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User acceptance of WAP services: test of competing theories
Shin-Yuan Hung*, Chia-Ming Chang
Department of Information Management, National Chung Cheng University, 160 San-Hsin Village, Ming Hsiung, Chia-Yi, Taiwan, ROC
Available online 4 November 2004
Abstract
Although wireless application protocol (WAP) service acceptance has long attracted considerable interest, the problem of
identifying the best theoretical model among the various prevalent models has been relatively neglected. Recently, a few
studies have attempted to examine this issue using the decomposed TPB model. It is rare for one model to be superior to all of
the other models in all criteria. WAP service acceptance involves competition among three well-established theoretical models,
as follows: the Technology Acceptance Model (TAM), the Theory of Planned Behavior (TPB), and the decomposed TPB
model. This study compares the effectiveness of these three models in understanding WAP services acceptance. Empirical data
were obtained from a field survey in Taiwan. Notable findings were reported for the three competing models, as follows: (1)
TPB and decomposed TPB are superior to TAM in terms of their ability to explain user acceptance of WAP services and (2)
while the decomposed TPB model provided more easily understood and managerially relevant factors, the TPB model was
more parsimonious and had very similar explanatory power to the decomposed TPB model. Finally, the implications of thisstudy are discussed.
D 2004 Elsevier B.V. All rights reserved.
Keywords: Wireless application protocol; Technology Acceptance Model; Theory of Planned Behavior; Decomposed Theory of Planned
Behavior; Information technology acceptance
1. Introduction
Wireless application protocol (WAP) service accept-
ance is crucial for WAP survival in the highlycompetitive mobile commerce market. WAP is partic-
ularly one of widespread technical standards to extend
Internet technologies to wireless networks. Since 1997,
Ericsson, Motorola, Nokia, and Unwired Planet have
taken the initiative to found the WAP Forum [25,26].
WAP has become an open, global specification,
enabling mobile users with wireless devices to easily
and instantly access and interact with information andservices [27]. Among the general public, WAP should
be held in very high esteem, owing to support from
numerous key industry players. However, problems for
low usage of WAP services do exist[17,21,24]. Clearly,
user acceptance is required for ensuring productivity
payoffs from any investments in IT services [10,19,23].
Thus, from a pragmatic perspective, WAP service
acceptance needs to be explored.
0920-5489/$ - see front matterD 2004 Elsevier B.V. All rights reserved.
doi:10.1016/j.csi.2004.10.004
* Corresponding author. Tel.: +886 5 2720411x34601; fax:
+886 5 2721501.
E-mail address: [email protected] (S.-Y. Hung).
Computer Standards & Interfaces 27 (2005) 359370
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One of numerous approaches for WAP services
involves assessing which model is best for under-
standing user acceptance. Early studies, focused on
examining WAP service acceptance, mostly exploredthe influence of critical factors on user acceptance. For
example, Teo and Pok [24] or Hung et al. [17] both
adopted similar approaches. However, previous studies
neglected to examine whether this model is the best
among the previous theoretical models. To consider
analysis build on the individual level and the WAP
services acceptance unit requires examining three
competing theoretical models, namely: the Technology
Acceptance Model (TAM) [10,11], the Theory of
Planned Behavior (TPB) [2,4,5], and the decomposed
TPB model [23].Simultaneous testing of competing theoretical mod-
els offers a helpful approach to understanding user
acceptance of WAP services. Numerous similar exam-
ples of testing simultaneously competing models exist.
For example, Mathieson [19] found that TPB is
superior to TAM in its ability to predict behavioral
intention. Professional healthcare provides another
example. Chau and Hu [8] found that TAM or TPB is
more limited in its explanatory ability [8]. Another
example has demonstrated that the three models (TAM,
TPB, Decomposed TPB model) are roughly equivalent
[23]. All these examples make it clear that (1)
simultaneous testing of competing models is a well-
established approach and (2) each theoretical model
has its own distinct advantages.
