Proceedings of the 38th Hawaii International Conference on System Sciences - 2005
Ambivalence and the Bivariate Nature of Attitudes in Information Systems
Research
Eric A. Walden
Rawls College of Business
Administration
Texas Tech University
[email protected]
Glenn J. Browne
Rawls College of Business
Administration
Texas Tech University
[email protected]
Abstract
People using information technologies experience both
joy and frustration, and thus develop both positive and
negative attitudes toward systems. Many researchers in
information systems have studied user attitudes toward
technology, and such attitudes have in fact been one of
the most frequently studied phenomena in the IS field.
User attitudes have generally been measured utilizing
bipolar scales, on which the low end represents strong
negative attitudes and the high end represents strong
positive attitudes. However, psychologists have long
recognized that the positive and negative dimensions of
attitudes can be separated, and attitudes are thus
bivariate. In the present research, we investigate whether
the positive and negative aspects of user attitudes are
separable using constructs from the Technology
Acceptance Model, one of the most frequently tested
models in information systems research. Our results
demonstrate that users can experience both positive and
negative attitudes toward a system, and that the positive
and negative attitudes are independent. Implications for
information systems theory and measurement are
discussed.
1. Introduction
In a wide variety of contexts, people express
simultaneous admiration for and frustration with
information technology. For example, the world wide
web has enhanced information search dramatically and
given access to information to billions of people around
the world. At the same time, however, the explosion of
available information has caused information overload for
nearly everyone who performs even the simplest of
searches. Thus, the same people who laud the availability
of information complain about the number of “hits” they
receive when using a search engine. Similarly, email
allows us to communicate with a vast number of people
with very little effort. However, at the same time, many
Jeff T. Larsen
Department of Psychology
Texas Tech University
[email protected]
people feel “trapped” by email, in the sense that they
believe they must always be logged on and must respond
immediately to messages received. In these and many
other contexts, the question arises as to whether people
have positive or negative feelings toward information
technology.
In the present research, we argue that people can have
both positive and negative feelings toward a particular
technology. Holding positive and negative feelings
simultaneously is termed ambivalence, and ambivalence
has profound implications for psychometric analysis of
attitudes toward technology because it challenges the
underlying assumption in nearly all attitude-based
research in IT.
To measure ambivalence, it is necessary to
conceptualize positive and negative feelings so that they
are not opposites. Instead, positive and negative feelings
are each independent forms of attitude and must be
measured using unipolar scales. However, psychometric
analyses of attitudes in IT overwhelmingly use bipolar
measures, thereby implicitly assuming that positive and
negative feelings are polar opposites.
To reconceptualize positive and negative feelings
toward IT, we introduce the idea of the evaluative space
model [1]. This model illustrates how positive and
negative attitudes can exist in bivariate space rather than
on a bipolar continuum. From this model we develop the
idea of ambivalence as the experience of both positive
and negative attitudes about a single object. We then test
the evaluative space model against the traditional bipolar
model in a well established IT setting and find that
positive and negative attitudes are not well correlated,
suggesting problems with the bipolar model.
Furthermore, we find that almost half of all subjects
experienced both positive and negative feelings,
suggesting not only that ambivalence is possible, but also
that it is prevalent with this particular attitude object
Finally, we discuss implications of these findings for IT
research, and suggest that ambivalence is likely to be a
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Proceedings of the 38th Hawaii International Conference on System Sciences - 2005
endeavor. At the same time, few human undertakings
entail the same level of fear, effort, and sacrifice.
2. Theory Development
Evaluative space
Bipolar scales
The evaluative space model [1] allows for negative and
positive attitudes to exist independently. Rather than as a
line, positivity and negativity are envisioned as two
orthogonal concepts, so that they can be activated
simultaneously. Nothing in the model prevents positive
and negative attitude from being negatively correlated,
and researchers believe that they usually are negatively
correlated. However, negative and positive attitudes are
not required in this model to be negatively correlated.
The model is illustrated in Figure 2.
