iBusiness, 2011, 3, 147-158
doi:10.4236/ib.2011.32021 Published Online June 2011 (http://www.scirp.org/journal/ib)
The Comparison of Three Major Occupations for
User Acceptance of Information Technology:
Applying the UTAUT Model
Yu-Shan Cheng*, Tsai-Fang Yu, Chin-Feng Huang, Chien Yu, Chin-Cheh Yu
Department of Applied Technology and Human Resource Development, National Taiwan Normal University, Taipei, China.
Receive January 22nd, 2011; revised April 3rd, 2011; accepted April 13th, 2011
ABSTRACT
This study investigated whether the differences of gender, age, and occupation for m-learning showed significance on
the utilization of the mobile devices and to figure out if the variation may influence the performance expectancy, effort
expectancy and the social influence to the behavioral intention or even to the behavior of usage. When the employees’
behavioral intention was low, the director of managers or HR department can suggest the employees’ colleagues, supe-
rior manager or friends to communicate with them to enhance their behavioral intention and to use it. And it suggested
that male employees and elder employees should be put more emphasis on the communication to enhance their behav-
ioral intention. UTAUT model with different kinds of businesses for m-learning but the conclusion did not investigate
the differences of the adoption of the mobile devices in each industry. Basing on this, this study attempted to investigate
whether the difference occupations showed significance on the utilization of the mobile devices.
Keywords: Unified Theory of Acceptance and Use of Technology (UTAUT), Manufacturing, Service, Bank
1. Introduction
Because m-learning (mobile learning) industry played
the role as the index of the development of knowledge
economy and digital economy and that it was also a high
value added industry which was the important base of
improving total competitive strength of the occupations
in a country, countries around the world have investi-
gated a lot of resources to promote the m-learning indus-
try. Therefore, m-learning industry has not been an in-
dustry issue in any country, but also the key to whether
the companies of the same business can have the advan-
tage across nations under the frame of knowledge econ-
omy.
The adoption of information and communication tech-
nology (ICT) can improve the learning when giving a
learner-centered lecture [1]. Therefore, triggered by the
marketing competition, service improvement and work-
ing performance, an organization would investigate a lot
of the information technology and apply it to the educa-
tion of the employees to deduce the training cost and
increase the learning will of the employees to improve
their performance, widen the training domain and short-
en the learning curve. etc. [2,3].
In 2007, the ratios among the occupations of manu-
facture, banking and service in buying or making e-learn-
ing platform was 12.4%, 37.1%, 14.3% respectively,
while the ratios went up to 16.7%, 54.3% and 25.7% in
2008 which showed a fair development [4]. In order to
figure out current situation of the three major occupa-
tions in Taiwan with m-learning when giving employees
training, the study adopted the UTAUT model which was
proposed by as in [5] to investigate the issue. They used
eight models to study the problems and found out that
the expected validity would have significant improved
after adding adjustment variables to six of the eight
models. Reference [5] adopted four different kinds of
corporations to test the model, namely the product de-
velopment department of the entertainment business, the
marketing department of the information service busi-
ness, the business account management of the banking
business and the account department of the pub-
lic-operated business. Understanding that as in [5] estab-
lished UTAUT model with different kinds of businesses
but the conclusion did not investigate the differences of
the adoption of the mobile devices in each industry. De-
spite that as in [6] investigated the demonstration and

The Comparison of Three Major Occupations for User Acceptance of Information Technology:
Applying the UTAUT Model
148
found out that the modulators of the technology accep-
tance model of the current users stood the significant
influences [7]. Basing on this, the study attempted to
investigate whether the differences of gender, age, and
occupation showed significance on the utilization of the
mobile devices and to figure out if the variation may
influence the performance expectancy, effort expectancy
and the social influence to the behavioral intention or
even to the behavior of usage. Finally, this study showed
some recommendations for future training are issued.
2. Unified Theory of Acceptance and Use
of Technology
Technology adoption research has flourished in recent
years [6-13].
Many researchers have made significant efforts in
building theories to examine and predict the determinants
of user technology acceptance [9,14,15].
Existing models of ICT acceptance have their founda-
tions in several diverse theories, most noticeably the in-
novation diffusion theory, where individuals’ perceptions
about using an innovation are considered to affect their
adoption behavior [14,15].
