Impact of Information Technology Change in the Operations System

International Journal of Business, Marketing, and Decision Sciences Volume 5, Number 2, Fall 2012 51
THE IMPACT OF INFORMATION TECHNOLOGY CHANGE
IN THE OPERATIONS SYSTEM BASED ON THE CAUSAL
LOOP DIAGRAM MODEL
Chin-Yen A. Liu
Texas A&M University-San Antonio
Wen-Hsing Liu
Texas Tech University
ABSTRACT
With the development of the information technology support system, industries have
saved multi-millions of dollars on transferring information through the upstream suppliers to the
end-users in the operations system during the past few decades. Electronic data interchange (EDI)
is an electronic means whose implementations have been shown to reduce a significant amount of
work in the supply chain. However, there have been few researches to demonstrate the
interrelationship between EDI and multiple operational factors at the same time. This study uses
one of the methods in the system dynamics (SD) model - causal loop diagram (CLD) to exhibit the
impact of EDI on the operations system. With a variety of input factors, the causes and effects of
EDI are considered simultaneously from a system dynamic perspective. By constructing a SD
model, the CLD provides a systematic feedback loop, and the study fills the gap between the
empirical-based studies and complex simulation studies.
Keywords: Electronic data interchange, bullwhip effect, operations system, supply chain, CLD, SD model
INTRODUCTION
According to Lederer, Mirchandani, and Sims (1997), electronic commerce (e-
commerce) is a series of market transactions that are facilitated by information technology, and
electronic data interchange (EDI) is a form to conduct business electronically. Since EDI allows
the entire process to be handled electronically, trading partners can use Internet transactions
instead of paper and do not need to be there physically to deliver messages or exchange data. The
implementation of EDI standardizes the process of trading and business documents, which bring
a certain degree of change to an organization (Millman, 1998), specifically in supply chain
management (SCM). Under the paperless environment, SCM focuses on managing the flow of
information through the supply chain, which facilitates a customer’s order and satisfies a
customer’s need at a reasonable cost (Collier & Evans, 2007).
In addition, a supply chain encompasses all activities associated with producing and
delivering a product or service from suppliers (and their suppliers) to customers (and their
customers) (Russell & Taylor, 2011). This study intends to investigate the impact of EDI
technology in the operations system using the system dynamic approach with a focus on the
bullwhip phenomenon. The remainder of the paper is organized as follows. Background of the
study is presented in section 2. Philosophy and methodology of system dynamics model are
described in section 3. A CLD guiding the overall modeling activities of EDI in the operations
system is exhibited in section 4. A detailed discussion of the feedback loops is presented in

52 International Journal of Business, Marketing, and Decision Sciences Volume 5, Number 1, Winter 2012
section 5. Finally, contributions of this research and potential future study areas are summarized
in section 6.
BACKGROUND OF THIS STUDY
Before the era of EDI, each member in a supply chain usually ordered enough to buffer its
own inventory (i.e. order amplification) due to the lack of transparency on the future demand.
After a certain delay, the entire supply chain increases the variability of orders and inventory
(i.e., oscillation), which may cause excessive or moderate inventories, uncertain production
planning, redundant expenditure, and unsatisfactory customer service. Both order amplification
and oscillation are examples of distortions in a supply chain, which are known as the bullwhip
effect (Frangoo & Wouters, 2000; Machuca & Barajas, 2004; Wu & Katok, 2006).
According to Wu and Katok (2006), the bullwhip phenomenon was first introduced by
Forrester (1958), the founder of system dynamics, who wrote one classic work on the study of
supply chains by using the system dynamics methodology. Since then, studies regarding the
bullwhip effect on a supply chain have been examined extensively. Janamanchi and Burns
(2007) point out that an easy way to alleviate the bullwhip effect is to shorten the duration of the
delays involved. Although the benefits of EDI are hard to perceive, the use of EDI is one of the
promising strategies that can solve this problem (Machuca & Barajas, 2004). When
implementing the EDI, an enterprise can obtain vastly shortened information delays to enhance
competitiveness by reducing the bullwhip effect.
From a top manager’s perspective, the ultimate goal of operating a business is to make a
profit. This study provides insight by causal mapping an overview structure with the systematic
feedback loops of using EDI in the operations system and offers a profound influence on the
holistic performance in the supply chain. Moreover, studies have shown that the bullwhip effect
leads to excessive ordering and shipping costs, high buffer inventories, poor customer service,
irregular production schedules, inaccurate capacity planning, etc. (Machuca & Barajas, 2004;
Wu & Katok, 2006; Janamanchi & Burns, 2007).
In a supply chain, members (i.e. supplier, company, and customer) are connected and
surrounded with different interrelated processes and activities; information technology (IT) is an
approach to engender systematic integration (Craig & Scudder, 2002). Previous research also
confirms that the implementation of EDI, an important class of IT, significantly reduces a
number of variable values, such as time to place an order, mean ordering cost, cumulative cost,
net excess stock, and amplification (Machuca & Barajas, 2004; Borden, 2004). In their original
work, Lee, Padmanabhan, and Whang (2004) identify four sources of the bullwhip effect: errors
in demand signal processing, inventory rationing, order batching, and price variations. Wu and
Katok (2006) later classify two categories to explain the bullwhip effect; they define these four
sources as the operational causes of that problem, and the other category is the behavioral causes
of that effect, whose purpose is to eliminate operational causes.
However, it is not easy to accomplish because the time delays and the individual’s
boundary are difficult to measure and control. According to Sterman (1989), when the incident is
related to indirect feedback or delayed situation, the decision-maker will find the whole event
difficult to control and follow. Nevertheless, causal diagrams are powerful tools that map the
feedback structure of complex systems and help users understand the model in a nontechnical
fashion. In this study, causal diagrams are utilized to characterize EDI and identify the leverage
points in the operations system.

