Quality Evaluation Models - High Level Models and Methodologies for Information Systems - page 111

longitudinal field studies, of six different departments from six large firms in six

different areas of industry. The goal was to arrive at a de nitive model for IS use

that synthesized general knowledge in this field (Anderson and Schwager 2003 ).

first concluded that, in general terms, there is a common basic
premise to user acceptance models, which is as follows: Individual reactions to
using IT lead to intentions to use IT, which leads to actual IT use. Use in turn will
in

The authors

uence other individual reactions, and so forth. All the models that were used to
arrive at UTAUT identify intention and/or usage as the key dependent variable, and
UTAUT aimed at understanding this variable in a comprehensive way (Venkatesh
et al. 2003 ).
From this premise, the authors then attempted to arrive at a commonality of
variables and factors that act upon intention to use. They arrived at four essential
factors: performance expectancy, effort expectancy, social in

uence, and facilitating

conditions (Venkatesh et al. 2003 ) (Fig. 6.12 ).
In the context of this model, performance expectancy relates to the users belief

in the IS capacity to help them achieve an enhanced job performance. It is regarded
as an in

'

uential factor in user behavior, since the more individuals expect a system
to be helpful in the execution of their job, the more likely they are to use that IS.
The concept of performance expectancy encompasses all other similar aspects that
were used in previous models: perceived usefulness, extrinsic motivation, job

t,

relative advantage, and outcome expectations (Venkatesh et al. 2003 ).
The second variable of this model, effort expectancy, concerns people s con-

viction of the system ' s ease of use. Individuals expecting a user-friendly system are
more prone to use it. In previous models, this variable was designated as perceived
ease of use, complexity, or ease of use (Venkatesh et al. 2003 ).
Social influence can be de

'

ned as the degree to which the user perceives that
others around him think he should use the system. A positive feedback from others
on his/her use of the system will in uence him/her toward using it (Wang and

Wang 2010 ). Previous models used the concepts of subjective norm, social factors,

Fig. 6.12 Unified theory of acceptance and use of technology, adapted from Venkatesh et al.
( 2003 )