are benefits, namely that users are confident that their performance will improve with this system. Thus, this study proposes:

H₂. Technology factor influences the behavioural intention to use the system

H₇. There is a relationship of fit between human and technology factors

3.3.  Organisational

Organisations are a group of people formally along with inseparable sources to achieve management goals and need to pay attention to their policies. This study has organisational factors consisting of several variables explored in Table 4.

Table 4. Definition of Variable Organizational Factors

Variable Organizational Factors
Facilitating Condition (FC)	The extent to which people believe that resources, facilities and infrastructure, training, and assistance facilities are available to support users in using SIMPEG [21].
Organisation Structure (OS)	The extent to which people believe that the organisation has carried out a strategy and plan for SIMPEG implementation [22].
Organisation Environment (OE)	The extent to which people believe that organisations have provided full support for SIMPEG implementation [22].

Organisational and human factors have an important role in developing and implementing SI. Besides, harmony between humans and organisations is needed to ensure the success of SI implementation by harmonising user needs, management, and work routines as the introduction of systems in complex ways affecting different dimensions of fit [23]. Furthermore, organisational suitability (goals, structures, and processes) and technology are important starting points in the implementation of SI because it is one of the strategies that can affect organisational performance [24]. Based on this, this study proposes:

H₃. Organisational factors influence the behavioural intention to use the system

H₈. There is a relationship of fit between technology and

organisational factors

H₉. There is a relationship of fit between organisational and human

      factors

3.4. User Satisfaction, Behavioral Intention, and Use Behavior

User Satisfaction (US), namely, feedback and response from users who have used the system [6]. The attitude of users towards information systems is a subjective criterion of how users like the system used. According to DeLone and McLean (2003), user satisfaction can be seen from the whole system of information presented [20]. In general, user satisfaction as a result of a comparison between expectations or needs of information systems with received system performance and benefits in the system input-output process that can affect user behaviour. Also, behavioural intention affects the use of using SIMPEG. Thus, this study proposes:

H₄. Behavioural factor intention influences user satisfaction

H₅. User satisfaction influences use behaviour

H₆. Behavioural intention influences use behaviour

4. Methodology

This research collects data using online surveys because it is by the conditions of the Indonesian people who mostly have internet access. Easy validation facilities provided online surveys to ensure respondents resolved all questions without error [25]. The collected data is validated and used for data analysis, with a 100% response rate. The sample of this study was 311 respondents who were employees of the Kemenkumham Region of West Sulawesi and were randomly selected. There are two parts to this questionnaire. The first part consists of questions related to information on respondents’ characteristics, which can be seen in Table 5. Furthermore, the second part consists of 28 questions representing measurement items in an integrated model between the UTAUT2 and HOT-Fit models (see Appendix A) using a 4 Likert scale.

Table 5. Characteristics of Respondents

Characteristics		Frequency	Percent(%)
Gender	Men	215	69.1
	Women	96	30.9
Age	18 – 25	82	26.4
	26 – 35	137	44.1
	36 – 45	43	13.8
	Above 45	49	15.8

Table 5 shows the respondents collected in this study consisted of 69.1% men and 30.9% of women with age 18-25 (26.4%), 26-35 (44.1%), 36 – 45 (13.8%) and over 45 years old around 15.8%. Furthermore, the data obtained will be analyzed using the Structure Equation Model (SEM) model in the form of a Generalized Structured Component Analysis (GeSCA). GeSCA was chosen because it can analyse the combined approach of factor analysis, structural models and path analysis together and can do three activities at once, namely testing reliability, testing between variables and testing the model used [16].

5. Result and Discussion

Two stages of SEM were applied in this study, namely the measurement model to measure the level of fit of the model adequately and to test the reliability and validity of latent constructs through confirmatory factor analysis. Then the structural model is tested to verify the integrated model related to the hypothesis in this study.

5.1. Measurement Model

5.1.1.    Overall Goodness of Fit

The overall size of the fit model is carried out before the structural model stage, including FIT, AFIT, GFI, SRM, and NPAR [8].

• FIT indicates the total variance that can be explained from all variables for model specifications. The FIT value ranges from 0 to 1. The FIT value recommended is ≥ 0.5. The higher the FIT value indicates, the greater the variance of the variable can be explained from the model specifications.
• Adjusted FIT (AFIT) is similar to FIT but takes into account the complexity of the model. The more AFIT values ​​close to FIT values ​​can be said to support the conclusion of FIT. The AFIT value can be calculated based on the FIT value using the following formula.
• The goodness of FIT Indices (GFI) and Standardized Root Mean Square Residual (SRMR) are fit criteria that indicate the difference between sample covariances and covariances generated from the estimated GeSCA parameters. The GFI value approaches 1, and the SRMR value close to 0 can be considered an indication of compatibility. The recommended GFI value for fit model size is> 0.90, and the recommended SRMR value for fit model size is ≤ 0.80.
• The number of Free Parameters (NPAR) is the sum of free parameter estimates, weights, loading and path coefficients.

