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Key factors to instructors’ satisfaction of learningmanagement systems in blended learning
Kamla Ali Al-Busaidi • Hafedh Al-Shihi
Published online: 17 November 2011
� Springer Science+Business Media, LLC 2011
Abstract Learning Management System (LMS) enables institutions to administer
their educational resources, and support their traditional classroom education and
distance education. LMS survives through instructors’ continuous use, which may
be to a great extent associated with their satisfaction of the LMS. Consequently, this
study examined the key factors that influence the instructors’ satisfaction of LMS in
blended learning, and how this satisfaction is related to their intention to continu-
ously use LMS in blended learning and purely for distance education. These
investigated factors are related to instructors’ individual characteristics (computer
anxiety, technology experience and personal innovativeness), LMS characteristics
(system quality, information quality and service quality), and organizational char-
acteristics (management support, incentives policy and training). The findings
indicated that computer anxiety, personal innovativeness, system quality, infor-
mation quality, management support, incentives policy and training are key factors
to instructors’ satisfaction of LMS in blended learning. Furthermore, instructors’
satisfaction is a significant determinant of their continuous intention to use LMS in
blended learning, and their intention to purely use LMS for distance education.
Keywords Learning management system � e-learning � Instructors’ satisfaction �Critical factors to LMS � Blended learning
Introduction
Learning Management Systems (LMSs) and e-learning have become an essential
tool for stakeholders in education and training. The global market for e-learning
reached US$27.1 billion in 2009 and revenues will reach $49.6 billion by 2014
K. A. Al-Busaidi (&) � H. Al-Shihi
Sultan Qaboos University, P.O. Box: 20, 123 Al-Khod, Muscat, Oman
e-mail: [email protected]
123
J Comput High Educ (2012) 24:18–39
DOI 10.1007/s12528-011-9051-x
(Ambient Insight Research 2011). A number of top universities around the world
have adopted LMS to enhance the instructional process (Browne et al. 2006;
Hawkins and Rudy 2007; National Center for Educational Statistics 2003). More
than 90% of all participating academic institutions in the US are adopting LMS
(Hawkins and Rudy 2007). Similarly, almost 95% of participating institutions in the
UK have adopted LMS (Browne et al. 2006). In Middle East and Africa, the demand
for e-learning products is growing by a 5-year compound annual growth rate of
more than 10% from 2009 to 2014 (Ambient Insight Research 2011).
Users’ satisfaction of an information system is critical to its continuous success.
Similarly for LMS, its success to a great extent depends on instructors’ satisfaction
of the system. Assessing individual users’ acceptance of the e-learning systems is a
‘‘basic marketing element’’ (Kelly and Bauer 2004). A survey of more than 800
instructors at 35 LMS-adopting institutions found that very few instructors use LMS
tools for assessing student learning or promoting community (Woods et al. 2004).
Prior studies also found that instructors’ adoption of LMS may be limited by fear of
technology and lack of time (Yueh and Hsu 2008). Therefore, instructors’
satisfaction of LMS is crucial and should be carefully studied to ensure successful
LMS deployment. LMS survive through instructors’ continuous use, which may be
to great extent linked to their satisfaction of the LMS.
Consequently, the objective of this study is to investigate the key factors
contributing to instructors’ satisfaction of LMS use in blended learning environ-
ment. These factors can be categorized as instructors’ individual characteristics
(computer anxiety, technology experience and personal innovativeness), LMS
characteristics (system quality, information quality and service quality), and
organizational characteristics (management support, incentives policy and training).
In addition, the study also assesses how instructors’ satisfaction of LMS use in
blended learning is related to their continuous intention to LMS use in blended
learning, and their intention to purely use LMS for distance education. Several
organizations initiate their LMS adoption by using them in blended learning
environment, to elevate the risks of a complete pure LMS use for distance
education. The following sections discuss the background literature, research
framework and methodology, analysis and results, and the conclusion.
Literature review
LMS and benefits
According to the World Bank (2010), LMS is a software package that automatically
administers education and trains human resources. It is the use of a Web-based
communication, collaboration, learning, knowledge transfer, and training to add
value to learners and businesses (Kelly and Bauer 2004). LMS supports e-learning
activities such as presenting information, managing course materials, and collecting
and evaluating student (Yueh and Hsu 2008). Several terminologies also are used to
refer to LMS such as Course Management Systems (CMS) and Learning Content
Management Systems (LCMS) (Yueh and Hsu 2008), and Computer-assisted
Key factors to instructors’ satisfaction of learning 19
123
Learning (CAL), Computer-based Learning (CBL), and Online Learning (Chan
et al. 2008). LMS can be used to support traditional face-to-face classroom learning
(blended learning); or it can be used to build a virtual classroom where learning is
done purely fully online for distance learning (Rainer et al. 2007). Also, LMS
applications have become an important tool in business for training purposes.
Approximately 20% of all training in large US corporations is delivered through
LMS (Reiser 2002).
There are several LMS applications in the market. The most popular LMS used at
academic organizations in the US is Blackboard (Falvo and Johnson 2007).
Blackboard has course management features that support integration with student
databases (Knecht and Reid 2009); it enables course delivery, content management
and community engagement (Blackboard 2011). Other commercial LMS solutions
are eCollege and Learn.com. Moodle, a free open-source software package, is
sometimes preferred over Blackboard (Beatty and Ulasewicz 2006). Moodle has
been adopted by many universities, schools, companies and independent teachers
around the world (Moodle 2011). ETutor, Claroline, eFront and Joomla are also
open-source applications.
