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Journal of Computing in Higher Education

, Volume 26, Issue 2, pp 159–181 | Cite as

Determinants of mobile wireless technology for promoting interactivity in lecture sessions: an empirical analysis

  • Chin Lay Gan
  • Vimala Balakrishnan
Article

Abstract

The aim of this paper is to identify adoption factors of mobile wireless technology to increase interactivity between lecturers and students during lectures. A theoretical framework to ascertain lecturers’ intentions to use mobile wireless technology during lectures (dependent variable) is proposed with seven independent variables. The independent variables were ease of use and usefulness from Technology Acceptance Model; trust from Wireless Internet via Mobile Devices model and Mobile Services Acceptance Model; self-efficacy from Social Cognitive Theory; enjoyment from Motivational Model; social influence from Unified Theory of Acceptance and Use of Technology; and uncertainty avoidance from Geert Hofstede’s cultural dimensions (Hofstede et al. in Cultures and organizations: software of the mind, 3rd edn. McGraw-Hill, New York, 2010). Four lecture observations were conducted and interaction barriers identified. Interviews with 22 selected lecturers were conducted to elicit perceptions of mobile wireless technology use during lectures and validate the framework’s variables. Interview results from thematic analysis strongly validated mobile wireless technology’s usefulness as a supporting, collaboration and real-time interaction tool, especially among introvert students. Ease of use, self-efficacy and enjoyment are supported through familiarity with mobile wireless technology. Majority of the respondents are apprehensive that mobile wireless technology might cause disruptions during lectures, with concerns of redundancy, dependency and misuses amongst students (attributes of uncertainty avoidance). None of the respondents are currently using mobile wireless technology for interaction during lectures, thus lending credence to social influence. Very few respondents agree that use of mobile wireless technology can reduce students’ boredom and make lectures more enjoyable, and few perceive intermittent wireless connection will affect user trust. Knowledge of significant mobile wireless technology adoption factors and concerns may be important and applicable to tertiary education in Malaysia.

Keywords

Interactivity Interactive lectures Technology acceptance models Mobile wireless technology Uncertainty avoidance 

Notes

Acknowledgments

The authors extend their gratitude to University of Malaya for supporting the study (FL004-2012).