Some theoretical models that attempted to explain the
relationship between user beliefs, attitudes, intentions
and actual system use include Theory of Reasoned Ac-
tion (TRA) [16,17], Theory of Planned Behavior (TPB)
[18] and Technology Acceptance Model (TAM) [8].
TAM was rooted in the TRA, a model concerned with
determinants of consciously intended behaviors [4,16,17].
TRA proposed that beliefs influence attitudes, which in
turn lead to intentions and then consequently generate
behaviors. Adopting TRA in the context of user tech-
nology acceptance, TAM assumed that beliefs about
usefulness and ease of use were the primary determinants
of user technology adaptation. Prior study has noted the
similarity between perceived usefulness and ease of use
beliefs in TAM and the relative advantage and complex-
ity constructs in diffusion theory [5,11,14,19].
TAM has received extensive empirical support
through validations, applications and replications for its
power to predict use of information technology [5,8,11,
14,20].
Perceived usefulness was the degree to which a person
believed that using a particular system would enhance
his or her job performance, and perceived ease of use is
the degree to which a person believes that using a par-
ticular system will be free of effort [8].
Attitude toward adoption has been found to play a key
role in technology acceptance within the consumer con-
text [10].
Deservedly, researchers have examined the acceptance
of technology, and several models have been proposed in
the literature. These models include the Technology Ac-
ceptance Model (TAM) [8], and its extension (TAM2)
[21], and models based on the Theory of Reasoned Ac-
tion [22], Innovation Diffusion Theory [19], Triandis
model [23], Motivation [24], Theory of Planned Behav-
ior [11], Social Cognitive Theory [25,26], and, recently,
the Unified Theory of Acceptance and Use of Technol-
ogy (UTAUT) [5]. Each model would have the same
dependent variable, usage, but used various antecedents
to understand acceptance of technology [27,28].
Based on eight prominent models in the field of IT
acceptance research, as in [5] proposed a unified model,
called the unified theory of acceptance and used of tech-
nology (UTAUT), which integrates element across the
eight models. The eight models consist of Theory of
Reasoned Action (TRA) [17], Technology Acceptance
Model (TAM) [8], Motivational Model (MM) [24], The-
ory of Planned Behavior (TPB) [29], the Combined
TAM and TPB (C-TAM-TPB) [30], Model of PC Utili-
zation (MPCU) [23,31], Innovation Diffusion Theory
(IDT) [19,32] and Social Cognitive Theory (SCT)
[25,33]. Based on the study of reference [5], briefly re-
viewed the core constructs in each of the eight models,
which have been theorized as the determinants of IT be-
havioral intention and/or behavior [28].
Various alternative approaches have used in analyzing
consumers’ acceptance of new technologies [8,34,35].
One of the more recent theories, the unified theory of
acceptance and use of technology (UTAUT) (see Figure
1) by as in [5] provided a comprehensive framework for
technology adoption analysis. The model was formulated
based on conceptual and empirical similarities across
eight technology acceptance models. UTAUT contains
four core determinants of behavioral intention-perform-
ance expectancy, effort expectancy, social influence and
facilitating conditions [5]. Although the UTAUT model
is relatively new, it has inspired researchers to try its
suitability in different contexts [36]. One of the strengths
of the UTAUT model is that it considers the role of sev-
eral moderating variables, namely gender, age, experi-
ence and voluntariness of use [5]. These moderators are
assumed to influence the significance of the four core
determinants [36].
The UTAUT aims to explain users’ behavioral inten-
tion to use an information system and their subsequent
usage behavior. The theory holds that four key constructs
(performance expectancy, effort expectancy, social in-
fluence, and facilitating conditions) were direct determi-
nants of behavioral intention and behavior [5]. But as in
[5] consider that when both performance expectancy
constructs and effort expectancy constructs are present,
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The Comparison of Three Major Occupations for User Acceptance of Information Technology:
Applying the UTAUT Model
149
facilitating conditions becomes non-significant in pre-
dicting intention.
The variables of gender, age, experience, and volun-
tariness of use are posited to moderate the impact of the
four key constructs on behavioral intention and behavior
[5]. These determinants and moderators will be used to
extend the proposed research model.
