COMPUTER SELF-EFFICACY - Human-Computer Interaction and Management Information Systems: Foundations - page 228

Interactions between the user and the support environment can be characterized by the vicarious
capability of individuals to learn from observing and/or interacting with these support mecha-
nisms to solve a technological problem (changed individual factors including the development of
computer self-efficacy, satisfaction, and new computer knowledge and skills).
From an SCT perspective, interaction with support mechanisms can constitute an opportunity
for people to learn through two processes, as influenced by the support mechanism chosen. By using
support mechanisms that involve interactions with others (help desks, peers, etc.), the user can learn
by observation of the performance and verbal explanations provided by others during the support
event—a process referred to as observational learning in Bandura's theory (1986). By using support
mechanisms in which the user performs his or her own problem solving using support resources
(manuals, online help, multimedia simulations), the user can also learn through observational learn-
ing by observing and studying verbal modeling (including written text) and symbolic modeling
(multimedia). In addition, he can learn through enactive learning processes as he experiments with
implementing solutions provided by the support mechanism chosen (Bandura, 1986). Enactive
mastery experiences are salient indicators of capability and produce strong information cues for
CSE development. Theoretically, regardless of the mode selected, an incident of using technical
support affords an everyday occasion, inevitably experienced by every computer user, wherein
individual characteristics such as computer self-efficacy may be influenced.
Within this research, specific computer self-efficacy judgments play a role in a number of
places, including selection of a technical support mechanism, and, after selection, the influence of
the chosen support mechanism on the development of users' specific computer self-efficacy judg-
ments about the problem experienced.
With regard to support resource choice, when users of technology experience a technical prob-
lem that they are unable to resolve on their own, they will be guided in their choice of support by
a number of factors, including their attributional analysis of the problem, their motivation to
maintain their self-image, and by their specific computer self-efficacy towards utilizing a partic-
ular mechanism (Wood and Bandura, 1989). In a given support event situation, an individual's
SCSE beliefs about being able to use a particular support mechanism based on prior experience
with it will influence his or her choice of which mechanism to use. For example, an individual
with a strong sense of SCSE judgments towards using a self-discovery support mechanism such
as an online help function will likely choose to use online support again. Also, SCSE judgments
influence motivation and persistence with a support mechanism. For example, the individual with
a strong sense of SCSE who uses the online help function for particular problems is more likely
to use it thoroughly and persistently than someone with a lower sense of SCSE towards online
help. SCSE beliefs of this type would be formed from the four sources mentioned previously—
prior enactive mastery (prior use of the resource), vicarious observation (of others use of the
resource), verbal persuasion (encouragement by others), and emotional arousal (level of anxiety
or excitement experienced).
In addition to the role that SCSE beliefs play in support resource choice, individual computer
self-efficacy continues to have a role once the choice has been made and the support event is under
way. In our research, specific computer self-efficacy was seen as being formed via the use of sup-
port and SCSE was seen as influencing other support outcomes such as learning and satisfaction.
More specifically, individuals learning observationally are accessing support mechanisms from
which they can gain valuable performance information and feedback from models on which to
construct and evaluate their own performances and specific computer self-efficacy judgments.
Individuals learning experientially are accessing support mechanisms that enable them to obtain
information and at the same time experiment with applying that information to solve their technical