References

  1. Adams, D., Nelson, R., & Todd, P. (1992). Perceived usefulness, ease of use and usage of information technology: A replication. MIS Quarterly, 16(2), 227–247.CrossRefGoogle Scholar
  2. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211.CrossRefGoogle Scholar
  3. Allen, D., & Tanner, K. (2005). Infusing active learning into the large-enrollment biology class: Seven strategies, from the simple to complex. Cell Biology Education, 4(4), 262–268.CrossRefGoogle Scholar
  4. Alzaza, N. S., & Yaakub, A. R. (2011). Students’ awareness and requirements of mobile learning services in the higher education environment. American Journal of Economics and Business Administration, 3(1), 95–100.CrossRefGoogle Scholar
  5. Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215.CrossRefGoogle Scholar
  6. Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52, 1–26.CrossRefGoogle Scholar
  7. Bandyopadhyay, K., & Fraccastoro, K. A. (2007). The effect of culture on user acceptance of information technology. Communications of the Association for Information Systems, 19(522–543), 23.Google Scholar
  8. Beekes, W. (2006). The ‘Millionaire’ method for encouraging participation. Active Learning in Higher Education, 7(1), 25–36.CrossRefGoogle Scholar
  9. Berg, B. L. (2000). Qualitative research methods for the social science. Boston, MA: Allyn and Bacon.Google Scholar
  10. Bryman, A., & Burgess, R. G. (1998). Qualitative Research: Sage Publication. CA: Thousand Oaks.Google Scholar
  11. Cheng, D., Liu, G, Song, Y. F., and Qian, C. (2008). Adoption of Internet banking: An integrated model. Paper presented at the 4th International Conference on Wireless Communications, Networking and Mobile Computing (WICOM ‘08), Dalian. Conference Publications retrieved from.Google Scholar
  12. Chong, J. L., Chong, A. Y. L., Ooi, K. B., & Lin, B. (2011). An empirical analysis of the adoption of m-learning in Malaysia. International Journal of Mobile Communications, 9(1), 1–18.CrossRefGoogle Scholar
  13. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.CrossRefGoogle Scholar
  14. 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
  15. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111–1132. doi: 10.1111/j.1559-1816.1992.tb00945.x.CrossRefGoogle Scholar
  16. DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A 10-year update. Journal of Management Information Systems, 19(4), 9–30.Google Scholar
  17. d’Inverno, R., Davis, H., & White, S. (2003). Using a personal response system for promoting student interaction. Teaching Mathematics Applications, 22(4), 163–169.CrossRefGoogle Scholar
  18. Dobson-Mitchell, S. (2011). Are big classes really a problem? Retrieved April 10, 2013, from http://oncampus.macleans.ca/education/2011/12/16/are-big-classes-really-a-problem/.
  19. Domagk, S., Schwartz, R. N., & Plass, J. L. (2010). Interactivity in multimedia learning: An integrated model. Computers in Human Behavior, 26(5), 1024–1033.CrossRefGoogle Scholar
  20. Doran, M. S., & Golen, S. (1998). Identifying communication barriers to learning in large group accounting instruction. Journal of Education for Business, 73(4), 221–224.CrossRefGoogle Scholar
  21. Draper, S. W., and Brown, M. I. (2002). Use of the PRS (Personal Response System) handsets at Glasgow University, Interim Evaluation Report: March 2002 Retrieved Nov 15, 2012, from http://www.psy.gla.ac.uk/~steve/evs/interim.html.
  22. Eckhardt, A., Laumer, S., & Weitzel, T. (2009). Who influences whom? Analyzing workplace referents’ social influence on IT adoption and non-adoption. Journal of Information Technology Education: Innovations in Practice, 24(1), 11–24.CrossRefGoogle Scholar
  23. Fishbein, M., & Azjen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.Google Scholar
  24. Gao, S., Moe, S. P., and Krogstie, J. (2010, 13-15 June). An empirical test of the mobile services acceptance model. Paper presented at the 2010 Ninth Global Mobility Roundtable.Google Scholar
  25. Gartner, Inc. (2013). Gartner Says Mobility Is Reshaping Consumer Gadget Spending and Behavior. Retrieved September 8, 2013, from http://www.gartner.com/newsroom/id/2370215.
  26. Geske, J. (1992). Overcoming the drawbacks of the large lecture class. College Teaching, 40(2), 151–154. doi: 10.1080/87567555.1992.10532239.CrossRefGoogle Scholar
  27. Grandon, E. E., Alshare, K., & Kwun, O. (2005). Factors influencing student intention to adopt online classes: A cross-cultural study. Journal of Computing Sciences in Colleges, 20(4), 46–56.Google Scholar
