Computers in Human Behavior 63 (2016) 249e255
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Computers in Human Behavior
journal homepage: www.elsevier.com/locate/comphumbeh
Full length article
Sharing instructors experience of learning management system: A
technology perspective of user satisfaction in distance learning course
Ibrahim Almarashdeh
Department of Management Information Systems, College of Applied Studies and Community Service, University of Dammam, Saudi Arabia
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 12 January 2016
Received in revised form
23 April 2016
Accepted 5 May 2016
The use of educational technology increased rapidly in higher education. Learning Management System
(LMS) is the most popular educational technology system used in distance learning. There are only a few
studies have been carried out to measure instructors satisfaction in distance learning courses, although
instructors satisfaction is considered as very important for the course involvement and increasing the
students interactions with the course content. Hence, this study proposed a detailed framework to
measure instructors’ satisfaction of using LMS. The findings prove that perceived usefulness and service
quality are taking the highest share on affecting the instructor satisfactions. This study limited to higher
education’s instructors and used a questionnaire survey to collect the data. Hence, the LMS should be
designed based on the needs of the instructors as well as the students, by adopting the latest technologies. In the contrary, building LMS without taking the instructors’ satisfaction into account will affect
negatively the distance learning course outcomes.
© 2016 Elsevier Ltd. All rights reserved.
Keywords:
Information quality
System quality
Service quality
Learning management system
Distance learning
User satisfactions
1. Introduction
Using learning management system (LMS) in online or distance
learning courses is very common in higher education (Navimipour
& Zareie, 2015). The integration of a LMS into learning and teaching
practices has been increased in higher education sectors
(Ashrafzadeh & Sayadian, 2015). Studies in the area of motivation
factors and instructional technology integration demonstrate a link
between instructional practices and the motivators (Gautreau,
2011). Instructors satisfaction is an important topic in view of the
rapid growth in the number of institutions using LMS in online
course and this leads to the need to evaluate the measurement of
the LMS effectiveness. It would be essential to understand how
teachers become experts in using online media which lead to high
level of satisfaction (Almeda & Rose, 2000).
Nowadays, the LMS has been used popularly in the learning
process. Therefore, the universities need to evaluate the effectiveness of computer usage by measuring user satisfaction with computers in a work place which is very important to the success of any
program or organization (Bergersen, 2004; Del Barrio-garcía, , &
Romero-frías, 2015). Measuring instructors’ satisfaction is important in terms of classroom quality improvement, since using online
E-mail address: ibramars@gmail.com.
http://dx.doi.org/10.1016/j.chb.2016.05.013
0747-5632/© 2016 Elsevier Ltd. All rights reserved.
platform and interacting with students online (without reading
their body language) requires the instructor to think twice before
using the online content (Swartz, Cole, & Shelley, 2010; Mclawhon
& Cutright, 2012). However, understanding the limited formal use
of LMS and the wide range of LMS implementation is very important for future success of the LMS (Naveh, Tubin, & Pliskin, 2010).
Unfortunately, most of researchers (eg (Cigdem & Topcu, 2015):
focused on measuring the instructor acceptance of LMS or intention
to use LMS in online course while measuring the instructor satisfaction and the outcomes of using such system can lead to better
view of the overall preferences. Since that only few studies focused
in instructors satisfaction (Swartz et al., 2010) and more focus is
needed in instructors satisfaction (Vasilica Maria, Carmen, &
Luis Montes, 2015), this study will focuses on the instructor’s
Jose
satisfaction which is the main player of learning process.
1.1. Research question
Continuing the investigation in faculty satisfaction with online
learning tools is needed to explore instructor satisfaction level and
which will lead to more system enhancement and improvement
(Swartz et al., 2010). For the purpose of investigating instructor
satisfaction in using LMS in distance learning course, this study is
designed to answer the following question: Which factors has statistically significant influence in the satisfaction of distance education
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I. Almarashdeh / Computers in Human Behavior 63 (2016) 249e255
System Quality
(SyQ)
H1
Service Quality
(SVQ)
H2
H3
Information
Quality (InQ)
User Satisfaction
(SAT)
H6
Net Benefit
(NB)
H4
H5
Perceived Ease of
Use (PEU)
Perceived
usefulness (PU)
Fig. 1. User satisfaction evaluation model (USEM).
instructor?
