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Editor’s Note: Predictors of success are important to effective introduction of a new technology. This is a complex and significant study of factors involved in introduction and development of E-learning.

Institutional Support and E-Learning Acceptance:
An Extension of the Technology Acceptance Model

Abdulhameed Rakan Alenezi,  Abdul Malek Abdul Karim,  Arsaythamby Veloo
Saudi Arabia and Malaysia


Owing to broad global attention given to e-Learning, various studies have been conducted by educational institutions and different organizations as well as the governments of various nations (Rosenberg, 2001). The Saudi Ministry of Higher Education is among those educational organizations that proposed the use of E-learning in Saudi Arabia. The Saudi Ministry of Higher Education recognised the need of integrating Information and Communication Technology (ICT) in various universities in Saudi Arabia. The Saudi Gazette (2008) by Madar Research reported that “the Saudi Arabian E-learning industry is projected to reach $125 million USD in 2008 and is set to grow at a compound annual rate of 33 per cent over the next five years”. However, several researches have indicated that the students are still unwilling to use E-learning tools and participate effectively in the online mode (Al-Jarf, 2007; Alenezi, Abdul karim, Veloo, 2010). Thus, this research extended Technology Acceptance Model (TAM) to include three institutional related variables: facilitating conditions, training and institutional technical support. Five universities participated in this research and 408 usable questionnaires were analysed. The findings showed that the TAM model was applicable, valid and reliable to investigate the students’ acceptance in higher education context In Saudi Arabia. The three examined institutional variables have significantly contributed to the students’ Acceptance of E-learning.

Keywords: Students’ E-learning Acceptance, TAM, facilitating conditions, training and institutional technical support.


The significance and relevance of E-learning to higher education has been palpably felt. Educational organizations and the governments of various nations realise that now is the opportune time to focus on the benefits derived from E-learning (Rosenberg, 2001). Saudi Arabia is one of those nations that promote the use of E-learning in its higher education institutions. The Saudi Arabian E-learning industry is projected to reach USD 125 million in 2008 and is set to grow at a compound annual rate of 33 per cent over the next five years, according to a recent study conducted by Madar Research (Saudi Gazette, 2008). Various research and studies have been conducted to promote the use of E-learning to foster better education worldwide (Webster & Hackley, 1997). Unfortunately, some research on E-learning, particularly in Saudi Arabia, did not develop optimum E-learning for various reasons. Al-Jarf (2004) has demonstrated that the Saudi students showed less reaction and participation in using E-learning compared to Ukrainian and Russian students when posting their responses under the discussion threads.

In recent days, the trend seems to be the same students are still unwilling to use E-learning tools and participate in the online mode (Al-Jarf, 2007; Alenezi, Abdul karim, Veloo, 2010). Al-Jarf (2007) pointed out that using the online system for her English course was a total failure.

Several internal and external institutional factors were found to have significant influence on online learning acceptance (Galletta et al., 1995; Igbaria et al., 1997; Yi et al., 2001). For example, Igbaria et al. (1997) confirmed that the organisational factor highly influences the technology acceptance. This research will consider three institutional variables namely facilitating conditions, training and institutional technical support. The reason behind this is the significant effects of proposed variables in influencing new technology acceptance (Amoako-Gyampah & Salam, 2004; Curtis, &Payne, 2008; Ngai, Poon & Chan, 2007).

In order to determine and investigate the factors that affect E-learning acceptance, the TAM has been chosen as the fundamental model for the current study. The reasons for the choice are the TAM’s applicability, validity, reliability and its tremendous popularity in acceptance studies in different settings (Landry, Rodger, & Hartman 2006; Masrom, 2007; Ngai et al., 2007; Roca, Chiu, & Martínez, 2006; Selim, 2003; Saadé & Bahli, 2005; Saadé & Galloway, 2005). Thus, this research empirically investigates the role of variables in influencing the students’ E-learning acceptance.

