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Moderating effect of innovation consciousness and quality consciousness on intention-behaviour relationship in E-learning integration

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Abstract

This study investigated moderating effect of factors of innovation consciousness and quality consciousness on relationship between intention and behaviour in the context of e-learning integration. Previous authors have suggested a deeper theoretical examination of conditions under which intention may or may not directly influence behaviour in order to better understand the inconsistency in relationship between intention and behaviour. In this paper, three hypotheses are introduced to investigate moderating effect of innovation consciousness and quality consciousness on relationship between intention to integrate e-learning and actual e-learning integration behaviour. Survey method was used to collect data from 100 preservice science teachers at one University in South Africa and partial least square structural equation modelling technique was applied for structural path analysis. The study found a strong support for the research hypotheses while implications for both theory and practice are succinctly discussed.

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References

  • Abdel Raheem, A. Y. (2012). Interactions quality in Moodle as perceived by learners and its relation with some variables. Turkish Online Journal of Distance Education-TOJDE, 13(1), 375–389.

    Google Scholar 

  • Adegbenro, J. B., & Olugbara, O. O. (2018). Investigating computer application technology teachers’ procedural knowledge and pedagogical practices in ICT-enhanced classrooms. Africa Education Review, 1-19.

  • Ageel, M. (2011). The ICT proficiencies of university teachers in Saudi Arabia: A case study to identify challenges and encouragement. Hummingbird, University of Southampton’s Doctoral Research Journal, 2, 55–60.

    Google Scholar 

  • Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.

    Article  Google Scholar 

  • Al-Ani, W. T. (2013). Blended learning approach using Moodle and student’s achievement at Sultan Qaboos University in Oman. Journal of Education & Learning, 2(3), 96–110.

    Article  Google Scholar 

  • Ali, G., Haolader, F. A., & Muhammad, K. (2013). The role of ICT to make teaching-learning effective in higher institutions of learning in Uganda. International Journal of Innovative Research in Science, Engineering & Technology, 2(8), 4061–4073.

    Google Scholar 

  • Allan, J. L., Johnston, M., & Campbell, N. (2011). Missed by an inch or a mile? Predicting the size of intention–behaviour gap from measures of executive control. Psychology & Health, 26(6), 635–650.

    Article  Google Scholar 

  • Al-Ruz, J. A., & Khasawneh, S. (2011). Jordanian pre-service teachers' and technology integration: A human resource development approach. Educational Technology & Society, 14(4), 77–87.

    Google Scholar 

  • Amandu, G. E., Muliira, J. K., & Fronda, D. C. (2013). Using Moodle e-learning platform to foster student self-directed learning: Experiences with utilization of the software in undergraduate nursing courses in a middle Eastern university. Procedia - Social and Behavioral Sciences, 93, 677–683.

    Article  Google Scholar 

  • Amireault, S., Godin, G., Vohl, M. C., & Pérusse, L. (2008). Moderators of the intention-behaviour and perceived behavioural control-behaviour relationships for leisure-time physical activity. International Journal of Behavioral Nutrition and Physical Activity, 5(7), 1–11.

    Google Scholar 

  • Babić, S. (2012). Factors that influence academic teacher's acceptance of e-learning technology in blended learning environment. In A. Guelfi (Ed.), E-learning-organizational infrastructure and tools for specific areas (pp. 1–18). Europe: InTech.

    Google Scholar 

  • Basak, S. K., Wotto, M., & Be’langer, P. (2018). E-learning, m-learning and d-learning: Conceptual definition and comparative analysis. E-Learning and Digital Media, 15(4), 191–216.

    Article  Google Scholar 

  • Behera, S. K. (2013). E- and M-learning: A comparative study. International Journal on New Trends in Education and Their Implications, 4(3), 65–78.

    Google Scholar 

  • Bhattacherjee, A., & Sanford, C. (2009). The intention-behaviour gap in technology usage: The moderating role of attitude strength. Behavior & Information Technology, 28(4), 389–401.

    Article  Google Scholar 

  • Burton-Jones, A., & Volkoff, O. (2017). How can we develop contextualized theories of effective use? A demonstration in the context of community-care electronic health records. Information Systems Research, 28(3), 468–489.

    Article  Google Scholar 

  • Chen, R.-J. (2010). Investigating models for pre-service teachers’ use of technology to support student-centered learning. Computers in Education, 55, 32–42.

    Article  Google Scholar 

  • Chigona, A., Chigona, W., & Davids, Z. (2014). Educators’ motivation on integration of ICTs into pedagogy: Case of disadvantaged areas. South African Journal of Education, 34(3), 1–8.