An individual may feel highly negative and not at all
positive, resulting in an extremely negative attitude (point
A). Conversely, an individual who feels highly positive
and not at all negative will have an extremely positive
attitude (point B). It is also possible for an individual to
experience low levels of both positive and negative
attitudes (point C), resulting in indifference. However, a
person may feel both positive and negative, yielding an
ambivalent attitude (point D)
Since Thurstone’s [2] insight that attitudes can be
measured, attitudes have typically been conceptualized on
a bipolar continuum ranging from extremely negative to
extremely positive. This model corresponds well with the
typical bipolar scales that have been used extensively in
IT research. Authors have applied bipolar theory to the
study of attitudes toward usage [3], chargeback systems
[4], end user satisfaction [5], information system
effectiveness in financial services [6], gender differences
in email use [7], and a myriad of other issues.
If the bipolar conceptualization is correct, then a
reasonable way to assess attitudes toward a technology
would be to have users rate how positive or negative they
feel about it. Intuitively, those who report a great deal of
positivity (point B in Figure 1) toward the technology feel
little negativity and those who report a great deal of
negativity (point A) feel little positivity.
The problem occurs in the middle of the scale (at Point
E).
Subjects who give middling ratings may be
expressing indifferent attitudes, such that they have few
positive or negative feelings toward the technology.
Another possibility, however, is that such subjects are
expressing ambivalent attitudes, ones characterized by
comparably intense positivity and negativity.
E) Ambivalent
or indifferent?
–
A) Very negative
+
B) Very positive
Figure 1: Bipolar model
A bipolar conceptualization does not allow for this
distinction. A bipolar scale assumes that a given feeling
is a single type of attitude that may be either positive or
negative, but the underlying assumption is that subjects
feel a single thing to some degree.
However, research contradicts this belief. Researchers
have documented ambivalence toward such attitude
objects as racial minorities [8], euthanasia and mandatory
HIV testing [9], and legalized abortion [10]. As another
example, recent research in economics has found that the
net value of a child is $0 [11]. It seems clear that this is
not because children are neither positive nor negative, but
rather because they are both highly positive and highly
negative simultaneously. Children offer a level of joy,
pride, and love that are not obtained by any other human
Positive Attitude
growing issue as systems evolve into ever more complex
configurations.
B) Very positive
D) Ambivalent
C) Indifferent
A) Very negative
Negative Attitude
Figure 2: Evaluative Space model
The important contribution of the evaluative state
model is to recognize indifference and ambivalence as
two distinct attitudinal states. Thus, this model allows us
to differentiate between indifference and ambivalence in
ways that bipolar models do not.
Why is ambivalence of particular and growing
importance in IT?
If attitudes are organized in a bivariate rather than
bipolar fashion, then ambivalence is possible but not
required. Unipolar attitudes are necessary but not
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Proceedings of the 38th Hawaii International Conference on System Sciences - 2005
sufficient for the difference between ambivalence and
indifference to be important. Unipolar measures can be
highly negatively correlated, which would mean that they
closely approximate bipolar measures.
Thus, it is
important to ask why we might expect ambivalence with
respect to IT.
For a stimulus to generate ambivalence, it must be
relatively complex [12]. It is difficult to understand how
one can be ambivalent about a simple stimulus such as,
for example, a rock. For ambivalence to exist, the
stimulus must have a variety of different aspects, some of
which may be regarded positively and some of which may
be regarded negatively. Moreover, the more aspects a
stimulus has, the more likely it is that there will be
extreme values for some aspects.
Consider, for example, word processing software.
Early word processors simply allowed a user to type and
print. There was not much functionality to cause attitude
formation one way or the other. However, over time
more functionality was added, such as spell checkers
(which were probably positive) and grammar checkers
(which were probably negative). Tables, styles, and
templates were added along with mail merge,
highlighting, and image processing. Some of these
functions were likely perceived as positive and some as
negative, and over time this increased complexity
probably generated a higher level of ambivalence than
was originally present.
IT is among the most complex things designed by
human beings [13]. Thus, if any human artifact has the
level of complexity necessary to generate both positive
and negative attitudes simultaneously, it is probably IT.
Moreover, the level of complexity is consistently rising
over time as systems become integrated and processing
power allows designers ever more leeway in design.
However, IT, particularly as used in business, is
something that may occupy as much as half of a person’s
waking hours. Moreover, this interaction is likely to have
profound implications for the person’s work. The use of
IT changes the amount and manner of users’ work. Thus,
IT has significant impact on the lives of users, and this is
only likely to increase over time.
