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