Having decided to adopt the simultaneous test
approach, it is beneficial to clarify the similarity and
differences among three competing models. Compar-
ison of the three theoretical models reveals the
following two similarities: (1) all three are intention-
based theoretical models and are grounded from social
psychology; (2) each one provides an appropriate
perspective for understanding individual IT serviceacceptance. Notable differences among the three
competing models include the following: (1) TAM,
proposed by Davis [10], is an adaptation of the Theory
of Reasoned Action (TRA) [3]; TPB extends the TRA
to explain behavioral conditions not entirely under
volitional control [2,4,5]; the decomposed TPB model
deconstructs belief structures of TPB into several
factors [23]; (2) to represent the antecedents of user
acceptance, TAM focuses on two factors, perceived
usefulness and ease of use [10,11]; TPB stresses the
influence of perceived behavioral control beliefs on
behavioral intention and actual behavior [2,5]; the
decomposed TPB model focuses on identifying various
beliefs factors that influence three determinants ofintention (namely attitude, subjective norm and per-
ceived behavioral control). Decomposition of belief
sets can identify more stable, easily understood, and
managerially relevant factors [23].
This investigation compares the three competing
models in terms of the extent to which they can be used
to better clarify user acceptance of WAP services. This
study uses overall model fit, explanatory power and
path significance to assess and compare models using
structural equation modeling (SEM). This investigation
established a sampling frame with the assistance ofseveral Taiwanese telecom companies and collected
data using a systematic sampling method. Empirical
self-reported data were obtained from 267 voluntary
users. Users indicate individuals that have registered in
the database of telecom companies and have heard of
WAP services, as well as those with actual use
experience.
The remainder of this study is organized as follows.
Section 2 reviews the related literature, especially the
literature on WAP and the three competing theoretical
models. Next, Section 3 describes the research
method. Meanwhile, the analytical results are reported
in Section 4. Finally, Section 5 discusses the results,
presents conclusions, and indicates the implications of
the study findings.
2. Literature review
2.1. WAP services
In mobile commerce, WAP is just one of many
competing technical standards. Competitors to WAPinclude GPRS, 3G, etc. In 1997, an industry organ-
ization known as the WAP Forum was established to
devise technical standards for bridging the gap between
mobile and Internet networks. Forum members include
Ericsson, Motorola, Nokia, and several key industry
players [25,26]. The development of WAP brought the
wireless world closer to the Internet via a set of
specifications based on technologies that improve the
experience of wireless users [26,27]. In technical
improvement, various newly released features include
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Push Services, User Agent Profile, WDP Tunneling,
WMLscript, CryptoLibrary, Wireless Telephony
Application, Wireless Application Environment, Mul-
timedia Message Service, Pictogram, etc. [26,27].However, support from key industry players and
technical improvements have not altered the problem
of lower user acceptance.
The Greater China economic region is a rapidly
growing market for WAP services. Numerous western
telecommunications enterprises are extremely inter-
ested in providing wireless services in Shanghai,
Hong Kong, Taipei, etc. Since these cities largely
share a common culture and language, the analytical
results and conclusions may provide a good reference
for global telecommunication enterprises to use inestablishing a development strategy for their far
eastern operations. Because of the importance of this
region, some previous studies of WAP services
acceptance have also focused on this area, including
Hung et al. [17] who investigated in Taiwan, and Teo
and Pok [24] who focused on Singapore.
2.2. Competing theories
The application of theoretical models to under-
stand user acceptance of any new IT services is a
well-established approach. Pursuing numerous theo-
retical models, for considering the analysis unit and
level of each theoretical model, three models
(namely TAM, TPB, and the Decomposed TPB
model) have been applied to examine the individual
acceptance of new IT services and suitable for WAP
services. The features of each theoretical model are
presented as follows.
2.2.1. Technology Acceptance Model (TAM)
TAM is a very powerful and parsimonious model
for explaining and predicting much of the variancein new IT acceptance [10,11]. TAM is an adaptation
of the Theory of Reasoned Action (TRA) [13].
TAM has the following features:
(1) TAM excludes the influence of social norm and
perceived behavioral control on behavioral
intention.
(2) Two belief factors (perceived usefulness and
perceived ease of use) determine attitude
towards behavioral intention.
(3) Behavioral intention is directly affected by
perceived usefulness and attitude.