2. Literature review
2.1. Distance learning
Moving from traditional face-to-face teaching to teaching online
can be a precarious process for instructors (Conrad, 2004).
Improving instructions of online classroom is based on the
instructor satisfaction with the online format and a better online
experience (Shea, Pelz, Fredericksen, & Pickett, 2002). As instructors always looking for attracting more students to make them
successfully finish their study, which is the main goal of every
university, latest studies have found that learners also expect fully
attention from online instructors as well (Conrad, 2004). The higher
satisfaction felt by the instructors promotes more usage of the LMS
(Hall, Corman, Drab, Meyer, & Smith, 2009). Hence, LMS and
educational technology adoption requires the users to experience
high level of satisfaction in order to increase usage and improve the
interaction in distance learning course (Keoduangsine & Goodwin,
2009; Kumar, Mukerji, Butt, & Persaud, 2007).
The format for delivering education from a distance has significantly changed since the introduction of new technologies such as
web based learning and mobile learning. Despite the advantages of
distance learning, its still has some limitations. For example, success may be dependent on technology; some individuals experience or technical issues with technology may provide some barriers
to learning. In addition, distance learning practise depends on
many factors such as user experience, motivation, technology,
expectation and time management. Since the technology always
deployed and the user needs always changed research indicates the
need for additional investigation in the area of distance learning
(Bertel & Pate, 2010). Furthermore, since the technology use in
distance learning course is the key factor to the success of the
course delivery and instructor satisfaction is one of the key factors
that may predict the overall expectation of the learning output.
Hence, investigating the instructor satisfaction of using LMS in
distance learning course is very important.
2.2. User satisfaction
Satisfaction of online instructors is an important topic because it
has the potential to influence the quality of instruction and student
outcomes (Bolliger, Inan, & Wasilik, 2014). Thus, Swartz et al. (2010)
claim that instructor satisfactions based on academic recognition,
technology availability, financial rewards and the degree of support
with online instruction (Swartz et al., 2010). When all these factors
are in place, it will motivate the instructors to more valuable as an
instructional platform than classroom instruction and more interactive and attractive to a more diverse student population in general. Additionally, quality of faculty work is likely to be perceived as
an extreme importance regarding student persistence and retention. Highly satisfied faculty members experience higher levels of
motivation to perform their duties (Mclawhon & Cutright, 2012). As
online teaching widely being used and instructors become experts
with on-line teaching, instructors satisfaction become more valuable to know how satisfied is the instructors as the delivery method
has been changed and teaching success based on the use of
instructional media (Almeda & Rose, 2000). However, there is a
number of studies published information on students satisfaction
with online course, but only few studies concerned with instructors
satisfaction topic especially in online course (Hall et al., 2009;
Swartz et al., 2010; Willett & Bouldin, 2004; Woodward, 1998).
User satisfaction is identified as the extent to which users
believe the information system available to them meets their
informational requirements (Ives, Olson, & Baroudi, 1983). User
satisfaction is the key differentiator in a competitive marketplace
(Gitman & Mcdaniel, 2008). Furthermore, analyzing the user
satisfaction is very useful for the product improvement (Li, Zhang,
Zhang, & Year, 2010). The faculty or instructors satisfaction is
defined as, “the extent to which faculty perceive that the institution
provides a climate ensuring professional autonomy and activity
commensurate with specialized expertise” (Pollicino, 1996).