System Dynamic Philosophy
Draper Kauffman (1980) defines a system as "a collection of parts which interact with
each other to function as a whole" (p. 1). In other words, a system cannot be split into separate
parts. When one part of the system is ignored or removed, the nature of the system is distorted. A
set of interacting, interrelated, and/or interdependent elements form an integrated whole, which
is made up from numerous subsystems. A subsystem is a set of elements which is a system itself
and a part of a larger system. Therefore, the major purpose of a system is to comprehend
something beyond cause and effect; the fundamental concept of system dynamic philosophy is to
view the whole system as a complex one which interconnects its subsystems (Bellinger, 2004a).
Systems thinking and system dynamics are two powerful conceptual tools that help us uncover
the straight shooters of the system. Therefore, it is very important for us to understand the idea
before we implement a system dynamics model.
Accelerating changes related to humans’ activities are transforming our world day after
day. The development of technology, the growth of population, the effect of global warming, etc.
raise consequences which make us face an unforeseen future. Humans used to think directly and
solve the problem based on what they see. As Senge (1990) says, “Small changes can produce
big results - but the areas of highest leverage are often the least obvious” (p. 63). People tend to
work on problems directly and individually within their comfort zones. However, the most
obvious answers usually do not work. They may improve matters in the short run, but they make
things worse in the long run.
One of the fundamental concepts of systems thinking demonstrates how a small,
precision action can create a significant improvement if it is taken in the right place and at the
right time. Senge (1990) also claims that “Today’s problems come from yesterday’s solutions”
(p. 57). Although events occur at different times and in different situations, they are all
connected to each other and within the same pattern. One influences another, which is also an
influence on the rest. Most of the time, the influence is hidden from the view. Consequently, it is
difficult for human beings to observe the whole picture. Traditional analysis in engineering
principles, therefore, will break down the whole system into individual pieces of what have being
studied as well as isolate smaller and smaller parts of the system.
A systems thinking, on the other hand, tackles these complex problems by stepping back
and looking at all factors which connect to a particular activity and the relationships between
these factors (Gyford, 1999). This theory focuses on the whole picture, and how things being
studied interact with other components of the system. It works by expanding the view to take
into considerations on larger and larger numbers of interactions while a topic is studied. By
observing the whole picture, the philosophy of the systems thinking allows people to clarify their
understandings on social systems and improve those systems in the similar way that other people
can understand and use engineering principles to improve their own understandings on the
complex systems (Aronson, 1996-1998). Therefore, when the system is too complex to
understand, system dynamics simulation can provide a clear image and a better idea.
Methodology – Causal Loop Diagram
A systems thinking is the foundation of the field of system dynamics, which provides a
conceptual approach, a body of knowledge and tools, which makes full patterns more clear and
helps us implement “what-ifs” theory more effectively. Sterman (2000) addresses that “system

dynamics is a method to enhance learning in a complex system” (p. 4). It also deals with
feedback loops and time delays that influence the behavior of the entire system. The use of
feedback loop diagram (i.e. causal loop diagram, CLD) and stock-and-flow diagram makes the
approach of systems dynamics different from others.
In this study, the authors use only CLD to explain the interrelationships among the
operational factors while implementing the EDI in the operations system. CLD is one of the
foundational tools used to capture the structure of system. It is a method that can focus on the
entire system, not just one interaction between two variables. A causal diagram consists of
variables connected by arrows (causal links) that show the causal influences among the variables.
Each causal link has its polarity of either positive (+) or negative (-) to indicate a change in one
variable causing the other one either in the same- or different- direction changes. As indicated in
Figure 1, A can positively (indicated by a "+" sign) influence B either in linear or nonlinear
formulation, thus increasing A. The alternative is that A can negatively (indicated by the "-"
sign) influence B either in linear or nonlinear formulation, thus decreasing B (Bellinger, 2004b).
Figure 1
Direct Proportional Relationship
Figure 2
Inverse Proportional Relationship
Reinforcing loops (R) and balancing loops (B) indicate positive and negative feedback
processes, respectively. Figure 3 indicates a population that involves both reinforcing and
balancing loops. It is a common structure in the complex system. In our later CLD of EDI effects
in an operations system (i.e. Figure 4), we will use only R and B as a notation in the loop
identifier. Time delays are indicated in the diagram by delay box. There are two types of delays
(i.e. information delay and flow delay) found in a supply chain. Whenever a process’ output lags
behind its input in some fashion, a delay occurs (Sterman, 2000). One important thing to know
about CLD is that it only tells you what would happen if there were a change, but not the
quantitative effects. This study provides an application of the evolutionary logic of EDI
implementation in a supply chain management context. Only through this systematic analysis
can an organization make changes that are lasting in nature and corresponding to its own
interests (Sterman, 2000).
A B
+
A B
-

