USING THE TECHNOLOGY ACCEPTANCE MODEL IN PREDICITNG
ACCEPTANCE OF IMPOSED TECHNOLOGY: A FIELD STUDY
Maureen Francis Mascha*
Department of Accounting
Marquette University
Milwaukee, WI – 53201
Maureen.mascha@mu.edu
(414) 288-0668
Monica Adya
Department of Management
Marquette University
Milwaukee, WI – 53201
Monica.adya@mu.edu
(414) 288-7526
*Corresponding author
Abstract
This paper extends an area of information systems into an AIS context. We describe a well
accepted model in information systems, the Technology Acceptance Model (TAM), and examine
whether or not the TAM is appropriate in determining acceptance of new technology where use
is not optional (e.g. mandatory). The TAM posits that intention to use new technology is shaped
by its perceived usefulness and the perceived ease of use of the technology. We describe the use
of this instrument in predicating acceptance of an imposed technology, a web-enabled student
registration system, as well as the effects of its use on acceptance employing a web-based
survey.
Results collected from 1,521 respondents indicate that, as expected, perceived usefulness
predicts acceptance, but contrary to the model, perceived ease of use does not predict acceptance.
New to the literature are the findings that trial time (time spent using the new technology) and
class year (i.e. freshman, sophomore, etc.) significantly affect subjects’ attitudes towards
acceptance. Specifically, as trial time increases, perceived ease of use declines and as class year
increases, (e.g. freshman to senior) perceptions of usefulness and ease of use also decline. These
findings suggest that attitude towards new technology is a function of use as well as familiarity
with the prior system. Together, these imply that acceptance of new technology is more complex
than originally proposed by the TAM and highlight the need for additional research on the
longer-term affects of new technology on users’ attitudes and acceptance.
2
I.
INTRODUCTION
As advances in information systems (IS) have changed the way people conduct
professional and personal lives, the study of user acceptance and willingness to use these systems
has gained interest. Studies in IS have consistently indicated that positive user attitude towards
an information system is critical to its success. User acceptance seems to be influenced by its
perceived ease of use and usefulness (Davis 1989; Szjana, 1994; Venkaetsh and Davis 2000;
Venkatesh, et al 2002); perceived voluntariness of usage (Agarwal and Prasad, 1997); and beliefs
about the technology (Moore and Benbasat, 1991) among others. User acceptance of
technologies can have a strong impact on an organization’s success at achieving the standards of
performance and return on investments through new technological investments (Al Gahtani and
King, 1999; Lucas and Spitler, 1999).
Many factors such as characteristics and usefulness of the technology and attitudes of
other users have been found to shape such attitudes. One tool that attempts to model the role of
user attitude towards new technology is the Technology Acceptance Model (TAM). Originally
proposed by Davis (1989) this simple yet powerful model suggests that the perceived usefulness
(PU) and perceived ease of use (PEOU) of a new technology are fundamental determinants of its
acceptance. PU is explained as a user’s assessment of his/her “subjective probability that using a
specific application system will increase his or her job performance within an organizational
context” (Davis 1989). PEOU is described as the “degree to which the user expects the target
system to be free of effort” (Davis 1989). Figure 1 illustrates the TAM and its constructs.
---------------------------- FIGURE 1 about here --------------------------Influenced by Ajzen and Fishbein’s (1980) Theory of Reasoned Action (TRA), TAM
constructs have demonstrated theoretical and psychometric support based on an extremely large
body of literature in IS. Significant research effort has been devoted to establishing the validity
and reliability of the constructs originally proposed by Davis (1989). Adams, et al (1992) was
one of the earliest studies to replicate the original work and demonstrate its consistency in two
different settings using multiple samples. Other replication and extension efforts include those by
Szjana (1994), Chau (1996), Chau and Hu (2002), and Dasgupta, et al (2002). The model has
been tested in multiple domains such as Internet marketing and online consumer behavior
(Koufaris, 2002), intranet use (Horton, et al 2001), medicine (Hu, et al 1999), distance education
3
(Lee, et al 2003), outsourcing decisions (Benamati and Rajkumar, 2002) among many. Most of
these studies have effectively demonstrated the ability of this model to explain much of user
acceptance of technologies, making TAM one of the most influential models in information
systems.
