yang et yoo_2004_all about attitude
Post on 30-May-2018
229 Views
Preview:
TRANSCRIPT
-
8/14/2019 Yang Et Yoo_2004_all About Attitude
1/13
Its all about attitude: revisiting the technology
acceptance model
Hee-dong Yanga,*, Youngjin Yoob,1
aCollege of Management, Ewha Womans University, 11-1 Daehyun-Dong, Sodaemun-Ku, Seoul 120-750, South KoreabInformation Systems Department, Weatherhead School of Management, Case Western Reserve University,
10900 Euclid Avenue, Cleveland, OH 44106, USA
Received 1 April 2002; accepted 1 February 2003
Available online 21 November 2003
Abstract
We expanded Davis et al.s technology acceptance model (TAM) by considering both the affective and the cognitive
dimensions of attitude and the hypothesized internal hierarchy among beliefs, cognitive attitude, affective attitude and
information systems (IS) use. While many of the earlier findings in TAM research were confirmed, the mediating role of
affective attitude between cognitive attitude and IS use was not supported. Our results cast doubts on the use of the affective
attitude construct in explaining IS use. Meanwhile, we found that cognitive attitude is an important variable to consider in
explaining IS usage behaviors. Our results suggest that attitude deserves more attention in IS research for its considerableinfluence on the individual and organizational usage of IS.
D 2003 Elsevier B.V. All rights reserved.
Keywords: Technology acceptance; Attitude; Structural equation model
1. Introduction
Davis [15] and Davis et al. [17] developed the
technology acceptance model (TAM) to explain theacceptance of information technology in performing
tasks and identified two important beliefs that influ-
ence the usage of information systems (IS): perceived
usefulness (PU) and ease of use (PEU). Perceived
usefulness is defined as the degree to which a person
believes that using a particular system would enhance
his or her job performance. It relates to job effec-
tiveness, productivity (time saving) and the relativeimportance of the system to ones job. On the other
hand, perceived ease of use refers to the degree to
which a person believes that using a particular system
would be free of effort, in terms of physical and
mental effort as well as ease of learning. It is these two
beliefs, according to TAM, that determine ones
intention to use technology. Thus, TAM has emerged
as a salient and powerful model that can be used to
predict potential IS usage by measuring users beliefs
after they are exposed to the system even for a short
0167-9236/$ - see front matterD 2003 Elsevier B.V. All rights reserved.doi:10.1016/S0167-9236(03)00062-9
* Corresponding author. Tel.: +82-2-3277-3582; fax: +82-2-
3277-2582.
E-mail addresses: hdyang@ewha.ac.kr (H. Yang),
yxy23@po.cwru.edu (Y. Yoo).1 Tel.: +1-216-368-0790.
www.elsevier.com/locate/dsw
Decision Support Systems 38 (2004) 1931
-
8/14/2019 Yang Et Yoo_2004_all About Attitude
2/13
period of time through training, prototype or mock-up
models [53].
Focusing primarily o n t h e affective aspect of
attitude, Davis et al. [17, p. 984] further found thatthe influence of attitude on IS use was at best modest
in predicting future IS use. They found that the
influence of attitude on IS use disappeared when PU
was considered to predict IS use. This led them to
conclude that attitude offers little value in predicting
IS use, leaving two users beliefsperceived useful-
ness and perceived ease of useas powerful and
parsimonious predictors. The validity and reliability
of constructs of the model have been well supported
by various studies [1,2,11,15,19,28,44].
The social psychology literature, however, clearly
suggests thatattitude has both affective and cognitive
components [1,4,5,13,51,56]. The affective compo-
nent of attitude refers to how much the person likes
the object of thought [34], while the cognitive com-
ponent refers to an individuals specific beliefs related
to the object[4,5]. In light of this, a close examination
of the measurement of attitude performed/provided by
Davis et al. raises the following issues. First, although
the underlying theory they used assumed no cognitive
component of attitude, the indicators for the attitude
construct they used included both the cognitive and
affective aspects. Second, provided attitude has bothcognitive and affective aspects, it should be examined
whether both aspects of attitudes mediate the impact
of PU and PEU on IS use. According to a dyadic view
of attitude [13,39,51,56], the cognitive and affective
components of attitude operate through different psy-
chological mechanisms. Therefore, one can argue that
one of the reasons that Davis et al. did not find a
significant influence of attitude in their study was
because the potentially significant influence of cogni-
tion was offset by the insignificant influence of affect.
In addition, attitude deserves more careful atten-tion in IS, not only in terms of refining the measure-
ments in TAM constructs, but also its potentially
powerful influence on the implementation of technol-
ogy and the diffusion of IT-enabled innovation in
organizations. According to attitude literature, attitude
is has a social function [22,36]. It is contagious,
malleable and fragile in that people influence each
others attitudes by affirming or contradicting them
through interactions and mutual experiences. By
better understanding which aspect of attitude is more
influential in the technology acceptance process, we
can support organizations efforts to implement infor-
mation technology.
To address these issues, we attempt to answer thefollowing two questions. First, can we empirically
distinguish the affective and cognitive aspects of
attitude in the context of explaining IS use? Second,
if the answer to the first question is presumably yes,
what is the causal relationship between two belief
constructs in TAM (PU and PEU) and two attitude
constructs (affective and cognitive attitude) leading to
IS use?