Figure 3
Population Involving both Reinforcing and Balancing Loops
Births
birth rate
Population
+
+
+
Deaths
death rate
+
+
-
Figure 4
CLD of EDI Effects in an Operations System
effort devoted to the
use of EDI
labor costs
IT costs
forecast accuracy efficiency of
production planning
accuracy of
inventorypaper work
error rate
inventory costs cumulative costs profit
revenue
order received
perceived training
requirements
training costs
customer
satisfaction customer loss rate
amount of work
morale of
employees
work pressure
R 3
B 3
R 2B 2
B 1 R 1
B 4
R 4
B 5
Delay
Delay
- +
+ -
+
+
+ +
+
- +
-
+
-
+ +
+
-
-
-
+
+
+
++--
+
+
number of
employees needed
Delay
Delay
Delay
DYNAMICS OF EDI IN THE OPERATIONS SYSTEM
Figure 4 represents the behavior of an organization evaluating the impact of EDI use in
the operations system. The organization compares the effort devoted to the use of EDI against
the frequency of the error. When an organization increases its effort devoted to the use of EDI, it
will reduce the amount of paperwork, which means less human intervention and data re-entry,
which reduce the possibility of mistakes and the chance of error, which brings the loop back to
the less effort on using EDI (see balancing loop B1). On the other hand, increasing the use of
EDI will further relieve the employees’ work pressure at some point; therefore, it will also

reduce the chance of error rate and form a positively influence on the use of EDI (see balancing
loop B2). However, if the work pressure stays too long and too high, fatigue sets in, which
increases the chance of making mistakes. As the error rate increases, several situations may take
place and boost the final costs.
First, the reinforcing loop R1 exhibits that the accuracy of inventory will be questioned
and the bullwhip phenomenon will actually happen, leading to a higher inventory cost, which
results in less profit. While a company’s profit falls, the level of employees’ morale will be
impacted negatively. Over an extended period of time (delay), work pressure will increase due to
decreased morale, and increased work pressure will bring an even higher error rate. Second, the
reinforcing loop R2 shows that because of the high error rate, the organization may require more
perceived training on its employees and thereby the training costs will increase.
Both inventory costs and training costs will cumulate the final costs, which bring less
profit than before. Third, the reinforcing loop R3 represents that as the error rate goes up; most
likely the customer satisfaction is going down. Low customer satisfaction means an organization
is losing its customers and orders. Both of those loops (i.e. R2 & R3) lead to a reduction of
revenue and profit, which bring the loop back to the increased error rate. The other balancing
loop B3 is formed due to the reduced orders causing the amount of work to be reduced. Thus,
after a certain period of time, the work pressure and error rate will be reduced as well. The other
balancing loop B4 is formed because when an organization increases its use of EDI, the company
therefore needs fewer employees to process the information and enter the data.
While fewer employees are needed, labor costs will fall and cumulative costs will go
down as a result. Additionally, increased effort devoted to the use of EDI will also increase a
company’s forecast accuracy, which raises the efficiency of production planning and the
accuracy of inventory level (see balancing loop B5) - this loop continues bringing a higher profit
and lower work pressure and error rate. However, increased effort devoted to the use of EDI will
also increase the IT costs, which boost up the cumulative costs, resulting in lower profit, lower
morale, higher work pressure, and higher error rate. Such a loop will lead a company to put more
effort on the use of EDI; therefore, another reinforcing loop (as mentioned in loop R4) appears
again.
DISCUSSION AND IMPLICATIONS
The previous section demonstrates that leverage points exist everywhere in an operations
system. A manager just needs to know where they are and how to use them. From this study, the
lack of EDI implementation results in a high error rate, which is the main determinant of
operational failure. Error rate is tightly coupled to other functions and processes in an operations
system among the supply chain members. Failure to account for these feedback effects in a
system leads to increase costs (work pressure) and decrease profit (employee morale), which
may cause permanent collapse within an organizational system. Our findings suggest that putting
less effort into the EDI has several side-effects since it increases the possibility of error rate,
which will bring higher error rate after the feedback activities (as appear in reinforcing loops R1,
R2, and R3). However, even though an increase of the use of EDI has its benefits resulting in a
reduction on error rate, the company may actually lose its concentration on investing the
infrastructure of EDI because of the diminished errors (as mentioned in balancing loops B1, B2,
B4, and B5).

Moreover, if the reinforcing loop R3 dominates the balancing loop B3, a decrease on
received orders would actually make the profit fall and lower the morale of employees, and the
amount of increase in work pressure may be more than offset by the decrease of work pressure
due to the less amount of work resulting from the reduced number of orders. The reinforcing
loop R4 suggests that when an organization raises its effort devoted to the use of EDI, not only
does it increase its IT costs which will reduce its profit, but it also increases the possibility of
error rate and gives a raise to the use of EDI. Yet, profit is determined by both cumulative costs
and revenue. Either of them alone cannot decide the amount of change in profit. Therefore, if an
increase in the use of EDI can bring a great reduction on the error rate, which will also carry a
significant number of orders (see balancing loop B3), it will greatly boost revenue. A positive
profit is still on its way even though the use of EDI technology will add to IT expenses.
Besides this study, one study done by Machuca and Barajas (2004) also reveals that the
use of EDI is expensive but the technology cuts lead time, and managers just need to justify these
expenses. Bergeron and Raymond (1992) had found that organizational support, the control
procedures, the implementation process, and the level of EDI integration are the major factors
influencing the success of EDI in the firm. Meanwhile, the benefits of EDI will reflect on
information quality, transaction speed, administrative costs, strategic advantages, and operations
management.
CONCLUSION
Top managers, specifically operations managers, know the operations systems of their
firms very well and make final decisions regarding such a system. CLD is a powerful tool that
improves the managers’ understandings on the whole system and helps them identify the
leverage points. Such a leverage point is an intervention of the EDI, which reduces errors caused
by re-entering order information and eliminates the need for rekeying entirely. The use of the
EDI reduces workload and increases accuracy, the result of which is a profitable operation.
Furthermore, EDI increases transaction speed, reduces administrative costs, and increases
strategic advantages; the only concern is the costs associated with the EDI implementation.
Therefore, operations managers must do everything they can to take care of this issue; otherwise,
the effect on profitability might be offset or even become negative.
The CLD in Figure 4 provides a total picture of how all effects of an EDI implementation
interact. In sum, the use of the system dynamics tool known as CLD allows the decision maker
to recognize the increasingly complex system and see through it with the underlying structure
changes. That is the purpose of this study - a small move in one thing can produce big changes in
everything else. By applying the method of CLD, a manager can achieve the holistic
performance in the operations system. However, to see the quantitative impact on the use of EDI,
the study needs to further convert the CLD into the SFD (Stock-and-Flow diagram), which is
another powerful tool of the system dynamics studies. One goal for future research will be to
collect some survey data, conduct a study by using the SFD, and observe the baseline behaviors
of those target variables.

