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Analysis of FIFA referees and assistant referees’ motivational factors towards the Multimedia Teaching Materials

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Abstract

The aim of our study is to improve the understanding of the different behavioral intentions of referees and assistant referees in different FIFA (International Federation of Association Football) confederations towards Multimedia Teaching Materials as learning tools. To achieve this goal, we carry out a survey of 214 elite referees and assistant referees and we propose a research model with the variables perceived usefulness, perceived ease of use, perceived enjoyment, perceived self-efficacy, multimedia instruction, previous experience with technology and self-paced learning. These variables were taken from previous educational technology research. Among these models we mainly take into account the Technology Acceptance Model, the Motivational Model and the Social Cognitive Theory. To assess the relationships between the constructs, we develop an analysis based on a Structural Equation Modeling (SEM), specifically Partial Least Squares (PLS). The results of this study confirm that referees and assistant referees are willing to use Multimedia Teaching Materials to assist them in their learning activities as long as they perceive the materials to be useful, enjoyable and easy to use and whenever the course contents are multimedia.

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Notes

  1. AVE is the average amount of variance in a set of indicators explained by their latent variable.

  2. The bootstrap re-sampling procedure (5000 sub-samples) is used to generate the standard errors and the t-values.

References

  • Albion, P. R. (2001). Some factors in the development of self-efficacy beliefs for computer Use among teacher education students. Journal of Technology and Teacher Education, 9(3), 321–347. Available at: http://www.editlib.org/p/8368.

  • Alenezi, A. R., Karim, A. M., & Veloo, A. (2010). An empirical investigation into the role of enjoyment, computer anxiety, computer self-efficacy and internet experience in influencing the students' intention to use e-learning: a case study from Saudi Arabian government universities. The Turkish Online Journal of Educational Technology, 9(4), 22–34.

    Google Scholar 

  • Amabile, T. M. (1993). Motivational synergy: toward new conceptualizations of intrinsic and extrinsic motivation in the workplace. Human Resource Management Review, 3(3), 185–201. doi:10.1016/1053-4822(93)90012-S.

    Article  Google Scholar 

  • Antonietti, A., & Giorgetti, M. (2006). Teachers beliefs about learning from multimedia. Computers in Human Behavior, 22(2), 267–282. doi:10.1016/j.chb.2004.06.002.

    Article  Google Scholar 

  • Armenteros, M., & Curca, D. (2008). Use of educational hypermedia for learning Laws of Game. FIFA Multimedia Teaching Materials. XII World Conference on Educational Multimedia, Hypermedia and Telecommunications. Vienna.

  • Armenteros M., Benítez, A.J., &Sillero, M. (2009). Interactive video test used for the training of football referees. Paper presented at the IADIS Multiconference on computer science and information systems. Algarve.

  • Armenteros M., Benítez, A.J., & Curca, D.G. (2010). El Trivia interactivo como recurso educativo para el aprendizaje de las reglas de juego del fúltbol. Edutec-E.Revista Electrónica de Tecnología Educativa, 33. Available at: http://edutec.rediris.es/Revelec2/Revelec33/pdf/Edutece_n33_Armenteros_Benitez_Curca.pdf.

  • Armenteros, M., Liaw, S.-S., Fernández, M., Díaz, R. F., & Sánchez, R. A. (2013). Surveying FIFA instructors’ behavioral intention toward the Multimedia Teaching Materials. Computers & Education, 61, 91–104. doi:10.1016/j.compedu.2012.09.010.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Bandura, A. (1986). Social foundations of thought and action. New Jersey: Englewood Cliffs.

    Google Scholar 

  • Barclay, D., Higgins, C., & Thompson, R. (1995). The Partial Least Squares (PLS) approach to causal modeling: personal computer adoption and use as an illustration. Technology Studies, Special Issue on Research Methodology, 2(2), 285–309.

    Google Scholar 

  • Buff, A. (2014). Enjoyment of learning and its personal antecedents: testing the change–change assumption of the control-value theory of achievement emotions. Learning and Individual Differences, 31, 21–29.

    Article  Google Scholar 

  • Chen, C.-M., & Sun, Y.-C. (2012). Assessing the effects of different multimedia materials on emotions and learning performance for visual and verbal style learners. Computers & Education, 59, 1273–1285. doi:10.1016/j.compedu.2012.05.006.

