1,661
Views
22
CrossRef citations to date
0
Altmetric
Survey Articles

Mobile Technology in the Classroom: What Drives Student-Lecturer Interactions?

&

References

  • Abu-Al-Aish, A., & Love, S. (2013). Factors influencing students’ acceptance of m-learning: An investigation in higher education. The International Review of Research in Open and Distributed Learning, 14(5), 82–107. doi:10.19173/irrodl.v14i5.1631
  • Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. doi:10.1016/0749-5978(91)90020-T
  • Al-Jabri, I. M., & Sohail, M. S. (2012). Mobile banking adoption: Application of diffusion of innovation theory. Journal of Electronic Commerce Research, 13(4), 379–391.
  • Armstrong, J. S., & Overton, T. S. (1977). Estimating nonresponse bias in mail surveys. Journal of Marketing Research, 14(3), 396–402. doi:10.2307/3150783
  • Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94. doi:10.1007/BF02723327
  • Bagozzi, R. P., Yi, Y., & Phillips, L. W. (1991). Assessing construct validity in organizational research. Administrative Science Quarterly, 36(3), 421–458. doi:10.2307/2393203
  • Balakrishnan, V. (2014). Using social networks to enhance teaching and learning experiences in higher learning institutions. Innovations in Education and Teaching International, 51(6), 595–606. doi:10.1080/14703297.2013.863735
  • Balakrishnan, V., Liew, T. K., & Pourgholaminejad, S. (2015). Fun learning with Edooware–A social media enabled tool. Computers & Education, 80, 39–47. doi:10.1016/j.compedu.2014.08.008
  • Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215. doi:10.1037/0033-295X.84.2.191
  • Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52, 1–26. doi:10.1146/annurev.psych.52.1.1
  • Bandyopadhyay, K., & Fraccastoro, K. A. (2007). The effect of culture on user acceptance of information technology. Communications of the Association for Information Systems, 19(1), 23.
  • Beldad, A. D., & Hegner, S. M. (2017). More photos from me to thee: Factors influencing the intention to continue sharing personal photos on an Online Social Networking (OSN) site among young adults in the Netherlands. International Journal of Human–Computer Interaction, 33(5), 410–422. doi:10.1080/10447318.2016.1254890
  • Bernard, R. M., Borokhovski, E., Schmid, R. F., Tamim, R. M., & Abrami, P. C. (2014). A meta-analysis of blended learning and technology use in higher education: From the general to the applied. Journal of Computing in Higher Education, 26(1), 87–122. doi:10.1007/s12528-013-9077-3
  • Blasco-Arcas, L., Buil, I., Hernandez-Ortega, B., & Sese, F. J. (2013). Using clickers in class. The role of interactivity, active collaborative learning and engagement in learning performance. Computers & Education, 62, 102–110. doi:10.1016/j.compedu.2012.10.019
  • Bollen, K. A. (2011). Evaluating effect, composite, and causal indicators in structural equation models. MIS Quarterly, 35(2), 359–372.
  • Briz-Ponce, L., & García-Peñalvo, F. J. (2015). An empirical assessment of a technology acceptance model for apps in medical education. Journal of Medical Systems, 39(11), 1–5. doi:10.1007/s10916-015-0352-x
  • 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), 32–40. doi:10.14742/ajet.95
  • Cheng, C. K., Paré, D. E., Collimore, L. M., & Joordens, S. (2011). Assessing the effectiveness of a voluntary online discussion forum on improving students’ course performance. Computers & Education, 56(1), 253–261. doi:10.1016/j.compedu.2010.07.024
  • Chilwant, K. S. (2012). Comparison of two teaching methods, structured interactive lectures and conventional lectures. Biomedical Research, 23(3), 363–366.
  • Chin, W. W. (1995). Partial least squares is to LISREL as principal components analysis is to common factor analysis. Technology Studies, 2(2), 315–319.
  • Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2), 295–336.
  • Chow, M., Herold, D. K., Choo, T. M., & Chan, K. (2012). Extending the technology acceptance model to explore the intention to use Second Life for enhancing healthcare education. Computers & Education, 59(4), 1136–1144. doi:10.1016/j.compedu.2012.05.011
  • Cohen, J. (1988). Statistical Power Analysis for the Behavioural Sciences (2nd ed.). Hillsdale, New Jersey: Lawrence Erlbaum Associates.
  • Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of measure and initial test. MIS Quarterly, 19(2), 189–211. doi:10.2307/249688
  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. doi:10.2307/249008
  • 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. doi:10.1080/07421222.2003.11045748
  • DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information Systems Research, 3(1), 60–95. doi:10.1287/isre.3.1.60
  • DeLone, W. H., & McLean, E. R. (2002, January). Information systems success revisited. In System Sciences, 2002. HICSS. Proceedings of the 35th Annual Hawaii International Conference on (pp. 2966–2976). Hawaii, USA: IEEE.
  • Delone, W. H., & Mclean, E. R. (2004). Measuring e-commerce success: Applying the DeLone & McLean information systems success model. International Journal of Electronic Commerce, 9(1), 31–47.
  • Deperlioglu, O., & Kose, U. (2013). The effectiveness and experiences of blended learning approaches to computer programming education. Computer Applications in Engineering Education, 21(2), 328–342. doi:10.1002/cae.v21.2
  • Detlor, B., Hupfer, M. E., Ruhi, U., & Zhao, L. (2013). Information quality and community municipal portal use. Government Information Quarterly, 30(1), 23–32. doi:10.1016/j.giq.2012.08.004
  • Doyle, G. J., Garrett, B., & Currie, L. M. (2014). Integrating mobile devices into nursing curricula: Opportunities for implementation using Rogers’ Diffusion of Innovation model. Nurse Education Today, 34(5), 775–782. doi:10.1016/j.nedt.2013.10.021
  • Dunn, J. E. (2015). The best secure mobile messaging apps 2015. Retrieved January 3, 2016 from http://www.techworld.com/security/best-secure-mobile-messaging-apps-2015-3629914/
  • 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.
  • Elavsky, C. M., Mislan, C., & Elavsky, S. (2011). When talking less is more: Exploring outcomes of Twitter usage in the large‐lecture hall. Learning, Media and Technology, 36(3), 215–233. doi:10.1080/17439884.2010.549828
  • Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, USA: Addison-Wesley.
  • Fornell, C., & Bookstein, F. L. (1982). Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing Research, 19(4), 440–452. doi:10.2307/3151718
  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. doi:10.2307/3151312
  • Gefen, D., Straub, D., & Boudreau, M. C. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the Association for Information Systems, 4(7), 1–77.
  • Giesbers, B., Rienties, B., Tempelaar, D., & Gijselaers, W. (2013). Investigating the relations between motivation, tool use, participation, and performance in an e-learning course using web-videoconferencing. Computers in Human Behavior, 29(1), 285–292. doi:10.1016/j.chb.2012.09.005
  • Gikas, J., & Grant, M. M. (2013). Mobile computing devices in higher education: Student perspectives on learning with cellphones, smartphones & social media. The Internet and Higher Education, 19, 18–26. doi:10.1016/j.iheduc.2013.06.002
  • Gorla, N., Somers, T. M., & Wong, B. (2010). Organizational impact of system quality, information quality, and service quality. The Journal of Strategic Information Systems, 19(3), 207–228. doi:10.1016/j.jsis.2010.05.001
  • Green, S. B. (1991). How many subjects does it take to do a regression analysis. Multivariate Behavioral Research, 26(3), 499–510. doi:10.1207/s15327906mbr2603_7
  • Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis. Upper Saddle River, NJ: Pearson Prentice Hall.
  • Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2014). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Los Angeles, Thousand Oaks: Sage.
  • Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152. doi:10.2753/MTP1069-6679190202
  • Henseler, J. (2010). On the convergence of the partial least squares path modeling algorithm. Computational Statistics, 25(1), 107–120. doi:10.1007/s00180-009-0164-x
  • Hess, T. J., McNab, A. L., & Basoglu, K. A. (2014). Reliability generalization of perceived ease of use, perceived usefulness, and behavioral intentions. MIS Quarterly, 38(1), 1–28. doi:10.25300/MISQ
  • Hofstede, G. (2001). Culture’s consequences: Comparing values, behaviours, institutions, and organisations across nations. Thousand Oaks, CA: SAGE Publications.
  • Hofstede, G., Hofstede, G. J., & Minkov, M. (2010). Cultures and organizations: Software of the mind: Intercultural cooperation and its importance for survival. New York, USA: McGraw-Hill.
