08832323.2011.586005
Journal of Education for Business
ISSN: 0883-2323 (Print) 1940-3356 (Online) Journal homepage: http://www.tandfonline.com/loi/vjeb20
Electronic Learning Systems in Hong Kong
Business Organizations: A Study of Early and Late
Adopters
Simon C. H. Chan & Eric W. T. Ngai
To cite this article: Simon C. H. Chan & Eric W. T. Ngai (2012) Electronic Learning Systems in
Hong Kong Business Organizations: A Study of Early and Late Adopters, Journal of Education
for Business, 87:3, 170-177, DOI: 10.1080/08832323.2011.586005
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Date: 11 January 2016, At: 22:00
JOURNAL OF EDUCATION FOR BUSINESS, 87: 170–177, 2012
C Taylor & Francis Group, LLC
Copyright
ISSN: 0883-2323 print / 1940-3356 online
DOI: 10.1080/08832323.2011.586005
Electronic Learning Systems in Hong Kong Business
Organizations: A Study of Early and Late Adopters
Simon C. H. Chan and Eric W. T. Ngai
Downloaded by [Universitas Maritim Raja Ali Haji] at 22:00 11 January 2016
The Hong Kong Polytechnic University, Kowloon, Hong Kong
Based on the diffusion of innovation theory (E. M. Rogers, 1983, 1995), the authors examined
the antecedents of the adoption of electronic learning (e-learning) systems by using a time-based
assessment model (R. C. Beatty, J. P. Shim, & M. C. Jones, 2001), which classified adopters
into categories upon point in time when adopting e-learning systems. Based on a structured
questionnaire survey from 143 business organizations, results indicated significant differences
in the reasons why adopters decided to adopt e-learning systems. Technical compatibility,
top management support, and social pressures had a greater influence on the adoption of the
e-learning systems by the early adopters than on the late adopters.
Keywords: adopters, electronic learning systems, time-based analysis
Electronic learning (e-learning) systems have long become
an alternative for organizations in their move toward their
vision of organizational learning. The context of learning is
evolving from the traditional classroom setting to the selfservice online format. The rapid development of e-learning
systems has prompted business organizations to reassess and
redesign the way individual learners learn via the Internet
and technologies (Halawi, McCarthy, & Pires, 2009; Zhang,
Zhao, Zhou, & Nunamaker, 2004). E-learning provides an
ideal medium to deliver learning materials, which eventually
improve knowledge sharing and organization performance
(Rosenberg, 2000). The speed and connectivity through the
network provide operational and administrative benefits to
organizations, benefits that ultimately improve their competitive advantages (Chang, Hsu, Smith, & Wang, 2004).
LITERATURE REVIEW
There are many definitions for e-learning, but there is real
no universal definition. E-learning can be defined as a webbased system that enables users and learners to share information (Sun, Tsai, Finger, Chen, & Yeh, 2008). In its broadest definition, e-learning is Internet-enabled learning, which
offers and delivers learning materials in multiple formats,
Correspondence should be addressed to Simon C. H. Chan, The Hong
Kong Polytechnic University, Department of Management and Marketing,
Hung Hom, Kowloon 852, Hong Kong. E-mail: mssimon@polyu.edu.hk
supported by a networked community of learners, instructors, content developers, and experts (Gunasekaran, McNeil,
& Shaul, 2002). In a narrower definition, e-learning is defined
as instructional content or a learning experience enabled by
electronic information technologies including the Internet,
intranets, and extranets (Wang, Wang, & Shee, 2007). Elearning system, in this study, is then defined as a web-based
system that creates, fosters, delivers, and facilitates learning
experience and training materials to individuals. The use of
interactive network technologies by individual learners is at
all time and elsewhere. The term e-learning environment is
also commonly used to describe the platform supporting the
self-learning process of individual learners (Sun et al., 2008).
The learning environment is a function of knowledge-derived
tools and devices by which individual learners acquire information and knowledge (Liao & Lu, 2008; Liaw, Chen, &
Huang, 2008).
Empirical studies have made valuable contributions to the
effectiveness of e-learning systems (e.g., Sun et al., 2008).
Alavi and Leider (2001) indicated that institutional strategy and information technology are important indicators of
the development of e-learning systems. The technology features engage the psychological learning processes of individuals through which learning occurs and results in the desired
learning outcomes. Johnson, Hornik, and Salas (2008) and
Piccoli, Ahmad, and Ives (2001) highlighted the characteristics of individual trainees and technology and emphasized
that the creation of a shared learning environment is significantly associated with e-learning outcomes. More recently,
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ELECTRONIC LEARNING SYSTEMS
Johnson, Gueutal, and Falbe (2009) found the significant
effects of individual characteristics and technologies on elearning outcomes. These findings show the positive influence on the relationship between the antecedents and the
adoption of e-learning systems.
As information technology (IT) systems adoption has
taken place and has been investigated over time (Harrison &
Waite, 2006), studies appear to identify the reasons for decisions on innovation adoption. Rogers’ (1983, 1995) diffusion
of innovation theory refers to the spread of abstract ideas and
concepts, technical information, and actual practices from
sources to adopters within a social system. This theory was
specifically used in determining the attributes of innovations
adopted by potential organizations, which are classified into
five adopter categories: a) pioneers, b) early adopters, c) early
majority, d) late majority, and e) laggards. Although the five
categories do not have a standard proportion, they follow
a bell-shaped curve based on the point in time when they
adopted IT systems (Beatty et al., 2001; Harrison & Waite,
2006). In a study of corporate website adoption, Beatty et al.
(2001) adopted the time-based assessment model to examine the relationship between the factors and the differences
among the different stages of adopters. Based on the rationality of adoption decisions by organizations at different
times (Beatty et al.), the adopters of e-learning systems are
classified into five main categories based on the number of
years they have been adopting e-learning systems: a) pioneers
(3 years or longer), b) early adopters (at least 2 years but less
than 3 years), c) early majority (at least 1 year but less than
2 years), d) late majority (less than 1 year), and e) laggards
(presently developing). To examine adoption decisions, pioneers, early adopters, and early majority adopters have been
defined as early adopters. Meanwhile, late majority adopters
and laggards are identified as later adopters.
