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Early and late adopters of IT innovations: Extensions to innovation diffusion theory

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Abstract This paper draws upon,innovation diffusion theory and more recent conceptualizations of IT adoption behavior to examine systematic differences among,Rogers’ adopter categories. We extend Rogers’ theory by characterizing adopter categories based on personality, belief, and attitudinal variables recently found to be salient in IT adoption behaviors. Theoretical predictions were empirically tested via a field study of 326 potential users of an IT innovation, which included early adopters as well as non-adopters in the sample. Results,provide,strong support,for the research hypotheses. Theoretical,and practical implications that follow are discussed. 1 Early and Late Adopters of IT Innovations: Extensions to Innovation Diffusion Theory
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Early and Late Adopters of IT Innovations: Extensions to Innovation
Diffusion Theory
Ritu Agarwal
College of Business
Florida State University
Tallahassee, FL 32306-1110
ragarwal@cob.fsu.edu
Manju Ahuja
College of Business
Florida State University
Tallahassee, FL 32306-1110
mahuja@cob.fsu.edu
Pamela E. Carter
College of Business
Florida State University
Tallahassee, FL 32306-1110
pec9319@garnet.acns.fsu.edu
Mitch Gans
Administrative Information Systems
Florida State University
Tallahassee, FL 32306
mgans@admin.fsu.edu
September 15, 1998
Early and Late Adopters of IT Innovations: Extensions to Innovation
Diffusion Theory
Abstract
This paper draws upon innovation diffusion theory and more recent conceptualizations of IT adoption
behavior to examine systematic differences among Rogersadopter categories. We extend Rogerstheory
by characterizing adopter categories based on personality, belief, and attitudinal variables recently found
to be salient in IT adoption behaviors. Theoretical predictions were empirically tested via a field study of
326 potential users of an IT innovation, which included early adopters as well as non-adopters in the
sample. Results provide strong support for the research hypotheses. Theoretical and practical
implications that follow are discussed.
1
Early and Late Adopters of IT Innovations: Extensions to Innovation
Diffusion Theory
Introduction
Information technology adoption behaviors persist as an important issue among academics and
practitioners alike. As a consequence, the past decade has witnessed considerable research activity
devoted to better understanding the processes underlying such behavior. A significant proportion of this
activity has focused on developing more robust theoretical models for the phenomenon (e.g., Davis, 1989;
Fulk et al., 1990), whereas the remainder has sought empirical support for proposed conceptualizations
through a variety of studies across a range of information technologies and contexts (e.g., Mathieson,
1990; Davis et al., 1989; Adams et al., 1992; Sjazna, 1996).
New information technologies represent innovations for potential adopters: an idea, practice, or object
that is perceived as new by an individual or other unit of adoption.(Rogers, 1995; p. 11). One popular
and enduring conceptualization of innovation adoption behavior is Rogerstheory of the diffusion of
innovations. Although the overall theory is rich and complex, its essence views the innovation adoption
process as one of information gathering and uncertainty reduction. Information about the existence of an
innovation, as well as its characteristics and features, flows through the social system within which
adopters are situated. Potential adopters engage in information seeking behaviors to learn about the
expected consequences of using the innovation. An assessment and evaluation of this information
manifests itself in the form of beliefs about the innovation, and is then a proximal antecedent of adoption
behavior. The theory also contains predictions regarding the spread of an innovation through a social
system, i.e., the diffusion process, which is postulated to follow a sigmoidal or S-shaped curve.
The S-shaped curve of cumulative adopters gives rise to a bell-shaped distribution of adopters. Rogers
utilizes this distribution to distinguish between five categories of adopters – ranging from innovatorsto
2
laggards” – derived from their time of adoption of the innovation. Based on a meta-analysis of findings
from a wide range of studies in several innovation domains, he also offers several generalizations
regarding early adopters versus the rest related to the socioeconomic status of adopters, personality
characteristics, and communication behaviors. Subsequent tests of these generalizations in the domain of
IT innovation (e.g., Brancheau and Wetherbe, 1990; Burkhardt and Brass, 1990) have produced mixed
results – while empirical support has been established for some generalizations, others have not yielded
results consistent with the theoretical predictions.