  • 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. doi:10.1080/15391523.2011.10782576
  • Hsu, C. K., Hwang, G. J., & Chang, C. K. (2013). A personalized recommendation-based mobile learning approach to improving the reading performance of EFL students. Computers & Education, 63, 327–336. doi:10.1016/j.compedu.2012.12.004
  • Hsu, M. H., Chang, C. M., Chu, K. K., & Lee, Y. J. (2014). Determinants of repurchase intention in online group-buying: The perspectives of DeLone & McLean IS success model and trust. Computers in Human Behavior, 36, 234–245. doi:10.1016/j.chb.2014.03.065
  • Huang, Y. M., Huang, Y. M., Huang, S. H., & Lin, Y. T. (2012). A ubiquitous English vocabulary learning system: Evidence of active/passive attitudes vs. usefulness/ease-of-use. Computers & Education, 58(1), 273–282. doi:10.1016/j.compedu.2011.08.008
  • Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20(2), 195–204. doi:10.1002/(ISSN)1097-0266
  • Hwang, G. J., Lai, C. L., & Wang, S. Y. (2015). Seamless flipped learning: A mobile technology-enhanced flipped classroom with effective learning strategies. Journal of Computers in Education, 2(4), 449–473. doi:10.1007/s40692-015-0043-0
  • Hwang, Y., & Lee, K. C. (2012). Investigating the moderating role of uncertainty avoidance cultural values on multidimensional online trust. Information & Management, 49(3), 171–176. doi:10.1016/j.im.2012.02.003
  • Internet Live Stats (2016). Malaysia Internet Users. Retrieved August 10, 2016 from http://www.internetlivestats.com/internet-users/malaysia/
  • Jang, H. Y., & Noh, M. J. (2011). Customer acceptance of IPTV service quality. International Journal of Information Management, 31(6), 582–592. doi:10.1016/j.ijinfomgt.2011.03.003
  • Joo, Y. J., Lim, K. Y., & Kim, E. K. (2011). Online university students’ satisfaction and persistence: Examining perceived level of presence, usefulness and ease of use as predictors in a structural model. Computers & Education, 57(2), 1654–1664. doi:10.1016/j.compedu.2011.02.008
  • Kay, R. H., & Lauricella, S. (2011). Exploring the benefits and challenges of using laptop computers in higher education classrooms: A formative analysis. Canadian Journal of Learning and Technology, 37(1), 1–18.
  • Kelly, H. (2014). A path analysis of educator perceptions of open educational resources using the technology acceptance model. The International Review of Research in Open and Distributed Learning, 15(2), 26–42. doi:10.19173/irrodl.v15i2.1715
  • King, M. F., & Bruner, G. C. (2000). Social desirability bias: A neglected aspect of validity testing. Psychology and Marketing, 17(2), 79–103. doi:10.1002/(ISSN)1520-6793
  • Ledford, C. J., Saperstein, A. K., Cafferty, L. A., McClintick, S. H., & Bernstein, E. M. (2015). Any questions? An application of Weick’s model of organizing to increase student involvement in the large-lecture classroom. Communication Teacher, 29(2), 116–128. doi:10.1080/17404622.2014.1003309
  • Lee, D. Y., & Lehto, M. R. (2013). User acceptance of YouTube for procedural learning: An extension of the technology acceptance model. Computers & Education, 61, 193–208. doi:10.1016/j.compedu.2012.10.001
  • Lee, D. Y., & Ryu, H. (2013). Learner acceptance of a multimedia-based learning system. International Journal of Human-Computer Interaction, 29(6), 419–437. doi:10.1080/10447318.2012.715278
  • Lee, M. K., Cheung, C. M., & Chen, Z. (2005). Acceptance of Internet-based learning medium: The role of extrinsic and intrinsic motivation. Information & Management, 42(8), 1095–1104. doi:10.1016/j.im.2003.10.007
  • Lee, Y. H., Hsieh, Y. C., & Hsu, C. N. (2011). Adding innovation diffusion theory to the technology acceptance model: Supporting employees’ intentions to use e-learning systems. Journal of Educational Technology & Society, 14(4), 124–137.
  • Lin, C. P., & Anol, B. (2008). Learning online social support: An investigation of network information technology based on UTAUT. CyberPsychology & Behavior, 11(3), 268–272. doi:10.1089/cpb.2007.0057
  • 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 & Education, 58(1), 88–99. doi:10.1016/j.compedu.2011.07.008
  • Lohmoller, J. B. (2013). Latent variable path modelling with partial least squares. Berlin: Springer Science & Business Media.