Our research makes important contributions to the
growing body of technology acceptance literature by
showing that a better understanding of the role of
attitude can enhance the models predictability about
users acceptance of information technology. Our
study shows that cognition and affect operate through
different psychological mechanisms in order to influ-
ence the use of IS. In particular, the cognitive dimen-
sion of attitude directly influences individuals IS use,
while the affective dimension needs to be treated as an
outcome variable of its own. Given the social function
of attitude [37], and the positive impact of its cogni-
tive dimension established in our study, organizations
can focus on improving the cognitive dimensions of
attitude in order to improve an individuals adoptionof information systems as well as an eventual, orga-
nization-wide implementation.
In addition, our research contributes to the attitude
literature by empirically demonstrating that two dimen-
sions of attitude operate through different psycholog-
ical mechanisms with respect to individual technology
use behaviors. In particular, we attempt to make
theoretical distinctions between similar constructs
the affective component of attitude, the cognitive
component of attitude and beliefsand empirically
demonstrate their differences.This paper is organized as follows. After the
introduction, we explain the backgrounds of our
research. We review the attitude construct and explain
how the affective and cognitive aspects of attitude are
different from each other. We also point out the
different mediating role of two attitudes between
PU, PEU and IS use. We introduce our research
models and followed by a description of research
methodology. Finally, we analyze the results and
discuss their implications.
H. Yang, Y. Yoo / Decision Support Systems 38 (2004) 193120
-
8/14/2019 Yang Et Yoo_2004_all About Attitude
3/13
2. Research rationale and motivation
Ajzen and Fishbeins [3] theory of action (TRA) is
a widely used general theory on the determinants ofconsciously intended behaviors. Building on TRA,
TAM posits that individuals form their intention to
perform certain behaviors based in part on their
affective feelings about the systemsa condition
labeled as attitudeand in part by their beliefs, which
also influence their attitude. In doing so, TAM con-
ceptualizes attitude as an affective uni-dimensional
construct.
Contrary to TAM, however, Petty et al. [39] have
argued that the most common classification for the
basis of attitude is affect and cognition (p. 613).2 The
affective dimension of attitude focuses on how much
the person likes the object of thought [34] and
measures the degree of emotional attraction toward
the object [4]. On the other hand, the cognitive
dimension of attitude refers to an individuals specific
beliefs related to the object[4,5] and consists of the
evaluation, judgment, reception, or perception of the
object of thought based on values [9].
The dyadic view presumes the affective and cog-
nitive dimensions to be independent variables that
affect behavioral intention. Weiss and Cropanzano
[56] introduced four empirical studies that identifiedthe independent influences of the affective and cog-
nitive components of attitude. Similarly, Triandis [51]
argued that a better understanding of the relationship
between attitude and behavior can be gained through
the separation of the affective and cognitive compo-
nents of attitude. Within the IS literature, Goodhue
[25] and Swanson [45] have both recognized that the
distinction between affective and cognitive dimen-
sions has frequently been overlooked in IS attitude
research. An evaluative disposition toward behavior
might be different depending on whether it is inferredfrom an affective or a cognitive response [35].
Crites et al. [13] listed the semantic pairs to measure
each aspect of an attitude construct. They defined the
affective scales as the position that best describes
respondents feelings toward the object, while the
cognitive scales indicate the position that best
describes the traits or characteristics of the object.
Therefore, 12 affective word pairs (love/hateful, de-lighted/sad, happy/annoyed, calm/tense, excited/
bored, relaxed/angry, acceptance/disgusted, joy/sor-
row, positive/negative, like/dislike, good/bad, and
desirable/undesirable) and 7 cognitive word pairs
(useful/useless, wise/foolish, safe/unsafe, beneficial/
harmful, valuable/worthless, perfect/imperfect, and
wholesome/unhealthy), respectively, constitute the
affective and cognitive versions of attitude [13].
Davis et al. [17] measured attitude according to the
following five items on 7-point semantic differential
rating scales: All things considered, my using Write-
One in my job is good/bad, wise/foolish, favorable/
unfavorable, beneficial/harmful, and positive/nega-
tive. According to the definition of Crites et al.
[13], Davis et al.s measures of attitude contain both
affective and cognitive aspects in a single attitude
construct.
Meanwhile, the affective dimension of attitude is
influenced by beliefs [49] and the beliefs can be
either evaluative or non-evaluative (true or false)
[37]. An evaluative belief can be considered as a
cognitive attitude, thus developed from non-evalu-
ative beliefs and values [50]. Values mean preferredend states and preferred ways of doing things
[39,49]. For example, building technology that is
useful for task completion and easy to use is gener-
ally accepted as having value in the IS community.
Upon the use of the tool, users can form non-
evaluative beliefs about the usefulness and ease of
use of the tool. Based on such non-evaluative beliefs,
users will form their evaluative beliefs about the
system. Such evaluative beliefs (i.e., the cognitive
attitude) in turn develop into users affective attitudes
(like or hate). Thus, there is a hierarchical relation-ship among these four constructs: affective attitude is
influenced by cognitive attitude, which is affected by
non-evaluative beliefs, which is in turn developed by
values [50].