    Article  Google Scholar 

  • Cheng, Y.-M. (2014). Roles of interactivity and usage experience in e-learning acceptance: a longitudinal study. International Journal of Web Information Systems, 10(1), 2–23. doi:10.1108/IJWIS-05-2013-0015.

    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. 1295–1336). Mahwah: Lawrence Erlbaum Associates.

    Google Scholar 

  • Chin, W. W. (2010). How to write up and report PLS analyses. In V. Esposito Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares: concepts, methods and applications (pp. 655–690). Berlin: Springer.

    Chapter  Google Scholar 

  • Chin, W. W., & Newsted, P. R. (1999). Structural equation modeling analysis with small samples using partial least squares. In R. Hoyle (Ed.), Statistical strategies for small samples research (pp. 307–341). Thousand Oaks: Sage.

    Google Scholar 

  • Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003). A partial least squares latent variable modeling approach for measuring interaction effects: results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information Systems Research, 14(2), 189–217. doi:10.1287/isre.14.2.189.16018.

    Article  Google Scholar 

  • Chiou, C.-C., Tien, L.-C., & Lee, L.-T. (2014). Effects on learning of multimedia animation combined with multidimensional concept maps. Computers & Education. doi:10.1016/j.compedu.2014.09.002.

    Google Scholar 

  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Hillsdale: Lawrence Erlbaum.

    MATH  Google Scholar 

  • Connell, S. L. (2006). Comparing blogs, wikis, and discussion boards as collaborative learning tools. In Wiki. Hyderabad: ICFAI (the Institute of Financial Analysts of India) University Press.

    Google Scholar 

  • Cornell, R. M., Eining, M. M., & Hu, P. J. H. (2011). The effects of process accountability on individuals’ use of a familiar technology. Journal of Information Systems, 25(1), 109–128. doi:10.2308/jis.2011.25.1.109.

    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. Available at: http://www.jstor.org/stable/249008.

  • Davis, S., & Wiedenbeck, S. (2001). The mediating effects of intrinsic motivation, ease of use and usefulness perceptions on performance in first-time and subsequent computer users. Interacting with Computers, 13(5), 549–580. doi:10.1016/S0953-5438(01)00034-0.

    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. doi:10.1287/mnsc.35.8.982.

    Article  Google Scholar 

  • Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111–1132. doi:10.1111/j.1559-1816.1992.tb00945.x.

    Article  Google Scholar 

  • FédérationInternationale de Football Association (2014). Activity Report 2013. Available at: http://www.fifa.com/mm/document/affederation/administration/02/33/87/89/fifa-ar2013_e_interactive_20140408_neutral.pdf.

  • Decharms, R. (1972). Personal causation training in the schools. Journal of Applied Social Psychology, 2(2), 95–113. doi:10.1111/j.1559-1816.1972.tb01266.x.

    Article  Google Scholar 

  • Deci, E. L. (1972). Intrinsic motivation, extrinsic reinforcement, and inequity. Journal of Personality and Social Psychology, 22(1), 113–120.

    Article  Google Scholar 

  • Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. New York: Plenum.

    Book  Google Scholar 

  • Escobar-Rodriguez, T., & Monge-Lozano, P. (2012). The acceptance of moodle technology by business administration students. Computers & Education, 58(4), 1085–1093. doi:10.1016/j.compedu.2011.11.012.

    Article  Google Scholar 

  • Fagan, M. H., Neill, S., & Wooldridge, B. R. (2008). Exploring the intention to use computers: an empirical investigation of the role of intrinsic motivation, extrinsic motivation, and perceived ease of use. Journal of Computer Information Systems, 48(3), 31–37.

    Google Scholar 

  • Falk, R. F., & Miller, N. B. A. (1992). Primer for soft modeling. Akron: University of Akron Press.

    Google Scholar 

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

    Google Scholar 

  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39–50. Available at: http://www.jstor.org/stable/31513.

  • Gangadharbatla, H. (2008). Facebook me: collective self-esteem, need to belong, and internet self-efficacy as predictors of the iGeneration's attitudes toward social networking sites. Cyberpsychology, Behavior & Social Networking, 14(12), 717–722.

    Google Scholar 

  • Gefen, D., Straub, D., & Boudreau, M. (2000). Structural equation modeling techniques and regression: guidelines for research practice. Communications of the Association for Information Systems, 7(7), 1–78.

    Google Scholar 

  • Geisser, S. (1975). The predictive sample reuse method with applications. Journal of the American Statistical Association, 70(350), 320–328. Available at: http://www.jstor.org/stable/2285815.

