Critical Success Factors for the of Acceptance and Use of an LMS

The Case of e-CLASS

Stavros Valsamidis

, Ioannis Kazanidis

, Vasilios Aggelidis

, Sotirios Kontogiannis

and Alexandros Karakos

Department of Accounting and Finance, TEI of East Macedonia and Thrace, Agios Loukas, 65404 Kavala, Greece

Department of Business Administration, TEI of East Macedonia and Thrace, Agios Loukas, 65404 Kavala, Greece

Department of Business Administration, TEI of West Macedonia,

6th National Road Kozani Grevena, 51100 Grevena, Greece

Department of Electrical and Computer Engineering, Democritus University of Thrace,

University Campus Kimeria, 67100 Xanthi, Greece

Keywords: e-Learning, Critical Success Factors, Instructor, Student, Technology, Support.

Abstract: Nowadays, as e-learning is increasingly used in education, it is useful to know what are the critical factors

for its successful implementation in higher education institutes. This research has two main objectives. The

initial objective is to clarify and categorize the Critical Success Factors (CSF) of education with the use of a

Learning Management System (LMS) from the perspective of students and then to investigate the

relationships among these factors, suggesting a new causal model. To achieve the above objectives, an

extensive, detailed and systematic study of available literature sources was held. Then, the critical success

factors were separated in four (4) broad categories: instructors' characteristics, students' characteristics,

information and communications technology used and technical support provided by the technical staff.

Each factor contains a number of deterministic variables which were adopted mainly by previous studies.

Also, for the collection of data for analysis, a questionnaire was distributed to students who use the LMS.

Technical multivariate analysis was used to examine the relevance of each variable determinant factor,

while for the evaluation of the causal model, structural equation systems were used.

1 INTRODUCTION

The use of Internet in education has created a new

context known as e-learning or web-based education

process, in which large amounts of information

about teaching–learning interaction are endlessly

generated and ubiquitously available. e-Learning is

one of the tools emerged from information

technology and it has been integrated in many

university programs. e-Learning describes the ability

to electronically transfer, manage, support, and

supervise learning and learning materials (Normark,

and Cetindamar, 2005). In its broadest sense e-

Learning can be defined as instruction delivered via

all electronic media including the Internet, intranets,

extranets, satellite broadcasts, audio/videotape,

interactive TV and CD-Rom (Urdanand Weggen,

2000).

e-Learning has been viewed as synonymous with

web-based learning (WBL), Internet-based training

(IBT), advanced distributed learning (ADL), web-

based instruction (WBI), online learning (OL) and

open/flexible learning (OFL) (Khan, 2001). e-

Learning is the effective learning process created by

combining digitally delivered content with learning

support services (Hara & Kling, 1999). Above are

the varied definitions and meanings that can be

ascribed to the modern pedagogy known as e-

learning. The categories of eLearning are depicted in

figure 1 (Siemens, 2004). e-Learning for the

purposes of this article refers to teaching and

learning that is web-enabled (Govindasamy, 2002).

The growth of e-learning led to the appearance of

Learning Management Systems (LMSs), which

provide a variety of features and operations

including the development, management,

distribution, diffusion and presentation of the

educational material as well as tools for the

management of users and courses. Some of the most

well-known commercial LMS are Blackboard,

Valsamidis, S., Kazanidis, I., Aggelidis, V., Kontogiannis, S. and Karakos, A.

Critical Success Factors for the of Acceptance and Use of an LMS - The Case of e-CLASS.

In Proceedings of the 8th International Conference on Computer Supported Education (CSEDU 2016) - Volume 2, pages 331-338

ISBN: 978-989-758-179-3

331

WebCT and TopClass while Moodle, Claroline and

aTutor are free distributed (Romero et al., 2008). In

Greece, the Greek University Network (GUNet) uses

Open eClass platform (GUNet, 2015), which is an

evolution of Claroline (Claroline, 2009). This

system is an asynchronous distant education

platform and is open source under a General Public

Licence (GPL).