A number of researchers believe that if the information system
(IS) meets the needs of the users, the users satisfaction with IS will
increase (Cyert & March 1992). Conversely, if IS does not provide
the required information, it will lead to dissatisfied instructor
(Bergersen, 2004). In our case, numerous instructors are dissatisfied with online classroom, and the dissatisfaction is derived from
perceptions of technical skills, personality type and unfamiliarity
(Llewellyn, 2011). Some of these factors are related to the technological difficulties such as using the available technology is not ease
of use, not user friendly, complex system, and low bandwidth and
accessibility. Hence, the next section will discuss the important
theories and factors in the field of information technology that
helps to predict the instructor satisfaction.
2.3. Research model
User satisfaction is regarded as one of the most important
measures of IS success, to determine user satisfaction includes
consumers’ total satisfaction of service performance, users’ opinion,
and national conditions (Delone & McLean, 2003). The D&M model
opened the gate for many researchers: they either empirically
tested the model in different contexts or criticized and enhanced
some of its aspects (Alshardan, Goodwin, & Rampersad, 2016).
Analyzing the improvements of certain quality elements may increases in satisfaction and a decreases in dissatisfaction. User
satisfaction affects the user loyalty to service enterprises, as high
level of satisfaction supports continuous use of the course (Hall
et al., 2009). In the service context, value and quality are proposed as antecedents of satisfaction; their influence on loyalty are
mediated by satisfaction (Llewellyn, 2011).
In LMS context, different researcher goes to different variable to
predict the user satisfaction in terms of capacity of use, culture
influenced or technology adoption. Furthermore, previous researchers in LMS perspective did not concern the importance of the
system quality such as availability and functionality of the LMS, or
I. Almarashdeh / Computers in Human Behavior 63 (2016) 249e255
251
from the view point of IS. In particular, the following important
factors are gathered from the continuous improvement and
development in designing, delivering and developing the required
services to the distance learners. Fig. 1 describes the characteristics
of the proposed User Satisfaction Evaluation Model (USEM).
2.4. Model hypothesis
Fig. 2. Reliability testing (Cronbach’s alpha).
the importance of service quality such as help desk, empathy and
follow up service, and information quality which are very important for the instructors to get the valuable information from the
LMS easily by ensuring the information accuracy, completeness,
legibility, consistency, availability, relevancy, timeliness and reliability (Cigdem & Topcu, 2015; Masud, 2016). All these factors are
equally important to the success of distance learning program.
Technology factors may give confidence to the instructors to teach
online if they find the LMS as a suitable environment and the ease
of use; and useful LMS with a better feature which will allow the
instructor to use it which facilitates the learning process for both
students and instructors.
The D&M model includes important factors to IS success namely
system quality, information quality, information system, service
quality, intention to use, usage, user satisfaction and net benefit
(Delone & Mclean, 2003). The TAM aims to predict the user
acceptance of new technology using perceived ease of use,
perceived usefulness, intention to use as a predictors that influence
the actual usage (Davis et al., 1989). This study proposed a research
model which built as the epitome of an association between
Technology Acceptance Model (TAM) (Davis, 1989) and the D&M
information system (IS) success model which has been used in
many empirical studies (Alshardan et al., 2016; Delone & Mclean,
1992).
In technology acceptance, perceived ease of use and usefulness
has been used as the mean variable to measure user behaviour. In
fact, the TAM did not concern the overall information, service and
quality even it has a significant influence in user behaviour (Wixom
& Todd, 2005). Whilst, the D&M IS success model did not represent
factors such as ease of use and usefulness of the technology
although previous studies found a strong influence on user
acceptance of new technology and user satisfaction (Rai, Lang, &
Welker, 2002).
It is necessary to invest more effort and time in the validation
and development of appropriate instruments for the assessment of
learning outcomes (Glaser, Chudowsky, & Pellegrino, 2001; Seel,
2011). This study used the user satisfaction as the predictor of the
overall user behaviour and defined it as possible impact of LMS and
the general evaluation of user’s experience upon using LMS in
distance learning course. Although user satisfaction are correlated
with net benefits. Subsequently, this study measured the net
benefit as the output of using LMS in distance learning course
because net benefit is the certain benefits that expected to be
achieved by using the system (Nguyen, Nguyen, & Cao, 2015) and
there is still the necessity to measure net benefits directly (Urbach
& Müller, 2012).