While the TAM is certainly robust, questions remain as to whether or not it is an able
predictor of user acceptance when use of new technology is mandatory, especially if the
predecessor technology is withdrawn and users are left with no alternative. At issue here is not
whether new technology will be used, but rather will new technology be accepted, recognizing
that the former may occur absent the latter. This is an important concern, since the acceptance of
new technology is often a necessary predecessor if the full benefits of the new technology
including return on investment are to be realized fully. Since new technology is often mandated
in accounting settings (e.g. enterprise systems, general ledger systems, etc.), this is an important
issue for system designers as well as management charged with implementing these systems.
A separate but equally important question concerns the ability of TAM in predicting PU
and PEOU over a period of time. If familiarity breeds acceptance, then more use of new
technology should increase the effects of PU and PEOU on acceptance. However, if increased
use leads to discovery of problems or glitches, increased use could lead to a diminished effect of
PU and PEOU on acceptance. Since the one prior study that investigated the effects of prior
exposure on PU and PEOU toward acceptance measured prior exposure as a dichotomous, not
continuous variable (Taylor and Todd 1995), we specifically address the issue here by measuring
prior exposure in terms of number of times subjects used the new system. This is particularly
salient since acceptance is a function of PU and PEOU, which can change over time, changing
attitudes from “acceptable” to “unacceptable”.
We examine these questions in a field study consisting of two on-sight surveys. These
surveys investigate the effect of PU and PEOU on acceptance of a web-based registration system
whose use was mandated (i.e. the prior system was withdrawn). We measure acceptance as the
degree to which subjects perceive the new technology to be more useful than and/or easier to use
than its predecessor. If the TAM is able to predict acceptance when technology is imposed,
particularly where no alternative exists, and/or can provide guidance on the effect of use over
time on acceptance, then the model’s relevance to applied research increases significantly.
4
The remainder of this paper is organized as follows. Section II discusses the literature
and develops research questions; Section III describes the research methodology; Section IV
presents the results; and finally, Section V summarizes the findings, suggest future areas for
research, and addresses the limitations.
II.
THE TECHNOLOGY ACCEPTANCE MODEL
In its simplest form, the TAM (Davis 1989) proposes that user acceptance of new
technology is a function of two factors: perceived usefulness (PU) and perceived ease of use
(PEOU). The TAM proposes that acceptance of new technology, defined very broadly, is a
function of how useful the subject finds the technology to be as well as how easy it is to use.
Referring to the model’s depiction in Figure One, the reader should notice two points: first PU
directly affects behavioral intent, defined as expected or anticipated usage and second, PEOU is
a function of PU. That is, PEOU in and of itself does not affect usage intent. If a new
technology is not first perceived to be useful, PEOU does not matter. Simply stated, if new
technology is not perceived to be useful, then the fact that it is easy to use does not sway
acceptance.
Prior studies have examined the TAM in a multitude of settings using many different
subjects and technologies suggest that the TAM provides an easy and relatively quick method for
determining acceptance of new technology in many different settings. Indeed, one study reports
the use of the TAM in predicting user acceptance of new technology based on exposure only to
the system prototype. (See Lee, et al (2003) and Ma and Liu (2004) for meta-analyses of studies
using the TAM.) The flexibility from requiring minimal exposure to the technology in question
is so important that Davis (1989) claims it to be the largest benefit of using the TAM.
Three important issues regarding TAM need to be stressed. The first is that acceptance is
proxied by respondents’ anticipated use; the greater the anticipated use, the greater the
acceptance. This can be problematic given the wide gap between expected and actual behavior.
Only one study to date has explored the difference between expected and actual usage of new
technology. That research found that respondents overstated significantly their expected usage
when compared with their actual use (Szajna 1996). His study is all the more telling as actual
use was not self-reported by the participants, but rather measured directly by the researcher,
suggesting that expected use may not be an effective proxy for actual use in all situations.