In reviewing prior IT adoption literature, the majority
of the reasons the affect adoption decision were generally
classified according to the characteristics of organizations
or the environment, and as benefits of IT systems (Downs
& Mohr, 1976; Hung, Hung, Tsai, & Jiang, 2010; Kuan
& Chau, 2001). Empirical studies have identified organizational size (Lee & Xia, 2006), organizational commitment
(Ramamurthy, Sen, & Sinha, 2008), top management support
(Lee & Shim, 2007), and innovation and environment characteristics (Premkumar & Roberts, 1999) as the antecedents of
adoption decision. Chau and Tam (1997) developed a model
for open systems adoption that incorporates three aspects,
namely, the external environment, the organizational technology, and the characteristics of the technology.
Several studies have emphasized organizational and individual readiness with regard to the adoption of IT systems
(e.g., Liu, 2005). The perceived advantages and the characteristics of individual learners (Shim, Shropshire, Park, Harris,
& Campbell, 2007) have been found as the antecedents influencing the adoption of IT systems. Top management support (Grover, 1993), technical compatibility (Raho, Beholav,
171
& Fiedler, 1987), and social pressures (Flanagin, 2000) are
also considered as antecedents influencing the adoption decision. Sabherwal and Sabherwal (2005) demonstrated that
the antecedents posited by the diffusion of information innovations theory underlie a variety of IT systems. In particular,
adopters put more emphasis on the perceived advantages and
technical compatibility of e-learning systems. As a result,
perceived advantages, IT expertise, technical compatibility,
top management support, and social pressures were identified
as potential factors of adoption of e-learning systems.
HYPOTHESIS DEVELOPMENT
In this study, five antecedents (perceived advantages, IT expertise, technical compatibility, top management support,
and social pressures) were included to examine the influence
of e-learning systems among adopters. In addition, adopters
were classified into five categories (pioneers, early adopters,
early majority, late majority, and laggards) based on the number of years that organizations have adopted the e-learning
systems. Figure 1 depicts the relationships of the five antecedents and the e-learning systems adoption over time.
Perceived Advantages
An e-learning system is a cost-effective training delivery
option for individual learners. The perceived advantages include reduced costs, delivery of consistent and timely information, and better communication between instructors and
individual learners (Gordon, 2003; Smith & Mitry, 2008).
The perceived advantages have been significantly associated
with e-learning systems adoption (Wang, 2003). If an organization has a quick response to e-learning systems adoption,
early adopters may benefit more than the later adopters. We
then hypothesized the following:
IT Expertise
Technical
Compatibility
Top
Management
Support
Perceived
Advantages
Adoption of Elearning System
Over Time
Social
Pressure
Pioneers
Early
Adopters
FIGURE 1
Early
Majority
Late
Majority
E-learning adoption model over time.
Laggards
172
S. C. H. CHAN AND E. W. T. NGAI
Hypothesis 1 (H 1 ): Earlier adopters place more importance
on the perceived advantages of having an e-learning system than do later adopters.
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Information Technology Expertise
An important factor in e-learning systems adoption depends
on the IT usage of individual learners. Individual learners’
basic knowledge and skills in computers, networks, and software systems are required in adopting e-learning systems.
The characteristics of individual learners are important and
are significantly associated with e-learning adoption decision
(Johnson et al., 2008; Piccoli et al., 2001). Early adopters are
more likely to adopt e-learning systems than later adopters
if individual learners have a higher level of IT expertise and
are familiar with an organization’s IT values. Hence,
H 2 : Earlier adopters have more individual learners with IT
expertise for e-learning systems than do later adopters.
Technical Compatibility
E-learning systems design is necessary for a good foundation
on technical infrastructure and compatibility of technology.
Technical compatibility is defined as the extent to which
the adoption of e-learning systems can be integrated into
the existing IT infrastructure and learning environment. The
incompatibility of IT adoption with an organization’s existing
hardware, software, or networks largely inhibits the adoption
of e-learning systems (Premkumar & Ramamurthy, 1995).
The more sophisticated the existing IT architecture is, the
higher the willingness of organizations to adopt e-learning
systems.
H 3 : The e-learning system is perceived as more technically
compatible by earlier adopters than by later adopters.
Top Management Support
Top management support is found to be significantly associated with the adoption of IT systems (Wong, 2005). Top
management support is defined as the extent to which there
is decision support from the top management of an organization (Lee & Shim, 2007). Their support is critical to the
success of adopting e-learning systems. In other words, an
earlier adopter would request for top management support to
adopt e-learning systems rather than the later adopters. We
propose that the support of top management is important for
the e-learning systems adoption process. Hence,
H 4 : Earlier adopters have greater top management support
on having e-learning systems than do later adopters.
Social Pressures
Social pressures are generally perceived to be associated
with the adoption of IT systems. Social pressures refer to
the degree in which organizations feel pressure from their
competitors or organizations in the same industry. Evidence
has shown that external forces of the environment can influence organizations’ adoption decision (Flanagin, 2000).
Organizations view the IT adoption as a business strategy
in competing with existing competitors. We then posited the
following:
H 5 : Earlier adopters face more social pressure to have an
e-learning system than do later adopters.
METHOD
Instrument
To test the hypotheses, a survey questionnaire was developed
to measure the relevant constructs. A pilot test was administrated to ensure the validity of the instruments. First, the
questionnaire was refined by a pilot test involving the assistance of two professors. Based on the comments, some
measurement items were modified. Second, 10 managerial
practitioners reviewed the questionnaire, as well as the clarity of the content and appropriateness of the items.
The questionnaire items used were mainly from existing
scales. The items for perceived advantages (9 items with
a 7-point scale) were largely based on the unique context
of e-learning systems. IT expertise (5 items with a 7-point
scale), developed by Flanagin (2000), was used to measure the extent to which individuals are sophisticated in the
use of IT. In addition, technical compatibility (4 items with
a 7-point scale), also developed by Flanagin (2000), was
used to measure technical support. Top management support
(4 items with a 7-point scale), developed by Grover (1993),
was used to measure top management attitude toward elearning adoption. Also, social pressure (6 items with a
7-point scale), developed by Flanagin (2000), was used to
measure external pressure from competitors.
Sample and Procedures
A sample was randomly drawn from the membership list of
an e-learning conference in Hong Kong. The conference contact list provided by the host was used, and 345 business organization managers were selected. A survey was conducted
by sending structured questionnaires, with the objective of
the study specified in the cover letter. The survey participants were requested to forward the survey questionnaires
to their staff responsible for e-learning systems adoption.