LMS applications offer instructors several functionalities that benefits and
contribute to teaching process (Burniske and Monke 2001). Course management
tools, group chat and discussion, assignment submission, and course assessment are
the primary tools in LMS (Yueh and Hsu 2008). In addition, LMS helps instructors
provide learners with educational materials and track their participation and
assessments (Falvo and Johnson 2007). LMS solutions also aim to increase interest
in learning and teaching among learners and instructors, respectively (Mahdizadeh
et al. 2008). Furthermore, LMS enhances teaching process efficiency and result in
cost-savings (Aczel et al. 2008; Naidu 2006).
Prior studies on LMS
Users’ satisfaction of LMS, as any other information system, is critical to their
continuous use (DeLone and McLean 2003). There are a number of studies that
have empirically investigated the learners’ acceptance, use and/or satisfaction of
LMS such as Klobas and McGill (2010), Liaw et al. (2007), Pituch and Lee (2006),
Raaij and Schepers (2008), Roca et al. (2006), Sun et al. (2008), and Wu et al.
(2006). However, limited quantitative studies have investigated instructors’
acceptance, use and/or satisfaction of LMS. In the LMS context, researchers have
studied LMS acceptance and success, from instructors’ perspective, in various ways.
Liaw et al. (2007) assessed factors influencing learners’ and instructors’ behavioral
intention to use e-learning, which is influenced by perceived usefulness, perceived
self-efficacy, and perceived enjoyment. Ball and Levy (2008) investigated the
impact of instructor’s individual characteristics on instructors’ intention to use
LMS. Teo (2009) assessed the instructor’s perceived usefulness of LMS and
perceived ease of use. However, user satisfaction of an information system is critical
to its continuous use and resulted benefits (DeLone and McLean 1992, 2003).
Moreover, key factors that might impact the instructors’ adoption of LMS can be
related to their individual characteristics (Ball and Levy 2008; Liaw et al. 2007;
20 K. A. Al-Busaidi, H. Al-Shihi
123
Raaij and Schepers 2008; Teo 2009), LMS characteristics (Pituch and Lee 2006;
Roca et al. 2006), and organization characteristics (Sumner and Hostetler 1999).
Al-Busaidi and Al-Shihi (2010) developed a theoretical model of the determinants
of instructors’ acceptance of LMS.
None of these studies, however, empirically investigated the direct impact of
instructors’ characteristics, LMS characteristics, and/or organizational characteris-
tics on instructors’ satisfaction. User satisfaction is an important indicator of IS
success (DeLone and McLean 2003). In addition assessing the impacts of
organization characteristics along with instructors’ characteristics and LMS
characteristics on instructors’ satisfaction is vital.
Instructor characteristics
The adoption and satisfaction of LMS may, critically, be determined by the
characteristics of its users. Several users characteristics have been recommended
and investigated as determinants of technology acceptance. In the context of
e-learning, few studies have investigated the impact of instructor dimensions on
LMS acceptance. Ball and Levy (2008) investigated the impact of self-efficacy,
computer anxiety, and technology experience on instructor intention to use
emerging learning experience in a small private university in the US and found
that self-efficacy was the only major determinant of instructor intention. Teo (2009)
found that computer self-efficacy directly impacts pre-service teacher’s perceived
usefulness, perceived ease of use, and behavioral intention in Singapore. Liaw et al.
(2007) found that perceived self-efficacy determines instructor behavioral intention
to use e-learning in Taiwan. Albirini (2006) investigated the perception of school
teachers of the use of ICT in education in Syria, and the results highlighted the
importance of teachers’ vision of technology, their experiences with it, and the
cultural conditions on their attitudes toward technology. Mahdizadeh et al. (2008)
found that a teacher’s previous experience with e-learning environments and ease of
use explain teachers’ perception of the usefulness of e-learning environments and
their actual use of these environments. Instructor innovativeness is important to the
satisfaction of e-learning (Raaij and Schepers 2008).
LMS characteristics
LMS characteristics may, to a great extent, impact the instructor’s satisfaction of
LMS. DeLone and McLean (2003) proposed that the major characteristics of any
information system include system quality, information quality, and service support
quality. System quality of LMS was found to be significant on instructors’ perceived
usefulness, perceived enjoyment, and perceived self-efficacy, which consequently
affect their intention to use the system (Liaw et al. 2007).
In the e-learning context, few studies have examined the general quality of the
technology or a specific dimension. For instance, from instructors’ and learners’
perspective, Liaw et al. (2007) investigated the impact of e-learning systems’
general quality on perceived usefulness, perceived enjoyment, and perceived self-
efficacy, which consequently affect their intention to use the system in the
Key factors to instructors’ satisfaction of learning 21
123
classroom, and found it significant. Albirini (2006) indicates that instructor vision of
technology impacts their attitudes toward the use of ICT in education. Two
significant studies on the impact of technology on user acceptance of LMS are
Pituch and Lee’s (2006) and Roca et al.’s (2006), but they are from the learner’s
perspective. Roca et al. (2006) investigated learner’s perception of system quality
from three dimensions (system quality, information quality, and service quality).
They found that a learner’s perceived system factors (system quality, information
quality, and service quality) directly affected e-learning satisfaction and intention to
use and indirectly their perceived usefulness. Pituch and Lee (2006) examined the
impact of system quality from three dimensions: the system’s functionality,
interactivity, and response.