  28. Hake, R. R. (1998). Interactive-engagement versus traditional methods: A six-thousand-student survey of mechanics test data for introductory physics courses. American Journal of Physics, 66(1), 64–74.CrossRefGoogle Scholar
  29. Halloran, L. (1995). A comparison of two methods of teaching: computer managed instruction and keypad questions versus traditional classroom lecture. Computers in Nursing, 13(6), 285–288.Google Scholar
  30. Hendrickson, A. R., Massey, P. D., & Cronan, T. P. (1993). On the test-retest reliability of perceived usefulness and perceived ease of use scale. MIS Quarterly, 17(2), 227–230.CrossRefGoogle Scholar
  31. Hofstede, G. J., & Minkov, M. (2010). Cultures and organizations: Software of the mind (3rd ed.). New York, USA: McGraw-Hill.Google Scholar
  32. Holden, H., & Rada, R. (2011). Understanding the influence of perceived usability and technology self-efficacy on teachers’ technology acceptance. Journal of Research on Technology in Education, 43(4), 343–367.CrossRefGoogle Scholar
  33. JuniperNetworks. (2012). Global survey reveals mobile technology adoption at risk if consumer trust not addressed. Retrieved September 10, 2013, from http://investor.juniper.net/investor-relations/press-releases/press-release-details/2012/Global-Survey-Reveals-Mobile-Technology-Adoption-at-Risk-If-Consumer-Trust-Not-Addressed/default.aspx.
  34. Kabilan, M. K., Ahmad, N., & Abidin, M. J. Z. (2010). Facebook: An online environment for learning of English in institutions of higher education? The Internet and Higher Education, 13(4), 179–187.CrossRefGoogle Scholar
  35. Kim, S. (2008). Appropriation of wireless technology: Direct impacting factors on the youth`s adoption intention and usage of the wireless application protocol phone. Information Technology Journal, 7(8), 1116–1124.CrossRefGoogle Scholar
  36. Kim, S., & Garrison, G. (2009). Investigating mobile wireless technology adoption: An extension of the technology acceptance model. Information Systems Frontiers, 11(3), 323–333.CrossRefGoogle Scholar
  37. Lee, M. K. O., Cheung, C. M. K., & Chen, Z. (2005). Acceptance of Internet-based learning medium: The role of extrinsic and intrinsic motivation. Information and Management, 42(8), 1095–1104.CrossRefGoogle Scholar
  38. Lin, C. P., & Anol, B. (2008). Learning online social support: an investigation of network information technology based on UTAUT. CyberPsychology and Behavior, 11(3), 268–272.CrossRefGoogle Scholar
  39. Lu, J., Yu, C. S., Liu, C., & Yao, J. E. (2003). Technology acceptance model for wireless Internet. Internet Research, 13(3), 206–222.CrossRefGoogle Scholar
  40. Mahat, J., Ayub, A. F. M., & Luan, S. (2012). An assessment of students’ mobile self-efficacy, readiness and personal innovativeness towards mobile learning in higher education in Malaysia. Procedia-Social and Behavioral Sciences, 64, 284–290.CrossRefGoogle Scholar
  41. Mäntymäki, M., & Salo, J. (2011). Teenagers in social virtual worlds: Continuous use and purchasing behavior in Habbo Hotel. Computers in Human Behavior, 27(6), 2088–2097.CrossRefGoogle Scholar
  42. Moreno, R., & Mayer, R. (2007). Interactive multimodal learning environments. Educational Psychology Review, 19(3), 309–326.CrossRefGoogle Scholar
  43. Nath, R., & Murthy, N. R. V. (2004). A study of the relationship between Internet diffusion and culture. Journal of International Technology and Information Management, 13(2), 123–132.Google Scholar
  44. Paladino, A. (2008). Creating an interactive and responsive teaching environment to inspire learning. Journal of Marketing Education, 30(3), 185–188.CrossRefGoogle Scholar
  45. Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university students’ behavioral intention to use e-learning. Educational Technology and Society, 12(3), 150–162.Google Scholar
  46. Parveen, F., & Sulaiman, A. (2008). Technology complexity, personal innovativeness and intention to use wireless Internet using mobile devices in Malaysia. International Review of Business Research Papers, 4(5), 1–10.Google Scholar
  47. Pituch, K. A., & Lee, Y. K. (2006). The influence of system characteristics on e-learning use. Computers and Education, 47(2), 222–244.CrossRefGoogle Scholar
  48. Poirier, C. R., & Feldman, R. S. (2007). Promoting active learning using individual response technology in large introductory psychology classes. Teaching of Psychology, 34(3), 194–196.CrossRefGoogle Scholar