The overall results of the measurement of the integrated model used in this study can be seen in Table 6. The FIT value is obtained at 0.609 or 60.9%, which indicates that the model can explain the total variance of all the variables in this study. Besides, the AFIT value is obtained at 0.609, which indicates that the AFIT value is close to the FIT value; it can be said to support the conclusion of the FIT. From the results of FIT and AFIT, it can be concluded that performance expectancy, effort expectancy, hedonic motivation, habit, system quality, information quality, service quality, organisation structure, organisation environment, behavioural intention, user authentication, and user behaviour can determine research models. Then the GFI value of 0.995 indicates that the model is feasible because it approaches the value 1. The SRMR value of 0.066 approaches the value of 0, indicating a good fit. The NPAR value shows the estimated number of free parameters of 60. The result means that the proposed integrated model is compatible with this study.

Table 6. Measurement Model

Model Fit
FIT	0.609
AFIT	0.607
GFI	0.995
SRMR	0.066
NPAR	60

5.1.2. Test Validity and Reliability

Before going further in the analysis of structural models, it is necessary to test the reliability of construction and validity. Table 7 describes the value of loading factors, average variance extracted (AVE) and composite reliability (CR). Convergent validity recommendation value is >0.7 [26]. Therefore, UB1 was dropped. Besides the value of the loading factor of other construct items is more than 0.7, it can be said that all indicators in the model have met the convergent recommendation for validity> 0.7. Likewise, the AVE value of all arranged variables is between 0.605 – 0.937 so that it can be said that the AVE values ​​of all the variables in the model meet good discriminant validity which is >0.50 and show that more than 50% of the variance of the indicators can be explained [27]. The CR was considered in this study. Latent variables can be said to be reliable if they have a value of CR> 0.70 [27]. All latent variables have a CR value above 0.70. Based on the results of the analysis obtained it can be concluded that all variables are valid and reliable because they have met the requirements of a good model.

Table 7. Loading Factor, AVE, CR

Factor	Item	Loading Factor	AVE	CR
Human	PE1	0.807	0.628	0.931
	PE2	0.753
	EE1	0.804
	EE2	0.798
	HM1	0.796
	HM2	0.846
	HT1	0.779
	HT2	0.757
Technology	SQ1	0.795	0.605	0.902
	SQ2	0.766
	IQ1	0.809
	IQ2	0.796
	SEQ1	0.761
	SEQ2	0.735
Organizational	FC1	0.774	0.605	0.902
	FC2	0.767
	OS1	0.723
	OS2	0.823
	OE1	0.781
	OE2	0.797
Behavioral Intention	BI1	0.820	0.804	0.925
	BI2	0.925
	BI3	0.941
User Satisfaction	US1	0.969	0.937	0.967
	US2	0.967
Use Behavior	UB1	-0.874	Dropped
	UB2	0.792	0.692	0.817
	UB3	0.868

Table 8. Overview of Structural Models

Path	Estimate	CR
Human à Behavioral Intention	0.796	32.64*
Technology à Behavioral Intention	0.094	0.94
Organizational à Behavioral Intention	0.460	5.67*
Behavioral Intention à User Satisfaction	0.604	13.3*
User Satisfaction à Use Behavior	-0.128	2.09*
Behavioral Intention à Use Behavior	-0.319	4.14*

Table 9. Analysis of the impact of moderating variable gender

Path	Gender
	Men		Women
	Estimate	CR	Estimate	CR
Human à Behavioral Intention	0.633	14.24*	0.676	12.95*

5.2.  Structural Model and Testing Hypothesis

The structural model analysis was conducted to determine the effect between variables hypothesised by this study. Also, testing using GeSCA can show hypotheses that are accepted or rejected. The hypothesis is accepted if it has a positive relationship that can be seen from the estimated and significant values which can be seen from the value of CR> 1.96 marked by a sign (*) [28]. The analysis of the moderating variable is done using a multigroup approach at GeSCA. The results of the analysis of structural models can be seen in Table 8 and an analysis of the impact of the moderating variables in this study is described in Table 9.

Table 8 shows the results of the structural model test. Organizational factors (Estimate = 0.460, CR = 5.67^*) are known to have a positive and significant effect on behavioral intention while technological factors (Estimate = 0.094, CR = 0.94) have a positive effect on behavioral intention but are not significant. Then, it is known that behavioural intention has a positive and significant effect on user satisfaction (Estimate = 0.604, CR = 13.3^*). However, behavioral intention (Estimate = -0.319, CR = 4.14 ^*) and user satisfaction (Estimate = -0.128, CR = 2.09^*) does not have a significant effect on use behavior.Also, Table 9 shows an analysis of human factors with moderating gender variables. The results of the analysis show that human factors with gender men and women moderation have a positive and significant effect on behavioural intention. Furthermore, a correlation test is carried out to determine whether there is a relationship between human, technological and organisational compatibility can be seen in Table 10.