As indicated, limited studies provide a detailed examination of the influence of
the three dimensions (system quality, information quality, service quality) of LMS
on instructor satisfaction. This study integrates these three dimensions of LMS on
the instructors’ satisfaction.
Organization characteristics
An organization’s characteristics can have a major impact on the behaviors of its
employees, including their use and satisfaction of any technology including LMS.
Corporate culture plays a major role in the success of any project. Culture is defined
as ‘‘the way we do things around here’’ (Schein 1985, p. 12). Cultural values outline
an organization’s customs and practices, which consequently influence employees’
behaviors such as LMS use and satisfaction. Organizational norms are considered a
reason for the use or resistance of a technology (such as LMS) in education (Lin
et al. 2010). In the e-learning context, management support, incentives, and training
are some organization’s characteristics that might be relevant to employees’
satisfaction of LMS.
Very limited empirical studies capture the impact of organization’s character-
istics on the acceptance and satisfaction of LMS. In the e-learning context, senior
management support, incentives and training are important for the instructor’s
adoption of e-learning. Senior managers should identify the alignment of e-learning
with the department and university curriculum and highlight its importance (Sumner
and Hostetler 1999). Motivators or incentives for instructors can be imposed by
having technology use as a factor in a nomination for teaching award, promotion,
and tenure (Sumner and Hostetler 1999). Finally, facilitating conditions including
training indirectly affect teachers’ acceptance of technology in education (Teo
2009).
Instructors’ satisfaction of LMS
Framework development
The objective of this study was to investigate the impact of instructor’s individual
characteristics, LMS characteristics, and organization characteristics on instructor
22 K. A. Al-Busaidi, H. Al-Shihi
123
satisfaction of LMS in blended learning, and consequently, on their continuous use in
blended learning and pure use intention for distance learning. Limited studies have
investigated this integrated examination of instructors’ satisfaction of LMS. Specifi-
cally, this study assessed the impact of individual characteristics (computer anxiety,
technology experience and personal innovativeness), LMS characteristics (system
quality, information quality, and service quality), and organization characteristics
(management support, incentives policy and training) on instructor satisfaction. The
impact of instructor self efficacy was also initially considered as part of instructors’
characteristics, but was dropped from the analysis because of low reliability and validity
of the construct in this study. Figure 1 illustrates this study model.
Instructor individual characteristics hypotheses
Computer anxiety hypothesis
Computer anxiety is an important factor for the acceptance of the technology (Ball
and Levy 2008; Piccoli et al. 2001; Raaij and Schepers 2008; Sun et al. 2008).
Fig. 1 A model of critical factors to instructors’ satisfaction of LMS in blended learning and its link totheir continuous intention to use LMS in blended learning and intention to use LMS purely for distanceeducation
Key factors to instructors’ satisfaction of learning 23
123
Computer anxiety is ‘‘the fear or apprehension felt by individuals when they used
computers, or when they considered the possibility of computer utilization’’
(Simonson et al. 1987, p. 238). The user’s acceptance of LMS and their perceived
satisfaction may negatively be affected by their fear of computers (Piccoli et al.
2001). The literature has different empirical evidence of the impact of computer
anxiety. Ball and Levy (2008) did not detect a significant link between computer
anxiety and instructors’ intention to use the e-learning. On the other hand, Sun et al.
(2008) found that computer anxiety significantly impacts the learners’ perceived
satisfaction of e-learning. Therefore:
Hypothesis 1 Instructors’ computer anxiety will be negatively associated with
their satisfaction of LMS.
Technology experience hypothesis
User experience with the technology plays a role in the acceptance of the
technology (Thompson et al. 2006; Venkatesh and Davis 2000). A user’s experience
with technology is his/her exposure to the technology along with the skills and
abilities that are gained through using a technology (Thompson et al. 2006). Thus,
instructors’ acceptance of LMS can be impacted by their technology experience.
Even though Ball and Levy’s (2008) empirical study found no significant impact of
technology experience on instructors’ intention to use LMS, researchers theoret-
ically emphasized its importance. Wan et al. (2007)underlined the importance of
technology experience on the learning processes and learning outcomes. Similarly,
Mahdizadeh et al. (2008) suggested that instructors’ prior experience with
e-learning may explain their perception of the usefulness of e-learning environments
and their actual use. Therefore:
Hypothesis 2 The instructor’s experience with the use of technology will be
positively associated with their satisfaction of LMS.
Personal innovativeness hypothesis
Personal innovativeness is another issue that may be critical factor on instructor
satisfaction of LMS. Personal innovativeness in information technology context is
an individual’s attitude reflecting a tendency to experiment with and to adopt new
information technologies independently of the communicated experience of others;
‘‘Being used to adapting to new systems and processes might reveal the usefulness
and ease of use more quickly to an innovative person than to a non-innovative
person’’ (Schillewaert et al. 2005, p. 843). Instructors’ innovativeness is important
to the satisfaction of e-learning (Raaij and Schepers 2008). Therefore,
Hypothesis 3 The instructor’s personal innovativeness will be positively associ-
ated with their satisfaction of LMS.