  49. Pollock, S. J. (2006). Transferring transformations: learning gains, student attitudes, and the impacts of multiple instructors in large lecture courses. AIP Conference Proceedings, 818(1), 141–144.CrossRefGoogle Scholar
  50. Renkl, A., & Atkinson, R. K. (2007). Interactive learning environments: Contemporary issues and trends. An introduction to the special issue. Educational Psychology Review, 19(3), 235–238.CrossRefGoogle Scholar
  51. Saadé, R. G., Nebebe, F., & Tan, W. (2007). Viability of the “technology acceptance model” in multimedia learning environments: A comparative study. Interdisciplinary Journal of Knowledge and Learning Objects, 3, 175–184.Google Scholar
  52. Sabry, K., & Barker, J. (2009). Dynamic interactive learning systems. Innovations in Education and Teaching International, 46(2), 185–197.CrossRefGoogle Scholar
  53. Scott, W. E., Farh, J., & Podaskoff, P. M. (1988). The effects of “intrinsic” and “extrinsic” reinforcement contingencies on task behavior. Organizational Behavior and Human Decision Processes, 41(3), 405–425.CrossRefGoogle Scholar
  54. Shen, R., Wang, M., Gao, W., Noval, D., & Tang, L. (2009). Mobile learning in a large blended computer science classroom: System function, pedagogies, and their impact on learning. IEEE Transactions on Education, 52(4), 538–546.CrossRefGoogle Scholar
  55. Shin, D. H. (2009). Determinants of customer acceptance of multi-service network: An implication for IP-based technologies. Information and Management, 46(1), 16–22.CrossRefGoogle Scholar
  56. Singer, D., Avery, A., & Baradwaj, B. (2008). Management innovation and cultural adaptivity in international online banking. Management Research News, 31(4), 258–272.CrossRefGoogle Scholar
  57. Stowell, J. R., Oldham, T., & Bennett, D. (2010). Using student response systems (“Clickers”) to combat conformity and shyness. Teaching of Psychology, 37(2), 135–140. doi: 10.1080/00986281003626631.CrossRefGoogle Scholar
  58. Subramanian, G. H. (1994). A replication of perceived usefulness and perceived ease of use measurement. Decision Sciences, 25(5/6), 863–874.CrossRefGoogle Scholar
  59. Van Dijk, L. A., Van Der Berg, G. C., & Keulan, V. (2001). Interactivies lectures in engineering education. European Journal of Engineering Education, 26(1), 15–18.CrossRefGoogle Scholar
  60. Veiga, J. F., Floyd, S., & Dechant, K. (2001). Towards modelling the effects of national culture on IT implementation and acceptance. Journal of Information Technology, 16(3), 145–158.CrossRefGoogle Scholar
  61. Venkatesh, V. (2000). Determinants of perceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342–365.CrossRefGoogle Scholar
  62. 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
  63. Venkatesh, V., & Zhang, X. (2010). Unified theory of acceptance and use of technology: U.S. vs. China. Journal of Global Information Technology Management, 13(1), 5–27.Google Scholar
  64. Verhagen, T., Feldberg, F., van den Hooff, B., Meents, S., & Merikivi, J. (2012). Understanding users’ motivations to engage in virtual worlds: A multipurpose model and empirical testing. Computers in Human Behavior, 28(2), 484–495.CrossRefGoogle Scholar
  65. Yeh, N.-C., Lin, J.-C.-C., & Lu, H.-P. (2011). The moderating effect of social roles on user behavior in virtual worlds. Online Information Review, 35(5), 747–769.CrossRefGoogle Scholar
  66. Yeow, P. H. P., YenYuen, Y., & Tong, D. Y. K. (2008). User acceptance of online banking service in Australia. Communications of the IBIMA, 1, 191–197.Google Scholar
  67. Yi, M. Y., & Hwang, Y. (2003). Predicting the use of web-based information systems: self-efficacy, enjoyment, learning goal orientation, and the technology acceptance model. International Journal of Human-Computer Studies, 59(4), 431–449.CrossRefGoogle Scholar
  68. 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
  69. Zakaria, M. H., Watson, J., & Edwards, S. L. (2010). Investigating the use of Web 2.0 technology by Malaysian students. Multicultural Education and Technology Journal, 4(1), 17–29.CrossRefGoogle Scholar
  70. Zhang, S., Zhao, J., & Tan, W. (2008). Extending TAM for online learning systems: An intrinsic motivation perspective. Tsinghua Science and Technology, 13(3), 312–317.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.University of MalayaKuala LumpurMalaysia

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