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Testing a Unified Model for Understanding Adoption of Technology in Classroom by Academicians

  • Received : 2022.09.04
  • Accepted : 2022.11.29
  • Published : 2023.03.31

Abstract

Flipped learning has become a leading practice in higher educational institutions to combat pedagogical challenges of instructors. The present research extends the Unified Theory of Acceptance and Use of Technology framework with technology self-efficacy and openness-to-change to examine the determinants of flipped classroom based instructional model adoption by the academicians of higher educational institutions. With the help of 243 sample data, the current study's structural model was tested using covariance-based structural equation modeling. Study model has shown that performance expectancy, effort expectancy, social influence, technical self-efficacy and openness to change predict the behavioral intention. Further behavioral intention and facilitating conditions predict the Use Behavior. Findings of the study led to derive a number of theoretical and practical implications.

Keywords

References

  1. Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.
  2. Alalwan, A. A., Dwivedi, Y. K., Rana, N. P., and Algharabat, R. (2018). Examining factors influencing Jordanian customers' intentions and adoption of internet banking: Extending UTAUT2 with risk. Journal of Retailing and Consumer Services, 40, 125-138.
  3. Albert, M., and Beatty, B. J. (2014). Flipping the classroom applications to curriculum redesign for an introduction to management course: Impact on grades. Journal of Education for Business, 89(8), 419-424.
  4. Aldunate, R., and Nussbaum, M. (2013). Teacher adoption of technology. Computers in Human Behavior, 29(3), 519-524.
  5. Alshehri, M. A. (2012). Using the UTAUT model to determine factors affecting acceptance and use of e-government services in the kingdom of Saudi Arabia. Griffith University.
  6. Anthony, B., Kamaludin, A., Romli, A., Raffei, A. F. M., Abdullah, A., Ming, G. L., Shukor, N. A., Nordin, M. S., and Baba, S. (2019). Exploring the role of blended learning for teaching and learning effectiveness in institutions of higher learning: An empirical investigation. Education and Information Technologies, 24(6), 3433-3466.
  7. Arpaci, I. (2015). A comparative study of the effects of cultural differences on the adoption of mobile learning. British Journal of Educational Technology, 46(4), 699-712.
  8. Arum, R., and Roksa, J. (2011). Academically adrift: Limited learning on college campuses. University of Chicago Press.
  9. Attuquayefio, S., and Addo, H. (2014). Using the UTAUT model to analyze students' ICT adoption. International Journal of Education and Development using ICT, 10(3), 75-86.
  10. Awwad, M. S., and Al-Majali, S. M. (2015). Electronic library services acceptance and use. The Electronic Library, 33(6), 1100-1120.
  11. Bagozzi, R. P., and Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94.
  12. Baker, J. W. (2000). The classroom flip. Using web course management tools to become the guide by the side. Paper presented at the 11th International Conference on College Teaching and Learning, Jacksonville, FL.
  13. Bandura, A. (1986). Social foundations of thought and action. NJ: Englewood Cliffs.
  14. Baylor, A. L., and Ritchie, D. (2002). What factors facilitate teacher skill, teacher morale, and perceived student learning in technology-using classrooms? Computers & Education, 39(4), 395-414.
  15. Bergmann, J., and Sams, A. (2012). Flip your classroom: Reach every student in every class every day. International society for technology in education.
  16. Bhatiasevi, V. (2016). An extended UTAUT model to explain the adoption of mobile banking. Information Development, 32(4), 799-814.
  17. Bland, L. (2006, June). Applying flip/inverted classroom model in electrical engineering to establish life-long learning. In ASEE Annual Conference & Exposition (pp. AC2006-856).
  18. Blau, I., and Peled, Y. (2012). [Chais] Teachers' openness to change and attitudes towards ICT: Comparison of laptop per teacher and laptop per student programs. Interdisciplinary Journal of E-Learning and Learning Objects, 8(1), 73-82.
  19. Bokolo Jr, A., Kamaludin, A., Romli, A., Mat Raffei, A. F., AL Eh Phon, D. N., Abdullah, A., Ming, G. L., Shukor, N. A., Nordin, M. S., and Baba, S. (2020). A managerial perspective on institutions' administration readiness to diffuse blended learning in higher Bryman, A., and Bell, E. 2014. Research Methodology: Business and Management Contexts. Oxford University Press Southern Africa.