24 K. A. Al-Busaidi, H. Al-Shihi
123
LMS characteristics hypotheses
System quality hypothesis
System quality is a critical factor to user satisfaction of any technology, including
LMS. The impact of system quality on technology acceptance, use or satisfaction
has been highlighted by several researchers such as Bailey and Pearson (1983),
DeLone and McLean (1992), and Seddon (1997). Instructor satisfaction of LMS
may be impacted to a great extent by system quality. The more functionalities,
interactivity, and response of LMS, the better its acceptance and utilization (Pituch
and Lee 2006). Several characteristics of e-learning system have a significant
impact on e-learning acceptance. Such system characteristics are system’s reliability
(Wan et al. 2007; Webster and Hackley 1997), accessibility (Wan et al. 2007), and
functionality, interactivity, and response (Pituch and Lee 2006). Instructor vision of
technology impacts their attitudes toward the use of ICT in education (Albirini
2006). Therefore:
Hypothesis 4 LMS system quality will be positively associated with the
instructor’s satisfaction of LMS.
Information quality hypothesis
Information quality can be significant for instructor satisfaction of LMS. Informa-
tion quality is the perceived output produced by the system. It is related to
information accuracy, relevance, timeliness, sufficiency, completeness, understand-
ability, format, and accessibility, which are important for the success of information
technology (Bailey and Pearson 1983; Seddon 1997). Limited research investigated
the impact of information quality on instructor satisfaction of LMS. From a
learner’s perspective, Roca et al. (2006) found that information quality was directly
significant for learner’s satisfaction and indirectly for perceived usefulness. They
assessed information quality of LMS by indicators related to relevance, timeliness,
sufficiency, accuracy, clarity, and format, Similarly, Lee (2006) found content
quality was significant for learner’s perceived usefulness. Consequently:
Hypothesis 5 LMS information quality will be positively associated with the
instructor’s satisfaction of LMS.
Service quality hypothesis
Service quality can be an important factor to instructor satisfaction of LMS. It is
related to the quality of support services provided to the system’s end-users. Service
quality is related to reliability, responsiveness, assurance, tangibles, and empathy of
support service (Parasuraman et al. 1988; Kettinger and Lee 1994). Limited studies
have investigated the impact of service quality on LMS adoption and success from
instructors’ perspective. Thus:
Key factors to instructors’ satisfaction of learning 25
123
Hypothesis 6 LMS service quality will be positively associated with instructor’s
satisfaction of LMS.
Organization characteristics hypotheses
Management support hypothesis
Management support can be a key factor for the instructors’ satisfaction of LMS.
Senior managers’ open approval of LMS promotes instructor adoption and
acceptance of LMS. Managers may support LMS adoption by encouraging
instructors to adopt it and clearly identify how it is aligned with the university
vision. There is very limited empirical research on the impact of management
support on instructor satisfaction of LMS. However, Sumner and Hostetler (1999)
indicated that senior managers should clearly identify the goal of LMS for the
university curriculum to encourage instructors to adopt and use the system.
Managers are acknowledged as a high authority (Ali 1990). Igbaria (1990) found
that management support of end-users significantly improves computer use.
Similarly, Teo (2009) found that administrative support, is a facilitating condition,
and indirectly affect instructors’ acceptance of technology in education.
Consequently:
Hypothesis 7 Management support will be positively associated with the
instructor’s satisfaction of LMS.
Incentives policy hypothesis
Incentives may have an impact on instructor satisfaction of LMS. Incentives are
important factors to encourage instructors to integrate LMS in their teaching.
Incentives can be in form of ‘‘non-trivial’’ monetary and non-monetary incentives.
There is an absence of research on the impact of incentives on LMS acceptance. In
e-learning context, incentives for instructors can be enforced by using the LMS as a
factor in nomination for a teaching award, promotion, and tenure (Sumner and
Hostetler 1999). Incentive policies may drive instructors’ use and satisfaction of
LMS. Therefore:
Hypothesis 8 An incentive policy will be positively associated with instructor’s
satisfaction of LMS.
Training hypothesis
Training may improves instructor adoption of LMS as it illustrates its potential
usefulness, and encourages its use in teaching. Limited research has investigated the
impact of training on instructor satisfaction of LMS. Training may include
workshops, online tutorials, courses, and seminars (Sumner and Hostetler 1999).
Teo (2009) found that facilitating conditions, including training, indirectly affect
teachers’ acceptance of technology in education. Thus:
26 K. A. Al-Busaidi, H. Al-Shihi
123
Hypothesis 9 Training will be positively associated with the instructor’s
satisfaction of LMS.
Usage and future intention hypotheses
Continuous blended learning intention hypothesis
The intention to use the technology is significantly determined by user acceptance:
perceived ease of use and perceived usefulness (Venkatesh and Davis 2000). The
higher the instructor satisfaction with LMS, the more likely it is that they will
continue to use it. Hayashi et al. (2004) indicated that continuous intention to
e-learning use is determined by perceived usefulness and satisfaction. Thus:
Hypothesis 10 The instructor satisfaction of LMS will be positively associated
with their intention to continuously use LMS in blended learning.
Pure use intention hypothesis
Some organizations begin their LMS adoption in blended learning environment (as
a supplementary tool to traditional classroom teaching), hoping that this supple-
mentary adoption will eventually promote the pure full use of LMS for distance
education. Instructors’ satisfaction of LMS in blended learning may have an
important impact on their future intention to use LMS purely for distance education.
When instructors realize the benefits of LMS and become satisfied with its use in
blended learning, they are more likely to adopt it purely for distance education.
Perceived usefulness of a technology is significant factor for their intention to use it
(Venkatesh and Davis 2000). Pituch and Lee (2006) found that perceived usefulness
and supplementary use were significant factors for learner use of e-learning for
distance education. Thus:
Hypothesis 11 The instructor satisfaction of LMS in blended learning will be
positively associated with their intention to purely use LMS for distance education.
