Advertisement

Educational Technology Research and Development

, Volume 67, Issue 6, pp 1613–1637 | Cite as

Cultural divides in acceptance and continuance of learning management system use: a longitudinal study of teenagers

  • Miaoting Cheng
  • Allan H. K. YuenEmail author
Cultural and Regional Perspectives
First Online:
  • 139 Downloads

Abstract

Drawing on the technology acceptance model, the theory of reasoned action, and the expectation-confirmation model, an integrated model was proposed to explore teenagers’ learning management system (LMS) acceptance and continuance. Based on the data collected from a longitudinal survey of 1182 junior secondary students in Hong Kong, the results of structural equation modelling (SEM) supported the hypothesised model. Key findings were peer and teacher influences and perceived ease of use demonstrated significant effects; whereas parental influence and perceived usefulness had no effect, on behavioural intention over time. Multi-group SEM was used to test whether the paths in the hypothesized model varied across teenagers with different immigrant backgrounds. The sample was classified into three cultural groups: 203 first-generation immigrant students (FG), 354 second-generation immigrant students (FG), and 521 non-immigrant student (Native). The results showed that cultural divides existed in the relations of the proposed model across the FG, SG, and Native groups. The FG group, who were Mainland China born immigrants, were significantly different from the Native group in terms of the effects of perceptions, use experience, parental influence, and peer influence on their learning satisfaction and behavioural intention. The SG and Native groups, students who were born in Hong Kong, were the least noticeable in significant path differences. To highlight, peer influence demonstrated significantly stronger relationships with the FG group’s intention at the initial use stage, and peer influence only had a significant relationship with satisfaction for the FG and SG group. Discussion and implications of the findings are presented.

Keywords

e-Learning environments Learning management systems Technology acceptance Immigrant students Secondary education 
This is a preview of subscription content, log in to check access.

