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Search Factor
23.60 ©
.931
.577
.219
(3.77 ©)
(7.74 ©)
(4.56 ©)
Cognitive Factor
.169
.002
-.002
.327
(.684)
(.282)
(-.556)
New & Unique
.604
.244
-.001
Factor
5.09
b
(2.443
a
)
(3.28
b
)
(-.256)
.888
.102
.007
Social Factor
4.65
b
(3.59
a
)
(1.37)
(1.51)
Entertainment Factor
9.61©
.820
.363
.005
(3.32
b
)
(4.87©)
(1.23)
a
= p < .05
b
= p < .01
as dependent variables. This analysis allows us to determine if certain motivations
for using the Web potentially result in differences in Web and computer use and
affinity with the computer. Multi-collinearity tests were conducted, and all variance
inflation factors were equal to one, indicating that multi-collinearity was non-
problematic. Results of the regression analysis are presented in Table 3.
As shown, the multivariate tests for four of the five independent variables were
significant. For the Search Factor, the multivariate F statistic was 23.60 (p <.001),
for the New & Unique Factor, the multivariate F statistic was 5.09 (p < .01), for
the Social Factor, the multivariate F statistic was 4.65 (p < .01) and for the
Entertainment Factor, the multivariate F statistic was 9.61 (p < .001). For the
Cognitive Factor, the F-test was not significant (F =.327, p > .10).
The univariate tests provide further understanding and details.
For example,
the searching motivation was significant (p < .001) for all three dependent variables.
That is, respondents with a higher motivation to search had a higher affinity with the
computer and appeared to use the computer and the Web more. Respondents with
motivations relating to finding new and unique things on the Web had a higher affinity
with the computer (F = 2.443, p < .05) and used the Web more frequently (F =
3.28, p < .01), but there was no significant relationship between this factor and
frequency of computer use (F = -.256, p > .10)
Social motivations had a positive and significant relationship with affinity with
the computer (F = 3.59, p < .05), but not with the two frequency measures. Finally,