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About the Authors:
Chin-Yen A. Liu is an Assistant Professor in the School of Business at Texas A&M University-San Antonio. Her
research interests include the areas of energy policy, operation research, risk management, higher education
performance, and Data Envelopment Analysis. Dr. Liu has presented/published many papers in professional
conferences and received many outstanding and/or best paper awards. She holds a Ph.D. in Business
Administration-Operations Management from Texas Tech University.
Wen-Hsing Liu is a Postdoctoral Research Associate in the Department of Industrial Engineering at Texas Tech
University. She received her Ph.D. in Industrial Engineering at Texas Tech University, her M.S. in Industrial and
Management Systems Engineering from West Virginia University, and her B.S. in Industrial Engineering from the
Chung Yuan Christian University in Taiwan. Her research interests include team performance management,
organizational performance evaluation and benchmarking, and data envelopment analysis. She is a member of the
Institute of Industrial Engineers, the American Society of Engineering Management, Alpha Pi Mu, and the Golden
Key International Honor Society.

AN ANALYSIS OF E-LEARNING ADOPTABILITY IN THE
DEVELOPING COUNTRIES: THE CASE OF THE KINGDOM
OF BAHRAIN
Iman Akour
New York Institute of Technology, Kingdom of Bahrain
ABSTRACT
The primary purpose of this study is to highlight the learning preferences and its impact
on adaptability to e-learning courses within a highly growth-oriented and competitive educational
system, in an emerging Middle-Eastern economy, namely, Bahrain. Utilizing a sample of high
school students in the Kingdom of Bahrain, the results of this study revealed that there are no
differences between students according to gender and age in e-learning adaptability. Further, the
study showed that the adaptability to e-learning can be predicted based on student’s preferences
for asynchronous learning, use of computers in learning, and asynchronous digital
communication. Recommendations for policy makers and researchers are presented. The
originality of this study stems from its value to provide further validation for e-learning
adaptability model within country variations and cross cultural dimensions.
Keywords: Asynchronous learning, learning styles, e-learning, Bahrain
INTRODUCTION
E-learning is all-permeate in education and learning as the most significant tool to
enhance knowledge in the academic as well as organizations. Since e-learning has several
advantage in terms of cost reduction, simplified training programs, flexibility, and convenience;
therefore, e-learning become an important component of information dissemination and sharing
(Fridrich & Hron, 2010). Nowadays, e-learning emerged as the new paradigm of modern
education supported by several favorable trends: for instance, the Global Industry Analyst (2012)
predicted that e-learning market will exceed US $600 Billion by 2015. The role of e-learning and
information technology in education are expected to expand in scope and complexity. The
American Society for Training and Development (2007) reported that organizations spending
annually over $40 billion in technology based training. Despite, the positive trends of e-learning,
e-learning poses a challenge for both schools and industry; since e-learning requires the
integration of information technology into teaching and learning process. For instance, there is
no explanations for why some users of e-learning stop using e-learning after their first initial
experiences; which impact levels of needed information communication technology innovations
(Ozkan, Koseler, & Baykal 2009; Mohamad, Ibrahim, & Mohd, 2010; Kaufman, Sauvé, &
Renaud, 2011).
Scholars argued these challenges imposes several difficulties in developing theoretical
concepts and methodological models to measure the effectiveness of e-learning and levels of
information communication innovations (Nishino et al., 2010). Particularly, models concerning
e-learning adaptability and learning styles are important in both education and learning
literatures (Kay & Knoack, 2008). Although the work of Nishino et al., (2010) involved a
process-model with asynchronous learning, use of computers in learning, asynchronous digital

