Computers & Education 57 (2011) 1919–1929

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Computers & Education

journal homepage: www.elsevier .com/locate/compedu

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

Abbas Keramati a,*, Masoud Afshari-Mofrad b, Ali Kamrani a

a Industrial Engineering Department, University of Tehran, P.O. Box 11155-4563, Tehran, Iranb Information Technology Management Department, Tarbiat Modares University, P.O. Box 14115-111, Tehran, Iran

a r t i c l e i n f o

Article history:Received 6 October 2010Received in revised form19 March 2011Accepted 13 April 2011

Keywords:E-LearningReadiness factorsOutcomesEvaluation methodologiesIran

* Corresponding author.E-mail addresses: keramati@ut.ac.ir (A. Keramati),

0360-1315/$ – see front matter � 2011 Elsevier Ltd. Adoi:10.1016/j.compedu.2011.04.005

a b s t r a c t

Although many researchers have studied different factors which affect E-Learning outcomes, there islittle research on assessment of the intervening role of readiness factors in E-Learning outcomes. Thisstudy proposes a conceptual model to determine the role of readiness factors in the relationship betweenE-Learning factors and E-Learning outcomes. Readiness factors are divided into three main groupsincluding: technical, organizational and social. A questionnaire was completed by 96 respondents. Thissample 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 theproposed model and prove that readiness factors variable plays a moderating role in the relationshipbetween E-Learning factors and outcomes. Also latent moderated structuring (LMS) technique andMPLUS3 software are used to determine each variable’s ranking. Results show that organizationalreadiness factors have the most important effect on E-Learning outcomes. Also teachers’ motivation andtraining is the most important factor in E-Learning. Findings of this research will be helpful for bothacademics and practitioners of E-Learning systems.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction

During recent years designing and implementing web-based education (E-Learning) systems have grown dramatically (Hogo, 2010) andthis type of education is playing an important role in teaching and learning (Franceschi, Lee, Zanakis, & Hinds, 2009). It is implementing asa new method of training which complements traditional methods (Vaughan & MacVicar, 2004) and its final ambition is to build anadvanced society for citizens and support creativity and innovation (Kim, 2005). In fact this new paradigm shifts education from teacher-centered to learner-centered (Lee, Yoon, & Lee, 2009). Advantages like cost reduction, elimination of time and space constraint and assistingthe traditional instruction, make it important and popular (Chao & Chen, 2009). In addition, quality of education in distance educationsystems depends on quality of electronic knowledge sources and other didactic materials instead of depending on teacher quality and hisability to share knowledge (Cohen & Nycz, 2006). The growth rate of E-Learning market is about 35.6% (Sun, Tsai, Finger, Chen, & Yeh, 2008)and recent researches have shown that only in America, nearly $40 billion is spent annually on technology-based training (Johnson, Gueutal,& Cecilia, 2009). According to such a heavily investing in this system, it is essential to evaluate its different aspects and understand factorswhich influence its effectiveness (Schreurs, Sammour, & Ehlers, 2008). Success in implementing and using this system is crucial because anunsuccessful attemptwill be clearly revealed in terms of the return of investment (Govindasamy, 2001). One of themost important variableswhich can have a critical effect on E-Learning successful outcomes is readiness factor and schools have to improve and upgrade theirreadiness to use this system (Wang, Zhu, Chen, & Yan, 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, inthis study the role of readiness factors in the relationship between E-Learning factors and outcomes is assessed. E-Learning readiness factorsare 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)?

m.afshari@ut.ac.ir (M. Afshari-Mofrad), kamrani.ali@gmail.com (A. Kamrani).

ll rights reserved.

