Analysis of Technology Acceptance of Enterprise Resource Planning (ERP) System in The Regional Office of PT. XYZ Throughout Indonesia

The companies that are starting to renew their system using the ERP-based systems, since 2000, the percentage of success reached 75%, and 25% is a failure. The ERP implements have several issues, such as the quality of human resources, not user-friendly system, incorrect format for data recording, system errors, unstable connections, a long-time process in the system, etc. Related to the issue, the company, which is a provider in the ERP SAP marketing, assists by local support to help end-users regarding the recording of selected divisions that use this system for daily work. It makes the researcher do the analysis related to what factors influence the ERP SAP acceptance technology with local support or implementor as a support for users of those companies in the regional office. In this study, to analyze the ERP system’s acceptance technology factors, we use the UTAUT2 method with two additional constructed variables, Trust and Learning Value, and for analyzing those hypotheses, we use the PLS-SEM method. The study results show that several variable factors significantly influence the technology acceptance of the ERP SAP in PT. XYZ, which is Hedonic Motivation, Price Value, and Habit. Local support associated with Effort Expectancy and Social Influence variables does not significantly influence the system’s acceptance. Researchers recommend that three factors should be considered more closely by the ERP system providers as a material reference that is related to the further strategy development and system development, such as improving the quality of human resources, etc.


Introduction
Innovations in rapidly developing technology had a significant impact nowadays, making the companies also updating their systems. It makes the companies updating and moving their system to ERP-based systems. Since 2000, previous research [1] implemented ERP systems, but the successful acceptance percentage is still 75% and 25% failure. The ERP implements have several issues, such as systems that are not user friendly, the extended system process, quality of human resources, errors in the recording process, frequent errors in the systems, unstable connection, and the connection to the system must be stable.
In 2019, finance company PT. XYZ applied an ERP system known as System Application Production (SAP) in its regional offices throughout Indonesia. In its implementation, there is a problem that the target user acceptance does not run according to the scheduled time, which impacts project delays. This paper aims to analyze the acceptance factors at all regional offices in Indonesia for 2020 2nd International Conference on Science & Technology (2020 2nd ICoST) Journal of Physics: Conference Series 1844 (2021) 012008 IOP Publishing doi: 10.1088/1742-6596/1844/1/012008 2 a company that implements SAP FICO (Financial Controlling) and SAP MM (Material Management) systems. The company who provides and markets SAP ERP system company not only provides services for the system and develops the system based on the client company request, some clients need assistance by local support to carry out the implementation in terms of the data recording process at selected divisions that use this system for their daily work. The expected of local support is to help overcome some problems that occur and know which part of the system is not user friendly so that it is possible to help users in data processing and recording. It led us to analyze the fundamental factors that affect the user's acceptance of the SAP ERP system technology at the company's regional offices. By looking at the performance of regional office users assisted by the local support, does the assistance by local support influence the implementation of the system in the company regional offices.
The model used to analyze this research is the Unified Theory of Acceptance and Use of Technology (UTAUT2). UTAUT [2] is a model with the latest technology acceptance concept, which is known to be the best method used by many technology industries chosen by experts. In this research, we attached the constructed variable, Trust [3], and Learning Value [4]. Hypotheses testing will be conducted using Partial Least Square Structural Equation Modelling (PLS-SEM) method. It is quantitative research through a survey approach to 87 respondents on the SAP FICO and MM modules from management to staff levels, where respondents are users who use ERP technology with SAP applications in all regional offices of PT. XYZ in Indonesia and local support used to accompany them. This study's expected results are to obtain the most influential factors in accepting ERP technology and recommendations for future use by ERP technology providers as references in further development and projects. If the assistance of local support does affect the system implementation, this should help regional office users smoothen the implementation process related to the recording if there is a possibility that recording will not be carried out according to the rules.

