"Cognitive Innovativeness as a Predictor of Student Attitudes and Intent: an
Application of the Theory of Planned Behavior to Online Learning Environments"
"Cognitive Innovativeness as a Predictor of Student Attitudes and Intent:
An Application of the Theory of Planned Behavior to Online Learning
Tracy Irani and Michelle O'Malley
University of Florida
College of Journalism and Communications
2000 Weimer Hall, P.O. Box 118400
University of Florida
Gainesville, FL 32611
[log in to unmask] phone: 352/392-6660
[log in to unmask] phone: 352/392-0502 ext. 225
This study, using the Theory of Planned Behavior as a framework,
investigates the effect of internal and external cognitive innovativeness on
attitudes, beliefs, and behavioral intentions related to desire to experience an
online Web-based course.
High internal and external innovators result in more positive attitudes
than low internal and external innovators. Regression analysis suggests that
attitude is predictive for high internal innovators, while for high cognitive
innovators, attitude and norms are predictive.
Despite numerous predictions over the past several years that development
of the Internet and the World Wide Web will revolutionize instruction and create
a new paradigm for teaching and learning, there is still a fair amount of
controversy about the viability of online learning environments. Although
colleges and universities around the country currently are making, or have plans
to make available a variety of online course offerings, students' attitudes
toward and acceptance of computerized teaching methods are still an issue
(Chronicle of Higher Education, 1998). For some students, the opportunity to
take an online course is attractive because it is a convenient and novel
approach to the process of education. For others, beliefs about some of the
drawbacks of online education, such as the lack of structure provided by a live
instructor or lack of access to a computer, may affect negatively attitudes and
intent to take an online course.
One of the motivating forces that underlies formation of attitudes and
beliefs with respect to adoption of new technologies is consumer innovativeness,
a personality characteristic that has been explored extensively in the consumer
behavior literature. Innovativeness, a construct that is derived from diffusion
theory, was originally associated with the adoption and use of new technological
innovations (Rogers and Shoemaker, 1971). Many consumer behavior studies have
used the concept of consumer innovativeness to examine purchasing behaviors
related to adoption of new products.
Purpose of the Study
Although it has been addressed conceptually, limited research has examined the
relationship between individual differences (such as innate traits) among
students and their attitudes and beliefs with respect to online education.
Hiltz (1987), in a study of adult learners, found that individual differences,
primarily differences in learning style, affected preference for online
education versus traditional teaching modes. Moore (1990), commenting on this
study and others, notes that research is "not yet sufficient to draw conclusions
about the extent of variations in preference for online education among the
adult population at large" (p.15).
Given the parallels between consumer adoption and use of new products and
students' similar ability to "adopt" and "use" (i.e., " take") an online course,
the purpose of this study is to attempt to extend the concept of consumer
innovativeness to the instructional setting in order to determine whether
students' levels of innovativeness can be used to predict attitude and intent to
enroll in an online course. Given the investment colleges and universities are
currently making in online education and distance learning, it is important to
have a better understanding of the factors that influence students' attitudes
and behavior towards acceptance of new communication technologies, with a view
towards developing message stimuli that may potentially create a more effective
Innovativeness as a Characteristic of Personality
In its original conception, Rogers defines innovativeness as "the degree
to which an individual is relatively earlier in adopting an innovation than
other members of his social system" (Rogers and Shoemaker,1971, p. 27).