3. Research models
Fig. 1 shows the two models we tested in this
study. Model I depicts the original TAM with mixed
2 While the tripartite model argues that attitude has affective,
cognitive and conative components [3,8,34,37], the affective and
conative scales loaded on the same factor in the study of Stephen et
al. (1994) [41]. This empirical result suggests that the conative
component is associated strongly with the affective aspects.
H. Yang, Y. Yoo / Decision Support Systems 38 (2004) 1931 21
-
8/14/2019 Yang Et Yoo_2004_all About Attitude
4/13
attitude measure. Model II is the revised TAM that
we developed in the previous section, i.e., cognitive
and affective attitude constructs are distinguished by
their different mediating roles. Furthermore, in Mod-el II, we hypothesize the internal hierarchy of
attitude between its cognitive and affective dimen-
sions. Comparisons of these two models allow us to
see the efficacy of the attitude construct in explain-
ing individuals use of information technology. In
particular, we can examine whether the weak role of
attitude in TAM is due to the constructs intrinsic
characteristics as explained by Davis et al. [17], or
due to the way the construct was measured in their
study.
There are four things in common in bothmodels. First, we choose IS use, not the behavior
intention (to use IS), as the dependent variable.
Ajzen and Fishbein [3] indicated intentions should
be measured close to the behavioral observation to
ensure an accurate prediction. Thus, behavioral
intention may lack practical value in predicting
long-term future IS use (Ref. [7, p. 135]). Fur-
thermore, stable use of IS, not early adoption after
a brief exposure to the technology, is a more
appropriate measure of technology innovation dif-
fusion [20]. Our interest here is to predict usage
behaviors rather than intentions to use. Thus, con-
sistent with a number of TAM studies in the past
that excluded intension and instead included IS use[1,43], we chose to use IS use as our dependent
variable.
Second, following Davis [15, pp. 477478], we
hypothesize that PEU would influence PU but not
vice versa. This hypothesized relationship has been
supported by much empirical evidence [2,10,15
17,31,33,4648,53].
Third, following the original TAM, we assume PU
directly influences IS use, whereas PEU does not.
Many empirical studies [10,1517,42,44,4648]
have consistently identified PU as a primary factorthat influences IS use, while PEU plays a much less
important role, particularly later in the adoption
process.
Fourth, attitude is hypothesized to mediate the
influences of PU and PEU on IS use. There are
many empirical studies supporting this hypothesis
[16,17,30,33]. The significance of these mediating
paths, along with affective or cognitive attitude
measures, is one of our main interests in this
study.
Fig. 1. Research models. Model I. Original TAM with mixed attitude measure. Model II. Revised TAM with separate affective and cognitive
attitudes.
H. Yang, Y. Yoo / Decision Support Systems 38 (2004) 193122
-
8/14/2019 Yang Et Yoo_2004_all About Attitude
5/13
4. Methodology
4.1. Data collection
Data were collected from undergraduate students
who major in management information systems (MIS)
at a college of management in the New England
region of the United States. Students were asked to
fill out spreadsheet usage surveys anonymously and
submit them to the class instructors on a voluntary
basis. One point (out of 100 total possible points) was
accredited to their voluntary participation to this
survey. It took 9 weeks to finish collecting surveys.
Harris and Schaubroeck [26] recommended a mini-
mum sample size of 200 to guarantee robust structural
equation modeling. In total, 211 completed question-
naires were returned for spreadsheet usage out of 420
handouts, satisfying this recommendation. The return
ratio was as 50.2%.
4.2. Measurements
Appendix A shows all the measurement items that
were used in the study. We used Davis et al.s [17]
original items for perceived usefulness (four items),
perceived ease of use (four items), and system use.
Through several empirical studies, these items valid-ity and reliability have been established [1,11,17,
46,53].
Out of their original 12 pairs of affective and 7
pairs of cognitive measurement of Crites et al. [13],
three items were chosen for each group. Three se-
mantic pairs for affective measures included good/
bad, happy/annoyed, and positive/negative. Three
semantic pairs for cognitive measures included wise/
foolish, beneficial/harmful, and valuable/worthless.
Two affective pairs (good/bad, positive/negative)
and two cognitive pairs (wise/foolish, beneficial/harmful) were chosen because they were used by
Davis et al. [17]. Two additional pairs, one for
affective and one for cognitive attitude, were chosen
from the list of Crites et al. [13].
5. Data analysis
Data analysis was conducted using a structural
equation modeling tool, Amos, to investigate the
influence of attitude operationalization and the rela-
tionship between two attitudes and the other variables.
Structural equation modeling has many advantages
over path analysis or regression analysis, especiallywhen the observed variables contain measurement
errors and the interesting relationship is among the
latent (unobservable) variables [24].
5.1. Test of the measurement model
Table 1 presents the results of the reliability testing
using Cronbach alpha coefficients, which ranged from
0.8341 to 0.9427. Construct validity was assessed
using confirmatory factor analysis. In our dataset, all
the measures loaded onto their underlying factors.
Generally, to show convergent validity, all item load-
ing scores need to be greater than 0.707 [23,29]. As
shown in Table 2, all factor loading scores were
higher than the suggested 0.707.