Because IT is complex, it has great potential to
engender ambivalent feelings in users. This seems to be a
much greater issue in IT than in other disciplines, because
IT is so pervasive and so complex. No other factor has
changed work over the past forty years as much as IT, and
that remains likely into the foreseeable future. Therefore,
it is time to expand our conceptualization of user
perceptions of IT to include the possibility of
ambivalence.
3. Empirical investigation
do individuals experience ambivalence when using IT?
Second, are positive and negative attitudes independent?
Third, what is the impact of accounting for positive and
negative attitudes separately? To answer these questions,
we conducted a study as detailed below.
Context
To address ambivalence, we must first establish the
research context. The dimensions we utilized are from
the most highly developed and widely validated IT
context, the technology acceptance model (TAM) [1416]. The variables used are the familiar TAM constructs
of perceived ease of use and perceived usefulness of
information systems. Our investigation is not one of
TAM per se, but rather of ambivalence in a TAM context.
Our measures permit us to test whether subjects are
ambivalent about the perceived ease of use and perceived
usefulness of a system.
We selected these variables specifically because they
are the most widely evaluated variables in IT research.
This allows us to be confident about the variables we are
measuring.
However, as noted, this work is not
concerned with whether users accept technology—it is
concerned with the possibility and implications of
ambivalence.
The TAM model posits that a user’s intention to use a
system is determined by the perceived ease of use and
perceived usefulness of the system (see Figure 3). In the
TAM model, both ease of use and usefulness are
measured using bipolar instruments. Thus, an individual
is constrained to feel either positive usefulness (ease of
use) or negative usefulness (ease of use), but not both.
Perceived
Usefulness
Intention to
Use
Perceived
Ease of Use
Figure 3: Technology Acceptance Model (TAM) using
bipolar measures as the basis for attitude
The evaluative space model suggests that positive
usefulness (ease of use) and negative usefulness (ease of
use) are independent constructs that can be used jointly to
determine the perceptions of usefulness (ease of use). See
Figure 4. Thus, both positive and negative feelings are
allowed to exist.
To assess the applicability of the evaluative space
model to IT research, we address three questions. First,
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Proceedings of the 38th Hawaii International Conference on System Sciences - 2005
Positive Feelings
About Usefulness
Perceived
Usefulness
Negative Feelings
About Usefulness
Intention to
Use
Positive Feelings
About Ease of Use
Negative Feelings
About Ease of Use
Perceived
Ease of
Use
items were adapted for this context from the validated
measures in [9]. All of the bipolar questions were
grouped together, as were all of the positive questions and
all of the negative questions. The unipolar questions were
presented first and the order of positive first or negative
first was randomized.
Data were collected from 374 participants. Participants
with data missing from two or more indicators of any
dependent measure were removed from all analyses
(n=22; 5.8%). The final sample consisted of 352
participants.
All measures showed acceptable reliabilities, as shown
in Table 1.
Table 1: Scale reliabilities and descriptive statistics
(n=352).
Figure 4: Technology Acceptance Model using the
Evaluative Space Model as a basis for attitude
Data
To collect data we asked undergraduate students at a
large research university to participate in a laboratory
study for class credit. The students were asked to use
online
software
called
Visual
Thesaurus
(http://visualthesaurus.com) to complete a task. The task
required the students to find synonyms for specific words
in a brief description of IT consulting (see appendix).
The students were then asked to complete a questionnaire
about the software.
Visual Thesaurus is a thesaurus that represents
synonyms in simulated three-dimensional space. It
includes a great deal of functionality for searching for
synonyms. However, it has a smaller word set than other
thesauruses and the representation can be somewhat
confusing. It is an appropriate tool for students, and they
should have a basis of comparison to other similar tools.
Subjects supplied synonyms in response to the task
requirements, but those responses were not of interest and
were not analyzed for this study.
The questionnaire included the standard bipolar TAM
items [14, 16] and unipolar items for positive and
negative attitudes. For example, subjects were asked, on
a seven point Likert scale, how much they agreed with the
statement “Using Visual Thesaurus would improve my
performance in my writing.” They were also asked
unipolar questions such as “Regardless of whatever
drawbacks it may have, do you find that Visual Thesaurus
has ADVANTAGES that would improve your performance
in writing?” To establish the level of activation, subjects
were then asked, “If YES, to what extent would these
ADVANTAGES improve your performance in writing?”