5
The second issue concerns the effect of usage over time, (referred to as prior exposure),
on user acceptance. To date, only two papers have examined the effect of prior exposure and
acceptance (Venkatesh and Dodd 2000; Taylor and Todd 1995). Interestingly, Taylor and Todd
find that prior exposure leads to increased reliance of PU in predicting acceptance. Their study
measured prior exposure as either present (at least one prior encounter) or absent (no prior
exposure). The authors fail to report the range of prior exposures, so it is difficult to infer if their
pattern of results holds when exposure increases.
Venkatesh and Davis 2000 examined the role of prior exposure on user acceptance over a
three month period. This study notes that the effect of PU and PEOU on users’ attitudes did
change over a three month period. Subjects’ attitudes were measured at three points: preimplementation, one month and three month intervals subsequent to implementation. While
Venkatesh and Davis investigate the effect of prior exposure on acceptance, their results were
based on three data points using a model adapted from, although different from, TAM. As a
result, the question regarding the ability of TAM in predicting acceptance over a period of time
remains unanswered.
Finally, the third issue addresses the question of whether mandatory usage affects
acceptance of new technology. Here again, Venkatesh and Davis (2000) report no difference in
acceptance between voluntary and mandatory settings. It is difficult to use their study in drawing
conclusions, however, since they employed a different model and base their findings on
responses from 43 subjects.
In summary, while prior research provides support for the TAM in a variety of settings,
issues related to prior exposure and conditions surrounding use (i.e. mandatory versus voluntary)
remain unanswered.
Research Questions
Based on the preceding discussion, it seems reasonable to inquire whether or not the
TAM accurately predicts the PU and PEOU of a new technology over its predecessor technology
when use is mandatory. A related inquiry concerns the effect that usage, or prior exposure, has
on attitude. Finally, a tertiary issue concerns the effect, if any, that gender plays in predicting
acceptance. While most studies are silent as to the effect of gender, one study (Gefen and Straub
1997) did specifically investigate its role. This study reports that gender significantly affects
6
perception of but not use of technology, suggesting that gender should at least be included in any
models measuring acceptance of technology.
Since there is no body of literature to guide directional hypotheses, the following research
questions are proposed.
RQ1: Does the TAM predict users’ perceptions of whether or not the new
technology is more useful and/or easier than the former system?
RQ2: Will attitude toward the new system change with use?
RQ3: Does gender affect the PU and PEOU of a new technology over
its predecessor?
III.
RESEARCH METHODOLOGY
The CheckMarq™ registration system was launched in March of 2004, in time for Fall
2004 registration at a large, private midwestern university. The new system, named
CheckMarq™, replaced a predominantly manual, telephone-response system. CheckMarq™
consists of various software tools, including on-line registration availability, and is accessed via
a web portal. The on-line registration feature was the object of our study since all returning
students, graduate as well as undergraduate, were required to use this system for Fall 2004
registration. The registration portal provided students access to course catalogs, course
offerings, registration information, and schedule maintenance. Measuring student attitudes
towards using CheckMarq™ would yield no new information given its mandatory use, but TAM
constructs could easily be adapted for measuring student perceptions of the new system as
compared to the previous one. Such as assessment is critical for assessing the third criteria for
successful project management – delivering a quality product that meets the needs of the user.
Survey Design and Administration
We used a multi-method approach to examine acceptance of the web registration system.
The TAM instrument as provided in Davis, et al (1989) was modified for CheckMarq™ and
served as the main source of empirical data. This instrument was administered via the web to all
11,000+ students at the university. Additionally, a second open-ended questionnaire soliciting
strengths and weaknesses of CheckMarq™ was administered to a select group of undergraduate
business students enrolled in a core course taught by one of the authors.
