April 2012 Volume 15 Number 2 - Educational Technology & Society

As shown in Table 5, it can be concluded that the full structural model meets all the goodness of fit indices.
Table 5. Goodness of fit indices
Fit Index Study Recommended values
Source
df 205
� 2 351.229
� 2 /df 1.713 ≤ 3.00 Bagozzi & Yi (1988)
GFI 0.852 ≥ 0.90 Chau & Hu (2001)
AGFI 0.800 ≥ 0.80 Chau & Hu (2001)
CFI 0.953 ≥ 0.90 Bagozzi & Yi (1988)
RMSEA 0.066 ≤ 0.08 Brown and Cudeck (1993)
NNFI (TLI) 0.943 ≥ 0.90 Bagozzi & Yi (1988)
Structural model
The structural model was estimated using the maximum likelihood estimate (MLE) offered by AMOS software
version 16. We added an extra path from EOU to INT in the full structural model. The reason for doing this is that in
structural equation modeling it is assumed that a full mediation is in place, whereas in this model there is a direct
effect from EOU to INT and it does not go through SAT. As presented in Table 5, the test of the overall model fit
yielded in a � 2 = 351.229 with 205 degrees of freedom and a p-value of less than 0.001. All the fit indices were
above the recommended values except for the Goodness-of-fit index (GFI) which was slightly below the 0.90 cutoff.
As shown in Figure 5, the explanatory power of the model ranges from as low as 0.57 for the effect on satisfaction.
For the outcome variables the R 2 was 0.58 for net benefit and 0.69 for intention to continue use. Motivation to learn,
management support and organization support were significant predictors of user satisfaction. Contrary to what was
hypothesized, self-efficacy, ease of use and content of training were not significant. User satisfaction was also found
not to influence net benefit but was a strong predictor of intention to continue usage as hypothesized.
Note. *p< 0.1, **p< 0.05, ***p< 0.01
Discussion
Figure 5. Modified structural model
Hypothesis 1 which posited that there is a positive relationship between computer self-efficacy and user satisfaction
was not supported, indicating that computer self-efficacy did not have an impact on user satisfaction in e-training.
133