Data to model the effect of awareness on the success of IT Governance implementation: A partial least squares structural equation modeling approach (PLS-SEM)

This article presents raw inferential statistical data that determine the influence of awareness on the successful implementation of IT Governance. Data were collected from respondents in all regions of Indonesia. Quantitative research methods are used to analyze data. The structured questionnaire was distributed to respondents in all regions of Indonesia who understood the field of IT Governance whose reliability and validity were confirmed. Structural equation modeling (SEM) using Smart PLS software, version 3, is used to present data. SEM path analysis shows an estimate of the relationship of the main constructs in the data. The results obtained from this dataset show a positive relationship between Risk Management, IT Resources, Budget, Stakeholder Involvement, Policy, Business Strategy, Organization, Commitment, Competence, Communication to awareness and consciousness also has a significant influence on the success of IT Governance implementation. However, politics has proven to have a negative and insignificant influence on the awareness and success of implementing IT Governance.


Data
Preliminary data were obtained through literature studies, as seen in Table 1. From the literature studies obtained later developed into a questionnaire. The questionnaire through an online survey can be accessed at the URL http://bit.ly/2XLNsPi, this questionnaire is then distributed to various communities that understand the field of IT Governance in all regions of Indonesia, as many as 260 copies of questionnaires are included, through a selection and feasibility process taken only 253 copy (97%). The survey questionnaire was chosen because it was considered the most preferred technique because of its many advantages and good quality [1]. To meet the quality feasibility, this data is then analyzed by considering the values: Cronbach's Alpha (0.6), Composite Reliability (0.7), AVE (0.5) and Loading Factor (0.7) [2,3]. To determine the level of a significant path coefficient, the bootstrap and T-Statistic processes are used above 1.96 at the 95% confidence interval [4]. The measurement accuracy data can be seen in Table 2 and the structural model can be seen in Fig. 1. As the last data, Table 3 displays the output model analysis data.

Experimental design, materials, and methods
The data presented is based on qualitative and quantitative research. Qualitative data were obtained based on literature studies to obtain awareness variables, as seen in Table 1. While quantitative data were obtained by distributing questionnaires to respondents. The survey method is considered as the right data collection method because it enables standardized data collection that allows researchers to Specifications

Value of the Data
This data is useful because it can be used as a reference, input, and consideration in implementing IT Governance in order to experience success in accordance with organizational goals, namely the creation of harmony between business objectives and IT goals. This data is useful for all parties involved, especially the top leaders of the organization, namely the board of directors and executive managers. This data can be developed into a measurement tool to determine the extent of awareness of IT Governance in an organization.
The added value of this data is to provide a valuable contribution in the development of knowledge in the field of IT Governance, specifically soft IT Governance that concentrates on human behavior in its role to achieve successful implementation of IT Governance.
produce information answering important variable questions that influence the awareness and success in implementing IT Governance. Respondents in the Indonesian country were selected for this study.
To test the data, researchers propose a model where Risk Management, IT Resources, Budget, Stakeholder Involvement, Policy, Business Strategy, Organization, Commitment, Politics, Competence, Table 1 Variable in awareness IT Governance for implementation success.

Area Sub Area
Risk Management (RM) -Risk management related to the use and application of IT methodology (RM1).
-Risk management related to control and supervision of IT resources (RM2).
-Risk management of the strengths and weaknesses of IT related to evaluation and analysis (RM3).

IT Resources (RS)
-Resources for availability and fulfillment related to data, technology and applications (RS1). -Resources for management and supervision related to data, technology and applications (RS2). -Resources for portfolio management related to IT strategic assets (RS3). Budget (BG) -The budget for IT investments is related to size and ability (BG1).
-Budget related to the availability of the IT budget needed (BG2). Stakeholder involvement (SH) -Stakeholder involvement in the implementation of IT Governance related to commissioners and board of directors (SH1). Communication are outcome variables. The model proposed by the researcher must be tested for validity from the proposed model and to determine whether the data, which has been collected in the field, matches the proposed conceptual model. The quality of the measurement model is determined based on its validity and reliability [2,3]. The results of testing the validity and reliability of the data are shown in Table 2.

Path model
The PLS estimation results for the structural model, path coefficients values as well as the item loadings for the research constructs are shown in Fig. 1 (Table 3).
The main data source (questionnaire) is used to collect data from respondents in the territory of Indonesia. The Microsoft Excel spreadsheet worksheet is used to enter all data and draw conclusions from the data obtained. The Statistical Package for Social Sciences (SPSS) and Smart PLS software for

Ethical considerations
The researcher guarantees that the respondents have adequate knowledge related to the purpose of this research, besides that they also obtain complete and transparent information. Respondents are guaranteed confidentiality about their personal data.

Academic, practical, and policy implications of this data article
The data presented in this article have implications for academics, for example, awareness directly influences the success of implementing IT governance in a positive and significant way as indicated by the path coefficient of (b ¼ 0.515).
Therefore, for academics in the field of IT Governance, this discovery can enhance their understanding of the relationship between awareness and success of IT Governance. This is a useful contribution to be used as literature. On the practitioner's side, the board of directors and executive managers can benefit from the implications of this discovery. For example, there is a strong relationship between awareness and risk management (b ¼ 0.560), executive managers must pay attention to risk management related to the use of methodology, monitoring of IT resources and evaluating weaknesses and strengths [5,6]. In addition, this data article offers implications for policymakers (board of directors) for the implementation of IT governance in order to improve company performance by paying attention to variables in consciousness. Thus, findings obtained from this research data collection can be used to generate new policies and assist in revising existing policies.