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Mobile Technology and Interactive Lectures: The Key Adoption Factors

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Mobile Learning Design

Part of the book series: Lecture Notes in Educational Technology ((LNET))

Abstract

Lecture classes are fundamental and essential for teaching and learning in higher education. The objective of this study is to investigate adoption factors for promoting interactive lectures in higher education from reviews of technology acceptance models, motivational factors, and cultural dimension theory. The study aims to elicit key factors influencing mobile technology adoption in the classrooms as an interaction tool, focusing on the notion of communication barriers caused by classes with large number of students. Survey involving higher education students enrolled in academic courses in Malaysia was conducted with a sample size of 396. Factor analysis produced three key factors: User system perception (USP), system and information quality (SIQ) and user uncertainty avoidance (UUA). Results of regression analysis revealed UUA as the strongest significant predictor of adoption (beta = −0.225, p < 0.001), and a high proportion of UUA was strongly explained by USP (r = −0.513) and SIQ (r = −0.537). This study underscores the need for researchers to further explore blended learning pedagogies using mobile technology.

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References

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Ashraf, A. R., Thongpapanl, N., & Auh, S. (2014). The application of the technology acceptance model under different cultural contexts: The case of online shopping adoption. Journal of International Marketing, 22(3), 68–93.

    Article  Google Scholar 

  • Ayoun, Baker M., & Moreo, Patrick J. (2008). The influence of the cultural dimension of uncertainty avoidance on business strategy development: A cross-national study of hotel managers. International Journal of Hospitality Management, 27(1), 65–75.

    Article  Google Scholar 

  • Balakrishnan, V., & Gan, C. L. (2013). Mobile wireless technology and its use in lecture room environment: An observation in Malaysian Institutes of higher learning. International Journal of Information and Education Technology, 3(6), 635–637.

    Google Scholar 

  • Bandura, Albert. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215.

    Article  Google Scholar 

  • Bandura, Albert. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52, 1–26.

    Article  Google Scholar 

  • Beekes, W. (2006). The ‘Millionaire’ method for encouraging participation. Active Learning in Higher Education, 7(1), 25–36.

    Article  Google Scholar 

  • Calisir, F., Altin Gumussoy, C., Bayraktaroglu, A. E., & Karaali, D. (2014). Predicting the intention to use a web-based learning system: Perceived content quality, anxiety, perceived system quality, image, and the technology acceptance model. Human Factors and Ergonomics in Manufacturing and Service Industries, 24(5), 515–531.

    Article  Google Scholar 

  • Chen, T. L., & Lan, Y. L. (2013). Using a personal response system as an in-class assessment tool in the teaching of basic college chemistry. Australasian Journal of Educational Technology, 29(1).

    Google Scholar 

  • Chickering, Arthur W., & Gamson, Zelda F. (1987). Seven principles for good practice in undergraduate education. New Direction for Teaching and Learning, 47, 1–7.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of measure and initial test. MIS Quarterly, 19(2), 189–211.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.

    Article  Google Scholar 

  • DeLone, W. H., & McLean, E. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9–30.

    Article  Google Scholar 

  • Detlor, B., Hupfer, M. E., Ruhi, U., & Zhao, L. (2013). Information quality and community municipal portal use. Government Information Quarterly, 30(1), 23–32.

    Article  Google Scholar 

  • Dobson-Mitchell, S. (2011). Are big classes really a problem? Retrieved November 10, 2014, from http://oncampus.macleans.ca/education/2011/12/16/are-big-classes-really-a-problem/.

  • Donovan, D., & Loch, B. (2013). Closing the feedback loop: Engaging students in large first-year mathematics test revision sessions using pen-enabled screens. International Journal of Mathematical Education in Science and Technology, 44(1), 1–13.

    Article  Google Scholar 

  • Gan, C. L., & Balakrishnan, V. (2014). Determinants of mobile wireless technology for promoting interactivity in lecture sessions: An empirical analysis. Journal of Computing in Higher Education, 26(2), 159–181.

    Article  Google Scholar 

  • Geske, J. (1992). Overcoming the drawbacks of the large lecture class. College Teaching, 40(4), 151–154.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Hofstede, G., Hofstede, G. J., & Minkov, M. (2010). Cultures and organizations: Software of the mind (3rd ed.): McGraw-Hill.