A. Keramati et al. / Computers & Education 57 (2011) 1919–19291920

2. Research background

With the increasing investment inE-Learning systems, it is essential to design and empirically investigate the factorswhich affect E-Learningoutcomes (Cukusic, Alfirevic, Granic, & Garaca, 2010). Many researchers have studied several aspects of E-Learning and many differentapproacheswere adopted (AbuSneineh&Zairi, 2010).Most of these researches have revealed that E-Learning is a suitablemethod for educationand it allows easy acceptance of novel strategies to improve learning outcomes (Wan,Wang, & Haggerty, 2008). Despite these researches, someother researchers demonstrated that E-Learning courses still have shortcomings andmost E-Learning courses have high drop out rate (Means,Toyama,Murphy, Bakia, & Jones, 2009; Stonebraker &Hazeltine, 2004;Welsh,Wanberg, Brown, & Simmering, 2003). Sincemost of universitiesand organizations are going to use this system, it is important for both practitioners and researchers to better understand how these short-comings can be overcome andhoworganizations could be ready to use this system. Some researchers studied different aspects of e-readiness tohelp organizations for using E-Learning more effectively. E-readiness refers to the capabilities of the organization for effective and efficientapplication of electronic media (Machado, 2007). Assessing E-Learning readiness can help managers and policy makers to adopt the mostappropriate 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-Learningoutcomes (Alavi & Leidner, 2001; Chen & Jang, 2010; Johnson, Hornik, & Salas, 2008; Piccoli, Ahmad, & Ives, 2001). 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.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 factorsof technological readiness, own level of readiness, etc. Aydin and Tasci (2005) have focused on human resource readiness as an importantvariable in E-Learning effectiveness in emerging countries. Darab and Montazer (2011) proposed an eclectic model for assessing E-Learningreadiness. Fathian, Akhavan, and Hoorali (2008) have evaluated e-readiness factors in Iran’s environment. They have extracted organiza-tional features, ICT infrastructures, ICT availability and security as critical issues for e-readiness assessment. In the second approach, someresearchers have considered readiness factors as an independent variable which affects E-Learning outcomes directly. For instance, Piccoliet al. (2001) and Sun et al. (2008) have considered some readiness factors (such as: technology dimension and design dimension) asindependent variables which affect E-Learning outcomes directly. Also Johnson et al. (2009) have assessed the effect of technology char-acteristics on E-Learning outcomes.

In this study, readiness factors are considered as a moderating variable because of twomain reasons. Firstly, a moderating variable refersto a variable which can affect the strength and direction of relationships between other variables. In this research we have considered some

Table 1Selected works on E-learning outcomes.

Researcher(s)/year Dependent variable (s) Methodology Results

Alavi (1994) Perceived skill, self-reportedlearning and grades

Evaluated effectiveness and efficiencyof computer-mediated collaborativelearning by an empirical study

Technology-mediated learningenvironments may improvestudents’ achievement

Schutte (1997) Students’ scores on exam Compared a traditional classroom anda virtual classroom by an experimentalresearch

Virtual class scored an average of20% higher than the traditional one

Maki, Maki, Patterson,and Whittaker (2000)

Students’ scores on exam Evaluated the online format relative tothe traditional lecture-test format, usinga pretest-posttest nonequivalent controlgroup design

The students in the online sectionsexpressed appreciation for coursecomponents and the convenienceof the course, but the lecture sections received higherratings on course

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

Presented a framework of Virtual LearningEnvironment (VLE) effectiveness andcompared it to a traditional classroom

Two classes of determinants includinghuman dimension and designdimension affect VLE effectiveness

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

Proposed a model on variables affectingE-Learning performance and confirmedit using an empirical study

There is a positive relationshipbetween individual, organizationaland online training design constructsand training effectiveness constructs

Wan et al. (2008) Learning effectiveness and satisfaction Using a survey, confirmed their hypothesizeswhich are asserted that prior experiencewith ICT and virtual competence are twosignificant factors that affected E-Learningoutcomes

Virtual competence and priorexperience with ICT affectE-Learning outcomes

Johnson et al. (2009) Perception of course utility, course satisfactionand course grade as E-Learning outcomes

Proposed a model of variables influencingE-Learning outcomes and assessed it by a survey

Technology characteristics, traineecharacteristics and Metacognitiveactivity affect E-Learning outcomes

Chu and Chu (2010) Perceived learning, persistence and satisfaction A survey to prove a proposed model toevaluate E-Learning outcomes for adult learners

Internet Self-Efficacy fully mediatesthe relationship between peersupport and E-Learning outcomes.

Chen and Jang (2010) Engagement, Achievement,Learning and satisfaction

Proposed and tested a model for the effectof online learner motivation and contextualsupport on online learning outcomes

The direct effect and indirect effectsof contextual support exertedopposite impacts on learning outcomes

A. Keramati et al. / Computers & Education 57 (2011) 1919–1929 1921

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, Keramati, and Razmi (2007)considered the intervening role of organizational readiness in the relationship between IT and firm performance. As the results of thiswork imply, to have a better level of performance, it is essential to invest on intervening variables in addition to invest in IT. Accordingly, it isimportant to knowwhich aspects of IT and readiness factors have the greater effect on performance. Such knowledge could be used tomakewiser investments in IT. Since E-Learning is an IT-enabled system, this paper aims to assess the role of E-Learning readiness factors in therelationship between E-Learning factors and outcomes based on aforementioned research.