Figure 1: Categories of eLearning.

Ingramet et al., (2000) argue that the term

Critical success factor (CSF) was coined in the

1980s. The fact that some organizations seemed to

be more successful than others caused the

investigation for this. Some factors appeared to be

critical for this success and characterized as critical.

A factor that is critical to the success of the project is

intuitively referred to as a Critical Success factor

(CSF). Therefore, critical success factors (CSFs) are

variables that are fundamental to the success of the

implementation, and an organization must handle

these CSFs well in order to have a successful

implementation (Frimpon, 2011). CSFs are “those

things that must be done if an organization is to be

successful” (Freund, 1988).

A complex technological initiative like an e-

learning deployment is an undertaking involving a

multiplicity of factors that impact the

implementation to varying degrees. In literature for

e-learning there is not much work for critical success

factors.

Drennan et al., (2005) derived measures of

perceptions of technology from research on the

Technology Acceptance Model and used locus of

control and innovative attitude as indicators of an

autonomous and innovative learning mode.

Sela and Sivan (2009) proposed nine success

factors for enterprise-wide e-learning. These factors

are divided into two categories: “must-have” factors

and “nice-to-have” factors. The must-have factors

include: useful and easy to use e-learning tools,

marketing, management support, the right

organizational culture, and the existence of a real

need for the organization. The “nice to have” factors

include: time to learn, support, mandatory learning,

and incentives.

Researchers have identified different CSFs in e-

learning. Volery and Lord (2000) identified

technology, instructor and previous use of

technology from a student’s perspective as the CSFs

in e-learning. More specifically define technology as

the factor that has relation with navigation and easy

of access as well as the interface of the LMS.

Regarding authors they measure attitudes towards

students and classroom interaction. Similarly Soong,

Chan, Chua, and Loh (2001)identified as e-learning

CSFs the human factors, technical competency of

participants, e-learning mindset, collaboration

between participants, and perceived information

technology infrastructure.At the same direction other

studiespropose as CSFs the technology, instructor

characteristics, and student characteristics (Leidner

and Jarvenpaa 1993; Guawardena, 1995).

According to Selim (2007), four CSFs were

identified and measured, namely instructor

characteristics, student characteristics, technology

infrastructure, and university support. Similar is the

approach by Frimpon (2001). Seventeen critical

success factors (CSFs) were obtained through an

exhaustive search, and were partitioned into 4

natural roles of Student, Instructor, Technology, and

Institution. The latter two papers inspired us for this

study.

In this paper, two main objectives have been set.

First, to clarify and categorize the Critical Success

Factors (CSFs) in an LMS from the perspective of

students and then, to investigate the relationships

between these factors, suggesting a new causal

model. 400 students from two departments of the

school of Business and Economy of TEI of East

Macedonia and Thrace were involved. They use the

LMS e-class taking advantage of most of its

features. E-class is installed and functions for almost

two decades in TEI of East Macedonia and Thrace

(former TEI of Kavala).The techniques of simple

descriptive statistics and multivariate analysis

techniques were used.

For the exploratory factor analysis and

descriptive statistics, correlations and reliability

validation, the statistical package SPSS 19 was used.

For the confirmatory factor analysis the structural

equation systems (Structural Equation Modeling -

SEM) with the AMOS software packagewas used.

CSEDU 2016 - 8th International Conference on Computer Supported Education

332

2 APPROACH

2.1 The Proposed Model

Four variables were defined as critical factors in the

proposed model; Instructors' characteristics

(Instructor), Students' characteristics (Student),

Information and Communications Technology

(Technology) and the Support by the school

(Support). Finally to measure the intention of

students to use the e-class, a fifth deterministic

variable (Intention to Use) is used.

A questionnaire that is consisting of 56 questions

was completed by students. The deterministic

variables are the questions of our questionnaire.

After a thorough analysis, the questionnaire

responses show us if the deterministic variables are

suitable to measure the hidden variables and how

they affect the formation of the students' intention in

the adoption and use of an LMS. The analysis of our

model, is focused on the four hidden variables

(factors), each determined by some deterministic

variables.