  20. Burke, A. S., and Fedorek, B. (2017). Does "flipping" promote engagement?: A comparison of a traditional, online, and flipped class. Active Learning in Higher Education, 18(1), 11-24.
  21. Boucher, B., Robertson, E., Wainner, R., and Sanders, B. (2013). "Flipping" Texas State University's physical therapist musculoskeletal curriculum: Implementation of a hybrid learning model. Journal of Physical Therapy Education, 27(3), 72-77.
  22. Campbell, D. T., and Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56-81.
  23. Carlson, M. P. (1999). The mathematical behavior of six successful mathematics graduate students: Influences leading to mathematical success. Educational Studies in Mathematics, 40(3), 237-258.
  24. Chu, T. H., and Chen, Y. Y. (2016). With good we become good: Understanding e-learning adoption by theory of planned behavior and group influences. Computers & Education, 92, 37-52.
  25. Compeau, D. R., and Higgins, C. A. (1995b). Computer self-efficacy: Development of a measure and initial test. MIS quarterly, 19(2), 189-211.
  26. Cooper, R. B., and Zmud, R. W. (1990). Information technology implementation research: A technological diffusion approach. Management Science, 36(2), 123-139.
  27. Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results (doctoral dissertation). Cambridge: Sloan School of Management, Massachusetts Institute
  28. Davis, F. D., Bagozzi, R. P., and Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003.
  29. DeLone, W. H., and McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information Systems Research, 3(1), 60-95.
  30. Demetry, C. (2010, October). Work in progress-an innovation merging "classroom flip" and team-based learning. 40 Th ASEE. In IEEE Frontiers in Education Conference (pp. 26-27).
  31. Dove, A. (2013, March). Students' perceptions of learning in a flipped statistics class. In Society for Information Technology & Teacher Education International Conference (pp. 393-398). Association for the Advancement of Computing in Education (AACE).
  32. Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., and Williams, M. D. (2019). Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers, 21(3), 719-734.
  33. Ertmer, P. (1999). Addressing first- and second-order barriers to change: Strategies for technology integration. Educational Technology Research and Development, 47(4), 47-61.
  34. Ertmer, P. A. (2005). Teacher pedagogical beliefs: The final frontier in our quest for technology integration? Educational Technology Research and Development, 53(4), 25-39.
  35. Ertmer, P. A., Ottenbreit-Leftwich, A. T., and Tondeur, J. (2014). Teachers' beliefs and uses of technology to support 21st-century teaching and learning. In H. Fives and M. Gill (Eds.), International Handbook of Research on Teachers' Beliefs (pp. 403-418). Routledge Abingdon.
  36. Escobar-Rodriguez, T., and Carvajal-Trujillo, E. (2014). Online purchasing tickets for low cost carriers: An application of the unified theory of acceptance and use of technology (UTAUT) model. Tourism Management, 43, 70-88.
  37. Fishbein, M., and Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research. MA: Addison-Wesley, Reading.
  38. Foertsch, J., Moses, G., Strikwerda, J., and Litzkow, M. (2002). Reversing the lecture/homework paradigm using eTEACH® web based streaming video software. Journal of Engineering Education, 91(3), 267-274.
  39. Fornell, C., and Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
  40. Garrison, D. R., and Kanuka, H. (2004). Blended learning: Uncovering its transformative potential in higher education. The internet and higher education, 7(2), 95-105.
  41. Gedik, N., Kiraz, E., and Ozden, M. Y. (2013). Design of a blended learning environment: Considerations and implementation issues. Australasian Journal of Educational Technology, 29(1), 1-19.
  42. Gefen, D., and Straub, D. (1997). Gender differences in the perception and use of e-mail: An extension to the technology acceptance model. MIS Quarterly, 21(4), 389-400.
  43. Goodwin, B., and Miller, K. (2013). Research says evidence on Flipped Classrooms is still coming in. Technology Rich Learning, 70(6), 78-80.
  44. Graham, C. R., Woodfield, W., and Harrison, J. B. (2013). A framework for institutional adoption and implementation of blended learning in higher education. The Internet and Higher Education, 18, 4-14.
  45. Hair, J. F., Anderson, R. E., Babin, B. J., and Black, W. C. (2010). Multivariate data analysis: A global perspective. Upper Saddle River, NJ: Pearson.
  46. Hall, D. T., and Mansfield, R. (1975). Relationships of age and seniority with career variables of engineers and scientists. Journal of Applied Psychology, 60(2), 201-210.
