Skip to main content

Advertisement

SpringerLink
Log in

Exploring instructors’ technology readiness, attitudes and behavioral intentions towards e-learning technologies in Egypt and United Arab Emirates

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

Abstract

This paper explores the association between technology readiness, (a meta-construct consisting of optimism, innovativeness, discomfort, and insecurity), attitude, and behavioral intention towards e-learning technologies adoption within an education institution context. The empirical study data is collected at two private universities located in Egypt and UAE. The research explores the role of instructors’ technology readiness level, in shaping their attitudes, preference to human interaction and ultimately behavioral intentions towards adopting e-learning technologies. Analysis of the data (Mann-Whitney U non-parametric test) shows no significant differences between instructors at the two universities in terms of technology readiness, attitudes, behavioral intentions, and preference to human interaction. The exploratory results provide evidence for the relationship between instructors’ technology, attitude, and behavioral intentions to adopt e-learning technologies. The study finds that preference to human interaction is equally important in Egypt and UAE with a strong potential to affect instructor’s behavioral intentions for adopting e-learning technologies. The research results provide initial insights to education managers on the nature and mechanisms of the relationship among the research variables, which would improve the ability of educational institutions to introduce and adopt e-learning technologies. An additional contribution is the validity and reliability tests for Technology Readiness (TR) scale, which shows its viability as a meaningful measurement instrument for use in an educational setting.

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
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. See Appendix Tables 9 and 10 for Total Variance Explained table and Rotated Component matrix

References

  • Ajzen, I. (2002). Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior. Journal of Applied Social Psychology, 32, 665–683.

    Article  Google Scholar 

  • Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice-Hall.

  • Armitage, S. & O’Leary, R. (2003) A guide for learning technologists, LTSN e-Learning Series No4. York: Learning and Teaching Support Network (LTSN).

  • Bachrach, D. G., Powell, B. C., Bendoly, E., & Richey, R. G. (2006). Organizational citizenship behavior and performance evaluations: exploring the impact of task interdependence. Journal of Applied Psychology, 91(1), 193–201.

    Article  Google Scholar 

  • Bagozzi, R. P. (2007). The legacy of the technology acceptance model and a proposal for a paradigm shift. Journal of the Association for Information Systems, 8(4), p.3.

  • Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37(2), 122–147.

    Article  Google Scholar 

  • Bandura, A. (1986). Social foundations of thought and action: a social cognitive theory. Englewood Cliffs: Prentice-Hall.

    Google Scholar 

  • Barnett, R. (2003). Beyond all reason: living with ideology in the university. Buckingham: Open University Press.

    Google Scholar 

  • Bates, A., & Sangra, A. (2011). Managing Technology in Higher Education: strategies for transforming teaching and learning. San Francisco: John Wiley & Co..

    Google Scholar 

  • Broadhead-Fearn, D., & White, K. M. (2006). The role of self-efficacy rule following behaviours for homeless youth: a test of the theory of planned behavior. The Journal of Social Psychology, 146(3), 307–325.

    Article  Google Scholar 

  • Cheung, D. (2009). Developing a scale to measure students’ attitudes toward chemistry lessons. International Journal of Science Education, 31(16), 2185–2203.

    Article  Google Scholar 

  • Conner, M., & Armitage, C. (1998). Extending the theory of planned behavior: a review and avenues for further research. Journal of Applied Social Psychology, 28(15), 1429–1464.

    Article  Google Scholar 

  • Curran, J. M., & Meuter, M. L. (2005). Self-service technology adoption: comparing three technologies. Journal of Services Marketing, 19(2), 103–113.

  • Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. psychometrika, 16(3), 297-334.

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

  • Davis, F., Bagozzi, R., & Warshaw, P. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management Science, 35(8), 982–1003.

    Article  Google Scholar 

  • Dearing, R. (1997). The national committee of enquiry into higher education, dearing report. Available at: www.leeds.ac.uk/educol/ncihe.

  • Field, A. (2009). Discovering statistics using SPSS. London: Sage publications.

    MATH  Google Scholar 

  • Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: an introduction to theory and research. Reading, MA: Addison-Wesley.

    Google Scholar 

  • Hennessy, M., Bleakley, A., & Fishbein, M. (2012). Measurement models for reasoned action theory. The Annals of the American Academy of Political and Social Science, 640(1), 42–57.

    Article  Google Scholar 

  • Hofstede, G. (1983). The cultural relativity of organizational practices and theories. Journal of International Business Studies, 14(2), 75–89 Special Issue on Cross-Cultural Management (autumn, 1983).

