AbstractMany study.Research modelIn this study, the four constructs

AbstractMany organizations are adopting different technologies such as cloud computing, Internet of Things, etc.;some are adopting more successful and far earlier than others. This study validates informationtechnology intent and usage based on the UTAUT (The unified theory of acceptance and use oftechnology) model using acquired dataset for this purpose. The dataset represents 722 participants with 21variables related to the four main constructs hypothesized in the research model. Data were analyzedusing multivariate analysis techniques and the result confirms previous results published in context of theUTAUT model.IntroductionThis research identifies the determinants of the intent and use of information technology based on theexisting UTAUT research model. The aim is to provide validation for the relevant factors influencing theintention and use of information technology. The research is based on the Unified Theory of Acceptanceand Use of Technology (UTAUT) developed by Venkatesh (Venkatesh, Morris, Davis, & Davis, 2003)who has shown that the UTAUT model accounted for 70% of the variance in usage intention. UTAUTpostulates that four constructs are determinant for accepting information technology and act as the directdeterminants of behavioral intention and eventually usage as shown in Figure 3.Figure 1: UTAUT Research Model (Venkatesh, Morris, Davis, & Davis, 2003)The four constructs are performance expectancy, effort expectancy, social influence and facilitatingconditions. In the original study, Venkatesh (Venkatesh, Morris, Davis, & Davis, 2003) used fourmoderators namely; gender, age, experience, and voluntariness of use which were hypothesized to havemoderating effects in the acceptance of information technology. However, the data for the moderatorswere not available in the dataset used. So, they will be excluded from the analysis in this study.Research modelIn this study, the four constructs as defined by Venkatesh (Venkatesh, Morris, Davis, & Davis, 2003), are:Performance Expectancy (PE): Degree of belief that when using the system, it will help to improve jobperformance.Effort Expectancy (EE): Degree of ease when using of the system.Social Influence (SN): Degree of perceiving other relevant believes that he or she should use the newtechnology.1/10Facilitating Conditions (FC): Degree of belief that an organizational and technical infrastructure isavailable to support usage of the system.Using the four core constructs of the UTAUT model, (excluding the four moderators not available in thedataset) five hypotheses were made as shown in Figure 2.Figure 2: UTAUT Hypothesis model (Liao, Shim, & Luo, 2004)H1: Performance expectancy positively affects intention to use information technology.H2: Effort expectancy positively affects intention to use information technology.H3: Social influence positively affects intention to use information technology.H4: Facilitating conditions positively affect actual use behavior.H5: Intentions to use information technology positively affect actual use behavior.Figure 3: UTAUT Research Model Project (SmartPLS, 2017)Research QuestionsWhat are the factors associated with intention and usage of information technology, when seen from atechnology acceptance perspective?Which statistical technique will be used to analyze the dataset and why?What is the magnitude of the effects using confidence intervals?Are there outliers? And if so, what can be done regarding them?Are there any missing data? If so, is there anything can be done to correct that?2/10Method & DataQuantitative approach is the common approach for collecting and converting data into numerical form.This allows us to test the UTAUT theory by examining the relationship among variables. The correlationbetween these variables can