  • Gong, M., Xu, Y., & Yu, Y. (2004). An enhanced technology acceptance model for web-based learning. Journal of Information Systems Education, 15(4), 365–374.

    Google Scholar 

  • Henry, J. W., & Stone, R. W. (1994). A structural equation model of end-user satisfaction with a computer-based medical information system. Information Resources Management Journal, 7(3), 21–33.

    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.

    Google Scholar 

  • Hong, T. (2006). The internet and tobacco cessation: the roles of internet self-efficacy and search task on the information-seeking process. Journal of Computer-Mediated Communication, 11, 536–556.

    Article  Google Scholar 

  • Hsu, C. L., & Lin, J. C. C. (2008). Acceptance of blog usage: the roles of technology acceptance, social influence and knowledge sharing motivation. Information & Management, 45(1), 65–74. doi:10.1016/j.im.2007.11.001.

    Article  MathSciNet  Google Scholar 

  • Huber, F., Herrmann, A., Frederik, M., Vogel, J., & Vollhardt, K. (2007). Kausalmodellierungmit Partial Least Squares—EineanwendungsorientierteEinführung. Wiesbaden: Gabler.

    Google Scholar 

  • Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: a review of four recent studies. Strategic Management Journal, 20, 195–204.

    Article  Google Scholar 

  • Igbaria, M., Parasuraman, S., & Baroudi, J. J. (1996). A motivational model of microcomputer usage. Journal of Management Information Systems, 13(1), 127–143. Available at: http://www.jstor.org/stable/40398206.

  • Johns, G. (1996). Organizational behavior (4th ed.). New York: Harper Collins.

    Google Scholar 

  • Kelly, T., & Bauer, D. (2004). Managing intellectual capital via e-learning at Cisco. In C. Holsapple (Ed.), Handbook on knowledge management 2: knowledge directions (pp. 511–532). Berlin: Springer.

    Google Scholar 

  • Kovačević, I., Minović, M., Milovanović, M., Ordonez de Pablos, P., & Starčevic, D. (2013). Motivational aspects of different learning contexts: “My mom won’t let me play this game”. Computers in Human Behavior, 29, 354–363.

    Article  Google Scholar 

  • Kuo, Y.-C., Walker, A. E., Schroder, K. E. E., & Belland, B. R. (2014). Interaction, Internet self-efficacy, and self-regulated learning aspredictors of student satisfaction in online education courses. Internet and Higher Education, 20, 35–50.

    Article  Google Scholar 

  • Law, K. M. Y., Lee, V. C. S., & Yu, Y. T. (2010). Learning motivation in e-learning facilitated computer programming courses. Computers & Education, 55(1), 218–228. doi:10.1016/j.compedu.2010.01.007.

    Article  Google Scholar 

  • Lawler, E. E., & Porter, L. W. (1967). The effect of performance on job satisfaction. Industrial Relations: A Journal of Economy and Society, 7(1), 20–28. doi:10.1111/j.1468-232X.1967.tb01060.x.

    Google Scholar 

  • Lee, M. K. O., Cheung, C. M. K., & 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.

    Article  Google Scholar 

  • Lee, Y.H, Hsieh, Y.C., & Chen, Y.H (2011). An investigation of employees' use of e-learning systems: applying the technology acceptance model. Behaviour& Information Technology, 173–189. doi:10.1080/0144929X.2011.577190.

  • Lee, Y.-H., Hsiao, C., & Ho, C.-H. (2014). The effects of various multimedia instructional materials on students’ learning responses and outcomes: a comparative experimental study. Computers in Human Behavior, 40, 119–132.

    Article  Google Scholar 

  • Liang, J. C., & Tsai, C. C. (2008). Internet self-efficacy and preferences toward constructivist internet-based learning environments: a study of pre-school teachers in Taiwan. Educational Technology & Society, 11(1), 226–237.

    Google Scholar 

  • Liang, J. C., & Wu, S. H. (2010). Nurses' motivations for web-based learning and the role of internet self-efficacy. Innovations in Education and Teaching International, 47(1), 25–37.

    Article  Google Scholar 

  • Liaw, S. S. (2004). Considerations for developing constructivist Web-based learning. International Journal of Instructional Media, 31(3), 309–321.

    Google Scholar 

  • Liaw, S. S. (2008). Investigating students’ perceived satisfaction, behavioral intention, and effectiveness of e-learning: a case study of the blackboard system. Computers & Education, 51(2), 864–873. doi:10.1016/j.compedu.2007.09.005.