7
The instrument containing TAM constructs consisted of a 31 item instrument; anchored
by “strongly disagree” and “strongly agree” measured using a five-point scale. This survey was
made available to all students between mid-April and early May. This period immediately
followed Fall 2004 registration. The intent was to capture student perceptions as close as
possible to actual use. The original instrument provided in Davis (1989) was modified for our
purposes. We retained all the original items as in the original study but added six more questions
related to population demographics and assessment of student perceptions of the old system
versus the CheckMarq system. Adding additional items required by the domain has frequently
been done in the past with no significant effect on PU or PEOU. (See Ma and Liu 2004 for a
detailed review.) Appendix 1 provides the instrument that was used and how our questions map
to the TAM instrument.
There were several reasons for using this web-based administration:
•
The web was the most cost effective method of reaching the student population.

The survey could be actively promoted via Student Commons, one of the most visited
sites on the student portal, and via e-mail communications.

Links could be provided in all electronic promotional material to provide easy access to
the instrument.

The software used virtually guaranteed that only surveys with all questions completed
would be accepted. If a student left a question blank, the software would immediately
inform the student of this error.

The survey could be accessed from anywhere the student had web access, making
completion accessible and easy.
Promotion of the instrument was critical in obtaining a reasonable sample size. To administer
the instrument we solicited the assistance of the Office of Public Affairs. This office had been
involved in campus-wide communications regarding CheckMarq™ from the early years of the
project and its role in surveying student acceptance fell naturally into place. An announcement
for CheckMarq™ survey was posted at the Student Commons site as well as sent out via e-mail
in mid-April. A reminder was sent out via e-mail two weeks later. The survey was withdrawn
from the website after first week of May, for a total on-line period of six weeks. Acceptance was
measured here with two variables: perception of whether or not CheckMarq™ was more useful
than and easier to use than its predecessor.
8
Students electing to respond to the web survey were asked to enter their unique student ID.
Subjects were promised confidentiality; no effort was to be made connecting their ID to their
responses. The ID was collected for two reasons, first to detect any duplicate responses and
second to identify raffle winners. As an inducement to participate, students who complete the
survey were automatically entered in a raffle. The prize was one of two $100 gift certificates to
a local store. This had been communicated to them via email and when they logged onto the
survey site. Winners were randomly selected by Student Services and notified of the prize after
the survey was withdrawn.
The second survey was designed to elicit specific feedback regarding users’ perceived
strengths and weaknesses of CheckMarq™. The subject pool consisted of students enrolled in a
core business course in the College of Business. Students were asked to list the top three
strengths or weaknesses of using CheckMarq™. This request came in the time period
immediately following registration. Because of the confidentiality associated with the online
survey, no effort was made to identify any “dual respondents”; therefore, it is entirely possible
that respondents to the first survey could also have responded to the second survey.
IV.
RESULTS
A total of 1,521 subjects responded to survey one. This represents an overall response
rate of 13.6%. Table 1 provides a breakdown of total enrolments versus respondents by gender
and status for the University. In general, respondents found CheckMarq™ to be both useful and
easy to use. A total of 39 subjects responded to survey two. The results from this survey are
incorporated into the discussion of results.
<TABLE 1 about here>
Preliminary Analyses
Correlation analyses performed on the variables indicate no significant correlation for
gender, work experience, or technological experience with either of the two dependent variables:
more useful than and easier to use than the previous system. However, trial time, measured as
the extent to which respondents had used CheckMarq™ and status (the year in school measured
on a 1-5 scale, with 1 being graduate student, 2 being senior, etc.) were both significantly
correlated with the dependent variables, “more useful” and “easier to use”.
Trial time could have a value from 1 (not used prior to registration) to “4” representing
prior use of forty-five minutes or more. Prior to use for registration, students were allowed, in
9
fact encouraged, to become acquainted with CheckMarq™ and a non-production version of the
system made available. The Information Technology Department, responsible for implementing
CheckMarq™, informed us that the trial version was exactly the same as the production version,
the only difference being that the production version created an actual registration record. Both
trial time and status were retained as potential covariates in the analyses that follow.