3. Research Model and Hypotheses
The study model tested in this study was shown in Fig-
ure 2. In this model, performance expectancy (PE), ef-
fort expectancy (EE) and social influence (SI) were hy-
pothesized to be determinants of behavioral intention (BI)
to use m-learning system. We also hypothesized that
gender, age and occupation differences would moderate
the influence of these determinants on behavioral inten-
tion and usage. The reason why this study did not discuss
Facilitating conditions was because as in [5] considered
that when both performance expectancy constructs and
effort expectancy constructs were present, facilitating
conditions becomes non-significant in predicting inten-
tion. The proposed constructs and hypotheses were sup-
ported by previous literature. The following sections
elaborated on the theory base and derive the hypotheses.
3.1. Performance Expectancy
Reference [5] defined performance expectancy as the
extent to which an individual believes that using an in-
formation system help him or her to attain benefits in job
performance. Performance expectancy has been justified
as a predictor of behavioral intention to use IT [5]. Per-
formance expectancy consisted of Perceived Usefulness
[8,22], Extrinsic Motivation [24], Job-fit [23], Relative
Advantage [19], and Outcome Expectations [25,26].
Prior studies suggested that performance expectancy
was significant in shaping an individual’s intention to
use new technology, and base on the UTAUT and previ-
ous literature (e.g., [1,5,21]). This study expected that
performance expectancy was a significant determinant of
behavioral intention to use m-learning. Thus, the follow-
ing hypothesis was tested:
Hypothesis 1: Performance expectancy has a positive
Figure 1. The UTAUT model. Source: Reference [5].
Figure 2. Conceptual framework and hypotheses.

The Comparison of Three Major Occupations for User Acceptance of Information Technology:
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150
effect on behavioral intention to use m-learning.
3.2. Effort Expectancy
Reference [5] defined effort expectancy as the degree of
ease associated with the use of the information system.
The three constructs from the different models that related
to effort expectancy are perceived ease of use (TAM/
TAM2), complexity (MPCU) and ease of use (IDT) [5].
Effort Expectancy consisted of Perceived Ease of Use
[8,22], Complexity [23], and Ease of Use [19].
Prior studies suggest that effort expectancy was sig-
nificant in shaping an individual’s behavioral intention to
use new technology, and base on the UTAUT and previ-
ous literature (e.g., [1,5,21,36]). This study expected that
effort expectancy was a significant determinant of be-
havioral intention to use m-learning. Thus, the following
hypothesis is tested:
Hypothesis 2: Effort expectancy has a positive effect
on behavioral intention to use m-learning.
3.3. Social Influence
Venkatesh et al. [5] defined social influence as the extent
to which a person perceives that important others believe
he or she should use a new information system. Three
constructs from the existing models captured the concept
of social influence: subjective norm (TRA, TAM2, TPB
and C-TAM-TPB), social factors (MPCU) and image
(IDT) [5].
Social Influence consisted of Subjective Norm [11,
17,22,29,30], Social Factors [23], and Image [19].
Prior studies suggested that social influence was sig-
nificant in shaping an individual’s intention to use new
technology [19,21,23]. Based on the UTAUT and previ-
ous literature (e.g., [5,12,36]). We expected that social
influence was a significant determinant of behavioral in-
tention to use m-learning. Thus, the following hypothesis
was tested:
Hypothesis 3: Social influence has a positive effect on
behavioral intention to use m-learning.
3.4. Behavioral Intention
Consistent with the underlying theory for all of the inten-
tion models discussed in this paper, we expected that
behavioral intention would have a significant positive
influence on technology usage [5].
Hypothesis 4: Behavioral intention has a significant
positive influence on usage.
3.5. Moderator Effects
Based on the UTAUT and previous literature [12], gen-
der and age were theorized to play a moderating role on
the influence of performance expectancy on behavioral
intention. That was, the influence of performance expec-
tancy on behavioral intention would be moderated by
gender and age, such that the effect would be stronger for
men and particularly for younger men [5].
From prior studies suggested performance expectancy
was significant in behavioral intention, and manufactur-
ing, service, and banking occupations may be affected
(e.g., [1,37,38]).Therefore, this study tested the following
hypotheses:
Hypothesis 5: Performance expectancy influences be-
havioral intention to use m-learning more strongly for
men than for women.