Hirschman (1980) defines innovativeness as the desire for new experience, and
traced the development of the construct to its roots in the diffusion and
personality literature. According to Venkatraman (1991), consumer innovativeness
can be defined as a latent personality trait that predisposes people to buy new
Leavitt & Walton (1974) critique the results of several previous diffusion
studies on the basis of smaller-than-expected variance in the dependent variable
of adoption. Calling personality variables the "soft underbelly" of the
problem, they postulate that a trait might exist that would underlie rational
media choice behaviors. The researchers argue that the many psychological
studies of close-mindedness, dogmatism, rigidity, etc., can be counterbalanced
by an attempt to scale a new open-minded, constructive trait they call
"innovativeness." The subsequent Leavitt & Walton innovativeness scale later
was tested for predictive validity by Craig & Ginter (1975) who concluded that
not all components of the original scale discriminated between innovators and
Along these same lines, Midgley and Dowling (1978) reconceptualize the
adoption-of-behavior approach as less a measure of time than a personality
construct that an individual could possess to a greater or lesser degree.
According to Midgley and Dowling, innovativeness is "the degree to which an
individual is receptive to new ideas and makes innovative decisions
independently of the communicated experience of others" (p 236). As such, the
construct is closely related to novelty seeking (Flavell, 1977), and to
creativity, especially productive thinking and problem solving (Welsh, 1975;
Although innovativeness is measured as a singular trait in the original
diffusion formulation, consumer innovativeness has also been viewed as having
more than one dimension. An example of this is Price and Ridgeway's
conceptualization of a three tiered hierarchy of innovativeness, which
differentiates between innovativeness as an innate trait and as an observed
behavior. Drawing on Alpert's (1937, 1961) notion of the hierarchical nature of
traits (cardinal, central, secondary) Price & Ridgeway's model views
innovativeness as an inherent central trait, and proposes a set of innate
secondary traits or tendencies that underlie observable behaviors.
A Multi-dimensional Model of Innovativeness
Some researchers have attempted to relate innovativeness to the internal need
for stimulation. They argue that when stimulation, diversely categorized as
complexity, arousal, enjoyment, risk, etc., falls below a certain level,
individuals will seek out stimulation through behaviors such as exploration,
variety seeking, and novelty seeking (Price and Ridgeway, 1985). Drawing on the
work of cognitive researchers, including Caccioppo and Petty (1982) and
Hirschman (1984) as well as the novelty seeking literature exemplified by
Pearson (1970), Zuckerman (1979) and Faison (1977), Venkatraman and Price (1990)
attempt to differentiate cognitive and sensory traits that predispose
individuals to seek stimulation of the mind, which they defined as cognitive
innovativeness, versus seeking stimulation of the senses (sensory
innovativeness). Adapting Pearson's Novelty Experiencing Scale (NES),
Zuckerman's Sensation Seeking Scale and Hirschman's Cognition Seeking Scale,
Venkatraman and Price (1990) are able to fit a hierarchical second order factor
model based on a multi-dimensional structure of innovativeness. (See Fig. 1
Internal External Internal External
Cognitive Cognitive Sensory Sensory
Second Order Factor Model of Innovativeness
In this model, cognitive and sensory dimensions form the higher-order
factors, and each of these is comprised of internal and external dimensions.
Conceptual definitions of these lower order factors, adapted from Pearson's 1970
study, clarified internal cognitive innovativeness as the "tendency to like
unusual cognitive processes that are focused on explanatory principles and
cognitive schemes"; while external cognitive innovativeness is the "tendency to
like finding out facts, how things work and learning to do new things."
Internal sensory innovativeness, on the other hand, is the "tendency to like
experiencing unusual dreams, fantasy or feelings that are internally generated";
while external sensory innovativeness is the tendency to like active physical
participation in thrilling activities." In the researchers' subsequent model,
they combine the internal and external factors of cognitive and sensory
innovativeness and average across each to form two eight-item scales (1990).
Theory of Planned Behavior
A seminal work in attempting to understand and predict behavior and
behavioral intentions is Ajzen's theory of planned behavior (1980). This theory
is the researcher's extension of the theory of easoned action (TORA). The basic
proposition of both models is that in order to predict a behavior, B (such as
enrolling in an online course), one must try to measure an individual's intent
to behave, BI (such as intent to take an online course), itself a function of
attitudes toward the target behavior and subjective norms.
