Skip to main content

Advertisement

SpringerLink
Log in

A cross-cultural validity of the E-learning Acceptance Measure (ElAM) in Lebanon and England: A confirmatory factor analysis

  • Published:
Education and Information Technologies Aims and scope Submit manuscript

Abstract

Despite the prevalence and significance of e-learning in education, there is a dearth of published instruments for educational researchers and practitioners that measure users’ acceptance of e-learning. To meet this need, Teo (2010) developed the E-learning Acceptance Measure (ElAM). The main objective of this paper is to validate the ElAM (Teo, 2010) across two cultures, one is from a European country: England, and the other from Asia: Lebanon. A total sample of 461 university students from two private universities in Lebanon (n = 209) and one university in England (n = 252) participated in this study. Using confirmatory factor analyses, our findings revealed that the original 3-factor solution for ElAM (Teo, 2010) was supported and found to be adequate for the British sample, whereas the results revealed a bad fit for the Lebanese sample. Despite the differences, the ElAM was found to possess an acceptable level of internal consistency and item reliability for the pooled sample. Theoretical and practical implications are discussed at the end of the paper.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  • Abouchedid, K., & Eid, G. M. (2004). E-learning challenges in the Arab world: Revelations from a case study profile. Quality Assurance in Education, 12(1), 15–27.

    Article  Google Scholar 

  • Akkoyunlu, B., & Yilmaz-Soylu, M. (2008). Development of a scale on learners’ views on blended learning and its implementation process. The Internet and Higher Education, 11(1), 26–32.

    Article  Google Scholar 

  • Arbuckle, J. (2009). Amos 18 user’s guide. Armonk: SPSS Incorporated.

    Google Scholar 

  • Baroud, F., & Abouchedid, K. (2010). Elearning in Lebanon: Patterns of E-learning development in Lebanon’s mosaic educational context. In U. Demiray (Ed.), E-learning practices: Cases on challenges facing e-learning and national development, institutional studies and practices (pp. 409–424). Eskisehir-Turkey: Anadolu University.

    Google Scholar 

  • Bernard, R. M., Brauer, A., Abrami, P. C., & Surkes, M. (2004). The development of a questionnaire for predicting online learning achievement. Distance Education, 25(1), 31–47.

    Article  Google Scholar 

  • Blunch, N. J. (2008). Introduction to structural equation modelling using SPSS and AMOS. London: Sage.

    Book  MATH  Google Scholar 

  • Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley.

    Book  MATH  Google Scholar 

  • Byrne, B. M. (2013). Structural equation modeling with Amos: Basic concepts, applications, and programming. New York: Routledge.

    Google Scholar 

  • Carmines, E. G., & Mciver, J. P. (1981). Analyzing models with unobserved variables: Analysis of covariance structures. In B. W. George & B. Edgar (Eds.), Social measurement: Current issues (pp. 65–115). Beverly Hills: Sage Publications, Inc.

    Google Scholar 

  • Cavus, N. (2013). Selecting a learning management system (LMS) in developing countries: Instructors’ evaluation. Interactive Learning Environments, 21(5), 419–437.

    Article  MathSciNet  Google Scholar 

  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.

    Article  Google Scholar 

  • Demiray, U. (2010). Cases on challenges facing e-learning and national development: Institutional studies and practices. Anadolu University Publications, 2(1), 63–75.

  • Devellis, R. F. (2011). Scale development: Theory and applications. Newbury Park: Sage.

    Google Scholar 

  • Gefen, D., Straub, D. W., & Boudreau, M. C. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of AIS, 4(7), 1–79.

    Google Scholar 

  • Hair, J. F. J., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2010). Multivariate data analysis. New Jersey: Prentice-Hall.

    Google Scholar 

  • Harb, J., Abu Bakar, N., & Krish, P. (2014). Gender differences in attitudes towards learning oral skills using technology. Education and Information Technologies, 19(4), 805–816.

    Article  Google Scholar 

  • Hofstede, G., & Hofstede, J. (2005). Cultures and organizations: Software of the mind. New York: McGraw-Hill.

    Google Scholar 

  • Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6, 1–55.

    Article  Google Scholar 

  • Janssens, W., De Pelsmacker, P., Wijnen, K., Van Kenhove, P. (2008). Marketing research with SPSS. Englewood Cliffs, NJ: Financial Times/Prentice Hall.

