The role of readiness factors in E-Learning outcomes: An empirical study

Abbas Keramati*

Keramati@ut.ac.ir

* Corresponding author

Industrial Engineering Department, University of Tehran, P.O. Box 11155-4563, Tehran, Iran

Masoud Afshari-Mofrad

M.Afshari@ut.ac.ir

Information Technology Management Department, Tarbiat Modares University, P.O. Box 14115-111,

Tehran, Iran

Ali Kamrani

Kamrani.ali@gmail.com

Industrial Engineering Department, University of Tehran, P.O. Box 11155-4563, Tehran, Iran

Reference to this paper should be made as follows: Keramati, A., Afshari-Mofrad, M. and Kamrani, A.

(2011) ‘The role of readiness factors in E-Learning outcomes: An empirical study’, Computers &

Education, Vol. 57, pp.1919-1929.

Abstract

Although many researchers have studied different factors which affect E-Learning outcomes, there is little

research on assessment of the intervening role of readiness factors in E-Learning outcomes. This study

proposes a conceptual model to determine the role of readiness factors in the relationship between E-

Learning factors and E-Learning outcomes. Readiness factors are divided into three main groups including:

technical, organizational and social. A questionnaire was completed by 96 respondents. This sample

consists of teachers at Tehran high schools who are utilizing a technology-based educating. Hierarchical

regression analysis is done and its results strongly support the appropriateness of the proposed model and

prove that readiness factors variable plays a moderating role in the relationship between E-Learning factors

and outcomes. Also latent moderated structuring (LMS) technique and MPLUS3 software are used to

determine each variable’s ranking. Results show that organizational readiness factors have the most

important effect on E-Learning outcomes. Also teachers’ motivation and training is the most important

factor in E-Learning. Findings of this research will be helpful for both academics and practitioners of E-

Learning systems.

Keywords:

E-Learning; Readiness factors; Outcomes; Evaluation methodologies; Iran

1. Introduction

During recent years designing and implementing web-based education (E-Learning) systems have grown

dramatically (Hogo, 2010) and this type of education is playing an important role in teaching and learning

(Franceschi, 2009). It is implementing as a new method of training which complements traditional methods

(Vaughan, 2004) and its final ambition is to build an advanced society for citizens and support creativity

and innovation (Kim, 2005). In fact this new paradigm shifts education from teacher-centered to learner-

centered (Lee et al., 2009). Advantages like cost reduction, elimination of time and space constraint and

assisting the traditional instruction, make it important and popular (Chao, 2009). In addition, quality of

education in distance education systems depends on quality of electronic knowledge sources and other

didactic materials instead of depending on teacher quality and his ability to share knowledge (Cohen,

2006). The growth rate of E-Learning market is about 35.6% (Sun et al., 2008) and recent researches have

shown that only in America, nearly $40 billion is spent annually on technology based training (Johnson,

2009). According to such a heavily investing in this system, it is essential to evaluate its different aspects

and understand factors which influence its effectiveness (Schreurs, 2008). Success in implementing and

using this system is crucial because an unsuccessful attempt will be clearly revealed in terms of the return

of investment (Govindasamy, 2001). One of the most important variables which can have a critical effect

on E-Learning successful outcomes is readiness factor and schools have to improve and upgrade their

readiness to use this system (Wang et al., 2009). Although there are many researches evaluating different

E-Learning aspects, there is little research on assessment of the intervening role of readiness factors in E-

Learning outcomes. To overcome this shortcoming, in this study the role of readiness factors in the

relationship between E-Learning factors and outcomes is assessed. E-Learning readiness factors are divided

into three main categories including: technical, organizational and social factors.

The following two main questions of this research are considered:

• Do readiness factors have a moderating effect on the relationship between E-Learning factors and

E-Learning outcomes?

• How much is the moderating effect for different aspects of readiness factors (which aspects are

more important)?

2. Research background

With the increasing investment in E-Learning systems, it is essential to design and empirically investigate

the factors which affect E-Learning outcomes (Cukusic, et al., 2010). Many researchers have studied

several aspects of E-Learning and many different approaches were adopted (AbuSneineh, 2010). Most of

these researches have revealed that E-Learning is a suitable method for education and it allows easy

acceptance of novel strategies to improve learning outcomes (Wan et al., 2008). Despite these researches,

some other researchers demonstrated that E-Learning courses still have shortcomings and most E-Learning

courses have high dropout rate (Welsh et al., 2003; Stonebraker & Hazeltine, 2004; Means et al., 2009).

Since most of universities and organizations are going to use this system, it is important for both

practitioners and researchers to better understand how these shortcomings can be overcome and how

organizations could be ready to use this system. Some researchers studied different aspects of e-readiness

to help organizations for using E-Learning more effectively. E-readiness refers to the capabilities of the

organization for effective and efficient application of electronic media (Machado, 2007). Assessing E-

Learning readiness can help managers and policy makers to adopt the most appropriate policies and

facilities for better implementation and utilization of E-Learning system (Kaur, 2004).

One of the best ways to understand how E-Learning limitations can be overcome is developing models of

E-Learning (Alavi & Leidner, 2001). In order to do so, several researchers have proposed several models

which address how different features can influence E-Learning outcomes (Alavi & Leidner, 2001; Piccoli

et al., 2001; Johnson et al., 2008; Chen & Jang, 2010). For instance, Piccoli et al. (2001) believe that

human dimension (including students and instructors) and design dimension (including learning model,

technology, learner control, content and interaction) affect E-Learning effectiveness. Also Johnson et al.

(2008) assert that creating a shared, peer-centered learning environment is important in E-Learning success.

Table 1 displays some previous researches on E-Learning outcomes.