Literature Review
Acceptance of technology can be defined as a user who is willing to work with technology that is designed as a support to carry out their tasks [5]. Many studies have been conducted from previous research to understand technology's acceptance in a business context because of the close relationship between appropriate technology and its profit limits. In many of these studies, researchers have attempted to identify and understand the type of user acceptance that influences the design and implementation process to avoid or minimize resistance or resistance when users interact with technology. Many experts emphasize the importance of proper education and training for both employees and managers. Many people will naturally resist changing the way they do their jobs. Many analysts noted that support from higher-up's management to achieve successful adoption and implementation is as important as company size changes. ERP is an integrated system for each division in a company to help the company's principal and critical administrative processes run in a logic database so that all divisions can store and retrieve their information in real-time [6]. The results of data integration in the system will produce the final report for the company. Companies and organizations can achieve the best competitive advantage by carefully managing training and education, as long as system activities are well integrated [7]. There are several causes of ERP implementation failure, which are described in the following conditions [1]: 1. Poor planning. Planning should include several business-related issues and user availability for system configuration decisions. 2. Poor project management. There are only a few companies that make ERP without involving a consultant. 3. Lack of software evaluation. Organizations or companies do not understand what and how the ERP system works and other reasons. In 1972, System Analysis and Program Development (SAP Company) were the first to develop ERP systems applications to globally complete 50% of business transactions. The vision of SAP is the development of standard software applications in real-time business processes. It was first created for accounting applications, and then SAP forms the basis for continuing application development [2]. SAP is a software that provides information from a business transaction resulting from the merger of 3 departments within the company as a facilitator for decision making [5]. UTAUT [6] is a model with the concept of acceptance of the latest technology, which is known to be the best method used by experts in many technology-related industries. This model is also known to collaborate with several older models such as Innovation Diffusion Theory (IDT), Motivational Model (MM), Model of PC Utilization (MPCU), Social Cognitive Theory (SCT), Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB), The Theory of Reasoned Action (TRA), and Combined TAM and TPB (c-TAM-TPB). UTAUT method can be used by many countries and is quite useful because it can be translated into various languages [8]. According to [7], where UTAUT2 is a continuation of the UTAUT method, it has seven constructs that affect BI and UB. It consists of four primary constructs; those are PE, EE, SI, FC. Then in this advanced method, we added three constructs, H, HM, and PV. However, the voluntariness moderator was removed. The construct is mediated using the mediators of age, gender, and experience. In this study, we added two construct variables, namely, Trust [3] and Learning Value [4]. According to previous research [9], the Trust (T) variable is added because it influences the technology's acceptance. In previous research, Learning Value (LV) is added as a variable because it also significantly influences the research. Illustration of the development of the UTAUT2 method for this study is presented in Figure 1.  [7], [10] The explanations related to the constructed model of UTAUT2 in Figure 1 are described as follows [7]. Performance Expectancy (PE): To find out to what extent a person believes that using this technology can improve his work performance. Effort Expectancy (EE): It is to know the level of ease related to technology use. Social Influence (SI): It is to know the level of environmental influence in using technology. Facilitating Conditions (FC): It aims to determine the organization's infrastructure and technical support to use the technology. Hedonic Motivation (HM): It is to find out does the user obtains some pleasure, enjoyment, or sense of happiness when using the technology. Price Value (PV): To know the level of comparison between costs to buy the technology and benefits obtained when using the technology. Habit (H): To determine to what extent individuals tend to behave because they knew it beforehand automatically. Learning Value (LV): Knowing from the utilization of technology products can hold value if they provide some benefits. Trust (T): It is to know the belief in technology use and cause interdependence. Behavioral Intention (BI): It is to find out behavioral intention, which is defined as a person who has a perceived probability or subjective probability that he will be involved in a particular behavior. Use Behavior (UB): It is to determine the actual frequency of users of technology usage. SEM is a statistical model that could analyze a pattern of relationships from a hypothetical relationship between variables on the indicator and its latent constructs, direct or indirect measurement errors, and latent constructs with each other [8]. There are two types of approaches in the SEM method, the variance approach (VB-SEM) with the Partial Least Squares-SEM (PLS-SEM) technique, and covariance-based SEM or also known as Covariance Based-SEM (CB-SEM). PLS is a powerful analytical method because it can be used in any type of data scale, such as ordinal, interval, nominal, and ratio, without using many assumptions that must be met. The difference between PLS-SEM and CB-SEM is that CB-SEM focuses more on making methods aimed at explaining the covariances of all construct indicators. Whereas PLS-SEM has a predictive objective and is more suitable because its approach assumes all variance measures are useful variances to explain [8].

Research Methodology
The procedure and method implemented in this research are presented in Figure 2 below. In this stage, we identify problems through observation and interviews. The observation was conducted by interacting and looking at how the management between the user and implementor uses it, how the user uses the application, etc. For the interview, we gave several questions to a few personnel, such as the marketing, HR, admin, consultant, local support, and system users at one of the regional offices at PT. XYZ. The questions are regarding the system, its utilization, and questions related to local support. After that, we determine the research objectives that determine what factors influence ERP technology acceptance using the UTAUT2 method. Then we analyze these factors using the PLS-SEM method. After formulating problems and research objectives, the next step examination, in this stage, examine some literature related to this research in order to know and understand the limits for this research.