Factor analyses were performed to determine whether the instrument measuring PU and
PEOS had factorial validity (Davis 1989). Results generated using the maximum likelihood
method with oblique rotation suggest that the variables loaded on two factors in the direction
predicted by the TAM and reported by Davis (1989), with the exception of the variables
cumbersome and closure; (closure designed to measure how well CheckMarq™ informed users
of where they were in the registration process). Because of the lack of discriminant ability of
these variables, cumbersome and closure were excluded from further analyses. However, even
with these variables, goodness of fit was .9275, suggesting that two factors adequately described
the underlying data pattern. Table 2 displays the variables and the factors on which each loaded.
ANOVA and Regression Analysis
Based on the output from the factor analyses, ANOVAs and regression analyses were
performed to determine the exact effect of each factor (PU and PEOU) on the dependent
variables, more useful than and easier than. (The variables captured subjects’ attitudes
comparing CheckMarq™ to the prior system.) Since an important tenant of the TAM concerns
the roles that PU and PEOU play in affecting acceptance, separate regressions were performed
examining the effect of each factor on each of the dependent variables. The first set of analyses
focused on the “more useful than” dependent variable. The first model regressed perceived
usefulness (PU) alone on “more useful than”. (Model: More Useful than = PU + error.) (Table
3, panels A, B, and C, detail these statistics.) Results indicate an R squared of .69628 and
significance of .0001 for the PU factor.
Next, the second factor, PEOU was added. The purpose of adding PEOU was to
determine the effect of PEOU while controlling for PU; (PU.PEOU in statistical terms, Davis
1989). This addition marginally increased the R squared to .696313; however, only the PU
factor was significant (p < .0001); the PEOU factor was not significant (p = .6854). Finally,
since trial time and status both correlated significantly with the dependent variable they were
added to the model without the PEOU factor (i.e. model: more useful than = PU + status + trial
10
time + error) yielding an R squared of .699586. Interestingly, after adding the covariates, only
PU and status were significant P < .0001 and P = .0002, respectively; trial time was not
significant (p = .1742).
<Insert Table 3 here>
The second set of analyses focused on the “easier than” dependant variable. Table 4,
panels A, B, and C, display the statistics. When the PEOU factor was regressed on the dependent
variable, (easier than = PEOU + error) the model indicated an R squared of .170202 and p of <
.001 for PEOU; adding PU to the model while holding PEOU constant increased R squared to
.682932. Results indicate that both factors were significant (p = .0085 and p < .0001,
respectively). Finally, including the two covariates, trial time and status, increased R squared to
.688697, but PEOU became non significant (.1995). Even more interesting is the fact that the
coefficient for trial time was negative, suggesting that as use increased, PEOU decreased.
<Insert Table 4 here>
Research Questions
Research Question One inquires as to the effect of each factor (PU and PEOU) on
acceptance. Analyses indicate that PU is significant in explaining the dependent variable more
useful than, while PEOU is not significant in explaining either dependent variable. Indeed,
results seem to highlight the dominance of the PU factor in predicting acceptance. Research
Question Two inquires as to the effect of acceptance over time. This study finds that trial time,
or prior exposure, has no effect in affecting the “more useful than” dependant variable, but does
have a significant negative effect in predicting the “easier than” dependent variable. Finally,
Research Question Three inquires about the effect of gender. Results indicate that gender played
no role in affecting either dependent variable.
IV.
DISCUSSION
In summary, this study finds that of the two factors proposed by the TAM, only PU
predicts acceptance of new technology when use is mandatory. Our findings confirm that of the
two factors, PU and PEOU, PU plays the dominant role is predicting acceptance. However,
unlike previous studies examining TAM, PEOU is not significant in affecting acceptance after
controlling for the covariates of usage and class year. Specifically, we note that class year
affects attitude as to whether the new system is more useful and easier than its predecessor and
that trial time affects attitude as to whether the new technology is easier than the former system.