Given the conceptual proximity among four con-
structs (affective attitude, cognitive attitude, PEU,
and PU), we examine the discriminant validity of
measures using three different measurement models
and Amos. The first measurement model assumes
that there is only one latent variable, having all
indicators loaded on a single factor. The second
model assumes that there are three latent variables(attitude, PEU, and PU). Finally, the last model
assumes that there are four latent variables (cognitive
attitude, affective attitude, PEU, and PU). The dif-
ference in Chi-square statistics was used to test the
superiority of one measurement model over another
in these comparisons [23]. Table 3 shows the results
of the hierarchical comparisons that we conducted on
our data set. The first comparison demonstrated the
superiority of the three-factor model over the one-
factor model. The second comparison demonstrated
the superiority of the four-factor model over thethree-factor model.
Table 1
Reliability estimates
Construct Items Cronbachs
alpha
PU 4 0.9427
PEU 4 0.8991
ATTITUDE 6 0.9177
IS USE 2 0.8341
H. Yang, Y. Yoo / Decision Support Systems 38 (2004) 1931 23
-
8/14/2019 Yang Et Yoo_2004_all About Attitude
6/13
We further examined the discriminant validity
using the square root of the average variance extracted
[14,21,40]. As shown in Table 4, all square roots of
the average variance extracted displayed on a diagonal
of a correlation matrix are greater than the off-diag-
onal construct correlations in the corresponding rows
and columns. Combined with the results from confir-
matory factor analysis, this indicates that each con-
struct shared more variance with its items than it
shared with other constructs, thereby confirming the
discriminant validity.
5.2. Test of the model
Structural equation modeling was conducted using
Amos to test the fit between the research models (Fig.
1) and the data set. In the literature, a variety of
measures are suggested to test the fit between the
model and data [6,21,27]. In general, the goodness-of-
fit is satisfactory when the Goodness of Fit Index
(GFI) is greater than 0.9, the Adjusted Goodness of
Fit Index (AGFI) is greater than 0.8, the Root MeanSquare Residual (RMSR) is lower than 0.1, and the
chi-square divided by degree of freedom (v2/df) is less
than 5 [27].3 Fig. 2 shows the fit indices of the
original TAM and the revised model.
As for the original TAM, the various goodness-of-
fit statistics indicate that the model shows a poor fit
with the data. In our dataset, the value of GFI is 0.73,
the AGFI is 0.64, the RMSR is 0.12, and the v2/dfis
5.7. On the other hand, for the revised model, the
value of GFI is 0.90, the AGFI is 0.85, the RMSR is
0.09, and v2/dfis 2.2. Thus, the revised model shows
the improved goodness-of-fit statistics in all four
fitness indices, compared to the original TAM. Fur-
thermore, all fitness indices of the revised model
passed the criterion-value. Overall, it is clear that
the revised model shows a better fit with the data,
demonstrating a superior explanatory power of the
technology usage by individuals.
Table 3
Competing measurement modeling
v2 df
One-factor Model (M1) 1379.40 77
Three-factor Model (M2) 513.46 74
Four-factor Model (M3) 161.62 71
Model comparisons Dv2 Ddf P
M1 M2 865.94 3 < 0.001
M2 M3 290.46 3 < 0.001
Table 4
Discriminant validity
PU PEU Affective
attitude
Cognitive
attitude
IS use
PU 0.847a
PEU 0.445b 0.834
Affective attitude 0.528 0.398 0.780
Cognitive attitude 0.480 0.348 0.614 0.788
IS use 0.311 0.364 0.255 0.372 0.898
a Diagonal: (average variance extracted from the observed
variables by the latent variables)1/2=(Sk2/q)1/2.b Off-diagonals: correlation between latent variables=(shared
variance)1/2.
Table 2
Confirmatory factor analysis model
Items PU PEU Affective
attitude
Cognitive
attitude
IS use
1 0.89
(na)
2 0.91
(19.91)
3 0.92
(20.55)
4 0.87
(18.23)
5 0.87 (na)
6 0.75 (12.80)
7 0.84 (15.25)
8 0.87 (16.34)
9 0.84 (na)
10 0.91 (17.60)11 0.95 (18.73)
12 0.92 (na)
13 0.90 (20.73)
14 0.91 (21.05)
15 0.78
(na)
16 0.91
(5.57)
na is set to metric.
The numbers in the parentheses are t-values. Loadings greater than
0.7 or t-values greater than 2.0 (which is significant at a = 0.05)
indicate the convergent validity.
3 More restrictive criteria are sometimes cited: e.g., 0.90 for
AGFI, 0.05 for RMSR, and 3:1 forv2/df[23].
H. Yang, Y. Yoo / Decision Support Systems 38 (2004) 193124
-
8/14/2019 Yang Et Yoo_2004_all About Attitude
7/13
Fig. 2 also shows the path coefficients in the
models. Because the revised model has a better
fit with our data set than the original TAM, we
would focus on the path coefficients of Model II.
In this model, all the paths were significant, except
for the one from the affective attitude to IS use
and the path from PEU to affective attitude. Most
findings of traditional TAM are repeated here:
PEU influences PU; PU has more influences on
attitude than does PEU; PU has a direct influence
on IS use.One significant difference was the important me-
diating role of the cognitive attitude between users
beliefs (PU and PEU) and IS use. Contrary to Davis et
al. [17], our results showed that the cognitive dimen-
sion of attitude played an important role in explaining
IS use. The beta coefficient from cognitive attitude to
IS use (0.51) is more than twice the value of the beta
coefficient from PU to IS use (0.25). On the other
hand, the path between affective attitude and IS use is
not significant, suggesting that affective attitude does
not mediate the relationship between cognitive atti-
tude and IS use.