3. Research model

Conceptual model of this study is depicted in Fig. 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 ofreadiness 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) anduniversity 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 et al., 2001). Especially in distance education,teachers’ conception of E-Learning and its usefulness plays a vital role (Zhao, McConnell, & Jiang, 2009) and their positive attitude toward usingthis system as a teaching assisted tool is required for E-Learning success (Liaw, Huang, & Chen, 2007). Previous research has shown that threeinstructor characteristics affect E-Learning success: attitude, teaching style and ITcompetency (Webster &Hackley,1997). In this research, basedon previous researches, four major characteristics of teachers are used to measure this factor including: motivation, attitude, training andteaching style.

Student is the most important participant in E-Learning (Aydin & Tasci, 2005). Since E-Learning is a student-centered environment, highmotivated and self-confident students can result in better E-Learning outcomes (Baeten, Kyndt, Struyven, & Dochy, 2010). In addition, studentsshould 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, andmultimedia 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-efficacyare themost important factorswhich are used in this research tomeasure ‘student’ factor.

Distance education is a result of information technology explosion and IT is the engine of E-Learning revolution (Selim, 2007). The role ofIT in this type of education is vital and it is essential to prepare suitable IT skills for success of E-Learning. Information technology hasreshaped 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 coursecontent delivery is critical (Lim, Lee, & Nam, 2007). In this research IT is measured by the ability of E-Learning system to provide a goodquality website design, the possibility of interact with classmates through the web, well structures/presented information and the possi-bility 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 failurebecause of lack of this factor (Soong, Chan, Chua, & Loh, 2001). Since E-Learning is an IT-based project, topmanager should know this systemand 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 et al., 2009). Prior research has proved thattechnical readiness is one of the most important factors influencing E-Learning outcomes (Brush et al., 2003) and it is crucial to match the

E-Learning Factors

- Student - Teacher - IT - Support

Readiness Factors

- Technical - Organizational - Social

Outcomes

Teachers Progress Students Progress Access to instruction

Interaction

Fig. 1. Research’s conceptual model.

Table 2Respondents’ 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

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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 forenrolling in future E-Learning courses (Carswell, Agarwal, & Sambamurthy, 2001). Several models have proposed to examine E-Learningoutcomes (Cukusic et al., 2010; Johnson et al., 2009; Piccoli et al., 2001;Wan et al., 2008;). In this research based onprevious researches threeimportant 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 hasshown that they do not always effectively predict learning transfer per se (Colquitt, LePine, & NOE, 2000). In this research, the role ofreadiness 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.

Fig. 1 depicts the conceptual model of this research.

4. Research methodology

Data gathering method, measurement instrument and method of analysis are described in this section. Hierarchical regression analysishas been used to confirm the moderating role of readiness factors and latent moderated structuring (LMS) technique is used to rank eachvariable.

4.1. Data

Case studies and empirical researches are appropriate ways for IT researches (Baroudi & Orlikowski, 1989). This research is an empiricalstudy by means of questionnaire in Iranian high schools. To recognize variables affecting E-Learning outcomes and identify researchvariables, 9 interviews were conducted of E-Learning specialists in the IT sector of Training Bureau in Tehran. Based on an extensive reviewof interview transcripts and reviewing the literature, research variables were recognized. An initial set of questions was developed tomeasure 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 levelsextending from 1 (Not at all) to 5 (Extreme). 96 respondents answer research questionnaires but four questionnaires were ignored sincethey were not complete. This sample consists of teachers at Tehran high schools who are utilizing a technology-based educating. Demo-graphic information of respondents are presented in the following tables (Tables 2–4).

4.2. Measurement instrument

In this section we will operationally define research variables and then introduce their measuring instruments. In this study, we tried touse different references for our research and in some cases tried to use different aspects of a statement from different references in order tocreate a comprehensive measurement instrument which has the most fitness with our purpose. Thus, our questions are partially selectedfrom different references. As stated before, we asked 2 academic experts to validate the questionnaire.

4.2.1. E-Learning factorsAs 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 thisnew 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 forutilizing E-Learning.