    Article  Google Scholar 

  • Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295–336). Mahwah, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Cidral, W. A., Oliveira, T., Di Felice, M., & Aparicio, M. (2018). E-learning success determinants: Brazilian empirical study. Computers & Education, 122, 273–290.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Demetriou, C., Ozer, B. U., & Essau, C. A. (2015). Self-Report Questionnaires. In R. L. Cautin & S. O. Lilienfeld (Eds.), The encyclopedia of clinical psychology (pp. 1–6). Inc: John Wiley & Sons.

    Google Scholar 

  • Edgeman, R. L., & Eskildsen, J. K. (2012). The C4 model of people-centered innovation: Culture, consciousness, and customer-centric co-creation. Journal of Innovation and Business Best Practices, 2012, 1.

    Google Scholar 

  • Fan, Y., Chen, J., Shirkey, G., John, R., Wu, S. R., Park, H., & Shao, C. (2016). Applications of structural equation modeling (SEM) in ecological studies: An updated review. Ecological Processes, 5(19), 1–12.

    Google Scholar 

  • Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.

    Google Scholar 

  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 48, 39–50.

    Article  Google Scholar 

  • Godin, G., Conner, M., & Sheeran, P. (2005). Bridging the intention-behaviour gap: The role of moral norm. British Journal of Social Psychology, 44(4), 497–512.

    Article  Google Scholar 

  • Goodhue, D. L., Lewis, W., & Thompson, R. (2012). Does PLS have advantages for small sample size or nonnormal data? MIS Quarterly, 36(3), 981–1001.

    Article  Google Scholar 

  • Govender, D. W. (2010). Attitudes of students towards the use of a learning management system (LMS) in a face-to-face learning mode of instruction. Africa Education Review, 7(2), 244–262.

    Article  Google Scholar 

  • Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. The Journal of Marketing Theory and Practice, 19(2), 139–152.

    Article  Google Scholar 

  • Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2014). A primer on partial least squares structural equation modeling (PLS-SEM) (1st ed.). Thousand Oaks: Sage Publications.

    MATH  Google Scholar 

  • Hameroff, S., & Penrose, R. (2014). Consciousness in the universe: A review of the ‘Orch OR’ theory. Physics of Life Reviews, 11, 39–78.

    Article  Google Scholar 

  • Hassan, L. M., Shiu, E., & Shaw, D. (2016). Who says there is an intention-behaviour gap? Assessing the empirical evidence of an intention-behaviour gap in ethical consumption. Journal of Business Ethics, 136(2), 219–236.

    Article  Google Scholar 

  • Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. Advances in International Marketing, 20, 277–320.

    Article  Google Scholar 

  • Howie, S. J., & Blignaut, A. S. (2009). South Africas readiness to integrate ICT into mathematics and science pedagogy in secondary schools. Education and Information Technologies, 14, 345–363.

    Article  Google Scholar 

  • Hsin, H. C., Chen, S. F., & Huang, C. Y. (2017). Willingness to adopt or reuse an e-learning system: The perspectives of self-determination and perceived characteristics of innovation. Innovations in Education and Teaching International, 54(5), 511–520.

    Article  Google Scholar 

  • Jeong, H. Y., & Hong, B. H. (2013). A practical use of learning system using user preference in ubiquitous computing environment. Multimedia Tools and Applications, 64, 491–504.

    Article  Google Scholar 

  • Jita, T. (2018). Exploring pre-service teachers’ opportunities to learn to teach science with ICTs during teaching practice. Journal of Education, 71, 73–90.

    Google Scholar 

  • Joseph, S., & Olugbara, O. O. (2018). Evaluation of municipal e-government readiness using structural equation modelling technique. The Journal for Transdisciplinary Research in Southern Africa, 14(1), 1–10.

    Article  Google Scholar 

  • Kalema, B. M., Olugbara, O. O., & Kekwaletswe, R. M. (2011). The application of structural equation modeling technique to analyse students’ priorities in using course management systems. International Journal of computing and ICT Research, 5(Special issue), 34–44.

    Google Scholar 

  • Kock, N. (2013). WarpPLS 4.0 user manual. Laredo, Texas: ScriptWarp Systems.

    Google Scholar 

  • Kock, N. (2015). WarpPLS 5.0 User Manual. Laredo, Texas: Script Warp Systems.

    Google Scholar 

  • Kotchoubey, B. (2018). Human consciousness: Where is it from and what is it for. Frontiers in Psychology, 9(567), 1–17.

    Google Scholar 

  • Lee, M. C. (2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation-confirmation model. Computers & Education, 54, 506–516.

    Article  Google Scholar 

  • Lee, J., Cerreto, F. A., & Lee, J. (2010). Theory of planned behavior and teachers' decisions regarding use of educational technology. Educational Technology & Society, 13(1), 152–164.

    Google Scholar 

  • Liao, H. L., & Lu, H. P. (2008). The role of experience and innovation characteristics in the adoption and continued use of e-learning websites. Computers in Education, 51, 1405–1416.