  47. Halverson, L. R., Graham, C. R., Spring, K. J., Drysdale, J. S., and Henrie, C. R. (2014). A thematic analysis of the most highly cited scholarship in the first decade of blended learning research. The Internet and Higher Education, 20(1), 20-34.
  48. Hamdan, N., McKnight, P., McKnight, K., and Arfstrom, K. M. (2013). A review of flipped learning. Flipped Learning Network. George Mason University: Harper and Row Ltd.
  49. Harman, H. H. (1976). Modern factor analysis. University of Chicago press.
  50. Higgins, K. M. (1997). The effect of year-long instruction in mathematical problem solving on middle-school students' attitudes, beliefs, and abilities. The Journal of Experimental Education, 66(1), 5-28.
  51. Hu, L. T., and Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1-55.
  52. Hung, Y. H., Wang, Y. S., and Chou, S. C. T. (2007). User acceptance of e-government services. PACIS 2007 Proceedings, 97.
  53. Huon, G., Spehar, B., Adam, P., and Rifkin, W. (2007). Resource use and academic performance among first year psychology students. Higher Education, 53(1), 1-27.
  54. Ifenthaler, D., and Schweinbenz, V. (2013). The acceptance of Tablet-PCs in classroom instruction: The teachers' perspectives. Computers in Human Behavior, 29(3), 525-534.
  55. Jelinek, R., Ahearne, M., Mathieu, J., and Schillewaert, N. (2006). A longitudinal examination of individual, organizational, and contextual factors on sales technology adoption and job performance. Journal of Marketing Theory and Practice, 14(1), 7-23.
  56. Jensen, J. L., Kummer, T. A., and Godoy, P. D. D. M. (2015). Improvements from a flipped classroom may simply be the fruits of active learning. CBE-Life Sciences Education, 14(1), 1-12.
  57. Karahanna, E., and Straub, D. W. (1999). The psychological origins of perceived usefulness and ease-of-use. Information & Management, 35(4), 237-250.
  58. Kember, D., McNaught, C., Chong, F. C., Lam, P. and Cheng, K. F. (2010). Understanding the ways in which design features of educational websites impact upon student learning outcomes in blended learning environments. Computers & Education, 55(3), 1183-1192.
  59. Kerres, M., and Witt, C. D. (2003). A didactical framework for the design of blended learning arrangements. Journal of Educational Media, 28(2-3), 101-113.
  60. Khalilzadeh, J., Ozturk, A. B., and Bilgihan, A. (2017). Security-related factors in extended UTAUT model for NFC based mobile payment in the restaurant industry. Computers in Human Behavior, 70, 460-474.
  61. Khan, I. U., Hameed, Z., Yu, Y., Islam, T., Sheikh, Z., and Khan, S. U. (2018). Predicting the acceptance of MOOCs in a developing country: Application of task-technology fit model, social motivation, and self-determination theory. Telematics and Informatics, 35(4), 964-978.
  62. Kim, J. E., Park, H., Jang, M., and Nam, H. (2017). Exploring flipped classroom effects on second language learners' cognitive processing. Foreign Language Annals, 50(2), 260-284.
  63. Kim, D. J., Ferrin, D. L., and Rao, H. R. (2008). A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents. Decision Support Systems, 44(2), 544-564.
  64. Kline, R. B. (2006). Principles and practice of structural equation modeling. New York: Guilford Press.
  65. Lee, J., Lim, C., and Kim, H. (2017). Development of an instructional design model for flipped learning in higher education. Educational Technology Research and Development, 65(2), 427-453.
  66. Long, T. T. (2016). Development and initial validation of a flipped classroom adoption inventory in higher education (PhD dissertation). University of Tennessee. Retrieved from https://trace.tennessee.edu/utk_graddiss/3940
  67. Long, T., Logan, J., and Waugh, M. (2016). Students' perceptions of the value of using videos as a pre-class learning experience in the flipped classroom. TechTrends, 60(3), 245-252.
  68. Lopes, A. P., and Soares, F. (2018). Perception and performance in a flipped Financial Mathematics classroom. The International Journal of Management Education, 16(1), 105-113.
  69. Lopez-Perez, M. V., Perez-Lopez, M. C., Rodriguez-Ariza, L., and Argente-Linares, E. (2013). The influence of the use of technology on student outcomes in a blended learning context. Educational Technology Research and Development, 61(4), 625-638.
  70. Louho, R., Kallioja, M., and Oittinen, P. (2006). Factors affecting the use of hybrid media applications. Graphic Arts in Finland, 35(3), 11-21.
