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:
King Saud University
King AbdulAziz University
King Faisl University
King Khalid University
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.
Actual Use (AU1)
Actual Use (AU1)
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)
Percentage of Variance Explained
Total Variance explained
Bartlett’s test of sphericity approx. chi square
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.
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)
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.
The Null Hypotheses
* 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.
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.
Al-Jarf, R. (2007, March). Cultural issues in online collaborative learning in EFL. Paper presented at the 3rd International Online Conference on Second and Foreign Language Teaching and Research.
Al-Jarf, R. (2004, June). Intercultural Communication: Saudi, Ukrainian and Russian Students online. Paper presented at the 37th National Educational Computing Conference (NECC). New Orleans, Louisiana, USA.
Alenezi, A. R., Abdul Karim, A. M., & Veloo, A. (2010). An Empirical investigation into the role of enjoyment, computer anxiety, computer self-efficacy and internet experience in influencing the students' intention to use e-learning: A case study from Saudi Arabian governmental universities. The Turkish Online Journal of Educational Technology, 9(4), 22-34. Retrieved from http://www.tojet.net/articles/943.pdf
Amoako, G. K., & Salam, A. F. (2004). An extension of the technology acceptance model in an ERP implementation environment. Information & Management, 41(6), 731-745.
Babenko, M. Y., Andrunsyszyn, M.A. & Goldenberg, D. (2004). Effects of computer-based clinical conferencing on nursing students’ self-efficacy. Journal of Nursing Education, 43(4), 149-156.
Behl, D., Fitzgerald, J. & Vrazalic, L. (2007). Barriers to e-learning for students in the United Arab Emirates and Oman. In Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education. (pp.2334-2338). Chesapeake, VA: AACE. Retrieved from http://www.editlib.org/p/26707.
Curtis, M. B., & Payne, E. A. (2008). An examination of contextual factors and individual characteristics affecting technology implementation decisions in auditing. International Journal of Accounting Information Systems, 9(2), 104-121. doi: 10.1016/j.accinf.2007.10.002
Ely, D. P. (1999). Conditions that facilitate the implementation of educational technology innovations. Educational Technology & Society, 39(3), 23-27.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.
Davis, F. (1986). A technology acceptance model for empirically testing new end- user information systems: Theory and results. (Unpublished Doctoral dissertation). Massachusetts Institute of Technology, Massachusetts, USA.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-339. doi: 10.2307/249008
Davis, F. D., Bagozzi, R. P., & Warshaw. P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003. doi: 10.1287/mnsc.35.8.982
Igbaria, M., Zinatelli, N., Cragg, P., & Cavaye, A. M. (1997). Personal computing acceptance factors in small firms: A structural equation model. MIS Quarterly, 21(3), 279-305. doi: 10.2307/249498
Klein, H. K., Hirschheim R., & Nissen H.E. (1991). A Pluralist Perspective of the Information Systems Research Arena. Elsevier, Amsterdam: Contemporary Approaches and Emergent Traditions Information Systems Research.
Gazette. (2008, April). Business: Kingdom's e-learning industry to reach $125 million this year. Saudi Gazette online, Retrieved from http://www.saudigazette.com.sa/index.cfm?method=home.regcon&contentID=200804244097.
Galletta, D. F., Ahuja, M., Hartman, A., Teo, T., & Peace, A. G. (1995). Social influence and end-user training. Communications of the ACM, 38(7), 70-79.
Guttman, L. (1954). Some necessary conditions for common factor analysis. Psychometrika, 19(2), 149-162. doi: 10.1007/BF02289162
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (1998). Multivariate data analysis. NJ: Prentice hall Upper Saddle River.
Landry, B. J. L., Rodger G., & Hartman S. (2006). Measuring student perceptions of blackboard using the technology acceptance mode. Decision Sciences Journal of Innovative Education 4(1), 87-99
Lee, Y., Lee, J., & Lee, Z. (2006). Social influence on technology acceptance behavior: Identity theory perspective. Information Systems, 37 (2&3), 60-75.
Krejcie, R., & Morgan, D. (1970). Determining sample size for research activities Educational and Psychological Measurement, 30, 607-610.
Masrom, M. (2007). Technology Acceptance Model and E-learning. Paper presented at the 12th International Conference on Education. Universiti Brunei Darussalam. Brunei.
Ngai, E. W. T., Poon, J. K. L., & Chan, Y. H. C. (2007). Empirical Examination of the Adoption of WebCT Using TAM. Computers & Education, 48(2), 250- 267. doi:10.1016/j.compedu.2004.11.007
Nunnaly, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York: McGraw-Hill.