Table 1

In general, two major approaches toward readiness factors can be determined in the literature. In the first

approach, many researchers tried to extract different readiness factors for implementing an effective E-

Learning system. For instance, Kaur (2004) presented the factors of technological readiness, own level of

readiness, etc. Aydin and Tasci (2005) have focused on human resource readiness as an important variable

in E-Learning effectiveness in emerging countries. Darab and Montazer (2011) proposed an eclectic model

for assessing E-Learning readiness. Fathian et al. (2008) have evaluated e-readiness factors in Iran’s

environment. They have extracted organizational features, ICT infrastructures, ICT availability and

security as critical issues for e-readiness assessment. In the second approach, some researchers have

considered readiness factors as an independent variable which affects E-Learning outcomes directly. For

instance, Piccoli et al. (2001) and Sun et al. (2008) have considered some readiness factors (such as:

technology dimension and design dimension) as independent variables which affect E-Learning outcomes

directly. Also Johnson et al. (2009) have assessed the effect of technology characteristics on E-Learning

outcomes.

In this study, readiness factors are considered as a moderating variable because of two main reasons.

Firstly, a moderating variable refers to a variable which can affect the strength and direction of

relationships between other variables. In this research we have considered some readiness factors (for

instance: organizational rules) which don’t have a direct influence on E-Learning outcomes (for instance:

students’ motivation) but can influence the relationship between E-Learning factors and outcomes.

Secondly, Albadvi et al. (2007) considered the intervening role of organizational readiness in the

relationship between IT and firm performance. As the results of this work imply, to have a better level of

performance, it is essential to invest on intervening variables in addition to invest in IT. Accordingly, it is

important to know which aspects of IT and readiness factors have the greater effect on performance. Such

knowledge could be used to make wiser investments in IT. Since E-Learning is an IT-enabled system, this

paper aims to assess the role of E-Learning readiness factors in the relationship between E-Learning factors

and outcomes based on aforementioned research.

3. Research model

Conceptual model of this study is depicted in figure 1. As shown in this figure, the model is composed

from three variables including: E-Learning factors, readiness factors and E-Learning outcomes. As

mentioned earlier, this research tries to examine moderating effect of readiness factors on the relationship

between E-Learning factors and E-Learning outcomes.

3.1. E-Learning Factors

Selim (2007) grouped E-Learning critical success factors into 4 categories namely instructor, student,

Information Technology (IT) and university support. In this research, these factors were used to determine

their effect on E-Learning outcomes.

The role of teacher is critical in effectiveness and success of all kinds of education (Piccoli, 2001).

Especially in distance education, teachers’ conception of E-Learning and its usefulness plays a vital role

(Zhao, 2009) and their positive attitude toward using this system as a teaching assisted tool is required for

E-Learning success (Liaw et al., 2007). Previous research has shown that three instructor characteristics

affect E-Learning success: attitude, teaching style and IT competency (Webster, 1997). In this research,

based on previous researches, four major characteristics of teachers are used to measure this factor

including: motivation, attitude, training and teaching style.

Student is the most important participant in E-Learning (Aydin & Tasci, 2005). Since E-Learning is a

student-centered environment, high motivated and self-confident students can result in better E-Learning

outcomes (Baeten, 2010). In addition, students should be familiar with computer skills to be successful in

this system. Prior research has presented that if E-Learning facilitates students’ learning and eliminates

time and space constraints, they tend to use it (Papp, 2000). Also Liaw et al. assert that self-paced, teacher-

led, and multimedia instructions are major factors influencing learners’ conceptions toward E-Learning as

an effective tool for education (Liaw et al., 2007). Motivation, attitude and computer self-efficacy are the

most important factors which are used in this research to measure ‘student’ factor.

Distance education is a result of information technology explosion and IT is the engine of E-Learning

revolution (Selim, 2007). The role of IT in this type of education is vital and it is essential to prepare

suitable IT skills for success of E-Learning. Information technology has reshaped the process of

acquisition, communication and dissemination of knowledge within the educating process (Darab &

Montazer, 2011). It allows both the teacher and student to be separated in terms of time, place, and space.

Thus an appropriate use of IT in course content delivery is critical (Lim, 2007). In this research IT is

measured by the ability of E-Learning system to provide a good quality website design, the possibility of

interact with classmates through the web, well structures/presented information and the possibility of

online registration for participants. These measurements exist in appendix 1.

Top management commitment and support is a critical success factor in many IT projects (Huang, 2010)

and IT projects face failure because of lack of this factor (Soong, 2001). Since E-Learning is an IT-based

project, top manager should know this system and believe in its advantages. Top management support,

commitment and knowledge about E-Learning advantages are measures for ‘support’ variable in this

research.

3.2. Readiness factors

Many researchers have examined the role of readiness factors in E-Learning outcomes (Zhao, 2009). Prior

research has proved that technical readiness is one of the most important factors influencing E-Learning

outcomes (Brush, 2003) and it is crucial to match the right technology with the right learning objective

(Kidd, 2010). In this research based on literature, authors’ experiences and interviewees’ statements,

readiness factors categorized into three groups namely technical, organizational and social factors.

Technical factors include: Hardware, software, content, internet access, bandwidth and school’s space.

Organizational factors include: experts, organizational rules, organizational culture and management

permanence.

Social factors include: society’s conception of E-Learning, governmental rules and administrative

instructions.

3.3. Outcomes

Assessing E-Learning outcomes is important because individuals who are less satisfied with this system

have fewer tendencies for enrolling in future E-Learning courses (Carswell, 2001). Several models have

proposed to examine E-Learning outcomes (Piccoli, 2001; Wan, et al., 2008; Johnson et al., 2009; Cukusic

et al., 2010). In this research based on previous researches three important factors have been examined

including: Teachers progress, students progress and access to education for all.

Many studies have conducted to evaluate the relationship between E-Learning factors and its outcomes but

reviewing the literature has shown that they do not always effectively predict learning transfer per se

(Colquitt, 2000). In this research, the role of readiness factors in the E-Learning outcomes is assessed.