Literature Review

Technology Acceptance Model (TAM)

The Technology Acceptance Model (TAM) is one of the most widely applied models in studies of individual intention and the usage of technologies. TAM was adapted from more general human behaviour, the Theory of Reasoned Action (TRA). The model was initially developed and validate by Davis (1986, 1989). Davis, et al. (1989) developed TAM as a theoretical basis to provide an explanation of the determinants human computer usage behaviour that is general directly from generic TRA (Fishbein & Ajzen, 1975). According to Davis (1986), this model is important in understanding use of the Information System as well as Information System Acceptance behaviours. TAM is an extension of the theory of reasoned action (TRA). However, the latter theory lacks distinction if the behaviour of users towards technology depends on intentions or attitudes (Klein, 1991). TAM believes that the individual’s intention to use the technology depends on how useful the technology is to the user and how easily it can be used in terms of functionality. It is also believed that the usefulness of the technology is directly proportional to the ease of use (Davis, 1989). Perceived usefulness is also seen as being directly impacted by perceived ease of use.

TAM suggests that perceived ease of use and perceived usefulness of Information Technology (IT) are the main determinants factors of IT usage. Davis (1989, p. 447) defines perceived ease of use (PEU) as, “the degree to which an individual believes that using a particular system would be free of physical and mental effort”. Moreover, Davis (1989) defined perceived usefulness (PU) as “the degree of which a person believes that using a particular system would enhance his or her job performance”. The two major key constructs of TAM, PU and PEOU, have capability to predict an individual’s attitude towards using a particular system. Both constructs PU and PEOU will influence an individual’s attitude (A). (Davis et al., 1989) defined attitude as individual’s positive or negative assessment of the behavior and is a function of Perceived Usefulness and Perceived Ease of Use: Attitude (A) will influence the Behavioral Intention (BI) of using particular system, and, in sequence, Actual use of use the system (AU). Actual use (AU) will be predicted by the individual’s Behavioral Intention (BI) which is considered in this study as the E-learning Acceptance concept. However, the Attitude was eliminated from this research based on the suggestion of Davis et al. (1989) findings, which demonstrated that the power of the TAM in predicting the individual's acceptance is equally good and parsimonious without the attitude mediating effects. Likewise, Venkatesh and Davis (1996) eliminated the attitude variable from their proposed model because the attitude, as a mediating construct, did not seem to mediate fully the effect of perceived usefulness and perceived ease of use on behavioural intention. This was confirmed also by Wolski and Jackson (1999), who stated that the relationship between Attitude and behavioural intention was not supported. Behavioral Intention (BI) refers to individual’s intention to perform a behavior and is a function of Attitude and Perceived Usefulness (Davis et al., 1989). The actual use will be included in this research through the researchers’ debates on this construct and its self-reported measures issue. The relationships between the mentioned constructs are presented in Figure 1, as shown below. Therefore, TAM model will be basic and theoretical grounds for the current study.

Figure 1.Technology Acceptance Model (TAM)

Source: Davis et al. (1989)

Institutional Influence and E-learning Acceptance

With the recent growth in investment in new technologies among institutions of higher education, the organizations have to be aware of their impact on the success and acceptance of these technologies. Several organisational internal and external factors revealed their influence on online learning acceptance (Galletta et al., 1995; Igbaria et al., 1997; Yi et al., 2001). The cited studies have shown the impact of internal and external organisational factors on perceived ease of use and perceived usefulness while other studies have suggested that training workshops have their impact on the students’ attitude and their intention to use online learning systems (Yi et al., 2001). Thus, the current research will investigate the role that the institutional factor plays on the students’ willingness to accept or reject using the E-learning system in Saudi universities. The facilitating conditions, training and technical support will be considered as institutional factors that could influence the students’ acceptance of E-learning implementation and will be studied from the organisational prospective.