In both the TORA model and the later theory of planned behavior (TOPB),
attitudes are a function of beliefs about and assessments of perceived
consequences of acting in a certain way, such as beliefs about the advantages or
disadvantages of taking an online course. Subjective norms refer to an
individual's interpretation of what important referents (people with whom the
subjects identify) think about the desirability of a behavior, combined with the
individual's desire and motivation to comply.
In an attempt to answer critics of the TORA, who argue that most behaviors
are neither volitional (as in the initial model formulation) nor involutional,
Ajzen adds an additional variable called perceived behavioral control, which
measures perceptions of individual control over the target behavior. The
resulting predictive equation can be written as follows:
where AB is attitude toward the behavior, SN is subjective norms, and PBC is the
degree of perceived behavioral control a subject feels s/he has over the
behavior. In the model, these three variables are weighted as follows:
AB - variables related to belief statements and evaluation of their
SN - variables related to normative beliefs of important referents and
their effect on a subject's motivation to comply; and
PBC - variables related to beliefs about the control a subject has over
the behavior and the power or degree of control.
The TOPB has been employed by researchers in several studies to predict
students' attitude and behavioral intentions. Prislin and Kovarlija (1992) in a
study of low and high self-monitoring, found that students' intentions to attend
a class lecture are best predicted by attitude of the low self-monitoring group
and subjective norms of the high self-monitoring groups. Crawley and Black
(1992) use the model to test causal linkages among attitudes, subjective norms,
and perceived behavioral control with respect to secondary science students'
intentions to enroll in physics classes. The model has also been used to
predict intention of tenth graders to enroll in subsequent mathematics courses
(Choe, 1982) as well as to predict success in an undergraduate computer science
course (Shaffer, 1990).
Rationale for the Study and Hypotheses
This study examines the demand economy currently driving adoption of online
education in colleges and universities around the country in light of its
similarities with the consumer-driven marketplace. Students in this
environment, much like consumer prospects, have the ability to choose to take
courses and in some cases, entire degree programs that are characterized by a
wide variety of delivery mechanisms and course technologies.
Given the volitional nature of the target behavior and students'
expectation and experience, that taking classes is a cognitively oriented
process, the researchers chose to employ the cognitive innovativeness sub-scale
by itself, focusing on differentiating the internal and external factors and
using these to predict attitude and intent. The rationale for this approach is
based on Vankatraman and Price's work, which showed a highly significant
relationship between higher education and cognitive innovativeness (F1,5.18, p<
.01), but not sensory innovativeness (p. 309).
The model for this study assumes that differences between
internal-cognitive innovators, who have a tendency to like unusual cognitive
processes and schemes or structures that allow for higher order thinking, versus
external-cognitive innovators, who like to ferret out factual information and
learn to do new things, might account for differences in the target behavior as
well as help predict how attitudes, subjective norms, and perceived behavioral
control influence behavioral intent.
As internally cognitive people like unusual cognitive processes and
cognitive schemes, a cognitively oriented stimulus should elicit a high degree
of liking. Conversely, those low in internal cognitive innovativeness receiving
the control stimulus should exhibit a lower degree of liking.
To test the effect of this potential difference, a cognitively oriented
stimulus, based on a detailed online course description and designed to tap into
cognitive innovativeness was introduced and assigned to half of the
participants, while the other half received a control stimulus.
Using this basis, the following hypotheses were derived:
When the information contained in the course description stimulus
is congruent with cognitive innovativeness (experimental stimuli),
compared to when it is discrepant (control stimulus), attitude toward
the target behavior should differ as follows:
H1) There should be an interaction effect between the attitude
scores for high and low internal and external cognitive innovators and
H2) There should be an interaction effect between the attitude
scores of high internal cognitive innovators and low internal cognitive
innovators with the stimuli.
H3) There should be an interaction effect between the attitude
scores of high external cognitive innovators and low external cognitive
innovators and the stimuli.