    Google Scholar 

  • Huang, Y. M., Liu, C. H., Huang, Y. M., & Yeh, Y. H. (2014). Adopt technology acceptance model to analyze factors influencing students’ intention on using a disaster prevention education system. In Advanced technologies, embedded and multimedia for human-centric computing (pp. 197–202). Springer Netherlands.

    Google Scholar 

  • Jang, H. Y., & Noh, M. J. (2011). Customer acceptance of IPTV service quality. International Journal of Information Management, 31(6), 582–592.

    Article  Google Scholar 

  • Krause, K. (2005). Understanding and promoting student engagement in university learning communities. Paper presented at the Sharing Scholarship in Learning and Teaching: Engaging Students, James Cook University, Townsville/Cairns, Queensland. http://learningspaces.edu.au/herg/assets/resources/StudengKrause.pdf.

  • Laver, K., George, S., Ratcliffe, J., & Crotty, M. (2012). Measuring technology self efficacy: Reliability and construct validity of a modified computer self efficacy scale in a clinical rehabilitation setting. Disability and Rehabilitation, 34(3), 220–227.

    Article  Google Scholar 

  • Lee, D. Y., & Lehto, M. R. (2013). User acceptance of YouTube for procedural learning: An extension of the technology acceptance model. Computers and Education, 61, 193–208.

    Article  Google Scholar 

  • Lee, J. A., Garbarino, E., & Lerman, D. (2007). How cultural differences in uncertainty avoidance affect product perceptions. International Marketing Review, 24(3), 330–349.

    Article  Google Scholar 

  • Lin, H. C. (2014). An investigation of the effects of cultural differences on physicians’ perceptions of information technology acceptance as they relate to knowledge management systems. Computers in Human Behavior, 38, 368–380.

    Article  Google Scholar 

  • Lin, W. S., & Wang, C. H. (2012). Antecedences to continued intentions of adopting e-learning system in blended learning instruction: A contingency framework based on models of information system success and task-technology fit. Computers and Education, 58(1), 88–99.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Matusitz, J., & Musambira, G. (2013). Power distance, uncertainty avoidance, and technology: Analyzing Hofstede’s dimensions and human development indicators. Journal of Technology in Human Services, 31(1), 42–60.

    Article  Google Scholar 

  • Mohamed, A. H. H., Tawfik, H., Al-Jumeily, D., & Norton, L. (2011, December). MoHTAM: A technology acceptance model for mobile health applications. In Developments in E-systems Engineering (DeSE), 2011 (pp. 13–18). IEEE.

    Google Scholar 

  • Oigara, J., & Keengwe, J. (2013). Students’ perceptions of clickers as an instructional tool to promote active learning. Education and Information Technologies, 18(1), 15–28.

    Article  Google Scholar 

  • Padilla-Meléndez, A., del Aguila-Obra, A. R., & Garrido-Moreno, A. (2013). Perceived playfulness, gender differences and technology acceptance model in a blended learning scenario. Computers and Education, 63, 306–317.

    Article  Google Scholar 

  • Pai, F. Y., & Huang, K. I. (2011). Applying the technology acceptance model to the introduction of healthcare information systems. Technological Forecasting and Social Change, 78(4), 650–660.

    Article  Google Scholar 

  • Park, E., Baek, S., Ohm, J., & Chang, H. J. (2014). Determinants of player acceptance of mobile social network games: An application of extended technology acceptance model. Telematics and Informatics, 31(1), 3–15.

    Article  Google Scholar 

  • Park, S. Y., Nam, M. W., & Cha, S. B. (2012). University students’ behavioral intention to use mobile learning: Evaluating the technology acceptance model. British Journal of Educational Technology, 43(4), 592–605.

    Article  Google Scholar 

  • Pavot, W., Diener, E., Colvin, C. R., & Sandvik, E. (1991). Further validation of the satisfaction with life Scale: Evidence for the cross-method convergence of well-being measures. Journal of Personality Assessment, 57(1), 149–161.

    Article  Google Scholar 

  • Reeves, T. C. (2006). How do you know they are learning? The importance of alignment in higher education. International Journal of Learning Technology, 2(4), 294–309.