Mixed results notwithstanding, the ability to distinguish early adopters of an IT innovation from the rest is
clearly a matter of considerable theoretical and practical relevance. Early adopters frequently serve as
opinion leaders who can persuade others to adopt the innovation by providing evaluative information
(Rogers, 1995). An understanding of the characteristics of such individuals can assist managers in
targeting new technology implementation appropriately. From the perspective of theory development,
knowledge about the characteristics of early adopters can help researchers develop richer theoretical
models that explain adoption behaviors across a range of adopter types.
Rogerstheory was developed in the broad context of innovations of all varieties, and not IT per se.
Since his articulation of the theory, others have identified salient influences specifically for IT adoption
behaviors by examining alternative models rooted in social psychology (Ajzen and Fishbein, 1980;
Fishbein and Ajzen, 1975; Ajzen and Madden, 1986), social learning theory (Bandura, 1997), and social
influence theories (Fulk et al., 1990). In this paper we draw upon innovation diffusion theory and more
recent conceptualizations of IT adoption behavior to examine systematic differences among Rogers
adopter categories. Rather than posit causal relationships between diverse antecedent conditions and
usage behavior as has been done in recent work (e.g., Davis, 1989; Taylor and Todd, 1995), we study
how early adopters differ from other potential users for the same set of variables. Thus, we extend
Rogerstheory by characterizing adopter categories based on variables recently found to be salient in IT
3
adoption behaviors. Theoretical predictions were empirically tested via a field study of 326 potential
users of an IT innovation, which included early adopters as well as non-adopters in the sample. Results
provide strong support for the research hypotheses. Theoretical and practical implications that follow are
discussed.
Theoretical Background and Research Hypotheses
According to Rogers (1983; 1995), the distribution of adopters of an innovation can be approximated by a
normal distribution of the time of adoption. Using the mean and standard deviation of this distribution as
a method of segmentation results in five adopter categories: innovators, early adopters, early majority,
late majority, and laggards. In discussing the dominant characteristics of each category, Rogers
characterizes innovators as venturesome, early adopters as opinion leaders who are widely respected in
their social circle, early majority members as deliberate, the late majority as skepticalabout the value
of an innovation, and laggards as traditional.For the purposes of the subsequent discussion, we use the
phrase early adoptersto include Rogerscategories of innovators and early adopters, and the phrase
later adoptersto connote individuals belonging to any of the three remaining categories1. In general,
early adopters use innovations even when the uncertainty surrounding potential use is high, and the
benefits of the innovation have not become widely visible and accepted.
Rogers uses innovativeness, operationalized as time of adoption, to derive adopter categories. However,
Agarwal and Prasad (1998), in reviewing prior work that has examined Rogersnotion of innovativeness,
present evidence suggesting that Rogersdefinition of a theoretical construct in operational terms suffers
from methodological limitations. Notable shortcomings include its measurement as an ex post descriptor
of behavior, thereby precluding its use as a predictor, and a lack of metrics to assess the reliability and
validity of the construct. To address these limitations, Agarwal and Prasad developed and validated a
construct labeled personal innovativeness in the domain of IT(PIIT) which they conceptually defined
4
as the willingness of an individual to try out any new information technology. In so far as early adopters
of an innovation are ones that have exhibited their willingness to use an innovation through overt
behavior, we propose:
H1: Early adopters of an IT innovation exhibit greater personal innovativeness in the domain of IT
than do later adopters.
Recent theories in technology acceptance, specifically the technology acceptance model (TAM), have
received considerable theoretical and empirical support (Davis et al., 1989; Taylor and Todd, 1995;
Mathieson, 1991). TAM, which is based upon the theory of reasoned action from the social psychology
literature (Ajzen and Fishbein, 1980), postulates that technology adoption behavior is an outcome of an
individuals affective response to, or attitude toward, the innovation. Thus, we expect early adopters of
an innovation to exhibit more positive affective responses, resulting in:
H2: Early adopters of an IT innovation have more positive attitudes toward the use of the
innovation than do later adopters.