The respondents were then asked to complete the questionnaire. A total of 143 questionnaires were returned, yielding
a response rate of 41.5%. The potential nonresponse bias
was examined by comparing the first half of the responses
received with the second half (the theoretical nonrespondents). The mean differences between the two groups of
responses in a random selection of measurement items in the
ELECTRONIC LEARNING SYSTEMS
TABLE 1
Demographic Profile of the Respondents
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Characteristic
Organization size
Less than 500 employees
Between 500 and 2,000 employees
More than 2,000 employees
Industry
Manufacturing
Nonmanufacturing
Hierarchical level
Director
Manager
Consultant
Officer
Miscellaneous
%
44.1
30.8
25.1
9.8
90.2
7.0
61.5
7.7
17.5
6.3
another factor (Messick, 1990, 1995). The reliability (i.e.,
internally consistent) of the score derived for each factor was
evaluated using Cronbach’s alpha (> .70; Cronbach, 1961;
Nunnally, 1978).
Following the EFA, the effects of the five adopter categories on perceived advantages, IT expertise, technical compatibility, top management support, and social pressures
were examined by using a multivariate analysis of variance
(MANOVA). It is an extension of an analysis of variance
(ANOVA) to accommodate more than one dependent variable (adopters; i.e., pioneers, early adopters, early majority,
late majority, and laggards). MANOVA measures the differences between two or more dependent variances based on
a set of categorical variables (Hair, Anderson, Tatham, &
Black, 1995).
questionnaire were tested. There were no significant differences between the respondents and nonrespondents.
Profile of Respondents
Table 1 presents the demographic profile of respondents. Organizations with fewer than 500 employees made up 44.1%
of the responding organizations, and those with over 500 employees comprised 55.9% of all responding organizations.
Nearly 90% of the participating organizations represented
nonmanufacturing industry sector. Over 75% of the respondents held managerial or equivalent positions, including director, manager, and consultant positions, and were among
decision makers for e-learning systems. The relative seniority of the respondents gives assurance that the sample is valid
because these respondents are more likely knowledgeable of
organizational strategies behind e-learning adoption.
Statistical Procedures
Data were analyzed by using the exploratory factor analysis (EFA) to assess the construct validity of the independent
variables (Doll & Torkzadeh, 1988; Kerlinger, 1986). EFA
offers a way of constructing an interrelated set of indicators,
meeting one of the conditions for construct validity (Zmud &
Boynton, 1991). A principal component factor analysis using
the Varimax criterion was performed to assess unidimensionality. A rotation to obtain a simple and interpretable result
was accomplished using the oblique rotation technique. Two
essential criteria are involved in determining the number of
factors in the analysis: (a) the magnitude of the eigenvalue
(with a minimum required value of 1.0; Kaiser, 1974) and
(b) the scree test (Cattell, 1966). Moreover, convergent validity is demonstrated if the items load strongly with factor
loadings > 0.50. Discriminant validity is achieved if each
item loads stronger on its associated factor than on any other
loadings. Items with factors of at least 0.30 and at least a
0.10 difference between their loading on other factors were
examined to determine if such items conceptually measured
173
RESULTS
EFA
Using the criteria as stated in the statistical analysis section,
an EFA of the antecedents of e-learning systems adoption was
performed. The total number of factors recommended was
six. A total of 26 items remained after two items were dropped
from the original 28 items due to cross loading in the factor
analysis. The factor structure resulted in 26 items measuring
six distinct factors and explaining 75.7% of the variance, as
shown in Table 2. The items predicted to measure perceived
advantages were loaded on two separate factors rather than
one single variable, representing perceived direct advantages
and perceived indirect advantages. Together, the structure of
the six factors went well with the structure of the items. The
results showed that items of the same construct distinctly
exhibited higher factor loading on a single construct rather
than on other constructs, suggesting adequate convergent and
discriminant validity.
The Cronbach’s coefficient of perceived direct advantages
(α = .76), IT expertise (α = .93), technical compatibility
(α = .92), top management support (α = .93), and social
pressures (α = .91) had coefficient alpha values exceeding
.70. Perceived indirect advantages exhibited marginal failure
(α = .67).
MANOVA
As shown in Table 3, MANOVA was used to test the hypotheses. The Wilks’s criterion for the test of overall statistical
effect was significant, F() = 0.94, p = .01, indicating that
the means for the five categories of adopters contain significant differences in the constructs of technical compatibility,
top management support, and social pressures (at α = .05).
The findings do not provide support for the hypotheses on
perceived direct and indirect advantages (H 1 ) and IT expertise (H 2 ). However, they support the hypotheses on technical
174
S. C. H. CHAN AND E. W. T. NGAI
TABLE 2
Operationalization of Research Variables
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Component
Factor 1
ITE1—Our organization has a high reliance on technology.
ITE2—Our organization has strength in technical infrastructure.
ITE3—Our organization relies on advanced technology in its
day-to-day operations.
ITE4—Advanced technology is central to my organization.
ITE5—Most employees are knowledgeable about information
technology.
TMS1—Top management in my organization is interested in its
implementation.
TMS2—Top management in my organization considers it to be
important to the organization.
TMS3—Top management in my organization has effectively
communicated its support for it.
TMS 4—Top management in my organization will support its use.
TC1—Most employees seem to have self-confidence in using
information technology.
TC2—Most employees have basic skills and capabilities in using
information technology.
TC3—Most employees have some experience in using information
technology.
TC 4—Most employees know how to use the Internet.
SP1—Typically, organizations similar to ours use it.
SP2—Organizations in the same field as our organization have their
own program.
SP3—Organizations in the same business as ours typically use it.
SP4—Organizations in our business have it these days.
SP 5—Organizations in my area of business typically don’t use it. (R)
SP6—Normally, organizations that do what we do don’t use it. (R)
PA1—Allows users to undertake training anytime, anywhere.
PA2—Users can go through a training program at their own pace.
PA3—More cost-effective than other forms of training.
PA6—Provides consistent delivery of content to each trainee.
PA7—Can be designed to access the most up-to-date information.
PA8—Provides consistent delivery of content to each trainee.
PA9—Improves communication between trainers and trainees.