Figure 2: The Research Model and its Assumptions.

The proposed model, for the acceptance of online

education by students is depicted in Figure 2. The

five variables are: (A1.) Instructors' characteristics,

(A2.) Students' characteristics, (A3.) Information

and Communications Technology, (A4.) The

information technology used by the school

(Support), (A6.) Intention of use of the LMS (e-

class).

In our research, the factor Instructors'

characteristics includes thirteen (13) deterministic

variables (A1.1-A1.13), in order to explore these

characteristics. The deterministic variables A1.1-

A1.7 were adopted by Selim (2007) and were

previously used by Volery and Lord (2000), to

examine the teaching styles. The deterministic

variables A1.8 and A1.9 were adopted by Lim et al.

(2008) to measure the availability of the instructor.

The deterministic variables A1.10-A1.11 were

adopted by the Selim (2007) and were previously

used by Volery and Lord (2000). The deterministic

variables A1.12-A1.13 were adopted by the Selim

(2007) and were previously used by Soong et al.

(2001). The deterministic variables A1.10-A1.13

will be used to verify the relationship of teacher

education through the LMS (e-class).

In order to determine the factor Students'

characteristics, twenty (20) deterministic variables

(A2.1-A2.20) were included. The deterministic

variables A2.1 and A2.2 measure the student's

motivation for use of education through the Internet.

The deterministic variables A2.3-A2.7 measured the

technical ability of the student. The deterministic

variables A2.8 - A2.14 measure the effectiveness of

the content of the course through the Internet as well

as the structure and design as perceived by students.

The deterministic variables A2.15-A2.20 are used, to

measure attitudes and behaviors of students toward

education through Internet. The first fourteen (14)

deterministic variables adopted by Selim (2007) and

the rest of the Lim et al. (2008). The deterministic

variables A2.1-A2.7 had been also previously used

by Soong et al. (2001).

The factor Information and Communications

Technology includes thirteen (13) deterministic

variables (A3.1-A3.13). These will be used to

measure the reliability, richness, consistency and

effectiveness of technology in the school.

The factor Support from the educational

institution was covered by five (5) deterministic

variables (A4.1-A4.5), which were adopted by Selim

(2007) and were used to investigate the effectiveness

and efficiency of technical support by the school,

library services, and the reliability of computer

laboratories.

For the factor Intention to use the e-class, five (5)

deterministic variables (A6.1 - A6.5) were used,

which has also been adopted by Selim (2007), in

order to m1easure the intention of the students to use

e-class. In other words, we want to measure how

willing are the students to follow that kind of

learning that was unknown to them.

2.2 Hypotheses

According to the research model (depicted in Figure

2), the instructors' characteristics, the students'

characteristics, the support and the technology

aresufficient to describe the acceptance of online

education from students' point of view and their

Critical Success Factors for the of Acceptance and Use of an LMS - The Case of e-CLASS

333

intention to use it as well. Therefore, we suggest the

following assumptions:

H1: The technology will positively affect

instructors' characteristics.

H2: The technology will positively affect students'

characteristics.

H3: The technology would have an immediate

positive effect on the intention to use.

H4: The technology will positively influence the

support.

H5: The support will positively affect the

instructors' characteristics.

H6: The support will positively affect students'

characteristics.

H7: The support will have a direct positive effect

on intention to use.

H8: The instructors' characteristics will have a

direct positive effect on intention to use.

H9: The students' characteristics will have a direct

positive effect on intention to use.

2.3 Test of the Research Hypotheses

For the test of research hypotheses, we estimate the

structural part of the research model, which

evaluates the causal relationships among factors that

make up the model. Consequently, the structural part

answers the research hypotheses of the research. The

assessment’s purpose of the overall adaptation of a

model is the degree of determination in the model

that is compatible with the empirical data.

