A Cross-cultural Exploration of Primary Students’ Learning Management System Use: A Mixed Methods Approach

  • Miaoting Cheng
  • Allan Hoi Kau Yuen
  • Qi Li
  • Ying Song
Conference paper
Part of the Educational Communications and Technology Yearbook book series (ECTY)


This study aims to explore and compare Hong Kong (HK) and Shenzhen primary students’ learning manage system (LMS) use and the factors affecting their LMS acceptance. The study was conducted in a mixed methods approach with a survey on 272 grade five students first and focus group interviews with 16 of the survey students followed by. The results of a structural equation modeling analysis on survey data confirm the technology acceptance model and indicate significant differences between two student groups on the model. Specifically, while all paths are supported among Shenzhen students, the effects of perceived ease of use on perceived usefulness and subjective norm on intention to LMS use are not significant among HK students. The results of analysis on interviews data reveal that intrinsic motivation may disassociate perceived ease of use with perceived usefulness. While LMS in HK provides multiple functions that facilitate playfulness, students from Shenzhen reported difficulties in using LMS for learning. Besides, voluntariness of LMS use may play an important role in influencing the effect of subjective norm on intention to use LMS. While HK students who use LMS under voluntary context may disregard social influence, Shenzhen students seem to derive motivation to use LMS from social pressure.


Technology acceptance E-learning Learning management system use Primary students Cross cultural Mixed methods 