Hypothesis 6: Performance expectancy influences be-
havioral intention to use m-learning more strongly for
younger than for older people.
Hypothesis 7: Performance expectancy influences be-
havioral to use m-learning intention relationship would
be the impact of different occupations.
Prior studies suggested that constructs associated with
effort expectancy would be stronger determinants of in-
dividuals’ intention for women [12,39] and for older
workers [12]. The influence of effort expectancy on be-
havioral intention would be moderated by gender and
age, such that the effect would be stronger for women,
particularly for older women [5]. Thus, this study tested
the following hypotheses:
Hypothesis 8: Effort expectancy influences behavioral
intention to use m-learning more strongly for women
than for men.
Hypothesis 9: Effort expectancy influences behavioral
intention to use m-learning more strongly for older than
for younger people.
From prior studies suggested performance expectancy
was significant in behavioral intention, and manufactur-
ing, service, and banking occupations might be affected
(e.g., [1,6,37]). Thus, the following hypothesis was tested:
Hypothesis 10: Effort expectancy influences behav-
ioral intention to use m-learning relationship would be
the impact of different occupations.
This study incorporated social influence to the study
model in order to explore the moderating effect of age
and gender differences on the relationships between so-
cial influence and behavioral intention. The effect of
social influence on behavioral intention would be mod-
erated by gender and age, such that the effect would be
stronger for women, particularly older women. Thus, the
following hypotheses were tested:
Hypothesis 11: Social influence influences behavioral
intention to use m-learning more strongly for women
than for men.
Hypothesis 12: Social influence influences behavioral
intention to use m-learning more strongly for older than
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The Comparison of Three Major Occupations for User Acceptance of Information Technology:
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151
for younger people.
From prior studies suggested performance expectancy
was significant in behavioral intention, and manufactur-
ing, service, and banking occupations might be affected
(e.g., [1,37,38]). Thus, the following hypothesis was
tested:
Hypothesis 13: Social influence influences behavioral
intention to use m-learning relationship would be the
impact of different occupations.
3.6. Measures
This study adopted SPSS18.0 and LISREL 8.53 statistics
software to study the hypothesis test and data analysis.
The statistic method adopted in this study includes de-
scriptive statistical analysis, common method variance,
reliability analysis, confirmatory factors analysis, and
correlation analysis. Then, this study adopted a structural
equation model to analyze the relation among the vari-
ables in the model and interpret the study model and hy-
pothesizes.
4. Methodology
4.1 Data Collection and Sample Characteristics
Taiwan Common Wealth Magazine would make lists for
manufacture business, service business and banking
business with the standards of revenue, total assets, af-
ter-tax net profit, stock holders’ equity, capital, profit-
ability, return on assets, return on equity, debt ratio, the
number of employees, and employee output. Basing on
the standards, the magazine listed the top 1000 compa-
nies of manufacture business, top 500 companies of the
service business and top 100 companies in banking busi-
ness.
The research objects in the study were the top 1600
enterprises in Taiwan. The questionnaires were delivered
with the method of general survey. The collected data
from 2009, October 1st to November 31st were used for
cross-sectional study. There were 350 questionnaires
collected with the recovery rate of 21.875%. After re-
moving the invalid questionnaires with obvious contra-
diction, there were 264 valid questionnaires collected
with the recovery rate of 16.5%. Among the valid ques-
tionnaires, 143 belong to manufacture business, 89 be-
long to service business and 32 belong to banking busi-
ness.
The repliers of the research were the managers of the
department of internal training which includes manage-
ment department, human resources department and in-
formation sections. In the 264 valid questionnaires, fe-
male repliers took the majority (N = 165; 62.5%). Most
of the informants fall between 31 to 35 years old (N = 74;
28.1%), then 26 to 30 years old (N = 63; 23.9%).
4.2 Common Method Variance
When giving test at the same time point with the self
report inventory and a single source informant, the prob-
lem of common method variance (CMV) was easily ob-
served when the questions were similar with each other
and the semantics was positive [40]. This study took the
Harman’s one-factor test which was the most used test
for common method variance to testy the severity of the
common method variance problem: 1) Each question is
evaluated with exploratory factor analysis to test the re-
sults of all non-shift factor analysis. If there are more
than two factors in the analysis or less than 50% of the
explained variance, then the effected level of the com-
mon method variance is not critical; 2) Then, all ques-
tions are put together to go through the single-factor con-
firmatory factor analysis. If the result shows that not all
the questions have more than 0.50 of the burden level or
poor verify model, then the effected level of the common
method variance is not critical.