  • Martin, F., & Ertzberger, J. (2013). Here and now mobile learning: An experimental study on the use of mobile technology. Computers & Education, 68, 76–85. doi:10.1016/j.compedu.2013.04.021
  • Mashable (2015). 15 mobile trends to watch in 2015. Retrieved April 10, 2015 from http://mashable.com/2015/01/02/mobile-trends-2015/
  • Mou, J., Shin, D. H., & Cohen, J. F. (2017). Tracing College Students’ Acceptance of Online Health Services. International Journal of Human–Computer Interaction, 33(5), 371–384. doi:10.1080/10447318.2016.1244941
  • Mueller, J. L., Wood, E., De Pasquale, D., & Cruikshank, R. (2012). Examining mobile technology in higher education: Handheld devices in and out of the classroom. International Journal of Higher Education, 1(2), 43–54. doi:10.5430/ijhe.v1n2p43
  • Nunnally, J. C. (1978). Psychometric theory (3rd ed.). New York, USA: McGraw-Hill.
  • 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. doi:10.1007/s10639-011-9173-9
  • Paladino, A. (2008). Creating an interactive and responsive teaching environment to inspire learning. Journal of Marketing Education, 30(3), 185–188. doi:10.1177/0273475308318075
  • Papadomichelaki, X., & Mentzas, G. (2012). e-GovQual: A multiple-item scale for assessing e-government service quality. Government Information Quarterly, 29(1), 98–109. doi:10.1016/j.giq.2011.08.011
  • 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. doi:10.1111/j.1467-8535.2011.01229.x
  • Peng, D. X., & Lai, F. (2012). Using partial least squares in operations management research: A practical guideline and summary of past research. Journal of Operations Management, 30(6), 467–480.
  • Petter, S., DeLone, W., & McLean, E. (2008). Measuring information systems success: Models, dimensions, measures, and interrelationships. European Journal of Information Systems, 17(3), 236–263. doi:10.1057/ejis.2008.15
  • Petter, S., DeLone, W., & McLean, E. R. (2013). Information Systems Success: The quest for the independent variables. Journal of Management Information Systems, 29(4), 7–62. doi:10.2753/MIS0742-1222290401
  • Podsakoff, P. M., & Organ, D. W. (1986). Self-reports in organizational research: Problems and prospects. Journal of Management, 12(4), 531–544. doi:10.1177/014920638601200408
  • Rai, A., Lang, S. S., & Welker, R. B. (2002). Assessing the validity of IS success models: An empirical test and theoretical analysis. Information Systems Research, 13(1), 50–69. doi:10.1287/isre.13.1.50.96
  • Rehman, R., Afzal, K., & Kamran, A. (2013). Interactive lectures: A perspective of students and lecturers. Journal of Postgraduate Medical Institute, 27(2), 152–156.
  • Reinartz, W., Haenlein, M., & Henseler, J. (2009). An empirical comparison of the efficacy of covariance-based and variance-based SEM. International Journal of Research in Marketing, 26(4), 332–344. doi:10.1016/j.ijresmar.2009.08.001
  • Rogers, E. M. (1995). Diffusion of Innovations. New York, USA: The Free Press.
  • Roopa, S., Bagavad Geetha, M., Rani, A., & Chacko, T. (2013). What type of lectures students want? A reaction evaluation of dental students. Journal of Clinical and Diagnostic Research: JCDR, 7(10), 2244–2246.
  • Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78. doi:10.1037/0003-066X.55.1.68
  • Sánchez-Prieto, J. C., Olmos-Migueláñez, S., & García-Peñalvo, F. J. (2016a). Enjoyment, resistance to change and mlearning acceptance among pre-service teachers. Proceedings of the Fourth International Conference on Technological Ecosystems for Enhancing Multiculturality (TEEM’16) (Salamanca, Spain, November 2-4, 2016) (pp. 691–697). New York, NY: ACM.
  • Sánchez-Prieto, J. C., Olmos-Migueláñez, S., & García-Peñalvo, F. J. (2016b). Informal tools in formal contexts: Development of a model to assess the acceptance of mobile technologies among teachers. Computers in Human Behavior, 55, 519–528. doi:10.1016/j.chb.2015.07.002
  • Sarwar, S., Razzaq, Z., & Saeed, I. (2014). Evaluation of interactive lectures: An innovative approach employed in a hybrid teaching system. Pak J Physiol, 10, 3–4.