Therefore, the main hypothesis of this research can be argued as follows:

• Readiness factors play a moderating role in the relationship between E-Learning factors and E-

Learning outcomes.

Figure 1 depicts the conceptual model of this research.

Figure 1

4. Research Methodology

Data gathering method, measurement instrument and method of analysis are described in this section.

Hierarchical regression analysis has been used to confirm the moderating role of readiness factors and

latent moderated structuring (LMS) technique is used to rank each variable.

4.1. Data

Case studies and empirical researches are appropriate ways for IT researches (Baroudi, 1989). This

research is an empirical study by means of questionnaire in Iranian high schools. To recognize variables

affecting E-Learning outcomes and identify research variables, 9 interviews were conducted of E-Learning

specialists in the IT sector of Training Bureau in Tehran. Based on an extensive review of interview

transcripts and reviewing the literature, research variables were recognized. An initial set of questions was

developed to measure each variable. 2 academic experts viewed each of the items on the questionnaire for

its content, scope and purpose.

The participants of the study filled out a 36-item questionnaire. The questionnaire was of a likert scale one

containing five levels extending from 1 (Not at all) to 5 (Extreme). 96 respondents answer research

questionnaires but four questionnaires were ignored since they were not complete. This sample consists of

teachers at Tehran high schools who are utilizing a technology-based educating. Demographic information

of respondents are presented in the following tables.

Table 2

Table 3

Table 4

4.2. Measurement instrument

In this section we will operationally define research variables and then introduce their measuring

instruments. In this study, we tried to use different references for our research and in some cases tried to

use different aspects of a statement from different references in order to create a comprehensive

measurement instrument which has the most fitness with our purpose. Thus, our questions are partially

selected from different references. As stated before, we asked 2 academic experts to validate the

questionnaire.

4.2.1. E-Learning factors

As mentioned before, to assess this variable, based on Selim (2007) we have used four factors including:

student, teacher, IT and support. “Student” refers to attitudes of students toward E-Learning, their

motivation to use this system and their computer self-efficacy. “Teacher” indicates the motivation of

teachers for using E-Learning, their attitude toward this system, their teaching style and training for using

this new paradigm of education. “IT” refers to some dimensions of Information Technology –like online

registration, information management, etc- which affect E-Learning outcomes. Finally, “support” is used to

determine the effect of top management commitment and support for utilizing E-Learning.

Questions of the first two factors are taken from similar works like: Gattiker and Hlavka (1992), Thurmond

et al. (2002), Webster and Hackley (1997) and Liaw et al. (2007). Questions of the last two factors are

customized from Sun et al. (2008) and Albadvi et al. (2007).

4.2.2. Readiness factors

In this research we have categorized readiness factors into three groups namely: technical, organizational

and social. Technical factors refer to the technical dimension of E-Learning like: hardware, software,

bandwidth, etc. Organizational factors stand for factors like: organizational culture, rules, etc. Finally,

social factors are used to determine the impact of some aspects of society and government –like social

culture, governmental regulations, etc- on E-Learning.

To our knowledge, there is no study to provide empirical study for intervening role of readiness factors in

the relationship between E-Learning and outcomes. Thus questions in this section of questionnaire are

adapted from semi-similar works like: Arbaugh (2000), Nagi et al (2007) and Sun et al. (2008).

4.2.3. Outcomes

To assess E-Learning outcomes we have defined measures in relation with teachers’ progress, students’

progress and access to instruction for all. “Progress” refers to satisfaction, effectiveness and productivity of

teachers and students. Since the final ambition of E-Learning is to build an intelligent society, we have

used access to instruction for all as an outcome of this system.

Teachers’ progress questions are taken from Webster and Hackley (1997), Thurmond et al (2002) and Sun

et al (2008). Students’ progress questions are adapted from Johnson et al (2009). Finally 2 questions about

access to instruction for all are self-development based on Kim (2005).

4.3. Reliability and validity analysis

We have presented the reliability and validity analysis of the questionnaire in this section.

4.3.1. Reliability analysis

The reliability analysis of a questionnaire determines its ability to yield the same results on different

occasions and validity refers to the measurement of what the questionnaire is supposed to measure (Cooper

and Schindler 2003). For reliability analysis, Cronbach’s alpha is calculated by SPSS. As demonstrated in

table 5, all variables have an alpha greater than 0.7 and we can conclude that the questionnaire is reliable.

4.3.2. Validity analysis

In this research, construct validity, content validity and predictive validity were analyzed to ensure the

validity of the instruments (Nunnally and Bernstein 1994).

Construct validity shows the extent to which measures of a criterion are indicative of the direction and size

of that criterion (Flynn et al. 1994). It also shows that the measures do not interfere with measures of the

other criteria (Flynn et al. 1994). Construct validity of measurement instrument is analyzed through factor

analysis. The most common decision-making technique to obtain factors is to consider factors with

eigenvalue of over one as significant (Olson et al. 2005). Thus factor analysis is done and KMO and

Bartlett’s significance levels are calculated to show validity of the questionnaire. Table 5 presents these

measurements. As shown in this table, the questionnaire is valid and reliable.

Table 5

Content validity indicates meeting the specific range of contents that have been selected (Nunnally and

Bernstein 1994). It also shows that measurement instruments have elements that cover all aspects of

variables under measurement. Content validity cannot be numerically measured, but we can measure it

subjectively and judgmentally. Basically, content validity depends on the appropriateness of the content

and the method of rendering (Nunnally and Bernstein 1994). Since the selection of research variables is

based on an intensive survey of literature and all the elements are supported by authentic research, the

instrument has content validity. Furthermore, 2 academic experts have examined the content of the

questionnaire during the pre-testing.