TAM also posits that attitude toward an IT innovation is determined by two salient beliefs: perceptions of
usefulness of the IT and perceptions of ease of use. In innovation diffusion theory, these beliefs are
labeled the perceived attributes of an innovation. The former belief, perceived usefulness, in TAM is
similar in spirit to Rogersconceptualization of the relative advantage of an innovation: the extent to
which the innovation offers better ways of performing a task than extant means for performance. The
latter perception is an individuals subjective assessment that use of the innovation will be relatively free
of cognitive burden, and represents the direct opposite of Rogersdescription of the complexity of an
innovation. Although there is wide spread empirical support for usefulness and ease of use as being
salient in technology adoption decisions, there is some discrepancy in the literature with regard to the
relative importance of these two perceptions as predictors of different technology acceptance outcomes.
For example, Davis (1993) found usefulness to be far more important than ease of use in predicting usage,
1 This method of categorization is similar to that utilized by Brancheau and Wetherbe (1990).
5
whereas Adams et al. (1992) obtained the opposite result. Agarwal and Prasad (1997) found ease of use
to be non-significant for current use as well as future use intentions of an IT innovation, while relative
advantage was significant only for future use intentions. Karahanna et al. (1998) posited that behavioral
beliefs underlying the attitude developed by users of an IT innovation would be richer and more complex
than those underlying attitudes of potential adopters. However, their empirical results indicated exactly
the opposite: individuals who had not yet adopted the new technology appeared to base their attitudes on
more complex belief sets than those who had adopted the technology. In other words, potential adopters
had several more distinct beliefs about the new technology than did individuals who had already adopted
the technology.
Based on these studies we expect that the belief structure of early adopters, who have already completed a
subjective evaluation of the new technology, is relatively monolithic. In contrast, later adopters, who
have not yet progressed to the adoption stage, are more likely to possess distinct beliefs about the
innovation. These expectations are summarized below:
H3: Usefulness and ease of use constitute distinct beliefs for later adopters of an IT innovation
but are indistinguishable for early adopters.
While the technology acceptance model has been widely accepted as a parsimonious and robust
conceptualization of IT adoption behaviors, recent work has examined more complex causal models that
include additional constructs as predictors of IT adoption (e.g., Taylor and Todd, 1995). Among these is
the theory of planned behavior (TPB), which represents an extension to the theory of reasoned action.
According to this theory, behavior is driven by a normative component (subjective norm), an affective
component (attitude toward the behavior), and a control component (perceived behavioral control). The
importance of subjective norms, or the extent to which an adopter perceives pressure from the social
environment in which she is situated, finds support in an alternate theory, viz., social influence models
(Fulk et al., 1995). Pressure emanating from the social environment to engage in a particular behavior is
6
likely to influence technology adoption because individuals do not always base adoption decisions on
strictly rational evaluations.
Who among early or late adopters is likely to perceive greater social pressure? At first glance it might
appear that innovators, by virtue of their risk-taking propensity, are relatively immune to social influence.
Consider, however, the observation made by Midgley and Dowling (1978): an individuals susceptibility
to interpersonal messages may be governed by psychological factors such as empathy, while the receipt of
these messages will be a function of their integration with a social system.(p. 236). Rogers (1995), in
describing differences in communication behavior among early and late adopters notes that early adopters
exhibit greater social participation, and are more highly interconnected through personal networks in the
relevant social system than later adopters. He also notes that earlier adopters exhibit greater empathy than
do later adopters. Arguably, to the extent that greater social participation creates more opportunities for
and potential sources of influence, it is reasonable to expect early adopters to perceive greater social
pressure than late adopters. Thus, we test:
H4: Subjective norms for using an IT innovation are more salient for early adopters of the innovation
than for later adopters.