11
This latter finding deserves further attention. In essence, we find that as class year
increases (i.e. from freshman to senior), PU and PEOU decline, suggesting that familiarity with
the prior system affects acceptance. This statement is made based on the positive coefficient for
the variable “status”. Since freshman status was coded as “5,” and decreased as class year
increased, (i.e. sophomores were coded as “4,” juniors as “3,” etc.). It seems clear that as class
status increased, acceptance decreased since use of the prior system would have increased as
class year increased; (e.g. seniors would most likely have used the prior system more frequently
than juniors, juniors more than sophomores, etc.). Thus, familiarity with the prior system, or at
least use thereof, negatively contributed towards acceptance of the new system.
Analysis of responses to the second survey indicates possible explanations for the pattern
of findings from the first survey. Overall, subjects agreed that CheckMarq was easier, faster, and
more useful in terms of information offered. This pattern seems to support the perception that
CheckMarq was more useful than its predecessor. However, subjects also felt that error
messages were not descriptive enough, particularly those concerning closed classes, and that at
times, CheckMarq was tedious in the number of screen visits required to obtain certain
information. These latter comments could explain why PEOU was not significant in predicting
attitude as respondents perceive key information to be difficult to obtain.
Taken together, these findings imply several things. Foremost, we note that the pattern of
results is different for mandated versus voluntary use. Our findings, while providing partial
support for the TAM, suggest that acceptance of new technology is more complex than originally
proposed by TAM. Specifically, length of usage of the new system negatively affects subjects’
attitudes towards PU and PEOU. This is important since it implies even brief encounters with
new technology can have a significant effect on acceptance.
Secondly, we note that familiarity with the former system affects acceptance, in this case
negatively, implying that measuring acceptance of new technology by solely focusing on the new
and ignoring the former can lead to inconclusive results. While prior studies focused on attitude
towards new technology, this study expanded the definition of attitude by comparing acceptance
of the new technology to its predecessor. Since most new technology installations occur as the
result of switching from one technology to another, this is an important issue that suggests that
factors outside of those proposed by TAM need to be identified and included when measuring
acceptance.
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Limitations
As with any study, there are limitations. First, this study compares a new system to one
that was highly manual and very limited in use. Such a comparison may affect generalizability
of these findings to other studies where the technology being replaced is more automated.
Second, this study limits it focus to a technology where use is mandatory. Care needs to be taken
in comparing this study’s finding with other studies where use was not mandatory since we did
not include voluntary use in this study. Finally, we define acceptance as difference in attitude
between the former and the current system. This definition differs from how acceptance is
measured in other studies and may also affect generalizability.
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15
Table 1: Student Registration Survey Responses by Status and Gender
Survey Reponses by Status and
Total
Survey
Response Rate
Gender
Enrolment
Respondents
(%)
Freshmen – Women
1129
88
7.79
Freshmen - Men
911
192
21.08
Sophomore - Women
1160
60
5.17
Sophomore - Men
968
100
8.62
Junior – Women
927
98
10.57
Junior – Men
700
224
32
Senior – Women
998
120
12.02
Senior – Men
832
269
32.33
Graduate/Professional – Women
1761
110
6.25
Graduate/Professional – Men
1819
260
14.29
TOTAL
11205
1521
13.57
16
Table 2: Factor Loadings by Variable*
(Standardized Regression Coefficients)
Variable
quality
control
accomplish
support
productive
effective
ease
overalluse
cumbersome
learn
frustrate
overallease
rigid
recall
effort
clear
skill
find
status
error
closure
alternative
overallease2
Factor1
Factor2
0.88739
0.84414
0.73724
0.66314
0.67827
0.87463
0.83781
0.83691
-0.39735
-0.05974
-0.34399
0.33990
-0.39591
-0.04491
0.05268
0.20497
0.08949
0.21628
-0.14392
0.08869
0.22330
0.45096
0.46052
0.01144
-0.02509
0.08794
0.08224
0.03920
0.03695
0.08760
0.09473
-0.39693
0.75911
-0.53988
0.53955
-0.42529
0.70007
-0.78607
0.68972
-0.79267
0.52136
0.04409
0.12050
0.22660
0.14666
0.50820
* The variables are presented in the order in which they appear on the survey.
