The overall SMC value, in this study, is high (0.78) (Figure 2), this finally confirms that the model fits the data well: the multiple regression equation fits the UGEM to predict a dependent latent variable (endogenous ([eta]) construct) from the five independent variables (exogenous ([zeta]) constructs) (Equation 1).
Overall, the UGEM establishes positive relationships between students' UGE for e-learning resources, and their Perceived e-Learning Experience; although two of the dimensions are not significant based on available quantitative data.
Although the 'Uses and Gratification Expectancy Model' (UGEM) (Figure 2), generated and developed in this study, fits the data well and provides a theoretically consistent set of findings, there may be other unexamined models that fit the data equally as well or even better fit.
In future research, it would be worthwhile to select diverse referent groups for use with UGEM in order to test and refine this model further.
A 'Uses and Gratification Expectancy Model' (UGEM) is developed based on both theory and empirical findings.
It is feasible to use the UGEM's parameters to predict the success of students' integration of technologies in their curriculum-based learning experience.