This process was repeated with disadvantages (e.g.,
“Regardless of whatever advantages it may have, do you
find that Visual Thesaurus has DRAWBACKS that would
hinder your performance in writing?”). The unipolar
Scale
Items
Usefulness (U)
4
Usefulness Positivity (UP)
4
4
Usefulness Negativity (UP)
Ease of Use (E)
4
4
Ease of Use Positivity (EP)
4
Ease of Use Negativity (EN)
Intention
2
Alpha
.896
.853
.782
.912
.836
.849
.954
Mean
1.13
1.87
0.61
1.12
1.82
0.72
-0.13
SD
1.07
0.89
0.74
1.39
1.06
0.88
0.93
Incidence of Ambivalence
The first question we address is whether individuals
are ambivalent about the usefulness and ease of use of
Visual Thesaurus.
In terms of its usefulness, 214 participants (61%)
found that Visual Thesaurus had both advantages and
drawbacks. Further, 191 (54%) endorsed both members of
at least one pair of oppositely worded items.
In terms of its ease of use, 190 participants (54%)
found that Visual Thesaurus had both advantages and
drawbacks. Moreover, 168 (48%) endorsed both members
of at least one pair of oppositely worded items.
This suggests that ambivalence was widespread in the
sample, with approximately half of the respondents
demonstrating some degree of ambivalence.
This
suggests that the evaluative space model is more accurate
in describing subjects’ responses than a simple bipolar
continuum.
Independence of positive and negative attitude
Even if individuals experience positive and negative
attitudes simultaneously, bipolar models may still be
acceptable if positive and negative attitudes are perfectly
correlated. Thus, we turn our attention to this question.
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Proceedings of the 38th Hawaii International Conference on System Sciences - 2005
Are UsefulnessPositivity and UsefulnessNegativity distinct?
UP and UN were moderately negatively correlated (r=
-.360, p < .001), but had unique effects on the TAM’s
measure of Usefulness. This was investigated with a pair
of two-step hierarchical regressions. In the analysis
investigating the unique effect of UP on Usefulness, UN
was entered in the first step and UP was entered in the
second step. Entering UN in the first step parses out its
effects on Usefulness. The inclusion of UP led to a
significant change in R2 from .29 to .59 (p < .001). Thus,
UP uniquely accounted for 30% of the variance in
Usefulness. In the analysis investigating the unique effect
of UN on Usefulness, UP was entered in the first step and
UN was entered in the second step. The inclusion of UN
led to a significant change in R2 from .49 to .59 (p <
.001). Thus, UP uniquely accounted for 10% of the
variance in Usefulness.
UP and UN also had unique effects on Intentions. After
parsing out the effect of UN, UP accounted for an
additional 7% of the variance in Intentions (p < .001).
After parsing out the effect of UP, UN accounted for an
additional 3% of the variance in Intentions (p = .001).
Are EasePositivity and EaseNegativity distinct?
EP and EN were moderately negatively correlated (r= .583), but had unique effects on the TAM’s measure of
Ease of Use. After parsing out the effect of EN, EP
accounted for an additional 18% of the variance in Ease
of Use. After parsing out the effect of EP, EN accounted
for an additional 12% of the variance in Ease of Use (p <
.001).
EP and EN also had unique (albeit small) effects on
Intentions. After parsing out the effect of EN, EP
accounted for an additional 1% of the variance in
Intention (p = .025). After parsing out the effect of EP,
EN accounted for an additional 4% of the variance in
Intention (p < .001).
These results suggest that positive and negative
attitudes are distinct constructs. Indeed, positivity and
negativity each accounted for unique portions of the
variance in the overall measure. In addition, positivity
and negativity had unique impacts on the intention to use
the system in the future. Overall, these results indicate
that the evaluative space model can be informative for
attitudes toward IT.
4. Discussion
We have demonstrated that user attitudes are not
bipolar, but rather that they can be bivariate. Negativity
and positivity can, and do, co-exist. Users can recognize
that systems contain both good and bad features, and
adjust their attitudes toward systems accordingly. Bipolar
measures simply do not capture the richness of human
attitudes, particularly toward objects as complex and
important as information systems.
Ours is the first investigation of the utility of unipolar
measures in IT, and certainly further studies should be
carried out in additional IT contexts. However, based on
empirical research in psychology, there is nothing
surprising about our findings. In a large variety of
contexts, people hold positive and negative attitudes
toward objects or events simultaneously [1, 17-20].