    Article  Google Scholar 

  • MacKinnon, D. P. (2011). Integrating mediators and moderators in research design. Research on Social Work Practice, 21(6), 675–681.

    Article  Google Scholar 

  • Mofokeng, P. L. S., & Mji, A. (2010). Teaching mathematics and science using computers: How prepared are south African teachers to do this? Procedia-Social & Behavioral Sciences, WCES, 2, 1610–1614.

    Article  Google Scholar 

  • Moghavvemi, S., Salleh, N. A. M., & Abessi, M. (2013). Determinants of IT-related innovation acceptance and use behavior: Theoretical integration of unified theory of acceptance and use of technology and entrepreneurial potential model. Social Technologies, 3(2), 243–260.

    Article  Google Scholar 

  • Moghavvemi, S., Salleh, N. A. M., Sulaiman, A., & Abessi, M. (2015). Effect of external factors on intention-behaviour gap. Behaviour & Information Technology, 34(12), 1171–1185.

    Article  Google Scholar 

  • Mohammad, H. A., & Nazila, B. (2015). An application of European customer satisfaction index (ECSI) in business to business (B2B) context. Journal of Business & Industrial Marketing, 30(1), 17–31.

    Article  Google Scholar 

  • Mohammadi, H. (2015). Investigating users’ perspectives on e-learning: An integration of TAM and IS success model. Computers in Human Behavior, 45, 369–374.

    Article  Google Scholar 

  • Mohiyeddini, C., Pauli, R., & Bauer, S. (2009). The role of emotion in bridging the intention-behaviour gap: The case of sports participation. Psychology of Sport and Exercise, 10(2), 226–234.

    Article  Google Scholar 

  • Nikolić, V., Kaljevic, J., Jović, S., Petković, D., Milovančević, M., Dimitrov, L., & Dachkinov, P. (2018). Survey of quality models of e-learning systems. Physica A, 511, 324–330.

    Article  Google Scholar 

  • Olugbara, C. T. (2018). A study of e-learning technology integration by preservice science teachers. Ed Thesis, University of Zululand, South Africa: Unpublished D.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Prenjasi, E., & Ahmetaga, S. (2015). Using learning management system to integrate physics courses with online activities: A case study. International conference on teaching/learning physics: Integrating research into practice, Palermo, Italy. 691-696.

  • Razak, N. S., Jalil, H. A., Krauss, S. E., & Ahmad, N. A. (2018). Successful implementation of information and communication technology integration in Malaysian public schools: An activity systems analysis approach. Studies in Educational Evaluation, 58, 17–29.

    Article  Google Scholar 

  • Reuter, T., Ziegelmann, J. P., Wiedemann, A. U., Lippke, S., Schüz, B., & Aiken, L. S. (2010). Planning bridges the intention–behaviour gap: Age makes a difference and strategy use explains why. Psychology and Health, 25(7), 873–887.

    Article  Google Scholar 

  • Rogers, E. M. (2003). Diffusion of innovations (5th ed.). New York: Free Press.

    Google Scholar 

  • Rogers, R. K., & Wallace, J. D. (2011). Predictors of technology integration in education: A study of anxiety and innovativeness in teacher preparation. Journal of Literacy & Technology, 12(2), 28–60.

    Google Scholar 

  • Sadaf, A., Newby, T. J., & Ertmer, P. A. (2012). Exploring actors that predict preservice teachers’ intentions to use web 2.0 technologies using decomposed theory of planned behavior. Journal of Research on Technology in Education, 15(2), 171–195.

    Article  Google Scholar 

  • Sallam, N., & Alzouebi, K. (2014). Teacher perceptions of the use of Moodle to enhance the quality of teaching and learning in a K-12 private school in the United Arab Emirates. Journal of Teaching & Teacher Education, 2(2), 93–102.

    Article  Google Scholar 

  • Seluakumaran, K., Jusof, F. F., Ismail, R., & Husain, R. (2011). Integrating an open-source course management system (Moodle) into the teaching of a first-year medical physiology course: A case study. Advances in Physiology Education, 35, 369–377.

    Article  Google Scholar 

  • Sheeran, P. (2002). Intention-behavior relations: A conceptual and empirical review. European Review of Social Psychology, 12(1), 1–36.

    Article  Google Scholar 

  • Simin, G., & Sani, I. M. (2015). Effectiveness of ICT integration in Malaysian schools: A quantitative analysis. International research Journal for Quality in Education, 2(8), 1–11.

    Google Scholar 

  • Sniehotta, F. F., Scholz, U., & Schwarzer, R. (2005). Bridging the intention-behaviour gap: Planning, self-efficacy, and action control in the adoption and maintenance of physical exercise. Psychology & Health, 20(2), 143–160.

