October 2011 Volume 14 Number 4 - Educational Technology ...

the average variance-extracted values were all above the threshold value of 0.50 (Hair, et al., 2006). Hence all three
conditions for convergent validity were met for the four measurement models (See Table 3).
Discriminant validity was obtained by comparing the shared variance between factors with the average variance
extracted from the individual factors (Fornell & Larcker, 1981). This analysis showed that the shared variances
between factors were less than the average variance extracted for the individual factors. Hence, discriminant validity
was assured (see Table 4). To sum up, the four measurement models reached satisfactory levels of reliability,
convergent validity and discriminant validity.
Table 4: Discriminant validity
Construct Interconstruct correlations
BI PU PEU CPA CPL ADV OB TRI
BI 0.828
PU 0.353 0.814
PEU 0.286 0.229 0.755
CPA 0.466 0.401 0.253 0.765
CPL 0.180 0.068 0.572 0.210 0.874
ADV 0.368 0.375 0.269 0.624 0.138 0.846
OB 0.138 0.052 0.094 0.123 0.061 0.138 0.819
TRI 0.228 0.095 0.271 0.240 0.240 0.185 0.203 0.784
Note. Diagonals represent the square root of average variance extracted, and the other matrix entries are the factor
correlation.
Structural model estimation and hypotheses testing
Descriptive statistics
The means and standard deviations for all constructs were determined and were displayed in Table 5. The highest
mean of 3.56 was for the trialability, while the lowest mean for complexity was 2.30 on a scale of 1 to 5. The means
for PU, PEU and behavioral intention were 3.79, 3.73, and 3.62, respectively.
Table 5: Descriptive statistics
Construct (# Items) Mean Standard deviation
BI (six items) 3.62 .774
PU (five items) 3.79 .708
PEU (four items) 3.73 .709
CPA (four items) 3.54 .808
CPL (three items) 2.30 .769
RA (five items) 3.46 .794
OB (three items) 3.39 .930
TRI (three items) 3.56 .794
Structural equation modeling (SEM)
SEM was performed to test the fit between the research model (Figure 1) and the obtained data. This technique was
chosen for its ability to simultaneously examine a series of dependence relationships, especially when there were
direct and indirect effects among the constructs within the model (Hair, et al., 2006). The first step in interpreting
SEM results includes reviewing fit indices, which provide evidence on how well the fit is between the data and the
proposed structural model. If the model fits the data well enough, a second step involves reviewing the feasibility of
each path in the model by examining whether the weights are statistically significant and practically significant.
Practical significance is evaluated on the basis of whether the effect size estimation (the R 2 ) regarding a given path in
the models is large enough.
131