For the implementation of this test, a

confirmatory factor analysis will be performed in a

model, which shows the averages of the variables

that define the conceptual factors. According to

Grapentine (1997, 2000), by using the above model,

allows better management of side effects resulting

from the multicollinearity of the variables that

determine the factors and focus more attention on

the concerned factors and the relationships between

them, despite the deterministic variables that are

used only to measure these factors.

The results of this analysis answer to the research

hypotheses and evaluate accordingly with regard: (a)

the accepted limits of adaptability coefficients, (b)

the recommendations of the modification indices

and (c) the statistical significance of the causal links

(paths of the model).

Based on the updated indices, some variables

were removed so as to maximize the adjustment of

the model. The decision to remove a variable or

relationship or the addition of a relationship, based

on revisions and corrections, reflects the substance.

So, for the redefinition of the model, only the

statistical significance of the relationship must be

taken into account, as well as the recommendations

of modification coefficients that should have such

values that the adaptability indices are within

acceptable limits of the adopted methodology

(Bollen, 1989; Green et al., 1999). In this way,

variables or relationship are deleted, when it is no

longer necessary to maintain the adjustment of the

model.

3 RESULTS

The results of confirmatory factor analysis, as

depicted in Figure 3, according to the statistical

significance of the relationship with the values of

adaptability coefficients suggest removing two

causal relationships of the model and the

simultaneous rejection of corresponding research

hypotheses (H1: technology affects directly and

positively to the formation of instructors'

characteristics, H6: Support directly and positively

affects the students' characteristics).

Figure 3: Schematic confirmatory factor analysis of the

research model (first step).

After removing the two relationships, a

confirmatory factor analysis again is performed. The

results show all the remaining causal relationships,

which are statistically significant (Figure 4).

But according to the modification indices, to

increase the reliability, validity and adaptability of

the model, a new relationship between the factor

instructors' characteristics and the factor students'

characteristics is necessary to insert, as depicted in

Figure 5.

CSEDU 2016 - 8th International Conference on Computer Supported Education

334

Figure 4: Schematic confirmatory factor analysis of the

research model (second step).

Figure 5: Schematic confirmatory factor analysis of the

research model (third step).

After the insertion of the new causal relationship,

taking into account the acceptable margins of the

adaptability coefficients, a generally very good fit of

the data with the concerned model is observed. In

particular, the indices CFI, GFI, RMR and NFI get

value greater than 0.9, which is considered the

threshold of reliability, validity and adaptability to

the data. The value of the index x

for the degrees of

freedom to be with less than 3 and the value of

RMSEA index is less than 0.1; these values were

adopted as the upper limit of a fitness model. So,

according to the values of adaptability coefficients,

the model is proved as a valid and reliable model for

analysis of the results and draw conclusions. Figure

5depicts the final proposed model for acceptance

and use of the LMS. It includes also the capacity of

causal relationships between factors that compose it,

and the explained rate fluctuates as well.

Table 1 contains the research hypotheses, as

these were determined during the creation of the

hypothetical research model, as well as a new

research hypothesis which has been added according

to the modification indexes. It also contains the

statistical analysis values, as those were resulted

from the confirmatory factor analysis

Table 1: The research hypotheses of acceptance and use

model of the LMS.

Research Hypotheses Estimate S.E. C.R P

Η1

Instructors'

characteristics



Technology

characteristics

0,133 0,088 1,508 0,132

Η2

Students'

characteristics



Technology

characteristics

0,224 0,057 3,918 ***

Η3

Intention of

use of e-class



Technology

characteristics

0,242 0,049 4,929 ***

Η4

Support

Characteristics



Technology

characteristics

0,523 0,093 5,638 ***

Η5

Instructors'

characteristics



Support

Characteristics

0,169 0,055 3,078 0,002

Η6

Students'

characteristics



Support

Characteristics

0,013 0,042

0,302

0,762

Η7

Intention of

use of e-class



Support

Characteristics

0,212 0,032 6,656 ***

Η8

Intention of

use of e-class



Instructors'

characteristics

0,259 0,037 7,018 ***

Η9

Intention of

use of e-class



Instructors'

characteristics

0,252 0,052 4,863 ***

Η10

Students'

characteristics



Instructors'

characteristics

0,208 0,044 4,731 ***

Table 2: Direct, indirect and total normalized effects

between the factors that make up the proposed model of

acceptance and use of LMS (D = Direct, I=Indirect, Τ =

Total Effect).