communication, and study sequence autonomy are determinants of e-learning adaptability.
Nishino et al., (2010) pointed out that e-learning adoptability is tailored to learner’s aptitudes;
customizability and usability of systems are all designed, developed, and tutored according to
user necessity of accessible e-learning. Therefore, users have to ensure usability, ease of use, and
attractiveness of interaction. Further, e-learning designed around “students centered learning”
considering the students’ needs and ability of learning.
This study is to replicate Nishino et al. (2010) e-learning adaptability model within a
highly growth-oriented and competitive educational system in an emerging Middle-Eastern
economy, namely, the Kingdom of Bahrain.
LITERATURE REVIEW
This section and the following sections provide a review of literatures related to e-
learning.
Background of the Study
In the early 1960s, Stanford University psychology professors Patrick Suppes and
Richard Atkinson experimented with using computers to teach math and reading to young
children in elementary schools in Palo Alto, California. In 1963, Bernard Luskin installed the
first computer in a community college for instruction, working with Stanford University and
others, developed computer assisted instruction. In early 1993, William Graziadei described an
online computer-delivered lecture, tutorial and assessment project using electronic mail. In 1997
Graziadei et al. published an article entitled "Building Asynchronous and Synchronous
Teaching-Learning Environments: Exploring a Course/Classroom Management System
Solution.” They described a process at the State University of New York of evaluating products
and developing an overall strategy for technology-based course development and management in
teaching-learning. In 1998, the British Prime Minister stated: "Technology has revolutionized the
way we work and is now set to transform education. Children cannot be effective in tomorrow's
world if they are trained in yesterday's skills. Nor should teachers be denied the tools that other
professionals take for granted."
Today many technologies can be, and are, used in e-learning, from blogs to collaborative
software, e-portfolios, and virtual classrooms. Most eLearning situations use combinations of
these techniques.
E-learning and Information Communication Technology (ICT)
E-learning is an ICT-based learning in which learning materials are delivered
electronically to remote learners via a computer network. As the new economy requires more
and more people to learn new knowledge and skills in a suitable and effective way, the
improvement of computer and networking technologies are providing a diverse means to support
learning in more personalized, flexible and portable way (Johnson, Hornik, & Salas, 2008).
The use of computers in education started in the 1980’s and was mainly focused on
teaching about computers rather than teaching through computers. Students were taught about
some basic applications that handle information or manipulate texts (Passerini & Granger, 2000).

The term ICT (Information and Communications Technology) tends to replace IT (Information
Technology), because it shows the importance of electronic communications such as email and
the internet as well as the computer aspect. ICT Defined as: “the combination of informatics
technology with other related technologies, specifically communication technology” UNESCO
(2002). The unique power of ICT enables users today to process, store, retrieve and
communicate information in whatever form it may take, unconstrained by distance, time, volume
and increasingly by cost. At the same time, ICT adds value to the processes of learning. Many
experts believe that ICT can transform education (Somekh, 2001).
Encouragement motivation to learn is one of the key principles for effective education
(Bransford, Brown, & Cocking, 2000). Many researchers have indicated positive correlations
between learner motivational levels and academic achievement (Maehr & Fyans, 1989). Student
motivation is very important with respect to use of cognitive strategies necessary for effective
learning (Pintrich & Groot, 1990). Kyong and Theodore (2011) argued that, if students are more
motivated to learn, it is less likely to drop out of online courses, which is an indicator of effective
e-learning. Effective e-learning defined by Johnson, Hornik, and Salas (2008) as “the integration
of instructional practices and the internet capabilities to direct a learner toward a specified level
of proficiency in a specified competency”. Further, Johnson (2011) defined effective e-learning
as the degree to which schools achieve their goals, in comparison with other schools that are
equalized in terms of student intakes.
Educational effectiveness is usually researched at both classroom and whole school level. Whole
school effectiveness deals with issues like leadership, civic engagement and outcomes, while
classroom effectiveness address issues such as motivation, attainment and autonomy. Cheng and
Mok (2008) argue that “classroom effectiveness” is a kind of future effectiveness that often
refers to the relevance of the “learning environment” to students’ multiple and sustainable
developments for the future.
Advantages and Disadvantages of E-learning:
Safavi (2008) identified some advantages of e-leaning include: 1) an inexpensive tool to
deliver education; 2) it is self-paced(usually, e-leaning courses can be taken when they are
necessary); 3) it is faster (learners can skip material they already know); 4) provides consistent
content (while in traditional learning different teachers may teach different material about the
same subject); 5) works anywhere and anytime (e-learners can take training sessions when they
want); 6) can be updated easily and quickly (online e-leaning sessions are especially easy to keep
up-to-date because the updated materials are simply uploaded to a server); 7) can lead to an
increased retention and a stronger grasp on the subject (because of the many elements that are
combined in e-learning to reinforce the message, such as video, audio, quizzes, interaction, etc.);
and 8) can be easily managed for large groups of students.
Also Safavi (2008) pointed out that disadvantages of e-learning may include: 1) it may
cost more to develop initially; 2) requires new skills in content producers; 3) enabling technology
might also be costly, especially in case of advanced visually-rich content; and 4) e-learning
requires more responsibility and self-discipline for the leaner to keep up with a more free and
unconstrained learning process and schedule.