Ong, C. S., Lai, J. Y., & Wang, Y. S. (2004). Factors affecting engineers’ acceptance of asynchronous e-learning systems in high-tech companies. Information & Management, 41(6), 795-804. doi: 10.1016/j.im.2003.08.012
Pituch, K. A., & Lee, Y. K. (2006). The influence of system characteristics on e-learning use. Computers & Education, 27(2), 222-244. doi:10.1016/j.compedu.2004.10.007
Pallant, J. (2001). SPSS survival manual: a step by step guide to data analysis using SPSS for windows (version 10 and 11). Buckingham: Open University Press.
Rezaei, M., Mohammadi, H.M., Asadi, A., & Kalantary, K. (2008). Predicting e-learning application in agricultural higher education using technology acceptance model. Turkish Online Journal of Distance Education, 9 (1), 85-95. Retrieved from http://tojde.anadolu.edu.tr/tojde29/pdf/Volume9Number1.pdf
Roca, J. C., Chiu C. M. & Martínez, F. J. (2006). Understanding e-learning continuance intention: An extension of the technology acceptance model. International Journal of human-computer studies 64(8), 683-696. doi:10.1016/j.ijhcs.2006.01.003
Rosenberg, M. J. (2001). E-Learning: Strategies for delivering knowledge in the digital age. NY: McGraw Hill.
Saadé, R., & Bahli, B. (2005). The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: An extension of the technology acceptance model. Info. & Mgt., 42(2), 317-327. doi: 10.1016/j.im.2003.12.013
Saadé, R. G., & Galloway, I. (2005). Understanding intention to use multimedia information systems for learning. The Journal of Issues in Informing Science and Information Technology 2(1), 287-296.
Schifter, C. C. (2000). Faculty participation in asynchronous learning networks: A case study of motivating and inhibiting factors. Journal of Asynchronous Learning Networks, 4(1), 15-22. Retrieved from http://sloanconsortium.org/sites/default/files/v4n1_schifter_1.pdf
Sekaran, U. (2000). Research methods for business: A skill building approach. NY: John Wiley & Sons, Inc.
Selim, H. M. (2003). An Empirical Investigation of Student Acceptance of Course Websites. Computers & Education, 40(4), 343-360. doi: doi:10.1016/S0360
Shannon, S., & Doube, L. (2004). Valuing and using web supported teaching: A staff development role in closing the gaps. Australasian Journal of Educational Technology, 20(1), 114-136. Retrieved from http://hdl.handle.net/2440/749
Succi, C. (2007). Corporate eLearning Acceptance: the role of Context and Communication (PhD dissertation).Retrieved from http://doc.rero.ch/lm.php?
Sun, P. C., Tsai, R. J., Finger, G., Chen, Y. Y., & Yeh, D. (2008). What drives a successful elearning? An empirical investigation of the critical factors influencing learner satisfaction. Computers & Education, 50(4), 1183-1202. doi: 10.1016/j.compedu.2006.11.007
Szajna, B. (1996). Empirical evaluation of the revised technology acceptance model. Management Science, 42(1), 85-92. doi: 10.1287/mnsc.42.1.85
Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly, 15(1), 124-143. doi:10.2307/249443
Tung, F. C., & Chang, S. C. (2008). An empirical investigation of students' behavioral intentions to use the online learning course websites. British Journal of Educational Technology, 39(1), 71-83.
Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences Journal of Innovative Education, 27(3), 451-482. doi:10.1111/j.1540-5915.1996.tb00860.x
Venkatesh, V. (2000). Determinants of perceived ease of use: integrating perceived behavioral control, computer anxiety and enjoyment into the technology acceptance model. Information Systems Research, 11(4), 342-365. doi:10.1287/isre.11.4.342.11872
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186-205. doi:10.1287/mnsc.184.108.40.20626
Venkatesh, V., & Morris M. G. (2000). Why don't men ever stop to ask for directions? gender, social influence, and their role in technology acceptance and usage behavior. MIS Quarterly, 24(1), 115-139. doi:10.2307/3250981
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.
Webster, J., & Hackley, P. (1997). Teaching effectiveness in technology-mediated distance learning. Academy of Management Journal, 40(6), 1282-1309. doi:10.2307/257034
Wolski, S. & Jackson, S. (1999). Technological Diffusion within Educational Institutions: Applying the Technology Acceptance Model. Proceedings of Society for Information Technology and Teacher Education International Conference 1999 (pp. 1718-1723). Chesapeake, VA: AACE. Retrieved from http://www.editlib.org/p/26707.
Yi, M. Y., Davis F. D. (2001). Improving computer training effectiveness for decision technologies: behavior modeling and retention enhancement. Decision Sciences Journal of Innovative Education, 32(3), 521-544. doi:10.1111/j.1540-5915.2001.tb00970.x
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.