Eigenvalue
Variance explained
Factor 2
Factor 3
Factor 4
Factor 5
Factor 6
0.815
0.764
0.871
0.792
0.845
0.805
0.823
0.831
0.853
0.837
0.849
0.774
0.775
0.742
0.811
0.826
0.740
0.791
0.790
0.880
0.890
0.520
9.967
38.336
3.034
11.669
2.321
8.926
1.912
7.354
0.534
0.615
0.751
0.745
1.383
5.320
1.066
4.102
Note. (R) indicates reverse coding. TC = technical compatibility; ITE = information technology expertise; PA = perceived advantages; SP = social
pressures; TMS = top management support.
compatibility (H 3 ), top management support (H 4 ), and social
pressures (H 5 ).
DISCUSSION
In the present study we examined the antecedents of the adoption of e-learning systems using a time-based assessment
model. Some findings from previous IT adoption literature
on the antecedents of the adopters of e-learning systems over
a particular time period (Rogers, 1983, 1995) have been confirmed. The results of the present study did not support perceived advantages (H 1 ) and IT expertise (H 2 ); instead, they
supported hypotheses related to technical compatibility (H 3 ),
top management support (H 4 ), and social pressures (H 5 ).
In contrast to the results of Beatty et al.’s (2006) website
adoption, there was no significant difference between the
early and late adopters of the perceived advantages (H 1 ) of
e-learning systems. This implied that early and late adopters
seem to favor reasonably the perceived advantages of elearning systems as awareness of IT innovations increases.
One possibility is that the perceived benefits of e-learning
systems, such as the stimulation of individuals’ learning motivation and attitude (Liaw et al., 2008) and cost reduction
(Chau & Tam, 2000), would apply among adopters over time
as the diffusion process continues. Early and late adopters of
e-learning systems perceive the advantages to organization
and individual development despite the early and late adoption (Barolli, Koyama, Durresi, & De Marco, 2006; Shim
et al., 2007).
ELECTRONIC LEARNING SYSTEMS
TABLE 3
MANOVA Statistic Results
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Statistic
Wilks’s lambda
Pillai’s trace
Hotelling-Lawley trace
Roy’s greatest root
Construct
Perceived direct advantages (H 1 )
Perceived indirect advantages (H 1 )
IT expertise (H 2 )
Technical compatibility (H 3 )
Top management support (H 4 )
Social pressure (H 5 )
∗∗ p
175
Limitations
F
Pr > F
0.94
0.85
2.03
6.55
.01∗∗
.01∗∗
.01∗∗
.01∗∗
0.95
0.35
2.09
3.90
5.07
6.41
.43
.83
.08
.01∗∗
.01∗∗
.01∗∗
< .01.
The nonsignificant result for IT expertise (H 2 ) is surprising. One possible explanation is that the adopters of
e-learning systems may not necessarily request individual
learners with high levels of expertise in IT. E-learning activities only require individual learners to possess basic technical skills while adopting systems such as an enterprise
resource planning (ERP) system that demands individual
learners with better IT knowledge and skills (Morton & Hu,
2008; Ngai, Law, & Wat, 2008; Waarts, van Everdingen,
& van Hillegersberg, 2002).
Consistent with the diffusion of innovation theory
(Rogers, 1983, 1985), early adopters have placed more emphasis on technical compatibility, top management support,
and social pressure when considering whether to acquire elearning systems. This highlights the importance of IT compatibility in affecting the adoption decision (Premkumar &
Ramamurthy, 1995). The e-learning system is perceived as
more technically compatible by early adopters than by late
adopters (H3). In addition, early adopters have strong top
management support for e-learning systems (H 4 ). Consistent
with the IT literature, top management support would ensure
the successful adoption of new skills and the maintenance of
competitiveness within the industry (Beatty et al., 2006; Lee
& Shim, 2007). This is a confirmation of top management
support as a champion for innovation adoption, including the
e-learning system. Finally, the early adopters of e-learning
systems are influenced by social pressure because they may
have eventually decided to adopt the e-learning systems
based on widespread acceptance among their competitors
(H 5 ). According to Flanagin (2000), organizations acquiring
e-learning systems because of their expected benefits is unlikely. Instead, they might have considered using e-learning
systems due to social pressure than from the existing competitive market. In summary, the adoption of e-learning systems
can meet the needs of organizations and provide a great learning environment and experience to individual learners.
Only a relatively small sample size of 143 adopters was utilized. Another potential limitation of the present study was
that data were collected from business organizations. More
research is needed for the application of the results to other
types of organizational settings such as those characterized
by the strictly regulated usage of e-learning systems (e.g.,
training institutes). Although in the present study we examined five adoption innovation antecedents and e-learning systems adoption over time, the characteristics of adopters such
as organization size (Lee & Xia, 2006) apparently have implications for potential e-learning adopters. Future researchers
should explore the relationship between the antecedents of
adoption innovation and the characteristics of adopters. Finally, this study was conducted using a common method
source questionnaire for all variables, which may lead to
common method bias.
Conclusion
The contribution of this study is the extension of e-learning
system literature through the examination of the relationship among the antecedents and the adopters of e-learning
systems over time. This approach adds value, as it compares the adopters of e-learning systems on a time-frame
basis. The findings suggest that technical compatibility, top
management support, and social pressures have significant
differences between early and late adopters. Compared with
late adopters, early adopters view e-learning systems as more
compatible with existing technology infrastructures, have potential to gain more support from top management, and are in
accordance with pressures from the social learning environment. However, the study also shows no significant difference
between the early and late adopters in terms of perceived advantages. This nonsignificance may be attributed to the fact
that the perceived benefits of e-learning systems become applicable among adopters over time as the diffusion process
continues.
This study also shows that the success of decisions on the
adoption of e-learning systems depends on the commitment
of top management and its technical infrastructure. Organizations also need to experience social pressures when consider
adopting e-learning systems. We believe that more research
is required to further develop our understanding of early and
late adopters in e-learning systems, and determine other differences between early and late adopters.
ACKNOWLEDGMENTS
The authors are grateful for the constructive comments of
the referees and the editors on an earlier version of this article. This research was supported in part by the Hong Kong
Polytechnic University under grant number 8CGP.
