Understanding Behavioral Intention in Implementation of the ICTs Based on UTAUT Model

Innovation on Information Communication and Technology (ICT) are not suddenly accepted and directly used by individuals in work and workplace, even some individuals refuse to work using adoption ICTs. Therefore this research needs to be done to reveal what factors influence this attitude. This article aims to analysis variables or factors such as performance expectancy (PE) as X1, effort expectancy (EE) as X2, social influence (SI) as X3 and facilitating condition (FC) as X4 that contribute to the behavioral intention (BI) as Y of individual in accepted or rejected innovation based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model perspective. The method was applied factor analysis. A technique of collecting data using the checklist of questionnaire instrument, with total the population of 85 people, then according to tables of Isaac and Michael obtained the sample of 68 respondents who came from the Government Employees in the Disdikpora Dharmasraya Regency. The data were analyzed with the software tools of the Statistical Package for the Social Sciences (SPSS) version 22. The data collection time starts from November to December 2018. We found that X1, X2, X3, and X4 have significant effects on user acceptation based on UTAUT model.


Introduction
Factors affecting the success of the application of Information and Communication Technology (ICT) innovation in an organization can be observed in one's behavior at work [1]. To be able to know the level of management awareness [2] from the adoption of ICT that has been carried out effectively or not, it needs a reliable evaluation tool (maturity) [3]. A person becomes the main attribute in the acceptance or rejection of innovation [4]. ICT is not suddenly acceptable and directly used by individuals in work and the workplace.
There have been many theories related to the rejection or acceptance of the use of innovations built on the various models developed. The first model is the Theory of Reasoned Action (TRA) [5]. The second theory is the Technology Acceptance Model (TAM) [6]. The third model is the Motivational Model (MM) [7]. The fourth model is the Theory of Planned Behavior (TPB) [8]. The fifth model is a combination of TAM and TPB [9]. The sixth model is Model of PC Utilization (MPCU) [10]. The seventh model is the Innovation Diffusion Theory (IDT) [11]. The eighth model is the Social Cognitive Theory (SCT) [12]. The last is the Unified Theory of Acceptance and Use Therefore, based on the concept of the UTAUT model, this research is very important to prove the hypothesis according to Figure 1. The independent variables (X) to be disclosed are performance expectancy (PE) as X1, effort expectancy (EE) as X2, social influence (SI) as X3 and facilitating of conditions (FC) as X4. The dependent variable (Y) is the behavioral intention (BI) to reject or accept the use of adoption or innovation, especially ICTs.

Figure 1. Study Model
The UTAUT model is the result of a synthesis of the theory or model of rejection or acceptance of the adoption of pre-existing ICT [19][20]. UTAUT is a new model that complements previous concepts that have more complete factors [21]. The original UTAUT consists of four major predictor constructions such as performance improvement, efforts, social environmental influences, and facility conditions, on one dependent variable that is the intention to behave in innovation especially computer-based technology [22]. What distinguishes between UTAUT 1 and UTAUT 2 is the factor motivation, investment, and work culture variables [23].
The concept of UTAUT has been proven to successfully reveal and explain up to 70% of variables that affect intentions that lead a person on behaving towards the rejection or acceptance of the use of information technology [24]. The UTAUT can be relied upon in explaining variables and factors in different places in different languages, cultures and developing countries [25]. Some research related UTAUT models that have been used and done in various countries [26][27][28][29][30][31][32][33][34][35][36][37][38][39].
According to [40], UTAUT can also be done with meta-analysis making it easier to apply in explaining invisible constructs to one's behavior on innovation. UTAUT is capable and very suitable to be used to get all the variables and factors that proved the most dominant of the behavior of individuals both within and outside the organization both government, private and consumer behavior [41]. The study that has been done [42][43][44][45][46] does not include all intervening variables and or moderate variables with the opinion that the variables or factors do not so impact on the object and subject observation because it will tend to be the same results in time cross-section.
UTAUT as a concept, theory, and model has been widely accepted as the most modern basic concept today in various parts of the world to express user acceptance of an innovation, especially in ICT. UTAUT has been massively used in various fields of science, various fields of work and countries for research needs. So it can be said that UTAUT is the result of analysis, synthesis, and evaluation of a number of theories that exist on the concept and theory of acceptance of the use of an innovation which has four (4) independent variables are performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), and one (1) dependent variable is behavioral intention (BI).
The definition of BI according to [47] is the amount of individual intention to perform certain acts. A person's intention to do something will be observable from behavioral intention [48]. BI can be interpreted as a feeling driven by the desire to do something [49]. BI is a storefront of one's behavior and attitude toward his/her perspective on new things [50]. It is understandable that BI is the power of hidden things that can only be seen from the behavior of a person on doing work.
The PE is defined as the amount of expectation that using and utilizing innovation will be able to support a person to gain performance benefits. This is consistent with the PE is directly proportional to the improvement of an organization's performance [51]. According to [52] PE is the high expectation of someone to improve the existing working conditions by utilizing innovation. The idea [53] states that the improvement of performance is the effectiveness and efficiency that one does in working with innovation. It is understood that PE as a benefit to be gained by someone involves innovation while working.
The EE is defined as the ease of using something that the user indication will be happy to adapt to something new. EE is how much duration of time spent getting familiar with the new thing [54]. According to [55] that EE is not a rumor of the use of innovation, so will be able to give birth to confidence, which ultimately brings a sense of security and comfortable wearing it. From both opinions can be said EE is easy to use, not difficult, simple, foster self-confidence, and comfortable in using to something new such as an innovation.
The SI is defined as having reached the extent to which a person believes and be sure when the individual in his or her sphere can influence to be able to use innovation [56]. The SI is indicated by the support of leaders, co-workers and the workplace environment [57]. This suggests that individuals will have a strong desire to utilize innovations such as ICTs if they have the support of other individuals.
The FC according to [58] is the feeling of the perception of behavioral control that is directed towards individual beliefs toward the approved environmental factors of observations that have boundaries of the inner and outer self. While according to [59] FC is that the condition of the facility or the completeness of the facility is believed to be able to influence a person to refuse or accept using an innovation.