Table 10. Relationship of Human, Technology, and Organization Factors

Relationship		Correlation Value of Latent Variables
Human	Technology	0.796
Technology	Organisational	0.758
Organisational	Human	0.736

Table 11. Summary of the Hypothesis

Hypothesis		Remarks
H1.	Human factors influence the behavioural intention to use the system with gender moderation	Yes
H2.	Technological factors influence the behavioural intention to use the system	No
H3.	Organisational factors influence the behavioural intention to use the system	Yes
H4.	Behavioural factor intention influences user satisfaction	Yes
H5.	User satisfaction influences use behaviour	No
H6.	Behavioural intention influences use behaviour	No
H7.	There is a relationship of fit between human and technology factors	Yes
H8.	There is a relationship of fit between technology and organisational factors	Yes
H9.	There is a relationship of fit between organisational and human factors	Yes

Table 10 shows the test results with the fit between human, technology and organisation factors that have a correlation value> 0.05, so the hypothesis is accepted [29]. Human factors with technology (0.782), factor technology with the organisation (0.758) and factors with human organisations (0.736) are known to have conformity relationships. These results indicate that there are nine hypotheses in this study consisting of 6 accepted hypotheses and 3 rejected hypotheses which can be seen in Table 11 and the overall summary of the results of the research model can be seen in Figure 2.

Figure 2. Summary of the results of the research model

According to the results of the analysis, human factors have a positive and significant effect on the behavioural intention with gender moderation (H₁). In the female sex, the effect is stronger in influencing behaviour intentions using SIMPEG than men. The results of this study support previous research, which states that gender differences are associated with difficulties in allocating attention to information at work so that it will affect intention or behaviour using the system [7,30].

Technological factors were found to have a positive effect on behavioural intention (H₂) but were not significant. The quality of the system, information, and services of a good system will have a strong influence on the intention to use the system [31]. However, in this case when the employee as a user accesses SIMPEG it is found that the quality of information produced is less accurate and relevant to the information needed by employees and the quality of service is not quick to deal with problems related to errors in the field that weaken the technological relationship with intention to use.

Organisational factors have a positive and significant effect on behavioural intention (H₃). This result is by previous studies conducted by Frendy and Holzmann [32,33]. Organizationally, the West Sulawesi Regional Office of Kemenkumham has provided support and implemented appropriate strategies based on the organisational environment to influence behavioural intentions using SIMPEG.

Behavioral intention has a positive and significant effect on user satisfaction (H₄). Yusof et al. said user satisfaction is an overall assessment of user experience in using information systems and their potential impact [6]. Increasing user satisfaction requires an effort to increase the intention to use SIMPEG. Increasing the intention to use SIMPEG can be done by increasing proven human, technological and organisational factors (H₁-H₃) having a positive effect on behavioural intentions using SIMPEG.

User satisfaction and behavioural intention to use the system have a negative and insignificant relationship to system usage behaviour (H₅-H₆). User satisfaction and interest in using information systems refer to the results of system performance received and benefits in the process of input-output systems that affect individual decisions to use or not use the system in completing a series of tasks. While in this case it was found that the system was less accurate and relevant to the information needed by employees and was not quick to deal with problems related to errors giving a negative relationship to the behaviour of using the system.

Furthermore, the results of the analysis of the correlation value in the 7th hypothesis correlate> 0.05 so that it can be said that human factors have a relationship of conformity with technology. The result can be explained from humans as users who come in direct contact with the system have several things that influence the decision making to use the system, namely users believe that their performance will increase with the use of systems and systems easy to use with the complete user guide. Also, users with certain information technology (IT) skills are not sufficient requirements for system use or acceptance, but their skills in using IT must be by the requirements of the system itself. The result shows the need for ‘conformity’ between humans and technology. The system failure can occur due to several causes, namely system failure, technical failure including hardware, software and communication errors. Also, usability failure is at the technical level when the system does not match the tasks needed in the organisation; failure as desired when the situation is technically correct and according to specific needs but the system is unsuccessful because the user does not approve it or rejected by them. Finally, it will result in a system rejecting behaviour if it is ‘fit’ between users or humans and low technology [34].

