# The Extended Information Systems Success Measurement Model: e-Learning Perspective
## 5 Results
### PICB Results  
EFA: The extraction results admitted an eight-factor solution for the eigenvalues greater than one. Due to the low loadings, the facilitating conditions factor with accompanied manifest variables was eliminated at that stage. The values of the scree plot also confirmed the eight-factor solution. Moreover, the eight-factor solution yielded a good result in the percent of the cumulative sample variance (68.97%). Factor analysis was conducted iteratively until an adequate model and factor structure that satisfies all the criteria were achieved. Among the remaining variables, eleven had low factor loading scores. Thus, these items were omitted from the matrix structure because they built other constructs that are not relevant to this research. Hence, the final model with eight factors and 30 items/manifest variables was accepted. CFA: After omitting items with shallow factor loading scores and accepting the final factor structure, CFA was conducted to test the reflective model and statistically confirm the factors obtained via EFA. The model with eight factors and 30 manifest variables had adequate model fit indices according to the recommended values. With a significance level of n < 0.01 for the Chi-square and the adequacy of all the suitability index values, it can be concluded that the measurement model fully describes the obtained data. According to these values, the measurement model has adequate goodness. Reliability and validity assessment: Calculated [PERSON]'s alpha values for each dimension were performance expectancy = 0.881, effort expectancy = 0.885, social influence = 0.720, behavioral intention = 0.957, use behavior = 0.806, proactivity = 0.558, adaptability = 0.792, and resilience = 0.833. According to [PERSON] [128], the minimum criterion for each dimension to be valid is 0.60, leading to the conclusion that all our dimensions satisfy the abovementioned criteria. The CR, AVE, MSV, and ASV coefficient values for all factors are shown in Table 5. Considering the calculated coefficient values, it could be concluded that the measurement model has adequate reliability, convergent validity, and discriminant validity. SEM: Fit indices for SEM show that all values are acceptable, indicating excellent model fit (\(\chi^{2}/\mathrm{df}=1.278\); NFI = 0.87; CFI = 0.97 and RMSEA = 0.043). Figure 3 shows the \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & **CR** & **AVE** & **MSV** & **ASV** & **P** & **PE** & **EE** & **A** & **SI** & **SU** & **BI** & **R** \\ \hline P & 0.817 & 0.619 & 0.238 & 0.097 & 0.787 a & & & & & & \\ PE & 0.894 & 0.630 & 0.407 & 0.163 & 0.170 & 0.794 a & & & & & \\ EE & 0.883 & 0.655 & 0.407 & 0.210 & 0.274 & 0.638 & 0.809 a & & & & \\ A & 0.756 & 0.620 & 0.367 & 0.183 & 0.488 & 0.254 & 0.428 & 0.787 a & & & \\ SI & 0.844 & 0.664 & 0.062 & 0.025 & 0.000 & 0.228 & 0.248 & 0.130 & 0.815 a & & \\ SU & 0.717 & 0.462 & 0.279 & 0.125 & 0.246 & 0.290 & 0.343 & 0.377 & 0.106 & 0.680 a & & \\ BI & 0.958 & 0.884 & 0.348 & 0.226 & 0.344 & 0.590 & 0.546 & 0.517 & 0.154 & 0.528 & 0.940 a & \\ R & 0.852 & 0.661 & 0.367 & 0.207 & 0.397 & 0.393 & 0.570 & 0.606 & 0.114 & 0.425 & 0.498 & 0.813 a & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: PICB—Discriminant and convergent validity of the measuring instrument. SEM model with question coefficient values (\(\beta\)), t-values, and multiple correlation squares (R\({}^{2}\)). The relation between the expected performance and the behavioral intention is one of the most significant in the model, as evidenced by the path coefficient (\(\beta\) = 0.32; t-value = 4.572). The relation between the effort expectancy and behavioral intention factors is positive and statistically significant (\(\beta\) = 0.09; t-value = 0.904). Social influence was statistically insignificant when observing its influence on the dependent behavioral intention factor, with a negative value of the path coefficient \(\beta\) = \(-\)0.01; t = \(-\)0.160.