Motivating Incentives, Self-efficacy, and Web Authoring
Student Paper
Visual Communication Division
1997 AEJMC Convention
MOTIVATING INCENTIVES, SELF-EFFICACY AND
THEIR CONSEQUENCES FOR WEB AUTHORING
by
Ghee-Young Noh
Mass Media Ph. D. Program
Michigan State University
500 W. Lake Lansing Rd. #C31
East Lansing, MI 48823
phone: 517-333-6696
e-mail: [log in to unmask]
MOTIVATING INCENTIVES, SELF-EFFICACY AND
THEIR CONSEQUENCES FOR WEB AUTHORING
The World Wide Web provides a unique presence platform. The Web is a hypermedia
system based on the client/server model. Hypertext is a non-sequential,
non-linear method for organizing and displaying information in the form of text,
graphics, animation, sound and video (Fkulund, 1995). For example, through
personal home pages, people present themselves, not necessarily through words
they have expressed their thoughts, but through an organized set of hot links.
The Web is also interactive platform. Users can interact with information
services to provided feedback, search databases, completing survey, and even
special custom programs for their own needs. Moreover, real-time audio and
video services are increasing on the Web.
Web authors create files of hypertext markup language, graphics, video and
audio on a Web server. However, he or she should acquire some Web authoring
tools for unique Web presence. Due to the diverse creating Web process, Web
authoring may be affected more by perceived self-efficacy of authors. Web
authoring involves using computer languages such as HTML, FORMS, ISMAP, and CGI
scripts. HTML documents are in plain text format and can be created using any
text editors. FORMS allow the user to submit information to the Web server with
several types of form input such as text entry fields, menus, and checkboxes and
ratio buttons. While a plain HTML document exists in a constant state, a CGI
scripts is executed in real-time, so that it can output dynamic information.
ISMAP is a graphical map of information resources with either the external
imagemap CGI script or the built-in imagemap. By clicking on different parts of
the overview image, user can transparently access any of the information
resources. Therefore, individuals who perceive themselves to be less competent
in their ability about Web authoring tools are likely to experience waning
interest in such activity.
The present study attempts to identify possible relationship between four
determinants derived from social cognitive theory and the degree of Web
authoring. Social learning model (Bandura, 1986) provides a successful
explanation about the acquisition process in which modeled events and matching
patterns through modeling. It explains on the aspects of learning rather than
that of action. However, modeling does not automatically produce a behavior
change. The implementation of Web authoring includes behavioral change through
learning. Self-efficacy theory may provide additional explanatory power for the
mechanism of implementation of Web authoring.
LITERATURE REVIEW
Social Learning Theory
Social learning theory distinguishes performance from acquisition because
people do not perform everything they learn. Motivational process in social
learning model plays a linkage role between learning and performance. The
self-regulation of motivation operates partly through internal standards and
evaluative reactions to one's own behavior. Human motivation and action are
self-regulated through a joint influence of proactive and feedback mechanism
(Bandura, 1989). In particular, the capability of forethought adds another
dimension to the motivational process. Most human behavior is regulated by
forethought. By being represented cognitively in the present, conceived futures
can have causal impact on current behavior (Bandura, 1994). Through the
exercise of forethought and self-regulative standards, they motivate themselves
and guide their actions anticipatorily (Bandura, 1989, 1179).
In the motivational process, when positive incentives are perceived by
forethought, social learning is promptly transformed into action. Performance
of learned behavior is influenced by three major types of incentive motivators:
direct, vicarious, and self-produced. Therefore, from the social learning
model, following theoretical statements are derived. Motivation is a function
of: (a) external incentives such as sensory, tangible, social, and control
incentives; (b) vicarious incentives such as observed benefits and observed
costs; (c) self-incentives such as tangible and self-evaluated incentives; (d)
observer attributes such as incentive preferences, social comparison biases, and
internal standards (Bandura, 1977, 1989, 1994).
On the other hand, attentional processes in social learning model determine
what is selectively observed in modeling influences and what information is
extracted from modeled events (Bandura, 1994). Attention is determined by
modeling events and observer attributes. The rate and level of observational
learning are affected by the salience, discriminability and complexity of
modeled activities (Bandura, 1986, p.51). Thus, much selective attention is
maintained by modeled events that are salient, evaluated positively, simple,
prevalent, accessible, and useful.
Bandura (1994) added accessibility to the attributes of modeled events. The
accessibility of modeled events reflects the advance of media technologies.
