Intentions to Use Information Technologies: An Integrative Model - End User Computing Challenges and Technologies: Emerging Tools and Applications - page 98

the complexity of the mechanisms through which
general beliefs such as personal innovativeness
and self-efficacy influence adoption intention, and
aids in building our understanding of the anteced-
ents of perceived usefulness and perceived ease
of use, the critical constructs in the Technology
Acceptance Model. We further echo the calls of
other researchers (e.g., Sun & Zhang, 2006) to
extend our conceptualization and understanding
of the role of experience in the formation of judg-
ments about information technologies.
While it has been argued that technol-
ogy acceptance is a mature model (Venkatesh,
2006; Venkatesh et al, 2003), we believe there
is substantial work to be done in further under-
standing the process of adoption. We agree with
Jasperson, Carter, and Zmud (2005) that richer
models are required which take into account the
varying features of different technologies, the
extent to which these features are used, and the
individual differences of the users themselves.
Initial research in this area has found that feature
specific self-efficacy predicts usage above and
beyond a more generalized operationalization of
self-efficacy (Hasan, 2006; Hsu & Chiu, 2003).
In addition to better prediction, richer models of
adoption can better inform the design and support
of information technologies in organizations.

Agarwal, R., Sambamurthy, V., & Stair, R.M.
(2000, December). Research report: The evolv-
ing relationship between general and specific
computer self-efficacy: An empirical assessment.
Information Systems Research, 11 (4), 418-430.

Ajzen, I. (1991). The theory of planned behavior.
Organizational Behavior and Human Decision
Processes, 50 , 179-211.

Bandura, A. (1986). Social foundations of thought
and action. Englewood Cliffs, NJ: Prentice
Hall.

Bandura, A. (1997). Self-efficacy: The exercise of
control. New York: W.H. Freeman & Co.

Barclay, D.W., Higgins, C.A., & Thompson, R.L.
(1995). The partial least squares approach to causal
modeling: Personal computer adoption and use as
an illustration. Technology Studies: Special Issue
on Research Methodology, 2 (2), 285-324.

Bhattacherjee, A., & Premkumar, G. (2004).
Understanding changes in belief and attitude
toward information technology usage: A theo-
retical model and longitudinal test. Management
Information Systems Quarterly, 28 (2), 229-254.

Chau, P., & Hu, P. (2001). Information technology
acceptance by individual professionals: A model
comparison approach. Decision Sciences, 32 (4),
699-719.

reFerenCes

Chin, W.W., & Frye, T. (2001). PLS-Graph (Ver-
sion 03.00). Soft Modeling Inc.

Agarwal, R., & Karahanna, E. (2000). Time flies
when you're having fun: Cognitive absorption and
beliefs about information technology usage. MIS
Quarterly, 24 (4), 665-694.

Cohen, J. (1988). Statistical power analysis for
the behavioral sciences. Hillside, NJ: L. Erlbaum
Associates.

Agarwal, R., & Prasad, J. (1998, June). A con-
ceptual and operational definition of personal
innovativeness in the domain of information
technology. Information Systems Research, 9 (2),
204-215.

Compeau, D.R., & Higgins, C.A. (1995a). Ap-
plication of social cognitive theory to training for
computer skills. Information Systems Research,
6 (2), 118-143.

Compeau, D.R., & Higgins, C.A. (1995b). Com-
puter self-efficacy: Development of a measure and
initial test. MIS Quarterly, 19 (2), 189-211.