6. Discussions and conclusions
The purposes of our study were (a) to empirically
examine whether two aspects of attitude (cognitive vs.
affective) can be separated with high degrees of
reliability and validity in the context of IS technology
acceptance and (b) to examine whether IS use isinfluenced by affective attitude, which is influenced
by cognitive attitude, which is in turn influenced by
PU and PEU. The answer to the first questions was
yes and the answer to the second question is in part
positive.
First, we found that, in the context of technology
acceptance, affective and cognitive attitudes are two
separate socio-psychological constructs. Since Davis
et al.s [17] finding that attitude adds little value in
explaining IS use, the attitude construct has often been
Fig. 2. Model path estimates: standardized estimates (t-values). Model I. Original TAM with mixed attitude measure. Model II. Revised TAM
with separated affective and cognitive attitude.
H. Yang, Y. Yoo / Decision Support Systems 38 (2004) 1931 25
-
8/14/2019 Yang Et Yoo_2004_all About Attitude
8/13
ignored in understanding technology acceptance.
Some studies even treated user beliefs and attitudes
as if they were interchangeable. Our results show that
they are not. This is an important point to note in thecontext of technology acceptance literature, which
often treats attitude as an affective construct, ignoring
its cognitive dimension (e.g., Ref. [17]). Future re-
search in this area should pay closer attention to the
differences between the cognitive and affective
dimensions of attitude in users acceptance of tech-
nology.
Second, consistent with many previous TAM stud-
ies, we found that PU has a direct influence on IS use.
We found, however, that only cognitive attitude medi-
ates the influence of PU and PEU on IS use. Contrary
to our expectation, affective attitude did not mediate
the influence of cognitive attitude on IS use. This
result raises a question about the plausibility of the
proposed hierarchical structure among affective atti-
tude, cognitive attitude, and non-evaluative beliefs as
hypothesized (non-evaluative beliefs! cognitive atti-
tude! affective attitude).
The results also explain why Davis et al. [17]
did not find that the role of attitude was significant
in their study. The weak relationship between at-
titude and IS use in their study might have been
due to the mixed measure of the attitude construct.As our results suggest, when the cognitive dimen-
sion of attitude is considered, attitude explains more
than twice as many variances of IS use as does PU.
This clearly suggests that we can significantly
enhance our understanding and prediction of IS
use by considering the cognitive dimension of at-
titude.
It is also important to note that the affective
dimension of attitude does not explain IS use at
all. Interestingly, however, cognitive attitude influen-
ces the affective attitude in various models of ourstudy. Taken together, we propose that the affective
attitude in technology acceptance needs to be treated
as a dependent variable of its own, not as a mediator.
Or, perhaps it is more directly related to another
important dependent variable in IS research, such as
user satisfaction. Given the significant differences
between the cognitive and affective dimensions of
attitude, we suggest focusing on the cognitive di-
mension of attitude in explaining or predicting IS
use.
Attitude has a social function. Attitude serves both
private and public identity concerns [37]. Even though
attitude has been treated as a vague and fragile
construct in the IS area, its importance on individualbehavior and social influence has been steadily rec-
ognized in psychology [37]. Attitude is contagious
and as people work together, they express their own
and listen to each others attitudes [50]. Therefore,
organizations and managers need to care about the
positive attitude change.
Changes in attitude occur quickly and require
less challenge than the changes in non-evaluative
beliefs or values [50]. Many theories and pro-
grams have been developed for positive attitude
change such as the direct influence of individuals
(e.g., enhancing peoples motivations, abilities, me-
mories, or moods), the improvement of contextual
cues (e.g., classical conditioning), or the consider-
ation of persuasive messages (e.g., message cred-
ibility, message memory, two-sided communication,
etc.).
Even though attitude can be changed quickly,
continuous efforts should also be given to maintain
the attitude because it is temporary, unstable, and
malleable [50]. Motivations, capability, experiences
and education all influence attitude development and
maintenance. Thus, attitude maintenance and changeshould be considered as a complementary tool to
traditional implementation techniques that can be
used to improve the users acceptance of new tech-
nology.
6.1. Limitations
The current study has several limitations. First, the
original TAM includes behavioral intention as a
mediator in the model, whereas we did not include
this factor in our model. For a more rigid investiga-tion of TAM, we could have used behavioral inten-
tion as well as self-reported use behavior. The value
of behavioral intention within the context of technol-
ogy acceptance is found in its early diagnostic func-
tion which enables management to predict the
potential acceptance (or rejection) of the systems by
the intended users after a short exposure to the
system. However, we believe that the research of
technology acceptance (including ours) is ultimately
concerned with explaining and predicting the users
H. Yang, Y. Yoo / Decision Support Systems 38 (2004) 193126
-
8/14/2019 Yang Et Yoo_2004_all About Attitude
9/13
usage behavior rather their intentions. Therefore, we
feel that our use of self-reported usage without
behavioral intention is consistent with the goal of
our study.Second, we used only perceptual measures of IS
use. Earlier studies have shown that individuals
perceptions of IS usage are sometimes different
from their actual usage pattern [12,18,43,52].
