Information Technology Reference
In-Depth Information
Personal innovativeness with information
technology exerted a strong positive influence
on computer self-efficacy (H8) at both time peri-
ods, and a strong positive influence on perceived
ease of use (H9) at Time 1. At Time 2, there
was no influence of personal innovativeness on
perceived ease of use. This is consistent with
the arguments of Venkatesh and Davis (1996)
who suggest that through experience, ease of
use perceptions become more rooted in specific
features of the software and less influenced by
general personal traits. Personal innovativeness
also exhibited a direct influence on intentions at
both time periods (H10).
month time period. We expected greater change
in the software specific constructs (usefulness,
ease of use, affect, perceived behavioral control)
than in the more general constructs (social fac-
tors, personal innovativeness, self-efficacy). To
perform this test, we computed a summed scale
score for all constructs at Time 1 and Time 2, and
then employed a t-test to see if the difference was
statistically significant.
The results (shown in Table 5) partly support
our expectation. Of the general factors, neither
PIIT nor self-efficacy changed. However, the
mean perception of social influence did increase
from Time 1 to Time 2. For the specific factors,
perceived usefulness, affect, and perceived be-
havioral control all increased. However, perceived
ease of use did not change, nor did long term
intentions.
supplemental analysis Concerning
experience
Our analysis, discussed above, suggests that
experience moderates many of the relationships
between constructs in technology acceptance
models. As subjects gain in experience, their
intentions are more strongly influenced by af-
fect and perceived behavioral control and less
influenced by perceived usefulness and personal
innovativeness. Computer self-efficacy exerts a
stronger influence on perceived ease of use and
affect, but a weaker influence on perceived useful-
ness. Thus, individuals become able to separate
the potential of the software from their ability to
use it. Personal innovativeness exerts a stronger
influence on self-efficacy following experience but
becomes a nonsignificant predictor of perceived
ease of use.
The findings that experience moderates
some relationships in the model are important
for researchers to understand as we attempt to
comprehend the forces involved in technology
adoption decisions. But it is equally important
to understand the direct effects of experience
on the constructs in the model. To examine this
aspect of the role of experience, we conducted
supplemental analyses, comparing the means
of each of our model constructs across the two
disCussion
In general, the results supported the hypothesized
relations. The model explained 44% of the variance
in intention at Time 1 and 40% at Time 2. While
improving on prediction was not our primary aim
in this chapter, examination of explained variance
is nonetheless a critical element of PLS analysis.
The R
2
values we obtained are less than some
other models have explained (e.g., Taylor & Todd,
1995 explained more than 60% of the variance
in intention). To provide an internally consistent
basis of comparison, we ran a model at each time
period based on TAM, using just PU, EOU, and
Future Intentions. These models explained 34%
and 30% of the variance in intention, compared
to 44% and 40% for our expanded model.
We also ran models based on DTPB, elimi-
nating the interlinkages among the independent
constructs. These models explained 34% of the
variance in intention (same at both time peri-
ods).
In general, then, an integrated model acknowl-
edging the linkages between behavioral, control,
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