Table 3Respondents’ 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

Table 4Respondents’ web-based educating background.

1 Year 2 Years 3 Years 4 Years

Percentage %54 %25 %12 %9

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Questions of the first two factors are taken from similar works like: Gattiker and Hlavka (1992), Liaw et al. (2007) and Webster andHackley (1997). Questions of the last two factors are customized from Albadvi et al. (2007) and Sun et al. (2008).

4.2.2. Readiness factorsIn 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 – likesocial 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 betweenE-Learning and outcomes. Thus questions in this section of questionnaire are adapted from semi-similar works like: Arbaugh (2000), Nagi,Poon, and Chana (2007) and Sun et al. (2008).

4.2.3. OutcomesTo assess E-Learning outcomeswe have definedmeasures in relationwith 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 tobuild an intelligent society, we have used access to instruction for all as an outcome of this system.

Teachers’ progress questions are taken from Sun et al. (2008), andWebster and Hackley (1997). Students’ progress questions are adaptedfrom 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 analysisThe 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 & Schindler, 2003). For reliability analysis, Cronbach’s alpha iscalculated by SPSS. As demonstrated in Table 5, all variables have an alpha greater than 0.7 and we can conclude that the questionnaire isreliable.

4.3.2. Validity analysisIn this research, construct validity, content validity and predictive validity were analyzed to ensure the validity of the instruments

(Nunnally & Bernstein, 1994).Construct validity shows the extent to which measures of a criterion are indicative of the direction and size of that criterion (Flynn,

Schroeder, & Sakakibara, 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 obtainfactors is to consider factors with eigenvalue of over one as significant (Olson, Slater, & Hult, 2005). Thus factor analysis is done and KMO andBartlett’s significance levels are calculated to show validity of the questionnaire. Table 5 presents these measurements. As shown in thistable, the questionnaire is valid and reliable.

Content validity indicates meeting the specific range of contents that have been selected (Nunnally & Bernstein, 1994). It also shows thatmeasurement instruments have elements that cover all aspects of variables under measurement. Content validity cannot be numericallymeasured, but we can measure it subjectively and judgmentally. Basically, content validity depends on the appropriateness of the contentand the method of rendering (Nunnally & Bernstein, 1994). Since the selection of research variables is based on an intensive survey ofliterature and all the elements are supported by authentic research, the instrument has content validity. Furthermore, 2 academic expertshave 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 & Bernstein, 1994). This validity is only possible through correlation between the predictor (independent variable) and criterion(dependent) variable (Nunnally & Bernstein, 1994). In this study, the results of two-variable and multi-variable correlation between

Table 5Reliability and validity analysis.

Variable Reliability analysis Descriptive results Factor analysis

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 61.96Readiness factors 13 0.828 87 3.498 0.506 0.624 4.864 0.000 19.45Outcomes 8 0.732 92 3.522 0.562 0.627 2.944 0.000 18.58

Fig. 2. Q-Q plots.

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A. Keramati et al. / Computers & Education 57 (2011) 1919–1929 1925

“E-Learning factors” as independent variables and “outcomes” as dependent variable have shown that there is significant correlationbetween 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 themoderating role of variables but in this research hierarchical regression analysis is used. To do so, afterdeveloping a conceptual model, normality of data is tested and finally regression analysis is performed.

4.4.1. Normality testIn order to test normality of data, two ways are used. Firstly, Kolmogorov-Smirnov (K-S) test is performed (E-Learning variable¼ 0.085,

Readiness factors¼ 0.917 and Outcomes¼ 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. Fig. 1 indicates these plots. This test confirmsthat all variables have normal data (Fig. 2).

4.4.2. Hierarchical regressionModerating 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. Fig. 3 demonstrates a model in which there are three ways. Path A reveals therelationship between dependent and independent variables. Path B reveals the relationship between dependent and moderating variablesand finally path C shows the relationship between dependent variable and the result of multiplying moderating and independent variables.

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.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, Leong, Ward,

and Krajewski (1997) argue that a variable couldn’t play a moderating role and mediating role simultaneously.

4.4.3. LMS techniqueThe LMS equations estimation method is particularly developed for the ML estimation of latent interaction effects (Klein &Moosbrugger,

2000). In a structural equation, the latent variables usually have a linear relationship in which the latent endogenous variables are linearfunctions of the latent exogenous variables. But in some cases another exogenous variable might have a moderating effect on the rela-tionship 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, latentinteraction 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 &Moosbrugger, 2003). In this study, to run LMS technique, MPLUS3 software is used.