17
Perceived
Usefulness
External
Variables
Attitude
toward use
Behavioral
intention
Actual Use
Perceived
Ease of Use
FIGURE 1: The Technology Acceptance Model – adapted from
Davis (1989)
18
APPENDIX 1
Modified TAM instrument
Use the prescribed scale of 1 - 5 with 1 being "strongly disagree" and 5 being
"strongly agree" to indicate your experiences.
Items adapted from Davis (1989)
1. Using Checkmarq improved the quality of the registration process.
2. Using Checkmarq gave me greater control over the registration process.
3. Checkmarq enabled me to accomplish the registration task more quickly.
4. Checkmarq supports critical aspects of my registration process.
5. Using Checkmarq allowed me to accomplish more work than would otherwise be
possible.
6. Using Checkmarq increased the effectiveness of the registration process.
7. Checkmarq made it easier for me to register for classes.
8. Overall, I find the Checkmarq system useful for class registration.
9. I find Checkmarq cumbersome to use.
10. Learning to use the Checkmarq system was easy for me.
11. Interacting with the Checkmarq system was often frustrating.
12. I found it easy to get Checkmarq to do what I want to do.
13. Checkmarq is rigid and inflexible to interact with.
14. It is easy for me to remember how to perform tasks using Checkmarq.
15. Interacting with Checkmarq required a lot of mental effort.
16. My interaction with Checkmarq was clear and understandable.
17. I find it takes a lot of effort to become skillful at using Checkmarq.
18. It was easy for me to find information on the Checkmarq site.
19. I felt I always knew what stage of the registration process I was in.
20.The Checkmarq system indicated to me when an error occurred.
21. The Checkmarq system indicated to me when the registration process was
complete.
22. The Checkmarq system made it easy for me to select between alternative courses.
23. Overall, I found Checkmarq easy to use.
24.Overall, I found CheckMarq easier to use than the prior registration system.*
25.Overall, I found CheckMarq more useful than the prior registration system. *
* used as the dependent variable
19
Demographic and Comparative items
26.Prior to this use of Checkmarq, for how long did you explore the trial version?