    Article  Google Scholar 

  • Liaw, S. S., Huang, H. M., & Chen, G. D. (2007). Surveying instructor and learner attitudes toward e-learning. Computers & Education, 49(4), 1066–1080. doi:10.1016/j.compedu.2006.01.001.

    Article  Google Scholar 

  • Lin, K.-M. (2011). e-learning continuance intention: moderating effects of user e-learning experience. Computers & Education, 56(2), 515–526. doi:10.1016/j.compedu.2010.09.017.

    Article  Google Scholar 

  • Liu, S. H., Liao, H. L., & Pratt, J. A. (2009). Impact of media richness and flow on e-learning technology acceptance. Computers & Education, 52(3), 599–607. doi:10.1016/j.compedu.2008.11.002.

    Article  Google Scholar 

  • Lohmöller, J. B. (1989). Latent variables path modeling with partial least squares. Heildelberg: Physica-Verlag.

    Book  MATH  Google Scholar 

  • Lu, Y., Zhou, T., & Wang, B. (2009). Exploring Chinese users’ acceptance of instant messaging using the theory of planned behavior, the technology acceptance model, and the flow theory. Computers in Human Behavior, 25(1), 29–39. doi:10.1016/j.chb.2008.06.002.

    Article  Google Scholar 

  • Malhotra, Y., & Galleta, J. (1999). Extending the technology acceptance model to account for social influence: Theorical Bases and Empirical Validation. Proceedings of 32th Hawaii International Conference on System Sciencie.

  • Martens, R. L., Gulikers, J., & Bastiaens, T. (2004). The impact of intrinsic motivation on e-learning in authentic computer tasks. Journal of Computer Assisted Learning, 20, 368–376.

    Article  Google Scholar 

  • Mayer, E. R. (2001). Multimedia learning. New York: Cambridge University Press.

    Book  Google Scholar 

  • Mayer, E. R. (2005). The Cambridge handbook of multimedia learning. New York: Cambridge University Press.

    Book  Google Scholar 

  • Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38, 43–52. doi:10.1207/S15326985EP3801_6.

    Article  Google Scholar 

  • Nunnally, J. (1978). Psychometric theory (2nd ed.). New York: McGraw-Hill.

    Google Scholar 

  • Ocepek, U., Bosni, Z., Serbec, I. N., & Rugelj, J. (2013). Exploring the relation between learning style models and preferred multimedia types. Computers & Education, 69, 343–355.

    Article  Google Scholar 

  • Pekrun, R., & Stephens, E. J. (2011). Academic emotions. In K. R. Harries, S. Graham, & T. Urdan (Eds.), APA educational psychology handbook, Vol. 2 (pp. 3–31). Washington: American Psychological Association.

    Google Scholar 

  • Pekrun, R., Goetz, T., Titz, W., & Perry, R. P. (2002). Positive emotions in education. In E. Frydenberg (Ed.), Beyond coping: meeting goals, visions, and challenges (pp. 149–173). Oxford: Oxford University Press.

    Chapter  Google Scholar 

  • Persico, D., Manca, S., & Pozzi, F. (2014). Adapting the technology acceptance model to evaluate the innovative potential of e-learning systems. Computers in Human Behavior, 30, 614–622.

    Article  Google Scholar 

  • Plass, J. L., Heidig, S., Hayward, E. O., Homer, B. D., & Um, E. (2014). Emotional design in multimedia learning: effects of shape and color on affect and learning. Learning and Instruction, 29, 128–140.

    Article  Google Scholar 

  • Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891.

    Article  Google Scholar 

  • Rains, S. A. (2008). Seeking health information in the information age: the role of internet self-efficacy. Western Journal of Communication, 72(1), 1–18.

    Article  MathSciNet  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Ringle, C. M., Wende, S., & Will, A. (2008). SmartPLS 2.0 (Beta). Hamburg: University of Hamburg. Retrieved March 21, 2010, from http://www.smartpls.de.

  • Roldán, J. L., & Sánchez-Franco, M. J. (2012). Variance-based structural equation modeling: guidelines for using partial least squares in information systems research. In M. Mora, O. Gelman, A. L. Steenkamp, & M. Raisinghani (Eds.), Research methodologies, innovations and philosophies in software systems engineering and information systems (pp. 193–221). USA: IGI Global.

    Chapter  Google Scholar 

  • Rovai, A. (2004). A constructivist approach to online college learning. The Internet and Higher Education, 7 (2), 79–93.