4.4.4. Ranking of elementsA rank for each element is determined considering readiness factor variables as moderating factor (Fig. 4). 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 andoutcomes (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 and8 using the results from the model. The tables show the rank of different factors based on the research model. For example, Total effect ofSTUDENT is calculated as:

Total effect of STUDENT ¼ 1:00*0:497þ 1:00*0:101 ¼ 0:598

In the above formula, the effect of STUDENT is calculated by the summation of the two possible paths from STUDENT to OC. Thecoefficient 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 ofELNG and IST on PER, calculated from the structural model.

Fig. 3. Moderating effect model.

Table 6Hierarchical regression results.

Step t-Test F-test

Measurement scale B Statistics Sig. R Square DR square Statistics Sig.

Technical: TECH1 TECH 0.374 1.974 0.000 0.099 0.099 8.240 0.0002 ELNG 0.467 4.238 0.000 0.182 0.083 9.472 0.0003 TECH*ELNG 0.711 4.564 0.000 0.345 0.163 14.73 0.000

Organizational: ORG1 ORG 0.515 4.735 0.000 0.209 0.209 22.418 0.0002 ELNG 0291 2.409 0.018 0.260 0.051 14.744 0.0003 ORG*ELNG 1.114 7.111 0.000 0.540 0.280 32.486 0.000

Social: SOC1 SOC 0.236 4.802 0.000 0.159 0.158 16.659 0.0002 ELNG 0.366 3.348 0.001 0.255 0.096 14.920 0.0003 SOC*ELNG 0.297 2.998 0.021 0.264 0.009 10.266 0.000

Readiness factors: RF1 IST 0.491 4.522 0.000 0.196 0.196 20.451 0.0002 ELNG 0.323 2.789 0.007 0.265 0.069 14.940 0.0003 RF*ELNG 6.046 1.018 0.000 0.491 0.227 26.409 0.000

A. Keramati et al. / Computers & Education 57 (2011) 1919–19291926

Total effect of STUDENT, shows the impact coefficient of student factor on E-Learning outcomes. In fact, 0.497 and 0.101 are regressioncoefficients and 1.00 is the weight of “student factor” in comparison with other factors which is calculated using LMS technique. Thus totaleffect is a weighted regression coefficient which shows the effect of different E-Learning factors on E-Learning outcomes.

In a similar procedure, different readiness factor (RF) elements are ranked based on their moderating effects. Table 8 shows the results.

5. Discussion and conclusion

Many researchers tried to evaluate readiness factors which affect E-Learning outcomes. For instance, Aydin and Tasci (2005) have focusedon human resource readiness as an important variable in E-Learning effectiveness in emerging countries. Also Rhee, Verma, Plaschka, andKickul (2007) have assessed technological readiness for implementing an effective E-Learning. Zoraini (2004) has mentioned some orga-nizational readiness factors such as culture and budget. Fathian et al. (2008) have evaluated e-readiness factors in Iran’s environment. Theyhave 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) asindependent variables which affect E-Learning outcomes directly. Although aforementioned researchers have studied different factorswhich affect E-Learning outcomes, there is little research on assessment of the intervening role of readiness factors. By reviewing theliterature, we have categorized readiness factors into three main factors including technical, organizational and social factors. Also, based onAlbadvi et al. (2007), we have tried to determine the intervening role of readiness factors in the relationship between E-Learning factors andits 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 aforementionedthree factors and also, this is the first study which evaluated the intervening role of readiness factors in the relationship between E-Learningfactors and outcomes.

In this research, firstly, we considered technical infrastructures as a readiness factor. Some factors such as: proper software and hardwareor bandwidth, can play a crucial role in E-Learning outcomes. Internet low speed and having problems while using the systemmay result indissatisfaction and drop out of students from the course. One of the most important difficulties in using E-Learning in Iran is the speed ofInternet (Darab & Montazer, 2011).

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

Fig. 4. Complete structural model.

Table 7Results of ranking different ELNG elements.

