# Perceived Usefulness of a Mandatory Information System
## 6 Results  
An evaluation of responses from research participants to 10 questionnaire items was conducted. It was found that responses to 9 items followed a binomial distribution, while the responses to one item showed a Poisson distribution, characterized by significant negative skewness and kurtosis. Consequently, for the construction of four latent constructs--perceived ease of use, perceived usefulness, symbolic adoption, and supervisor influence--and their use in testing four hypotheses via structural equation modeling (SEM), the responses were transformed to approximate normality. These transformations, carried out using the R package 'bestNormalize' [48; 49], are presented in Table 1. They show the normality transformations across 72 observations corresponding to the 10 questionnaire items. This transformation process facilitated the selection of the most suitable normalization method for each specific questionnaire item. Table 1 presents a comprehensive analysis of how different normalization techniques affect the distribution of responses to various questionnaire items. This analysis is crucial for selecting the most appropriate method for subsequent analysis in structural equation modeling (SEM). The effectiveness of these techniques is assessed using the Estimated Normality Statistics (ENS), which measures the closeness of the transformed data to a normal distribution. A lower ENS value indicates a closer approximation to normality. The evaluation method used is out-of-sample cross-validation, conducted with 10 folds and 5 repeats. After applying the necessary transformations to achieve normality and standardizing the 10 questionnaire items, four latent constructs were identified: perceived ease of use, perceived usefulness, supervisor influence, and symbolic adoption. Figure 2 illustrates the revised structural equation model that includes these constructs, showing the hypothesized relationships among them. A maximum likelihood estimation was employed to estimate the hypothesized research model. The model demonstrated a close model fit, \(\chi^{2}=38.09\) (df = 30, N = 72, \(p=0.15\)), CFI = 0.98, NFI = 0.92, RMSEA = 0.06. Figure 3 provides a comprehensive overview of the final structural equation model. Table 2 presents the standardized path coefficients for the final model, detailing the measures of the model and the estimated model parameters, including direct and indirect effects. Table 2 outlines the relationships between dependent and independent constructs, with significance levels marked (* \(p<0.05\); ** \(p<0.01\)). It includes two-tailed bootstrap significance (TTBS), bootstrap standard errors (BSE), and 95% bootstrap confidence intervals (BC), with lower and upper bounds. Significant findings include a positive total effect of perceived ease of use on perceived usefulness (\(\beta=0.64^{**}\), \(H_{1}\)) and a positive total effect of perceived usefulness on symbolic adoption (\(\beta=0.85^{*}\), \(H_{2}\)). Supervisor influence was found to have a significant positive total (direct) effect on perceived usefulness (\(\beta=0.29^{*}\), \(H_{3}\)). The relationship between perceived ease of use and symbolic adoption is fully mediated by perceived usefulness (\(\beta^{*}_{STE}=0.70=\left[\beta_{SDE}=0.16\right]+\left[\beta^{**}_{SIE}=0.54 \right]\), \(H_{4}\)). Similarly, the relationship between supervisor influence and symbolic adoption is fully mediated by perceived usefulness (\(\beta^{*}_{STE}=0.20=\left[\beta_{SDE}=-0.04\right]+\left[\beta^{*}_{SIE}=0.2 4\right]\), \(H_{5}\)).