176
S. C. H. CHAN AND E. W. T. NGAI
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ISSN: 0883-2323 (Print) 1940-3356 (Online) Journal homepage: http://www.tandfonline.com/loi/vjeb20
Electronic Learning Systems in Hong Kong
Business Organizations: A Study of Early and Late
Adopters
Simon C. H. Chan & Eric W. T. Ngai
To cite this article: Simon C. H. Chan & Eric W. T. Ngai (2012) Electronic Learning Systems in
Hong Kong Business Organizations: A Study of Early and Late Adopters, Journal of Education
for Business, 87:3, 170-177, DOI: 10.1080/08832323.2011.586005
To link to this article: http://dx.doi.org/10.1080/08832323.2011.586005
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Date: 11 January 2016, At: 22:00
JOURNAL OF EDUCATION FOR BUSINESS, 87: 170–177, 2012
C Taylor & Francis Group, LLC
Copyright
ISSN: 0883-2323 print / 1940-3356 online
DOI: 10.1080/08832323.2011.586005
Electronic Learning Systems in Hong Kong Business
Organizations: A Study of Early and Late Adopters
Simon C. H. Chan and Eric W. T. Ngai
Downloaded by [Universitas Maritim Raja Ali Haji] at 22:00 11 January 2016
The Hong Kong Polytechnic University, Kowloon, Hong Kong
Based on the diffusion of innovation theory (E. M. Rogers, 1983, 1995), the authors examined
the antecedents of the adoption of electronic learning (e-learning) systems by using a time-based
assessment model (R. C. Beatty, J. P. Shim, & M. C. Jones, 2001), which classified adopters
into categories upon point in time when adopting e-learning systems. Based on a structured
questionnaire survey from 143 business organizations, results indicated significant differences
in the reasons why adopters decided to adopt e-learning systems. Technical compatibility,
top management support, and social pressures had a greater influence on the adoption of the
e-learning systems by the early adopters than on the late adopters.
Keywords: adopters, electronic learning systems, time-based analysis
Electronic learning (e-learning) systems have long become
an alternative for organizations in their move toward their
vision of organizational learning. The context of learning is
evolving from the traditional classroom setting to the selfservice online format. The rapid development of e-learning
systems has prompted business organizations to reassess and
redesign the way individual learners learn via the Internet
and technologies (Halawi, McCarthy, & Pires, 2009; Zhang,
Zhao, Zhou, & Nunamaker, 2004). E-learning provides an
ideal medium to deliver learning materials, which eventually
improve knowledge sharing and organization performance
(Rosenberg, 2000). The speed and connectivity through the
network provide operational and administrative benefits to
organizations, benefits that ultimately improve their competitive advantages (Chang, Hsu, Smith, & Wang, 2004).
LITERATURE REVIEW
There are many definitions for e-learning, but there is real
no universal definition. E-learning can be defined as a webbased system that enables users and learners to share information (Sun, Tsai, Finger, Chen, & Yeh, 2008). In its broadest definition, e-learning is Internet-enabled learning, which
offers and delivers learning materials in multiple formats,
Correspondence should be addressed to Simon C. H. Chan, The Hong
Kong Polytechnic University, Department of Management and Marketing,
Hung Hom, Kowloon 852, Hong Kong. E-mail: mssimon@polyu.edu.hk
supported by a networked community of learners, instructors, content developers, and experts (Gunasekaran, McNeil,
& Shaul, 2002). In a narrower definition, e-learning is defined
as instructional content or a learning experience enabled by
electronic information technologies including the Internet,
intranets, and extranets (Wang, Wang, & Shee, 2007). Elearning system, in this study, is then defined as a web-based
system that creates, fosters, delivers, and facilitates learning
experience and training materials to individuals. The use of
interactive network technologies by individual learners is at
all time and elsewhere. The term e-learning environment is
also commonly used to describe the platform supporting the
self-learning process of individual learners (Sun et al., 2008).
The learning environment is a function of knowledge-derived
tools and devices by which individual learners acquire information and knowledge (Liao & Lu, 2008; Liaw, Chen, &
Huang, 2008).
Empirical studies have made valuable contributions to the
effectiveness of e-learning systems (e.g., Sun et al., 2008).
Alavi and Leider (2001) indicated that institutional strategy and information technology are important indicators of
the development of e-learning systems. The technology features engage the psychological learning processes of individuals through which learning occurs and results in the desired
learning outcomes. Johnson, Hornik, and Salas (2008) and
Piccoli, Ahmad, and Ives (2001) highlighted the characteristics of individual trainees and technology and emphasized
that the creation of a shared learning environment is significantly associated with e-learning outcomes. More recently,
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ELECTRONIC LEARNING SYSTEMS
Johnson, Gueutal, and Falbe (2009) found the significant
effects of individual characteristics and technologies on elearning outcomes. These findings show the positive influence on the relationship between the antecedents and the
adoption of e-learning systems.
As information technology (IT) systems adoption has
taken place and has been investigated over time (Harrison &
Waite, 2006), studies appear to identify the reasons for decisions on innovation adoption. Rogers’ (1983, 1995) diffusion
of innovation theory refers to the spread of abstract ideas and
concepts, technical information, and actual practices from
sources to adopters within a social system. This theory was
specifically used in determining the attributes of innovations
adopted by potential organizations, which are classified into
five adopter categories: a) pioneers, b) early adopters, c) early
majority, d) late majority, and e) laggards. Although the five
categories do not have a standard proportion, they follow
a bell-shaped curve based on the point in time when they
adopted IT systems (Beatty et al., 2001; Harrison & Waite,
2006). In a study of corporate website adoption, Beatty et al.
(2001) adopted the time-based assessment model to examine the relationship between the factors and the differences
among the different stages of adopters. Based on the rationality of adoption decisions by organizations at different
times (Beatty et al.), the adopters of e-learning systems are
classified into five main categories based on the number of
years they have been adopting e-learning systems: a) pioneers
(3 years or longer), b) early adopters (at least 2 years but less
than 3 years), c) early majority (at least 1 year but less than
2 years), d) late majority (less than 1 year), and e) laggards
(presently developing). To examine adoption decisions, pioneers, early adopters, and early majority adopters have been
defined as early adopters. Meanwhile, late majority adopters
and laggards are identified as later adopters.