Thus, accessibility is considered as an important element to determine effects
of modeling, because advanced telecommunications have become the dominant means
for disseminating symbolic information. Markus (1993) also argues that
diffusion of interactive media requires operational access to a medium.
Operational access is provided by infrastructure, access devices, usage
knowledge and skills.
Unlike conventional communication media, the Internet varies in access and
representation capacity. Leased-line network speeds have advanced from 56 Kbps
to 1.5 Mbps known as T-1 lines, and then to 45 Mbps (T-3) in the early 90s.
Lines of 155 Mbps are now available, though not yet widely used. A logical
extension of brief discussion is that implementation of Web authoring is
motivated by external and vicarious incentives, and by accessibility.
Therefore, the following hypotheses have been addressed.
Hypothesis 1: The degree of web authoring will be positively
related to motivating incentives.
Hypothesis 2: The degree of web authoring will be positively
related to the degree of accessibility.
Self-efficacy Theory
Self-efficacy functions as an important set of proximal determinants of
human motivation, affect, and action. Bandura (1986) defined self-efficacy as
follows:
"People's judgments of their capabilities to organize and execute
courses of action required to attain designated types of performances.
It is concerned not with the skills one has but with judgments of what
one can do with whatever skills one possesses." (p.391)
Empirical research findings suggest that individuals who have high
self-efficacy tend to perform better than individuals low in self-efficacy
(Barling & Beattie, 1983; Taylor, Locke, Lee & Gist, 1984; Gist, Schwoerer &
Rosen, 1989, Saks, 1995). Efficacy expectancy is distinguished from outcome
expectancy (Bandura, 1977). While an outcome expectancy is defined as a
person's estimate that a given behavior will lead to certain outcomes, an
efficacy expectancy means the conviction that one can successfully execute the
behavior. The person's assessments of both external and self-produced outcomes
depend heavily on that person's judgments of his or her behavioral capabilities
(Cahill, 12). Perceived self-efficacy determines whether people even consider
changing their behavior and how well they maintain the changes they have
achieved (Bandura, 1994, p.81). While changing behavior involves adoption,
maintaining behavioral changes is related to implementation. It may be derived
that perceived self-efficacy determines the implementation of Web authoring.
Self-efficacy beliefs are obtained from four principal sources of information:
performance accomplishments, vicarious experience, verbal persuasion, and
physiological states (Bandura, 1977). One of the major sources of
self-efficacy, both ability and past performance have consistently been found to
be positively related to self-efficacy (Bandura, 1986, Wood & Bandura, 1989).
Performance accomplishment is especially influential because it is based on
personal mastery experiences. Thus, successes raise mastery expectations;
repeated failures lower them.
People who have a strong sense of efficacy focus that attention on analyzing
and figuring out solutions to problems (Bandura & Jourdan, 942). Self-efficacy
beliefs usually affect cognitive functioning through the joint influence of
motivational and information-processing operations. People's self-efficacy
beliefs also determine their level of motivation, as reflected in how much
effort they will exert in an endeavor and how long they will persevere in the
face of obstacles. Finally, the most important effect of self-efficacy beliefs
may be through selection process. Judgments of personal efficacy also affect
the selection of environments (Bandura, 1989).
Gist & Mitchell (1992) summarized three important aspects of self-efficacy.
First, self-efficacy involves a comprehensive summary or judgment of one's
perceived capability for performing a specific task. Second, self-efficacy
involves a mobilization or motivational component. Third, self-efficacy is a
dynamic construct that changes over time and in response to new experiences and
information. Thus, an assessment of self-efficacy reflects more than just an
ability assessment. Capability, although based heavily on ability, also reflects
a forward-looking prediction of how hard one will work and an integration of
both of these factors (Mitchell et al. 1994, 506).
Another issues about self-efficacy research is its measurement. Lee and Nobko
(1994) classified the self-efficacy measures into three: self-efficacy
magnitude, strength and composite. While self-efficacy magnitude is formed by
summing the total positive responses given by a subject, self-efficacy strength
is formed by summing the confidence ratings across all performance levels. On
the other hand, some researchers have employed a combination of the two kinds of
self-efficacy indexes. They found that the self-efficacy magnitude and strength
measures have generally weaker predictive validity and correlations than the
self-efficacy composites have (Taylor et. al., 1984; McAuley et al., 1991; Gist,
Schwoerer, & Rosen, 1989).