This study extracted three factors with the questions
analyzed with exploratory factor analysis. The result
showed that the sample data of the research did not have
the problem of severe common method variance. The
single-factor confirmatory factor analysis also indicated
that it was not the case that all questions were beyond the
burden level of 50. In the model test result, the value of
χ2 was 1196.53, with the freedom level set as 77 and the
significance test ratio probability value p-value as .000
which is significant. Besides, other fit index value
showed: GFI = 0.61, AGFI = 0.46, NFI = 0.81, NNFI =
0.78, CFI = 0.82, that was more than .90 below the fit
standard while the RMSEA is 0.235 which is more than
0.05. All of these values showed that the single-factor
confirmatory factor analysis has indicated poor fit on the
model.
4.3 Validity and Reliability
Before performing structural model analysis, must deal
with the issues of validity and reliability. This study also
adopted Cronbach’s α and confirmatory factor analysis to
test the reliability and validity of the questionnaire.
When studying basic issues, it would be better for the
reliability to be more than 0.70 [41]. The result of the
analysis showed that Cronbach’s α value of performance
expectancy was 0.870, Cronbach’s α value of effort ex-
pectancy was 0.937, and Cronbach’s α value of social
influence was 0.899, which indicated that the question-
naire would have quite fine reliability. Since behavioral
intention and the actual usage were both single observed
variable, the reliability was not included.
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In the confirmatory factor analysis, most potential di-
mensions took composite reliability value (CR) as the
standard. The composite reliability was used to test the
internal agreement (similar with Cronbach’s α) of the
questions in a questionnaire. High reliability value means
high agreement. Then, on the aspect of composite reli-
ability test, Fornell and Larcker [42] suggested the CR
value to be higher than 0.60. The CR value of the per-
formance expectancy was 0.8834, effort expectancy was
0.9375 and social influence was 0.8913. From the above
mentioned, it showed that the composite reliability were
all higher than 0.60 and so the three measuring methods
should have a fine reliability [42].
Convergent validity was used to test multiple ques-
tions derived from a single variable to see if the result
will converge to a single factor. In the confirmatory fac-
tor analysis, converge validity of the dimension took the
average variance extracted (AVE) as a base. AVE was
mainly used to calculate the explanatory power of all
observed variable (measuring questions) of the dimen-
sion to the average variations. The higher AVE was the
higher reliability and converge validity of the dimension
was. What comes next was to test the average amount of
variance extracted. Reference [43] suggested that AVE
should be higher than 0.50. When a measure method
would have good reliability and validity, it would have
better internal structure fit. AVE value of performance
expectancy was 0.6605, effort expectancy was 0.7904,
and social influence was 0.6806 (Table 1).
On the aspect of discriminate validity, if a measuring
model should have discriminate validity, the correlation
level across the potential dimensions should be less than
Table 1. Validity and reliability.
Observation
Variable
Standarized
factor
loadings
Squared
Multiple
Correlations
Composite
Reliability AVE
PE1 0.84 0.71
PE2 0.92 0.84
PE3 0.87 0.75
Performance
expectancy
(PE)
PE4 0.58 0.34
0.8834 0.6605
EE1 0.80 0.63
EE2 0.85 0.73
EE3 0.95 0.89
Effort
expectancy
(EE)
EE4 0.95 0.91
0.9375 0.7904
SI1 0.97 0.94
SI2 0.97 0.94
SI3 0.66 0.43
Social
influence
(SI)
SI4 0.64 0.41
0.8913 0.6806
the correlation level inside the potential dimensions.
Therefore, it used the correlation matrix across the di-
mensions to do the test. The square root of AVE of the
potential variables should be greater than the correlation
coefficients of other dimensions [44]. The square root of
AVE of each dimension was calculated below: AVE
value of performance expectancy was 0.6000; The
square root of AVE was 0.813. AVE value of effort ex-
pectancy was 0.7904; The square root of AVE was 0.889.
AVE value of social influence was 0.6806; The square
root of AVE was 0.825 (Ta ble 2).