Learning Preferences and E-Learning
Four learning styles have been identified asking students their preferences in
studying, understanding, questioning, and doing homework in terms of asynchronous
learning and the use of ICT Nishino et al., (2010). The preferences in e-learning include:
1. Preference for asynchronous learning which concerns the place, time, and the content of
asynchronous learning. Asynchronous learning is a student-centered teaching method that uses
online learning resources to facilitate information sharing outside among a network of people.
2. Preference for the use of computers in learning is about the use of computers in studying and
understanding. Computer-based learning is a method that allows students to obtain information
in formats that cannot be presented by teachers and it gives the students control of the
information.
3. Preference for asynchronous digital communication which concerns the communication
matters which there is no timing required for transmission and in which the start of each
character is individually signaled by the transmitting device.
4. Study sequences autonomy concerns the autonomy of deciding the study sequence which is
based on the learner’s willingness and capacity to control or oversee her own learning. More
specifically, someone qualifies as an autonomous learner when he/she independently chooses
aims and purposes and sets goals, chooses materials, methods and tasks, exercises choice, and
purpose in organising and carrying out the chosen task (Cheng & Mok 2008; Safavi, 2008;
Mohamad, Ibrahim, & Mohd, 2010).
Studies of E-Learning
Many developed and developing countries have applied e-learning in education and
reported successful results. For the sake of space, limited studies from different regions will be
briefly explained below:
E-Learning in Developed Countries:
In USA, a study of high school teachers used a wide variety of tools (content,
communication, and management) and approaches (course management system and project-
based learning) within an online, secondary-level, social studies course. By the completion of the
course, students received credits that could apply toward a high school diploma. Findings from
this study indicated that secondary online teachers and students use a variety of tools to engage
in meaningful learning in online courses.
In Canada a study was used to determine whether playing an online educational games to
improve the secondary school students’ cognitive skills as and if there are any differences
between males and females in cognitive skills developed by the game. The results of the paired t-
tests showed significant improvements in a variety of cognitive skills after students played the
game on laptops in their classrooms for 40-60 minutes. No differences were found between
males and females. These results are encouraging for teachers who wish to use educational
digital games in their classrooms (Kaufman, Sauve, & Renaud, 2011)
A study in Germany was conducted to evaluate the implementing and testing the E-
Learning system in German high schools (Grade 11 and 12) which consisted of high-quality
learning content in the form of interactive multimedia presentations and subject specific learning
tools (lexica, algebra tools, mind mapping software) that could be used inside and outside the

classroom. Results perceived usefulness was a significant positive predictor of students'
acceptance, with perceived usefulness of the Learning Management Systems having the greatest
weight. Computer related attitude, self-efficacy, and gender had no influence on acceptance and
the findings indicate the critical importance of perceived usefulness in predicting students’
acceptance of the E-Learning system, as known from acceptance research on IT at the work
place (Friedrich & Hron, 2010).
E-Learning in Developing Countries
In a study in Malaysia, M-Learning (mobile and wireless technologies) implemented for
Smart primary school students in Malaysia by using an open source technology of a new mobile
learning environment named Mobile Math which focuses on learning mathematics and allows
learners to do lessons, quizzes, tests, and performance tracking with automated graph. The result
shows that the mobile phones can be useful in learning mathematics as most of primary school
students already use them through many communication activities. Mathematics teachers should
start implementing the M-Learning to allow students to independently explore the lesson taught
with flexible access to the content and construct the effective teaching environment. The authors
propose the M-Learning for mathematics by allowing the extension of technology in the
traditional classroom in term of learning and teaching (Mohamad, Ibrahim, & Taib, 2010).
In Jordan, a study in Applied Science University presents conceptual framework
architecture of an e-Learning system that could be used to prepare graduated students to take an
ETS-like, the international exam during their last semester; which is critical to maintain the
quality of higher education among competing private and public universities. Students can
prepare for the exam by reviewing the material and taking the mock tests from different
locations, such as on campus or off campus; and using different hardware platforms, such as
desktop, laptop, or PDA. The study findings confirmed that the system provides students a better
preparation, savings in preparation time, system verification, and documenting lessons learned.
In Kuwait, a study investigated the impact of using e-learning models' to enhance the
critical thinking skills of students in higher education institutions. The study examines the
effectiveness of e-learning model in enhancing critical thinking of students at Kuwait University.
The effectiveness is measured by a critical thinking test. The findings confirmed that there was
an increase of critical thinking for those who used the e-learning models (Salah & Abdulwahed,
2006).
In Unite Arab Emirates (UAE) a study aims to reflect the development of a new learning
environment within the library at the University of Sharjah (UOS). It seeks to discuss e-learning,
and how it can be supported by the library web-based services. The findings of this study
confirmed that the capabilities of learning management systems (LMS) such as Blackboard have
a great effect on libraries and become an active partner in the learning process. On the other
hand, strategies adopted by the UOS library place it in a strong position to play an effective role
in e-learning environment through the Blackboard platform.
E-LEARNING MODEL IN RELATION TO EDUCATIONAL SYSTEM IN BAHRAIN
The researcher envisions Nishino et al. (2010) E-Learning Adaptability Model to be
applicable in the context of Bahraini schools as shown in figure 1. The model includes
asynchronous learning, use of computers in learning, asynchronous digital communication, and
study sequence autonomy as determinants of e-learning adaptability. The linkage between the

use of computers in learning and e-learning adaptability is based on the logic that a use of
computer in learning would translate into better e-learning adoptability, which in turn, would
result into better students learning.
The hypothesized relationships in the model are likely to hold in terms of their effects
and directions (Kuada & Buatsi, 2005). This is because Bahrain educational system is one of the
fastest growing industries not only in Bahrain but also in the entire Middle-East. A free market
economic policy led to the emergence of diverse types of schools including public, local-private,
western schools. This situation is favorable for e-learning adaptability activities to evolve.
Therefore, educational system can be considered a fertile ground for a robust test of Nishino et
al., (2010) e-learning adaptability model. Thus, the study formulates the following hypotheses:
Figure 1
Conceptual Model of Asynchronous Learning and E-Learning Adoptability
LITERATURE REVIEW
Hypothesis 1: There are significant differences between male and female students in their e-
learning adaptability in the public schools in Bahrain.
Hypothesis 2: There is significant difference among age groups and their e-learning adaptability
in the public schools in Bahrain.
Gender
Asynchronous
Learning
The Use of
Computers in
Learning
Asynchronous
Digital
Communication
Age
Study Sequences
Autonomy
E-Learning
Adaptability