    Article  Google Scholar 

  • Horton, W. K., (2011). E-learning by design. 2nd edn. San Francisco, CA:Wiley, John & Sons

  • Holmes, B., & Gardner, J. (2006). E-learning: Concepts and practice. Sage.

  • Jiun-Sheng, C. L., & Hsieh, P.-L. (2012). Refinement of the technology readiness index scale: a replication and cross-validation in the self-service technology context. Journal of Service Management, 23(1), 34–53.

    Article  Google Scholar 

  • Jones, A. (2004). A review of the research literature on barriers to the uptake of ICT by teachers. Coventry: Becta.

    Google Scholar 

  • Leem, J., & Lim, B. (2007). The current status of e-learning and strategies to enhance educational competitiveness in Korean higher education. International Review of Research in Open and Distance Learning, 8(1), 1–18.

    Google Scholar 

  • Lightner, S. L., Doggett, A. M., & Whisler, V. R. (2009). Entrance requirements and learning styles for online graduate programs. IIE Annual Conference. Proceedings, 263–270.

  • Lin, J. S. C., & Hsieh, P. L. (2006). The role of technology readiness in customers’ perception and adoption of self-service technologies. International Journal of Service Industry Management, 17(5), 497–517.

    Article  Google Scholar 

  • Lin, J. S. C., & Chang, H. C. (2011). The role of technology readiness in self-service technology acceptance. Managing Service Quality: An International Journal, 21(4), 424–444.

  • Lin, C.-P., Tsai, Y.-H., Joe, S.-W., & Chiu, C.-K. (2011). Modeling IT product recall intention based on the theory of reasoned action and information asymmetry: a qualitative aspect. Quality and Quantity, 47(2), 753–759.

    Article  Google Scholar 

  • Litteljohn, A., & Pegler, C. (2007). Preparing for blended e-learning. New York: Routledge.

    Google Scholar 

  • Mathews, P., & Bhanugopan, R. (2014). Predictors of effective web-based international business management courses in China: students’ perceptions on course interaction and satisfaction. Journal of Teaching in International Business, 25(1), 60.

    Article  Google Scholar 

  • Meuter, M. L., Ostrom, A. L., Roundtree, R. I., & Bitner, M. J. (2000). Self-service technologies: understanding customer satisfaction with technology-based service encounters. Journal of marketing, 64(3), 50–64.

  • Mick, D. G., & Fournier, S. (1998). Paradoxes of technology: Consumer cognizance, emotions, and coping strategies. Journal of Consumer research, 25(2), 123–143.

  • Muddox, J. (2000). Self-efficacy: the power of believing you can. Handbook of positive psychology. New York: Oxford University Press.

    Google Scholar 

  • Pallant, J. (2007). SPSS survival manual: a step by step guide to data analysis using SPSS for windows. Maidenhead: Open University Press.

    Google Scholar 

  • Parasuraman, A. (2000). Technology readiness index (TRI): a multiple item scale to measure readiness to embrace new technologies. Journal of Service Research, 2, 307–320.

    Article  Google Scholar 

  • Parasuraman, A., Edvardsson, B., & Gustafsson, A. (2004). The impact of national culture on technology readiness, adoption, and use. Paper presented at the Ninth International Research Symposium on Service Excellence in Management. Sweden: Karlstad.

    Google Scholar 

  • Parasuraman, A., Valarie A. Z., & Arvind M. (2005), “E-S-QUAL: A Multiple-Item Scale for - Assessing Electronic Service Quality,” Journal of Service Research, 7 (3), 213–3.

  • Parnell, J. A., & Carraher, S. (2003). The management education by internet readiness (MEBIR) scale: developing a scale to assess personal readiness for internet mediated management education. Journal of Management Education, 27(4), 431–446.

    Article  Google Scholar 

  • Parnell, J. A., & Carraher, S. (2005). Validating the management education by internet readiness (MEBIR) scale with samples of American, Chinese, and Mexican students. Journal of Education for Business, 81(1), 47–54.

    Article  Google Scholar 

  • Patry, A. L., & Pelletier, L. G. (2001). Extraterrestrial beliefs and experiences: an application of the theory of reasoned action. The Journal of Social Psychology, 141(2), 199–217.

    Article  Google Scholar 

  • Patsiotis, A., Hughes, T., & Webber, D. (2012). Adopters and non-adopters of internet banking: a segmentation study. The International Journal of Bank Marketing, 30(1), 20–42.

    Article  Google Scholar 

  • Peattie, K. (2001). Towards sustainability. The third age of green marketing. The Marketing Review, 2(2), 129–146.