  1. Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behaviour. Englewood Cliffs, NJ: Prentice Hall.Google Scholar
  2. Amah, O. E. (2010). Multi-dimensional leader member exchange and work attitude relationship: The role of reciprocity. Asian Journal of Scientific Research, 3(1), 39–50.CrossRefGoogle Scholar
  3. Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94.CrossRefGoogle Scholar
  4. Bhattacherjee, A., & Premkumar, G. (2004). Understanding changes in belief and attitude toward information technology usage: A theoretical model and longitudinal test. MIS Quarterly, 28(2), 229–254.CrossRefGoogle Scholar
  5. Cheung, R., & Vogel, D. (2013). Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers & Education, 63, 160–175.CrossRefGoogle Scholar
  6. Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189–211.CrossRefGoogle Scholar
  7. Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems. Doctoral dissertation, Massachusetts Institute of Technology.Google Scholar
  8. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.CrossRefGoogle Scholar
  9. 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.CrossRefGoogle Scholar
  10. Education Manpower Bureau (EMB). (1998). Information technology for learning in a new era: Five-year strategy 1998/99 to 2002/03. The Bureau: Hong Kong.Google Scholar
  11. Education Manpower Bureau (EMB). (2004). Empowering learning and teaching with information technology. Hong Kong: Education and Manpower Bureau.Google Scholar
  12. Education Manpower Bureau (EMB). (2008). Right technology at the right time for the right task. Hong Kong: Education and Manpower Bureau.Google Scholar
  13. Education Manpower Bureau (EMB). (2014). The fourth strategy on information technology in education: Realising IT potential. In unleashing learning power. Hong Kong: Education and Manpower Bureau.Google Scholar
  14. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley Pub.Google Scholar
  15. Fornell, C., & Larker, D. (1981). Structural equation modeling and regression: Guidelines for research practice. Journal of Marketing Research, 18(1), 39–50.CrossRefGoogle Scholar
  16. Hofstede, G., & Hofstede, G. J. (1991). Cultures and organizations: Software of the mind. London: McGraw-Hill.Google Scholar
  17. Johnson, R. B., & Onwuegbuzie, A. J. (2004). Mixed methods research: A research paradigm whose time has come. Educational Researcher, 33(7), 14–26.CrossRefGoogle Scholar
  18. King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information Management, 43(6), 740–755.CrossRefGoogle Scholar
  19. Lee, M. (2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model. Computers & Education, 54(2), 506–516.CrossRefGoogle Scholar
  20. Leidner, D. E., & Kayworth, T. (2006). Review: A review of culture in information systems research: Toward a theory of information technology culture conflict. MIS Quarterly, 30(2), 357–399.CrossRefGoogle Scholar
  21. Liu, I.-F., Chen, M. C., Sun, Y. S., Wible, D., & Kuo, C.-H. (2010). Extending the TAM model to explore the factors that affect intention to use an online learning community. Computers & Education, 54(2), 600–610.CrossRefGoogle Scholar
  22. Ma, W. W. K., & Yuen, A. H. K. (2011). E-learning system acceptance and usage pattern. In T. Teo (Ed.), Technology acceptance in education (pp. 201–216). Rotterdam, The Netherlands: Sense Publishers.CrossRefGoogle Scholar
  23. Mou, J., Shin, D.-H., & Cohen, J. (2017). Understanding trust and perceived usefulness in the consumer acceptance of an e-service: A longitudinal investigation. Behaviour & Information Technology, 36(2), 125–139.CrossRefGoogle Scholar
  24. Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university students’ behavioral intention to use e-learning. Educational Technology & Society, 12(3), 150–162.Google Scholar
  25. Spector, J. M., & Yuen, A. H. K. (2016). Educational technology program and project evaluation. New York: Taylor and Francis.Google Scholar
  26. Straub, D. W. (1994). The effect of culture on IT diffusion: E-mail and FAX in Japan and the US. Information Systems Research, 5(1), 23–47.CrossRefGoogle Scholar
  27. Straub, D. W., Keil, M., & Brenner, W. (1997). Testing the technology acceptance model across cultures: A three country study. Information Management, 33(1), 1–11.CrossRefGoogle Scholar
  28. Teo, T. (2009). Modelling technology acceptance in education: A study of pre-service teachers. Computers & Education, 52(2), 302–312.CrossRefGoogle Scholar
  29. Teo, T. (2014). Unpacking teachers’ acceptance of technology: Tests of measurement invariance and latent mean differences. Computers & Education, 75, 127–135.CrossRefGoogle Scholar
  30. Teo, T., Luan, W. S., & Sing, C. C. (2008). A cross-cultural examination of the intention to use technology between Singaporean and Malaysian pre-service teachers: An application of the Technology Acceptance Model (TAM). Educational Technology and Society, 11(4), 265–280.Google Scholar
  31. Venkatesh, V., Brown, S. A., & Bala, H. (2013). Bridging the qualitative-quantitative divide: Guidelines for conducting mixed methods research in information systems. MIS Quarterly, 37(1), 21–54.CrossRefGoogle Scholar
  32. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.CrossRefGoogle Scholar
  33. 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.CrossRefGoogle Scholar
  34. Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178.Google Scholar
  35. Wang, W. T., & Wang, C. C. (2009). An empirical study of instructor adoption of web-based learning systems. Computers & Education, 53(3), 761–774.CrossRefGoogle Scholar
  36. Wu, & Zhang, C. (2014). Empirical study on continuance intentions towards E-learning 2.0 systems. Behaviour & Information Technology, 33(10), 1027–1038.CrossRefGoogle Scholar
  37. Yuen, A. H. K. (2011). Exploring teaching approaches in blended learning. Research and Practice in Technology Enhanced Learning, 6(1), 3–23.Google Scholar
  38. Yuen, A. H. K., Fox, R., Sun, A., & Deng, L. (2009). Course management systems in higher education: Understanding student experiences. Interactive Technology and Smart Education, 6(3), 189–205.CrossRefGoogle Scholar
  39. Yuen, A. H. K., & Ma, W. W. K. (2008). Exploring teacher acceptance of e-learning technology. Asia-Pacific Journal of Teacher Education, 36(3), 229–243.CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Miaoting Cheng
    • 1
  • Allan Hoi Kau Yuen
    • 1
  • Qi Li
    • 1
  • Ying Song
    • 1
  1. 1.The University of Hong KongPok Fu LamHong Kong

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