In reviewing prior IT adoption literature, the majority
of the reasons the affect adoption decision were generally
classified according to the characteristics of organizations
or the environment, and as benefits of IT systems (Downs
& Mohr, 1976; Hung, Hung, Tsai, & Jiang, 2010; Kuan
& Chau, 2001). Empirical studies have identified organizational size (Lee & Xia, 2006), organizational commitment
(Ramamurthy, Sen, & Sinha, 2008), top management support
(Lee & Shim, 2007), and innovation and environment characteristics (Premkumar & Roberts, 1999) as the antecedents of
adoption decision. Chau and Tam (1997) developed a model
for open systems adoption that incorporates three aspects,
namely, the external environment, the organizational technology, and the characteristics of the technology.
Several studies have emphasized organizational and individual readiness with regard to the adoption of IT systems
(e.g., Liu, 2005). The perceived advantages and the characteristics of individual learners (Shim, Shropshire, Park, Harris,
& Campbell, 2007) have been found as the antecedents influencing the adoption of IT systems. Top management support (Grover, 1993), technical compatibility (Raho, Beholav,
171
& Fiedler, 1987), and social pressures (Flanagin, 2000) are
also considered as antecedents influencing the adoption decision. Sabherwal and Sabherwal (2005) demonstrated that
the antecedents posited by the diffusion of information innovations theory underlie a variety of IT systems. In particular,
adopters put more emphasis on the perceived advantages and
technical compatibility of e-learning systems. As a result,
perceived advantages, IT expertise, technical compatibility,
top management support, and social pressures were identified
as potential factors of adoption of e-learning systems.
HYPOTHESIS DEVELOPMENT
In this study, five antecedents (perceived advantages, IT expertise, technical compatibility, top management support,
and social pressures) were included to examine the influence
of e-learning systems among adopters. In addition, adopters
were classified into five categories (pioneers, early adopters,
early majority, late majority, and laggards) based on the number of years that organizations have adopted the e-learning
systems. Figure 1 depicts the relationships of the five antecedents and the e-learning systems adoption over time.
Perceived Advantages
An e-learning system is a cost-effective training delivery
option for individual learners. The perceived advantages include reduced costs, delivery of consistent and timely information, and better communication between instructors and
individual learners (Gordon, 2003; Smith & Mitry, 2008).
The perceived advantages have been significantly associated
with e-learning systems adoption (Wang, 2003). If an organization has a quick response to e-learning systems adoption,
early adopters may benefit more than the later adopters. We
then hypothesized the following:
IT Expertise
Technical
Compatibility
Top
Management
Support
Perceived
Advantages
Adoption of Elearning System
Over Time
Social
Pressure
Pioneers
Early
Adopters
FIGURE 1
Early
Majority
Late
Majority
E-learning adoption model over time.
Laggards
172
S. C. H. CHAN AND E. W. T. NGAI
Hypothesis 1 (H 1 ): Earlier adopters place more importance
on the perceived advantages of having an e-learning system than do later adopters.
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Information Technology Expertise
An important factor in e-learning systems adoption depends
on the IT usage of individual learners. Individual learners’
basic knowledge and skills in computers, networks, and software systems are required in adopting e-learning systems.
The characteristics of individual learners are important and
are significantly associated with e-learning adoption decision
(Johnson et al., 2008; Piccoli et al., 2001). Early adopters are
more likely to adopt e-learning systems than later adopters
if individual learners have a higher level of IT expertise and
are familiar with an organization’s IT values. Hence,
H 2 : Earlier adopters have more individual learners with IT
expertise for e-learning systems than do later adopters.
Technical Compatibility
E-learning systems design is necessary for a good foundation
on technical infrastructure and compatibility of technology.
Technical compatibility is defined as the extent to which
the adoption of e-learning systems can be integrated into
the existing IT infrastructure and learning environment. The
incompatibility of IT adoption with an organization’s existing
hardware, software, or networks largely inhibits the adoption
of e-learning systems (Premkumar & Ramamurthy, 1995).
The more sophisticated the existing IT architecture is, the
higher the willingness of organizations to adopt e-learning
systems.
H 3 : The e-learning system is perceived as more technically
compatible by earlier adopters than by later adopters.
Top Management Support
Top management support is found to be significantly associated with the adoption of IT systems (Wong, 2005). Top
management support is defined as the extent to which there
is decision support from the top management of an organization (Lee & Shim, 2007). Their support is critical to the
success of adopting e-learning systems. In other words, an
earlier adopter would request for top management support to
adopt e-learning systems rather than the later adopters. We
propose that the support of top management is important for
the e-learning systems adoption process. Hence,
H 4 : Earlier adopters have greater top management support
on having e-learning systems than do later adopters.
Social Pressures
Social pressures are generally perceived to be associated
with the adoption of IT systems. Social pressures refer to
the degree in which organizations feel pressure from their
competitors or organizations in the same industry. Evidence
has shown that external forces of the environment can influence organizations’ adoption decision (Flanagin, 2000).
Organizations view the IT adoption as a business strategy
in competing with existing competitors. We then posited the
following:
H 5 : Earlier adopters face more social pressure to have an
e-learning system than do later adopters.
METHOD
Instrument
To test the hypotheses, a survey questionnaire was developed
to measure the relevant constructs. A pilot test was administrated to ensure the validity of the instruments. First, the
questionnaire was refined by a pilot test involving the assistance of two professors. Based on the comments, some
measurement items were modified. Second, 10 managerial
practitioners reviewed the questionnaire, as well as the clarity of the content and appropriateness of the items.
The questionnaire items used were mainly from existing
scales. The items for perceived advantages (9 items with
a 7-point scale) were largely based on the unique context
of e-learning systems. IT expertise (5 items with a 7-point
scale), developed by Flanagin (2000), was used to measure the extent to which individuals are sophisticated in the
use of IT. In addition, technical compatibility (4 items with
a 7-point scale), also developed by Flanagin (2000), was
used to measure technical support. Top management support
(4 items with a 7-point scale), developed by Grover (1993),
was used to measure top management attitude toward elearning adoption. Also, social pressure (6 items with a
7-point scale), developed by Flanagin (2000), was used to
measure external pressure from competitors.
Sample and Procedures
A sample was randomly drawn from the membership list of
an e-learning conference in Hong Kong. The conference contact list provided by the host was used, and 345 business organization managers were selected. A survey was conducted
by sending structured questionnaires, with the objective of
the study specified in the cover letter. The survey participants were requested to forward the survey questionnaires
to their staff responsible for e-learning systems adoption.