Methodology
Participants
The study’s sample included 82 instructors from Sultan Qaboos University (SQU) in
Oman. SQU adopted WebCT application initially, but later switched to the open-
source Moodle application. Instructors could voluntarily adopt LMS to supplement
their traditional classes (blended learning).
The majority of participants (62%) were male, whereas 38% were female.
Participants included assistant lecturers (5% of them), lecturers (27%), assistant
professors (50%), associate professors (13%), and full professors (5%). Eight
percent of the participants were in their 20s, 26% were in their 30s, 16% in their 40s,
and 32% were 50 or over. Almost 44% had less than 6 years of work experience,
Key factors to instructors’ satisfaction of learning 27
123
30% had less than 11 years, 16% had less than 16 years, 7% had less than 21 years,
and 2% had more than 20 years. When asked to rate their computer skills, 71%
reported above average computer skills, 23%, about average, and only 6% reported
below average skills. The majority, 59%, had used the LMS for classes for 3 years
or more; 30% have used it for 1–2 years; and 11% have used it for less than 1 year.
Research questionnaire
The data were collected electronically. An invitation to participate in the
questionnaire, including the website link of the questionnaire, was posted on the
Moodle login webpage for a month. An email invitation was also sent to 646
instructors who were registered with the Moodle application. This list of instructors
was obtained from the Moodle support team. Instructors were asked to complete the
study questionnaire either online or using an attached MS Word document. A
reminder was sent 2 weeks after the initial invitation. However, only 82 instructors
(12.69% of invited instructors) completed the questionnaire. Most of the instructors
completed the questionnaire online (95%).
The questionnaire included several measures of the study’s constructs, along with
demographic questions (e.g., gender, age, degree, LMS usage experience, work
experience, and job title). Construct measurements items were phrased according to
a five-point Likert-type scale (1 = strongly disagree; 2 = disagree; 3 = Neutral;
4 = agree and 5 = strongly agree). To statistically evaluate the study framework,
39 indicators were used. Tables 1 and 2 show the total indicators used for each
construct. The LMS characteristic constructs (system quality, information quality,
and service quality) were adopted and modified from Pituch and Lee (2006) and
Roca et al. (2006). Individual characteristics constructs (computer anxiety and
technology experience) were adopted from Ball and Levy (2008); while the personal
innovativeness construct was adopted from (Raaij and Schepers 2008). Organiza-
tional characteristics’ constructs (management support, incentives, and training)
were self-developed, based on Sumner and Hostetler (1999). The user satisfaction
construct was adopted from Sun et al. (2008), and continuous blended learning and
pure LMS intention were adopted and modified from Pituch and Lee (2006).
Data analysis & results
PLS analysis methodology
PLS-Graph 3.0 software was used for data analysis. PLS (partial least square) is a
variance-based structural equation model (SEM) technique that allows path analysis
of models with latent variables (Chin 1998, 2001). The PLS approach is a variance-
based SEM that assists researchers in obtaining determinate values of latent
variables for predictive purposes. The PLS does that by minimizing the residual
variances of all dependent variables rather than using the model to explain the co-
variation of all indicators (Chin 1998; Chin and Newsted 1999). Thus, the model
28 K. A. Al-Busaidi, H. Al-Shihi
123
Table 1 Independent constructs measures and loadings
Construct measures Loading
Computer anxiety
I believe that working with computers is very difficult 0.8717
Computers make me feel uncomfortable 0.9493
I get a sinking feeling when I think of trying to use a computer 0.8961
Technology experience
I feel confident using the e-learning system 0.7617
I feel confident downloading/uploading necessary materials from the Internet 0.8460
I feel confident using online communication tools 0.6333
Personal innovativeness
I like to experiment with new information technologies 0.6713
Among my peers, I am usually the first to try out new information technologies 0.9735
System quality
The system offers flexibility in teaching as to time and place 0.7046
The system offers multimedia (audio, video, and text) types of course content 0.7225
The response time of the system is reasonable 0.7017
The system enables interactive communication between instructor and students 0.8190
Information quality
The information provided by the system is relevant for my job 0.8537
The information in the system is very good 0.9060
The information from the e-learning system is up-to-date 0.8457
The information provided by the system is complete 0.8186
Service quality
The system support services give me prompt service 0.8485
The system support services have convenient operating hours 0.8388
The system support services are reliable 0.8859
The system support services are easy to communicate with 0.8769
Management support
Senior administrators strongly support the use of e-learning system 0.8811
I get support by department chair or dean on my use of e-learning system 0.8253
My mangers highlight the importance of e-learning system on my curriculum 0.8624
Senior administrators clearly identify the importance of e-learning to the curriculum 0.7517
Incentives
The use of e-learning is a factor in the nomination for teaching award 0.9396
The use of e-learning system is a factor in determining promotion 0.9620
The use of e-learning system is a factor in annual elevation of teaching 0.9685
Training
I receive training workshops on how to use e-learning tools 0.8015
I receive on-line manuals on how to use e-learning tools 0.7993
I receive seminars on the use of e-learning tools 0.8761
Key factors to instructors’ satisfaction of learning 29
123
paths are estimated based on the ability to minimize the residual variances of the
dependent variables.
