Computers & Education 57 (2011) 1919–1929 Contents lists available at ScienceDirect Computers & Education journal homepage: www.elsevier.com/locate/compeduThe role of readiness factors in E-learning outcomes: An empirical studyAbbas Keramati a, *, Masoud Afshari-Mofrad b, Ali Kamrani aa 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, Irana r t i c l e i n f o a b s t r a c tArticle history: Although many researchers have studied different factors which affect E-Learning outcomes, there isReceived 6 October 2010 little research on assessment of the intervening role of readiness factors in E-Learning outcomes. ThisReceived in revised form study proposes a conceptual model to determine the role of readiness factors in the relationship between19 March 2011 E-Learning factors and E-Learning outcomes. Readiness factors are divided into three main groupsAccepted 13 April 2011 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.Keywords: Hierarchical regression analysis is done and its results strongly support the appropriateness of theE-LearningReadiness factors proposed model and prove that readiness factors variable plays a moderating role in the relationshipOutcomes between E-Learning factors and outcomes. Also latent moderated structuring (LMS) technique andEvaluation methodologies MPLUS3 software are used to determine each variable’s ranking. Results show that organizationalIran 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. Ó 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 $40 billion is spent annually on technology-based training (Johnson, Gueutal,& Cecilia, 2009). According to such a heavily investing in this system, it is essential to evaluate its different aspects and understand factorswhich inﬂuence its effectiveness (Schreurs, Sammour, & Ehlers, 2008). Success in implementing and using this system is crucial because anunsuccessful attempt will be clearly revealed in terms of the return of investment (Govindasamy, 2001). One of the most important variableswhich can have a critical effect on E-Learning successful outcomes is readiness factor and schools have to improve and upgrade theirreadiness to use this system (Wang, Zhu, Chen, & Yan, 2009). Although there are many researches evaluating different E-Learning aspects,there is little research on assessment of the intervening role of readiness factors in E-Learning outcomes. To overcome this shortcoming, inthis study the role of readiness factors in the relationship between E-Learning factors and outcomes is assessed. E-Learning readiness factorsare divided into three main categories including: technical, organizational and social factors. The following two main questions of this research are considered: Do readiness factors have a moderating effect on the relationship between E-Learning factors and E-Learning outcomes? How much is the moderating effect for different aspects of readiness factors (which aspects are more important)? * Corresponding author. E-mail addresses: email@example.com (A. Keramati), firstname.lastname@example.org (M. Afshari-Mofrad), email@example.com (A. Kamrani).0360-1315/$ – see front matter Ó 2011 Elsevier Ltd. All rights reserved.doi:10.1016/j.compedu.2011.04.005
1920 A. Keramati et al. / Computers Education 57 (2011) 1919–19292. Research background With the increasing investment in E-Learning systems, it is essential to design and empirically investigate the factors which affect E-Learningoutcomes (Cukusic, Alﬁrevic, Granic, Garaca, 2010). Many researchers have studied several aspects of E-Learning and many differentapproaches were adopted (AbuSneineh Zairi, 2010). Most of these researches have revealed that E-Learning is a suitable method 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 and most E-Learning courses have high drop out rate (Means,Toyama, Murphy, Bakia, Jones, 2009; Stonebraker Hazeltine, 2004; Welsh, Wanberg, Brown, Simmering, 2003). Since most 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 and how organizations 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 researcherstried 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 two main 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 someTable 1Selected works on E-learning outcomes. Researcher(s)/year Dependent variable (s) Methodology Results Alavi (1994) Perceived skill, self-reported Evaluated effectiveness and efﬁciency Technology-mediated learning learning and grades of computer-mediated collaborative environments may improve learning by an empirical study students’ achievement Schutte (1997) Students’ scores on exam Compared a traditional classroom and Virtual class scored an average of a virtual classroom by an experimental 20% higher than the traditional one research Maki, Maki, Patterson, Students’ scores on exam Evaluated the online format relative to The students in the online sections and Whittaker (2000) the traditional lecture-test format, using expressed appreciation for course a pretest-posttest nonequivalent control components and the convenience group design of the course, but th e lecture sections received higher ratings on course Piccoli et al. (2001) Performance (e.g. achievement, Presented a framework of Virtual Learning Two classes of determinants including recall), self-efﬁcacy and satisfaction. Environment (VLE) effectiveness and human dimension and design compared it to a traditional classroom dimension affect VLE effectiveness Lim et al. (2007) Learning performance (to what degree Proposed a model on variables affecting There is a positive relationship the trainees learn?) and Transfer performance E-Learning performance and conﬁrmed between individual, organizational (how well the trainees applied what they it using an empirical study and online training design constructs learned to their job tasks) and training effectiveness constructs Wan et al. (2008) Learning effectiveness and satisfaction Using a survey, conﬁrmed their hypothesizes Virtual competence and prior which are asserted that prior experience experience with ICT affect with ICT and virtual competence are two E-Learning outcomes signiﬁcant factors that affected E-Learning outcomes Johnson et al. (2009) Perception of course utility, course satisfaction Proposed a model of variables inﬂuencing Technology characteristics, trainee and course grade as E-Learning outcomes E-Learning outcomes and assessed it by a survey characteristics and Metacognitive activity affect E-Learning outcomes Chu and Chu (2010) Perceived learning, persistence and satisfaction A survey to prove a proposed model to Internet Self-Efﬁcacy fully mediates evaluate E-Learning outcomes for adult learners the relationship between peer support and E-Learning outcomes. Chen and Jang (2010) Engagement, Achievement, Proposed and tested a model for the effect The direct effect and indirect effects Learning and satisfaction of online learner motivation and contextual of contextual support exerted support on online learning outcomes opposite impacts on learning outcomes
A. Keramati et al. / Computers Education 57 (2011) 1919–1929 1921readiness 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 know which aspects of IT and readiness factors have the greater effect on performance. Such knowledge could be used to makewiser 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 IT competency (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-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 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, top manager 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 Outcomes Interaction Teachers Progress Students Progress Access to instruction Readiness Factors - Technical - Organizational - Social Fig. 1. Research’s conceptual model.