The square root values of AVE were all higher than
the relevant value in the rows and the columns. This in-
dicated that the performance expectancy, effort expec-
tancy and social influence have discriminate validity.
4.4 Descriptive Statistics and Inter-Correlations
among Research Variable
The dimension mean scores of the performance expec-
tancy, effort expectancy and the social influence were
3.39, 3.48 and 3.08 respectively which showed their be-
havioral intention to the e-learning have higher identity
with the influential factors of the usage. With standard
deviation were 0.62, 0.65 and 0.68, the informants did
not show difference with the perception level, instead the
agreement degree was quite high (Table 3). When the
behavioral intention mean score fell on 2.86 which was
below than the mean score 3, the informants were not
passionate to the adoption of the e-learning. On the as-
pect of the usage, it included those with an e-learning
system and those without. The mean score was 1.45
which showed that most of the informants use e-learning
system when performing relevant business. The stan-
Table 2. Average variance extracted (AVE).
Variable PE EE SI
PE 0.813
EE 0.728 0.889
SI 0.595 0.590 0.825
Table 3. Descriptive statistics and inter-correlations among
research variable.
VariablesM SD 1 2 3 4 5
1. PE 3.39 0.62 1
2. EE 3.48 0.65 0.73** 1
3. SI 3.08 0.68 0.60** 0.59** 1
4. BI 2.86 0.97 0.38** 0.39** 0.58** 1
5. Usage1.45 0.50 0.11 0.16* 0.14* 0.18**1
N = 264, *p < 0.05, **p < 0.01, ***p < 0.001.
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The Comparison of Three Major Occupations for User Acceptance of Information Technology:
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153
dardized deviation was 0.50. fluence. This showed that by lifting the employees’ be-
havioral intention could trigger him/her to perform usage.
Accordingly, hypothesis 4 was not rejected. Finally, R2
value of the behavioral intention was 34% and that of the
usage was 1.1%.
Except for the performance expectancy, the correlation
coefficient of the usage was 0.111. When any two of
them showed no correlative significance, other correla-
tion coefficients showed a relationship between every
two dimensions which fell between 0.139 to 0.728 for
the with significance (p < 0.05). It indicated that the two
variables were positively correlated.
4.6. Results of Moderator Effects
Basing on prior studies on the variables of the moderator
effects, the study was designed to investigate the issue
from gender, age and occupation. This approach used a
pre-established level of a moderator, which emerges
naturally from the study and cannot be modified by re-
searchers. For example, a person’s gender, recorded as
male or female, naturally forms two moderator levels. To
identify a moderation level for age, the dataset was di-
vided to form two sets, each representing individuals
who belong to a particular generation. An analysis of age
distribution demonstrates that two major age groups
emerged: Senior and Journal groups. Representatives of
these generations may be fundamentally different in
terms of various characteristics, perceptions, and behav-
iors [45]. Reference [46] used 40 years of age at the day
of the survey as a cut-off point. In addition, the classifi-
cation of the industry is based on Taiwan Common
Wealth Magazine which investigated the manufacture
business, the service business and the banking business
annually.
To sum up, when informants would have higher
awareness level to the coordination facility condition of
the performance expectancy, effort expectancy and social
influence, they would have higher intention to perform
the behavior. Besides, when the awareness level of the
performance expectancy, social influence and behavioral
intention were higher, the correlation with the usage
would be higher as well.
4.5. Structural Paths and Hypotheses Tests
On the measuring model of the performance expectancy,
effort expectancy, social influence, behavioral intention
and usage in this study, the χ2 value of the test result was
2.59, and when the freedom level was 3, the significance
test probability p-value was 45961. To see it from other
fitness index: GFI = 1.00, AGFI = 0.98, NFI = 0.99, CFI
= 1.00, SRMR = 0.026, RMSEA = 0.00.
Regarding the exogenous variable and the endogenous
variable, only the social influence and behavioral inten-
tion showed significance influence (Figure 3). The stan-
dardized coefficient value was 0.54 (p < 0.001) which
was more than significant and showed that when the
people by the employees reveal their expectation for the
employee to access e-learning system, then the em-
ployee’s behavioral intention would be positively af-
fected. In addition to this, the performance expectancy,
effort expectancy and behavioral intention did not show
any significance. Accordingly, hypothesis 3 was not re-
jected.