Predictive validity is in fact the correlation between measurement instrument and an independent variable

taken from relating criteria (Nunnally 1978). This validity is only possible through correlation between the

predictor (independent variable) and criterion (dependent) variable (Nunnally and Bernstein 1994). In this

study, the results of two-variable and multi-variable correlation between “E-Learning factors” as

independent variables and “outcomes” as dependent variable have shown that there is significant

correlation between intended criteria under measurement in this study (Sig.: 0.000 and Correlation

coefficient: 0.425).

4.4. Method of analysis

There are different ways to test the moderating role of variables but in this research hierarchical regression

analysis is used. To do so, after developing a conceptual model, normality of data is tested and finally

regression analysis is performed.

4.4.1. Normality test

In order to test normality of data, two ways are used. Firstly, Kolmogorov-Smirnov (K-S) test is performed

(E-Learning variable = 0.085, Readiness factors = 0.917 and Outcomes = 0.559). Since non-significant

result (Sig value of more than .05) indicates normality, all variables (CSFs, Readiness factors and

Outcomes) have normal data. The other way is normal Q-Q plot. Figure 1 indicates these plots. This test

confirms that all variables have normal data.

Figure 2

4.4.2. Hierarchical regression

Moderating variable is generally defined as a variable (quantitative or qualitative) which affect direction or

strength of the relationship between a dependent variable and an independent variable. Figure 3

demonstrates a model in which there are three ways. Path A reveals the relationship between dependent and

independent variables. Path B reveals the relationship between dependent and moderating variables and

finally path C shows the relationship between dependent variable and the result of multiplying moderating

and independent variables.

Figure 3

Moderation hypothesis is proved if path C is statistically significant.

Table 6 presents the results of hierarchical regression of this research. As shown in this table, all three

paths are statistically significant.

Table 6

As shown in this table, hierarchical regression results proved that in 95% confidence level, readiness factor

variable plays a moderating role in the relationship between E-Learning factors and its outcomes.

Note that, we have assessed the mediating role of readiness factors but it was not significant statistically.

Moreover, Boyer et al. (1997) argues that a variable couldn’t play a moderating role and mediating role

simultaneously.

4.4.3. LMS technique

The LMS equations estimation method is particularly developed for the ML estimation of latent

interaction effects (Klein and Moosbrugger, 2000). In a structural equation, the latent variables usually

have a linear relationship in which the latent endogenous variables are linear functions of the latent

exogenous variables. But in some cases another exogenous variable might have a moderating effect on the

relationship between endogenous and exogenous variables. Therefore, a latent interaction effect influences

the latent model structure. In fact, by recognition of the new exogenous variable, the slope of the regression

of the endogenous variable on the exogenous variable will vary. The interaction effect is applied by adding

a product of latent exogenous variables in the structural equation. In other words, latent interaction models

include non-linear structural relationships in the structural equation. LMS executes alternative methods, for

instance: LISREL or 2SLS, with regard to statistical power, efficiency and the capability of detecting latent

interaction (Schermelleh-Engel et al., 2003). In this study, to run LMS technique, MPLUS3 software is

used.

4.4.4. Ranking of elements

Figure 4

A rank for each element is determined considering readiness factor variables as moderating factor.

For this purpose, using direct effect coefficients gained from the structural model, the real effect of each

factor is calculated considering all paths between that factor and outcomes (OC), and then the factors are

ranked in the order of calculated values for their effect on OC. The rankings are shown in Tables 7 and 8

using the results from the model. The tables show the rank of different factors based on the research model.

For example, Total effect of STUDENT is calculated as:

Total effect of STUDENT = 1.00*0.497 + 1.00*0.101 = 0.598

In the above formula, the effect of STUDENT is calculated by the summation of the two possible paths

from STUDENT to OC. The coefficient of 1.00 is gained from the measurement model, 0.497 is the direct

effect of ELNG on PER and 0.101 is the effect of interaction of ELNG and IST on PER, calculated from

the structural model.

Total effect of STUDENT, shows the impact coefficient of student factor on E-Learning outcomes. In fact,

0.497 and 0.101 are regression coefficients and 1.00 is the weight of “student factor” in comparison with

other factors which is calculated using LMS technique. Thus total effect is a weighted regression

coefficient which shows the effect of different E-Learning factors on E-Learning outcomes.

Table 7

In a similar procedure, different readiness factor (RF) elements are ranked based on their moderating

effects. Table 8 shows the results.

Table 8

5. Discussion and conclusion

Many researchers tried to evaluate readiness factors which affect E-Learning outcomes. For instance,

Aydin and Tasci (2005) have focused on human resource readiness as an important variable in E-Learning

effectiveness in emerging countries. Also Rhee et al. (2007) have assessed technological readiness for

implementing an effective E-Learning. Zoraini (2004) has mentioned some organizational readiness factors

such as culture and budget. Fathian et al. (2008) have evaluated e-readiness factors in Iran’s environment.

They have extracted organizational features, ICT infrastructures, ICT availability and security as critical

issues for e-readiness assessment. Finally, Piccoli et al. (2001) and Sun et al. (2008) have considered some

readiness factors (such as: technology dimension and design dimension) as independent variables which

affect E-Learning outcomes directly. Although aforementioned researchers have studied different factors

which affect E-Learning outcomes, there is little research on assessment of the intervening role of readiness

factors. By reviewing the literature, we have categorized readiness factors into three main factors including

technical, organizational and social factors. Also, Based on Albadvi (2007), we have tried to determine the

intervening role of readiness factors in the relationship between E-Learning factors and its outcomes.

Results of this study show that readiness factors play a moderating role and they strengthen the relationship

between E-Learning factors and E-Learning outcomes. To our knowledge, this is the first study which

categorized readiness factors into aforementioned three factors and also, this is the first study which

evaluated the intervening role of readiness factors in the relationship between E-Learning factors and

outcomes.

In this research, firstly, we considered technical infrastructures as a readiness factor. Some factors such as:

proper software and hardware or bandwidth, can play a crucial role in E-Learning outcomes. Internet low

speed and having problems while using the system may result in dissatisfaction and drop out of students

from the course. One of the most important difficulties in using E-Learning in Iran is the speed of internet

(Darab & Montazer, 2011).