Not at all
Less than 30 minutes
30-45 minutes
More than 45 minutes
27. What motivated you to try the system before the actual registration process?
Prizes offered for trial
Friends
Instructors
Desire to learn about system
Other
28. What is your academic status?
Graduate student
Senior
Junior
Sophomore
Freshman
29. How many years of work experience do you have?
None at all
Less than 1 year
1-2 years
2-3 years
More than 3 years
30. How comfortable do you feel you are with computer technology?
Very uncomfortable
Uncomfortable
Neutral
31. What is your gender?
Female
Male
20
Comfortable
Very Comfortable
TABLE 3
Panel A
ANOVA of: More Useful = PU + error
Source
DF
Sum of
Squares
Mean Square
F Value
Pr > F
Model
1
1638.227881
1638.227881
3482.31
<.0001
Error
1519
714.601836
0.470442
Corrected Total
1520
2352.829717
R-Square
Coeff Var
Root MSE
usefultvr Mean
0.696280
18.95413
0.685888
3.618672
Source
PU
DF
Type III SS
Mean Square
F Value
Pr > F
1
1638.227881
1638.227881
3482.31
<.0001
Parameter
Estimate
Standard
Error
t Value
Pr > |t|
Intercept
factor1
-.6844713627
0.1341040229
0.07501167
0.00227252
-9.12
59.01
<.0001
<.0001
Panel B
ANOVA of: More Useful = PU + PEOU + error
R-Square
0.696313
Source
Coeff Var
18.95935
DF
PU
PEOU
Intercept
PU
PEOU
Estimate
-.7656822272
0.1336285792
0.0027282169
usefultvr Mean
0.686077
Type III SS
1
1
Parameter
Root MSE
3.618672
Mean Square
1284.316550
0.077256
Standard
Error
0.21403876
0.00255821
0.00673418
21
F Value
1284.316550
0.077256
t Value
-3.58
52.24
0.41
2728.52
0.16
Pr > |t|
0.0004
<.0001
0.6854
Pr > F
<.0001
0.6854
Panel C
ANOVA of: More Useful = PU + status + trial time + error
Source
PU
Status
trial time
Parameter
Intercept
PU
Status
Trial time
DF
Type III SS
Mean Square
F Value
Pr > F
1
1
1
1040.351025
6.685186
0.861113
1040.351025
6.685186
0.861113
2232.82
14.35
1.85
<.0001
0.0002
0.1742
Estimate
-.7053212785
0.1280587485
0.0768627957
-.0302765371
Standard
Error
t Value
Pr > |t|
0.09515210
0.00271008
0.02029188
0.02227095
-7.41
47.25
3.79
-1.36
<.0001
<.0001
0.0002
0.1742
22
TABLE 4
Panel A
ANOVA of: Easier Than = PEOU + error
Source
DF
Sum of
Squares
Mean Square
F Value
Pr > F
Model
1
436.724830
436.724830
311.57
<.0001
Error
1519
2129.192329
1.401707
Corrected Total
1520
2565.917160
R-Square
Coeff Var
Root MSE
easiertvr Mean
0.170202
33.97035
1.183937
3.485207
Source
PEOU
Source
PEOU
DF
Type I SS
Mean Square
F Value
Pr > F
1
436.7248305
436.7248305
311.57
<.0001
DF
Type III SS
Mean Square
F Value
Pr > F
1
436.7248305
436.7248305
311.57
<.0001
Parameter
Estimate
Standard
Error
t Value
Pr > |t|
Intercept
PEOU
-2.959553472
0.182266615
0.36637630
0.01032599
-8.08
17.65
<.0001
<.0001
Panel B
ANOVA of: Easier Than = PEOU + PU + error
Source
DF
Sum of
Squares
Mean Square
F Value
Pr > F
Model
2
1752.345678
876.172839
1634.80
<.0001
Error
1518
813.571482
0.535950
Corrected Total
1520
2565.917160
R-Square
Coeff Var
Root MSE
easiertvr Mean
0.682932
21.00552
0.732086
3.485207
Source
PEOU
PU
DF
Type III SS
Mean Square
F Value
Pr > F
1
1
3.724994
1315.620848
3.724994
1315.620848
6.95
2454.75
0.0085
<.0001
Parameter
Estimate
Standard
Error
t Value
Pr > |t|
Intercept
PEOU
PU
-1.524467524
0.018944128
0.135247325
0.22839243
0.00718578
0.00272977
-6.67
2.64
49.55
<.0001
0.0085
<.0001
Panel C
ANOVA of: Easier Than = PEOU + PU + status + trial time + error
Source
DF
Sum of
Squares
Mean Square
F Value
Pr > F
Model
4
1767.138400
441.784600
838.46
<.0001
23
Error
1516
798.778760
Corrected Total
1520
2565.917160
0.526899
R-Square
Coeff Var
Root MSE
easiertvr Mean
0.688697
20.82740
0.725878
3.485207
Source
PEOU
PU
Status
Trial time
Parameter
Intercept
PEOU
PU
Status
Trial time
DF
Type III SS
Mean Square
F Value
Pr > F
1
1
1
1
0.8679961
962.7269743
11.4868596
2.6099400
0.8679961
962.7269743
11.4868596
2.6099400
1.65
1827.16
21.80
4.95
0.1995
<.0001
<.0001
0.0262
Estimate
Standard
Error
t Value
Pr > |t|
-1.247009152
0.009562581
0.128414966
0.105275322
-0.052788942
0.23387126
0.00745041
0.00300419
0.02254704
0.02371873
-5.33
1.28
42.75
4.67
-2.23
<.0001
0.1995
<.0001
<.0001
0.0262
24