be estimated using multivariate analysis. Multivariate analysis is often usedto test hypotheses such as the one on the current study (Pahor, 2017). This will help identifying andunderstanding the factors that influence and/or predict the outcome. This section outlines how the datawas acquired, as well as provides a rudimentary description of it.Data AcquisitionThe dataset used in this analysis is offered by SmartPLS Company based on data provided by Venkatesh(Venkatesh, Morris, Davis, & Davis, 2003). It is part of a survey conducted to examine the adoption of ITin an organization in an effort to produce a unified model that explains the information technologyadoption (UTAUT). The dataset can be found in SmartPLS website available for download at thefollowing link (https://www.smartpls.com/documentation/sample-projects/utaut) as shown in Figure 3.Data DescriptionAs mentioned earlier, the dataset represents responses to a survey. It consists of 722 observations ofresponses to 21 variables. The variables can be found in the original article (a concise simplified versionis included in the appendix under core variables to provide guidance and clarification). This dataset hasalready been processed, and since this is an attempt at replicating the previous work, it is assumed thedata have been properly sanitized. All outliers have been removed, and the dimensions have beenappropriately reduced.An outlier is generally defined as data point that lies far from the norm of a variable and they can havedevastating effects on statistical analyses (Osborne & Overbay, 2004). They can increase the errorvariance and as such they will reduce the power of statistical tests. In multivariate analyses techniquewhich is the one selected for this study, they can violate multivariate normality with the result of makingboth Type I and Type II errors. However, this is not the case in this study as explained in the previousparagraph.AnalysisIn an effort to expand on Venkatesh’s findings (Venkatesh, Morris, Davis, & Davis, 2003), the results oftheir PCA approach will be modeled in two different approaches. The first is using structural equationmodeling (SEM). The second is using multiple regression analysis as a confirmatory step. The analysiswould be conducted using JASP, a free statistical software tool provided by the University of Amsterdamas shown in Figure 3.SEM was chosen as an initial step, due to its ability to reflect relationships between multiple variables, as-given the way the survey was constructed- the dependent variable was measured by 4 different latentvariables. Other modeling techniques would not be able to reflect that. Partial Least Squares technique isessentially a variant of SEM, and linear regression will not be able to capture multiple dependentvariables (Pahor, 2017). Also, since Venkatesh (Venkatesh, Morris, Davis, & Davis, 2003) had alreadyattempted PCA- full name, it would not add much (neither from a confirmatory perspective nor from anexploratory perspective) to simply repeat the experiment. As for the regression, it was required toexamine the relationships between each of the USAGE latent variables and the rest of the variables, inorder to gain a better understanding how changes in performance expectancy, effort expectancy, socialinfluence, and behavior intention affected the different aspects of technology usage.3/10Structural Equation ModelingBased on the provided data, and the relationships already hypothesized between the variables byVenkatesh (Venkatesh, Morris, Davis, & Davis, 2003) as shown in Figure 3, a model was put togetherthat seemed to best represent the data. The lavaan syntax of the model is as follows:PE =~ PE1 + PE2 + PE3 + PE4EE =~ EE1 + EE2 + EE3 + EE4SN =~ SN1 + SN2 + SN3FC =~ FC1 + FC2 + FC3BI =~ BI1 + BI2 + BI3 + PE + EE + SNUSE =~ USE1 + USE2 + USE3 + USE4USE ~ FC + BIAs can be seen in the model, performance expectancy (PE) is measured by four latent variables collectedby the survey, namely PE1, PE2, PE3, and PE4 (as clarified in the appendix). The same applies to effortexpectancy (EE), social influence (SN), and facilitating conditions (FC). However, behavioral intention(BI) is slightly different. Venkatesh (Venkatesh, Morris, Davis, & Davis, 2003) finds that -in addition toits latent variables- there is also some relationship between it and PE, EE, and SN. As such, the BI portionof the model is slightly different from its predecessors.Multiple RegressionAs mentioned earlier, multiple regression here is used as both a confirmatory step, as well as anexploratory step to examine in more detail the intricacies of the relationships between each of the USAGElatent variables and the rest of the variables. Each latent usage variable is designated as a dependentvariable, while the remaining PE, EE, SN, FC, and BI variables are modeled as independent variables.The relationship between BI and PE, EE, and SN is not explicitly modeled, due to the nature of regressionwhich specifies only the one dependent variable. In addition to the regression model, JASP will also runan ANOVA test, to determine statistical significance.ResultsThe following section contains the results of both approaches as they were carried out the by the JASPtool.Structural Equation AnalysisOn running the previous model in JASP, the following results were obtained as shown in Table 1. As seenin the table, the estimated parameters produce a very low standard error, and each is statisticallysignificant, having a p-value less than 0.05. The lower and upper limits of the confidence interval alsoprovide a reasonable approximation of the population mean. The results also show, from the estimatedparameters, the effect of each latent variable on the USE variable. For instance, it can be seen that as FCincreases, so does USE, given the positive coefficient, but the increase follows a more logarithmic trend.The same is true for the BI variable as well.Parameter Estimateslabel est se z p CI (lower) CI (upper) std (lv) std (all) std (nox) groupPE =~ PE1 . 1.000 0.000 . . 1.000 1.000 0.721 0.720 0.720 .PE =~ PE2 . 1.278 0.059 21.558 < .001 1.162 1.394 0.922 0.862 0.862 .PE =~ PE3 . 1.347 0.062 21.678 < .001 1.226 1.469 0.972 0.870 0.870 .PE =~ PE4 . 1.013 0.061 16.508 < .001 0.893 1.134 0.731 0.650 0.650 .EE =~ EE1 . 1.000 0.000 . . 1.000 1.000 1.013 0.772 0.772 .4/10EE =~ EE2 . 0.904 0.047 19.309 < .001 0.812 0.995 0.916 0.728 0.728 .EE =~ EE3 . 0.975 0.047 20.759 < .001 0.883 1.067 0.988 0.779 0.779 .EE =~ EE4 . 1.070 0.049 21.923 < .001 0.974 1.166 1.084 0.825 0.825 .SN =~ SN1 . 1.000 0.000 . . 1.000 1.000 1.478 0.918 0.918 .SN =~ SN2 . 1.025 0.025 40.999 < .001 0.976 1.074 1.515 0.946 0.946 .SN =~ SN3 . 0.862 0.027 32.051 < .001 0.810 0.915 1.275 0.832 0.832 .FC =~ FC1 . 1.000 0.000 . . 1.000 1.000 0.452 0.342 0.342 .FC =~ FC2 . 2.978 0.397 7.504 < .001 2.201 3.756 1.346 0.745 0.745 .FC =~ FC3 . 2.854 0.379 7.522 < .001 2.110 3.598 1.289 0.729 0.729 .BI =~ BI1 . 1.000 0.000 . . 1.000 1.000 0.643 0.552 0.552 .BI =~ BI2 . 0.845 0.096 8.831 < .001 0.657 1.033 0.544 0.427 0.427 .BI =~ BI3 . 1.418 0.121 11.728 < .001 1.181 1.655 0.912 0.658 0.658 .BI =~ PE . 0.709 0.069 10.304 < .001 0.574 0.844 0.633 0.633 0.633 .BI =~ EE . 1.091 0.100 10.943 < .001 0.895 1.286 0.692 0.692 0.692 .BI =~ SN . 1.046 0.117 8.975 < .001 0.817 1.274 0.455 0.455 0.455 .USE =~ USE1 . 1.000 0.000 . . 1.000 1.000 1.190 0.772 0.772 .USE =~ USE2 . 0.738 0.046 16.132 < .001 0.648 0.828 0.878 0.728 0.728 .USE =~ USE3 . 0.715 0.049 14.532 < .001 0.618 0.811 0.851 0.627 0.627 .USE =~ USE4 . 1.054 0.084 12.562 < .001 0.889 1.218 1.254 0.534 0.534 .USE ~ FC . 0.431 0.163 2.650 0.008 0.112 0.750 0.164 0.164 0.164 .USE ~ BI . 