Skip to main content

Advertisement

SpringerLink
Log in

Using the HOT-fit model to predict the determinants of E-learning readiness in higher education: a developing Country’s perspective

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

Abstract

E-learning readiness has been initiated in higher education institutions (HEI) as an attempt to improve institutions’ service delivery. Meeting and managing the expectations of students using e-learning systems to facilitate teaching and learning activities is a prominent way to make HEI competitive. The purpose of this study is to investigate the impact of human, organizational, and technological factors on students’ e-learning readiness. This study was conducted by using a survey method in a private university in the north region of Iran with a total number of 153 respondents. Survey data were analyzed using the partial least squares (PLS) method while Smart PLS was used to test the hypotheses and to validate the proposed model. The results indicated that computer self-efficacy, management support, relative advantage, compatibility, and complexity are significant factors that influence students’ e-learning readiness. The findings provide a basis for assessing the determinants of e-learning readiness in developing countries.

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

Similar content being viewed by others

References

  • Acharya, B., & Lee, J. (2018). Users’ perspective on the adoption of e-learning in developing countries: The case of Nepal with a conjoint-based discrete choice approach. Telematics and Informatics, 35(6), 1733–1743.

    Google Scholar 

  • Ahmadi, H., Nilashi, M., & Ibrahim, O. (2015). Organizational decision to adopt hospital information system: An empirical investigation in the case of Malaysian public hospitals. International Journal of Medical Informatics, 84, 166–188.

    Google Scholar 

  • Ahmed, M. U., Hussain, S., & Farid, S. (2018). Factors influencing the adoption of e-learning in an open and distance learning institution of Pakistan. Electronic Journal of e-Learning, 16(2), 148–158.

    Google Scholar 

  • Al-araibi, A. A. M., Naz’ri bin Mahrin, M., Yusoff, R. C. M., & Chuprat, S. B. (2019). A model for technological aspect of e-learning readiness in higher education. Education and Information Technologies, 24(2), 1395–1431.

    Google Scholar 

  • Albarrak, A. (2010). Designing e-learning systems in medical education: A case study. International Journal of Excellence in Healthcare Management, 3(1), 1–8.

    Google Scholar 

  • Ansong, E., Boateng, S. L., Boateng, R., & Effah, J. (2016). Determinants of e-learning adoption in universities: Evidence from a developing country. Paper presented at 49th Hawaii international conference on system sciences, USA.

  • Ashtari, S., & Eydgahi, A. (2017). Student perceptions of cloud applications effectiveness in higher education. Journal of Computational Science, 23, 173–180.

    Google Scholar 

  • Aung, T. N., & Khaing, S. S. (2015). Challenges of implementing e-learning in developing countries: A Review. Paper presented at international conference on genetic and evolutionary computing, China.

  • Awidi, I. T., & Cooper, M. (2015). Using management procedure gaps to enhance e-learning implementation in Africa. Computers & Education, 90, 64–79.

    Google Scholar 

  • Aydin, C., & Tasci, D. (2005). Measuring readiness for e-learning: Reflections from an emerging country. Educational Technology & Society, 8(4), 244–257.

    Google Scholar 

  • Bates, R., & Khasawneh, S. (2007). Self-efficacy and college students’ perceptions and use of online learning systems. Computers in Human Behavior, 23(1), 175–191.

    Google Scholar 

  • Bhuasiri, W., Xaymoungkhoun, O., Zo, H., Rho, J. J., & Ciganek, A. P. (2012). Critical success factors for e-learning in developing countries: A comparative analysis between ICT experts and faculty. Computers & Education, 58(2), 843–855.

    Google Scholar 

  • Bora, U. J., & Ahmed, M. (2013). E-learning using cloud computing. International Journal of Science and Modern Engineering, 1(2), 9–12.

    Google Scholar 

  • Borotis, S., & Poulymenakou, A. (2004). E-learning readiness components: Key issues to consider before adopting e-learning interventions. Paper presented at the world conference on E-learning in corporate, government, healthcare, and higher education, Virginia.

  • Bowles, M. (2004). What is electronic learning? In M. Bowles (Ed.), Relearning to E-learn: Strategies for electronic learning and knowledge (pp. 3–19). Carlton: Melbourne University Press.

    Google Scholar 

  • Bush, T. (2008). Leadership and management development in education. Thousand Oaks: Sage.

    Google Scholar 

  • Cao, Q., Griffin, T. E., & Bai, X. (2009). The importance of synchronous interaction for student satisfaction with course web sites. Journal of Information Systems Education, 20(3), 331–338.