    Article  Google Scholar 

  • Stols, G., Ferreira, R., Pelser, A., Olivier, W. A., Van der Merwe, A., De Villiers, C., & Venter, S. (2015). Perceptions and needs of south African mathematics teachers concerning their use of technology for instruction. South African Journal of Education, 35(4), 1–13.

    Article  Google Scholar 

  • Tan, P. J. B. (2013). Applying the UTAUT to understand factors affecting the use of English e-learning websites in Taiwan. SAGE Open, 3(4), 1–12.

    Article  Google Scholar 

  • Tan, P. J. B. (2015). English e-learning in the virtual classroom and the factors that influence ESL (English as a second language): Taiwanese citizens’ acceptance and use of the modular object-oriented dynamic learning environment. Social Science Information, 54(2), 211–228.

    Article  Google Scholar 

  • Tan, P. J. B., & Hsu, M.-H. (2018). Designing a system for English evaluation and teaching devices: A PZB and TAM model analysis. Eurasia Journal of Mathematics, Science and Technology Education, 14(6), 2107–2119.

    Article  Google Scholar 

  • Tarhini, A., Hone, K., & Liu, X. (2013). Factors affecting students’ acceptance of e-learning environments in developing countries: A structural equation modeling approach. International Journal of Information & Education Technology, 3(1), 45–59.

    Google Scholar 

  • Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6, 144–176.

    Article  Google Scholar 

  • Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers in Education, 57, 2432–2440.

    Article  Google Scholar 

  • Teo, T., & Lee, C. B. (2010). Explaining the intention to use technology among student teachers: An application of the theory of planned behavior (TPB). Campus-Wide Information Systems, 27(2), 60–67.

    Article  MathSciNet  Google Scholar 

  • Teo, T., & Tan, L. (2012). The theory of planned behaviour (TPB) and pre-service teachers’ technology acceptance: A validation study using structural equation modeling. Journal of Technology and Teacher Education, 20(1), 89–104.

    Google Scholar 

  • Touray, A., Salminen, A., & Mursu, A. (2013). The impact of moderating factors on behavioral intention towards internet: A transnational perspective. International Journal of Computer & Information Technology, 2(6), 1035–1041.

    Google Scholar 

  • Turan, A., Tunc, A. O., & Zehir, C. (2015). A theoretical model proposal: Personal innovativeness and user involvement as antecedents of unified theory of acceptance and use of technology. In 4th international conference on leadership, technology, innovation and business management. Procedia & Behavioural Sciences, 210, 43–51.

  • Umugiraneza, O., Bansilal, S., & North, D. (2018). Exploring teachers’ use of technology in teaching and learning mathematics in KwaZulu-Natal schools. Pythagoras, 39(1), 1–13.

    Article  Google Scholar 

  • Urbach, N., & Ahlemann, F. (2010). Structural equation modeling in information systems research using partial least squares. Journal of Information Technology Theory & Application (JITTA), 11(2), 5–40.

    Google Scholar 

  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unifying view. MIS Quarterly, 27, 425–478.

    Article  Google Scholar 

  • Wong, K. T., Osman, R., Goh, P. S. C., & Rahmat, M. K. (2013). Understanding student teachers’ behavioural intention to use technology: Technology acceptance model (TAM) validation and testing. International Journal of Instruction, 6(1), 89–104.

    Google Scholar 

  • Yanuschik, O. V., Pakhomova, E. G., & Batbold, K. (2015). E-learning as a way to improve the quality of educational for international students. International conference for international education and cross-cultural communication. Problems and solutions (IECC-2015), Tomsk, Russia: Procedia-Social & Behavioural Sciences, 215, 147–155.

  • Yapici, İ. Ü., & Hevedanli, M. (2012). Pre-service biology teachers’ attitudes towards ICT using in biology teaching. Procedia - Social and Behavioral Sciences, 64, 633–638.

    Article  Google Scholar 

  • Zadry, H. R., & Yusof, S. M. (2006). Total quality management and theory of constraints implementation in Malaysian automotive suppliers: A survey result. Total Quality Management & Business Excellence, 17(8), 999–1020.

    Article  Google Scholar 

  • Zhou, M., Chan, K. K., & Teo, T. (2016). Modeling mathematics teachers’ intention to use the dynamic geometry environments in Macau: An SEM approach. Educational Technology & Society, 19(3), 181–193.

    Google Scholar 

  • Ziphorah, R. M. (2014). Information and communication technology integration: Where to start, infrastructure or capacity building? Procedia - Social and Behavioral Sciences, 116, 3649–3658.

    Article  Google Scholar 

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Olugbara, C.T., Imenda, S.N., Olugbara, O.O. et al. Moderating effect of innovation consciousness and quality consciousness on intention-behaviour relationship in E-learning integration. Educ Inf Technol 25, 329–350 (2020). https://doi.org/10.1007/s10639-019-09960-w

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