Facilitating Conditions (FC)

Facilitating Conditions are defined as “the degree to which an individual believes that an organizational and technical infrastructure exists to support the use of the system” (Venkatesh et al, 2003, p.453). In other words, the facilitating conditions can be those fulfilled by universities in providing their students with the basic knowledge, necessary resources and assistance while the students are using an E-learning system. Facilitating Conditions have been identified to be a strong predictor for usage behavior (Venkatesh et al, 2003).

Ely (1999) cited in Succi (2007), identified eight conditions that affect the success of the implementation of innovative educational technologies: 1) “Dissatisfaction with the status quo” which indicates the users’ attitude towards new techniques, 2) “Knowledge and Skills” which indicates that level of users’ knowledge about the implemented system, 3)” Adequate Resources” which refers to available resources necessary to a particular system,4) “Adequate Time” which refers to the availability of the time provided by the institution to educate the users in using the system, 5)” Rewards or Incentives” which indicates the role of the organisation in providing the users with external motivation elements such as non-financial and financial rewards, 6) “Participation” which refers to the institution’s effort to encourage the users to use the system, 7)” Commitment” which stands for the organisation management’s commitment to use the system, 8)”Leadership” which refers to the management’s active contribution in the implementation of the system. The proposed conditions have appeared to be significant in terms of the organisational context. Accordingly, this study will consider the proposed conditions in the measurement of the facilitating conditions construct.

Thompson et al. (1991) adapted the Triandis model of human behavior to build the Model of PC Utilization. They predicted the usage behavior rather than the intention to use. Their findings indicated that the facilitating conditions play crucial role to simulating the users’ behavior and not only the intention. These findings opened the door for further investigation into the role of facilitating conditions. For instance, Bock and Kim (2000) investigated the model of PC by expanding the facilitating conditions factor to include rewards. They found that facilitating conditions also had a positive effect on users’ behaviour.

Venkatesh et al. (2003) built the Unified Theory of Acceptance and Use of Technology (UTAUT). The facilitating conditions were direct determinants of users’ usage behaviour and have shown enormous impact on the users’ acceptance. Venkatesh et al. (2003, p.470) suggested that future research should attempt to “test additional boundary conditions of the model in an attempt to provide an even richer understanding of technology adoption and usage behaviour”. The researchers have suggested investigating more conditions due to its impact on technology acceptance.

Based on the information provided above, the present research will investigate the direct relationship between the facilitating conditions and the students’ acceptance and use of the
E-learning system.

Training (TR)

Training (TR) in this study is defined as institution’s effort to teach and train their students to acquire E-learning skills. The studies that extended the TAM have included Internal Training as a significant factor influencing the students’ acceptance of using online learning (Igbaria et al, 1997; Wolski & Jackson,1999). The studies concluded that training had a positive impact on users’ acceptance and their intention to use a particular system.

Thus, the training provided by the institutions will be considered a key factor for the successful implementation of E-learning. Its relationship with the students’ intention to use E-learning will be investigated.

Institutional Technical Support (ITS)

Institutional Technical support (ITS) in the current study is defined as institution capability to provide qualified people to support the E-learning system users when they encounter any system difficulties i.e. a help desk and online support. The lack of technical support was cited as one of most important barriers to E-learning implementation (Behl et al., 2007, Schifter, 2000; Shannon & Doube, 2004). Kleinman and Entin (2002) stated that technical support must be available during the online courses in order to offer a sense of confidence for the online learners.

Several studies have been tested to determine the influence of technical support on students’ acceptance of technology (Igbaria et al., 1996, 1997; Ngai et al., 2005; Venkatesh& Davis, 2000). The studies indicated that different types of support such as management support and internal computing support have influenced the users’ perception towards using specific technology.