H4) There should be a main effect between internal and external
innovators - high internal cognitive innovators should have a more
positive attitude than low internal cognitive innovators, and high
external innovators should have a more positive attitude than low
external cognitive innovators.
Finally, in attempting to draw some inferences about which variables are
the best predictors of intent to take an online course, the TOPB model was
tested using intent to take an online course as outcome variable. This model
takes into account potentially contributory variables to the target behavior
(taking an online course), and precedent for its use has been established in
other studies focusing on students' behavioral intentions.
Subjects and Experimental Design
Participants in the study were college students (n=356) in three large
introductory public relations and animal science writing classes at a large
Southeastern university. The mean age was 20.9; average class standing was
sophomore year. The study, based on a 2x2x2 factorial design, consisted of a
questionnaire comprised of three elements:
1) A set of scale items measuring internal and external cognitive
2) A randomly assigned stimulus consisting of either a cognitively
oriented written course description for an online Web-based course or a
control version; and
3) A set of scale items measuring the variables in the theory of
planned behavior model with intent to take an online Web-based course as
dependent target behavior variable.
At the beginning of the session, subjects were randomly assigned to one of
the two (cognitive or control stimuli) experimental conditions, which were
incorporated into the copy of the questionnaire each subject received. After
filling out the cognitive innovator scale, the participants were then asked to
read the course description and answer the rest of the items based on the
description they received.
Validity stimuli was assessed prior to the study through a pre-test that asked
a panel of 15 judges to match each course description to a hypothetical highly
innovative student respondent, using an example of how such a student would have
responded to items on the scales as part of the description of the student.
The validity and reliability of the theory of planned behavior theoretical
framework, when used for the purpose of predicting behavioral intent and the
conceptual rationale behind each of the variables in the model, has been
extensively documented. A thorough analysis of the model and its application to
predicting human behavior can be reviewed in Ajzen, 1991.
To measure attitude toward the behavior, subjects were asked to rate the
target behavior (taking an online Web-based course) on a set of seven-point (-3,
+3), Likert-type, four bipolar scales, whose anchors are good-bad;
pleasant-unpleasant; harmful-beneficial; positive-negative.
Subjective norms were measured by a series of bipolar scales that rated first
the referents and then motivation to comply with each referent's opinion of the
respondent's engaging in the target behavior. These scales were also measured
on a Likert-type seven-point scale (-3,+3).
Perceived behavioral control was examined by having students rate the
degree of control they felt they had over taking an online class, as well as how
difficult or easy it would be and whether or not they felt the decision to take
an online class was up to them. These scales were measured on a Likert-type
seven-point scale (-3,+3).
Confirmatory factor analysis was conducted on all of the variables in the
study and all of the scales had a one-factor solution. The Statistical Package
for Social Sciences (SPSS) 7.0 was used to conduct the analyses.
Reliability analyses for the scale items used in the instrument were
measured by Chronbach's alpha and are reported as follows: Internal cognitive
innovativeness ((=.72); external cognitive innovativeness ((=.62); attitude
((=.91); subjective norms ((=.98); and behavioral intention ((=.91). Perceived
behavioral control was a one-item measure for each of its components.
Hypotheses one through four were tested using ANOVAS. To conduct these
tests, internal and external cognitive innovativeness scores were recoded on the
basis of a median split into four groups (High Internal Cognitive; Low Internal
Cognitive; High External Cognitive; Low External Cognitive)
Hypothesis one, that there should be an interaction effect between the
attitude scores for high and low internal and external cognitive innovators and
the stimulus, was supported at the (=.05 level. F(7, 2.29) p = .016.