Therefore, our results need to be cautiously inter-
preted.
Third, while the goodness of fit indices of our
model meet the minimum requirements suggested
by Hayduk [27], it does not meet more stringent
requirements (i.e., RMSR = 0.05) recommended by
Gefen et al. [23], which is more respected by IS
researchers. They warned that [i]t is important to
note that large [RMSR] values mean high residual
variance, and that such values reflect a poorly
fitting model (p. 35). Therefore, our results
could have been influenced by correlated residual
variances.
Finally, our sample consisted of college students
learning these tools for course credit. Therefore, no
organizational setting is considered in our data set.
However, past research suggests that social, orga-
nizational, and cultural contexts influence individu-
als decisions about technology acceptance [22,32,38,54,55]. Our study did not consider those so-
cial variables. Future research can study how
social norms and existing social practices influence
the formation of individuals attitude in the con-
text of technology acceptance. In particular, one
can examine the relative influences of the mechan-
ical characteristics of the technology as well as
social norms on individuals attitudes toward tech-
nology.
6.2. Implications for future research
Despite these limitations, our results offer several
insights into the technology acceptance process.
First, given the important role of attitude (particu-
larly cognitive attitude) in the technology accep-
tance process, it is critical to examine the evolution
patterns of both cognitive and affective attitudes
over time and how their relationships change. It is
well known that individuals attitudes and beliefs
toward technology change over time as they become
more experienced. In this light, one can expand the
TAM into a reciprocal model in which attitudeinfluences IS use, which in turn influences the
users attitude in the subsequent phases. It would
require further theorization efforts and more sophis-
ticated empirical techniques to examine such dy-
namic relationships.
Also, one can examine the relationship between
TAM and another well-studied dependent variable in
IS research, user satisfaction [18]. Our results show
that affective attitude should be treated as the
dependent variable rather than as the mediator. We
see a possible conceptual linkage between affective
attitude and user satisfaction, through which TAM
can be expanded to include user satisfaction as
another important dependent variable in addition to
IS use.
6.3. Implications for practice
Two important implications for IS managers can
be drawn from the results. First, by replacing Davis
et al.s five attitude measurement items with our
three-item cognitive attitude measure, managers can
predict IS use more successfully. Second, and moreimportantly, our results suggest that individual users
attitudes do influence technology acceptance. Par-
ticularly, our results suggest that encouraging among
users a positive cognitive approach to the systems
can also substantially improve the users acceptance
of the technology. Also, managers can indirectly
improve the users acceptance of the technology
by affecting individuals cognitive attitude. While
the importance of users beliefs underscores the
value of system design and users training, the im-
portance of users attitudes underscores the signifi-cance of communication with users about the
systems.
Acknowledgements
We thank Sora Kang for her assistance in
conducting statistical analyses for this paper.
H. Yang, Y. Yoo / Decision Support Systems 38 (2004) 1931 27
-
8/14/2019 Yang Et Yoo_2004_all About Attitude
10/13
Appendix A. Measurement instrument
H. Yang, Y. Yoo / Decision Support Systems 38 (2004) 193128
-
8/14/2019 Yang Et Yoo_2004_all About Attitude
11/13
References
[1] D.A. Adams, R.R. Nelson, P.A. Todd, Perceived usefulness,
ease of use, and usage of information technology: a replica-
tion, MIS Quarterly (1992, June) 227247.
[2] R. Agarwal, J. Prasad, Are individual differences germane to
the acceptance of new information technologies? Decision
Sciences 30 (1999) 2.
[3] I. Ajzen, M. Fishbein, Understanding Attitudes and Predict-
ing Social Behavior, Prentice-Hall, Englewood Cliffs, NJ,
1980.
[4] R.P. Bagozzi, R.E. Burnkrant, Attitude organization and the
attitude behavior relationship, Journal of Personality and So-
cial Psychology 37 (1979) 6.
[5] R.P. Bagozzi, R.E. Burnkrant, Attitude organization and the
attitude behavior relationship: a reply to Dillon and Ku-mar, Journal of Personality and Social Psychology 49
(1985) 1.
[6] P.M. Bentler, D.G. Bonett, Significance tests and goodness of
fit in the analysis of covariance structures, Psychological Bul-
letin 88 (1980) 3.
[7] F. Bergeron, L. Raymond, S. Rivard, M.-F. Gara, Determi-
nants of EIS use: testing a behavioral model, Decision Support
Systems 14 (1995) 2.
[8] L. Berkowitz, Attitudes and Action: A Survey of Social Psy-
chology, CBS College Publishing, New York, 1986.
[9] S. Chaiken, C. Stangor, Attitudes and attitude change, Annual
Review of Psychology 38 (1987) 575630.
[10] P.Y.K. Chau, An empirical assessment of a modified technol-
ogy acceptance model, Journal of Management Information
Systems 13 (1996) 2.
[11] W.W. Chin, P.A. Todd, On the use, usefulness, and ease of use
of structural equation modeling in MIS research: a note of
caution, MIS Quarterly 19 (1995) 2.
[12] F. Collopy, Biases in retrospective self-reports of time use: an
empirical study of computer users, Management Science 42
(1996) 5.
[13] S.L. Crites Jr., L.R. Fabrigar, R.E. Petty, Measuring the affec-
tive and cognitive properties of attitudes: conceptual and
methodological issues, Personality and Social Psychology
Bulletin 20 (1994) 6.