Research Methods
Type of this research conducted with a quantitative approach. The methods have been applied using factor analysis. Type of data collection is primary and secondary data. Primary data was obtained directly from the data subject and secondary data type was obtained from the literature review. A technique of collecting data using the checklist of questionnaire instrument, with the total population of 85 peoples, with proportional random sampling based on tables Isaac and Michael, obtained as many as 68 samples as respondents. The assessment of the questionnaire instrument was carried out by 5 experts invited as validators in the ongoing process of the Focus Group Discussion (FGD) activity. Object data comes from government employees working on Disdikpora Dharmasraya District. Data were analyzed with the help of software SPSS version 22. Data collection starts from November to December 2018.
The steps taken are a literature review, compiling and establishing indicators that will be used as measuring instruments in the form of statement items or questions that are inserted on the sheet of questionnaire instruments, collecting data, analyzing and displaying the results of the process. The instrument is given to three experts judgment as of the validator. Instruments are then repaired and ready to carry the spaciousness. After all the required data is collected, inserted to be processed with SPSS tools by testing the data normality, linearity, and multicollinearity. Then by clicking the analyze menu, proceed by choosing the regression button and then selecting the linear button. Input all exogenous (free) variables to be analyzed into independent boxes and endogenous variables into the dependent box. Click the button by selecting the enter technique and finally output is displayed that is the coefficient of regression, from the result of analyzing the correlation and the coefficient of the determinant. Figure 2 shows the steps in these studies.

Result
Before the data is analyzed by factor analysis method, the work to be done is the test of data normality. The normalization of the data applied by the Kolmogorov-Smirnov test technique at an error level of 95%. Data is said to be normal if the Cronbach alpha (α > 0,05). The table of normality test as shown in table 1. Performance expectancy (PE), effort expectancy (EE), social influence (SI) and facilitating of conditions (FC) as dependent variables.

Table 1. Test Normalization
The linearity test is performed on two variables which are said to have linearity correlation using Test for Linearity (Analysis of Variance) technique with a significance error of 0.05. At least two factors will be said to be linearly related if the significance is less than 0.05 and the deviation from linearity is greater than 0.05 (> 0.05). We used the analysis of variance (ANOVA) approach is able to explain how much difference in influence between one independent variable and the other independent variables and on the dependent variable in this study. F-test is used for discovering a ratio of the one group of variance or two variances influential. Degrees of freedom (df) is practiced to demonstrate indicate or to coefficient estimates hypothesis in a regression model for this study. Significance probability (Sig. / p-value) refuted that the null hypothesis is true in our sample, so that there is no correlation or no linear relationship between the independent variable and the dependent variable, if higher than or equal to Sig., we preserve the null hypothesis. The linearity test results are shown in Table 2. The testing with multicollinearity techniques was performed to determine whether or not multicollinearity symptoms in all independent variables can be recognized from a large number of Variance Inflation Factor (VIF). The limit of the VIF is less than 10 and the Tolerance number must be greater than 0.1. Table 3 shows the multicollinearity test results.  The result of hypothesis test which has been done by factor analysis method with simple regression technique can be disclosed that all exogenous variables such as PE, EE, SI, and FC together there is an influence of significance to endogen variable that is BI. These are consistent with some of the research findings conducted by other earlier researchers who also excluded moderate variables.  Whether or not the multiple regression model is established, should be proven by testing the feasibility of the model using the F test. Table 8 shows the results of the F coefficient test.  15.129 with a significant value of 0.000. This value is smaller than α = 0.05. This indicates that the model or regression equation is made good or feasible to use. These means that there is a significant influence of factors PE, EE, SI, and FC to BI. The summary of the hypothetical test results from this study can be seen in table 9.