Secondly, we regarded organizational readiness factors. Factors such as: organizational rules, culture and

experts, are considered in these factors. These factors can lay the ground for an appropriate move from

traditional education system to E-Learning system. It means that educating organizations should try to

adapt their rules and culture and train sufficient E-Learning experts to empower E-Learning outcomes after

implementation.

Social readiness factor is another factor which can moderate the relationship between E-Learning factors

and E-Learning outcomes. E-Learning not only affects students and teachers life, but also has an important

effect on parents and society. Therefore it is essential to consider social rules on E-Learning outcomes.

Factors like: society’s conception of E-Learning, governmental rules and administrative instructions, are

the most important social factors.

Our interviewees believe that one of the most difficulties in implementing and using E-Learning in Iran is

lack of readiness in teachers. Thus it is important to train them for using this system. Also, interviewees

argue that top managers in training bureau don’t believe in advantages of E-Learning yet. Another

difficulty in using E-Learning is management permanence. Some managers who were investing on

implementation of E-Learning have been changed and new managers don’t continue their programs. Our

experts also remark some concerns about E-Learning budget, cultural readiness and internet bandwidth.

Based on Selim (2007), E-Learning factors grouped into 4 categories namely: Student, teacher, IT and

support. Also based on previous researches and authors’ experiences readiness factors grouped into three

groups including: Technical, Organizational and Social. A survey conducted and 96 teachers In Tehran

high schools filled research questionnaire. Using hierarchical regression analysis, it is proved that

“readiness factors” variable plays a moderating role in this relationship. For instance, Organizational

readiness factor has a moderating effect. This means that organizational factors like management

permanence and organizational rules cannot have a direct effect on E-Learning outcomes like teachers’

motivation but these factors can affect E-Learning outcomes indirectly by influencing top management

support (which is an E-Learning factor in our model). In fact, management permanence can assure

managers to implement E-Learning which is an expensive and lengthy project appropriately. A successful

implementation of E-Learning will result in its effectiveness and motivates teachers to use this system.

The moderating effect of social readiness factors implies that some social factors (such as governmental

regulations) cannot play a direct role in E-Learning outcomes but these factors may affect the strength of

this relationship. In the other hand, some technical factors (such as school’s space) don’t have a direct

impact on teachers’ productivity but a better environmental condition will affect E-Learning outcomes

indirectly.

Finally, LMS technique and MPLUS 3 software were used to rank the effects of different aspects of

variables. Results demonstrated that organizational readiness factors are the most important aspects

influencing E-Learning outcomes. Technical and social readiness factors are in lower levels of importance.

The results of the ranking of different E-Learning elements show that in spite of previous researches who

have shown that student is the most important element of E-Learning (Aydin & Tasci, 2005), teachers’

motivation and training factor plays the most important role in E-Learning outcomes in Iranian high

schools. This result revealed that to shift learning environment from teacher-centered to learner-centered,

the first step is to train teachers and clarify advantages of this new paradigm for them.

5.1 Managerial recommendations

Based on results of the questionnaire and interviews, there are two important problems in utilizing E-

Learning in Iran. The first problem is management support. Our interviewees stated that many managers

still don’t believe in this system and therefore they don’t support it. The other major problem is teacher’s

conception of E-Learning. Our interviewees believe that teachers attitude toward this system is not

appropriate. Also they believe that students are more familiar and comfortable with new technologies like

internet and multimedia. Therefore it is important to first convince managers and second train teachers and

clarify advantages of this new paradigm for them. The results of LMS technique emphasize on this

argument. Also, LMS technique reveals that organizational readiness factor is the most important factor to

achieve better E-Learning outcomes. Thus managers should provide a good organizational environment to

implement and use E-Learning system more effectively.

5.2 Limitations and future research directions

This study has some limitations that should be taken into consideration, especially due to the fact that this

study is based on Iranian high schools. The suggested model might show different results in other

countries. Subsequent research can explore these issues using a broader research sample. Also some

researchers believe that E-Learning factors are more than 4 elements which can be taking into account for

future researches.

Acknowledgement

The authors would like to thank the reviewers for their constructive and invaluable comments.

References

AbuSneineh, W. & Zairi, M. (2010). An Evaluation Framework for E-Learning Effectiveness in the Arab

World. International Encyclopedia of Education , pp. 521-535.

Alavi, M. (1994). Computer-Mediated Collaborative Learning: An Empirical Evaluation. MIS Quarterly,

18(2), 159-174.

Alavi, M. & Leidner, D.E. (2001). Technology mediated learning: a call for greater depth and breadth of

research. Information Systems Research, 12(1), 1-10.

Albadvi, A., Keramati A. & Razmi, J. (2007). Assessing the impact of information technology on firm

performance considering the role of intervening variables: organizational infrastructures and business

processes reengineering. International Journal of Production Research, 45(12), 2697–2734.

Arbaugh, J. B. (2000). Virtual classroom characteristics and student satisfaction with internet-based MBA

courses. Journal of Management Education, 24(1), 32-54

Aydin, C. H. & Tasci, D. (2005). Measuring readiness for E-Learning: reflections from an emerging

country. Educational Technology & society, 8(4), 244-257.

Baeten, M., Kyndt, E., Struyven, K. & Dochy, F. (2010). Using student-centred learning environments to

stimulate deep approaches to learning: Factors encouraging or discouraging their effectiveness.

Educational Research Review, In Press.

Baroudi, J.J. & Orlikowski, W.J. (1989). The problem of statistical power in MIS research. MIS Quarterly,

13(1), 87-106.

Boyer, K.K., Leong, G.K., Ward, P.T. & Krajewski, L.J. (1997). Unlocking the potential of advanced

manufacturing technologies. Journal of Psychology Management, 15, 331–347.