Hypotheses Methodology
In this section, we present the hypotheses related to UTAUT2 that will be tested in this research. x

Data Collection
The questionnaire's questions are also structured questions with alternative answers that have been provided online through several communication media using the Google Forms website. The data were obtained online from 87 users of the SAP ERP system in all Indonesia regional offices of PT. XYZ, accompanied by local support. Users of the SAP ERP system are users of the SAP FICO and SAP MM modules. Assessment of the questionnaire using scale Likert from 1 point to 5 points, the data will be used to determine what factors affect the acceptance of ERP technology.

Data Analysis using PLS-SEM Method
PLS-SEM method process the data obtained from the completed questionnaire. Here we use the outer model and inner model testing. Inner model testing using R 2 calculation aims to determine BI and UB's value and their effect on ERP technology acceptance. The next step is to analyze the influencing variables using t-value analysis and bootstrapping on the PLS-SEM method. Based on [8], there are several steps in the PLS-SEM analysis; it consists of: Defining the outer model. The outer model represents the relationship between indicator variables and constructs variables that measure it in determining the nature of the formative or reflective types, and for constructs, it has a single item and multi-item measurement types. The formula for the outer model equation is as follows.
Where: matrix of variable manifest independent and dependent; matrix of variable latent independent and dependent; matrix coefficient (matrix loading); residue matrix outer model. Defining the inner model. Defining the inner model aims to model the relationship between latent variables and define it. It is necessary to pay attention to describing a relationship and hypotheses on the theory being tested. The formula for the inner model equation used is explained as follows.
Where: matrix construct latent endogen; coefficient matrix variable endogen; coefficient matrix variable exogen; matrix construct latent endogen; residual of the inner matrix model.
Also, to know the R-square value, there is an evaluation by looking at Q-square, which is predictive of its relevance by the model and its parameter estimation. If the Q-square value is > 0, it means that the model has a relevant prediction. On the other hand, if the Q-square value is ≤ 0, it indicates that the model has less relevant predictions. The Q-square formula can be described as follows.
Where: is R-Square of endogen variable; Q 2 , which has a value in the range 0 < Q 2 < 1, means that the closer to the value 1, the better. The magnitude of Q 2 is equivalent to the total coefficient of determination in the path analysis. model evaluates with average variance extracted to assess convergent validity, and composite reliability to assess internal consistency and individual indicator reliability. Also, to assess discriminant validity using cross-loading calculations and Fornell-Lacker criterion. For the structural model, the objective is to determine the relationship between latent variables and the path coefficient, indicating whether there is a significant relationship in this research method or not. The step taken is to look at the R-Square (R 2 ) value for each structural model prediction. This value will explain a specific exogenous (latent variable) effect on endogenous (latent variable) or its influence. There are several types of Goodness of Fit; those are outer reflective, formative, and GOF inner models. In the reflective model, the model evaluated based on the convergent seen from loading factor value, the value that can be considered sufficient between 0.5 to 0.6 and preferably greater than 0.7, while for the number of latent variables between 3 to 7. Furthermore [11], for discriminant validity, it is seen based on the existing AVE value at a number > 0.5, while composite reliability is > 0.6. The equation of AVE calculation is stated below.
Where: = loading factor; = 1-. Formula to calculate composite reliability: In the formative model using the outer model, the evaluation is based on the substance content, which can be seen on the multicollinearity's value significance and weight. In GOF, the inner model is measured by predicting the relevant Q-square. The Q-square equation used is as follows.
Where: is R-Square of endogen variable in the model; Q 2 is a total coefficient of determination in the path analysis as R 2 in regression. Results Interpretation and Conclusion. The results obtained come from the p-value, t-value, or bootstrapping confidence interval significance tests. Bootstrapping is also needed to assess the significance of the path coefficient. Hypothesis testing is carried out based on the research method with the hypothesis, then processed using PLS, which will produce a P-value. This value is used to determine whether to accept the hypothesis by comparing this value with the alpha (α) = 5% to the following conditions. P-value ≤ α means the hypothesis is accepted. P-value > α means the hypothesis is rejected.

Result and Analysis
This chapter will discuss the data from the results of user questionnaires from 87 respondents, the results of the PLS-SEM method's application process, and the UTAUT2 method using the SmartPLS tool in data analysis research hypothesis testing [12].