This research illustrates the value of measuring LMS success
The proposed USEM, which adapted and incorporated aspects of
two models of IS success model and technology acceptance model,
presents the possible effects of five exogenous variables (INQ, SVQ,
SYQ, PEU, PU) and two endogenous variables (SAT and NB). The
proposed model is called User Satisfaction Evaluation Model
(USEM) throughout the rest of this research.
According to Delone and Mclean (2003), System Quality (SYQ) is
defined as suitability, reliability of the system, and stability of the
software and hardware whereby information needed are supported. In the same model, Information Quality (INQ) referred as
student’s records, images reports and transcription or prescriptions
with information system (IS), and Service Quality (SVQ) is referred
to the distinctive standard of deliverables and supports by system
service provider (Delone & Mclean, 2003).
H1. System quality has a positive influence on instructor satisfaction in using LMS.
H2. Information quality has a positive influence on instructor
satisfaction in using LMS.
H3. Service quality has a positive influence on instructor satisfaction in using LMS.
As proposed by TAM, Perceived Ease of Use (PEU) concerns with
the users’ expectation that using a target system would be
errorefree and hassle-free, Perceived Usefulness (PU) is defined as
the users’ expectation that using a target system would contribute
and facilitate the work performance (Davis, 1989).
H4. Perceived ease of use has a positive influence on instructor
satisfaction in using LMS.
H5. Perceived usefulness has a positive influence on instructor
satisfaction in using LMS.
The User Satisfaction (SAT) is defined as possible impact of LMS
and the general evaluation of user’s experience upon using LMS.
Subsequently, Net Benefit (NB) captures the balance of negative and
positive impacts on the user behaviour. Thus, net benefit can be
measured using job effectiveness, efficiency, effects, error reduction
and decision quality (Lee-post, 2009; Delone & Mclean, 2003).
H6. Instructor satisfaction has a positive influence on net benefit
(outcomes) of using LMS.
3. Research methodology
This section outlines the data collection, data analysis, model fit
and hypotheses testing.
3.1. Data collection
This study is targeted instructors in distance learning course,
specifically in higher education. The distance learning instructor
was defined as an instructor who teaches a 100% online course or a
hybrid course. The sample and population are identified and the
questionnaire for the study is designed based on the study requirements and based on literature. Pre-testing have been conducted by using pilot test and professional review, the pilot data has
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I. Almarashdeh / Computers in Human Behavior 63 (2016) 249e255
years experience in using the LMS. Only 19.1 had less than one year
experience using the LMS. Furthermore, 14.5 of the responses had
more than 10 years experience using the LMS as shown in Table 1
above.
3.4. Descriptive statistics
Fig. 3. Standard deviation.
Table 1
Demographic information.
Gender
Frequency
Percentage
Male
Female
Job positions
Professor
Associate professor
Senior lecturer
Lecturer
Other
LMS Usage
Less than 1 year
1e3 years
3e7 years
7e10 years
More than 10 years
Total
53
57
Frequency
9
8
15
66
12
Frequency
21
22
29
22
16
110
48.2
51.8
Percentage
8.2
7.3
13.6
60.0
10.9
Percentage
19.1
20.0
26.4
20.0
14.5
100
been gathered and analyzed and the findings show reliable instruments have been used, in addition, participants’ suggestions
and comments are taken into consideration. The collected data is
analyzed begins by describing the demographic information of the
sample, reliability test and validity analysis. Instructors involved in
distance learning course in one of the four universities (two public
and two private) are the targeted sample. In July 2015, the targeted
sample size of 110 respondents was accomplished. A 5 point-Likertscales ranging from strongly disagree to strongly agree is used to
measure the questionnaire items.