0.724 0.118 6.128 < .001 0.492 0.955 0.391 0.391 0.391 .PE1 ~~ PE1 . 0.483 0.029 16.421 < .001 0.426 0.541 0.483 0.482 0.482 .PE2 ~~ PE2 . 0.293 0.025 11.590 < .001 0.243 0.342 0.293 0.256 0.256 .PE3 ~~ PE3 . 0.304 0.027 11.129 < .001 0.250 0.358 0.304 0.244 0.244 .PE4 ~~ PE4 . 0.730 0.042 17.268 < .001 0.647 0.813 0.730 0.577 0.577 .EE1 ~~ EE1 . 0.697 0.047 14.726 < .001 0.604 0.790 0.697 0.404 0.404 .EE2 ~~ EE2 . 0.744 0.047 15.759 < .001 0.651 0.836 0.744 0.470 0.470 .EE3 ~~ EE3 . 0.631 0.044 14.505 < .001 0.546 0.716 0.631 0.393 0.393 .EE4 ~~ EE4 . 0.551 0.043 12.800 < .001 0.467 0.636 0.551 0.319 0.319 .SN1 ~~ SN1 . 0.408 0.038 10.785 < .001 0.334 0.483 0.408 0.157 0.157 .SN2 ~~ SN2 . 0.270 0.036 7.596 < .001 0.201 0.340 0.270 0.105 0.105 .SN3 ~~ SN3 . 0.721 0.045 16.113 < .001 0.633 0.809 0.721 0.307 0.307 .FC1 ~~ FC1 . 1.542 0.085 18.087 < .001 1.375 1.709 1.542 0.883 0.883 .FC2 ~~ FC2 . 1.455 0.164 8.859 < .001 1.133 1.777 1.455 0.446 0.446 .FC3 ~~ FC3 . 1.464 0.154 9.492 < .001 1.162 1.767 1.464 0.468 0.468 .BI1 ~~ BI1 . 0.945 0.057 16.458 < .001 0.832 1.057 0.945 0.695 0.695 .BI2 ~~ BI2 . 1.325 0.075 17.716 < .001 1.179 1.472 1.325 0.818 0.818 .BI3 ~~ BI3 . 1.092 0.075 14.547 < .001 0.944 1.239 1.092 0.568 0.568 .USE1 ~~ USE1 . 0.962 0.085 11.345 < .001 0.796 1.128 0.962 0.404 0.404 .USE2 ~~ USE2 . 0.684 0.052 13.039 < .001 0.581 0.787 0.684 0.470 0.470 .USE3 ~~ USE3 . 1.119 0.071 15.700 < .001 0.979 1.259 1.119 0.607 0.607 .USE4 ~~ USE4 . 3.949 0.232 17.003 < .001 3.494 4.404 3.949 0.715 0.715 .PE ~~ PE . 0.312 0.033 9.495 < .001 0.248 0.376 0.600 0.600 0.600 .EE ~~ EE . 0.534 0.057 9.361 < .001 0.422 0.646 0.520 0.520 0.520 .SN ~~ SN . 1.732 0.118 14.698 < .001 1.501 1.963 0.793 0.793 0.793 .5/10FC ~~ FC . 0.204 0.050 4.049 < .001 0.105 0.303 1.000 1.000 1.000 .BI ~~ BI . 0.414 0.059 7.032 < .001 0.298 0.529 1.000 1.000 1.000 .USE ~~ USE . 1.069 0.109 9.841 < .001 0.856 1.281 0.754 0.754 0.754 .FC ~~ BI . 0.149 0.025 5.849 < .001 0.099 0.199 0.513 0.513 0.513 .Table 1: SEM Parameter EstimateMultiple RegressionOn running the regression models in JASP, the following results were obtained:Linear Regression with USE1 as Dependent VariableModel SummaryModel R R² Adjusted R² RMSE1 0.503 0.253 0.235 1.350ANOVAModel Sum of Squares df Mean Square F p1 Regression 433.9 17 25.521 14.00 < .001Residual 1283.3 704 1.823Total 1717.1 721CoefficientsModel Unstandardized Standard Error Standardized t p 2.5% 97.5%1 (Intercept) 0.573 0.437 1.312 0.190 -0.285 1.431PE1 -0.104 0.069 -0.067 -1.497 0.135 -0.240 0.032PE2 0.080 0.079 0.056 1.010 0.313 -0.076 0.236PE3 -0.052 0.075 -0.038 -0.691 0.490 -0.200 0.096PE4 0.003 0.058 0.002 0.047 0.962 -0.110 0.116EE1 -0.207 0.056 -0.176 -3.683 < .001 -0.317 -0.097EE2 0.154 0.057 0.125 2.717 0.007 0.043 0.265EE3 0.107 0.059 0.088 1.824 0.069 -0.008 0.222EE4 0.042 0.060 0.036 0.700 0.484 -0.076 0.161SN1 -0.084 0.066 -0.087 -1.259 0.209 -0.214 0.047SN2 0.081 0.071 0.084 1.146 0.252 -0.058 0.221SN3 -0.072 0.057 -0.071 -1.253 0.211 -0.184 0.041FC1 0.046 0.047 0.039 0.971 0.332 -0.047 0.139FC2 0.020 0.035 0.023 0.562 0.575 -0.049 0.088FC3 0.060 0.036 0.069 1.684 0.093 -0.010 0.131BI1 0.071 0.052 0.054 1.372 0.170 -0.031 0.173BI2 0.440 0.044 0.363 10.103 < .001 0.354 0.525BI3 0.128 0.045 0.115 2.842 0.005 0.040 0.216Table 2: Linear Regression with USE1 as Dependent VariableLinear Regression with USE2 as Dependent VariableModel SummaryModel R R² Adjusted R² RMSE1 0.451 0.204 0.184 1.090ANOVA6/10Model Sum of Squares df Mean Square F p1 Regression 214.0 17 12.587 10.59 < .001Residual 836.9 704 1.189Total 1050.9 721CoefficientsModel Unstandardized Standard Error Standardized t p 2.5% 97.5%1 (Intercept) 2.210 0.353 6.262 < .001 1.517 2.902PE1 0.012 0.056 0.010 0.217 0.828 -0.098 0.122PE2 0.078 0.064 0.069 1.217 0.224 -0.048 0.204PE3 -0.079 0.061 -0.073 -1.292 0.197 -0.198 