Discussion
Based on H1 can be explained that there is a significant influence between the PE against the BI with large t count of 3.703 on the significance of 0.000. This value is smaller than 0.05 (p <0.05) which indicates its significance. The value of correlation (r) variable of PE (X1) to BI (Y) is 0,415. The value of this correlation is it the level of moderate relationship with the direction of a positive correlation. These means that the better the PE employees, the better the BI in using innovation. The magnitude of the effect of PE to BI is shown by the determinant coefficient of 17.2%. These illustrate that the variable of PE can explain the BI of 17.2%, while the rest is influenced by other variables outside the regression equation Y = 30.039 + 0.340X1.
The H2 can be explained that there is a significant influence between EE on BI with a significant number of 0.000. The p value < 0,05 indicates significant. The value of correlation (r) variable EE to BI is 0,559. This correlation value is it the level of moderate relationship with positive (r) relationship direction. This means that the better EE, than the better BI in the implementing of innovation. This illustrates that the EE variable can explain BI by 31.2%, while the rest can be perceived or influenced by other variables outside the regression equation. The regression equation obtained is Y = 27.843 + 0.602X2.
For the H3, there is significant influence between SI and BI with t value count is 4,451 has significant. The p <0.05 have shown significant. The value of correlation (r) variable SI with BI is 0.480. This correlation value is the level of moderate relationship with positive (r) relationship direction. This means SI is good, then the better is also to Bl in the implementation of innovation. The magnitude of the effect shown by the number R 2 is 23.1%. This illustrates that the SI variable can explain BI by 23.1%, while the rest is perceived or influenced by other variables outside the regression equation. The regression equation obtained is Y = 28.003 + 0.494X3.
The H4 explained that there is a significant influence between FC and BI in the implementation of innovation with a significance value of 0.001. This value is smaller than 0.05 (p <0.05) which indicates significant. The value of correlation (r) of FC variable to BI is 0,400. The value of this correlation is the level of moderate relationship with the direction of a positive relationship because of the value of r positive. This means the better the FC, the better the BI in the implementation of innovation. The amount of influence indicated by the value of the determinant coefficient of 0. 16 The significance value of all exogenous variables together with the endogenous variables is smaller than alpha 0.05 which indicates a significant influence between PE, EE, SI, FC together with BI. The correlation value (r) of the independent variables together is 0.700. The coefficient of determination or R square is 0.490 which implies that the influence of variables of PE (X1), EE (X2), SI (X3), FC (X4) together to BI (Y) is 49% while the rest can be perceived to be influenced by other variables outside the regression equation. Analysis of regression model that aims to see the direction of the relationship of exogenous variables to endogenous variables in the research that is applied by looking at the value of the coefficient of Beta (B) of each variable. The constant value (a) is 0,087, coefficient B X1 equal to 0,248, coefficient B X2 equal to 0,274, coefficient B X3 equal to 0,256 and coefficient of B X4 equal to 0,480, so that obtained by equation of multiple regression model that is Y = 0,087+ 0,248 X1 + 0,274 X2 + 0,256 X3 + 0,480 X4. The results of this research indicate that the method with a simple linear regression is estimated to be able to explain the effect of an exogenous variable on endogenous variables, thus also can be concluded that the UTAUT model is acceptable and suitable for use in this study.

Conclusion
All exogenous variables have been shown to have a significant influence on endogen variables PE, EE, SI, and FC are explanatory factors of BI in the implementation of innovation, especially ICT in employees Disdikpora Dharmasraya Regency. The better the PE, EE, SI, and FC, the better the BI. In this research, the adopted UTAUT model has ruled out all the variables so as to produce findings that are not the same as other studies that include all moderate variables such as age, gender, motivation, work culture, and the others. The instruments used as measuring instruments with different indicators are considered to also differentiate the final results of the study. Although this questionnaire has been declared valid by the expert and declared reliable, however, it can not be ruled out that the grain of statement or problem is not free from bias condition. Hopefully, this research model can be developed more deeply and expanded by adding other independent variables such as interpersonal and include intervening variables such as lifestyles so that new theories outweigh the popularity of the UTAUT model.