Brush, T., Glazewski, K., Rutowski, K., Berg, K., Stromfers, C. & Van-Nest, M.H. (2003). Integrating

technology in a field-based teacher training program: The PT3@ASU project. Educational Technology

Research and Development, 51(1), 57–72.

Carswell, A.D., Agarwal, R. & Sambamurthy, V. (2001). Development and validation of media property

perception measures. paper presented at the Americas Conference on Information Systems, Boston, MA.

Chang, T.Y. & Chen, Y.T. (2009). Cooperative learning in E-Learning: A peer assessment of student-

centered using consistent fuzzy preference. Expert Systems with Applications, 36, 8342-8349.

Chao, R.J. & Chen, Y.H. (2009). Evaluation of the criteria and effectiveness of distance E-Learning with

consistent fuzzy preference relations. Expert Systems with Applications, 36, 10657-10662.

Chen, K. C. & Jang, S. J. (2010). Motivation in online learning: Testing a model of self-determination

theory. Computers in Human Behavior, 26, 741–752.

Chu, R.C. & Chu, A.Z. (2010). Multi-level analysis of peer support, Internet self-efficacy and e-learning

outcomes – The contextual effects of collectivism and group potency. Computers & Education, 55, 145-

154.

Cohen, E.B. & Nycz, M. (2006) Learning Objects and E-Learning: an Informing Science Perspective.

Interdisciplinary Journal of Knowledge and Learning Objects, 2, 23-34.

Colquitt, J.A., LePine, J.A. & Noe, R.A. (2000). Toward an integrative theory of training motivation: a

meta-analytic path analysis of 20 years of research. Journal of Applied Psychology, 85 (5), 678-707.

Cooper, D.R. & Schindler, P.S. (2003). Business Research Methods, 8th ed., 23–240, (McGraw Hill: New

York).

Cukusic, M., Alfirevic, N., Granic, A. & Garaca, Z. (2010). E-Learning process management and the E-

Learning performance: Results of a European empirical study. Computers & Education, 55(2), 554-565.

Darab, B. & Montazer, G.A. (2011). An eclectic model for assessing e-learning readiness in the Iranian

universities. Computers & Education, 56(3), 900-910.

Fathian, M., Akhavan, P. & Hoorali, M. (2008). E-readiness assessment of non-profit ICT SMEs in a

developing country: The case of Iran. Technovation, 28(9), 578-590.

Flynn, B.B, Schroeder, R.G. & Sakakibara, S. (1994). A framework for quality management research and

an associated measurement instrument. Journal of Operation Management, 11, 339–366.

Franceschi, K., Lee, R.M., Zanakis, S.H. & Hinds,D. (2009). Engaging Group E-Learning in Virtual

Worlds. Journal of Management Information Systems, 26(1), 73-100.

Gattiker, U. E., & Hlavka, A. (1992). Computer attitudes and learning performance: Issues for management

education and training. Journal of Organizational Behavior, 13(1), 89–101.

Govindasamy, T. (2001). Successful implementation of E-Learning: Pedagogical considerations The

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

Hogo, M.A. (2010). Evaluation of E-Learning systems based on fuzzy clustering models and statistical

tools. Expert Systems with Applications, In Press.

Huang, M. (2010). The Dynamic Effect of the Top Management Support and Departmental Manager

Knowledge on IT Application Maturity. Advanced Materials Research, 121-122, 769-774.

Johnson, R.D., Hornik, S.R. & Salas, E. (2008). An empirical examination of factors contributing to the

creation of successful e-learning environments. International Journal of Human-Computer Studies, 66,

356-69.

Johnson, R.D., Gueutal, H. & Cecilia, M.F. (2009). Technology, trainees, metacognitive activity and E-

Learning effectiveness. Journal of Managerial Psychology , 24(6), 545-566.

Kaur, K. (2004). An assessment of e-learning readiness at the Open University Malaysia, International

conference on computers in education.

Kidd, T. (2010). Online Education and Adult Learning: New Frontiers for Teaching Practices, Information

science reference, Hershey, New York.

Kim, C.J. (2005). Construction of E-Learning Environments in Korea. ETR&D, 53(4), 108-115.

Klein, A. & Moosbrugger, H. (2000). Maximum likelihood estimation of latent interaction effects with the

LMS method. Psychometrika, 65, 457-474.

Lee, B.C., Yoon, J.O. & Lee, I. (2009). Learners’ acceptance of E-Learning in South Korea: Theories and

results. Computers & Education, 53(4), 1320-1329.

Liaw, S.S., Huang, H.M. & Chen, G.D. (2007). Surveying instructor and learner attitudes toward E-

Learning. Computers & Education, 49(4), 1066-1080.

Lim, H., Lee, S.G. & Nam, K. (2007). Validating E-Learning factors affecting training effectiveness.

International Journal of Information Management, 27(1), 22-35.

Machado, C. (2007). Developing an e-readiness model for higher education institutions: results of a focus

group study. British Journal of Educational Technology, 38(1).

Maki, R. H., Maki, W, S., Patterson, M. & Whittaker, P. D. (2000). Evaluation of a Web-Based

Introductory Psychology Course: L Learning and Satisfaction in On-Line Versus Lecture Courses.

Behavior Research Methods, Instruments, and Computers, 32(2), 230-239.

Means, B., Toyama, Y., Murphy, R., Bakia, M. & Jones, K. (2009). Evaluation of Evidence-Based

Practices in Online Learning: A Meta-Analysis and Review of Online Learning Studies. U.S. Department

of Education, Washington, D.C.

Nagi, E., Poon, J. & Chana, Y. (2007). Empirical examination of the adoption of WebCT using TAM.

Computers & Education, 48(2), 250-267.

Nunnally, J.C. & Bernstein, I.H. (1994). Psychometric Theory, 3rd ed., 214–286, (McGraw Hill: New

York).