    Article  Google Scholar 

  • Scornavacca, E., Huff, S., & Marshall, S. (2009). Mobile phones in the classroom: If you can’t beat them, join them. Communications of the ACM, 52(4), 142–146.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Shroff, R. H., Deneen, C. D., & Ng, E. M. (2011). Analysis of the technology acceptance model in examining students’ behavioural intention to use an e-portfolio system. Australasian Journal of Educational Technology, 27(4), 600–618.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Subramanian, G. H. (1994). A replication of perceived usefulness and perceived ease of use measurement. Decision Sciences, 25(5/6), 863–874.

    Article  Google Scholar 

  • Šumak, B., HeričKo, M., & Pušnik, M. (2011). A meta-analysis of e-learning technology acceptance: The role of user types and e-learning technology types. Computers in Human Behavior, 27(6), 2067–2077.

    Article  Google Scholar 

  • Tan, C. W., Benbasat, I., & Cenfetelli, R. T. (2013). IT-mediated customer service content and delivery in electronic governments: An empirical investigation of the antecedents of service quality. MIS Quarterly, 37(1), 77–109.

    Google Scholar 

  • Tarhini, A., Hone, K., & Liu, X. (2014). Measuring the moderating effect of gender and age on e-learning acceptance in England: A structural equation modeling approach for an extended technology acceptance model. Journal of Educational Computing Research, 51(2), 163–184.

    Article  Google Scholar 

  • Tesch, F., Coelho, D., & Drozdenko, R. (2011). The relative potency of classroom distracters on student concentration: We have met the enemy and he is us*. Paper presented at the ASBBS Annual Conference, Las Vegas.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Venema, S., & Lodge, J. M. (2013). Capturing dynamic presentation: Using technology to enhance the chalk and the talk. Australasian Journal of Educational Technology, 29(1).

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315.

    Article  Google Scholar 

  • Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.

    Article  Google Scholar 

  • Wang, W. T., & Wang, C. C. (2009). An empirical study of instructor adoption of web-based learning systems. Computers and Education, 53(3), 761–774.

    Article  Google Scholar 

  • Wang, Y. S., Yeh, C. H., & Liao, Y. W. (2013). What drives purchase intention in the context of online content services? The moderating role of ethical self-efficacy for online piracy. International Journal of Information Management, 33(1), 199–208.

    Article  Google Scholar 

  • Wixom, B. H., & Todd, P. A. (2005). A theoretical integration of user satisfaction and technology acceptance. Information Systems Research, 16(1), 85–102.

    Google Scholar 

  • Yoo, S. J., & Huang, W. H. D. (2011). Comparison of Web 2.0 technology acceptance level based on cultural differences. Educational Technology and Society, 14(4), 241–252.

    Google Scholar 

  • Zheng, Y., Zhao, K., & Stylianou, A. (2013). The impacts of information quality and system quality on users’ continuance intention in information-exchange virtual communities: An empirical investigation. Decision Support Systems, 56, 513–524.

    Article  Google Scholar 

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Acknowledgments

The authors extend their gratitude to University of Malaya (RP028A-14AET) and Multimedia University for supporting the study.

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Authors and Affiliations

  1. University of Malaya, Kuala Lumpur, Malaysia

    Vimala Balakrishnan

  2. Multimedia University, Cyberjaya, Malaysia

    Chin Lay Gan

Authors
  1. Vimala Balakrishnan
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  2. Chin Lay Gan
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Correspondence to Vimala Balakrishnan .

Editor information

Editors and Affiliations

  1. Faculty of Education, The University of Hong Kong, Hong Kong, Hong Kong

    Daniel Churchill

  2. BGI College, Genomics, Educational Resource BGI College, Genomics, Hong Kong, Hong Kong

    Jie Lu

  3. Faculty of Education, The University of Hong Kong Faculty of Education, Hong Kong, Hong Kong

    Thomas K.F. Chiu

  4. Faculty of Arts and Social Sciences, University of New South Wales Faculty of Arts and Social Sciences, Sydney, Australia

    Bob Fox

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Balakrishnan, V., Gan, C.L. (2016). Mobile Technology and Interactive Lectures: The Key Adoption Factors. In: Churchill, D., Lu, J., Chiu, T., Fox, B. (eds) Mobile Learning Design. Lecture Notes in Educational Technology. Springer, Singapore. https://doi.org/10.1007/978-981-10-0027-0_7

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  • DOI: https://doi.org/10.1007/978-981-10-0027-0_7

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  • Print ISBN: 978-981-10-0025-6

  • Online ISBN: 978-981-10-0027-0

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