(4) Through two beliefs factors, numerous external
factors (i.e., system design characteristics, usecharacteristics, facilitating support, training, etc.)
can affect behavioral intention.
(5) Two belief factors are easy to understand and
manipulate in information system design and
implementation.
(6) The use of self-reported measurements may
cause low ability to predict actual behavior [11].
2.2.2. Theory of Planned Behavior (TPB)
TPB extends the TRA to consider perceived
behavioral control for reflecting user perceptions
regarding possible internal and external constraints
on behavior [2,5]. TPB emphasizes that behavior
includes non-volitional aspects under certain circum-
stances. Some TPB features and early study results are
described as follows:
(1) TPB includes the possible influence of perceived
behavioral control on behavioral intention and
actual behavior.
(2) Behavioral intention and perceived behavioral
control can directly affect behavior.
(3) Attitude and perceived behavioral control both
determine behavioral intention.
(4) In the early IT implementation phase, the factor
(subjective norm) is important for users with
limited direct experience [16].
(5) Monolithic belief sets in TPB may be inconsis-
t e nt l y r el a te d t o t h e t h re e d e te rm in a nt s
of intention and thus may be difficult to opera-
tionalize [23].
2.2.3. Decomposed Theory of Planned Behaviormodel (Decomposed TPB model)
The decomposed TPB model is created by Taylor
and Todd [23]. This model is focused on decom-
posing three sets of belief structures into a multi-
dimensional belief construct. The advantages of this
model include: (1) representing clear, easily under-
stood, and stable sets of beliefs; (2) easily oper-
ationalizing these beliefs; (3) focusing on more
managerially relevant beliefs, rather the two factors
proposed in TAM [23].
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A variety of early investigations [8,9,11,19,23] have
compared competing theoretical models. These pre-
vious works have demonstrated that tests among these
models have no consistent results. For example,Mathieson [19] found that TAM is superior to TPB
for predicting behavioral intention to use spreadsheet
packages. Taylor and Todd [23] observed that decom-
posed TPB is superior to TPB and TAM in under-
standing behavioral intention. For professional
workers, Chau and Hu [8] proposed that TAM is
superior to TPB for explaining behavioral intention.
The findings of these studies highlight the need to
develop simultaneous tests of these competing models
for user acceptance of new IT services.
3. Research method and design
3.1. Measures and pretests
Regarding instrument construction, the items used
to operationalize the constructs of each investigated
variable were mainly adopted from relevant previous
studies, with validation and wording changes as
necessary (see Table 1). Specifically, measures of
perceived usefulness and ease of use were adapted
from Davis [10], measures of user satisfaction adapted
from Doll and Torkzadeh [12], measures of personal
innovativeness adapted from Agarwal and Prasad [1],
and measures of subjective norms, perceived behav-
ioral control, and attitudes were derived from Taylor
and Todd [23]. Furthermore, measures of behavioral
intention were derived from both the above sources.
Additionally, constructs shared by different investi-
gated models were measured using the same items. All
items were measured on a seven-point Likert-type scale
with anchors ranging from bstrongly agreeQ tobstrongly disagreeQ. Appendix A lists the items used
to measure each variable. To achieve the desired
balance and randomness in the questionnaire, half of
the items were worded with proper negation and all
items in the questionnaire were randomly sequenced
to reduce the potential ceiling (or floor) effect, which
induces monotonous responses to the measures of a
particular construct. Furthermore, the final question-
naire was validated by two professional translators to
ensure no syntax and semantic errors during the
translation from English to Chinese. Appendix Acontains the questionnaire used in this study.
To ensure data validity and reliability, this study
first pre-tested the questionnaire through review by
several consumers and telecommunication professio-
nals. Following final survey administration, analysis
of the responses of 25 random respondents found the
survey design free of problems. Regarding reliability,
the survey exhibited strong internal consistency, with
all multiple-item constructs achieving Cronbachs
alpha of 0.73 or higher. Moreover, regarding validity,
previously validated measurements were used to
ensure the measurement validity. Factor analysis was
conducted to clarify convergent and discriminant
validity, after which all factors were extracted that
had an eigenvalue N1.0 and all items displayed
loading 0.63 or higher on their respective factors.