The moderator effects of user variables were tested by
comparing, and the path coefficients produced for each
moderator. Path coefficients were calculated using
t-values suggested by Chow [45,46].
From Ta b l e 4 and Figure 4, it could find that gender
would cause adjustment to the social influence influ-
enced behavioral intention. Regarding the path coeffi-
cient, females have higher social influence influenced
behavioral intention(  = 0.64; p < 0.001) than males (  =
0.33; p < 0.01). Gender did not cause any adjustment
effect with other paths. Accordingly, Hypothesis 11 was
not rejected.
When two endogenous variables (behavioral intention
and usage) would have the direct effect as 0.20 which
was higher than significance which showed that the em-
ployees’ behavioral intention usage caused directive in- Secondly, on the aspect of age, informants under 40
0.01
(0.09)
0.07
(0.88)
0.54***
(8.21)
0.20**
(2.99)
Usage
Denotes not significant
Effort
Expectancy
Performance
expectancy
Social
Influence
Behavioral
Intention
Figure 3. Result of the research model. *p < 0.05; **p < 0.01; ***p < 0.001.

The Comparison of Three Major Occupations for User Acceptance of Information Technology:
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Table 4. The results of moderate effects—gender, age and occupation.
Gender Age Occupation
Path
Male Female Senior Junior Manufacturing Service Bank
PE→BI 0.26 –0.12 –0.07 0.23 0.11 0.02 –0.27
EE→BI 0.06 0.06 0.05 0.23 0.07 –0.07 0.29
SI→BI 0.33** 0.64*** 0.63*** 0.18 0.50*** 0.58*** 0.46*
N = 264, *p < 0.05, **p < 0.01, ***p < 0.001.
Figure 4. Results of moderator effects. *p < 0.05; **p < 0.01; ***p < 0.001
years old show that their social influence would affect
the behavioral intention significantly (
 = 0.63; p <
0.001). Accordingly, hypothesis 9 was rejected.
Besides, from the perspective of occupation, manu-
facture business, service business and banking business
all showed significant adjustment effect regarding social
influence influenced behavioral intention. Among all, the
service business (
 = 0.58; p < 0.001) would have the
highest strength, then the manufacture business (
 = 0.50;
p < 0.001) and the banking business (  = 0.46; p < 0.05).
Accordingly, hypothesis13 was not rejected.
Hypothesized results are as Table 5.
5. Discussion and Conclusions
5.1 Discussion
Several hypotheses can be drawn from this study.
1) In the model assumed, only social influence influ-
enced behavioral intention and usage would cause posi-
tive significance. The influence of the performance ex-
pectancy and effort expectancy influence behavioral in-
tention would not cause positive significance. Therefore,
the adoption of UTAUT model in this study was only
partial applicable.
2) UTAUT model was proposed by Venkatesh et al. [5]
explain 70% of the behavioral intention. But it was only
explained 34% of the behavioral intention in this study
which adopted UTAUT model. The difference between
the two explanatory results might be due to the reasons
listed below:
a) The employees or managers of the three major oc-
cupations in Taiwan did not think mobile learning would
help their working performance and thus it was not a
factor to affect their behavioral intention.
b) The employees or managers of the three major oc-
cupations in Taiwan are not because the devices are
easy-to-use and to use them.
c) The employees of the three major occupations
would use mobile instrument to access the e-learning for
the users think that their significant other may think that
he/she should use the new system. The motivation was
not hard to understand. In Taiwan, if an employee
wanted to advance his/her job training; he/she would
only need to pay partial of the fee. Council of Labor af-
fairs of Republic of Taiwan would support 80% of the
credit fees. Employees older than 45 years old can even
get full support from the government. The up-limit of the
support was around 950 USD within three years. There-
fore, behavioral intention to use the technologies was
mostly external factors, instead of self-triggered motiva-
tion. From Lee and Huang [46] indicated because of the
“external expectation”, the “social relationship” and the
“working behaviors” that push the employees to use the
mobile learning system.
d) Since UTAUT model established by Venlatesh et al.
[5] was a model established for the acceptance of the
new information technology by the businessmen and
there were some cognitive of latent construct difference
between Eastern and Western cultures, it might also re-
sult in the explanatory power of the behavioral intention
and usage is lower.