3.2. Data analysis
For the purposes of statistical analysis, missing data were
deleted pair-wise. The data analyzed using SPSS 17 and SEM AMOS
18. The first part of the data analysis is reliability testing. Reliability
test (cronbach’s alpha) ranges between 0.80 and 0.90 which indicates that the scale is well-constructed (Sekaran, 2003). The
reliability testing of the conducted questionnaire value is 0.865
which indicates a well-constructed scale. Fig. 2 show the reliability
testing result of each construct.
3.3. Demographic information
The sample background information shows that majority of the
instructors of female gender represent 51.8% of the sample size,
while males represent 48.2% of the total sample size as shown in
Table 1 bellow. Academic Job positions, we can see that most of the
responses are lecturer in distance learning course which represent
60% of the total sample size. As the lower responses goes to associate professors which represent 7.3% of the total sample size.
In terms of LMS usage level, 26.4% of the instructors had 3e7
The purpose of using descriptive statistics is to consolidate a
large amount of data into simpler summary and be transformed
into a graphic diagrams and numerical procedures which would
provide clear and easy way for the readers to interpret and understand (Podsakoff, Mackenzie, Lee, & Podsakoff, 2003). Descriptive statistics and frequency distributions were used to provide an
overall view and expose the attributes of the collected data
(Trochim, 2003). Descriptive statistics including means (Table 2)
and standard deviation (Fig. 3) were generated for the scaled variables, as shown in Table 2 below.
The descriptive statistics result exhibits that the means for all
factors are above 3.9, and were similar. Thus, the values were
packed closely around the mean; this illustrates that all shared
opinions were similar from instructors’ point of view. In this study,
all of the standard deviations are less than 1.00, which shows the
variations in instructors’ opinions are small.
3.5. Measure of fit
The model evaluation is likely to be one of the most difficult
issues related to SEM (Arbuckle, 2005). It is essential to know how
to evaluate the model prior to analyze the structural model. The
CFA (confirmatory factor analysis) in SEM are categorized into
different types and each type has a specific capability in model
evaluation, such as population discrepancy, minimum sample
discrepancy function, measures of parsimony, comparison to a
baseline model, population discrepancy, and goodness of fit index
(Maccallum, 1990; Holmes-smith, 2000; Arbuckle, 2003; Byrne,
2009; Steiger, 1990).
The first step to build a structural model is to use empirical
research and knowledge of the theory to draw the relationship
between the observed variables and subsequently to use statistical
technique to test the hypothesis. CFA is a statistical technique used
to verify variables structure and enable the researcher to test the
model hypothesis. The CFA testing influenced by the requirement of
sufficient sample size, multivariate normality, the research hypothesis being tested, measurement instruments, outliers, interpretation of model fit indices, missing data as well as parameter
identification (Schumacker & Lomax, 2004).
In terms of fit measures, the chi-square statistic (c2) is by far the
most universally reported index of fit in structural equation
modeling (Davey & Savla, 2009). The chi-square value measures the
extent to which the data were incompatible with the hypotheses.
Thus, the higher the P (probability value) associated with CMIN, the
closer the fit between the hypothesized model and the perfect fit
(Arbuckle, 2009; Byrne, 2009). The chi-square value in the research
model is 14.988 with P value 0.010 which indicates the research
model is correct and no difference from the previous theories. The
overall fit for the research model indicate Chi-sq/df (2.998), at 5 df
which indicates a very good fit model. Table 3 summarizes the
USEM the measures of fit.
Fit measures of the USEM is concluded in the Table above which
exhibited that the model is clearly fit the data. The goodness of fit
measures presented in the table above based on the recommendations of previous research. The chi-square measure of discrepancy tests how much the implied and sample covariance matrices
differ under the null hypothesis that they do not. The result of the
test indicates the model hypotheses are accepted, which does auger
I. Almarashdeh / Computers in Human Behavior 63 (2016) 249e255
Table 2
Descriptive statistics.
No
Factors and items
Mean
1
2
3
4
5
6
7
System quality
Information quality
Service quality
Perceived ease of use
Perceived usefulness
User satisfaction
Net benefit
4.1500
4.0568
4.1477
4.2333
4.2182
4.1848
3.9591
Table 3
Summary of the fit measures used in this study.