Ngai et al. (2005) investigated the students’ perception towards the WebCT tools. The researchers investigated around 836 students in Hong Kong. They extended the Technology Acceptance Model (TAM) to include technical support as an external factor. The study findings indicated that perceived usefulness and perceived ease of use are able to predict the students’ acceptance of web course tools through a positive attitude among students. The Institutional Technical Support (ITS) had a direct influence on both: perceived usefulness and perceived ease of use. The researchers concluded that technical support was a significant factor that could influence the students’ acceptance of WebCT. Furthermore, Venkatesh and Davis (2000) examined the role of Management support, Internal computing support and External computing support on users’ acceptance. The researchers confirmed that support systems provided by the management or technical staff seemed to be vital factors influencing the users’ intention to use computer technology.

Igbaria et al. (1996) tested microcomputer usage through their motivational model. The model used organisational support as a critical factor affecting the usage together with Complexity, Usefulness, Enjoyment and social pressure. Their model explained approximately 28% of the variance. They found that organisational support had significantly influenced the users’ usage of microcomputer.

In brief, the technical support provided by the institutions seems to be a crucial issue particularly with the E-learning system. As it is a new form of technology, many students will encounter some technical difficulties that will need to be resolved. The lack of technical support will be crucial in E-learning implementation. In this present research, the influences of Facilitating conditions, Training, and Institutional technical support on students’ E-learning acceptance were investigated.

Research Model and Hypotheses

Based on the original TAM model and based on the previous literature review regarding these three variables, null hypotheses were summarised as follows and the Research model is proposed (as depicted in Figure 2).

H01: Facilitating Conditions (FC) have no influence on the students' E-learning acceptance.

H02: Training (TR) has no influence on the students' E-learning acceptance.

H03: Perceived Usefulness (PU) has no influence on the students' E-learning acceptance.

H04: Perceived Ease of Use (PEU) has no influence on the students' E-learning acceptance.

H05: Institutional Technical Support (ITS) has no influence on students' attitudes toward the using E-learning.

H06: Perceived ease of use has no influence on students' perceived usefulness.

H07: Students’ acceptance has no influence on the students’ Actual E-learning System use.

Research design

Measurement Scales

The questionnaire consisted of 34 Items in order to measure the proposed research model factors. The measurement was adapted from prior research (Amoako-Gyampah, K. & Salam, A. F,2004;Curtis, M. & Payne, E.,2008; Ngai, Poon, & Chan, 2007; Suh & Lee, 2007). A pilot study was conducted in order to develop the measurements and the adapted scales. Moreover, the pilot study was performed in order to check the internal consistency and reliability of the utilised questionnaire. The questionnaire was distributed to 50 students from Al-Jouf University in session one 2009/2010. The returned and usable questionnaires were 48 and two questionnaires were excluded from the analysis due to the enormous number of unanswered questions. The analysis of internal consistency was obtained from the interval scale items only. Overall, the pilot study data revealed an acceptable high alpha reliability coefficient of all items which were above 0.70. Therefore, all items were retained for the main study. Thus, the questionnaire distribution to the targeted sample can be justified.

Sample and data collection

Based on research population which is 156, 429 students, it is appropriate to select a minimum sample of 384 students from the entire research population ( Krejcie, & Morgan, 1970). Four hundred and eighty questionnaires were randomly distributed to the students at five universities in Saudi Arabia. The usable response rate was 85% with 408 undergraduate students from five different governmental universities. The profile of respondents is portrayed in Table 1.