  • Scott, W. E., Farh, J. L., & Podsakoff, P. M. (1988). The effects of “intrinsic” and “extrinsic” reinforcement contingencies on task behavior. Organizational Behavior and Human Decision Processes, 41(3), 405–425. doi:10.1016/0749-5978(88)90037-4
  • Seddon, P. B. (1997). A respecification and extension of the DeLone and McLean model of IS success. Information Systems Research, 8(3), 240–253. doi:10.1287/isre.8.3.240
  • Segars, A. H. (1997). Assessing the unidimensionality of On Device Research: A paradigm and illustration within the context of information systems research. Omega, 25(1), 107–121. doi:10.1016/S0305-0483(96)00051-5
  • Seilhamer, R., Chen, B., & Sugar, A. (2013). A framework for implementing mobile technology. In Z. Berge & L. Muilenburg (Eds.), Handbook of mobile learning. New York, USA: Routledge Taylor and Francis Group.
  • Singh, J. (2014). Mobile messaging through android phones: An empirical study to unveil the reasons behind the most preferred mobile messaging application used by college going students. International Journal of Multidisciplinary and Current Research, 2, 367–372.
  • Spence, A., & McKenzie, S. (2014, December). Using interactive technology for lectures in higher education information technology. In Teaching, Assessment and Learning (TALE), 2014 International Conference on (pp. 224–230). New Zealand: IEEE.
  • 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. doi:10.2190/EC.51.2.b
  • Teo, T., Lee, C., & Chai, C. (2012). An empirical study to validate the technology acceptance model in explaining the intention to use technology among educational users. Advancing Education with Information Communication Technologies: Facilitating New Trends, 6, 282–294.
  • Thomas, J. D. (1996). The importance of package features and learning factors for ease of use. International Journal of Human‐Computer Interaction, 8(2), 165–187. doi:10.1080/10447319609526146
  • Torrisi-Steele, G., & Drew, S. (2013). The literature landscape of blended learning in higher education: The need for better understanding of academic blended practice. International Journal for Academic Development, 18(4), 371–383. doi:10.1080/1360144X.2013.786720
  • Tossell, C. C., Kortum, P., Shepard, C., Rahmati, A., & Zhong, L. (2014). You can lead a horse to water but you cannot make him learn: Smartphone use in higher education. British Journal of Educational Technology, 46(4), 713–724. doi:10.1111/bjet.12176
  • Toven-Lindsey, B., Rhoads, R. A., & Lozano, J. B. (2015). Virtually unlimited classrooms: Pedagogical practices in massive open online courses. The Internet and Higher Education, 24, 1–12. doi:10.1016/j.iheduc.2014.07.001
  • Tsai, W. H., Lee, P. L., Shen, Y. S., & Lin, H. L. (2012). A comprehensive study of the relationship between enterprise resource planning selection criteria and enterprise resource planning system success. Information & Management, 49(1), 36–46. doi:10.1016/j.im.2011.09.007
  • Turel, O., Serenko, A., & Giles, P. (2011). Integrating technology addiction and use: An empirical investigation of online auction users. MIS Quarterly, 35(4), 1043–1062.
  • Vallerand, R. J. (1997). Toward a hierarchical model of intrinsic and extrinsic motivation. Advances in Experimental Social Psychology, 29, 271–360.
  • Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. doi:10.1287/mnsc.46.2.186.11926
  • 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.
  • Wang, Y. S., & Liao, Y. W. (2008). Assessing e-Government systems success: A validation of the DeLone and McLean model of information systems success. Government Information Quarterly, 25(4), 717–733. doi:10.1016/j.giq.2007.06.002
  • Ward, R. (2013). The application of technology acceptance and diffusion of innovation models in healthcare informatics. Health Policy and Technology, 2(4), 222–228. doi:10.1016/j.hlpt.2013.07.002
  • Woodcock, B., Middleton, A., & Nortcliffe, A. (2012). Considering the Smartphone Learner: An investigation into student interest in the use of personal technology to enhance their learning. Student Engagement and Experience Journal, 1(1), 1–15. doi:10.7190/seej.v1i1.38
  • Wu, J. H., & Wang, Y. M. (2006). Measuring KMS success: A respecification of the DeLone and McLean’s model. Information & Management, 43(6), 728–739. doi:10.1016/j.im.2006.05.002
  • Yoo, S. J., Han, S. H., & Huang, W. (2012). The roles of intrinsic motivators and extrinsic motivators in promoting e-learning in the workplace: A case from South Korea. Computers in Human Behavior, 28(3), 942–950. doi:10.1016/j.chb.2011.12.015
  • Yoo, S. J., & Huang, W. H. D. (2011). Comparison of Web 2.0 technology acceptance level based on cultural differences. Educational Technology & Society, 14(4), 241–252.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.