Information Technology Reference
In-Depth Information
Table 4. Correlation matrix of study variables
Hours
using
Internet
and e-mail
Use (
= Y 0 =
N)
Ease of
Use
Buy #
of times
Buy
freq
Spent
Online
Age
Educ
Trust
Usefulness
Age
Education
.*
Hours using
Internet and
e-mail
.0
-.0
Trust
.0
.0
-.**
Ease of Use
.*
-.0
-.*
.***
Usefulness
.*
-.00
-.0**
.***
.***
Use
( = Y 0 = N)
-***
.
.**
-.***
-.*
-.***
Buy # of
times
-.
.
.***
.**
-.0
-.***
.***
Buy
frequency
-.***
.0
.***
-.***
-.**
-.***
.***
.***
Spent online
-.0*
.00
.0
-.
-.**
-.***
.**
.**
.0**
* p < .05; ** p < .01; *** p < .001.
Cronbach's alpha was calculated for each
multi-item scale to assess the internal reliability.
Nunnally (1978) suggests that alphas near .9 repre-
sent highly consistent scales, those near .7 reflect
a moderate level of consistency and alphas below
.3 indicate that the items have little in common.
As shown in Table 3, the alpha scores are within
the guidelines for research (Kerlinger, 1986;
Nunnally, 1978). Table 4 contains the correlation
matrix for the studied variables.
Given the sample size, structural equation
modeling (SEM) was not advisable. Kelloway
(1998) recommends that SEM, a method of
examining the quality of the measurement and
examining predictive relationships simultane-
ously, only be conducted when sample sizes are
a minimum of 200. Since the sample size was
only 110, multiple regression was used to see
if there was support for the proposed model. It
was necessary to include, and therefore control,
demographic variables that may cause spurious
effects. The choice of control variables was gov-
erned by theory and prior empirical studies as
well as dictated by the current data. The number
of hours spent using the Internet and e-mail were
significantly correlated with the study variables
and have been suggested as determinants of
online shopping (Bellman et al., 1999). It was
therefore necessary to control these characteris-
tics. The number of hours spent per week using
the Internet and e-mail were included as having
a direct effect on the use of electronic commerce
and an indirect effect via the three independent
variables, ease of use, perceived usefulness and
trust. Since electronic commerce participation
(Y or N) is dichotomous, logistic regression was
used to assess the affect of the study variables
on this outcome. The results are presented in
Table 5a and 5b and the supported relationships
are shown in Figure 2. As summarized in Table
6, the proposed model was partially supported.
Trust only had a direct impact on one of the four
dimensions of usage but did have a positive impact
on ease of use and usefulness. Ease of use did not
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