Meanwhile, this study also found that technological factors have a relationship with an organisation (H₈). These results support previous studies by Yusof and Mohamadali [22,23]. Yusof stated that the lack of compatibility between the main organisational elements contributed to a large number of system failures that needed support from the organisation. One form of this suitability, namely strategy, planning and support from the organisation in implementing SIMPEG can be a budget for facilities and infrastructure. The selection of SI needs to support the strategy and goals of the organisation. Each system needs to be aligned with organisational settings. The system shows the need for ‘fit’ between technology and organisation. Also, evaluation of information systems not only discusses how well the system works but also needs to discuss how well a system works in certain settings with certain users and further what functions the system itself and why the system works like that [35]. The clearly shows the need to evaluate technology together with organisations, as well as humans who use the system, namely conformity with the factors that influence acceptance. One thing that can be done to improve the performance and productivity of employees is to increase voting, information technology infrastructure that supports the implementation of the SIMPEG.

Then, the results of the analysis in this study found that human factors have a relationship of conformity with the organisation (H₉). Organisations are a group of people formally along with inseparable sources to achieve management goals and need to pay attention to policy. One form of this suitability is, for example, if certain individuals or users do not have the skills to use the system, organisational management is responsible for providing the necessary training [22,36]. To achieve a management information system (MIS), that is successful and has a positive impact on the organisation, and the information system must first have an impact on the individual. The MIS will ultimately affect users to accept technology so that it can improve the performance and productivity of employees.

6. Conclusion and Future Research

This study used 311 respondents with men (69.1%) and women (30.9%) to test the factors that influence SIMPEG user acceptance and identify user behaviour. To achieve this goal, the researchers propose an integrated model based on HOT-Fit and UTAUT2 that has a Goodness of Fit Index (GFI) of 0.995 and a Standardized Root Mean Square Residual (SRMR) of 0.079 indicating the model is compatible with this study. Based on the results of the analysis and discussion concluded from the nine hypotheses proposed, there are six hypotheses accepted, and three hypotheses rejected, among others:

• Human factors with the moderation of gender, technology, and organisation have a positive and significant effect on behavioural intention. While technology has a positive effect on behavioural intention but is not significant.
• The behavioural intention has a significant effect on user satisfaction.
• Behavioural intention and user satisfaction do not have a significant effect on user behaviour. It can occur because the system is less accurate and relevant to the information needed by employees and is not quick to deal with problems related to errors that weaken the relationship to behaviour to use the system.
• Humans and technology are compatible, technology and organisations, as well as organisations and humans in the adoption of information systems.

Future research will expand research into other fields (for example education, health, and media) which include human factor variables with age and experience moderation, and other variables such as social influence and price value. Finally, the new integration between the HOT-Fit and UTAUT2 models is a contribution to the current information system literature and provides an understanding of the importance of technology and the involvement of humans and organisations to achieve successful acceptance of the technology itself.

Appendix A. Questionnaire

Factor	Items	Question	Sources
Human	PE1	I feel New SIMPEG is easy to use	Venkatesh et al. (2012)
	PE2	Using SIMPEG New can help me get things done quickly
	EE1	I easily learn how to use SIMPEG New
	EE2	It’s easy to master how to use SIMPEG New
	HM1	I feel that using SIMPEG New is fun
	HM2	I enjoy using SIMPEG New
	HT1	The use of New SIMPEG has become a habit for me
	HT2	I have to use SIMPEG New
Technology	SQ1	The New SIMPEG process does not require a long time	Yusof et al. (2006)
	SQ2	SIMPEG New has high security regarding data integration
	IQ1	The application of the New SIMPEG reduces the errors in the staff data management process
	IQ2	The application of SIMPEG New creates accurate information and staffing
	SEQ1	There is a guide to using SIMPEG New
	SEQ2	I get help quickly when an error occurs
Organisational	FC1	Regional Office of Kemenkumham West Sulawesi has the tools needed to use SIMPEG New
	FC2	SIMPEG New is compatible with the technology that I use
	OS1	The application of New SIMPEG is the Kemenkumham strategy for performance improvement
	OS2	The implementation of New SIMPEG has been well planned by the Ministry of Law and Human Rights
	OE1	SIMPEG New has adequate financial support in providing the hardware needed from the Ministry of Law and Human Rights
	OE2	All agencies of the West Sulawesi Kemenkumham Regional Office support and assist in the implementation of New SIMPEG
Behavioural Intention	BI1	I intend to use New SIMPEG in the future	Venkatesh et al. (2012)
	BI2	I will always try to use SIMPEG New in my daily work life
	BI3	I plan to use SIMPEG New often
User Satisfaction	US1	SIMPEG New simplifies the staff administration process	Yusof et al. (2006)
	US2	SIMPEG New accelerates the staff administration process
Use Behavior	UB1	Every working day I regularly use SIMPEG New	Venkatesh et al. (2012)
	UB2	I rarely use New SIMPEG in a week
	UB3	I rarely use New SIMPEG in a month

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