[14] A.M. Croteau, L. Raymond, F. Bergeron, Testing the validity
of miles and snows typology, Academy of Information andManagement Sciences Journal 2 (1999) 2.
[15] F.D. Davis, Perceived usefulness, perceived ease of use, and
user acceptance of information technology, MIS Quarterly
(1989, September) 318340.
[16] F.D. Davis, User acceptance of information technology:
system characteristics, user perceptions and behavioral im-
pacts, International Journal of Man Machine Studies 38
(1993) 3.
[17] F.D. Davis, R.P. Bagozzi, P.R. Warshaw, User acceptance of
computer technology: a comparison of two theoretical models,
Management Science 35 (1989) 8.
[18] W.H. DeLone, E.R. McLean, Information systems success: the
quest for the dependent variable, Information Systems Re-
search 3 (1992) 1.[19] W.J. Doll, A. Hendrickson, X. Deng, Using Daviss perceived
usefulness and ease-of-use instruments for decision making: a
confirmatory and multigroup invariance analysis, Decision
Sciences 29 (1998) 4.
[20] R.G. Fichman, C.F. Kemerer, The illusory diffusion of inno-
vation: an examination of assimilation gaps, Information Sys-
tems Research 10 (1999) 3.
[21] C. Fornell, Issues in the application of covariance structure
analysis: a comment, Journal of Consumer Research 9 (1983)
443448.
[22] J. Fulk, Social construction of communication technology,
Academy of Management Journal 36 (1993) 5.
[23] D. Gefen, D.W. Straub, M.-C. Boudreau, Structural equation
modeling and regression: guidelines for research practice,
Communications of the Association for Information Systems
4 (7) (2000) 177.
[24] A.S. Goldberger, Structural equation models: an overview, in:
A.S. Goldberger, O.D. Duncan (Eds.), Structural Equation
Models in the Social Science, Chapter 1, Seminar Press,
New York, 1973, pp. 118.
[25] D. Goodhue, I/S attitudes: toward theoretical and definitional
clarity, Database 19 (3/4) (1988) 615.
[26] M. Harris, J. Schaubroeck, Confirmatory modeling in organ-
izational behavior/human resource management: issues and
applications, Journal of Management 16 (1990) 2.
H. Yang, Y. Yoo / Decision Support Systems 38 (2004) 1931 29
-
8/14/2019 Yang Et Yoo_2004_all About Attitude
12/13
[27] L.A. Hayduk, Structural Equation Modeling with LISREL:
Essentials and Advances, Johns Hopkins Univ. Press, Balti-
more, MD, 1987.
[28] A.R. Hendrickson, P.D. Massey, T.P. Cronan, On the test
retest reliability of perceived usefulness and perceived easeof use scales, MIS Quarterly (1993, June) 227230.
[29] K.G. Joreskog, D. Sorbum, LISREL 7: A Guide to the Pro-
gram and Applications, 2nd ed., SPSS, Chicago, IL, 1989.
[30] E. Karahanna, D. Straub, N.L. Chervany, Information tech-
nology adoption across time: a cross-sectional comparison of
pre-adoption and post-adoption beliefs, MIS Quarterly 23
(1999) 2.
[31] H.C. Lucas Jr., V.K. Spitler, Technology use and performance:
a field study of broker workstations, Decision Sciences 30
(1999) 2.
[32] M.L. Markus, Toward a Critical Mass theory of interactive
media, in: J. Fulk, C. Steinfield (Eds.), Organizations and
Communication Technology, Sage, Newbury Park, CA,
1990, p. 194.
[33] K. Mathieson, Predicting user intentions: comparing the tech-
nology acceptance model with the theory of planned behavior,
Information Systems Research 2 (1991) 3.
[34] W.J. McGuire, Attitudes and attitude change, in: G. Lindzey,
E. Aronson (Eds.), Handbook of Social Psychology, Chapter
19, Random House, New York, 1985, pp. 233346.
[35] N.P. Melone, A theoretical assessment of the user-satisfaction
construct in information systems research, Management Sci-
ence 36 (1990) 1.
[36] J.C. Nunnally, I.H. Bernstein, Psychometric Theory, 3rd ed.,
McGraw-Hill, New York, 1994.
[37] J.M. Olson, M.P. Zanna, Attitudes and attitude change, Annual
Review of Psychology 44 (1993) 117154.[38] W.J. Orlikowski, The duality of technology: rethinking the
concepts of technology in organizations, Organization Science
3 (1992) 3.
[39] R.E. Petty, D.T. Wegener, L.R. Fabrigar, Attitudes and attitude
change, Annual Review of Psychology 48 (1998) 609648.
[40] D.S. Staples, J.S. Hulland, C.A. Higgins, A self-efficacy
theory explanation for the management of remote workers
in virtual organizations, Journal of Computer-Mediated Com-
munication 3 (1998) 4.
[41] L.C. Stephen Jr., R.F. Leandre, E.P. Richard, Measuring the
Affective and Cognitive Prosperities of Attitudes: Conceptual
and Methodological Issues, Personality and Social Psychology
Bulletin 20 (1994).
[42] D. Straub, M. Keil, W. Brenner, Testing the technology ac-ceptance model across cultures: a three country study, Infor-
mation and Management 33 (1997) 1.