    Google Scholar 

  • Chen, M. P. (2009). An evaluation of the ELNP e-learning quality assurance program: Perspectives of gap analysis and innovation diffusion. Journal of Educational Technology & Society, 12(1), 18–33.

    Google Scholar 

  • Cheon, J., Lee, S., Crooks, S. M., & Song, J. (2012). An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Computers & Education, 59(3), 1054–1064.

    Google Scholar 

  • Choshaly, S. H., & Mirabolghasemi, M. (2019). Using SEM-PLS to assess users satisfaction of library service quality: Evidence from Malaysia. Library Management, 40(3/4), 240–250.

    Google Scholar 

  • Chugh, R., Ledger, S., & Shields, R. (2017). Curriculum Design for Distance Education in the tertiary sector. Turkish Online Journal of Distance Education, 18(2), 4–15.

    Google Scholar 

  • Cigdem, H., & Topcu, A. (2015). Predictors of instructors’ behavioral intention to use learning management system: A Turkish vocational college example. Computers in Human Behavior, 52, 22–28.

    Google Scholar 

  • Compeau, D., Higgins, C. A., & Huff, S. (1999). Social cognitive theory and individual reactions to computing technology: A longitudinal study. MIS Quarterly, 23(2), 145–158.

    Google Scholar 

  • Contreras, J., & Hilles, S. M. (2015). Assessment in e-learning environment readiness of teaching staff, administrators, and students of faculty of nursing - Benghazi University. International Journal of the Computer, the Internet and Management, 23(1), 53–58.

    Google Scholar 

  • Dada, D. (2006). E-readiness for developing countries: Moving the focus from the environment to the users. The Electronic Journal on Information Systems in Developing Countries, 27(6), 1–14.

    Google Scholar 

  • Duan, Y., He, Q., Feng, W., Li, D., & Fu, Z. (2010). A study on e-learning take-up intention from an innovation adoption perspective: A case in China. Computers & Education, 55(1), 237–246.

    Google Scholar 

  • Dwyer, D. C., Ringstaff, C., & Sandholtz, J. H. (1997). Teacher beliefs and practices part II: Support for change. The evolution of teachers' instructional beliefs and practices in high-access-to-technology classrooms first-fourth year findings. CA: Apple Computer.

  • El Alfy, S., Gómez, J. M., & Ivanov, D. (2017). Exploring instructors’ technology readiness, attitudes and behavioral intentions towards e-learning technologies in Egypt and United Arab Emirates. Education and Information Technologies, 22(5), 2605–2627.

    Google Scholar 

  • Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1–4.

    Google Scholar 

  • Faul, F., Erdfelder, E., Buchner, A., & Lang, A. G. (2009). Statistical power analyses using G* power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160.

    Google Scholar 

  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 34(2), 161–188.

    Google Scholar 

  • Furneaux, B., & Wade, M. R. (2011). An exploration of organizational level information systems discontinuance intentions. MIS Quarterly, 35(3), 573–598.

    Google Scholar 

  • Goi, C., & Ng, P. (2009). E-learning in Malaysia: Success factors in implementing e-learning program. International Journal of Teaching and Learning in Higher Education, 20(2), 237–246.

    Google Scholar 

  • Hair, J. F., Jr., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2013). A primer on partial least squares structural equation modeling (PLS-SEM). Thousand Oaks: Sage.

    MATH  Google Scholar 

  • Hashim, H., & Tasir, Z. (2014). E-learning readiness: A literature review. Paper presented at international conference on teaching and learning in computing and engineering, Vietnam.

  • Hsu, C. L., & Lu, H. P. (2004). Why do people play on-line games? An extended TAM with social influences and flow experience. Information & Management, 41, 853–868.

    Google Scholar 

  • Jucevičienė, P., & Valinevičienė, G. (2010). A conceptual model of social networking in higher education. Electronics and Electrical Engineering, 102(6), 55–58.

    Google Scholar 

  • Karahanna, E., & Straub, D. W. (1999). The psychological origins of perceived usefulness and ease of use. Information & Management, 35, 237–250.

    Google Scholar 

  • Kaur, K., & Abas, Z. W. (2004). An Assessment of e-learning readiness at the Open University Turkey. Paper presented at international conference on computers in education, Turkey.

  • Keramati, A., Afshari-Mofrad, M., & Kamrani, A. (2011). The role of readiness factors in e-learning outcomes: An empirical study. Computers & Education, 57(3), 1919–1929.

    Google Scholar 

  • Korhonen, A. M., Ruhalahti, S., & Veermans, M. (2019). The online learning process and scaffolding in student teachers’ personal learning environments. Education and Information Technologies, 24(1), 755–779.