3.2. Survey respondents
Two-hundred and sixty-seven survey respondents
were usable data. Only 50 respondents had actual
experience of using WAP services. A large proportion
of the respondents had only heard of WAP services buthad not used them. While such respondents were
considered usable respondents in the data analysis, the
representativeness of such data is questionable. The
authors were conscious of this problem before data
collection. To avoid this problem, during the research
design phase, two supplementary works were consid-
ered, as follows:
(1) The sampling frame is built with the assistance
of several telecommunication companies. Thus,
Table 1
Research variables and measurements
Construct Source
User satisfaction Doll and Torkzadeh [12]
Personal innovativeness Agarwal and Prasad [1]
Ease of use Davis [10]
Usefulness Davis [10]
Peer influence Taylor and Todd [23]
External influence Taylor and Todd [23]
Self-efficacy Taylor and Todd [23]
Facilitating condition Taylor and Todd [23]
Attitude Taylor and Todd [23]
Subjective norm Taylor and Todd [23]
Perceived behavior control Taylor and Todd [23]
Intention Taylor and Todd [23]
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all survey respondents are registered WAP
users.
(2) To check the understanding of respondents of
WAP services, this study added the questionsbHave ever heard of the following mobile
services: (1) WAP, (2) GPRS, (3) 3G, (4) GSM, Q
to the first part of questionnaire. All survey
respondents had heard of WAP services and other
competing options.
3.3. Statistical analysis
This investigation used the structural equation
modeling (SEM) for hypotheses testing. Following
the two-stage approach proposed by Anderson andGerbing [6], data from 267 samples was analyzed in
two stages. First, the measurement model was
estimated using confirmatory factor analysis to test
whether the constructs possessed sufficient validation
and reliability. To ensure data validity and reliability,
internal consistency, convergent validity, and dis-
criminant validity were demonstrated. Second, the
structural model that best fitted the data was
identified, and then the hypotheses were tested.
SEM has been identified as an appropriate cova-
riance-based approach in studies with a strong basis
on a priori theory [18,7]. This study is well suited
for confirmatory testing of the fit of the proposed
theoretical model to observed data using SEM.
This study chose AMOS for windows (version
4)as the SEM software for model estimation.
AMOS is a covariance-based approach similar to
LISREL, in which the covariance structure obtained
from the observed data is used to simultaneously fit
measurement and structural equations specified in
the model. AMOS estimated both the measurement
and structural models using the full information
maximum likelihood estimator.
3.4. Data collection and sample representativeness
Using a systematic sampling method, 500 ques-
tionnaires were mailed to individuals. Initially, the
questionnaires were mailed to respondents who were
given 15 days to respond. After 15 days, follow-ups
were sent to the non-respondents. Following a
further 10-day wait, responses were solicited from
remaining non-respondents via telephone.
Of the 280 returned questionnaires, 13 were
excluded due to incomplete answers, leaving 267
usable responses (profiled in Table 2). The response
rate thus was 53.4%, comparing favorably with similarmail surveys.
The chi-square goodness-of-fit test was used to test
whether the sample data ratio, including WAP users
and non-users, derived from the population with a
specific distribution. The results indicated that this
sample was representative of the WAP user popula-
tion of Taiwan (v2=0.11, p=0.745). Furthermore, this
study also tested for response bias between the
responses and non-responses using the independent
sample t-test. The analytical results demonstrated no
significant differences among the respondent andnon-respondent groups in terms of gender, age,
education level, annual income, and marriage status.
Thus, no response bias existed in this study.
4. Results
4.1. Measurement model results
Following the two-step approach suggested by
Anderson and Gerbing [6], the first stage measures
Table 2
Profile of the respondents
Variable Count Percentage
Gender Male 166 62.2
Female 101 37.8
Age b20 years old 10 3.7
2130 years old 132 49.4
3140 years old 90 33.7
4150 years old 32 12.0
N50 years old 3 1.1
Education Junior high school 2 0.7
Senior high school 26 9.7Bachelor 209 78.3
Master or above 30 11.2
Annual income b NT$240,000 44 16.5
NT$240,000NT$480,000 108 40.4
NT$480,000NT$720,000 85 31.8
NT$720,000NT$960,000 20 7.5
N NT$960,000 10 3.7
Marriage status Single 155 58.1
Married 112 41.9
WAP use Non-users 217 81.3
Users 50 18.7
US$ 1cNT$34.90.