Hypothesis 3: There is a significant relationship between students’ preference for asynchronous
learning and the e- learning adoptability in the public school in Bahrain.
Hypothesis 4: There is a significant relationship between students’ preference for the use of
computers in learning and the e- learning adoptability in the public school in
Bahrain.
Hypothesis 5: There is a significant relationship between students’ preference for asynchronous
digital communication and the e- learning adoptability in the public school in
Bahrain.
Hypothesis 6: There is a significant relationship between students’ study sequences autonomy
and the e- learning adoptability in the public school in Bahrain.
METHODOLOGY
Sample and Data Collection
Data were acquired from high school students in Bahrain. A convenient sample of
200 students from four schools participated in this research. The survey instrument was
distributed during the class time. 190 students completed the questionnaire resulting in a
response rate of 95 percent. 54 percent were male students, and 46 percent of the participants
were female.
Data Analysis
To examine the data of the study, descriptive and quantitative analysis were used.
The responses to the questionnaire were analyzed using the Statistical Package for Social
Science 20.0 software program (SPSS).
Measures of Constructs
Because this study replicates Nishino et al., (2010) e-learning adaptability, their
scales for all the constructs have been adopted. All 5 constructs have a total of 46 items. The
questionnaire (instrument is presented in Appendix A). All the items in the questionnaire
were utilized according to likert 5 point scale ranged from (1 strongly disagree to 5 strongly
agree).
RESULTS OF THIS STUDY
Exploratory factor analysis and coefficient alpha were estimated to assess the
psychometric proprieties of the scales (Hair et al., 1987). The results of factor analyses
identified 4 items with factor loadings less than 0.40 out of 40 items which belong to
learning styles dropped from the scale as shown in Appendix A. Cronbach alphas ranged
from 0.70 to 0.80 similar to that of Nunnlly (1978) and psychometric proprieties of the
scales are similar to those of Hair et al. (1987).

HYPOTHESES TESTING
In order to investigate the relationship between the adaptability of e-learning courses and
the demographic factors (gender and age) and learning styles includes asynchronous learning,
use of computers in learning, asynchronous digital communication, and study sequence
autonomy, two multiple regression analysis were conducted. The first multiple regression model
was undertaken in response to H1 and H2 that related to age and gender impact on students’ e-
learning adoptability.
The regression results of model 1 indicate that hypothesis H1 and H2 were not supported.
The findings illustrate no variation in the respondents’ demographic characteristics. Gender and
age of students have no significant impact on their e-learning adaptability. These findings are in
contrast with previous studies findings that computer education skewed toward young male
students’ more than female students. Further, the second multiple regression model was
undertaken to investigate the relationship between e- learning adoptability and each of the
learning styles in the public school in Bahrain (H3-H6).
Table 1 showed that the regression coefficients of asynchronous learning, use of
computers in learning, and asynchronous digital communication are relatively high and the p-
value less than 0.01. The regression results indicated that H3, H4, and H5 were supported. While
the regression coefficient for study sequence autonomy is low with high p-value, this indicates
that H6 was not supported. The study showed that the adaptability to e-learning can be predicted
based on student’s preference for asynchronous learning, use of computers in learning, and
asynchronous digital communication.
It is expected that many students will take fully online courses in the future. However,
not all students who take e-learning courses prefer asynchronous learning and the use of ICT.
Furthermore, students who do not prefer asynchronous learning and the use of ICT, are expected
to score low in the adaptability to e-learning courses and make sure that the system will provide
them with instructions before they are enrolling in the online courses (Chen, Shang, Harrris,
2006; Akhtar and Dutta, 2011; Ciudad-Gomez, 2012).
Table 1
Regression Results of Model 1
Independent Variable Regression
Coefficient
P
Asynchronous Learning 0.128 0.001
Use of Computers in Learning 0.513 0.004
Asynchronous Digital Communication 0.203 0.001
Study Sequence Autonomy 0.017 0.643
Multiple R-Square 0.74

DISCUSSION AND CONCLUSION
In this study, Nishino et al.’s (2010) E-Learning Adaptability model was replicated
within a highly growth oriented and competitive industry in an emerging Middle-Eastern
economy. Not only e-Learning adaptability research is rare in such context but also the context
has the characteristics that allow for a robust test for the complete e-learning adaptability model.
The study findings are generally resonate with the results of Nishino et al.’s (2010) and offer one
more support for the robustness of Nishino et al.’s (2010) e-learning adaptability model.
However, a closer look into the results reveals some interesting insights. First, the
influence of demographic traits, gender and age on e-learning adaptability is not fairly stable
across diverse contexts. Gender and age of students have no significant impact on their e-
learning adaptability. These findings are in contrast with previous studies findings that computer
education skewed toward young males more their counterpart female students. Similarly,
asynchronous learning, use of computers in learning, and asynchronous digital communication
are fairly stable across diverse contexts.
These findings demonstrated that students’ adaptability to e-learning of public schools in
Bahrain can be predicted based on student’s preference for asynchronous learning, use of
computers in learning, and asynchronous digital communication. Similarly to other study
findings, study sequence autonomy was insignificant to e-learning adaptability in Bahrain. This
finding might be explained by the fact that Bahraini culture is characterized to be high in
collectivism and low in individualism. In other words, all of these learning styles preferences are
crucial for students’ e-learning adoptability. Despite the positive relationships between the
dependent and independent variables, a word of caution before applying the asynchronous
learning in Bahraini-Arab culture has to be considered.
This culture according to Hofestede’s (1997) typology might not be receptive of e-
learning. Hofstede identified Arab culture, including Bahrain as having a fixed set of cultural
traits such as high in power distance, collectivism, uncertainty avoidance, and femininity that are
not conducive for e-learning. Therefore, any implementation for e-learning has to consider such
traits and how it enhance or hinder learners’ motivation to learn, creating meaningful and
memorable experiences, and adjust learners’ behavior to non-traditional means of learning.
Further, this study confirms that e-learning adaptability may be not a culture-bound. Finally, the
study recommend that similar studies to be conducted in Bahrain.
As with any study, there are several potential factors that might limit the generalizability
of the study findings. One limitation is that this study is limited contextually where attention
should be made not to generalize the findings beyond the empirical findings within limited
number of participants; another limitation is that the study findings might be culture bound to
oil-rich-Arab-state, namely, Bahrain.
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About the Author:
Iman Akour is an Assistant Professor of Information Systems at New York Institute of Technology, Bahrain. Dr.
Akour holds a DBA in Business Administration/Information Systems from Louisiana Tech University, USA. Her
current research areas include applications of Technology Acceptance Model into Arab cultures as well other
emerging economies. Additionally, her research covers the impact of information technology, social, political, and
economical changes.