A second component of TPB, perceived behavioral control, has been shown to be significant for IT
adoption outcomes. Perceived behavioral control captures an individuals beliefs about the presence or
absence of requisite resources and opportunities,(Ajzen and Madden, 1986). Higher perceived
behavioral control is posited to be related to intentions to perform a particular behavior, as well as the
actual behavior. Recently Taylor and Todd (1995) decomposed the theory of planned behavior and
suggested that perceived behavioral control is jointly influenced by facilitating conditions and self-
efficacy. While the former captures resource availability, the latter is an individuals assessment of their
personal capability to perform the desired behavior. Self-efficacy derives its conceptual foundations from
a rich literature related to social learning theory (Bandura, 1997), whereas facilitating conditions are
based on work by Triandis (1979). Rogers (1995) generalizes that early adopters are less fatalistic than
7
late adopters, where fatalism is described as the the degree to which an individual perceives a lack of
ability to control his or her future.(1995: p. 273.) Following from these arguments, we hypothesize that
in the context of the adoption of a new IT, perceived behavioral control, perceptions regarding facilitators
in the environment, and self-efficacy with regard to IT will be higher for early adopters than for later
adopters. The relationship between early adoption and self-efficacy in the domain of IT has also been
posited and empirically tested by Burkhardt and Brass (1990).
H5: Perceived behavioral control over the use of an IT innovation is higher for early adopters of the
innovation than for later adopters.
H6: Perceptions of facilitating conditions for the use of an IT innovation are higher for early adopters
of the innovation than for later adopters.
H7: Early adopters of an IT innovation exhibit greater self-efficacy with regard to IT than do later
adopters.
As noted earlier, an ability to characterize early adopters has theoretical and practical relevance. Rogers
generalizations focus primarily on demographic differences and less so on beliefs and attitudes. The
explicit examination of differences along these dimensions can allow for the development of more
focused interventions when managers desire to implement new information technologies.
Methodology and Results
The target innovation examined in this study is a Web registration system at a large university. Recent
implementation of this technology afforded an excellent opportunity to collect data on early versus late
adopters. A brief history of the development of the system, followed by the sampling method used for the
study, the operationalization of research variables, and results are provided below.
The IT Innovation and Study Context
In 1985, the only available method of registering for college classes at this university consisted of an
iterative process whereby all students stood in numerous queues while attempting to gain entry to their
requested classes. One could wait hours just to learn that a particular class had been filled by a student in
8
the same line earlier. This then entailed selecting another course and repeating the wait in line. In 1986,
this non-automated system was replaced by telephone course registration, where students could add and
drop classes through an interactive voice response application2
From 1986 until 1998, the university registered all of its matriculated students by either telephone or by
assisted terminal entry. Terminal entry was provided as a service primarily to conform to the Americans
with Disabilities Act. In the spring semester of 1998, the university implemented Internet Course
Registration for the summer semester. The application also featured a searchable on-line Directory of
Classes, in real time. The searchable on-line Directory of Classes is in fact "smarter" and more up-to-date
than the traditional printed Directory of Classes which is only accurate up until approximately the date of
publication. As classes are filled, or added or canceled, the changes are transparently reflected upon the
client browser session because the Web Registration application is completely functional within real time.
Designed to supplement, rather than replace, telephone course registration, the Web Registration
application was released as a usable system in summer 1998. The summer semester was selected
because, historically, registration traffic for the summer semester is lower than for fall and spring
semesters. Thus, any problems that arose could theoretically be handled quickly. Whether in their
hometowns or at the university, students merely have to place a local telephone call to their respective
Internet Service Providers3 and employ an Internet browser in order to access and use the Web
Registration application. With the new system, three modes of registration are available to students:
terminal, telephone, and Internet.
Generally the same messages and information available through the telephone registration system are
used for Web Registration. The front-end applications are identical in function, although verbiage for the
2 The university was the first in the United States to offer course registration over the telephone.
3 The university also provided all students with ppp accounts.
9
various media output has been adjusted to enable universal understanding of the systems by all students,
even if they first select one mode of registration and then another at a later date. Students who request
courses that are closed, cancelled, or full, are told this information, and for classes that are full, sections of
the same course available at other times are suggested.