  71. Lynott, P. P., and McCandless, N. J. (2000). The impact of age vs. life experience on the gender role attitudes of women in different cohorts. Journal of Women & Aging, 12(1-2), 5-21.
  72. Mason, G. S., Shuman, T. R., and Cook, K. E. (2013). Comparing the effectiveness of an inverted classroom to a traditional classroom in an upper-division engineering course. IEEE Transactions on Education, 56(4), 430-435.
  73. McLaughlin, J. E., Roth, M. T., Glatt, D. M., Gharkholonarehe, N., Davidson, C. A., Griffin, L. M., Esserman, D. A., and Mumper, R. J. (2014). The flipped classroom: a course redesign to foster learning and engagement in a health professions school. Academic Medicine, 89(2), 236-243.
  74. Miller, J. B. (1976). Toward a new psychology of women. Boston.
  75. Minton, H. L., and Schneider, F. W. (1985). Differential Psychology. IL: Waveland Press Inc.
  76. Mohamed, H., and Lamia, M. (2018). Implementing flipped classroom that used an intelligent tutoring system into learning process. Computers & Education, 124, 62-76.
  77. Moore, G. C., and Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192-222.
  78. Morahan-Martin, J. (1998). Males, females and the Internet In J. Gackenbach (Ed.), Psychology and the Internet: Intrapersonal, Interpersonal, and Transpersonal Implications (pp. 169-197). San Diego, CA, USA: Academic Press.
  79. Morris, M. G., and Venkatesh, V. (2000). Age differences in technology adoption decisions: implications for a changing workforce. Personnel Psychology, 53(2) pp. 375-403.
  80. Noh, M., Lee, K., Kim, S., and Garrison, G. (2013). Effects of collectivism on actual s-commerce use and the moderating effect of price consciousness. Journal of Electronic Commerce Research, 14(3), 244-260.
  81. Nov, O., and Ye, C. (2009). Resistance to change and the adoption of digital libraries: An integrative model. Journal of the American Society for Information Science and Technology, 60(8), 1702-1708.
  82. Nunnally, J. C., and Bernstein, I. H. (1994). The assessment of reliability. Psychometric Theory, 3, 248-292.
  83. Oliveira, T., Faria, M., Thomas, M. A., and Popovic, A. (2014). Extending the understanding of mobile banking adoption: When UTAUT meets TTF and ITM. International Journal of Information Management, 34(5), 689-703.
  84. Pavon, F., and Brown, I. (2010). Factors influencing the adoption of the World Wide Web for job-seeking in South Africa. South African Journal of Information Management, 12(1), 1-9.
  85. Plude, D., and Hoyer, W. (1985). Attention and performance: Identifying and localizing age deficits. In N. Charness (Ed.), Aging and Human Performance (pp. 47-99). NY: John Wiley and Sons.
  86. Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., and Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879.
  87. Podsakoff, P. M., MacKenzie, S. B., and Podsakoff, N. P. (2012). Sources of method bias in social science research and recommendations on how to control it. Annual Review of Psychology, 63, 539-569.
  88. Porter, L. W. (1963). Job attitudes in management: II. Perceived importance of needs as a function of job level. Journal of Applied Psychology, 47(2), 141-148.
  89. Pynoo, B., Devolder, P., Tondeur, J., Van Braak, J., Duyck, W., and Duyck, P. (2011). Predicting secondary school teachers' acceptance and use of a digital learning environment: A cross-sectional study. Computers in Human Behavior, 27(1), 568-575.
  90. Rhodes, S. R. (1983). Age-related differences in work attitudes and behavior: A review and conceptual analysis. Psychological Bulletin, 93(2), 328-367.
  91. Rogers, C. R. (1962). The interpersonal relationship. Harvard Educational Review, 32(4), 416-429.
  92. Sang, G., Valcke, M., Van Braak, J., and Tondeur, J. (2010). Student teachers' thinking processes and ICT integration: Predictors of prospective teaching behaviors with educational technology. Computers & Education, 54(1), 103-112.
  93. Shamir-Inbal, T., Dayan, J., and Kali, Y., 2009. Assimilating online technologies into school culture. Interdisciplinary Journal of E-Learning and Learning Objects, 5(1), 307-334.
  94. Shareef, M. A., Dwivedi, Y. K., Kumar, V., and Kumar, U. (2017). Content design of advertisement for consumer exposure: Mobile marketing through short messaging service. International Journal of Information Management, 37(4), 257-268.