    Google Scholar 

  • Kurt, Ö. E. (2019). Examining an e-learning system through the lens of the information systems success model: Empirical evidence from Italy. Education and Information Technologies, 24(2), 1173–1184.

    Google Scholar 

  • Lennon, J., & Maurer, H. (2003). Why it is difficult to introduce e-learning into schools and some new solutions. Journal of Universal Computer Science, 9(10), 1244–1257.

    Google Scholar 

  • Link, T. M., & Marz, R. (2006). Computer literacy and attitudes towards e-learning among first year medical students. BMC Medical Education, 6(1), 34–42.

    Google Scholar 

  • Lopes, C. (2007). Evaluating E-learning readiness in a health sciences higher education institution. Paper presented at the proceedings of IADIS international conference of E-learning, Portugal.

  • Lynn, T., Liang, X., Gourinovitch, A., Morrison, J. P., Fox, G., & Rosati, P. (2018). Understanding the determinants of cloud computing adoption for high performance computing. Paper presented at 51st Hawaii international conference on system sciences, USA.

  • Micheni, E. M. (2015). Using the technology organization environment framework for adoption and implementation of cloud computing in institutions of higher learning in Kenya. International Journal of Engineering Science, 4(9), 37–43.

    Google Scholar 

  • Mirabolghasemi, M., & Iahad, N. A. (2013). Social network intervention for the enhancement of students’ social presence in relation to satisfaction and perceived learning. International Journal of Web Based Communities, 9(2), 233–242.

    Google Scholar 

  • Mirabolghasemi, M., Iahad, N. A., & Rahim, N. Z. A. (2016). Students’ perception towards the potential and barriers of social network sites in higher education. Social Networking and Education. Lecture Notes in Social Networks, Springer.

  • Mohamad, H., & Md Idris, K. (2009). E-learning adoption: the role of relative advantages, trialability and academic specialisation. Campus-Wide Information Systems, 26(1), 54–70.

    Google Scholar 

  • Mosa, A. A., Naz’ri bin Mahrin, M., & Ibrrahim, R. (2016). Technological aspects of e-learning readiness in higher education: A review of the literature. Computer and Information Science, 9(1), 113–127.

    Google Scholar 

  • Mosadegh, H., Kharazi, K., & Bazargan, A. (2011). Conducting feasibility of e-learning in gas companies in Yazd province. Journal of Science and Technology Information, 3, 547–569.

    Google Scholar 

  • Namisiko, P., Munialo, C., & Nyongesa, S. (2014). Towards an optimization framework for e-learning in developing countries: A case of private universities in Kenya. Journal of Computer Science and Information Technology, 2(2), 131–148.

    Google Scholar 

  • Naresh, B., & Reddy, B. S. (2015). Challenges and opportunity of E-learning in developed and developing countries-a review. International Journal of Emerging Research in Management &Technology, 4(6), 259–262.

    Google Scholar 

  • Neyland, E. (2011). Integrating online learning in NSW secondary schools: Three schools' perspectives on ICT adoption. Australasian Journal of Educational Technology, 27(1), 152–173.

    Google Scholar 

  • Njihia, J. M., & Oketch, H. A. (2014). E-learning readiness assessment model in Kenyas’ higher education institutions: A case study of university of Nairobi. International Journal of Scientific Knowledge, 5(6), 1–64.

    Google Scholar 

  • Nyeko, J. S., & Ogenmungu, C. (2017). Determinants of electronic learning adoption in higher institutions of learning in Uganda: A learners’ perspective. Global Journal of Computer Science and Technology, 17(1), 1–15.

    Google Scholar 

  • Nyoni, J. (2014). E-readiness of open and distance learning (ODL) facilitators: Implications for effective mediation. Perspectives in Education, 32(3), 78–91.

    Google Scholar 

  • Obi, I. E., Charles-Okoli, A. N., Agunwa, C. C., Omotowo, B. I., Ndu, A. C., & Agwu-Umahi, O. R. (2018). E-learning readiness from perspectives of medical students: A survey in Nigeria. Nigerian Journal of Clinical Practice, 21(3), 293–300.

    Google Scholar 

  • Oketch, H. A., Njihia, J. M., & Wausi, A. N. (2014). E-learning readiness assessment model in Kenya’s higher education institutions: A case study of the University of Nairobi. International Journal of Scientific Knowledge, 5(6), 29–41.

    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.

    Google Scholar 

  • Petković, D., Jovic, S., & Golubovic, Z. (2018). Object-oriented modeling approach of universal education software. Computer Applications in Engineering Education, 26(3), 543–558.