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Table 5. Hypothesized results.
H3 Social influence has a positive effect on behavioral intention to use m-learning. not rejected
H4 Behavioral intention has a significant positive influence on usage. not rejected
H11 Social influence influences behavioral intention to use m-learning more strongly for women than for men. not rejected
H13 Social influence influences behavioral intention to use m-learning relationship would be the impact of dif-
ferent occupations. not rejected
3) It found in this study that gender and age would
cause significant inference to the derivation of the path
of social influence influenced behavioral intention. This
agreed with the suggestion by Venlatesh et al. [5]. Mean-
while, it was also observed that different business would
have significant inference to the path of the social influ-
ence influenced behavioral intention which matches with
researches on different occupations (e.g., [1,37,38]).
4) On the aspect of moderator variables, it observed
that social influence influenced behavioral intention to
use m-learning more strongly for women than for men.
This finding could relate with general findings that
women were generally more empathic than were men
[47-49], women sought out and received more emotional
social support and overall support than men, and women
were more likely than men to seek support, received
needed support, and would have higher perceptions of
social support [50-52].
5) On the aspect of moderator variables, it also found
that employees under forty years old would have
stronger social influence influenced behavioral intention
than those over forty years old. It was something worth
of attention. Though Venlatesh et al. [5] mentioned in the
literature that elders would be easier to be influenced by
social influence than the youth, as now the unemploy-
ment rate has broken the records and many jobs was pro-
vided with relationship-oriented factor, the youth in Tai-
wan would gradually emphasize on the social influence.
That is to say, the youth would observe the needs of the
interested people. Furthermore, since there were 73% of
the employments between 15 years old to 24 years old
were in the service business, it was critical to enhance
the quality of the business service and competitiveness.
6) On the aspect of moderator variables, it found that
among all the business, employees of the service busi-
ness were more easily affected by social influence than
the employees of manufacture or banking business. It
was obvious that each business would have its own
unique resource perception on the social influence. Ac-
cording to Taiwan government survey, the GPD ratio of
service business in Taiwan was 73.2%, the employment
number of the service business was 6 million and 40
thousand which takes 58% of the overall employment
number. Plus the environmental influence in Taiwan, all
of these may be the reason why service business was
much easier affected by behavioral intention than other
business.
5.2. Conclusions
The result of the research indicates that social influence
would have a positive effect on behavioral intention to
use m-learning. That means when the employees in the
three major occupations in Taiwan are using mobile de-
vice to access mobile learning, the more their significant
others (i.e., colleagues, superior manager or friends)
thought that they should use the mobile device, the more
they would have behavioral intention to use one. There-
fore, when the director of managers department or HR
department are introducing mobile device for mobile
learning, they shell pay attention to the situation of the
usage of the mobile device. When the behavioral inten-
tion is low, they can suggest their colleagues, superior
manager or friends to communicate with them to en-
hance their intention and to use it.
From the analysis of the moderator variable of the re-
search, it can found that gender was an important mod-
erator. The paths of each social influence influenced be-
havioral intention significantly interfered where the path
relation was stronger among young females. This showed
that when a mobile device was introduced for mobile
learning, the perception of the social influence would
cause higher intention for young females than males.
Therefore, when a corporation is trying to introduce a
mobile device to perform mobile learning, it suggests
that male employees and elder employees shell is put
more emphasis on the communication to enhance their
behavioral intention.
5.3. Study Limitations and Future Directions
There were three limitations in this study. The first limi-
tation concerned the explanatory power of the models.
Most of the existing studies account for less than 60% of
variance explained, especially those using field studies
with professional users [7]. Therefore, when UTAUT
model was used in different countries, the model should
be adjusted to fit the conditions of each country.
Regarding the timing of the research, this study took
single-time approach which was different from the
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UTAUT model which was proposed by Venkatesh et al.
[5] where three measuring time of the same groups of
samples should be applied. This may also be the reason
that causes the result is different.
Since the result of the research indicated that the users
of the three major occupations in Taiwan was mostly
motivated by social influence, it suggests that future
study can focus on the application relation between the
social influence and the users’ behavioral intention for
new technologies.
6. Acknowledgements
The authors would like to thank Taiwan Institute for In-
formation Industry provides the information.
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