  • Kay, R. H., & Knaack, L. (2008). A multi-component model for assessing learning objects: The learning object evaluation metric (LOEM). Australasian Journal of Educational Technology, 24(5), 574–591.

    Article  Google Scholar 

  • Keengwe, J., & Malapile, S. (2014). Factors influencing technology planning in developing countries: A literature review. Education and Information Technologies, 19(4), 703–712.

    Article  Google Scholar 

  • Kline, R. B. (2010). Principles and practice of structural equation modeling (3rd ed.). New York: Guilford Press.

    MATH  Google Scholar 

  • Kontkanen, S., Dillon, P., Valtonen, T., Renkola, S., Vesisenaho, M., & Väisänen, P. (2014). Pre-service teachers’ experiences of ICT in daily life and in educational contexts and their proto-technological pedagogical knowledge. Education and Information Technologies, 19(4), 1–25.

    Google Scholar 

  • Lee, C.B., Teo, T., Chai, C.S., Choy, D., Tan, A., Seah, J. (2007). Closing the gap: Pre-service teachers’ perceptions of an ICT based, student centred learning curriculum. In ICT: Providing choices for learners and learning. Proceedings ascilite Singapore 2007.

  • Malapile, S., & Keengwe, J. (2013). Information communication technology planning in developing countries. Education and Information Technologies, 19(4), 691–701.

    Article  Google Scholar 

  • Mirza, A. A., & Al-Abdulkareem, M. (2011). Models of e-learning adopted in the Middle East. Applied Computing and Informatics, 9(2), 83–93.

    Article  Google Scholar 

  • Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory. New York: McGraw.

    Google Scholar 

  • Paechter, M., & Maier, B. (2010). Online or face-to-face? Students’ experiences and preferences in e-learning. The Internet and Higher Education, 13(4), 292–297.

    Article  Google Scholar 

  • Pallant, J. (2010). SPSS survival manual: A step by step guide to data analysis using SPSS. Buckingham: Open University Press.

    Google Scholar 

  • Park, S. Y., Nam, M. W., & Cha, S. B. (2012). University students’ behavioral intention to use mobile learning: Evaluating the technology acceptance model. British Journal of Educational Technology, 43(4), 592–605.

    Article  Google Scholar 

  • Schumacker, R. E. & Lomax, R. G. (2010) A beginner's guide to structural equation modeling. New York: Routledge: Lawrence Erlbaum.

  • Sriram, B. (2014). Factors influencing the internet resource users satisfaction: An analytical study on omani undergraduate learners. Education and Information Technologies, 18(1), 1–17.

  • Stewart, I., Hong, E., & Strudler, N. (2004). Development and validation of an instrument for student evaluation of the quality of web-based instruction. American Journal of Distance Education, 18(3), 131–150.

    Article  Google Scholar 

  • Straub, D., Keil, M., & Brenner, W. (1997). Testing the technology acceptance model across cultures: A three country study. Information & Management, 33(1), 1–11.

    Article  Google Scholar 

  • Suhr, D. D. (2006). Exploratory or confirmatory factor analysis? In Statistics and data analysis (pp. 200–231). Cary: SAS Institute.

    Google Scholar 

  • Tabachnick, B. G., & Fidell, L. S. (2007) Using multivariate statistics. Pearson Education.

  • Tarhini, A., Hone, K., & Liu, X. (2013a). Factors affecting students’ acceptance of E-learning environments in developing countries: A structural equation modeling approach. International Journal of Information and Education Technology, 3(1), 54–59.

    Article  Google Scholar 

  • Tarhini, A., Hone, K., & Liu, X. (2013b). User acceptance towards web-based learning systems: Investigating the role of social, organizational and individual factors in European higher education. Procedia Computer Science, 17, 189–197.

    Article  Google Scholar 

  • Tarhini, A., Hone, K., & Liu, X. (2013c). Extending the TAM model to empirically investigate the students’ behavioural intention to use e-learning in developing countries. In Science and Information Conference (SAI) (pp. 732–737), United Kingdom.

  • Tarhini, A., Hone, K., & Liu, X. (2014a). A cross-cultural examination of the impact of social, organisational and individual factors on educational technology acceptance between British and Lebanese university students. British Journal of Educational Technology. doi:10.1111/bjet.12169.