For this study, PLS SEM was selected over the traditional covariance-based SEM
mainly because it requires a smaller sample size than covariance-based SEM. With
PLS, the rule of thumb suggests that the sample-size is to equal 5–10 times the
larger of the following: (1) the scale with the largest number of formative indicators
(scales with reflective indicators can be ignored), or (2) the largest number of
structural paths directed at a particular construct in the structural model (Chin and
Newsted 1999). In reflective mode indicators, 82 sample-size satisfies the second
rule; it is between 45 (5 times 9 paths) and 90 (10 times 9 paths). Reflective mode
was selected for this study rather than formative mode because reflective indicators
are appropriate for explanation and prediction studies (Chin and Newsted 1999).
In reflective mode, all the indicators of a specific construct measure the same
underlying construct, hence they all correlate. On the other hand, in formative
mode, indicators are not considered to be correlated; indicators are assumed to be
causing rather than being caused by the latent construct.
The PLS algorithm uses an iterative process for the estimation of weights and
latent variables scores. The process almost converges to a stable set of weight
estimates. The evaluation of the model is based on (1) the assessment of the model
measurements by assessing their validity, reliability, and discriminant validity, (2)
the analysis of the paths of the structural model (Chin 1998). Tables 1 and 2 show
the independent and dependent construct measures and loading respectively.
Constructs validity and reliability
The applicability of adopted measurements was empirically assessed by two
criteria: reliability and validity. Reliability refers to the consistency of the measures
(indicators) of a specific latent variable; while, validity refers to how well the
Table 2 Dependant constructs measures and loadings
Construct measures Loading
User satisfaction (SAT)
I am satisfied with the performance of the e-learning system 0.8078
I am pleased with the experience of using the e-learning system 0.9133
My decision to use the e-learning system was a wise one 0.8684
Continuous intention to LMS use in blended learning (CUI)
I will frequently use e-learning system to do a teaching task 0.8743
I will use e-learning system on regular basis to supplement my classes in the future 0.8645
I will always try to use the e-learning system to do a teaching task whenever it has a useful
feature
0.8917
Intention to pure LMS use (PUI)
I plan to teach purely online courses for distance learners 0.9393
I will use e-learning system to teach purely online courses 0.9594
I plan to teach purely online courses in as many occasions as possible 0.9304
30 K. A. Al-Busaidi, H. Al-Shihi
123
concept is defined by the measures (Crano and Brewer 2002; Hair et al. 1998). With
PLS, in the reflective mode, loading can determine the amount of variance in that
indicator that the prospective latent variable is able to account for. The reliability of
the measurements was evaluated by internal consistency reliability, and the validity
was measured by the average variance extracted (AVE), which refers to the amount
of variance a latent construct captures from its indicators. AVE was developed by
Fornell and Larcker (1981) to assess construct validity. The recommended level for
internal consistency reliability is at least 0.70, and is at least 0.50 for AVE (Chin
1998). Tables 1 and 2 show the model constructs’ measurements and loading.
Table 3 shows that the study constructs’ reliability and AVE are above the
recommended levels for all the constructs.
To achieve the discriminant validity of the constructs, Fornell and Larcker (1981)
suggest that the square root of AVE of each construct should exceed the correlations
shared between the constructs and other constructs in the model. The discriminant
validity is used to ensure the differences among constructs (Barclay et al. 1995;
Chin 1998). Table 4 shows that the model constructs satisfy that rule, as the square
root of the AVE (on the diagonal) was greater than the correlations with other
constructs. Thus, all the model constructs have a satisfactory discriminant validity
construct.
Model evaluation and paths analysis
With PLS, R-square (R2) values are used to evaluate the predictive relevance of a
structural model for the dependent latent constructs, and the path coefficients are
used to assess the effects of the independent constructs (Chin 1998). The
significance of the model paths was assessed based on their t values. Only paths’
coefficients with significance level (a) of at least 0.05 were considered significant.
Table 5 shows the R2 values of the endogenous dependent constructs. The
analysis indicated that the model explains 47.1% of variance in the instructor
Table 3 Constructs reliability and validity
Construct Total items Reliability AVE
Computer anxiety (CA) 3 0.932 0.821
Technology experience (TE) 3 0.794 0.566
Personal innovativeness (PI) 2 0.818 0.699