Fit measures
Model fit
Recommended value
P value
CMIN/Df
RMSEA
CFI
0.010
2.998
0.135
0.971
>0.05
<3
<0.08
>0.90
well for the proposed model.
3.6. Hypotheses testing
SEM is well suited to test a group of hypotheses simultaneously
(in the form of a model), but it helps to unpack these hypotheses
and to consider each one individually (Hoyle & Smith, 1994)
(Table 4).
In this hypothesized model, all of the paths were statistically
significant except perceived ease of use have no significant influence in instructor satisfaction. AMOS prints a quantity called the
critical ratio (C.R) which is a coefficient divided by its standard
error. The C.R shows that the lowest C.R is H4 between the
instructor satisfaction and perceived ease of use where the C.R
value is 0.441 which is not significant (p value 0.659 is not accepted
on the 0.05 level); while the highest is H5 between instructors
satisfaction and perceived usefulness (3.96). The service quality
also has the second strongest impact on instructor satisfaction (H2)
with C.R 3.46 and significant p value in the 0.01 level. The C.R shows
that the service quality affects the user satisfactions more than the
system quality and information quality.
4. Discussion
Several studies have been conducted on students satisfaction
with online course, but only few studies concerned with instructors
satisfaction topic especially in online course (Hall et al., 2009;
Petersen, 2015; Swartz et al., 2010; Willett & Bouldin, 2004;
Woodward, 1998), which makes instructor satisfaction an important topic to discuss. As user satisfaction is an impertinent and wide
topic, different researchers used different factors to measure it
(Alharbi & Drew, 2014; Ibrahim & Silong, 1997). In this study, as the
Table 4
User satisfaction evaluation model (USEM) hypotheses.
No
Hypothesis
H1
H2
H3
H4
H5
H6
SyQ
SvQ
InQ
PEU
PU
SAT
SAT
SAT
SAT
SAT
SAT
NB
C.R.
2.460
3.466
2.014
0.441
3.967
3.082
P
Result
0.014
Support
Support
Support
Not support
Support
Support
***
0.044
0.659
***
0.002
*** Regression weight is significantly different from zero at the 0.001 level (twotailed).
253
technological issues had a large share of influencing the user
satisfaction (Mohe, 2006), we investigate the instructor satisfaction
with using technology on online course specifically the LMS. The
findings indicate that the service quality, perceived usefulness,
system quality and information quality has significant affect on
user satisfaction which mediate the influence to the net benefit of
using the LMS. This study contributes the user satisfaction studies
by adopting the most important factors that affect the user satisfactions from the past researches. This study found that perceived
ease of use have no significant influence in the instructors satisfaction of using the LMS. Analyzing the LMS using the USEM shows
the interrelationships within the model are hypothesized to measure the cause and effect between the success factors as well as the
success measures. USEM is hypothesized to affect (positively or
negatively) the quality of the LMS and, consequently, to affect user
satisfaction and the net benefit.
The findings of this study offer implications in a number of
areas. First, the model shows that the perceived usefulness of LMS
would make the instructor to use online course more and would
make instructors encourage students to use as well, due to it plays
as the most attractive factor for the instructor satisfaction of using
this online platform to interact with students of distance learning.
Second, as the service quality is the second most affected factor on
the instructor satisfaction, which means, if the LMS provides good
service quality such as high reliability and availability of support
24/7, useful service ready for the instructor use with high level of
training, the instructor satisfaction would be higher (Ba &
Johansson, 2008) and that will promote the usage, just as what
previous studies found if the instructor become an expert in using
the platform, the satisfaction level will be increased (Conrad, 2004).