Both hypothesis two, which predicted differences in attitude between high
internal cognitive innovators receiving a cognitive stimulus and low internal
cognitive innovators receiving a control stimulus (H2), and hypothesis three,
which predicted that attitudes would differ for high external cognitive
innovators receiving a cognitive stimulus compared to low external cognitive
innovators receiving the control stimulus (H3), were supported at the (=.05
Two-way interactions were observed in both cases; the High Internal group
(M .458, sd 1.43) had a more positive attitude than the Low Internal group (M
-1.21, sd 1.44), F(3, 3.70) p = .012. The High External group (M 5.42, sd 1.26)
also had a more positive attitude than the Low External group (M -.003, sd
1.56); F(3, 4.14) p = .007.
Hypothesis four, which predicted a main effect between high and low
internal and external cognitive innovators, was also supported. The main effect
between the High Internal (M .486, sd 1.47) and Low Internal Cognitive (M .184,
sd 1.4) and the High External Cognitive (M .583, sd 1.33) and Low External
Cognitive. (M .146, sd 1.54) groups was significant at the (=.05 level (F 3,
3.855) p = .004. (See Figure 2).
[--- WMF Graphic Goes Here ---]
Figure 2. Attitudes toward taking an online course.
Prediction of Behavioral Intention
To examine the relative contribution of attitudes, subjective norms, and
perceived behavioral control to the prediction of behavioral intentions for high
and low cognitive and sensory innovators, linear regression analyses were
performed using SPSS. Testing of the TOPB model indicates that although all
factors are significant contributions to behavioral intent, attitudes and
perceived behavioral control are most important (Table 1).
Prediction of Behavioral Intent to take an Online-Web Based Course
Group/Measures r Beta R2
Attitudes .81 .72 **
Subjective Norms .54 .08*
PBC .41 09** .667
* p = .05, ** p < .01
For all subjects, attitude proved to be a better predictor of behavioral
intent than either perceived behavioral control or subjective norms, suggesting
that intent to take an online course is a behavior perceived to be, to a great
extent, under one's own control and not subject to significant influence by
peers, advisers, relatives and other referents.
Regressions were then performed for high and low internal and external
cognitive innovators, in an attempt to see if these factors had any influence on
the outcome variable.
Prediction of behavioral Intent for High and Low Internal and External Cognitive
* p < .05, **p < .01
For all groups, attitudes remained the strongest predictors of intent to
take an online course. Interestingly, for high internal cognitive innovators,
attitude alone was highly significant in contributing to the TOPB model, while
both attitude and subjective norms were contributory for high external cognitive
innovators. For both low internal and low external cognitive innovators,
attitude and perceived behavioral control were important, but norms were not,
suggesting that high external cognitive innovators are more concerned about what
relevant referents might advise and less concerned about feelings of control or
efficacy over the behavior.
Discussion and Conclusions
Online education is a technological innovation that would seem likely to
appeal most to those who have highly innovative personalities, as opposed to
those who are less attracted to new experiences and novel technologies. In
addition to lack of face-to-face interaction, online learning represents a major
change from the traditional educational environment. In the live classroom,
the instructor serves as an anchor and a source capable of providing explanation
and context for a linear sequence of activities. In addition to potential
unfamiliarity with the technology, students in an online course have to deal
with a fair amount of ambiguity and lack of structure; the environment is less
predictable and may require some ability to figure things out without benefit of
experience or rules.
The differences observed between high and low internal and high and low
external cognitive innovators may relate to their relative levels of tolerance
for ambiguity. It seems possible that this may be one of the factors that
influences people who are low in innovative tendencies to have a negative
opinion about experiences involving technology. The introduction of technology
may add a degree of unpredictability and ambiguity to what was formerly a
relatively familiar terrain.
Conversely, highly innovative people may find the relative lack of
structure and "rules" in an online course environment mentally challenging. As
the literature suggests, cognitive innovators are problem solvers and thinkers;
they like to figure out puzzles, problems, etc. and they are motivated to take
risks and enjoy mastering the complex.