0.041PE4 0.002 0.046 0.002 0.042 0.966 -0.089 0.093EE1 -0.103 0.045 -0.112 -2.270 0.024 -0.192 -0.014EE2 0.209 0.046 0.217 4.565 < .001 0.119 0.298EE3 -0.006 0.047 -0.007 -0.137 0.891 -0.099 0.086EE4 -0.012 0.049 -0.013 -0.239 0.811 -0.107 0.084SN1 -0.039 0.054 -0.052 -0.723 0.470 -0.144 0.066SN2 0.049 0.057 0.066 0.861 0.389 -0.063 0.162SN3 -0.063 0.046 -0.080 -1.368 0.172 -0.154 0.028FC1 0.043 0.038 0.047 1.119 0.263 -0.032 0.118FC2 -0.025 0.028 -0.038 -0.888 0.375 -0.081 0.030FC3 0.091 0.029 0.133 3.132 0.002 0.034 0.147BI1 0.069 0.042 0.067 1.663 0.097 -0.013 0.151BI2 0.277 0.035 0.292 7.882 < .001 0.208 0.346BI3 0.036 0.036 0.041 0.981 0.327 -0.036 0.107Table 3: Linear Regression with USE2 as Dependent VariableLinear Regression with USE3 as Dependent VariableModel SummaryModel R R² Adjusted R² RMSE1 0.534 0.285 0.268 1.162ANOVAModel Sum of Squares df Mean Square F p1 Regression 379.1 17 22.301 16.50 < .001Residual 951.3 704 1.351Total 1330.4 721CoefficientsModel Unstandardized Standard Error Standardized t p 2.5% 97.5%1 (Intercept) -0.821 0.376 -2.182 0.029 -1.560 -0.082PE1 0.038 0.060 0.028 0.638 0.523 -0.079 0.155PE2 0.066 0.068 0.052 0.963 0.336 -0.068 0.200PE3 -0.046 0.065 -0.038 -0.710 0.478 -0.174 0.081PE4 0.013 0.050 0.011 0.263 0.793 -0.084 0.110EE1 -0.055 0.048 -0.053 -1.130 0.259 -0.150 0.040EE2 0.093 0.049 0.086 1.913 0.056 -0.002 0.189EE3 0.045 0.050 0.042 0.888 0.375 -0.054 0.1447/10CoefficientsModel Unstandardized Standard Error Standardized t p 2.5% 97.5%EE4 0.002 0.052 0.002 0.048 0.962 -0.100 0.105SN1 -0.065 0.057 -0.077 -1.132 0.258 -0.177 0.047SN2 0.075 0.061 0.088 1.219 0.223 -0.045 0.194SN3 -0.121 0.049 -0.136 -2.448 0.015 -0.217 -0.024FC1 0.192 0.041 0.187 4.693 < .001 0.112 0.272FC2 0.015 0.030 0.020 0.501 0.617 -0.044 0.074FC3 0.033 0.031 0.043 1.058 0.290 -0.028 0.093BI1 -0.024 0.045 -0.021 -0.545 0.586 -0.112 0.063BI2 0.425 0.037 0.399 11.336 < .001 0.351 0.499BI3 -0.023 0.039 -0.023 -0.588 0.556 -0.099 0.053Table 4: Linear Regression with USE3 as Dependent VariableLinear Regression USE4 as Dependent VariableModel SummaryModel R R² Adjusted R² RMSE1 0.491 0.241 0.222 2.074ANOVAModel Sum of Squares df Mean Square F p1 Regression 959.4 17 56.438 13.12 < .001Residual 3027.4 704 4.300Total 3986.9 721CoefficientsModel Unstandardized Standard Error Standardized t p 2.5% 97.5%1 (Intercept) -2.631 0.671 -3.919 < .001 -3.948 -1.313PE1 0.019 0.107 0.008 0.181 0.856 -0.190 0.229PE2 0.020 0.122 0.009 0.165 0.869 -0.220 0.260PE3 0.107 0.116 0.051 0.927 0.354 -0.120 0.335PE4 0.151 0.088 0.072 1.709 0.088 -0.022 0.324EE1 -0.156 0.086 -0.087 -1.804 0.072 -0.325 0.014EE2 0.114 0.087 0.061 1.313 0.189 -0.056 0.285EE3 0.017 0.090 0.009 0.188 0.851 -0.160 0.194EE4 0.024 0.093 0.013 0.258 0.796 -0.158 0.206SN1 -0.093 0.102 -0.064 -0.915 0.361 -0.293 0.107SN2 0.012 0.109 0.008 0.111 0.912 -0.202 0.226SN3 -0.141 0.088 -0.092 -1.600 0.110 -0.313 0.032FC1 0.234 0.073 0.132 3.210 0.001 0.091 0.377FC2 0.075 0.054 0.058 1.403 0.161 -0.030 0.181FC3 0.120 0.055 0.091 2.189 0.029 0.012 0.228BI1 -0.089 0.079 -0.044 -1.123 0.262 -0.245 0.067BI2 0.659 0.067 0.357 9.849 < .001 0.527 0.790BI3 -0.005 0.069 -0.003 -0.078 0.938 -0.141 0.130Table 5: Linear Regression with USE4 as Dependent Variable8/10For each of the 4 latent usage variables, we get a low R2 score, essentially establishing the factthat the data is not linearly distributed. As such, these models unfortunately do not adequatelyexplain those relationships. The models were also run with a confidence interval of 95%, as such,the estimated parameters are expected to be within 2 standard errors of the population mean forthe respective coefficients. According the values obtained in that regard, it is not a vastdifference indicating a rather small standard error. However, given the opportunity, I wouldrecommend running the models