1922 A. Keramati et al. / Computers Education 57 (2011) 1919–1929Table 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 %20right 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 on previous 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 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 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
A. Keramati et al. / Computers Education 57 (2011) 1919–1929 1923 Table 4 Respondents’ web-based educating background. 1 Year 2 Years 3 Years 4 Years Percentage %54 %25 %12 %9 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 factors In this research we have categorized readiness factors into three groups namely: technical, organizational and social. Technical factorsrefer 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. Outcomes To assess E-Learning outcomes we have deﬁned measures in relation with teachers’ progress, students’ progress and access to instructionfor 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), and Webster 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 analysis The reliability analysis of a questionnaire determines its ability to yield the same results on different occasions and validity refers to themeasurement 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 analysis In 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 betweenTable 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 61.96 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
1924 A. Keramati et al. / Computers Education 57 (2011) 1919–1929 Fig. 2. Q-Q plots.
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 the moderating 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 test In order to test normality of data, two ways are used. Firstly, Kolmogorov-Smirnov (K-S) test is performed (E-Learning variable ¼ 0.085,Readiness factors ¼ 0.917 and Outcomes ¼ 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 regression Moderating variable is generally deﬁned as a variable (quantitative or qualitative) which affect direction or strength of the relationshipbetween 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 moderatingrole 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 technique The 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 elements A rank for each element is determined considering readiness factor variables as moderating factor (Fig. 4). For this purpose, using directeffect 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*0:497 þ 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.
1926 A. Keramati et al. / Computers Education 57 (2011) 1919–1929Table 6Hierarchical regression results. Step t-Test F-test Measurement scale B Statistics Sig. R Square DR 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 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 system may 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). Student 1.00 Teacher 1.506 Teachers Prog 1.00 0.497 IT 0.888 0.814 Students Prog ELNG OC 0.818 Support 0.837 Access 0.101 0.123 ELNG*R Technical 1.00 Organizational 1.365 RF 0.726 Social Fig. 4. Complete structural model.
A. Keramati et al. / Computers Education 57 (2011) 1919–1929 1927 Table 7 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 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 are more than 4 elements which can be taking into account for futureresearches. Table 8 Results of ranking different RF elements. Rank Readiness factors element Total effect 1 Organizational 0.137 2 Technical 0.101 3 Social 0.073
1928 A. Keramati et al. / Computers Education 57 (2011) 1919–1929Acknowledgement 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. Questionnaire Based on your valuable experiences in using E-Learning, please indicate the extent to which each following element affects E-Learningoutcomes, ranging from 1 to 5: (1 ¼ not at all, to 3 ¼ moderate effect, to 5 ¼ 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 online 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 innovationReferencesAbuSneineh, W., Zairi, M. (2010). An evaluation framework for E-learning effectiveness in the Arab World. International Encyclopedia of Education, 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, 5(3), 243–260.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.
A. Keramati et al. / Computers Education 57 (2011) 1919–1929 1929Chao, 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.). New York: McGraw Hill. 23–240.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, 37(10), 6891–6903.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, 356–369.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. Hershey, New York: Information Science Reference.Kim, C. J. (2005). Construction of E-learning environments in Korea. ETRD, 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. Washington, D.C: U.S. Department of Education.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.). New York): McGraw Hill. 214–286.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 E-learning: 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.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? Northridge, CA: California State University.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., Simmering, M. J. (2003). E-learning: emerging uses, empirical results and future directions. International Journal of Training and Development, 7, 245–258.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.