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variance in attitude, and 44% of the variance in
perceived usefulness.
4.4. Model 2the Theory of Planned Behavior
As indicated in Fig. 2, most path coefficients were as
hypothesized. The path from perceived behavioral
control to intention unexpectedly was insignificant.
Fig. 2 also showed that the TPB model can explain 11%
of the variance in WAP service use and 38% of the
variance in behavioral intention. The path coefficient
(0.107) between perceived behavioral control and
use indicated that perceived behavioral control signifi-
cantly suppresses actual WAP services use.
4.5. Model 3The Decomposed TPB
Fig. 3 showed that the decomposed TPB model
explains 12% of the variance in WAP services use, 38%
of the variance in behavioral intention, 54% of the
variance in attitude, 63% of the variance in subjective
norm, and 52% of the variance in perceived behavioral
control. This figure exhibited the same significant path
as the TPB model in Fig. 2. For example, in these two
models, the path from perceived behavioral control tointention is insignificant. Additionally, through the
decomposing approach used in this model, the follow-
ing observations can be made:
(1) For attitude, connection speed, user satisfaction,
personal innovativeness, and two factors (per-
ceived usefulness and ease of use) are significant
determinants of attitude towards WAP services.
Meanwhile, the path from current service costs
(usage charge) to attitude is insignificant.
(2) For subjective norm, the significant determi-
nant is peer influence, while the path fromexternal influence to subjective norm is insig-
nificant.
(3) Self-efficacy is a significant determinant of
perceived behavioral control. In comparison,
the path from facilitating condition to perceived
behavioral control is insignificant.
From comparison of three competing models, the
following discussions demonstrate how each model
provides understanding of user acceptance of WAP
services.
5. Discussion and conclusions
This study performs a model comparison among
three competing theoretical models (TAM, TPB,
Decomposed TPB model) for explaining user accept-
ance of WAP services. In terms of overall model fit
criteria, all models provide comparable fit to the data,
with a few exceptions. Consequently, by comparing the
models with one another, reflections on comparative
findings in the Results section clarify that:
(1) The comparative results in the explained variance
of use demonstrate that TPB and decomposed
TPB model have better explanation ability than
TAM (R2=0.12 for decomposed TPB, R2=0.11
for TPB, R2=0.08 for TAM). However, explan-
ation ability is low for all three models. The
possible reasons of low explanation ability are
that: (a) Self-reported measures of WAP services
use. Some empirical studies [11,22,23] have
Fig. 1. Results of TAM model.
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pointed this method problem. For example, Davis
et al. [11] reports that the explained variance of
self-reported IT usage is just 12%. Szajna [22] or
Taylor and Todd [23] both emphasize self-
reported measures problem. (b) Training or direct
usage experience. For example, owing to actual
usage experience over a 15-week period in the
study of Szajna [22], the explained variance of
usage increases from 8% to 32%.
(2) In all models, behavioral intention is the neces-
sary precursor to use of WAP services, and in the
decomposed TPB and TPB models, perceived
behavioral control is an additional precursor of
use. Ajzen [2,5] suggested that the link between
Fig. 3. Results of the decomposed TPB model.
Fig. 2. Results of TPB model.
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perceived behavioral control and use is actual
control rather than perceived control. Addition-
ally, the improvement in explained variance of
use by considering perceived behavioral controlwas small. Consequently, actual use of WAP
services is a relatively straightforward task and is
affected mainly by attitude and subjective norm
rather than perceived behavioral control.
(3) Regarding the explanatory power of behavioral
intention among three models, both models (TPB
and decomposed TPB) exhibit the same explan-
ation ability (R2=0.38), TAM reveal lower
explanation ability (R2=0.32). Thus, in terms of
behavioral intention, both the decomposed TPB
and TPB models can provide better understand-ing of intentions than TAM in WAP services.
