Determinants of the Use of Technological Innovation in Distance Learning: A Study with Business School Instructors

Year 2015, Volume: 16 Issue: 1, 19 - 37, 27.02.2015

Edvalda ARAÚJO Leal Alberto LUIZ Albertın

https://doi.org/10.17718/tojde.70207

Cited By: 2

Abstract

This study’s overall purpose is to identify the factors determining the use of technological innovation in Distance Learning (DL), as perceived by instructors of Business Education programs. The theoretical basis for the study is the Innovation Diffusion Theory (IDT). The study’s sample is made up of 436 instructors; we used a quantitative approach and applied Confirmatory Factor Analysis and multiple regression. We found that not all of the attributes selected for analysis as proposed by IDT showed a direct effect for the use of innovation for the instructors investigated. The identified attributes were: compatibility, which shows how consistent innovation is with their values, practices and needs; relative advantage, indicating an innovation’s perceived improvement from its predecessor; and demonstrated results, according to which instructors understand the tangible results obtained from the use of innovation.

Keywords

Instructors; Diffusion of Innovation Theory; Distance Learning.

References

• Agarwal, R. & Karahanna, E. ( 2000).Time flies when you're having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, v.24, n.4, p.665-694.
• Agarwal, R. & Prasad, J. (1997). The role of innovation characteristics and perceived voluntariness in the acceptance of information technology.Decision Science, v. 28, n. 3, p. 557-582.
• Agarwal, R. & Prasad, J. (1999). Are individual differences germane to the acceptance of new information technologies? Decision Sciences, v. 30, n. 2, p. 361-91.
• Bagozzi, Richard P. & Philips, L.W. (September, 1982). Representing and testing organizational theories: a holistic construal. Administrative Science Quarterly, v.27, n.3, p.459-489.
• Behar, P. A. (2008). Modelos Pedagógicos em Educação a Distância.Porto Alegre: Artmed.
• Brown, T. (2006). Confirmatory factor analysis: for applied research. New York: Guilford Publications.
• Chen, J. V.; Yen, D. C. & Chen, K. (2009). The acceptance and diffusion of the innovative smart phone use: a case study of a delivery service company in logistics. Information & Management, v. 46, p. 241-248.
• Cheng, B., Wang, M., Yang, S., & Peng, K. (2011). Acceptance of competency-based workplace e- learning systems: Effects of individual and peer learning support. Computers & Education, v. 57, n. 2, p. 1317-1333.
• Compeau, D.R., Meister, D.B. & Higgins, C.A. (2007). From prediction to explanation: reconceptualizing and extending the perceived characteristics of innovating. Journal of the Association for Information Systems, v. 8, n. 8, p. 409-439.
• Davis, F. D., Bagozzi, R. P. & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management Science, v. 35, n. 8, p. 982-1003.
• Dong, L., Sun, H. & Fang, Y. (2007). Do perceived leadership behaviors affect user technology beliefs? An examination of the impact of project champions and direct managers. Communications of the Association for Information Systems, v. 19, p. 655-664.
• Fornell, C., Larcker, D. F. (1981). Evaluating Structural Equations Models with Unobservable
• Variables and Measurement Error. Journal of Marketing, v.18, n.1, p.39-50. Gong, M, Xu, Y & Yu, Y. (2004). An enhanced technology acceptance model for web-based learning. Journal of Information Systems Education. v. 15, n. 4, p.365.
• Hair J. F. Jr., Anderson; R. E.; Tatham, R. L & Black, W. C. (2009). Análise Multivariada de dados. ed. Porto Alegre: Bookman.
• He, Q., Duan, Y., Fu, Z. & Li, D. (2006). An innovation adoption study of online e-payment in
• Chinese companies. Journal of Electronic Commerce in Organizations, v. 4, n. 1, p. 48-69. Holak, S., Lehmann, D. R. (1990). Purchase Intentions and Dimensions of Innovation: An
• Exploratory Model. Journal of Product Innovation Management, v.7, p. 59-73. Hong, J., Hwang, M., Hsu, H., Wong, W., & Chen, M. (2011). Applying the technology acceptance model in a study of the factors affecting usage of the Taiwan digital archives system. Computers & Education, v. 57, n. 3, p. 2086-2094.
• Huang, A., Yang, S., & Liaw, S. A. (2012). Study of user’s acceptance on situational mashups in situational language teaching. British Journal of Education Technology, v. 43, n.1, p.52.
• Huertas, A. (2007). Teaching and learning logic in a virtual learning environment. Oxford
• University Press, v. 15, n. 4, p.321-331. Huff. S. L., Mcnaughton, J. ( 1991). You and the computer; Diffusion of an Information Technology
• Innovation. Business Quarterly. London: Summer, v.56, n.1. Karahanna, E.; Straub, D. W.; Chervany, N. L. (1999). Information technology adoption across time: a cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Quarterly, v. 23, n. 2, p. 183-213.
• Kenski, V. M. Tecnologias e Ensino Presencial e a Distância. (2009). 7ª ed. Campinas: Papirus.
• Kline, R. B. (2005). Principles and Practice of Structural Equation Modeling.2. ed. New York,
• London: The Guilford Press. Lewis, W., Agarwal, R. & Sambamurthy, V. (2003). Information Technology Use: An Empirical
• Study of Knowledge Workers. MIS Quarterly, v. 27, n.4, p. 657-678. Masetto, M. T. (2003). Competência Pedagógica do Professor Universitário. São Paulo: Summus.
• Malhotra, N. K., Rocha, I., Laudisio, M.C., Altheman, E., & Borges, F.M. (2005). Introdução à
• Pesquisa de Marketing. São Paulo: Pearson Prentice Hall. Mathieson, K. (1991). Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, v.2, p. 173-191.
• Moore, G. C. & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, v. 2, n. 3.
• Pituch, K. A., Lee, Y. (2006). The influence of system characteristics on e-learning use. Computer & Education, v. 47, n. 2, p. 222-244.
• Plouffe, C. R., Hulland, J. & Vandenbosch, M. (2001). Richness versus Parsimony in
• Modeling Technology Adoption Decisions: Understanding Merchant Adoption of a Smart Card- Based Payment System. Information Systems Research, v.12, n.2, p.208-222. Rogers, E. M. (1983). Diffusion of innovation. 3. edition. New York: The Free Press.
• Sugar, W., Crawley, F. & Fine, B. (2005). Critiquing Theory of Planned Behaviour as a method to assess teachers’ technology integration attitudes. British Journal of Educational Technology, v.36, n.2, p. 331-334.
• Teo, T., Noyes, J. (2011). An assessment of the influence of perceived enjoyment and attitude on the intention to use technology among pre-service teachers: A structural equation modeling approach. Computers & Education, v. 57, n. 2, p. 1645-1653.
• Venkatesh, V. & Davis, F. D. (2000). A theoretical extension of the technology acceptance model:
• Four longitudinal field studies. Management Science, v. 46, p. 186–204. Venkatesh, V., Morris, M. G., Davis, G. B. & Davis, F. D. (2003). User acceptance of Information
• Technology: toward a unified view. MIS Quarterly, v. 27, n. 3, p. 425-478. Welsh, E. T., Wanberg, C. R., Brown, E. G. & Simmering, M. J. (2003). E-learning: emerging uses, empirical results and future directions. International Journal of Training and Development, v.7, p. 245–258.
• Zabalza, M. A. (2006). Competencias docentes del profesorado universitario. Calidad y desarrollo profesional. Madrid-Es.: Editora Narcea.