    Google Scholar 

  • Punnoose, A. C. (2012). Determinants of intention to use eLearning based on the technology acceptance model. Journal of Information Technology Education: Research, 11(1), 301–337.

    Google Scholar 

  • Ramayasa, I. P. (2015). Evaluation model of success and acceptance of e-learning. Journal of Theoretical and Applied Information Technology, 82(3), 462–469.

    Google Scholar 

  • Ramdani, B., Kawalek, P., & Lorenzo, O. (2009). Predicting SMEs' adoption of enterprise systems. Journal of Enterprise Information Management, 22(1/2), 10–24.

    Google Scholar 

  • Raouf, J. B., Naser, I. S., & Jassim, B. K. (2012). Determinants of e-learning implementation success in the Iraqi MoHE. Engineering and Technology Journal, 30(4), 659–671.

    Google Scholar 

  • Ringle, C. M., Sarstedt, M., & Straub, D. W. (2012). Editor's comments: A critical look at the use of PLS-SEM in MIS quarterly. MIS Quarterly, 36(1), iii–xiv.

    Google Scholar 

  • Rogers, E. M. (2003). Diffusion of innovations. New York: Free Press.

    Google Scholar 

  • Sife, A., Lwoga, E., & Sanga, C. (2007). New technologies for teaching and learning: Challenges for higher learning institutions in developing countries. International journal of education and development using ICT, 3(2), 57–67.

    Google Scholar 

  • Sims, R. (2008). Rethinking (e) learning: A manifesto for connected generations. Distance Education, 29(2), 153–164.

    Google Scholar 

  • Smaldino, S. (1999). Instructional design for distance education. TechTrends, 43(5), 9–13.

    Google Scholar 

  • Summers, M. (1990). New student teachers and computers: An investigation of experiences and feelings. Educational Review, 42(3), 261–271.

    Google Scholar 

  • Tagoe, M., & Abakah, E. (2014). Determining distance education students' readiness for mobile learning at University of Ghana using the theory of planned behavior. International Journal of Education and Development using Information and Communication Technology, 10(1), 91–106.

    Google Scholar 

  • Tarhini, A., Hone, K., Liu, X., & Tarhini, T. (2017). Examining the moderating effect of individual-level cultural values on users’ acceptance of E-learning in developing countries: A structural equation modeling of an extended technology acceptance model. Interactive Learning Environments, 25(3), 306–328.

    Google Scholar 

  • Tarus, J. K., Gichoya, D., & Muumbo, A. (2015). Challenges of implementing e-learning in Kenya: A case of Kenyan public universities. The International Review of Research in Open and Distributed Learning, 16(1), 120–141.

  • Tashkandi, A. N., & Al-Jabri, I. M. (2015). Cloud computing adoption by higher education institutions in Saudi Arabia: An exploratory study. Cluster Computing, 18(4), 1527–1537.

    Google Scholar 

  • Teo, T. S., Lim, G. S., & Fedric, S. A. (2009). The adoption and diffusion of human resources information systems in Singapore. Asia Pacific Journal of Human Resources, 45(1), 44–62.

    Google Scholar 

  • Tornatzky, L. G., Fleischer, M., & Chakrabarti, A. K. (1990). Processes of technological innovation. Lexington: Lexington Books.

    Google Scholar 

  • Tyilo, N. (2017). E-learning as instructional innovation in higher education institutions (HEI’s): Lessons learnt from the literature. Journal of Communication, 8(1), 87–93.

    Google Scholar 

  • Urbach, N., & Ahlemann, F. (2010). Structural equation modeling in information systems research using partial least squares. Journal of Information Technology, Theory and Applications, 11(2), 5–40.

    Google Scholar 

  • Van Raaij, E. M., & Schepers, J. J. (2008). The acceptance and use of a virtual learning environment in China. Computers & Education, 50(3), 838–852.

    Google Scholar 

  • Vijayasarathy, L. R. (2004). Predicting consumer intentions to use on-line shopping: The case for an augmented technology acceptance model. Information & Management, 41, 747–762.

    Google Scholar 

  • Wang, M., Chen, Y., & Khan, M. J. (2014). Mobile cloud learning for higher education: A case study of Moodle in the cloud. The International Review of Research in Open and Distributed Learning, 15(2), 254–267.

    Google Scholar 

  • Watkins, R., Leigh, D., & Triner, D. (2004). Assessing readiness for e-learning. Performance Improvement Quarterly, 17(4), 66–79.

    Google Scholar 

  • Weber, A. S., & Hamlaoui, S. (Eds.). (2018). E-Learning in the Middle East and North Africa (MENA) Region. Berlin: Springer.