    Google Scholar 

  • Tarhini, A., Hone, K., & Liu, X. (2014b). The effects of individual differences on e-learning users’ behaviour in developing countries: A structural equation model. Computers in Human Behavior, 41, 153–163.

    Article  Google Scholar 

  • Tarhini, A., Hone, K., & Liu, X. (2014c). Measuring the moderating effect of gender and Age on e-learning acceptance in England: A structural equation modeling approach for an extended technology acceptance model. Journal of Educational Computing Research, 51(2), 163–184

    Article  Google Scholar 

  • Tarhini, A., Scott, M., Sharma, K.S., & Abbasi, M.S. (2015a). Differences in Intention to Use RSS Feeds between Lebanese and British students: A Multi-Group Invariance Analysis Based on the Technology Acceptance Model. Electronic Journal of e-Learning, 13(1), 14-29.

  • Tarhini, A., Hassouna, M., Abbasi, M.S. & Orozco, J. (2015b). Towards the Acceptance of RSS to Support Learning: An empirical study to validate the Technology Acceptance Model in Lebanon. Electronic Journal of e-Learning, 13 (1), 30-41

  • Teo, T. (2009a). The impact of subjective norm and facilitating conditions on pre-service teachers’ attitude toward computer use: A structural equation modeling of an extended technology acceptance model. Journal of Educational Computing Research, 40(1), 89–109.

    Article  Google Scholar 

  • Teo, T. (2009b). Modelling technology acceptance in education: A study of pre-service teachers. Computers & Education, 52(2), 302–312.

    Article  Google Scholar 

  • Teo, T. (2010). Development and validation of the E-learning Acceptance Measure (ElAM). The Internet and Higher Education, 13(3), 148–152.

    Article  Google Scholar 

  • Teo, T., & Noyes, J. (2010). Exploring attitudes towards computer use among pre-service teachers from Singapore and the UK: A multi-group invariance test of the technology acceptance model (TAM). Multicultural Education & Technology Journal, 4(2), 126-135.

  • Teo, T. (2011). Assessing the cross-cultural validity study of the E-learning Acceptance Measure (ElAM): A structural equation modeling approach. International Journal of Educational and Psychological Assessment, 8(2), 43–53.

  • Teo, T., Lee, C. B., Chai, C. S., & Choy, D. (2009). Modelling pre-service teachers’ perceived usefulness of an ICT-based student-centred learning (SCL) curriculum: A Singapore study. Asia Pacific Education Review, 10(4), 535–545.

    Article  Google Scholar 

  • Teo, T., Wong, S. L., Thammetar, T., & Chattiwat, W. (2011). Assessing the e-learning acceptance of university students in Thailand. Australasian Journal of Educational Technology, 27(8), 1356–1368.

    Article  Google Scholar 

  • Thompson, B. (2004). Exploratory and confirmatory factor analysis: Understanding concepts and applications. Washington: American Psychological Association.

    Book  Google Scholar 

  • Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a aesearch agenda on interventions. Decision Sciences, 39(2), 273–315.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • Zhang, Y. (2007). Development and validation of an internet use attitude scale. Computers & Education, 49(2), 243–253.

    Article  Google Scholar 

  • Zhang, S., Zhao, J., & Tan, W. (2008). Extending TAM for online learning systems: An intrinsic motivation perspective. Tsinghua Science and Technology, 13(2), 312–317.

    Article  Google Scholar 

  • Zhu, C., Valcke, M., & Schellens, T. (2009). Cultural differences in the perception of a social‐constructivist e‐learning environment. British Journal of Educational Technology, 40(1), 164–168.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information System, Brunel University London, Middlesex, UK

    Ali Tarhini

  2. Department of Education and Social Science, University of Macau, Tapia, China

    Timothy Teo

  3. Department of Electrical and Computer Engineering, Texas A&M University Qatar, Doha, Qatar

    Takwa Tarhini

Authors
  1. Ali Tarhini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Timothy Teo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Takwa Tarhini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ali Tarhini.

Appendix

Appendix

Table 5 Survey items and scales
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tarhini, A., Teo, T. & Tarhini, T. A cross-cultural validity of the E-learning Acceptance Measure (ElAM) in Lebanon and England: A confirmatory factor analysis. Educ Inf Technol 21, 1269–1282 (2016). https://doi.org/10.1007/s10639-015-9381-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10639-015-9381-9

Keywords

Search

Navigation