Defining and Analyzing Inner Model (Structural Model) and Outer Model (Measurement Model).
The inner model is more simple than the outer model, and it is defined by combining several latent variables, exogenous variables, and endogenous variables. The outer model's determination was carried out by combining several latent variables, exogenous and endogenous variables. An overview of the combination of these variables is presented in a diagram presented in Figure 3. Because of this, it is necessary to eliminate the seven indicator variables. So, for the next run, there will be no indicator variables that do not meet the outer loading standard value < 0.7, and the research can be continued to the next stage, which is internal consistency reliability. Internal Consistency Reliability. Internal consistency reliability is measured by observing Cronbach's Alpha and Composite Reliability; the value given good reliability has passed a standard value > 0.7. The result of this measurement can be seen in Table 4 below. Table 2. Cronbach's Alpha and Composite Reliability. After executing the data, the LV construct variable obtained a Cronbach's Alpha value of less than 0.7 so that we deleted the indicator that has a small value, those are LV2, then LV3. The data is then re-executed, new data is generated below, which shows the Cronbach's Alpha value with good reliability values. After that, we check the composite data reliability. From this process, it is identified that the value of each variable produces good reliability. Convergent Validity. Convergent validity is used to determine the suitability between indicator variables and theoretical concepts that describe these indicators' existence. Average Variance Extracted (AVE) testing can be said to have a good value if the minimum value is 0.5, and the results are shown in Table 5 that the value of each AVE of all variables has a value following the standard so that the construct variables can be said to be all valid and usable data.  [14]. The value of f 2 is 0.02 (weak), 0.15 (moderate), and 0.35 (large) can be ignored or can be said not to affect [10]. The results of F-square (f 2 ) value for variable relations of H towards BI is 0,035 (weak effect), H towards UB is 0,121 (weak effect), HM towards BI is 0,059 (weak effect), LV towards BI is 0,039 (weak effect), PV towards BI is 0,097 (weak effect), SI towards BI is 0,046 (weak effect).  Figure 4 and Figure 5 below. Figure 4 shows that there are Hedonic Motivation and PV variables that affect BI. Moderator variables such as age can be said to have a significant effect on the Hedonic Motivation variable. The moderator variable for experience can also significantly affect Hedonic Motivation and PV variables. Whereas in Figure 5, it is known that Habit affects UB, there are also moderator variables such as experience, which significantly affect the Habit variable.

Conclusion and Discussion
In this research, three variable factors significantly affect the acceptance of SAP ERP technology: Hedonic Motivation, Habit, and Price Value. In contrast, two additional variables, Learning Value and Trust, do not seem to have a significant effect. Based on all variables, we can conclude that the local support associated with the Social Influence and Effort Expectancy variables does not significantly influence the system's acceptance.
H5: Regarding variable age, gender, and experience, does HM significantly affect BI. The result is significant. Only two reliable measurement indicator variables were associated with Hedonic Motivation (HM) left, pleasant use (HM1), and willingness to use (HM3). In this case, there is a kind of happiness that users get because they get new experiences when using a technology that is new in their environment, and it can be a desire that more users pay attention to and use it. There is also an influence of the moderate variables, age, and experience in the Hedonic Motivation variable. HM is a kind of happiness obtained by users because they get new experiences when using new technology for their environment. It can be a desire for more users to pay attention to and use it [15]. H6: Regarding variable age and gender, does PV significantly affect BI. The result is significant. In the PV variable, there are two reliable measurement indicator variables, benefits compared to costs (PV2) and value compared to costs (PV3), it means that users feel the benefits and values are greater than the costs incurred so that users are willing to apply new technology to their environment. This difference is probably due to the environmental effect and the type of technology so that users accept the use of technology and become more productive in improving their performance. For variable PV, users feel the benefits are greater than the costs incurred, so users are willing to use new technology for their environment [16]. H9: Regarding variable age, gender, and experience, does H significantly affect UB. The result is significant. There are two reliable measurement indicator variables, namely habit 2020 2nd International Conference on Science & Technology (2020 2nd ICoST) Journal of Physics: Conference Series 1844 (2021) 012008 IOP Publishing doi:10.1088/1742-6596/1844/1/012008 10 (H1) and necessity (H2). These factors are like a routine that is carried out daily and has become a habit that is quite difficult to leave. The number of transactions and records that must be done will help the user accept this technology and become a habit [17].
Based on the conducted research, system users recommend adding multiple user accounts to faster work without using the system and synchronizing data faster. Adjust the language used on the system with the company environment so that it is easier for users to understand, perform automatic data backup to prevent data loss when the system suddenly goes offline, or an error occurred when the user is inputting data. The efficiency of work processes should be more efficient and comfortable for users to make transactions, provide a more detailed explanation of technical guidelines (user manual) related to system processes so that users can more easily learn about the system. Future research can be conducted by adding other construct variables that are likely to affect this system or adding the number of samples with more users. Also, research using other acceptance methods can be implemented to compare with the natural methods of this study, or comparing data analysis from other companies with company data in this study.