Technology

characteristics

Support

Characteristics

Instructors'

characteristics

Students'

characteristics

Support

Characteristics

0,346

Instructors'

characteristics

0,198

0,068

0,068 0,198

Students'

characteristics

0,239 0,289

0,020 0,057

0,259 0,057 0,289

Intention of use

D 0,243 0,321 0,336 0,236

I 0,195 0,080 0,068

T 0,438 0,401 0,404 0,236

In conclusion, table 2 presents the determinant of

direct, indirect and total normalized (in unit) effects

among the factors that comprise the proposed model

for the acceptance and use of the LMS by the

students. The coefficients of the paths can be used to

Critical Success Factors for the of Acceptance and Use of an LMS - The Case of e-CLASS

335

decompose the correlations between the factors,

which form the model of direct and indirect effects,

corresponding to the direct and indirect paths shown

by the arrows of causality model. The indirect effect

of a variable i to variable j, according to the rules of

linear systems, is calculated from the summary of

the coefficients multiplications of all the indirect

paths from i to j.

4 DISCUSSION AND

CONCLUSIONS

As the results from the descriptive and statistical

analysis reported and interpreted above, here we will

report and explain the results from the test of the

hypotheses.

A) The model was initiated, having set these

hypotheses for the relationships between the factors

to be considered together and the intended use of

students on the e-class. However, other cases were

confirmed and others were rejected. Especially, after

the analysis, a new relationship between two factors

derived. More specifically, the relationship between

Technology and Instructors' Characteristics and the

relationship between the Support and Students'

characteristics were rejected. On the other hand, a

new relationship between Instructors' Characteristics

and Students' characteristics arose.

These findings are reasonable, as the technical

background of the instructor is not affected by the

Institute's technology but also the characteristics of

students are not affected by the quality and quantity

of technical support. On the other hand, the

characteristics of students are affected and shaped by

the characteristics of instructors. Every instructor

should be the coach and the advisor of the student.

Generally, the characteristics and the progress of

each student are dependent, to some extent, on the

interactions s/he had with his/her instructors.

B) It is worth noting that the strongest relationship

in our final model, is that between technology and

the support from the school. The weakest is

observed between the support of the educational

institute and instructor's characteristics. This makes

sense because a school as good technical support

has, the better it will be or at least seems to be the

technology to students. And vice-versa, as finer

technology has, the better it will be or at least seems

to students, the technical support. On the other hand,

the only relationship between instructor and support

is when the instructor assumes such responsibility,

i.e. to fix something or help a student to something

relevant.

C) The order of significancy among the five factors,

based on the average of the coefficients are:

students' characteristics, the intention of use, the

technology, the instructors' characteristics and the

support from the school. This result does not agree

with the corresponding conclusion of Lim et al.

(2008), who had found as the most significant factor

the technical support from the school. It agrees, with

Poon et al., (2004), who had also found students'

characteristics, as the most important category.

D) Table 2 can give us enough results worth

interpret. Initially, we observe that the instructor's

characteristics have the strongest direct relationship

with the intention to use. This probably indicates

that the attitude and the knowledge of the instructor

play an important role in the student's intention of

use the LMS. On the other hand, the student's

characteristics are weakest directly related to the

intention of use. His/her own characteristics i.e., no

shape his/her intention as strongly as other external

factors.