At 8:00 am on March 14, 1998, Web registration opened for the summer semester on the same day and
time that telephone registration began. Parallel implementation was deemed necessary because of the
mission critical nature of the application, and it was decided that the two systems would continue to
function in tandem indefinitely. The Web registration system offered a unique opportunity to segment the
user population into Rogersadopter categories. Its use as the target application for testing the research
hypotheses is appropriate for several reasons: it is a volitional system; there are alternate means available
for accomplishing the same task; and the application itself is completely new to the student population.
Of the 15,513 students who registered using all means for the summer semester of 1998, approximately
11.5% (1,772) students registered via the Web application.
Sample and Data Collection
The overall research strategy employed was a field survey of students who registered for summer classes.
Of the total population of approximately 15,500 students, a stratified random sample was constructed.
We used stratification to ensure representation from all major academic areas (as reflected in colleges and
schools) within the university. The summer Directory of Classes was utilized to construct the sample. A
total of 326 students in 16 different classes offered in 5 academic areas were surveyed; frequency of
respondents by college and by academic level is shown in Table 1. As the data indicate, the sample
consisted of a broad range of respondents in terms of the degree program they were enrolled in. Of the
326 respondents, 54 (approximately 16% of the total sample) indicated they used the Web registration
system, while the remainder used an alternate method for registration. The proportion of users obtained
10
in this sample is close to the proportion of early adopters in the overall population, further underscoring
the representativeness of the sample.
****Table 1 here****
Operationalization of Research Variables
All research hypotheses were constructed to test expected differences between early adoptersand late
adopters.For the purposes of this study, early adopters were those who indicated on the survey that they
had utilized the Web registration system to register for summer classes. All those who selected other
methods of registration (i.e., the telephone or in person at the registrars office) were categorized as late
adopters. Such a method for distinguishing early adopters from the rest is reasonable in that data were
collected early in the summer semester, only a few weeks after the innovation had been implemented.
Moreover, the population proportion of early adopters as compared with sample proportion lends
credence to the fact that data were collected at an appropriate stage of the diffusion of this innovation.
The remainder of the research constructs were measured using multi-item scales developed and validated
in prior studies. Personal innovativeness in the domain of IT was operationalized using the four-item
scale developed by Agarwal and Prasad (1998). Ajzen and Fishbeins (1980) definition of attitude
yielded the three-item measure used in this research. Usefulness and ease of use beliefs were measured
using four-item scales developed by Davis (1989; 1993); while subjective norms, perceived behavioral
control, facilitating conditions, and self-efficacy scales were based on those utilized by Taylor and Todd
(1995). All scales and items are listed in the Appendix. Respondents circled their level of agreement or
disagreement on a 7-point Likert scaled anchored with the values Strongly Disagreeand “Strongly
Agree.” Table 2 summarizes the internal consistency of each scale as measured by Cronbachs alpha.
With the exception of subjective norm, all scales exhibited adequate reliability with Cronbachs alpha
being close to or above the recommended 0.7 level (Nunnally, 1978). The less than ideal reliability for
subjective norm, however, should not diminish the relationships found to be significant.
****Table 2 here****
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Results
T-tests were utilized to test Hypotheses 1 and 2, and 4 through 7; results are shown in Table 3. The t-
statistic utilized is a 2-tailed one; when the homoscedasticity assumption was violated for the two
samples, the appropriate adjusted t-statistic is reported. Hypothesis 3, which posited that usefulness and
ease of use beliefs for early adopters would be indistinguishable from each other was tested via a
principal components factor analysis with varimax rotation on the 8 items comprising the usefulness and
ease of use scales. The analysis was conducted for both early adopters and later adopters. Tables 4a and
4b present results of the factor analysis for both groups. All seven research hypotheses were supported by
the empirical data: early adopters exhibited significantly greater personal innovativeness in the domain of
IT and significantly more positive attitudes toward use of the IT innovation (Hypotheses 1 and 2).