  95. Shaw, H., Ellis, D. A., and Ziegler, F. V. (2018). The Technology Integration Model (TIM). Predicting the continued use of technology. Computers in Human Behavior, 83, 204-214.
  96. Shin, D. H. (2009). Towards an understanding of the consumer acceptance of mobile wallet. Computers in Human Behavior, 25(6), 1343-1354.
  97. Slade, E. L., Dwivedi, Y. K., Piercy, N. C., and Williams, M. D. (2015). Modeling consumers' adoption intentions of remote mobile payments in the United Kingdom: Extending UTAUT with innovativeness, risk, and trust. Psychology & Marketing, 32(8), 860-873.
  98. Strayer, J. F. (2012). How learning in an inverted classroom influences cooperation, innovation and task orientation. Learning Environments Research, 15(2), 171-193.
  99. Sun, Z., Xie, K., and Anderman, L. H. (2018). The role of self-regulated learning in students' success in flipped undergraduate math courses. The Internet and Higher Education, 36, 41-53.
  100. Sun, H., and Zhang, P. (2006). The role of moderating factors in user technology acceptance. International Journal of Human-computer Studies, 64(2), 53-78.
  101. Taylor, S., and Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144-176.
  102. Thong, J. Y., Venkatesh, V., Xu, X., Hong, S. J., and Tam, K. Y. (2011). Consumer acceptance of personal information and communication technology services. IEEE Transactions on Engineering Management, 58(4), 613-625.
  103. Tibenderana, P., Ogao, P., Ikoja-Odongo, J., and Wokadala, J. (2010). Measuring levels of end-users' acceptance and use of hybrid library services. International Journal of Education and Development using ICT, 6(2), 33-54.
  104. Tosuntas, S. B., Karadag, E., and Orhan, S. (2015). The factors affecting acceptance and use of interactive whiteboard within the scope of FATIH project: A structural equation model based on the Unified Theory of acceptance and use of technology. Computers & Education, 81, 169-178.
  105. Van Ryzin, G. G., Muzzio, D., Immerwahr, S., Gulick, L., and Martinez, E. (2004). Drivers and consequences of citizen satisfaction: An application of the American Customer Satisfaction Index Model to New York City. Public Administration Review, 64(3), 331-341.
  106. Venkatesh, V., and Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451-481.
  107. Venkatesh, V., Morris, M. G., and Ackerman, P. L. (2000). A longitudinal field investigation of gender differences in individual technology adoption decision-making processes. Organizational Behavior and Human Decision Processes, 83(1), 33-60.
  108. Venkatesh, V., and Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204.
  109. Venkatesh, V., Morris, M. G., Davis, G. B., and Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.
  110. Venkatesh, V., Thong, J. Y., and Xu, X. (2016). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems, 17(5), 328-376.
  111. Wagner, D., Laforge, P., and Cripps, D. (2013). Lecture material retention: A first trial report on flipped classroom strategies in electronic systems engineering at the University of Regina. Proceedings of the Canadian Engineering Education Association (CEEA).
  112. Wang, C. (2021). Employing blended learning to enhance learners' English conversation: A preliminary study of teaching with Hitutor. Education and Information Technologies, 26(2), 2407-2425.
  113. West, S. G., Finch, J. F., Curran, P. J. (1995). Structural equation models with non-normal variables: problems and remedies. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues and applications (pp. 56-75). Newbery Park, CA: Sage.
  114. Wijewardene, U., Azam. S. M., and Khatibi, A. (2018). Students' Acceptance of Online Courses and Perceived Risk: A Study of UTAUT in the Sri Lankan State Universities. International Journal of Advances in Scientific Research and Engineering, 4(1), 15-22.
  115. Williams, M. D., Rana, N. P., and Dwivedi, Y. K. (2015). The unified theory of acceptance and use of technology (UTAUT): A literature review. Journal of Enterprise Information Management, 28(3), 443-488.
  116. Wong, K. T., Hwang, G. J., Choo Goh, P. S., and Mohd Arrif, S. K. (2020). Effects of blended learning pedagogical practices on students' motivation and autonomy for the teaching of short stories in upper secondary English. Interactive Learning Environments, 28(4), 512-525.
  117. Zuiderwijk, A., Janssen, M., and Dwivedi, Y. K. (2015). Acceptance and use predictors of open data technologies: Drawing upon the unified theory of acceptance and use of technology. Government Information Quarterly, 32(4), 429-440.