again with an SVM (support vector machine) which might bemore suited to the non-linear distribution of the data. The results, however, do not seem to bestatistically significant.DiscussionThe results provided by the SEM analysis confirm the hypotheses already discussed. Performanceexpectancy does positively affect the intention to use technology, as given by the positive coefficientestimated by the model (0.709). The population mean is even estimated to be between 0.574 and 0.844according to the confidence interval. The same is true for the second hypothesis, where effort expectancyis found to have a positive effect on intention. The coefficient's estimate places the population meansquarely between 0.895 and 1.286 according to the confidence interval. Again, the same applies to socialinfluence, where the population mean falls between 0.817 and 1.274. The last two remaining hypothesesare also verified where facilitating conditions and behavioral intention do positively affect the usage oftechnology with populations' means falling between 0.112 and 0.75, and 0.492 and 0.955 respectively.Unfortunately, the variable's effects on each of the usage variables as attempted by the multiple regressionapproach did not yield any statistically significant results. However, for the sample at hand, they doindicate mixed effects, with some aspects of performance expectancy, effort expectancy, social influence,facilitating conditions, and behavioral intention affecting each usage variable at times positively, andsometimes negatively as well.ReferencesLiao, Q., Shim, J. P., & Luo, X. (2004). Student acceptance of web-based learning environment: Anempirical investigation of an undergraduate IS course., (p. 377).Osborne, J. W., & Overbay, A. (2004). The power of outliers (and why researchers should always checkfor them). Practical assessment, research & evaluation , 9 (6), 1-12.Pahor, M. (2017). Research Skills: Quantitative statistical methods and big data analytics. 15-25.University of Ljubljana, Facutly of Economics.SmartPLS. (2017). Unified Theory of Acceptance and Use of Technology (UTAUT) Project SmartPLS.Retrieved January 2018, from SmartPLS Product: https://www.smartpls.com/documentation/sampleprojects/utautVenkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of informationtechnology: Toward a unified view. MIS quarterly , 425-478.9/10Appendix:Core VariablesPerformance Expectancy (PE)PE1- I would find the system useful in my job.PE2- Using the system enables me to accomplish tasks more quickly.PE3- Using the system increases my productivity.PE4- If I use the system, I will increase my chances of getting a raise.Effort Expectancy (EE)EE1- My interaction with the system would be clear and understandable.EE2- It would be easy for me to become skillful at using the system.EE3- I would find the system easy to use.EE4- Learning to operate the system is easy for me.Social Influence (SN)SN1- People who influence my behavior think that I should use the system.SN2- People who are important to me think that I should use the system.SN3- The senior management of this business has been helpful in the use of the system.Facilitating Conditions (FC)FC1- I have the resources necessary to use the system.FC2- I have the knowledge necessary to use the system.FC3- The system is not compatible with other systems I use.Behavioral Intention (BI)BI1- I intend to use the system in the next months.BI2- I predict I would use the system in the next months.BI3- I plan to use the system in the next months.Usage Context (USE)USE1- I often use information technology to do my work.USE2- I often use information technology to improve my work.USE3- I often use information technology to prove my work abilities.USE4- I often use information technology to solve work issues.10/10

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