If our results hold in future studies in different IT
contexts, it will have profound implications for a
considerable body of IT research. Does this mean that we
should throw out prior research based on bipolar
measures as fundamentally flawed?
Certainly not.
Bipolar measures are a first approximation to true
attitudes. However, first approximations are not as
accurate as second approximations. To extend knowledge
of attitudes toward IT requires that, as a discipline, our
measures be founded on the strongest theoretical evidence
available. Based on our findings with the TAM variables
in this study, and considerable research in the psychology
literature, it is clear that bipolar measures can lead to
improper conclusions about data. Thus, when bipolar
measures are used, comments concerning limitations of
the study are at least warranted. When subjects were not
highly polarized toward negative or positive responses,
prior conclusions based on bipolar measures may
profitably be reconsidered.
This is not to say that bipolar measures have no place
in attitudinal research. There are some situations in which
bipolar measures might be appropriate. Unipolar
measures require twice as many questions as bipolar
measures, and it may be preferable to capture fewer
answers with subjects focused on the questions than to
capture superior measures when subjects’ concentration
and interest are waning. Bipolar measures may also be
very close approximations if subjects are very positive or
very negative.
In addition to the measurement issues, bivariate
measures allow a richer way to conceptualize attitudes.
This, in turn, should lead to richer theory and richer
practice about how attitudes influence IT. Rather than
simply conceptualizing an IT artifact as good or bad,
research can discuss the level of attitude and the direction
of attitude. Research can now consider the effects of
negative and positive conflicts.
In this study, we found that negative feelings toward
ease of use are more correlated with overall ease of use,
but that positive feelings toward usefulness are more
correlated with the overall usefulness of a system.
Intentions and/or behavior may be fully moderated by
only one type of feeling. Negative feelings may be
wholly irrelevant as long as there are some positive
feelings (other research in progress by one of the authors
suggests this is the case for smoking behavior).
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Proceedings of the 38th Hawaii International Conference on System Sciences - 2005
For practice, our results reveal some issues in IT that
would not be meaningful (or even apparent) without the
bivariate conceptualization of attitudes. The correlations
suggest that the two most important things one can do to
encourage acceptance are to make the system more useful
and to make it less difficult to use (which is distinct from
making it easier to use). One intuitive way to make a
system more useful is to add features. However, adding
more features often makes a system more difficult to use,
which tends to decrease acceptance. This dilemma
becomes
apparent
only
with
the
bivariate
conceptualization of attitudes.
Another area in which the bivariate conceptualization
is relevant is in the design of instruments. A colleague
once commented that reverse scored questions frequently
do not “load properly” in analyses. If we recognize that
people experience attitudes independently, then this
colleague’s experience makes perfect sense. If a subject
is asked, “Is this technology good?,” she may strongly
agree. When later asked, “Is this technology bad?,” she
may also strongly agree. In a bipolar model this is
illogical, but in a bivariate model this makes can be
perfectly sensible.
As with any study, there are limitations to the present
research that should be acknowledged. We did not
measure whether intentions to use were different between
high and low ambivalent subjects, which would have
made comparisons with prior TAM research clearer. This
limits the significance of our findings considerably, so
this relationship should be investigated in future research.
Further, we used only student subjects in the current
study. Although students are heavy users of information
technology, their behavioral patterns may not generalize
to the population as a whole.
To summarize, a bivariate conceptualization offers a
richer way to conceptualize attitudes and a more
scientifically valid way to measure them. As we advance
beyond the initial stages of researching various
phenomena, and the quality of measures becomes a more
important issue, we should move away from the
restrictive bipolar measurement model and take advantage
of the greater flexibility of the bivariate conceptualization.
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6. Appendix
Problem Statement
Outsourcing is an important factor for management
information systems (MIS).
Outsourcing includes
everything from the licensing of software to the leasing of
computer equipment to full-blown outsourcing of entire
business processes. Virtually all companies of any size
outsource some amount of their MIS, and many of those
company report good results.
The driving force behind outsourcing is the contract.
The contract details all of the important considerations for
the outsourcing arrangement.
Sadly, the contracts
themselves are often poorly written. If you look at the
statistics, you find that up to fifty percent of contracts
must be renegotiated. This is an expensive and timeconsuming process that often creates ill will between the
parties.