In the self-efficacy theory, choice behaviors are governed in part by
perceptions of self-efficacy rather than by a drive condition. Differences in
the knowledge, skills, and resources particular innovations require produce
variations in rate of acquisition. However, acquiring skills does not mean
using them. Human competency requires not only skills, but also self-belief in
one's capabilities to use those skills well (Bandura, 1994). In case of Web
authoring, programming competence may be a drive condition. On the basis of
this literature discussion, the following hypotheses are set forth.
Hypothesis 3: The degree of web authoring will be positively
related to perceptions of self-efficacy about web authoring tools.
Hypothesis 4: The degree of web authoring will be positively
related to programming competence.
METHODS
The goal of secondary analysis is to shed new light on previous data and
conclusion (Rubin, Rubin, & Piele, 1996). Present study intends to reanalyze
previous dataset with different concepts and analysis techniques. The primary
advantage of secondary survey analysis is its potential for resource savings.
Another advantage is that secondary analysis circumvents data collection
problems (Kiecolt & Nathan, 1985). Most of all, when used in exploratory
research prior to fielding a new survey, secondary analysis can uncover aspects
of research problem that require elaboration and the need to refine and improve
existing measures.
GTRC's WWW User Survey Archives
Gerogia Tech Reseach Corporation (GTRC)'s survey archive is the oldest and the
largest Web-based survey to date as measured in terms of the number of responses
by the number of questions asked. The Fourth Survey datasets, collected from
October 10 through to November 10, 1995, contains over 23,000 responses. They
comprise eight kinds of datasets: General Demographics, WWW browser Usage,
Authoring Information, Consumer Attitudes & Preferences and Web Service
Providers. Even if the datasets were collected by non-probabilistic sample, GVU
used diverse media to collect responses for minimizing any sampling bias: high
exposure WWW pages, Usenet newsgroups, computer and Internet trade magazines,
daily newspapers, and mailing list. As a result, the Fourth Survey's ratio for
gender and other demographics like income, marriage, etc., were reported as
almost exactly those reported by North American based random sampling surveys
(GTRC, 1995). Moreover, the object of this study is to examine patterns of
relationships between variables rather than to estimate population statistics.
Present study used the Authoring Information dataset[1] among six datasets for
the analysis of web authoring implementation. The sample contained 5,300 Web
authors. According to Web Authoring Survey results, the most common types of
pages authored were work-related and personal. The most common server
connection speed was 10 Mbps/sec (38.9%), followed by 1 Mbps/sec with 12.9% and
56 Kbps/sec with 11.1% of the responses. In case of programming skill, the
respondents who have HTML skills, were reported as learning the basics in under
3 hours, with 79.4% learning in under 6 hours.
Operational Measures
The present study focuses on the implementation of the Web authoring. The
simple adoption of Web authoring should be distinguished from the implementation
of it. Implementation occurs when an individual puts an innovation into use
(Rogers, 1983). Toranatzky and Klein (1982) argued that simple measurement of
adoption may mislead the real relationship between innovation characteristics
and diffusion. Rogers (1986) also suggested that implementation should be the
dependent variable in studies of the new communication technologies, rather than
adoption. The degree of Web authoring measure was defined as dependent variable
in this study. Four independent variables relating to perceived self-efficacy,
motivating incentives, the degree of accessibility, and programming competence,
also were defined. Original survey did not contain precise indicators of the
concepts present study wanted to test. Therefore, researcher developed new
schemes for measuring items of interest.
Degree of Web authoring
The degree of Web authoring was derived from the estimated number of documents
authored using HTML directly or converters/translators. Original GVU dataset
contained a series of 5-point scales about the estimated number of documents
authored using HTML directly and converters separately. Scale range included
'none', '1 to 10 documents', '11 to 50 documents', '51 to 100 documents', and
'101 documents or more'. Although original scales take a 5-point interval form,
they are not actual interval scale because the intervals between adjacent points
on the scale were not of equal value. However, they contained a minimum value
within each interval. Therefore, the original data were recorded as minimum
scores for statistical analysis. Next, the implementation of Web authoring
measure combined two items by summing the scores.
Motivating Incentives
Motivating incentives measure was derived from the response to two questions:
"How much do you charge for companies to advertise on your pages?" and "For
roughly how many people do you maintain documents?" Motivating incentives
measure combined by summing both five-point scale scores. Because Web
publishing business runs under the hybrid costs recovery system, the motivating
incentives should be considered in two aspects of revenue sources: consumer and
advertising. While the first question represents advertising revenue
incentives, the latter may represent consumer revenue incentives.