Appendix A
The Result of Factor Analysis of E-Learning and Learning Styles Questionnaire Data (After Rotation)
Factor
1 2 3 4
q1) I understand better when I study at my convenient time rather than learning
in class with other people.
0.791 0.110 0.095 0.110
q2) I can familiarize myself better when I study independently at my convenience
than studying with others at one place.
0.829 0.170 0.181 0.128
q3) I would rather study alone at the place and time convenient to me than learn
in class with other people.
0.721 0.065 0.219 0.052
q4) I can be more creative when I study alone than studying with others at one
place.
0.693 0.210 0.147 0.129
q5) I feel more motivated when I study at my convenience than learning in class
with other people.
0.745 0.231 0.050 0.096
q6) I can learn better when I study at the time I decide than when I study at the
time decided by others.
0.862 0.096 0.263 0.053
q7) I tend to learn more actively when I study alone than studying with others at
one place.
0.655 0.047 0.110 0.092
q8) I study at my own pace and do not care how others study. 0.632 0.053 0.320 0.241
q9) I can concentrate better when I study independently at my convenience than
studying with others at one place.
0.745 0.328 0.065 0.213
q10) I feel less tired when I study independently at my convenience than studying
with others at one place.
0.879 0.140 0.290 0.089
q11) I wan to study at the same pace with other students. 0.654 0.053 0.082 0.170
q12) When I study through computers, I tend not to care how others study. 0.576 0.011 0.422 0.390
q13) I wan to study at my own pace. 0.432 0.230 0.116 0.074
q14) I tend to learn more actively using computers than studying in class. 0.221 0.531 0.095 0.341
q15) It is easier for me to memorize what is on a computer rather than to review
printed materials.
0.324 0.572 0.048 0.095
q16) I can be more creative when I think on paper than using computers. 0.231 0.628 0.265 0.251
q17) I would rather do group learning through computers than face to face. 0.221 0.634 0.192 0.198
q18) I can concentrate better looking at a computer screen than looking at a
blackboard or a large screen in a classroom.
0.342 0.611 0.110 0.094
q19) I feel more motivated when I study using computers than learning from
teachers in person.
0.302 0.642 0.257 0.165
q20) I understand better when I learn through computers than when I learn by
reading books.
0.210 0.582 0.049 0.057
q21) I can be more creative when I think using computers than thinking on paper. 0.027 0.667 0.370 0.022
q22) It is easier for me to communicate through computers or cell phones than to
communicate face to face.
0.092 0.698 0.190 0.039
q23) I would rather follow the computer instruction rather than study reading
textbooks.
0.198 0.510 0.385 0.123
q24) I prefer learning through computers to learning by reading books. 0.212 0.784 0.345 0.141
q25) I feel less tired looking at a computer screen than looking at a blackboard or
a large screen in a classroom.
0.066 0.349 0.290 0.084
q26) It is easier for me to take test on a computer than on paper. 0.020 0.310 0.510 0.190
q27) I would rather submit my report in an electronic format than in a paper and
pencil format.
0.091 0.114 0.628 0.310
q28) It is easier for me to take test individually than to take one in a place with
others.
0.301 0.262 0.690 0.210

q29) I would rather receive answers later from teachers via mail than asking
questions in person or through chat.
0.112 0.096 0.720 0.073
q30) I prefer communicating via email to communicating through telephones. 0.110 0.120 0.686 0.104
q31) I am familiar with computers. 0.153 0.053 0.741 0.086
q32) I prefer taking notes using a computer than writing on paper. 0.231 0.296 0.562 0.271
q33) I would rather ask questions using email or bulletin boards than asking
teachers in person.
0.105 0.320 0.713 0.832
q34) I would rather study reading textbooks rather than follow the computer
instruction.
0.065 0.215 0.651 0.661
q35) I want to decide the study sequence on my own. 0.011 0.092 0.172 0.578
q36) I want to follow the study sequence which my teacher decides. 0.124 0.162 0.121 0.496
q37) I prefer being assessed individually upon completion of the assignment to
being assessed at the same time with others.
0.286 0.190 0.110 0.539
q38) I want to drill what I have learnt repeatedly. 0.201 0.119 0.085 0.290
q39) It is easier for me to tackle with the project I decide than the one assigned to
me.
0.198 0.085 0.270 0.310
q40) I prefer looking my grade online to being given it on paper. 0.223 0.252 0.381 0.298

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