Table 1
Profile of Respondents



King Saud University



King AbdulAziz University



King Faisl University



King Khalid University



Aljouf University


Data Analysis and Findings

Reliability and Factor analysis

Construct validity and reliability analysis were examined to ensure that the obtained responses are valid and reliable for further analysis. Exploratory Factor Analyses (EFA) represented by principal components analysis (PCA) with Varimax rotation were performed. All required criterion to perform the factor analysis were achieved. Kaiser-Guttman criterion was applied regarding the number of variables to be extracted. Only variables with eigenvalues equal to or greater than one can be extracted (Guttman, 1954; Kaiser & Dickman, 1959). The items with loading 0.300 or greater were considered to be acceptable (Hair et al., 1998). The factor analysis has individually been performed on each of the following scales because the ratio of five subjects per item (5:10) suggested by Coakes and Steed (2003) and the ratio of ten subjects per item (1:10) to run a single factor analysis were not achieved (Hair et al., 1998). Therefore, the factor analysis was performed separately for the original TAM constructs and the technological factors namely system performance, system response, system interactivity and system functionality. The Cronbach’s alpha coefficient above 0.60 is considered as acceptable and justified (Nunnally& Bernstein, 1994; Sekaran, 2000). Therefore, the suggested acceptable cut-off level of 0.60 was applied in this research. Table 2 represents the obtained results from factor analysis of the TAM model. Table 3 represents the results obtained from factor analysis of a total of 14 items that were used to measure the Institutional Factor (IF). An institutional factor consists of three variables: Institutional Technical Support (ITS), Facilitating Conditions (FC), and Training (TR). It has respectively 5 items, 5 items and 4 items. Table 3 provides the results of the factorability on the Institutional variables items.

Table 2
Factor analysis of TAM constructs

Actual Use (AU1)







Actual Use (AU1)







Behavioral Intention1(BI1)







Behavioral Intention2(BI2)







Behavioral Intention3(BI3)







Behavioral Intention4(BI4)







Perceived Ease of Use1 (PEU1)







Perceived Ease of Use2 (PEU2)







Perceived Ease of Use3 (PEU3)







Perceived Ease of Use4 (PEU4)







Perceived Ease of Use5 (PEU5)







Perceived Ease of Use6 (PEU6)







Perceived Usefulness1(PU1)







Perceived Usefulness2(PU2)







Perceived Usefulness3(PU3)







Perceived Usefulness4(PU4)







Perceived Usefulness5(PU5)







Percentage of Variance Explained







Total Variance explained














Bartlett’s test of sphericity approx. chi square















       . 000


. 000

 . 000


According to Table 2, the overall KMO exceeded the minimum requirement of 0.50. The probability association with Bartlett's test of sphericity was significant (p<0.05). The results for factor analysis yielded that the two factors (AU, BI) have eigenvalues greater than one that explained 65.71, 58.64, and 62.914 respectively of the total Variance explained. Perceived usefulness with eigenvalues of 1.98 explained about 47.62% of the total variance Perceived ease of use with eigenvalues of 3.25 explained about 24.09% of the total variance. The factor loading for all examined variables were acceptable and justified. Therefore, the results indicated a goodness of the current study factors' measurements and consider acceptable for further analysis.

Table 3
Factor loading for the Institutional Factor (IF)

Institutional Technical Support1 (ITS1)





Institutional Technical Support2 (ITS2)





Institutional Technical Support3 (ITS3)





Institutional Technical Support4 (ITS4)





Institutional Technical Support5 (ITS5)





Facilitating Condition1 (FC1)





Facilitating Condition2 (FC2)





Facilitating Condition3 (FC3)





Facilitating Condition4 (FC4)





Facilitating Condition5 (FC5)





Training1 (TR1)





Training2 (TR2)





Training3 (TR3)





Training4 (TR4)










Percentage of Variance Explained





Total Variance Explained










Bartlett’s test of sphericity approx. chi square
















According to Table 3, the overall KMO was 0.76 which exceeds the minimum requirement of 0.50. The probability association with Bartlett's test of sphericity was significant (p<.05). The principle component methods revealed the presence of three main components with eigenvalues exceeding one, explaining 54.63 of the total variance. Institutional Technical Support (ITS), which includes four items, accounted for 33.28% of the total variance explained with an eigenvalue of 4.66. The factor loading of its items was acceptable as it ranged from 0.59 to 0. 69. Facilitating Conditions (FC) (eigenvalue = 1.66) contributed 11.85 % of the total variance explained. Its factor loading ranged from 0.64 to 0.72. Thus, the factor items met the current research criteria and five items were retained. Training (TR), represented by 4 items accounted for 9.49 of the total variance explained with an eigenvalue of 1.33. The items factor loading ranged from 0.52 to 0.85. The results of analysing the factorability of the Institutional Factors (IF) items met the research criteria and resulted in retention of all 14 items for further data analysis. 