That differences were observed between the internal and external dimensions
of cognitive innovativeness would suggest implications for marketing efforts
aimed at attracting students to take an online course. Internal cognitive
innovators seem most influenced by their attitudes and sense of control over the
technology related to online environments, such as familiarity with and access
to computers. Highly internal cognitive innovators seem to feel a stronger
sense of control over this type of experience than low cognitive innovators;
and for these students, the attraction of an unusual cognitive process is
appealing in itself.
In addition to attitude, external cognitive innovators seem more
influenced by situational norms than internal cognitive innovators. For this
group, the feelings of peers and academic advisers may come into play more so
than for other groups, because these individuals are externally oriented, yet
they also tend to feel a strong sense of control over the target behavior. On
the other hand, for both low internal and external cognitive innovators,
attitude and perceived behavioral control are important predictors. These
groups are less attracted to ambiguous and unstructured situations, this may
affect their feelings of lack of control over both the technology and their own
ability to function and perform well in the online course environment.
Venkatraman et al. (1990) found that cognitive innovators are more
persuaded by factual ads than evaluative messages. The results of this study
suggest that it also may be important to take the internal and external
predispositions of cognitive innovators into account when designing messages.
For high internal cognitive innovators, a message emphasizing the mentally
challenging aspects of mastering a course in an unpredictable and
technologically challenging environment may hold appeal, while high external
cognitive innovators might respond better to an appeal that adds the support of
important referents such as family, friends, and academic faculty and advisers.
Finally, for those groups low in cognitive innovativeness, it seems that
lack of perceived behavioral control may be an important issue, one that might
be addressed by crafting messages aimed at bolstering confidence in the level of
control over the technology and the structure of the experience.
From a theoretical standpoint, this study, in keeping with the literature,
supports the contention that innovativeness is not a homogenous construct, but
one that can be defined according to internal and external aspects of the
cognitive innovativeness trait. As results of the study show, internal and
external cognitive innovators differ, not only in terms of their relative levels
of innovativeness (high vs. low), but also in how the internal or external
aspects of the trait may influence evaluations of behavior and behavioral
In the context of online course development and promotion, these
differences have important implications for constructing course environments
that appeal to cognitive tendencies. In addition, the results suggest support
for the importance of both designing communication messages that tap into the
internal and external dimensions of cognitive innovativeness, and attempting to
reassure students concerned about normative evaluations and beliefs related to
perceived behavioral control.
Ajzen, I. (1991). The theory of planned behavior. Organizational
Behavior and Human Decision Processes, (50), 179-211.
Ajzen, I., and Fishbein, M., (1980). Understanding attitudes and
predicting social behavior, Englewood Cliffs, NJ: Prentice-Hall, Inc.
Allport, G.W., (1937) Personality: A Psychological Interpretation,
New York: Henry Holt.
Allport, G.W., (1961). Pattern and growth in personality. New
York: Holt, Rinehart and Wilson.
Cacioppo, J.T., and Petty, R.E., (1982). The need for cognition.
Journal of Personality and Social Psychology (42) 116-131.
Capon, N., and Burke, M., (1980). Individual, Product Class and
task-related Factors in Consumer Information Processing. Journal of
Research, (7), 314-326.
Childers, T.L., Houston, M.J., and Heckler, S.E., (1985).
Measurement of individual differences in visual vs. verbal information
processing. Journal of Consumer, (12) 125-134.
Choe, S., (1992). An analysis of tenth graders intention to enroll
in subsequent marketers: An application of the theory of planned behavior,
unpublished dissertation, the University of Texas at Austin.
Chronicle of Higher Education, (Mar 15,1998). Universities
Targeting Distance Courses to Traditional Students. (3), 24-25.
Craig, C.S., and Ginter, J.L., (1975). An empirical test of a scale
for innovativeness. Advances in Consumer Research, (2) 555-562.