Subsequently, the high system quality have a big share on the
instructor satisfaction as some factors like availability and accessibility which may hinder the usage and that consequently leads to
dissatisfaction and usage will be reduced. The fourth implication is
the information quality which has the lower share among the
above mentioned factors because the information quality would be
based on the content quality and accuracy which is depend on the
instructor use and based on the service provided by the LMS. Even
though user satisfaction was strongly influenced by all factors, but
perceived ease of use was not deemed to be the most important
factors among all (Adam, 2000).
As previous researcher claim that most if not all measures have
been adopted to test the students readiness were not for instructors
(Mclawhon & Cutright, 2012). The USEM would give a readers and
the faculty to look into the instructors’ readiness from the viewpoint of their satisfaction with technology. A number of researchers
believed that if the information system (IS) meets the needs of the
users, user satisfaction with IS will be increased (Cyert & March
1992). Future studies might consider focusing more on the importance of services quality, the usefulness of the online system, reliability and accuracy of content, and adequacy of training. If we
found the good match between the instructors and the platform
and its support such as good level of training and useful features,
we believe the satisfaction level would be increased and the
encouragement to the instructor to use the platform will be
increased and that would lead to a successful education process as
we believe the technology these days plays important role in the
success, as previous researchers claim that the high level of satisfaction will leads to a continuous use of online course (Hall et al.,
2009).
Lastly, ongoing questions about instructor’s reactions to LMS
used in distance education have led us to seek understanding about
the effects of technology factors on instructor’s satisfaction; such
as: which factors has statistically significant influence in the satisfaction of distance education instructor? As evidenced from the
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I. Almarashdeh / Computers in Human Behavior 63 (2016) 249e255
result of this study and review of relevant literature, universities
whom provide distance learning courses or programs should seek
to satisfy the instructors by providing a supported technology to
their needs as motivation. Consequently, this will contributes to
overall program quality by providing the consistency among individual courses and subsequently leads to intended program outcomes such as to attract more diverse student population and more
encouragement to use the LMS. As the findings prove that
perceived usefulness and service quality was taking the highest
share on affecting the instructor satisfactions. Hence, the LMS
should be designed based on the needs of the instructors as well as
the students, by adopting the latest technologies. In the contrary,
building the LMS without taking into account the instructors in
distance learning course requirements will affect negatively the
distance learning course outcomes and benefits.
This study provide future researchers in the field of information
system and educational technology with a user satisfaction model
which built as an epitome of previous research studies in the field.
Also, this study describes the evaluation criteria and critical success
factors that influencing the instructors satisfaction. I hope more
research to be conducted in how to motivate the instructors to use
more available technology in teaching which will indirectly
encourage students to use these technologies in learning process. I
hope the future researchers can investigate which features are
really used in LMS and how to motivate the instructors to use such
unused features and why they don’t use it?
5. Conclusions
Nowadays, the use of the technology in education such as the
learning management systems (LMS) has becoming imperative.
The LMS includes full spectrum of tools that provides academic and
learning institutions an effective and efficient means to support
distance learning course. Instructors’ satisfaction is essential for the
deployment of LMS. The success of LMS in any institution starts by
instructors’ satisfaction, which in turns initiates and promotes
learners’ utilization of LMS.
Consequently, the objective of this paper was to examine the
factors that influence instructor satisfaction. This study used survey
questionnaire in sample size of 110 instructors to validate the User
Satisfaction Evaluation Model (USEM) and to gather required data
to answer the research question. The result indicates that the service quality, perceived usefulness, system quality and information
quality has a significant effect on user satisfaction which mediate
the influence to the net benefit of using LMS. In addition, the results
found that perceived ease of use have no influence on instructor
satisfaction. Among all tested hypotheses, the highest affecting
factor on instructor’s satisfaction was perceived usefulness and
services quality on using the LMS in distance education.
This study proposed and validated a detailed framework to
measure the instructors’ satisfaction of using LMS. The model can
help future researchers in the field of information system and
educational technology to investigate the user satisfaction. Thus,
more research is needed on the services quality and the usefulness
of the LMS. I hope more research to be conducted in how to
motivate the instructors to use more available technology in
teaching which will indirectly encourage students to use these
technologies in learning process.
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