Notes

Funding

This study was funded by the Research Grants Council of the Hong Kong Special Administrative Region (Project No.: 17411414).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
  2. Al-Emran, M., Mezhuyev, V., & Kamaludin, A. (2018). Technology acceptance model in m-learning context: A systematic review. Computers & Education,125, 389–412.CrossRefGoogle Scholar
  3. Alhirz, H., & Sajeev, A. (2015). Do cultural dimensions differentiate ERP acceptance? A study in the context of Saudi Arabia. Information Technology & People,28(1), 163–194.CrossRefGoogle Scholar
  4. Arbuckle, J. L. (2014). Amos 23.0 user’s guide. Chicago: IBM SPSS.Google Scholar
  5. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
  6. Bentler, P., & Appelbaum, Mark I. (1990). Comparative fit indexes in structural models. Psychological Bulletin,107(2), 238–246.CrossRefGoogle Scholar
  7. Berry, J. (1997). Immigration, acculturation, and adaptation. Applied Psychology,46(1), 5–34.Google Scholar
  8. Bhattacherjee, A. (2001a). An empirical analysis of the antecedents of electronic commerce service continuance. Decision Support Systems,32(2), 201–214.CrossRefGoogle Scholar
  9. Bhattacherjee, A. (2001b). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly,25(3), 351–370.CrossRefGoogle Scholar
  10. 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
  11. Bourgonjon, J., Valcke, M., Soetaert, R., & Schellens, T. (2010). Students’ perceptions about the use of video games in the classroom. Computers & Education,54(4), 1145–1156.CrossRefGoogle Scholar
  12. Census and Statistics Department. (2011). Babies born in Hong Kong to Mainland Women. Hong Kong: Census and Statistics Department.Google Scholar
  13. Chan, R., (2002). Acculturation of young new arrivals from mainland China to Hong Kong. (Doctoral dissertation, The Chinese University of Hong Kong).Google Scholar
  14. Cheng, M., & Yuen, A. H. K. (2018). Student continuance of learning management system use: A longitudinal exploration. Computers & Education,120, 241–253.CrossRefGoogle Scholar
  15. 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
  16. Chong, S. (2004). A critical perspective of culturally diverse children in the changing school population in Hong Kong. (Doctoral dissertation, University of Toronto).Google Scholar
  17. Chou, S., & Liu, C. (2005). Learning effectiveness in a Web-based virtual learning environment: A learner control perspective. Journal of Computer Assisted Learning,21(1), 65–76.CrossRefGoogle Scholar
  18. d’Addio, A. C. (2007). Intergenerational transmission of disadvantage: Mobility or immobility across generations? OECD Social, Employment, and Migration Working Papers (p. 52).Google Scholar
  19. Dağhan, G., & Akkoyunlu, B. (2016). Modeling the continuance usage intention of online learning environments. Computers in Human Behavior,60, 198–211.CrossRefGoogle Scholar
  20. Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems. (Doctoral dissertation, Massachusetts Institute of Technology).Google Scholar
  21. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly,13(3), 319–340.CrossRefGoogle Scholar
  22. 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
  23. Delone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems,19(4), 9–30.CrossRefGoogle Scholar
  24. EDB. (2009). Main report: Working group on textbooks and e-Learning resources development. Hong Kong: Education Bureau, Government of the Hong Kong Special Administrative Region.Google Scholar
  25. EDB. (2012). Report on the review surveys of the THIRD strategy on information technology in education. Hong Kong: Education Bureau, Government of the Hong Kong Special Administrative Region.Google Scholar
  26. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.Google Scholar
  27. Fornell, C., & Larker, D. (1981). Structural equation modeling and regression: Guidelines for research practice. Journal of Marketing Research,18(1), 39–50.CrossRefGoogle Scholar
  28. Friedrich, H. F., & Hron, A. (2010). Factors influencing pupils’ acceptance of an e-learning system for secondary schools. Journal of Educational Computing Research,42(1), 63–78.CrossRefGoogle Scholar
  29. Gil-Aluja, J. (2004). Fuzzy sets in the management of uncertainty. Berlin, New York: Springer.CrossRefGoogle Scholar
  30. Greener, S. (2017). Cultural diversity and learning technology. Interactive Learning Environments,25(8), 947–948.CrossRefGoogle Scholar
  31. Gu, M. M. (2011). ‘I am not qualified to be a Honkongese because of my accented Cantonese’: Mainland Chinese immigrant students in Hong Kong. Journal of Multilingual and Multicultural Development,32(6), 515–529.CrossRefGoogle Scholar
  32. Hatcher, L., & O’Rourke, N. (2013). A step-by-step approach to using SAS for factor analysis and structural equation modeling. Cary, NC: SAS Institute.Google Scholar
  33. Ho, K. (2006). Stories of marriage migration: Identity negotiation of Chinese immigrant women in Hong Kong. (Doctoral dissertation, The University of Hong Kong).Google Scholar