Olson, E.M., Slater, S.F. & Hult, G.T.M. (2005). The performance implication of fit among business

strategy, marketing organization structure, and strategic behavior. Journal of Marketing, 69, 49-65.

Papp, R. (2000). Critical success factors for distance learning. Paper presented at the Americas Conference

on Information Systems, Long Beach, CA, USA.

Piccoli, G., Ahmad, R. & Ives, B. (2001). Web-based virtual learning environments: a research framework

and a preliminary assessment of effectiveness in basic IT skills training. MIS Quarterly, 25(4), 401-426.

Rhee, B., Verma, R., Plaschka, G.R. & Kickul, J.R. (2007). Technology Readiness, Learning Goals, and

eLearning: Searching for Synergy. Decision Sciences Journal of Innovative Education, 5(1), 127-149.

Schreurs, J., Sammour, G. & Ehlers, U. (2008). ERA - E-Learning Readiness Analysis: A eHealth Case

Study of E-Learning Readiness. WSKS, CCIS, 267–275.

Selim, H.M. (2007). Critical success factors for E-Learning acceptance: Confirmatory factor models.

Computers & Education, 49(2), 396-413.

Shaw, R.S. (2010). A study of learning performance of E-Learning materials design with knowledge maps.

Computers & Education, 54(1), 253-264

Schermelleh-Engel, K. & Moosbrugger, H. (2003). Evaluating the Fit of Structural Equation Models: Tests

of Significance and Descriptive Goodness-of-Fit Measures. Methods of Psychological Research Online,

8(2), 23-74

Schutte, J. G. (1997). Virtual Teaching in Higher Education: The New Intellectual Superhighway or Just

Another Traffic Jam?, California State University, Northridge, CA.

Soong, B.M.H., Chan, H.C., Chua, B.C. & Loh, K.F. (2001). Critical success factors for on-line course

resources. Computers & Education, 36(2), 101–120.

Stonebraker, P.W. & Hazeltine, J.E. (2004). Virtual learning effectiveness. The Learning Organization,

11(2-3), 209-225.

Sun, P.C., Tsai, R.J., Finger, G., Chen, Y.Y. & Yeh, D. (2008). What drives a successful E-Learning?: An

empirical investigation of the critical factors influencing learner satisfaction. Computers & Education,

50(4), 1183-1202

Vaughan, K. & MacVicar, A. (2004). Employees’ pre-implementation attitudes and perceptions to E-

Learning: A banking case study analysis. Journal of European Industrial Training, 28(5), 400-413.

Wan, Z., Wang, Y. & Haggerty, N. (2008). Why people benefit from E-Learning differently: The effects of

psychological processes on E-Learning outcomes. Information & Management, 45(8), 513-521.

Wang, Q., Zhu, Z., Chen, L. & Yan, H. (2009). E-Learning in China. Campus-Wide Information systems,

26, 47- 61.

Webster, J., & Hackley, P. (1997). Teaching effectiveness in technology-mediated distance learning.

Academy of Management Journal,. 40(6), 1282–1309.

Welsh, E.T., Wanberg, C.R., Brown, K.G. and Simmering, M.J. (2003). E-learning: emerging uses,

empirical results and future directions. International Journal of Training and Development, 7, 245-58.

Zhao, J., McConnell, D. & Jiang, Y. (2009). Teachers' conceptions of E-Learning in Chinese higher

education: A phenomenographic analysis. Campus-Wide Information Systems, 26(2), 23-35.

Zoraini, A. (2004). E-Learning Readiness: A Crucial Factor in Managing Teaching and Learning.

Education Management Through Technology Conference, Malaysia.

Figures list

Figure 1. Research’s conceptual model

Figure 2. Q-Q plots

Figure 3. Moderating effect model

Figure 4. Complete structural model

Tables list

Table1. Selected works on E-Learning outcomes

Table2. Respondents’ computer skills

Table3. Respondents’ educating background

Table4. Respondents’ web-based educating background

Table5. Reliability and validity analysis

Table6. Hierarchical regression results

Table7. Results of ranking different ELNG elements

Table8. Results of ranking different RF elements

Fig 1. Research conceptual model

E-Learning Factors

- Student

- Teacher

- IT

- Support

Readiness Factors

- Technical

- Organizational

- Social

Outcomes

- Teachers Progress

- Students Progress

- Access to instruction

Interaction

Fig. 2. Q-Q plots

Fig. 3. Moderating effect model (Adapted from Albadvi et al. 2007)

Fig. 4. Complete structural model

0.726

0.497

0.101

1.365

1.00

0.818

0.888

1.506

1.00

0.814

1.00

ELNG

RF

ELNG*R

OC

0.123

Teachers Prog

Students Prog

Access

Student

Teacher

IT

Support 0.837

Technical

Organizational

Social

Table1. Selected works on E-Learning outcomes

Researcher(s) year Dependent variable (s) methodology results

Alavi 1994 Perceived skill, self-reported learning and grades

Evaluated effectiveness and

efficiency of computer-

mediated collaborative

learning by an empirical study

Technology-mediated learning

environments may improve

students' achievement

Schutte 1997 Students’ scores on exam Compared a traditional

classroom and a virtual

classroom by an experimental

research

Virtual class scored an average

of 20% higher than the

traditional one

Maki et al. 2000 Students’ scores on exam Evaluated the on-line format

relative to the traditional

lecture-test format, using a

pretest-posttest nonequivalent

control group design

The students in the on-line

sections expressed appreciation

for course components and the

convenience of the course, but

the lecture sections received

higher ratings on course

Piccoli et al. 2001 Performance (e.g. achievement, recall), self-efficacy and satisfaction.