    Google Scholar 

  • Yadegaridehkordi, E., Shuib, L., Nilashi, M., & Asadi, S. (2019). Decision to adopt online collaborative learning tools in higher education: A case of top Malaysian universities. Education and Information Technologies, 24(1), 79–102.

    Google Scholar 

  • Yilmaz, R. (2017). Exploring the role of e-learning readiness on student satisfaction and motivation in flipped classroom. Computers in Human Behavior, 70, 251–260.

    Google Scholar 

  • Yuen, A. H., & Ma, W. W. (2008). Exploring teacher acceptance of e-learning technology. Asia-Pacific Journal of Teacher Education, 36(3), 229–243.

    Google Scholar 

  • Yusof, M. M., Kuljis, J., Papazafeiropoulou, A., & Stergioulas, L. K. (2008). An evaluation framework for health information systems: Human, organization and technology-fit factors (HOT-fit). International Journal of Medical Informatics, 77, 386–398.

    Google Scholar 

  • Zhang, D., Zhao, J. L., Zhou, L., & Nunamaker, J. F., Jr. (2004). Can e-learning replace classroom learning? Communications of the ACM, 47(5), 75–79.

    Google Scholar 

  • Zheng, Y., Wang, J., Doll, W., Deng, X., & Williams, M. (2018). The impact of organisational support, technical support, and self-efficacy on faculty perceived benefits of using learning management system. Behaviour & Information Technology, 37(4), 311–319.

    Google Scholar 

Download references

Acknowledgements

The authors would like to thank anonymous reviewers and those who aided in ensuring the high quality of this paper through the review process.

Author information

Authors and Affiliations

  1. Islamic Azad University, Lahijan Branch, Lahijan, Iran

    Marva Mirabolghasemi & Sahar Hosseinikhah Choshaly

  2. Universiti Teknologi Malaysia, Johor Bahru, Malaysia

    Noorminshah A. Iahad

Authors
  1. Marva Mirabolghasemi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sahar Hosseinikhah Choshaly
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Noorminshah A. Iahad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marva Mirabolghasemi.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix A. Question Wording

Appendix A. Question Wording

Computer Self-efficacy (CSE)

1- I could complete the tasks using e-learning if I had only the software manuals for reference.

2- I could complete the tasks using e-learning if I had seen someone else using it before trying it myself.

3- I could complete the tasks using e-learning if I had enough time.

4- I could complete the tasks using e-learning if I had just the built-in help facility for assistance.

5- I could complete the tasks using e-learning if someone showed me how to do it first.

6- I could complete the tasks using e-learning if I had used similar software.

Subjective Norm (SN)

1- People who are important to me would strongly support using e-learning in my study.

2- People who their opinions are important to me prefer that I use e-learning in my study.

IS/IT Knowledge (ISTK)

1- The institution has an extensive e-learning technical knowledge.

2- E-learning is a familiar type of technology to use.

3- I have the knowledge to use e-learning services.

4- I have enough understanding of using e-learning services.

Management Support (MS)

1- Managers are interested in the use of e-learning services.

2- Managers are willing to invest on the necessary resources.

3- Managers are willing to take risks involved in e-learning usage.

Relative Advantage (RA)

1- E-learning enhances user’s satisfaction which leads to improves in academic performance.

2- E-learning offer convenience in service provision.

3- E-learning usage is better than the use of face to face teaching and learning.

4- Using e-learning reduce the cost in service delivery.

5- Using e-learning improves the effectiveness in my study.

Compatibility (CMPA)

1- Using e-learning is compatible with the institution’s goals and objectives.

2- The use of e-learning is compatible with all aspects of our institution’s operations.

3- E-learning is compatible with the institutions’ existing culture and values.

4- E-learning is compatible with the institution’s existing IT infrastructure.

Complexity(CMPL)

1- The skills needed to use e-learning are very complex.

2- Integrating e-learning in the current practices will be a challenge.

3- Overall, I believe that e-learning is easy to use.

E-learning Readiness (ELR)

1- I have positive belief towards the efficiency of e-learning in my study.

2- I am a pioneer of using e-learning in my study.

3- If I want to use e-learning in my study, I prefer to use basic features.

4- I distrustful of using e-learning in my study.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mirabolghasemi, M., Choshaly, S.H. & Iahad, N.A. Using the HOT-fit model to predict the determinants of E-learning readiness in higher education: a developing Country’s perspective. Educ Inf Technol 24, 3555–3576 (2019). https://doi.org/10.1007/s10639-019-09945-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10639-019-09945-9

Keywords

Search

Navigation