Degree of Accessibility
The degree of accessibility was operationalized as "what is the speed of the
network connection to your server?" The original data were collected by 10
discrete scale: 'under 28.8 Kb/sec', '28.8 Kb/sec SLIP/PPP', '56 Kb/sec', '128
Kb/sec', '1 Mb/sec Ethernet', '10 Mb/sec Ethernet', '45 Mb/sec Ethernet', '100
Mb/sec FDDI', 'Faster than 100 Mb/sec' and 'unsure of network connection'. For
statistical analysis they were recorded as continuous scale.
Perceived Self-efficacy
The perceived self-efficacy measure was operationalized with a set of four
statements: "overall, learning HTML was:," "overall, learning FORMS was:,"
"overall, learning ISMAP was:," "overall, learning to write CGI scripts was:."
A nine-point scale, ranging from 'easy' to 'hard', was provided for each
statement from original dataset. The statements reflect evaluations and
perceptions of self-efficacy about four Web authoring tools. Subjective
perceptions of performance success can be used as a perceived self-efficacy
measure (McAuley et. al., 1991, Spink & Roberts, 1980). While the four levels
may measure a magnitude of perceived self-efficacy, the scores in each level may
mean an intensity of the perceived self-efficacy. Therefore, the four levels
with nine-point scale may be a composite measure of perceived self-efficacy on
the Web authoring. Each score was reversely recorded, ranging from 'hard' to
'easy', and then sum across only those perceived self-efficacy magnitude levels
to which respondents answered.
Programming Competence
Programming competence was operationalized as "How many years of computer
programming experience do you have, if any?" Five point scale was used, ranging
from 'none' to '13 years or more' in the original survey. Present study used
the original 5-point scale data without recording for programming competence.
Analysis
Multiple regression analysis was used to assess the relationship of perceived
self-efficacy, motivating incentives, degree of accessibility, and programming
competence to Web authoring implementation. The four independent variables were
entered in stepwise regressions. Pairwise deletion procedure of missing data was
used because there was a large loss in cases due to use of a listwise procedure.
Since regression analysis depends on correlations for estimating the regression
coefficients, we can use the available cases for each individual correlation
coefficient.
RESULTS
Four independent variables entered into the regression predicting the degree of
Web authoring implementation. Table-1 (see Appendix) displays means, standard
deviations, and zero-order Pearson product-moment correlation coefficients for
the dependent and independent variables used to test the hypotheses for the
present study. High correaltions were not observed among the four independent
variables. Therefore, the regressions based on these data do not seem to run
the risk of multicollinearity. These four variables explained 54 percent of the
variance in Web authoring implementation, significant at the .001 level (
F(4,1836) = 540.4). The results are shown in Table-2.
Hypothesis 1, stating that the degree of Web authoring will be positively
related to its motivating incentives was strongly supported. The motivating
incentives significantly and positively affected the implementation of Web
authoring (Beta =.640, p = .000). As the beta weights show, the motivating
incentives seem to be the best predictor of all the four independent variables
(Beta = .284, p < .001).
Hypothesis 2, stating that the degree of Web authoring will be positively
related to the degree of accessibility, was not supported. No significant
relationship between the degree of accessibility and Web authoring
implementation was observed, although it tended in the hypothesized direction
(Beta = .030, p = .063). In other words, the degree of accessibility appeared
to play an insignificant role in predicting the implementation of Web authoring.
Hypothesis 3, stating that the degree of Web authoring will be positively
related to perceptions of self-efficacy about web authoring tools, was supported
(Beta =.175, p = .000). The perceive self-efficacy about web authoring tools
was significantly and positively affected the degree of Web authoring
implementation. Further, to examine patterns of interaction among the magnitude
levels in perceived self-efficacy such as HTML, CGI, FORMS, and ISMAP, the four
subsets of perceived self-efficacy were entered in multiple regression. The
results, shown in Table-3, indicate that all the independent variables were
statistically significant in their relation to authoring performance. These four
kinds of perceived self-efficacy variables explained 41 percent of the variance
in Web authoring implementation, significant at .001 level (F(4, 5292) = 332.3).
As the beta weights show, ISMAP seems to be the best predictor of all the four
self-efficacy magnitude levels (Beta = .284, p < .001). FORMS (Beta = .117, p <
.001) was a better predictor than CGI (Beta = .096, p <.001). HTML accounted
for the least amount of variance (Beta = .066, p <.001).
Hypothesis 4, stating that the degree of web authoring will be positively
related to programming competence, was moderately supported (Beta =.049, p <
.01). Although programming competence was significantly related to Web
authoring implementation, it accounts for a very small amount of implementation
variance. Therefore, programming competence seem to be a less important factor
to predict the implementation of Web authoring, than perceived self-efficacy
and motivating incentives.