  • Ruf, T., & Ploetzner, R. (2014). One click away is too far! How the presentation of cognitive learning aids influences their use in multimedia learning environments. Computers in Human Behavior, 38, 229–239.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Ryan, R. M., & Stiller, J. (1991). The social contexts of internalization: parent and teacher influences on autonomy, motivation and learning. In P. R. Pintrich & M. L. Maehr (Eds.), Advances in motivation and achievement: goals and self regulatory processes (Vol. 7, pp. 115–149). Greenwich: JAI.

    Google Scholar 

  • Saadé, R. G., Nebebe, F., & Tan, W. (2007). Viability of the “Technology Acceptance Model” in multimedia learning environments: a comparative study. Interdisciplinary Journal of Knowledge and Learning Objects, 3, 175–184.

    Google Scholar 

  • Saadé, R. G., Morin, D., & Thomas, J. D. E. (2012). Critical thinking in E-learning environments. Computers in Human Behavior, 28(5), 1608–1617. doi:10.1016/j.chb.2012.03.025.

    Article  Google Scholar 

  • Sánchez, R. A., & Hueros, A. D. (2010). Motivational factors that influence the acceptance of Moodle using TAM. Computers in Human Behavior, 26(6), 1632–1640. doi:10.1016/j.chb.2010.06.011.

    Article  Google Scholar 

  • Schmid, E. C. (2008). Potential pedagogical benefits and drawbacks of multimedia use in the English language classroom equipped with interactive whiteboard technology. Computers & Education, 51(4), 1553–1568. doi:10.1016/j.compedu.2008.02.005.

    Article  Google Scholar 

  • Schmidt-Weigand, F., Kohnert, A., & Glowalla, U. (2010). A closer look at split visual attention in system- and self-paced instruction in multimedia learning. Learning and Instruction, 20, 100–110.

    Article  Google Scholar 

  • Schoonenboom, J. (2014). Using an adapted, task-level technology acceptance model to explain why instructors in higher education intend to use some learning management system tools more than others. Computers & Education, 71(2), 247–256.

  • Selim, H. M. (2007). Critical success factors for e-learning acceptance: confirmatory factor models. Computers & Education, 49, 396–413. doi:10.1016/j.compedu.2005.09.004.

    Article  Google Scholar 

  • Sheng, Z., Jue, Z., & Weiwei, T. (2008). Extending TAM for online learning systems: an intrinsic motivation perspective. Tsinghua Science and Technology, 13(3), 312–317. doi:10.1016/S1007-0214(08)70050-6.

    Article  Google Scholar 

  • Simons, J., Dewite, S., & Lens, W. (2004). The role of different types of instrumentality in motivation, study strategies, and performance: know why you learn, so you’ll know what you learn! British Journal of Educational Psychology, 74, 343–360.

    Article  Google Scholar 

  • Sosik, J. J., Kahai, S. S., & Piovoso, M. J. (2009). Silver bullet or voodoo statistics? A primer for using the partial least squares data analytic technique in group and organization research. Group & Organization Management, 34(1), 5–36. doi:10.1177/1059601108329198.

    Article  Google Scholar 

  • Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society, 36(2), 111–147. Available at: http://www.jstor.org/stable/2984809.

  • Sun, J., & Rueda, R. (2012). Situational interest, computer self-efficacy and self-regulation: their impact on student engagement in distance education. British Journal of Educational Technology, 43(2), 191–204.

    Article  Google Scholar 

  • Tang, T. L. P., & Austin, M. J. (2009). Students’ perceptions of teaching technologies, application of technologies, and academic performance. Computers & Education, 53(4), 1241–1255. doi:10.1016/j.compedu.2009.06.007.

    Article  Google Scholar 

  • Taylor, S., & Todd, P. (1995). Assessing IT usage: the role of prior experience. MIS Quarterly, 19(4), 561–570. Available at: http://www.jstor.org/stable/249633.

  • Tenenhaus, M., Vinzi, V. E., Chatelin, Y. M., & Lauro, C. (2005). PLS path modelling. Computational Statistics & Data Analysis, 48(1), 159–205. doi:10.1016/j.csda.2004.03.005.

    Article  MathSciNet  MATH  Google Scholar 

  • Teo, T., Lee, C. B., & Chai, C. S. (2008). Understanding pre-service teachers' computer attitudes: applying and extending the technology acceptance model. Journal of Computer Assisted Learning, 24(2), 128–143. doi:10.1111/j.1365-2729.2007.00247.