Rank E-learning element Total effect

1 Teacher 0.9002 Student 0.5983 IT 0.5314 Support 0.489

A. Keramati et al. / Computers & Education 57 (2011) 1919–1929 1927

Secondly, we regarded organizational readiness factors. Factors such as: organizational rules, culture and experts, are considered in thesefactors. These factors can lay the ground for an appropriate move from traditional education system to E-Learning system. It means thateducating organizations should try to adapt their rules and culture and train sufficient E-Learning experts to empower E-Learning outcomesafter 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 toconsider social rules on E-Learning outcomes. Factors like: society’s conception of E-Learning, governmental rules and administrativeinstructions, 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 inadvantages of E-Learning yet. Another difficulty in using E-Learning is management permanence. Some managers who were investing onimplementation of E-Learning have been changed and new managers don’t continue their programs. Our experts also remark someconcerns 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 previousresearches and authors’ experiences readiness factors grouped into three groups including: Technical, Organizational and Social. A surveyconducted 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 moderatingeffect. This means that organizational factors like management permanence and organizational rules cannot have a direct effect onE-Learning outcomes like teachers’ motivation but these factors can affect E-Learning outcomes indirectly by influencing top managementsupport (which is an E-Learning factor in our model). In fact, management permanence can assure managers to implement E-Learningwhich is an expensive and lengthy project appropriately. A successful implementation of E-Learning will result in its effectiveness andmotivates teachers to use this system.

The moderating effect of social readiness factors implies that some social factors (such as governmental regulations) cannot play a directrole in E-Learning outcomes but these factors may affect the strength of this relationship. In the other hand, some technical factors (such asschool’s space) don’t have a direct impact on teachers’ productivity but a better environmental condition will affect E-Learning outcomesindirectly.

Finally, LMS technique and MPLUS3 software were used to rank the effects of different aspects of variables. Results demonstrated thatorganizational readiness factors are the most important aspects influencing E-Learning outcomes. Technical and social readiness factors arein lower levels of importance. The results of the ranking of different E-Learning elements show that in spite of previous researches who haveshown that student is the most important element of E-Learning (Aydin & Tasci, 2005), teachers’ motivation and training factor plays themost 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 problemis 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 notappropriate. 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. Theresults of LMS technique emphasize on this argument. Also, LMS technique reveals that organizational readiness factor is the mostimportant factor to achieve better E-Learning outcomes. Thus managers should provide a good organizational environment to implementand 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 highschools. The suggested model might show different results in other countries. Subsequent research can explore these issues using a broaderresearch sample. Also some researchers believe that E-Learning factors aremore than 4 elements which can be taking into account for futureresearches.

Table 8Results of ranking different RF elements.

Rank Readiness factors element Total effect

1 Organizational 0.1372 Technical 0.1013 Social 0.073

A. Keramati et al. / Computers & Education 57 (2011) 1919–19291928

Acknowledgement

The authors would like to thank the reviewers for their constructive and invaluable comments. The authors also would like to declaretheir appreciation to University of Tehran for the financial support for this study (Grant Number 7314812/1/03).

Appendix 1. QuestionnaireBased 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: (1¼ not at all, to 3¼moderate effect, to 5¼ extreme effect).

Question Not at all 1 2 3 4 Extreme 5Readiness factors1- Appropriate hardware in school2- Appropriate software in school3- Appropriate course content4- The speed of internet5- Proper school space for E-Learning system6- Cultural readiness for change in learning style7- Adequate skilled employees in E-Learning8- Appropriate organizational rules9- Managerial readiness to implement the system10- Sufficient budget and investment in E-Learning11- Society’s conception of E-Learning12- Appropriate governmental rules and regulations13- Appropriate administrative recipe

Question Not at all 1 2 3 4 Extreme 5E-Learning Factors14- Teachers’ motivation for using E-Learning15- Teachers training for using this system16- Teachers’ attitude toward the technology17- Teachers’ teaching style18- Students attitude toward E-Learning19- Students motivation to use this system20- Students ability to use a computer to display or present information in a desired manner21- Existence of required IT experts in school22- The possibility of interact with classmates through the web23- Good design of website24- Well structured/presented information25- The possibility of registering courses online26- Top management knowledge about advantages of the system27- Top management support28- 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 51- Enhancing students effectiveness in learning2- Improving students satisfaction with the course3- Enhancing students productivity4- Enhancing teachers effectiveness in educating5- Improving teachers satisfaction with the educating environment6- Enhancing teachers productivity7- Access to instruction for all8- Build an advanced society for citizens to support creativity and innovation

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