System quality (SQ) 4 0.827 0.545
Information quality (IQ) 4 0.917 0.734
Service quality (SvQ) 4 0.921 0.744
Management support (MS) 4 0.899 0.692
Incentives (IN) 3 0.970 0.915
Training (TR) 3 0.866 0.683
User satisfaction (SAT) 3 0.898 0.747
Continuous supplementary use intention (CUI) 3 0.909 0.769
Pure use intention (PUI) 3 0.960 0.889
Key factors to instructors’ satisfaction of learning 31
123
Ta
ble
4C
onst
ruct
sco
rrel
atio
ns
and
dis
crim
inan
tval
idit
y
Co
nst
ruct
CA
TE
PI
SQ
IQS
vQ
MS
INT
RS
AT
CU
IP
UI
Com
pu
ter
anx
iety
(CA
)0
.906
Tec
hn
olo
gy
exp
erie
nce
(TE
)-
0.1
53
0.7
52
Per
son
alin
no
vat
iven
ess
(PI)
-0
.295
0.5
51
0.8
36
Sy
stem
qu
alit
y(S
Q)
-0
.092
0.1
59
0.2
60
0.7
38
Info
rmat
ion
qu
alit
y(I
Q)
-0
.078
0.1
79
0.2
09
0.6
33
0.8
57
Ser
vic
eq
ual
ity
(Sv
Q)
-0
.027
0.0
56
0.1
28
0.4
72
0.6
89
0.8
63
Man
agem
ent
sup
po
rt(M
S)
0.1
99
-0
.174
0.1
25
0.2
98
0.2
26
0.2
29
0.8
32
Ince
nti
ves
(IN
)0
.227
-0
.224
-0
.106
0.1
58
0.1
24
0.1
42
0.5
30
0.9
57
Tra
inin
g(T
R)
0.0
20
0.0
03
0.1
65
0.2
71
0.3
48
0.3
53
0.2
41
0.2
97
0.8
26
Use
rsa
tisf
acti
on
(SA
T)
-0
.338
0.1
82
0.3
33
0.4
91
0.4
97
0.3
24
0.2
26
0.2
09
0.3
88
0.8
64
Con
tin
uou
ssu
pp
lem
enta
ryu
sein
ten
tio
n(C
UI)
-0
.329
0.3
73
0.4
93
0.4
88
0.3
65
0.1
91
0.1
73
0.1
63
0.3
40
0.7
64
0.8
77
Pu
reu
sein
ten
tio
n(P
UI)
0.0
08
0.1
23
0.3
74
0.1
03
0.0
54
-0
.026
0.0
72
0.0
94
0.1
13
0.3
55
0.4
35
0.9
43
Bo
ldn
um
ber
sin
the
dia
go
nal
rep
rese
nt
the
SQ
RT
(AV
E)
of
the
con
stru
ct;
toac
hie
ve
the
dis
crim
inan
tv
alid
ity
of
the
con
stru
cts,
the
SQ
RT
(AV
E)
of
each
con
stru
ctsh
ould
exce
edth
eco
rrel
atio
ns
shar
edb
etw
een
the
con
stru
ctan
do
ther
con
stru
cts
inth
em
od
el
32 K. A. Al-Busaidi, H. Al-Shihi
123
satisfaction of LMS in blended learning. The analysis also showed that instructor
satisfaction of LMS in blended learning explains 58.4% of variance in their
intention to continuously use LMS in blended learning, and 12.6% of their intention
to use LMS purely for distance education.
Table 5 also shows the paths coefficients analysis between the exogenous
independent constructs (instructors characteristics, LMS characteristics, and orga-
nization characteristics) and the endogenous dependent construct (instructors’
satisfaction of LMS in blended learning), and, consequently, intention (continuous
LMS use in blended learning, and LMS pure use for distance education).
The analysis showed that most of the instructor characteristics, the LMS
characteristics and the organization characteristics to some extent have impact on
the instructor’s satisfaction of LMS in blended learning. First, instructor computer
anxiety negatively impacts satisfaction of LMS (b = - 0.3058, p \ 0.0005); thus
Hypothesis 1 was supported. Second, the impact of instructor experience with the
technology did not have a significant effect on their satisfaction of LMS
(b = 0.0587, p \ 0.20); thus Hypothesis 2 was not supported. Third, instructor
personal innovativeness positively impacted their satisfaction of LMS (b = 0.2371,
p \ 0.001); thus, Hypothesis 3 was supported. Fourth, system quality significantly
impacted instructor satisfaction of LMS (b = 0.1808, p \ 0.025); thus, Hypothesis
4 was supported. Fifth, information quality significantly impacted instructor
satisfaction of LMS (b = 0.2371, p \ 0.001); thus, Hypothesis 5 was supported.
Sixth, service quality did not significantly impact instructor satisfaction of LMS
(b = 0.0398, p \ 0.25); thus Hypothesis 6 was not supported. Seventh, manage-
ment support significantly impacted instructor satisfaction of LMS (b = 0.1272,
p \ 0.025); thus, Hypothesis 7 was supported. Eighth, incentives policy signifi-
cantly impacted instructor satisfaction of LMS (b = 0.1476, p \ 0.01); thus,
Hypothesis 8 was supported. Ninth, training significantly impacted the instructor
Table 5 Model evaluation and paths analysis
Dependent construct Path Beta (b) p value Hypothesis
Satisfaction (SAT) (R2 = 0.471) CA ? SAT -0.3058 \0.0005 H1: supported
TE ? SAT 0.0587 \0.20 H2: not
supported
PI ? SAT 0.1115 \0.025 H3: supported
SQ ? SAT 0.1808 \0.025 H4: supported
IQ ? SAT 0.2371 \0.001 H5: supported
SvQ ? SAT 0.0398 \0.25 H6: not
supported
MS ? SAT 0.1272 \0.025 H7: supported
IN ? SAT 0.1476 \0.01 H8: supported
TR ? SAT 0.2046 \0.001 H9: supported
Continuous supplementary use intention (CUI)
(R2 = 0.584)
SAT ? CUI 0.7693 \0.0005 H10:
supported
Pure use intention (PUI) (R2 = 0.126) SAT ? PUI 0.3592 \0.0005 H11 supported
Key factors to instructors’ satisfaction of learning 33
123
satisfaction of LMS (b = 0.2046, p \ 0.001); thus, Hypothesis 9 was supported. In
addition, instructor satisfaction of LMS in blended learning significantly impacted
their intention to continuously use LMS in blended learning (b = 0.7693,
p \ 0.0005), and their intention to purely use LMS for distance education
(b = 0.3592 p \ 0.0005); thus, Hypothesis 10 and Hypothesis 11 respectively
were supported. Figure 2 illustrates these significant factors to instructors’
satisfaction of LMS.