Presented a framework of

Virtual Learning Environment

(VLE) effectiveness and

compared it to a traditional

classroom

Two classes of determinants

including human dimension and

design dimension affect VLE

effectiveness

Lim et al. 2007 Learning performance (to what degree the trainees learn?) and Transfer performance (how well the trainees applied what they learned to their job tasks)

Proposed a model on variables affecting E-Learning performance and confirmed it using an empirical study

There is a positive relationship between individual, organizational and online training design constructs and training effectiveness constructs

Wan et al. 2008 Learning effectiveness and satisfaction

Using a survey, confirmed

their hypothesizes which are

asserted that prior experience

with ICT and virtual

competence are two

significant factors that

affected E-Learning outcomes

Virtual competence and prior

experience with ICT affect E-

Learning outcomes

Johnson et al. 2009 Perception of course utility, course satisfaction and course grade as E-Learning outcomes

Proposed a model of variables

influencing E-Learning

outcomes and assessed it by a

survey

Technology characteristics,

trainee characteristics and

Metacognitive activity affect E-

Learning outcomes

Chu & Chu 2010 Perceived learning, persistence and satisfaction

A survey to prove a proposed model to evaluate E-Learning outcomes for adult learners

Internet Self-Efficacy fully

mediates the relationship

between peer support and E-

Learning outcomes.

Chen & Jang 2010 Engagement, Achievement, Learning and satisfaction

Proposed and tested a model for the effect of online learner motivation and contextual support on online learning outcomes

The direct effect and indirect effects of contextual support exerted opposite impacts on learning outcomes

Table2. Respondents’ computer skills

I don’t use computer at all I am familiar with computer I am a professional user of computer

Percentage %5 %75 %20

Table3. Respondents’ educating background

0-5 (yrs) 5-10 (yrs) 10-15 (yrs) 15-20 (yrs) 20-25 (yrs) 25-30 (yrs)

Percentage %13 %09 %18 %25 %21 %14

Table4. Respondents’ web-based educating background

1 year 2 years 3 years 4 years

Percentage %54 %25 %12 %9

Table5. Reliability and validity analysis

Reliability Analysis Descriptive results Factor Analysis

Variable No. of questions

Cronbach’s Alpha

N Mean Std. Deviation

Extraction Eigenvalue KMO and Bartlett’s sig

% of Variance

E-Learning 15 0.700 92 3.549 0.504 0.607 2.982 0.000 61.96

Readiness factors

13 0.828 87 3.498 0.506 0.624 4.864 0.000 19.45

Outcomes 8 0.732 92 3.522 0.562 0.627 2.944 0.000 18.58

Table6. Hierarchical regression results

t-test

F-test

Step Measurement Scale B Statistics Sig. R Square ∆R

square Statistics Sig.

Technical: TECH

1 TECH 0.374 1.974 0.000 0.099 0.099 8.240 0.000

2 ELNG 0.467 4.238 0.000 0.182 0.083 9.472 0.000

3 TECH*ELNG 0.711 4.564 0.000 0.345 0.163 14.73 0.000

Organizational: ORG

1 ORG 0.515 4.735 0.000 0.209 0.209 22.418 0.000

2 ELNG 0291 2.409 0.018 0.260 0.051 14.744 0.000

3 ORG*ELNG 1.114 7.111 0.000 0.540 0.280 32.486 0.000

Social: SOC

1 SOC 0.236 4.802 0.000 0.159 0.158 16.659 0.000

2 ELNG 0.366 3.348 0.001 0.255 0.096 14.920 0.000

3 SOC*ELNG 0.297 2.998 0.021 0.264 0.009 10.266 0.000

Readiness factors: RF

1 IST 0.491 4.522 0.000 0.196 0.196 20.451 0.000

2 ELNG 0.323 2.789 0.007 0.265 0.069 14.940 0.000

3 RF*ELNG 6.046 1.018 0.000 0.491 0.227 26.409 0.000

Table7. Results of ranking different ELNG elements

Rank E-Learning element Total effect

1 Teacher 0.900

2 Student 0.598

3 IT 0.531

4 Support 0.489

Table8. Results of ranking different RF elements

Rank Readiness factors element Total effect

1 Organizational 0.137

2 Technical 0.101

3 Social 0.073

Appendix 1: Questionnaire

Based on your valuable experiences in using E-Learning, please indicate the extent to which each

following element affects E-Learning outcomes, ranging from 1 to 5: (1 = not at all, to 3 = moderate effect,

to 5 = extreme effect)

Question Not at

all

1

2

3

4

Extreme

5

Readiness factors

1- Appropriate hardware in school

2- Appropriate software in school

3- Appropriate course content

4- The speed of internet

5- Proper school space for E-Learning system

6- Cultural readiness for change in learning style

7- Adequate skilled employees in E-Learning

8- Appropriate organizational rules

9- Managerial readiness to implement the system

10- Sufficient budget and investment in E-Learning

11- society’s conception of E-Learning

12- Appropriate governmental rules and regulations

13- Appropriate administrative recipe

Question Not at

all

1

2

3

4

Extreme

5

E-Learning Factors

14- Teachers’ motivation for using E-Learning

15- Teachers training for using this system

16- Teachers’ attitude toward the technology

17- Teachers’ teaching style

18- Students attitude toward E-Learning

19- Students motivation to use this system

20- Students ability to use a computer to display or

present information in a desired manner

21- Existence of required IT experts in school

22- The possibility of interact with classmates

through the web

23- Good design of website

24- Well structured/presented information

25- The possibility of registering courses on-line

26- Top management knowledge about advantages of

the system

27- Top management support

28- Top management commitment

Please indicate the extent to which using E-Learning system affects each following criterion

Question Not at all

1

2

3

4

Extreme

5

1- Enhancing students effectiveness in learning

2- Improving students satisfaction with the course

3- Enhancing students productivity

4- Enhancing teachers effectiveness in educating

5- Improving teachers satisfaction with the educating

environment

6- Enhancing teachers productivity

7- Access to instruction for all

8- Build an advanced society for citizens to support

creativity and innovation