Consistent with the predictions of Hypothesis 3, factor analysis of usefulness and ease of use belief items
for early adopters yielded a single factor that explained 71.5% of the variance, while, for later adopters,
two factors were extracted that together explained 79% of the variance. As expected, items for usefulness
and ease of use loaded on the conceptual constructs they were intended to measure. Finally, Hypotheses
4 through 7, which posited differences among early and late adopters on constructs derived from the
theory of planned behavior, were all supported:
***Tables 3, 4a, 4b here ***
Prior to discussing the implications of the findings, the limitations of this research must be acknowledged.
The first relates to our characterization of later adoptersas those who had not used the target innovation
during the first possible useperiod. Arguably some individuals within this category may eventually be
non-adopters in that they continue to use the old method of registration. Nevertheless, for the purposes of
distinguishing innovators from others,this method of classification is satisfactory. The second
limitation of this study is the low reliability for subjective norm. However, as noted earlier, the reliability
should not affect the interpretation of the relationship found to be significant. Moreover, the items for
subjective norm used here are identical to those used by Taylor and Todd (1995); the reliability of this
scale for their sample was above 0.8.
12
Discussion and Conclusions
In this study we sought to extend Rogerstypology of innovators and later adopters and generalizations
derived from this typology specifically to the domain of IT. We juxtaposed Rogersgeneralizations with
belief and attitudinal constructs recently found to be salient in technology adoption, and derived
predictions regarding expected differences for these beliefs and attitudes among those who are early
adopters of an IT innovation and those who are relatively late in adoption. Results provided strong
support for the theoretical predictions. Several implications for theory and practice follow.
The ability to understand how innovators in IT differ from others has implications for the development of
theoretical models explaining IT usage behaviors. The technology acceptance model suggests that
behavioral intentions to use an IT are determined by attitude and usefulness beliefs, while attitude is an
outcome of usefulness and ease of use beliefs. Our results suggest that these beliefs are indistinguishable
for the early adopters of an IT innovation. Although the length of time subsequent to adoption for which
these beliefs remain indistinguishable is a question for future work, the finding does raise an issue about
the appropriateness of using TAM to predict future use intentions for individuals who have already
adopted an innovation. In other words, TAM might be more suitable for predicting the behavior of non-
adopters rather than current users. The finding that belief structures become diffuse and undifferentiated
upon adoption is certainly an issue that merits further work.
One plausible explanation of this finding may reside in the proclivity of early adopters to take risks. As
indicated earlier in this paper, PIIT captures an individuals willingness to try out new information
technology. Individuals with high PIIT are likely to be impulsive by nature and may not think through
the reasons and implications for their actions. In other words, they may dive inand try the technology
due to their curious and risk-taking nature, and not necessarily base their decision on the concrete
13
advantages for doing so. Individuals low in PIIT may carefully consider the reasons and consequences
for adopting technology, thereby forming concrete beliefs regarding its usefulness and ease of use.
We proposed and confirmed a paradoxical relationship that rather than being immune to social influence,
early adopters perceive stronger pro-innovation messages from their social circle than do later adopters.
However, it is important to distinguish between social messages that encourage an individual to act like
an innovative person in general and those that encourage the use of a specific IT innovation. For
example, early adopters may not be susceptible to generalized social influence, i.e., they will not become
more or less innovative due to social influence but they may be more inclined to try a specific innovation
if their curiosity is aroused by a social interaction. A fruitful area of future research then would be to
examine how early and late adopters differ with regard to social norms emanating from generalized pro-
innovation messages versus specialized messages that suggest the use of a specific innovation.
Further, the exact nature of social influence may represent an avenue of future research. The marketing
literature (e.g., Herr, et al., 1991) as well as some research in the field of information systems (Galletta,
et. al., 1995) has suggested that negative messages are more salient than positive messages in determining
innovation adoption attitudes and behaviors. Researchers can test the impact of these two types of social
influence on various stages of Rogers S curve.
In conclusion, a contribution of this paper is to extend Rogerstheory to include constructs derived from
recent conceptualizations of innovation adoption in the domain of IT. A review of the empirical research
in IT adoption indicates that the majority of studies have been conducted with target innovations that have
been in existence for some time. Thus, to the best of our knowledge, little is known about the belief and
attitudinal structures of new users.Such knowledge could be of great value in developing managerial
prescriptions for improving the new technology adoption process.