Hypotheses Testing

Table 4
The Regression Analysis Results
The Null Hypotheses








H1: Rejected








H2: Rejected








H3: Rejected








H4: Rejected








H5: Rejected








H6: Rejected








H7: Rejected

* p <.05, ** p <.01

ELA: E-Learning Acceptance, FC: Facilitating Conditions, TR: Training, PU: Perceived Usefulness, PEU: Perceived Ease of Use, ITS: Institutional Technical Support, AU: Actual Use.

Conclusions, Discussions and Suggestions

As portrayed in Table 4, the results yielded that the Perceived Usefulness and Perceived Ease of Use significantly influenced the Students’ E-learning Acceptance. It also confirmed the significant relationship and influence between the Perceived Usefulness and Perceived Ease of Use. Moreover, the results indicated that the Actual Use was significantly driven by E-learning acceptance.

The research findings were consistent with the majority of previous researches on the TAM model, particularly the effect of both TAM predictors' namely perceived usefulness and perceived ease of use on the users' behavioral intention (E-learning acceptance) to use new technology (Landry, Rodger, & Hartman 2006; Masrom, 2007; Ngai et al., 2007; Roca, Chiu, & Martínez, 2006; Selim, 2003; Saadé & Bahli, 2005; Saadé & Galloway, 2005). The obtained findings indicated that the attitude towards using E-learning fully mediated the relationship between perceived usefulness and E-learning acceptance. It is also partially mediated the relationship between perceived ease of use and E-learning acceptance. The results contradicted the Davis et al. (1989) findings, which demonstrated that the power of the TAM in predicting the individual's acceptance is equally good and parsimonious without the attitude mediating effects. Likewise, Venkatesh and Davis (1996) eliminated the attitude variable from their proposed model because the attitude as a mediating construct did not seem to mediate fully the effect of perceived usefulness and perceived ease of use on behavioural intention as confirmed also by Wolski and Jackson (1999), who stated that the relationship between Attitude and behavioural intention was not supported. In this research, TAM model showed the power and parsimonious of the TAM model in predicting the individual's acceptance without the attitude mediating effects.

As pointed out earlier, the finding indicated that there was a positive relationship between perceived ease of use and perceived usefulness. This can be confirmed by the majority of technology acceptance research findings particularly E-learning acceptance findings (Babenko-Mould, Andrunsyszyn, & Goldenberg, 2004; Davis et al., 1992; Gefen & Straub, 2000; Masrom, 2007; Ngai et al., 2007; Ong et al., 2004; Rezaei, Mohammadi, Asadi, and Kalantary, 2008; Selim, 2003;; Sun, Tsai, Finger, Chen, & Yeh, 2008; Szajna, 1996; Tung & Chang, 2008; Saadé & Bahli, 2005). Consistent with this research finding, Sun, Tsai, Finger, Chen, & Yeh (2008) conducted an empirical study to investigate the significant factors affecting online system satisfaction. The research confirmed the positive relationship between perceived ease of use in relation to perceived usefulness. The findings also indicated that perceived usefulness of the online learning system would positively influence the learners’ satisfaction with this system. Furthermore, Tung and Chang (2008) utilised the TAM in order to investigate the students’ intention to use online courses. This study investigated whether the Taiwanese students accepted the online courses or not. The study findings also indicated the original positive relationship between ease of use and usefulness as proposed by Davis et al. (1989). In line with this research finding, Ong and Lai (2004) conducted a research to examine the students’ acceptance of E-learning by extending the TAM with gender as a demographic characteristic. The study showed that the students who had a high level of belief that online courses were easy to use showed an increase in their acceptance of online learning. In addition, they found that the perceived ease of use has a significant relationship with the perceived usefulness of using E-learning system. Therefore, the relationship between perceived ease of use and perceived usefulness was possibly justified because of their nature that related to the E-learning system characteristics and their proven influence on the users’ beliefs, attitudes and their behavioural Intentions.