(4) In terms of the determinants of behavioral
intention, one similarity of three models is that
behavioral intention is directly influenced by
attitude. To consider attitude, all three models are
suitable for explaining WAP service acceptance.
To consider the role of subjective norm in WAP
services acceptance, TPB and the decomposed
TPB are superior to TAM. To consider simulta-
neously two determinants (attitude and perceived
usefulness), TAM is the best choice. Additionally,
in TPB or decomposed TPB, the path from
perceived behavioral control to intention is
insignificant. More research needs to be con-
ducted to examine this path in WAP service
setting.
(5) Regarding the ability to explain attitude, TAM
predicted attitude towards WAP services better
than the decomposed TPB model (R2=0.58 for
TAM and R2=0.54 decomposed TPB model).
Good evidence exists that TAM is more parsimo-
nious [11] and provides a more efficient method
of assessing individual attitude regarding WAPservices.
(6) In terms of determinants of attitude towards
WAP services, the decomposed TPB model has
its advantage. By contrast, the decomposed
TPB model indicates connection speed, user
satisfaction, personal innovativeness, and two
factors in TAM (perceived usefulness and ease
of use) are significant determinants of attitude,
while TAM indicates just perceived usefulness
is significant determinant. To consider approach
to affecting attitude, the decomposed TPB
model provides more easily understood and
managerially relevant information to guide
WAP services design efforts. Additionally,regarding the determinants of subjective norm
or perceived behavioral control, the decom-
posed TPB model is also superior to the other
two models. From this model, peer influence
can directly affect subjective norm and self-
efficacy can directly influence perceived behav-
ioral control. Consequently, in WAP service
setting, the decomposed TPB model can pro-
vide leverage points to guide WAP services
design efforts. The implications for WAP
service design are that peer referents opinionshave priority over external media advisements,
and individual self-efficacy precedes facilitating
resources.
Finally, interpreting the results of this is limited by
the fact that the study was conducted in Taiwan.
Although the cultural and linguistic similarities exist
within the Greater China economic region, threats to
external validity of this investigation in Taiwan cannot
be avoided. The current study suggests that further
research should be anticipated to further extend the
population of WAP service users to the Greater China
economic region. This study could be also expanded in
the other direction. Although this investigation has
argued that modeling WAP service acceptance is
appropriate based on the three competing theories, it
is also important to extend current research to clarify
why WAP users either continue or discontinue using
WAP services. Additionally, longitudinal research
conducted in field settings is also necessary because
technical and environmental changes of WAP services
take place over time.
Acknowledgment
The authors would like to thank the National
Science Council (NSC) of the Republic of China,
Taiwan under Contract No. NSC 90-2416-H-194-
0 32 a nd t he M in is tr y o f E du ca ti on ( MO E)
Program for Promoting Academic Excellence of
Universities under grant number 91-H-FA08-1-4 for
their financial support.
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Gender (1) Male (2) Female
Age (1) 20 or less (2) 2130 (3) 3140 (4) 4150 (5) 51 or aboveEducation (1) Junior high school (2) Senior high school (3) Bachelor (4) Master or above
Annual Income (thousand NT dollars) (1) 239 or less (2) 240480 (3) 480720 (4) 720960 (5) 961 or above
Marriage status (1) Single (2) Married
Computer experience (years) (1) 1 or less (2) 23 (3) 45 (4) 67 (5) 8 or above
Average times you use WAP services in a week (1) None (2) 1 or less (3) 24 (4) 57 (5) 8 or above
Item Rating scale
Extremely
Likely
Quite
Likely
Slightly
Likely
Neither Slightly
Unlikely
Quite
Unlikely
Extremely
Unlikely
Connection speed
I would accept current connection speed of WAP services.
Usage cost
I would accept current charge for WAP services.
User satisfaction
The WAP services provide the precise information I need.
The information content of the WAP services meets I need.
The WAP services provide reports that seem to be just about
exactly what I need.
The WAP services provide sufficient information.
Personal innovativeness
I am generally cautious about accepting new ideas.
I find it stimulating to be original in my thinking and behavior.