Crawley, F., and Kaballa, T.R., Jr., (1992). Hispanic-American
Students' attitudes toward enrolling in high school chemistry. A Study of
Planned Behavior and Belief-Based Change, Hispanic Journal of Behavioral
Sciences, (14)4, 469-485.
Crawley, F.E., and Black, C.B. (1992). Causal modeling of sociology
science students' intention to enroll in physics, Journal of Research in
Science Teaching, 29(6), 585-599.
Fasion, E.W.J., (1997). The neglected variety: A useful concept for
consumer behavior. Journal of Consumer Research, (4), 172-5.
Flavell, J.H., (1977). Cognitive Development. Englewood Cliffs,
NJ: Prentice-Hall, Inc.
Golsmith, R.E., and Hofacker, C.F., (1991). Measuring consumer
innovativeness. Journal of the Academy of Marketing Science, (19)3,
Guilford, J.P., (1965). Intellectual factors in productive
thinking. Productive Thinking in Education, Washington, D.C.: National
Education Association, 5-20.
Henry, W.A., (1980). The effects of information processing ability
on processing accuracy, Journal of Consumer Research, (7) 42-48.
Hiltz, S.R., Teaching in a Virtual Classroom: A Virtual Classroom on
EIES: Final Evaluation Report, (2) ( ERIC ED 315 039), 1987.
Hirschman, E.C., (1980). Innovativeness, novelty seeking, and
consumer creativity. Journal of Consumer Research, (7) 283-295.
Hirschman, E.C., (1984). Experience seeking: A subjectivistic
perspective for consumption, Journal of Business Research, (12) 115-136.
Leavitt, C., and Walton, J., (1975). Development of a scale for
innovativeness, Advances in Consumer Research, (2) 545-554.
McGuire, W.J., (1976). Some internal psychological factors
influencing consumer choice, Journal of Consumer Research (2) 302-319.
Midgley, D.F., and Dowling, G.R., (1978). Innovativeness: The
concept and its measurement. Journal of Consumer Research, (4) 227-242.
Moore, M.G., (1973). Towards a theory of independent learning and
teaching. Journal of Higher Education, (44) 661-679.
Pearson, P., (1970). Relationships between global and specific
measures of novelty seeking. Journal of Consulting and Clinical Psychology,
Price, L.L., and Ridgeway, N.M., (1983), Development of a role to
measure use innovativeness. Advances in Consumer Research, (10) 679-684.
Prislin, R., and Kovrlya, N., (1992). Predicting behavior of high
and low self monitors: An application of the theory of planned behavior.
Psychological Reports, (70), 1131-1138.
Ridgeway, N., and Price, L.L., (1994). Exploration in product
usage: A model of use innovativeness. Psychology and Marketing, (11)1,
Rogers, E.M., (1983). Diffusions of Innovations. New York:: The
Rogers, E.M., and Shoemaker, F.F., (1971) Communication of
Innovations. New York: The Free Press.
Shaffer, D., (1990). Predicting Successes in the Undergraduate
Introductory Computer Science Course Using the Theory of Planned Behavior,
unpublished dissertation, University of Texas at Austin.
Venkatraman, M.P., and Price, L.L., (1990). Differentiating between
cognitive and sensory innovativeness: Concepts, measurement, and
Journal of Business Research, (20), 293-315.
Venkatraman, M.P., Marlino, D., Kordes, F.R., and Sklar, K.B.,
(1990). Effects of individual difference variables on responses to factual
and evaluative ads. Psychology and Marketing, 102-107.
Wallach, M.A., and Kogan, N., (1965). Modes of Thinking in Young
Children. New York: Holt, Rinehart and Wilson.
Welsh, G.S., (1975). Creativity and Intelligence, Institute for
Research in Social Science, Chapel Hill, NC: University of North Carolina
Zuckerman, M., (1979). Sensation-Seeking Beyond the Optimal Level
of Arousal, Hillsdale, NJ: Lawrence Erlbaum Associates.