  34. Home Affairs Department & Immigration Department. (2006). Statistics on new arrivals from the Mainland (Fourth quarter of 2006). Hong Kong: Home Affairs Department & Immigration Department.Google Scholar
  35. Home Affairs Department & Immigration Department. (2012). Statistics on new arrivals from the Mainland (Fourth quarter of 2012). Hong Kong: Home Affairs Department & Immigration Department.Google Scholar
  36. Home Affairs Department & Immigration Department. (2016). Statistics on new arrivals from the Mainland (Fourth quarter of 2016). Hong Kong: Home Affairs Department & Immigration Department.Google Scholar
  37. Hong Kong Government. (2013a), LCQ2: One way permit scheme. Retrieved from http://www.info.gov.hk/gia/general/201303/20/P201303200372.htm.
  38. Hong Kong Government. (2013b), LCQ12: Immigration policy. Retrieved from http://www.info.gov.hk/gia/general/201303/20/P201303200372.htm.
  39. Hofstede, G. (2013). Values survey module 2013 questionnaire Chinese (Hong Kong) version. Retrieved from https://geerthofstede.com/wpcontent/uploads/2017/10/VSM2013_HongKongVersion.pdf.
  40. Hofstede, G., Hofstede, G. J., & Minkov, M. (2010). Cultures and organizations: Software of the mind (Rev. and expanded (3rd ed.). New York: McGraw-Hill.Google Scholar
  41. Hofstede, G., & Minkov, M. (2013). Values survey module 2013 manual. Retrieved from https://geerthofstede.com/wp-content/uploads/2016/07/Manual-VSM-2013.pdf.
  42. Hossain, L., & Silva, A. D. (2009). Exploring user acceptance of technology using social networks. The Journal of High Technology Management Research,20(1), 1–18.CrossRefGoogle Scholar
  43. Islam, A. N. (2013). Investigating e-learning system usage outcomes in the university context. Computers & Education,69, 387–399.CrossRefGoogle Scholar
  44. Islam, A. N., & Azad, N. (2015). Satisfaction and continuance with a learning management system: Comparing perceptions of educators and students. The International Journal of Information and Learning Technology,32(2), 109–123.CrossRefGoogle Scholar
  45. Kline, R. B. (2005). Principles and practice of structural equation modeling. Methodology in the social sciences (2nd ed.). New York: Guilford Press.Google Scholar
  46. Lau, G. K. (2014). Digital divide in education: A shift to ethical usage. (Doctoral dissertation, The University of Hong Kong).Google Scholar
  47. Lau, W. W., & Yuen, A. H. K. (2014). Internet ethics of adolescents: Understanding demographic differences. Computers & Education,72, 378–385.CrossRefGoogle Scholar
  48. Law, K.-Y., & Lee, K.-M. (2006). Citizenship, economy and social exclusion of mainland Chinese immigrants in Hong Kong. Journal of Contemporary Asia,36(2), 217–242.CrossRefGoogle Scholar
  49. 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
  50. Lee, S.-G., Trimi, S., & Kim, C. (2013). The impact of cultural differences on technology adoption. Journal of World Business,48(1), 20–29.CrossRefGoogle Scholar
  51. 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
  52. Limayem, M., & Cheung, C. M. K. (2008). Understanding information systems continuance: The case of Internet-based learning technologies. Information & Management,45(4), 227–232.CrossRefGoogle Scholar
  53. Limayem, M., & Cheung, C. M. K. (2011). Predicting the continued use of Internet-based learning technologies: The role of habit. Behaviour & Information Technology,30(1), 91–99.CrossRefGoogle Scholar
  54. Lin, X., & Hatano, G. (2003). Technology, culture, and adaptive minds: An introduction. Mind, Culture, and Activity,10(1), 3–8.CrossRefGoogle Scholar
  55. Lin, C. S., Wu, S., & Tsai, R. J. (2005). Integrating perceived playfulness into expectation-confirmation model for web portal context. Information & Management,42(5), 683–693.CrossRefGoogle Scholar
  56. 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
  57. Ma, W. K., & Yuen, H. K. (2011). e-Learning system acceptance and usage pattern. In T. Teo (Ed.), Technology acceptance in education: Research and issues (pp. 201–216). Rotterdam: Sense Publishers.CrossRefGoogle Scholar
  58. Marks, G. N. (2005). Accounting for immigrant non-immigrant differences in reading and mathematics in twenty countries. Ethnic and Racial Studies,28(5), 925–946.CrossRefGoogle Scholar
  59. McGill, T., Hobbs, V., & Klobas, J. E. (2003). User developed applications and information systems success: A test of DeLone and McLean’s model. Information Resources Management Journal,16(1), 24–45.CrossRefGoogle Scholar
  60. Metallo, C., & Agrifoglio, R. (2015). The effects of generational differences on use continuance of Twitter: An investigation of digital natives and digital immigrants. Behaviour & Information Technology,34(9), 869–881.CrossRefGoogle Scholar
  61. OECD. (2004). Learning for tomorrow’s world: First results from PISA 2003. Paris and Washington, DC: Organisation for Economic Co-operation and Development.CrossRefGoogle Scholar
  62. OECD. (2007). PISA 2006: Science competencies for tomorrow’s world. Paris and Washington, DC: Organisation for Economic Co-operation and Development.CrossRefGoogle Scholar