The present research findings indicated that there is a positive relationship between perceived usefulness and E-learning acceptance, which was indicated through the behavioural intention variable. The previous research findings were confirmed and support this research finding of the relationship between perceived usefulness and students' acceptance (Davis et al., 1992; Gefen and Straub, 2000; Ong et al., 2004; Masrom, 2007; Ngai et al., 2007; Rezaei, Mohammadi, Asadi, & Kalantary, 2008; Saadé & Bahli, 2005; Selim, 2003; Szajna, 1996; Tsai, Finger, Chen, & Yeh, 2008, Tung and Chang, 2008). For instance, Rezaei, Mohammadi, Asadi, and Kalantary (2008) conducted a research in order to predict the factors affecting the E-learning system in Agriculture schools in higher education. The study showed “a strong direct influence of perceived usefulness on students’ intention to use e-learning” (Rezaei et al., 2008, p.90). It also indicated that there was a positive relationship between students’ intention to use E-learning and perceived usefulness besides the internet experience, computer self-efficacy and affect.

Several implications were obtained from the research findings. The applicability and validity of the TAM and its related original constructs were confirmed in the Educational context especially in the area of E-learning in Saudi Arabian institutions of higher education as consistent with the research that examined the TAM’s applicability in the area of E-learning (Lee et al., 2006; Masrom,2007; Rezaei, Mohammadi, Asadi, & Kalantary,2008; Saadé, Tan, & Nebebe,2008). The perceived ease of system use influenced the perceived usefulness and both constructs were significantly influenced E-learning acceptance through the mediating effects of the students’ attitude.  Thus, it also confirmed that the TAM is able to include additional factors that could influence technology acceptance besides the confirmed original directions and relationships between TAM's constructs.

There are limitations related to the sample size and number of universities that participated in this study. However, it would be useful for future research to implement the research examining these factors and instrumentations with more universities’ either governmental or private ones, in order to obtain a better representation for the entire population and ultimately represent optimum generalization. Furthermore, the research was limited only to university students, it is therefore future research should consider other university members such as research assistants, lecturers and administrators in order to identify their trend to accept
E-learning and determine the important factors that could affect their acceptance. This study is also limited to subjective measure of the Actual use (Self-reported) which influences the accuracy of measuring the students’ actual system usage. Therefore, future research should examine the actual system usage using objective measures such as actual system access frequency recorded by a computerised system. The reported R-square yielded other additional variables that might be needed particularly from the Institutional perspective since the Institutional variable significantly contributed towards the E-learning Acceptance. Therefore, future research could investigate and test more additional Institutional related variables. The level of students’ participations in their E-learning courses still weak and they are still unwilling to use E-learning tools and participate effectively in the online learning mode. Future research should investigate in depth this phenomenon and conduct further studies in the area of E-learning readiness and perceptions as well as evaluate the current learning management systems adopted by the universities.


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About the Authors

Abdulhameed Rakan Alenezi is Lecturer at Aljouf University, Department of Instructional Technology, Saudi Arabia and a Ph.D. candidate in eLearning at the Department Information and Communication Technology (ICT), Universiti Utara Malaysia, Malaysia.

Abdul Malek Abdul Karim is at the Department of Education, Universiti Utara Malaysia, Malaysia.

Arsaythamby Veloois is at the Department of Education, Universiti Utara Malaysia, Malaysia.
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Februqry 2011
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