    Article  Google Scholar 

  • Pepicello, B., & Pepicello, S. (2003). Determining the significance of “no significant difference”. In G. Richards (Ed.), Proceedings of world conference on E-learning in corporate, government, healthcare, and higher education. Chesapeake: AACE.

    Google Scholar 

  • 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, 5(4), 401–426.

    Article  Google Scholar 

  • Richey, R. G., & Autry, C. (2009). Assessing interfirm collaboration/technology investment tradeoffs: the effects of technological readiness and organizational learning. The International Journal of Logistics Management, 20(1), 30–56.

    Article  Google Scholar 

  • Ringle, C. M., Sarstedt, M., & Straub, D. (2012). A critical look at the use of PLS-SEM in MIS Quarterly. MIS Quarterly (MISQ), 36(1).

  • Rogers, E. M. (1995). Diffusion of innovations. New York: Free Press.

    Google Scholar 

  • Rosce, J. T. (1975). Fundamental research statistics for the behavioral sciences (2nd ed.). New York: Holt Rinehart & Winston.

    Google Scholar 

  • Rose, J., & Fogarty, G. (2010). Technology readiness and segmentation profile of mature consumers. In Proceedings of 4th Biennial Conference of the Academy of World Business, Marketing and Management Development, vol. 4, pp. 57–65. From: 4th Biennial Conference of the Academy of World Business, Marketing and Management Development, 12–15 July 2010, Oulu, Finland.

  • Rosenberg, M. L., & Rosenberg (2000). E-learning: strategies for delivering knowledge in the digital age. New York: McGraw-Hill.

    Google Scholar 

  • Sawang, S., Newton, C., & Jamieson, K. (2013). Increasing learners’ satisfaction/intention to adopt more e-learning. Education and Training, 55(1), 83–105.

    Article  Google Scholar 

  • Sekaran, U., & Bougie, R. (2010). Research methods for business: a skill building approach (5th ed.). West Sussex: Wiley.

    Google Scholar 

  • Soderlund, M., & Ohman, N. (2003). Behavioral intentions in satisfaction research revisited. Journal of Consumer Satisfaction, Dissatisfaction and Complaining Behavior, 16, 53–66.

    Google Scholar 

  • Thurstone, L. L. (1947). Multiple factor analysis. Chicago: University of Chicago Press.

    MATH  Google Scholar 

  • Tucker, J. P., & Gentry, G. R. (2009). Developing an e-learning strategy in higher education. Foresight, 11(2), 43–49.

  • Verhoef, P. C., Lemon, K. N., Parasuraman, A., Roggeveen, A., Tsiros, M., & Schlesinger, L. A. (2009). Customer experience creation: determinants, dynamics and management strategies. Journal of Retailing, 85(1), 31–41.

    Article  Google Scholar 

  • Welsh, E., Wanberg, C., Brown, K., & Simmering, M. (2003). E-learning: emerging uses, empirical results and future directions. International Journal of Training and Development, 7(4), 245–258.

    Article  Google Scholar 

  • Westaby, J. D. (2005). Behavioral reasoning theory: identifying new linkages underlying intentions and behavior. Organizational Behavior and Human Decision Processes, 98(2), 97–120.

    Article  Google Scholar 

  • Yi, M. Y., Fiedler, K. D., & Park, J. S. (2006). Understanding the role of individual innovativeness in the acceptance of IT-based innovations: comparative analyses of models and measures. Decision Sciences, 37(3), 393–426.

    Article  Google Scholar 

  • Zhang, D., & Nunamaker, J. F. (2003). Powering e-learning in the new millennium: an overview of e-learning and enabling technology. Information Systems Frontiers, 5(2), 207–218.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Higher Colleges of Technology, University City, Sharjah, United Arab Emirates

    Shahira El Alfy & Danail Ivanov

  2. Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany

    Jorge Marx Gómez

Authors
  1. Shahira El Alfy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jorge Marx Gómez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Danail Ivanov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shahira El Alfy.

Appendix

Appendix

SPSS Tables 9 and 10 for factor Analysis.

Table 9 Rotated Component Matrixa
Full size table
Table 10 Factor Analysis Data
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

El Alfy, S., Gómez, J.M. & Ivanov, D. Exploring instructors’ technology readiness, attitudes and behavioral intentions towards e-learning technologies in Egypt and United Arab Emirates. Educ Inf Technol 22, 2605–2627 (2017). https://doi.org/10.1007/s10639-016-9562-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10639-016-9562-1

Keywords

Search

Navigation