The respondents were then asked to complete the questionnaire. A total of 143 questionnaires were returned, yielding
a response rate of 41.5%. The potential nonresponse bias
was examined by comparing the first half of the responses
received with the second half (the theoretical nonrespondents). The mean differences between the two groups of
responses in a random selection of measurement items in the
ELECTRONIC LEARNING SYSTEMS
TABLE 1
Demographic Profile of the Respondents
Downloaded by [Universitas Maritim Raja Ali Haji] at 22:00 11 January 2016
Characteristic
Organization size
Less than 500 employees
Between 500 and 2,000 employees
More than 2,000 employees
Industry
Manufacturing
Nonmanufacturing
Hierarchical level
Director
Manager
Consultant
Officer
Miscellaneous
%
44.1
30.8
25.1
9.8
90.2
7.0
61.5
7.7
17.5
6.3
another factor (Messick, 1990, 1995). The reliability (i.e.,
internally consistent) of the score derived for each factor was
evaluated using Cronbach’s alpha (> .70; Cronbach, 1961;
Nunnally, 1978).
Following the EFA, the effects of the five adopter categories on perceived advantages, IT expertise, technical compatibility, top management support, and social pressures
were examined by using a multivariate analysis of variance
(MANOVA). It is an extension of an analysis of variance
(ANOVA) to accommodate more than one dependent variable (adopters; i.e., pioneers, early adopters, early majority,
late majority, and laggards). MANOVA measures the differences between two or more dependent variances based on
a set of categorical variables (Hair, Anderson, Tatham, &
Black, 1995).
questionnaire were tested. There were no significant differences between the respondents and nonrespondents.
Profile of Respondents
Table 1 presents the demographic profile of respondents. Organizations with fewer than 500 employees made up 44.1%
of the responding organizations, and those with over 500 employees comprised 55.9% of all responding organizations.
Nearly 90% of the participating organizations represented
nonmanufacturing industry sector. Over 75% of the respondents held managerial or equivalent positions, including director, manager, and consultant positions, and were among
decision makers for e-learning systems. The relative seniority of the respondents gives assurance that the sample is valid
because these respondents are more likely knowledgeable of
organizational strategies behind e-learning adoption.
Statistical Procedures
Data were analyzed by using the exploratory factor analysis (EFA) to assess the construct validity of the independent
variables (Doll & Torkzadeh, 1988; Kerlinger, 1986). EFA
offers a way of constructing an interrelated set of indicators,
meeting one of the conditions for construct validity (Zmud &
Boynton, 1991). A principal component factor analysis using
the Varimax criterion was performed to assess unidimensionality. A rotation to obtain a simple and interpretable result
was accomplished using the oblique rotation technique. Two
essential criteria are involved in determining the number of
factors in the analysis: (a) the magnitude of the eigenvalue
(with a minimum required value of 1.0; Kaiser, 1974) and
(b) the scree test (Cattell, 1966). Moreover, convergent validity is demonstrated if the items load strongly with factor
loadings > 0.50. Discriminant validity is achieved if each
item loads stronger on its associated factor than on any other
loadings. Items with factors of at least 0.30 and at least a
0.10 difference between their loading on other factors were
examined to determine if such items conceptually measured
173
RESULTS
EFA
Using the criteria as stated in the statistical analysis section,
an EFA of the antecedents of e-learning systems adoption was
performed. The total number of factors recommended was
six. A total of 26 items remained after two items were dropped
from the original 28 items due to cross loading in the factor
analysis. The factor structure resulted in 26 items measuring
six distinct factors and explaining 75.7% of the variance, as
shown in Table 2. The items predicted to measure perceived
advantages were loaded on two separate factors rather than
one single variable, representing perceived direct advantages
and perceived indirect advantages. Together, the structure of
the six factors went well with the structure of the items. The
results showed that items of the same construct distinctly
exhibited higher factor loading on a single construct rather
than on other constructs, suggesting adequate convergent and
discriminant validity.
The Cronbach’s coefficient of perceived direct advantages
(α = .76), IT expertise (α = .93), technical compatibility
(α = .92), top management support (α = .93), and social
pressures (α = .91) had coefficient alpha values exceeding
.70. Perceived indirect advantages exhibited marginal failure
(α = .67).
MANOVA
As shown in Table 3, MANOVA was used to test the hypotheses. The Wilks’s criterion for the test of overall statistical
effect was significant, F() = 0.94, p = .01, indicating that
the means for the five categories of adopters contain significant differences in the constructs of technical compatibility,
top management support, and social pressures (at α = .05).
The findings do not provide support for the hypotheses on
perceived direct and indirect advantages (H 1 ) and IT expertise (H 2 ). However, they support the hypotheses on technical
174
S. C. H. CHAN AND E. W. T. NGAI
TABLE 2
Operationalization of Research Variables
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Component
Factor 1
ITE1—Our organization has a high reliance on technology.
ITE2—Our organization has strength in technical infrastructure.
ITE3—Our organization relies on advanced technology in its
day-to-day operations.
ITE4—Advanced technology is central to my organization.
ITE5—Most employees are knowledgeable about information
technology.
TMS1—Top management in my organization is interested in its
implementation.
TMS2—Top management in my organization considers it to be
important to the organization.
TMS3—Top management in my organization has effectively
communicated its support for it.
TMS 4—Top management in my organization will support its use.
TC1—Most employees seem to have self-confidence in using
information technology.
TC2—Most employees have basic skills and capabilities in using
information technology.
TC3—Most employees have some experience in using information
technology.
TC 4—Most employees know how to use the Internet.
SP1—Typically, organizations similar to ours use it.
SP2—Organizations in the same field as our organization have their
own program.
SP3—Organizations in the same business as ours typically use it.
SP4—Organizations in our business have it these days.
SP 5—Organizations in my area of business typically don’t use it. (R)
SP6—Normally, organizations that do what we do don’t use it. (R)
PA1—Allows users to undertake training anytime, anywhere.
PA2—Users can go through a training program at their own pace.
PA3—More cost-effective than other forms of training.
PA6—Provides consistent delivery of content to each trainee.
PA7—Can be designed to access the most up-to-date information.
PA8—Provides consistent delivery of content to each trainee.
PA9—Improves communication between trainers and trainees.