Interests are the results of the overall

relationships, namely computing and indirect from

direct. The strongest relationship longer observed

between technology and the intention to use. While

the direct relationship between them was particularly

weak, the indirect was particularly strong with the

result as a whole to have the strongest correlation

model. The so strong indirect relationship, probably

due to the fact that technology is related to all the

factors which affect. We have not to forget that

education through Internet is a methodology that

uses advanced technology. It is worth noting that the

difference of total relationship among factors and

intention of use is small. In particular, the

relationships of intention to use with technology,

students' characteristics, instructors' characteristics

and support are in the same range. It is striking that

the student's characteristics have a significantly

weaker relationship with the intention to use. The

same occurred with the direct relationship. The most

likely explanation in this case is that the student is

more influenced by external factors than by its own

characteristics.

E) In general, we see that the education through

LMS is still at an early investigation stage in Greece.

Nevertheless, students seem willing to walk the new

paths that appear in front of them and adopt the e-

class and other relevant LMSs. The factors affecting

the intent of students are many. Some of them are

definitely the student's and instructor's

characteristics, information technology and technical

CSEDU 2016 - 8th International Conference on Computer Supported Education

336

support from the university. This has also been

noticed by previous studies (Selim, 2007; Volery

and Lord, 2000; Friesen, 2005; Soong et al., 2001)

and now it was confirmed. The relationships

between the factors are influenced by other factors

affecting the students’ intention to use.

F) All the above tables, figures and analysis show

us that the constructed model can be considered

reliable and can fulfil its mission successfully.

It should be noted that the above results related

to the test of hypotheses, largely agree with most

previous studies (Selim, 2006; Volery and Lord,

2000; Al-Fadhli, 2009; Abbad et al., 2009).

Concluding, we point out with some general

remarks. The presented causal model explains 54%

of education acceptance criteria through an LMS.

The strongest relationship in our final model is

between Information Technology and Technical

Support from the school. The weaker relationship is

observed between the Technical Support and

Instructors' Characteristics.The study revealed the

following order of significancy of the five factors

used, according to the average of the responses of

the students; the most significant factors in

descending order are: Students' characteristics,

Intention of use, Technology, Instructors'

characteristics and Technical Support.

However the limitations of the study are the

sample size, the questionnaire size, the objectivity of

the respondents, the level of education through LMS

in Greece, the associative nature of the research and

the adaptability indices of confirmatory factor

analysis.

Suggestions for further research are the repeat of

study with new larger sample, to be applied in other

Universities to confirm the findings of the study.

Since this causal model covers only the 54% of all

the possible factors, there are more factors that have

to be discovered. A twofold evaluation with research

to other entities apart of the students (i.e. proper

questionnaires for teachers, executives of school,

companies of advanced technology) would be

useful.

REFERENCES

Abbad, M. M., Morris, D., and de Nahlik, C., 2009.

Looking under the Bonnet: Factors Affecting Student

Adoption of e-Learning Systems in Jordan,

International Review of Research in Open and

Distance Learning, 10, 2., ISSN: 1492-3831

Al-Fadhli, S., 2009. Factors Influencing the Acceptance of

Distance Learning: A Case Study of Arab Open

University in Kuwait. Online Journal of Distance

Learning Administration, 12, 3.

Bollen, K. A., 1989.Structural Equations with Latent

Variables. Wiley Series in Probability and

Mathematical Statistics. New York: Wiley.

Claroline (2010). Retrieved January 05, 2010 from

http://www.claroline.net.

Dillon, L., and Gunawardena, C., 1995. A framework for

the evaluation of telecommunicationsbased distance

education in Stewart, D. (Ed.), Selected Papers from

the 17th Congress of the International Council for

Distance Education, 2, 34851. Open University,

Milton Keynes.

Drennan, J., Kennedy, J. and Pisarki, A., 2005. Factors

affecting student attitudes toward flexible online

learning in management education. Journal of

Educational Research, 98 6, 331-338.

Freund, Y. P., 1988. Critical success factors. Planning

Review, 16, 4, 20-25. DOI=

http://dx.doi.org/10.1108/eb054225.

Friesen, N., 2005. Interoperability and Learning

Objectives: An Overview of eLearning

Standardization. Interdisciplinary Journal of

Knowledge and Learning Objects.1, 22-31.