I am challenged by ambiguities and unsolved problems.
I must see other people using innovations before I will considerthem.
Ease of use
Learning to use WAP services would be easy for me.
I would find it easy to gather information using WAP services.
It would be easy for me to become skillful at using WAP services.
I would find WAP services easy to use.
Usefulness
Using WAP services would improve my performance in gathering
information.
Using WAP services would improve my productivity in gathering
information.
Using WAP services would enhance my effectiveness in gathering
information.
I would find WAP services useful in gathering information.Peer influence
My peers/colleagues/friends thought that I should use WAP
services for gathering information.
People I knew thought that using WAP services was a good idea.
People I knew influenced me to try out WAP services for gathering
information.
External influence
I read/saw news reports that using WAP services
was a good way of gathering information.
The popular press depicted a positive sentiment for using
WAP services.
Mass media reports influenced me to try out WAP services.
Appendix A. The Questionnaire
Please indicate your agreement with the next set of statements using the following rating scale.
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Self-efficacy
I would feel comfortable using WAP services on my own.
I would be able to use WAP services reasonably well on my own.
I would be able to use WAP services even if there was no one around
to help me.
Facilitating conditions
Resources required to use WAP services were available to me.
I had access to hardware, software, and services needed to use
WAP services.
I was constrained by the lack of resources needed to use WAP services.
Attitude
Using WAP services would be a good idea.
Using WAP for gathering information would be a foolish idea.
I like the idea of using WAP services for gathering information.
Using WAP services would be a pleasant experience.
Subjective norm
People (peers and experts) important to me supported my use of
WAP services.People who influenced my behavior wanted me to use WAP
services instead of any alternative means.
People whose opinions I valued preferred that I use WAP services.
Perceived behavioral control
I would be able to use WAP services well.
Using WAP services was entirely within my control.
Intention
I intend to use WAP services in the near future
(i.e., next three months).
It is likely that I will use WAP services in the near future
(i.e., next three months).
I expect to use WAP services in the near future
(i.e., next three months).
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http://www.iec.org/online/tutorials/wap/.
Shin-Yuan Hung is an Associate Profes-
sor of Information Systems, and the Head
of Division of Information Management
of Computer Center at National Chung
Cheng University. He holds a Ph.D. in
Information Systems from the National
Sun Yat-sen University. His current
research interests include decision support
systems, electronic commerce, and knowl-
edge management. Dr. Hung has pub-
lished a number of papers in Information
and Management, Decision Support Systems, Electronic Com-
merce Research and Applications, Information Technology and
People, Computer Standard and Interfaces, Industrial Management
and Data Systems, International Journal of Management Theory
and Practice, Journal of Chinese Information Management, among
others.
Chia-Ming Chang is a Doctoral Student in
the MIS program at the National Chung
Cheng University. He received his Masters
degree in MIS from the same university.
His current research interests include deci-
sion support systems, electronic commerce,
and user interface design. He has published
articles in Electronic Commerce Research
and Applications, Information Management
& Computer Security, Journal of Chinese
Information Management, and so on.
S.-Y. Hung, C.-M. Chang / Computer Standards & Interfaces 27 (2005) 359370370
http://www.archive.devx.com/wireless/articles/WAP/WAPjp112000.asphttp://www.wapforum.org/what/WAPWhite_Paper1.pdfhttp://www.iec.org/online/tutorials/wap/http://www.iec.org/online/tutorials/wap/http://www.iec.org/online/tutorials/wap/http://www.archive.devx.com/wireless/articles/WAP/WAPjp112000.asphttp://www.wapforum.org/what/WAPWhite_Paper1.pdfhttp://www.wapforum.org/what/WAPWhite_Paper1.pdfhttp://www.iec.org/online/tutorials/wap/http://www.wapforum.org/what/WAPWhite_Paper1.pdfhttp://www.archive.devx.com/wireless/articles/WAP/WAPjp112000.asphttp://www.iec.org/online/tutorials/wap/http://www.wapforum.org/what/WAPWhite_Paper1.pdfhttp://www.archive.devx.com/wireless/articles/WAP/WAPjp112000.asp