Therefore, it is necessary to enlist help when
negotiating original outsourcing contracts. The client
needs to hire both legal help and MIS help. On the MIS
side, the client needs more than a simple consultant, he
needs and advisor—someone who acts in the best interest
of the client over the long term. Having the appropriate
help during the initial contract negotiation will make
outsourcing more successful.
Measure
Now we would like to ask you several questions about the
ADVANTAGES (that is, positive aspects or benefits) of
Visual Thesaurus. As you answer these questions, please
try to ignore any drawbacks (that is, negative aspects or
weaknesses) of Visual Thesaurus.
1.
Regardless of whatever drawbacks it may have,
do you find that Visual Thesaurus has
ADVANTAGES that would improve your
performance in writing?
O YES O NO
If YES, to what extent would these ADVANTAGES
improve your performance in writing?
O Somewhat
O Moderately
O Quite a bit
O Extremely
(Questions 2-16 also follow this form)
2. Regardless of whatever drawbacks it may have,
do you find that Visual Thesaurus has
ADVANTAGES that would increase your
productivity?
3. Regardless of whatever drawbacks it may have,
do you find that Visual Thesaurus has
ADVANTAGES that would enhance your
effectiveness in writing?
4. Regardless of whatever drawbacks it may have,
do you find that Visual Thesaurus has
ADVANTAGES that would be useful in your
writing?
5. Regardless of whatever drawbacks it may have,
do you find that Visual Thesaurus has
ADVANTAGES that make your interaction
with it clear and understandable?
6. Regardless of whatever drawbacks it may have,
do you find that Visual Thesaurus has
ADVANTAGES that make your interaction
with it require little mental effort?
7. Regardless of whatever drawbacks it may have,
do you find that Visual Thesaurus has
ADVANTAGES that make it easy to use?
8. Regardless of whatever drawbacks it may have,
do you find that Visual Thesaurus has
ADVANTAGES that make it easy to get it to do
what you want it to do?
Now we would like to ask you several questions about the
DRAWBACKS (that is, negative aspects or weaknesses) of
Visual Thesaurus. As you answer these questions, please
try to ignore any advantages (that is, positive aspects or
benefits) of Visual Thesaurus.
9.
Regardless of whatever advantages it may have,
do you find that Visual Thesaurus has
DRAWBACKS that would hinder your
performance in writing?
10. Regardless of whatever advantages it may have,
do you find that Visual Thesaurus has
DRAWBACKS that would reduce your
productivity?
11. Regardless of whatever advantages it may have,
do you find that Visual Thesaurus has
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Proceedings of the 38th Hawaii International Conference on System Sciences - 2005
12.
13.
14.
15.
16.
DRAWBACKS that would impair your
effectiveness in writing?
Regardless of whatever advantages it may have,
do you find that Visual Thesaurus has
DRAWBACKS that would not be useful in your
writing?
Regardless of whatever advantages it may have,
do you find that Visual Thesaurus has
DRAWBACKS that make your interaction with
it unclear and difficult to understand?
Regardless of whatever advantages it may have,
do you find that Visual Thesaurus has
DRAWBACKS that make your interaction with
it require a lot of mental effort?
Regardless of whatever advantages it may have,
do you find that Visual Thesaurus has
DRAWBACKS that make it hard to use?
Regardless of whatever advantages it may have,
do you find that Visual Thesaurus has
DRAWBACKS that make it hard to get it to do
what you want it to do?
Now we would like to get your overall impressions of
working with Visual Thesaurus.
17)
Using Visual Thesaurus would improve my
performance in my writing.
Strongly
Disagree
-3
-2
-1
0
+1
+2
+3
ż
ż
ż
ż
ż
ż
ż
Strongly
Agree
(Questions 18-26 also follow this form.)
18) Using Visual Thesaurus in my writing would
increase my productivity.
19) Using Visual Thesaurus would enhance my
effectiveness in my writing.
20) I would find Visual Thesaurus useful in my
writing.
21) My interaction with Visual Thesaurus was clear
and understandable.
22) Interacting with Visual Thesaurus did not require
a lot of my mental effort.
23) I found Visual Thesaurus was easy to use.
24) I found it easy to get Visual Thesaurus to do what
I wanted it to do.
25) Assuming I had access to Visual Thesaurus, I
intend use it.
26) Given that I had access to Visual Thesaurus I
predict I would use it.
0-7695-2268-8/05/$20.00 (C) 2005 IEEE
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