  63. OECD. (2012). Untapped skills: Realising the potential of immigrant students. Paris and Washington, DC: Organisation for Economic Co-operation and Development.CrossRefGoogle Scholar
  64. OECD. (2015). Helping immigrant students to succeed at school—and beyond. Paris and Washington, DC: Organisation for Economic Co-operation and Development.CrossRefGoogle Scholar
  65. Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research,17(4), 460–469.CrossRefGoogle Scholar
  66. Pelgrum, W. (2008). School practices and conditions for pedagogy and ICT. In N. Law, W. J. Pelgrum, & T. Plomp (Eds.), Pedagogy and ICT use (pp. 67–120). Dordrecht: Springer.CrossRefGoogle Scholar
  67. Phillion, J. (2008). Multicultural and cross-cultural narrative inquiry into understanding immigrant students’ educational experience in Hong Kong. Compare: A Journal of Comparative and International Education,38(3), 281–293.CrossRefGoogle Scholar
  68. Pong, S.-L. (2009). Grade level and achievement of immigrants’ children: Academic redshirting in Hong Kong. Educational Research and Evaluation,15(4), 405–425.CrossRefGoogle Scholar
  69. Schleicher, A. (2006). Where immigrant students succeed: A comparative review of performance and engagement in PISA 2003. Intercultural Education,17(5), 507–516.CrossRefGoogle Scholar
  70. Shiue, Y. M., & Hsu, Y. C. (2017). Understanding factors that affecting continuance usage intention of game-based learning in the context of collaborative learning. Eurasia Journal of Mathematics Science and Technology Education,13(10), 6445–6455.CrossRefGoogle Scholar
  71. Srite, M., & Karahanna, E. (2006). The role of espoused national cultural values in technology acceptance. MIS Quarterly,30(3), 679–704.CrossRefGoogle Scholar
  72. 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
  73. 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
  74. Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research,6(2), 144–176.CrossRefGoogle Scholar
  75. Teo, T., & Noyes, J. (2014). Explaining the intention to use technology among pre-service teachers: A multi-group analysis of the unified theory of acceptance and use of technology. Interactive Learning Environments,22(1), 51–66.CrossRefGoogle Scholar
  76. Teo, A. C., Tan, G. W. H., Cheah, C. M., Ooi, K. B., & Yew, K. T. (2012). Can the demographic and subjective norms influence the adoption of mobile banking? International Journal of Mobile Communications,10(6), 578–597.CrossRefGoogle Scholar
  77. Teo, T., Wong, S. L., & Chai, C. S. (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). Journal of Educational Technology & Society,11(4), 265–280.Google Scholar
  78. Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences,39(2), 273–315.CrossRefGoogle Scholar
  79. 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
  80. 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.CrossRefGoogle Scholar
  81. 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
  82. Venkatesh, V., Speier, C., & Morris, M. G. (2002). User acceptance enablers in individual decision making about technology: Toward an integrated model. Decision Sciences,33(2), 297–316.CrossRefGoogle Scholar
  83. 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.CrossRefGoogle Scholar
  84. Venkatesh, V., & Zhang, X. (2010). Unified theory of acceptance and use of technology: US vs China. Journal of Global Information Technology Management,13(1), 5–27.CrossRefGoogle Scholar
  85. Wong, Y.-C. (2011). The challenges for educational achievements of young Mainland Chinese migrants in Hong Kong. Asia Pacific Journal of Education,31(3), 277–291.CrossRefGoogle Scholar
  86. Wu, J.-H., Tennyson, R. D., & Hsia, T.-L. (2010). A study of student satisfaction in a blended e-learning system environment. Computers & Education,55(1), 155–164.CrossRefGoogle Scholar
  87. Yuen, A. H. K., Law, N. W., Lee, M. W., & Lee, Y. (2010). The changing face of education in Hong Kong: Transition into the 21st century. Hong Kong: Centre for Information Technology in Education, The University of Hong Kong.Google Scholar
  88. Zhang, J. (2007). A cultural look at information and communication technologies in eastern education. Educational Technology Research and Development,55(3), 301–331.CrossRefGoogle Scholar
  89. Zhou, Z., Fang, Y., Vogel, D. R., Jin, X.-L., & Zhang, X. (2012). Attracted to or locked in? Predicting continuance intention in social virtual world services. Journal of Management Information Systems,29(1), 273–306.CrossRefGoogle Scholar
  90. Zhu, Y., & Leung, F. K. (2011). Mathematics achievement of mainland immigrant students in Hong Kong. Asia Pacific Journal of Education,31(4), 471–485.CrossRefGoogle Scholar

Copyright information

© Association for Educational Communications and Technology 2019

Authors and Affiliations

  1. 1.Division of Information Technology Studies, Faculty of EducationThe University of Hong KongPok Fu LamHong Kong

Personalised recommendations

Cite article

Buy options