[43] D. Straub, M. Limayem, E. Karahanna-Evaristo, Measuring
system usage: implications for IS theory testing, Management
Science 41 (1995) 8.
[44] G.H. Subramanian, A replication of perceived usefulness
and perceived ease of use measurement, Decision Sciences
25 (5/6) (1994) 863 874.
[45] E.B. Swanson, Measuring user attitudes in MIS research: a
review, OMEGA: The International Journal of Management
Science 10 (1982) 2.
[46] B. Szajna, Empirical evaluation of the revised technology
acceptance model, Management Science 42 (1996) 1.
[47] S. Taylor, P.A. Todd, Understanding information technology
usage: a test of competing models, Information Systems Re-
search 6 (1995) 2.[48] S. Taylor, P.A. Todd, Assessing IT usage: the role of prior
experience, MIS Quarterly 19 (1995) 4.
[49] A. Tesser, D.R. Shaffer, Attitudes and attitude change, Annual
Review of Psychology 41 (1990) 479.
[50] R.C. Thompson, J.G.J. Hunt, In the black box of alpha, beta,
and gamma change: using a cognitive-processing model to
assess attitude structure, Academy of Management Review
21 (1996) 3.
[51] H.C. Triandis, Value, attitudes, and interpersonal behavior,
Nebraska Symposium on Motivation, University of Nebraska
Press, Lincoln, NE, 1980.
[52] A.W. Trice, M.E. Treacy, Utilization as a dependent variable
in MIS research, Proceedings of the International Conference
of Information Systems, 1986.
[53] V. Venkatesh, F.D. Davis, A model of the antecedents of per-
ceived ease of use: development and test, Decision Sciences
27 (1996) 3.
[54] V. Venkatesh, F.D. Davis, A theoretical extension of the tech-
nology acceptance model: four longitudinal field studies,
Management Science 46 (2000) 186204.
[55] K.E. Weick, Technology as equivoque: sensemaking in new
technologies, in: P.S. Goodman, L.E. Sproull, et al. (Eds.),
Technology and Organizations, Jossey-Bass, San Francisco,
1990, pp. 144.
[56] H.M. Weiss, R. Cropanzano, Affective events theory: a theo-
retical discussion of the structure, causes and consequences of
affective experiences at work, in: B.M. Staw, L.L. Cummings(Eds.), Research in Organizational Behavior, JAI Press,
Greenwich, CT, 1996, pp. 174.
Further reading
[1] J.C. Anderson, D.W. Gerbing, Structural equation modeling in
practice: a review and recommended two-step approach, Psy-
chological Bulletin 103 (1988) 3.
[2] E. Babbie, The Practice of Social Research, 7th ed., Wads-
worth, Belmont, CA, 1995.
[3] R.M. Baron, D.A. Kenny, The moderator mediator variabledistinction in social psychological research: conceptual, strate-
gic, and statistical considerations, Journal of Personality and
Social Psychology 51 (1986) 6.
[4] J. Etezadi-Amoli, A.F. Farhoomand, A structural model of end
user computing satisfaction and user performance, Information
and Management 30.
[5] L.R. James, J.M. Brett, Mediators, moderators, and tests for
mediation, Journal of Applied Psychology 69 (1984) 2.
[6] F.N. Kerlinger, Foundations of Behavioral Research, 3rd ed.,
Holt, Rinehart and Winston, New York, 1986.
[7] V. Venkatesh, F.D. Davis, A theoretical extension of the tech-
H. Yang, Y. Yoo / Decision Support Systems 38 (2004) 193130
-
8/14/2019 Yang Et Yoo_2004_all About Attitude
13/13
nology acceptance model: four longitudinal field studies, Man-
agement Science 46 (2000) 2.
[8] N. Venkatraman, The concept of fit in strategy research: toward
verbal and statistical correspondence, Academy of Management
Review 14 (1989) 3.
Hee-Dong Yang is an Assistant Professor in the College of
Management at Ewha Womans University in Korea. He has a
PhD from Case Western Reserve University in Management of
Information Systems. He was previously an Assistant Professor at
the University of Massachusetts-Boston. His research interests
include electronic commerce, the organizational impact of informa-
tion technology, the technology acceptance model, and strategic use
of information systems. His papers have appeared in the Journal of
Information Technology Management, International Journal of
Electronic Commerce, and have been presented at many leading
international conferences (ICIS, HICSS, Academy of Management,
ASAC).
Youngjin Yoo is an Assistant Professor in the Information Systems
Department at the Weatherhead School of Management at Case
Western Reserve University. He holds a PhD in Information
Systems from the University of Maryland and an MBA and BS in
Business Administration from Seoul National University. His re-search interests include knowledge management in global organ-
izations and technology-enabled organizational transformation. His
papers have been presented at several leading conferences (the
Academy of Management, ICIS, AIS, and HICSS) and have
appeared in leading academic and practitioner journals such as
Information Systems Research, MIS Quarterly, the Academy of
Management Journal, Journal of Strategic Information Systems,
Journal of Management Education, and International Journal of
Organizational Analysis and Information Systems Management. He
serves on the editorial board of the Journal of AIS.
H. Yang, Y. Yoo / Decision Support Systems 38 (2004) 1931 31
top related