    Article  Google Scholar 

  • Tsai, C. C. (2012). The development of epistemic relativism versus social relativism via online peer assessment, and their relations with epistemological beliefs and internet self-efficacy. Educational Technology & Society, 15(2), 309–316.

    Google Scholar 

  • Tsai, C. C., Chuang, S. C., Liang, J. C., & Tsai, M. J. (2011). Self-efficacy in internet-based learning environments: a literature review. Educational Technology & Society, 14(4), 222–240.

    Google Scholar 

  • Van der Heijden, H. (2004). User acceptance of hedonic information systems. MIS Quarterly, 28(4), 695–704. Available at: http://www.jstor.org/stable/25148660.

  • Van Genuchten, E., Scheiter, K., & Schüler, A. (2012). Examining learning from text and pictures for different task types: does the multimedia effect differ for conceptual, causal, and procedural tasks? Computers in Human Behavior, 28, 2209–2218.

    Article  Google Scholar 

  • Varol, E. S., Tarcan, E., & Ozmen, I. (2010). The acceptance and use of e-learning systems in Turkey. International Journal of Innovation and Learning, 8(1), 90–105. doi:10.1504/IJIL.2010.034016.

    Article  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. doi:10.1287/isre.11.4.342.11872.

    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. doi:10.1287/mnsc.46.2.186.11926.

    Article  Google Scholar 

  • 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. Available at: http://www.jstor.org/stable/30036540.

  • Wang, S.-K., & Reeves, T. C. (2007). The effects of a web-based learning environment on student motivation in a high school earth science course. Educational Technology Research and Development, 55, 169–192.

    Article  Google Scholar 

  • Wang, Y., Peng, H., Huang, R., Hou, Y., & Wang, J. (2008). Characteristics of distance learners: research on relationships of learning motivation, learning strategy, self-eficacy, attribution and learning results. Open Learning, 23, 17–28.

    Article  Google Scholar 

  • Werts, C. E., Linn, R. L., & Jöreskog, K. G. (1974). Intraclass reliability estimates: testing structural assumptions. Educational and Psychological Measurement, 34(1), 25–33. doi:10.1177/001316447403400104.

    Article  Google Scholar 

  • Wu, J. H., Tennyson, R. D., & Hsia, T. L. (2010). A study of student satisfaction in a blended e-learning system environment. Computers & Education, 55(1), 155–164. doi:10.1016/j.compedu.2009.12.012.

    Article  Google Scholar 

  • Yi, M. Y., & Hwang, Y. (2003). Predicting the use of web-based information systems: self-efficacy, enjoyment, learning goal orientation, and the technology acceptance model. International Journal of Human-Computer Studies, 59(4), 431–449. doi:10.1016/S1071-5819(03)00114-9.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • You, J. W., & Kang, M. (2014). The role of academic emotions in the relationship between perceived academic control and self-regulated learning in online learning. Computers & Education, 77, 125–133.

    Article  Google Scholar 

  • Zhang, Y., Fang, Y., Wei, K. K., & Wang, Z. (2012). Promoting the intention of students to continue their participation in e-learning systems: the role of the communication environment. Information Technology and People, 25(4), 356–375. doi:10.1108/09593841211278776.

    Article  Google Scholar 

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Authors and Affiliations

  1. Journalism and Audiovisual Communication Department, Carlos III University, C/ Madrid 133, 28903, Getafe, Spain

    M. Armenteros & M. Fernández

  2. General Education Center, China Medical University, 91 Shiuesh Road, Taichung, 404, Taiwan, China

    Shu-Sheng Liaw

  3. Department of Business Administration and Marketing, University of Sevilla, Avda. Ramón y Cajal 1, 4101, Sevilla, Spain

    M. J. Sánchez-Franco

  4. Department of Financial Economics and Accounting, University of Huelva, Pza. Merced 11, 21002, Huelva, Spain

    R. Arteaga Sánchez

Authors
  1. M. Armenteros
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  2. Shu-Sheng Liaw
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  3. M. J. Sánchez-Franco
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  4. M. Fernández
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  5. R. Arteaga Sánchez
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Correspondence to M. Armenteros.

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Armenteros, M., Liaw, SS., Sánchez-Franco, M.J. et al. Analysis of FIFA referees and assistant referees’ motivational factors towards the Multimedia Teaching Materials. Educ Inf Technol 22, 841–872 (2017). https://doi.org/10.1007/s10639-015-9460-y

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