Frimpon, M., 2011. A Re-Structuring of the Enterprise

Resource Planning Implementation Process.

International Journal of Business and Social Science,

3, 1.

Govindasamy, T. 2002. Successful implementation of e-

Learning; Pedagogical considerations. The Internet

and Higher Education, 4(3-4), 287-299.

Grapentine, T., 1997. Managing multicollinearity.

Marketing Research. 9, 3, 10-21.

Grapentine, T., 2000. Path analysis vs. structural equation

modelling: do the relative merits of path analysis and

structure equation modelling outweigh their

limitations? Marketing Research, 12, 312 -20.

Green, S.B., Thompson, M.S., and Poirier, J., 1999.

Exploratory Analyzes to Improve Model Fit: Errors

Due to Misspecification and a Strategy to Reduce

Their Occurrence. Structural Equation Modelling.6, 1,

113-126.

GUNet. 2015. OPEN eClass, Retrieved May 15, 2015

from http://eclass.gunet.gr/.

Hara, N., and Kling, R., 1999. Students' frustrations with a

web-based distance education course. First Monday,

4(12), 5.

Ingram, H., Biermann, K., Cannon, J., Neil, J., and

Waddle, C., 2000. Internalizing action learning: a

company perspective. Establishing critical success

factors for action learning courses. International

Journal of Contemporary Hospitality Management,

12,2, 107-113. DOI= http://dx.doi.org/10.1108/

09596110010307369

Khan, B. H., 2001.Web-based training: An introduction.

In B.H. Kahn (Ed.) Web-based Training. Englewood

Cliffs, NJ.: Educational Technology Publications.

Leidner, D.E. and Jarvenpaa, S.L., 1995. The Use of

Information Technology to Enhance Management

School Education: A Theoretical View. MlS

Critical Success Factors for the of Acceptance and Use of an LMS - The Case of e-CLASS

337

Quarterly. 19, 3, 265-291.

Lim B., Hong, K. S., and Tan, K. W., 2008. Acceptance of

e-learning among distance learners: A Malaysian

perspective, In Hello! Where are you in the landscape

of educational technology? Proceedings ascilite

Melbourne 2008.

Normark, O. R., and Cetindamar, D., 2005. e-Learning in

a competitive firm setting. Innovations in Education

and Teaching International, 42, 4, 325-335. DOI=

10.1080/14703290500062581.

Poon, W.C., Low, L.T., and Yong, G. F., 2004.A study of

Web-based learning (WBL) environment in Malaysia.

The International Journal of Educational

Management, 18, 6, 374-385.

Romero, C., Sebastian, V., and Garcia, E., 2008.Data

Mining in course management systems: Moodle case

study and tutorial”. Computers and Education, Vol.

51, No.1, pp. 368-384.

Sela, E. and Sivan, Y., 2009. Enterprise e-Learning

Success Factors: An Analysis of Practitioners’

Perspective (with a Downturn Addendum).

Interdisciplinary Journal of e-Learning and Learning

Objects. 5, 1, 335-343.

Selim, H.M., 2007. Critical success factors for e-learning

acceptance: Confirmatory factor models. Computers

and Education.49, 2, 396-413. DOI=

10.1016/j.compedu.2005.09.004.

Siemens, G., 2004.Categories of eLearning. Retrieved

from http://www.elearnspace.org/Articles/elearning

categories.htm.

Soong, B. M. H., Chan, H. C., Chua, B. C., and Loh, K. F.,

2001. Critical success factors for on-line course

resources. Computers and Education, 36, 2, 101–120.

DOI= 10.1016/S0360-1315(00)00044-0

Urdan, T. A., and Weggen, C. C., 2000. Corporate e-

learning: exploring a new frontier. W.R. Hambrecht

& Co.

Volery, T. and Lord, D., 2000. Critical Success Factors in

Online Education, The International Journal of

Educational Management. 14, 5, 216-223. DOI=

http://dx.doi.org/10.1108/09513540010344731.

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