14
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16
Table 1.
Sample Demographics
College Valid N Missing
Undeclared
Arts and Sciences*
Business
Education
Engineering
Humanities and Social Science**
5
34
108
38
77
58
6
Level
Freshman
Sophomore
Junior
Senior
Graduate
2
9
64
171
75
5
Notes: *Includes Communications, Consumer Sciences,
Criminology
** Includes Music, Theater, Social Work
Table 2.
Scale Reliabilities
Construct Number of
Items Reliability*
Personal innovativeness
Attitude
Usefulness
Ease of use
Subjective norms
Behavioral control
Facilitating conditions
Self-efficacy
4
3
4
4
2
3
2
3
.80
.87
.92
.93
.52
.81
.66
.93
Notes: *Cronbachs alpha is reported
17
Table 3.
Early Adopters Versus Others: T-tests
Early Adopters Others
Construct Mean S.D. Mean S.D. t-value p-value
Personal innovativeness
Attitude
Subjective norms
Behavioral control
Facilitating conditions
Self-efficacy
5.67
5.26
4.42
6.19
2.78
6.20
1.04
0.76
1.15
1.12
1.32
1.17
4.88
4.67
4.08
5.34
3.27
5.19
1.17
0.76
1.00
1.39
1.45
1.41
-4.58
-5.21
-2.02
-4.84
2.30
-5.60
.000
.000
.048
.000
.022
.000
Table 4a.
Factor Analysis of Usefulness and Ease of Use Beliefs
Early Adopters
Item Loading
EOU1
EOU2
EOU3
EOU4
USEFUL1
USEFUL2
USEFUL3
USEFUL4
0.869
0.862
0.714
0.902
0.823
0.736
0.915
0.918
Eigen Value 5.72
Percent of Variance 71.5
Table 4b.
Factor Analysis of Usefulness and Ease of Use Beliefs
Later Adopters
Item Factor1 Factor2
EOU1
EOU2
EOU3
EOU4
USEFUL1
USEFUL2
USEFUL3
USEFUL4
0.809
0.756
0.860
0.876
0.184
0.374
0.346
0.429
0.376
0.439
0.216
0.306
0.854
0.796
0.852
0.747
Eigen Value 5.32 1.00
Cumulative Percent of Variance 66.59 79.15
18
Appendix
Scales and Items
Personal Innovativeness in the Domain of IT
1. I like to experiment with new information technologies.
2. If I heard about a new information technology, I would look for ways to experiment with it.
3. Among my peers, I am usually the first to try out new information technologies.
4. In general, I am hesitant to try out new information technologies*.
Attitude
1. Using the Web registration system is a good idea.
2. Using the Web registration system is a wise idea.
3. I like the idea of using the Web registration system.
Usefulness
1. Using Web registration enables me to accomplish tasks more quickly.
2. Using Web registration increases my productivity.
3. Using the Web makes it easier for me to register.
4. I find the Web to be a useful way of registering.
Ease of Use
1. Learning to use the Web registration system was easy for me.
2. I find it easy to do what I want to do with the Web registration system.
3. How to use the Web registration is clear and understandable.
4. I have found the Web registration system to be easy to use.
Subjective Norm
1. People who influence my behavior think that I should use the Web registration system.
2. People who are important to me think that I should use the Web registration system.
Behavioral Control
1. I would be able to use the Web registration system.
2. Using the Web registration system is entirely within my control.
3. I have the resources and the knowledge and the ability to make use of the Web registration system.
Facilitating Conditions
1. There will not be enough computers around for everyone to use the Web registration system.
2. I wont be able to find a computer to use the Web registration system when I want to.
Self-Efficacy
1. I would feel comfortable using the Web registration system on my own.
2. I could use the Web registration system even if there was no one around to help.
3. If I wanted to, I could easily use the Web registration system on my own.
Notes: *reverse scaled item
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