DISCUSSION AND CONCLUSION
Regression analyses were successful in identifying determinants effects on the
implementation of Web authoring. Over fifty-four percent of the variance in the
implementation of Web authoring was accounted for. Results indicate that the
motivating incentives and the perceived self-efficacy may be the most important
of the factors derived from social cognitive theory that predict the degree of
Web authoring.
First, the degree of Web authoring implementation appears to primarily depend
on factors that involve external or vicarious incentives, including advertising
revenue and the number of sites supported. The Web authoring seems to be
motivated by these incentives. In addition, the concept of motivating
incentives is compatible with that of relative advantages in diffusion theory.
The present findings imply that conventional innovation characteristic like
relative advantage may be still an influential determinant of the implementation
of Web authoring.
Further research is needed to examine the incentives effects using incentive
structure which is patterned by the relationship among diverse forms of
incentives in Web authoring. Various incentives exist in motivational process.
Self-incentives, self-evaluated incentives and incentive preferences are also
likely to influence the degree of Web authoring.
Second, the perceived self-efficacy appears to partially influence Web
authoring. Among four types of Web authoring tools, the perceived self-efficacy
about ISMAP was the best predictor for the degree of implementation. ISMAP
reflects the highest level of current Web authoring in which multimedia and
interactivity are executed. However, computer programming competence was not an
important determinant of implementation. This result suggests that the
perceived self-efficacy could be a more influential predictor than real
competence.
Third, the degree of accessibility was not related to the degree of Web
authoring. The 155 Mbps, 128 Kbps and 1Mbps users implemented more Web
authoring than other type access users. Surprisingly, many middle-lower level
users are producing more Web pages than other higher level access users. One
interpretation may be that there is an optimal level of access capability to
implement Web authoring, rather than a maximization of access capability. It
seems to reflect economies of Web authoring in which costs and benefits are
considered by Web authors. Under the current access infrastructure and
conditions, better accessibility does not provide a good predictor for Web
authoring implementation.
Furthermore, the measurement for accessibility in this study reflects the
intensity dimension of technology resources. Operational access for
implementation of new communication technology should include not only intensity
but also magnitude dimension such as technology cluster.
Although the present results are interesting and suggest some important links
among Web authoring, motivating incentives, and perceived self-efficacy, some
notes of causation are appropriate. The major limitation of present study is
likely to stem from secondary analysis design. Some indexes for the present
study may provide partial validation since the original survey did not contain
precise indicators of the concept present study examines. For example, perceived
self-efficacy may depends on a variety of conditions - reading instructions,
listening instructions, watching behavior, and with instructor. Finally, the
scientific verification for the Web survey method has not been established.
Possible errors made by Web platform survey could not be identified within the
present study design. Nevertheless, the present study suggests that social
cognitive theory may provide additional explanatory power for the mechanism of
implementation of Web authoring.
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Table 1: Pearson Product-Moment Correlations
Variable
M
SD
2
3
4
5
1. Implementation
32.793
47.109
.075**
.438***
.712***
.125***
2. Accessibility
5.154
1.783
.062**
.049*
.074**
3. Self-efficacy
17.096
8.803
.387***
.278**
4. Incentives
4.914
1.587
.040*
5. Competence
2.988
1.437
* p < .05; ** p < .01 *** p < .001
Table 2: Multiple Regression Analysis
Independent Variables
R2 Change
r
Beta
t
Prob.
Degree of Accessibility
.006
.075
.030
1.859
.063
Perceived Self-efficacy
.189
.438
.175
9.783
.000
Motivating Incentives
.344
.712
.640
37.219
.000
Programming Competence
.002
.125
.049
2.937
.003
Multiple R
.735
R2
.541
F (4, 1836)
540.428
* Variables are presented in order of entry.
Table 3: Multiple Regression Analysis: Magnitudes of Perceived Self-efficacy
Independent Variables
Change in R2
R
Beta
Significance
CGI
.104
.322
.096
.000
FORMS
.040
.346
.117
.000
HTML
.005
.181
.066
.000
ISMAP
.052
.411
.284
.000
Multiple R 448
R Square 201
F(4,5292) 332.3
[1] Copyright 1995 Georgia Tech Research Corporation. All rights Reserved.
Source: GVU's Fourth WWW User Survey
URL:http://www.cc.gatech.edu/gvu/user_surveys/.
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