Discussion
LMS offers several benefits for academic and training organizations. LMS can be
used to efficiently support distance education, supplement traditional teaching and
capture educational materials for future reuse. This study examined the impact of
instructor characteristics (computer anxiety, technology experience and personal
innovativeness), LMS characteristics (system quality, information quality, and
service quality), and organization characteristics (management support, incentives,
and training) on instructor satisfaction of LMS in blended learning, and,
Fig. 2 Significant factors to instructors’ satisfaction of LMS in blended learning and its link to theircontinuous intention to use LMS in blended learning and intention to use LMS purely for distanceeducation
34 K. A. Al-Busaidi, H. Al-Shihi
123
consequently, their future intention of using LMS in blended learning and in pure
e-learning for distance education. Findings suggested that individual instructor
characteristics, LMS characteristics, and organization characteristics have impact
instructor satisfaction of LMS in blended learning.
The study found that instructor computer anxiety negatively impacts satisfaction
of LMS. The study found that instructor computer anxiety was the key factor
influencing instructor satisfaction of LMS. Empirical studies have shown mixed
impacts of computer anxiety on LMS adoption (perceived ease of use, perceived
usefulness and satisfaction). These mixed results could be linked to computer
literacy or cultural issues. Nevertheless, organizations need to investigate the causes
of computer anxiety in order to eliminate it and consequently improve the adoption
of LMS in their organizations. Second, even though qualitative research has
suggested that technology experience might contribute to the LMS adoption and
satisfaction, this study was unable to find a significant impact on instructor
satisfaction, which is consistent with Ball and Levy’s (2008) findings. Third, the
current study found that instructor personal innovativeness is another positive key
factor to satisfaction of LMS in blended learning, which was consistent with Raaij
and Schepers (2008). Thus, improving instructor personal innovativeness could
improve satisfaction of LMS.
The study found system quality and information quality as key factors to
instructor satisfaction of LMS. This finding was consistent with Roca et al. (2006)
study on learner satisfaction of e-learning. For a successful deployment of LMS
organizations should ensure that system is with high functionalities and has quality
information. This study was unable to detect a significant impact of service quality
on instructor satisfaction as found by Roca et al. (2006). This study, compared to
Roca et al.’s study on learners, investigated additional factors. Thus, service quality
may be an important factor, but it is not a key factor to instructor satisfaction of
LMS compared to the other factors.
Management support, incentives policy and training were found to be key factors
to instructor satisfaction of LMS. Prior qualitative research has suggested these
findings; there were few quantitative studies that have reported this impact on LMS
satisfaction. Support of the LMS initiative and encouragement to use the system by
organizations is important. Senior managers should also integrate LMS use in their
incentives policy such as a factor in nomination for a teaching award, promotion,
and tenure. Finally, the organization should provide sufficient training to instructors;
this training program can be in form of workshops, on-line manuals or/and
seminars.
Finally, the study found that instructor satisfaction of LMS is a key determinant
of their continuous use of LMS in blended learning. The study also found that
instructor satisfaction of LMS in blended learning is a key determinant of their
intention to purely use LMS for distance education. Few studies have examined the
link between instructor use of LMS in blended learning to their intention of pure
e-learning. This study found that organizations that were not ready for pure
e-learning, that the use of LMS in blended learning was a valuable option to prepare
organizations and instructors to complete digital transformation through the use of
LMS purely for distance education.
Key factors to instructors’ satisfaction of learning 35
123
This study offered some implications for researchers and practitioners. The study
has showed that individual characteristics, LMS characteristics, and organization
characteristics were key factors to instructor satisfaction of LMS in blended
learning, and that instructor satisfaction of LMS was a significant in impacting their
future intentions for blended learning or pure e-learning. In addition, organizations
should deploy high quality LMS’ (in terms of system quality and information
quality) systems to encourage instructor adoption and use. Finally, to improve
instructor satisfaction of LMS in blended learning, organizational senior managers
should support its deployment and integrate LMS use in their incentives policy.
Limitations and future research
This study had few limitations First, the sample was from one academic institution
in Oman; more research can be conducted in several organizations in different
countries to improve the generalization of the findings. Second, the response rate
(12.69%) of invited participants was low; other effective invitation methods (e.g.
post mail, phone calls etc.) could be utilized to enhance response rate. Third, the
adopted measurements of self-efficacy were of low reliability and validity; new
measurements should be developed to improve its reliability and validity across
different countries. Moreover, future research may also examine in detail the
outcomes of LMS for instructors, and the critical factors to instructors’ satisfaction
of full e-learning and the critical factors to organizations’ deployment of LMS.
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38 K. A. Al-Busaidi, H. Al-Shihi
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Author Biographies
Kamla Ali Al-Busaidi is an Assistant Professor of information systems at Sultan Qaboos University in
Oman. She received her PhD in management information systems from Claremont Graduate University in
California in 2005. Her research interests include knowledge management systems, learning management
systems, decision support systems and the deployment of information and communication technologies in
Arab countries. She has published in several conference proceedings, book chapters and journals,
including International Journal of Knowledge Management, Journal of Global Information Technology
Management and International Journal of Global Management Studies. She also served as a reviewer for
several international conference proceedings, books and journals.
Hafedh Al-Shihi is an Assistant Professor at the College of Commerce and Economics in Sultan Qaboos
University. He obtained his PhD from Victoria University, Australia in 2006. He is a member of the
Association of Information Systems (AIS) and the Special Interest Group on Electronic Government
(SIGeGov). He is also a Research Associate in the Center for International Corporate Governance
Research at Victoria University, Australia. He has several publications on e-government/m-government,
e-learning and technology adoption and dissemination and has attended several local and international
conferences.
Key factors to instructors’ satisfaction of learning 39
123