on the assumption that all behavior involves a choice and that individuals are quite rational,
making systematic use of the information available.
This cognitive emphasis has been questioned by other researchers who consider that
cognitive evaluations are important, but are not the sole influence on behavior, and that under
different circumstances, individuals might develop other attitude formation processes
(Bettman, 1982; Holbrook and Hirschman, 1982; Zajonc and Markus, 1982; Gatignon and
Robertson, 1985). For these researchers, cognitive and affective factors may interplay in a
variety of combinations and dominances.
As this research covers behaviors that are considered innovative (usage of
technology-based service deliveries), and that might be explained with either a general or
with a more specific model, consumer adoption models were also reviewed. Models such as
the Technology Acceptance Model (Davis et al., 1989), the Innovation Diffusion Theory
(Rogers, 1995), and Gatignon and Robertson’s Diffusion and Adoption Model (1985) have
been developed to explain consumers’ adoption of innovations. These models are based on a
cognitive appraisal, favoring the attributes pertaining to the characteristics of an innovation as
the determining factors of its adoption. Dabholkar (1996) studying service quality found that
consumers deal with unfamiliar or new situations, such as technology self-service options, in
a rational and cognitive way. Parasuraman (2000) proposed the ‘Technology Readiness
Scale’ to represent people’s propensity to embrace and use new technologies for
accomplishing goals. Although this construct is intended to reflect an overall state of mind, it
favors in its components a cognitive evaluation. Similarly, the empirical studies reported on
the adoption of innovations tend to concentrate on cognitive determinants.
MODEL DEVELOPMENT
Based on the literature reviewed, a model is proposed to explain consumer decisions on usage
of technology-based channels. It is assumed that consumer behavior is driven by cognitive
and affective factors. While conclusions from the theories reviewed favor a cognitive-based
evaluation of new technologies, research has also produced examples of the importance of
affect as a behavior determinant (Mehrabian and Russel, 1974; Black et al., 2002). The
combined analysis of these two streams (cognitive and affective-based evaluations) has not
yet been researched for technology-based service deliveries and for their preference over
human-based deliveries.
Consumer behavior is both fascinating and complex, and these two perspectives
complement each other and enrich consumer research. Trying to describe consumer behavior