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Electronic Commerce and Data Privacy 223<br />
compare the relative goodness of fit of competing models, thereby assessing the need<br />
for, and strength of, different path models (Bentler, 1990; Hartwick & Barki, 1994).<br />
Overall Goodness of Fit<br />
There is not one generally accepted measure of overall model goodness of fit,<br />
or even a set of optimal tests. Thus, we must rely on the use of multiple fit criteria.<br />
In this study, four goodness of fit indices were used. The first is the � 2 statistic,<br />
which tests the proposed model against a fully saturated model–meaning that all<br />
variables are correlated (Bentler, 1990; Hartwick & Barki, 1994). A nonsignificant<br />
� 2 value indicates good fit. However, the � 2 is sensitive to sample size. In large<br />
samples, the � 2 is almost always significant.<br />
A better measure using the � 2 statistic is to divide it by its degrees of <strong>free</strong>dom.<br />
In this case, the smaller the value, the better the fit. The literature gives several<br />
thresholds for reasonable fit: 5.0 or less (Wheaton, Muthen, Alwin, & Summers,<br />
1977) and 3.0 or less (Carmines & McIver, 1981) and between 1.0 and 2.0 (Hair,<br />
Anderson, Tatham, & Black, 1998).<br />
The most widely used overall goodness of fit indices are the goodness of fit<br />
index (GFI) and the adjusted goodness of fit index (AGFI). GFI measures the<br />
absolute fit of the measurement and structural models to the data. AGFI adjusts the<br />
value of the GFI to the degrees of <strong>free</strong>dom in the model. Thresholds for these indices<br />
are above 0.90 and above 0.80, respectively Chin and Todd, 1995, and Segars and<br />
Grover, 1993. A more restrictive threshold of above 0.90 for AGFI is often cited in<br />
IS research (Chin & Todd, 1995; Hair et al., 1998).<br />
Another measure of goodness of fit is the comparative fit index (CFI), which<br />
is appropriate for all sample sizes and is thought to provide a more stable estimate<br />
than some of the other fit indices (Bentler, 1990; Hartwick & Barki, 1994). Values<br />
greater than 0.90 reflect acceptable fit.<br />
Finally, the root mean square error of approximation (RMSEA) index measures<br />
the discrepancy in the population between the observed and estimated<br />
covariance matrices per degree of <strong>free</strong>dom. Thus, RMSEA is not affected by sample<br />
size (Garver & Mentzer, 1999). RMSEA is acceptable if the value is 0.08 or less<br />
(Hair et al., 1998).<br />
The initial measurement model did not fit the data adequately (� 2 = 123.54,<br />
GFI = 0.88, AGFI = 0.79, CFI = 0.61, RMSEA = 0.12) as illustrated in Table 2. Thus,<br />
the model was altered using the modification index provided by the LM test. The LM<br />
test represents the expected � 2 decrease due to model modification (Bentler &<br />
Chou, 1993). The modifications based on this index are shown in Figure 2. The<br />
following were added to the model: covariances between some of the antecedent<br />
variables and direct paths between GENDER and IMPPP, between TK and IMPPP,<br />
and between PERUSE and REGPREF. The covariances added are supported by the<br />
significant correlations between the antecedent variables (see Table 1). Previous<br />
studies showing that gender and technology skills/knowledge have an impact on<br />
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