Eigenvalue
Variance explained
Factor 2
Factor 3
Factor 4
Factor 5
Factor 6
0.815
0.764
0.871
0.792
0.845
0.805
0.823
0.831
0.853
0.837
0.849
0.774
0.775
0.742
0.811
0.826
0.740
0.791
0.790
0.880
0.890
0.520
9.967
38.336
3.034
11.669
2.321
8.926
1.912
7.354
0.534
0.615
0.751
0.745
1.383
5.320
1.066
4.102
Note. (R) indicates reverse coding. TC = technical compatibility; ITE = information technology expertise; PA = perceived advantages; SP = social
pressures; TMS = top management support.
compatibility (H 3 ), top management support (H 4 ), and social
pressures (H 5 ).
DISCUSSION
In the present study we examined the antecedents of the adoption of e-learning systems using a time-based assessment
model. Some findings from previous IT adoption literature
on the antecedents of the adopters of e-learning systems over
a particular time period (Rogers, 1983, 1995) have been confirmed. The results of the present study did not support perceived advantages (H 1 ) and IT expertise (H 2 ); instead, they
supported hypotheses related to technical compatibility (H 3 ),
top management support (H 4 ), and social pressures (H 5 ).
In contrast to the results of Beatty et al.’s (2006) website
adoption, there was no significant difference between the
early and late adopters of the perceived advantages (H 1 ) of
e-learning systems. This implied that early and late adopters
seem to favor reasonably the perceived advantages of elearning systems as awareness of IT innovations increases.
One possibility is that the perceived benefits of e-learning
systems, such as the stimulation of individuals’ learning motivation and attitude (Liaw et al., 2008) and cost reduction
(Chau & Tam, 2000), would apply among adopters over time
as the diffusion process continues. Early and late adopters of
e-learning systems perceive the advantages to organization
and individual development despite the early and late adoption (Barolli, Koyama, Durresi, & De Marco, 2006; Shim
et al., 2007).
ELECTRONIC LEARNING SYSTEMS
TABLE 3
MANOVA Statistic Results
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Statistic
Wilks’s lambda
Pillai’s trace
Hotelling-Lawley trace
Roy’s greatest root
Construct
Perceived direct advantages (H 1 )
Perceived indirect advantages (H 1 )
IT expertise (H 2 )
Technical compatibility (H 3 )
Top management support (H 4 )
Social pressure (H 5 )
∗∗ p
175
Limitations
F
Pr > F
0.94
0.85
2.03
6.55
.01∗∗
.01∗∗
.01∗∗
.01∗∗
0.95
0.35
2.09
3.90
5.07
6.41
.43
.83
.08
.01∗∗
.01∗∗
.01∗∗
< .01.
The nonsignificant result for IT expertise (H 2 ) is surprising. One possible explanation is that the adopters of
e-learning systems may not necessarily request individual
learners with high levels of expertise in IT. E-learning activities only require individual learners to possess basic technical skills while adopting systems such as an enterprise
resource planning (ERP) system that demands individual
learners with better IT knowledge and skills (Morton & Hu,
2008; Ngai, Law, & Wat, 2008; Waarts, van Everdingen,
& van Hillegersberg, 2002).
Consistent with the diffusion of innovation theory
(Rogers, 1983, 1985), early adopters have placed more emphasis on technical compatibility, top management support,
and social pressure when considering whether to acquire elearning systems. This highlights the importance of IT compatibility in affecting the adoption decision (Premkumar &
Ramamurthy, 1995). The e-learning system is perceived as
more technically compatible by early adopters than by late
adopters (H3). In addition, early adopters have strong top
management support for e-learning systems (H 4 ). Consistent
with the IT literature, top management support would ensure
the successful adoption of new skills and the maintenance of
competitiveness within the industry (Beatty et al., 2006; Lee
& Shim, 2007). This is a confirmation of top management
support as a champion for innovation adoption, including the
e-learning system. Finally, the early adopters of e-learning
systems are influenced by social pressure because they may
have eventually decided to adopt the e-learning systems
based on widespread acceptance among their competitors
(H 5 ). According to Flanagin (2000), organizations acquiring
e-learning systems because of their expected benefits is unlikely. Instead, they might have considered using e-learning
systems due to social pressure than from the existing competitive market. In summary, the adoption of e-learning systems
can meet the needs of organizations and provide a great learning environment and experience to individual learners.
Only a relatively small sample size of 143 adopters was utilized. Another potential limitation of the present study was
that data were collected from business organizations. More
research is needed for the application of the results to other
types of organizational settings such as those characterized
by the strictly regulated usage of e-learning systems (e.g.,
training institutes). Although in the present study we examined five adoption innovation antecedents and e-learning systems adoption over time, the characteristics of adopters such
as organization size (Lee & Xia, 2006) apparently have implications for potential e-learning adopters. Future researchers
should explore the relationship between the antecedents of
adoption innovation and the characteristics of adopters. Finally, this study was conducted using a common method
source questionnaire for all variables, which may lead to
common method bias.
Conclusion
The contribution of this study is the extension of e-learning
system literature through the examination of the relationship among the antecedents and the adopters of e-learning
systems over time. This approach adds value, as it compares the adopters of e-learning systems on a time-frame
basis. The findings suggest that technical compatibility, top
management support, and social pressures have significant
differences between early and late adopters. Compared with
late adopters, early adopters view e-learning systems as more
compatible with existing technology infrastructures, have potential to gain more support from top management, and are in
accordance with pressures from the social learning environment. However, the study also shows no significant difference
between the early and late adopters in terms of perceived advantages. This nonsignificance may be attributed to the fact
that the perceived benefits of e-learning systems become applicable among adopters over time as the diffusion process
continues.
This study also shows that the success of decisions on the
adoption of e-learning systems depends on the commitment
of top management and its technical infrastructure. Organizations also need to experience social pressures when consider
adopting e-learning systems. We believe that more research
is required to further develop our understanding of early and
late adopters in e-learning systems, and determine other differences between early and late adopters.
ACKNOWLEDGMENTS
The authors are grateful for the constructive comments of
the referees and the editors on an earlier version of this article. This research was supported in part by the Hong Kong
Polytechnic University under grant number 8CGP.
176
S. C. H. CHAN AND E. W. T. NGAI
Downloaded by [Universitas Maritim Raja Ali Haji] at 22:00 11 January 2016
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