Factores determinantes de la aceptación de Cisco Netscapeestudio empírico basado en TAM

  1. Solano Cóndor, Julio 1
  2. Abella García, Víctor 2
  1. 1 Universidad Privada de Santa Cruz de la Sierra UPSA
  2. 2 Universidad de Burgos
    info

    Universidad de Burgos

    Burgos, España

    ROR https://ror.org/049da5t36

Revue:
Pixel-Bit: Revista de medios y educación

ISSN: 1133-8482

Année de publication: 2017

Número: 51

Pages: 211-225

Type: Article

DOI: 10.12795/PIXELBIT.2017.I51.14 DIALNET GOOGLE SCHOLAR lock_openAccès ouvert editor

D'autres publications dans: Pixel-Bit: Revista de medios y educación

Objectifs de Développement Durable

Résumé

The LMS have provided a impulse to both online learning and blended learning. The aim of this research was to determine what factors influence the satisfaction of the teachers who develop their work in b-learning environments with CISCO NetScape platform. For this purpose we used as a theoretical framework the Technology Acceptance Model (TAM) and using as statistical method the Partial Least Squares (PLS) regression. The study included 115 CISCO Academy instructors from 18 countries in Latin America. In the first place, the measurement model was evaluated, showed adequate psychometric properties; its reliability as well as the convergent and discriminant validity were adequate. Results revealed that the model explained less than 2% of Use (U) and yet 36% of Intention of use (IU) of the LMS by instructors. Likewise, Intention of use (IU) is positively affected by the Perceived usefulness. On the other hand, the effect of Perceived ease of use (PEU) on the Intention of Use (IU) is not significant, however, the indirect effect of this factor on the Intended Use (IU) through Perceived Utility (PU) was significative.

Références bibliographiques

  • Abella, V., López, C., Ortega, N., Sánchez, P., & Lezcano, F. (2011). Implantación de UBUVirtual en la Universidad de Burgos: evaluación y expectativas de uso. EDUTEC: Revista Electrónica de Tecnología Educativa, 38. Recuperado de: http://edutec.rediris.es/Revelec2/Revelec38/implantacion_ubuvirtual_universidad_burgos.html
  • 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-Azawei, A., Parslow, P., & Lundqvist, K. (2017). Investigating the effect of learning styles in a blended e-learning system: An extension of the technology acceptance model (TAM). Australasian Journal of Educational Technology, 33(2), 1-23. doi:10.14742/ajet.2741
  • Alharbi, S., & Drew, S. (2014). Using the Technology Acceptance Model in understanding academics’ behavioural intention to use Learning Management Systems. International Journal of Advanced Computer Science and Applications, 5(1), 143-155. doi:10.14569/IJACSA.2014.050120#sthash.5OCEPrDy.dpuf
  • Asiri, M. S., Mahmud, R., Abu-Bakar, K., & Ayub, A. F. (2012). Factors influencing the use of learning management system in Saudi Arabian Higher Education: A theoretical framework. Higher Education Studies, 2, 125-137. doi:10.5539/hes.v2n2p125
  • Barclay, D., Higgins, C., & Thompson, R. L. (1995). The partial least squares (PLS) approach to causal modeling: personal computer adoption and uses an illustration. Technology Studies, 2(2), 285-309.
  • Barroso, M. C., Cepeda, A., & Roldán, J. L. (2007). Investigar en economía de la empresa: ¿Partial Least Squares o modelos basados en la covarianza? Comunicación presentada en el XIX Congreso anual y XV Congreso Hispano Francés de AEDEM, Vitoria. Recuperado de: https://dialnet.unirioja.es/descarga/articulo/2480048.pdf
  • Bartolomé, A. (2004). La red como instrumento de formación. Blended Learning. Conceptos básicos. Pixel-Bit. Revista de Medios y Educación, 23, 7-20. Recuperado de: http://acdc.sav.us.es/ojs/index.php/pixelbit/article/view/828/761
  • Bartolomé, A. (2008). Entornos de aprendizaje mixto en Educación Superior. RIED-Revista Iberoamericana de Educación a Distancia, 11(1), 15-51. doi:10.5944/ried.1.11.955
  • Benson, V., Anderson, D., & Ooms, A. (2011). Educators’ perceptions, attitudes and practices: blended learning in business and management education. Research in Learning Technology, 19(2), 143-154. doi:10.1080/21567069.2011.586676.
  • Bollen, K. (1989). Structural equations with latent variables. New York: Wiley.
  • Buchanan, T., Sainter, P., & Saunders, G. (2013). Factors affecting faculty use of learning technologies: implications for models of technology adoption. Journal of Computing in Higher Education, 25, 1–11. doi:10.1007/s12528-013-9066-6
  • Cepeda, G., & Roldán, J. L. (2004). Aplicando en la práctica la técnica PLS en la Administración de Empresas. Comunicación presentada en el XIV Congreso Nacional ACEDE, Murcia. Recuperado de: https://personal.us.es/jlroldan/Sitio_web/Partial_Least_Squares_(PLS)_files/Cepeda,%20Roldan%20%282004%29%20ACEDE.pdf
  • 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: Lawrence Erlbaum Associates.
  • Cuesta, I. I., Abella, V., Alegre, J. M. (2014). Evaluación del módulo de cuestionarios del entorno de trabajo UBUVirtual mediante el Modelo de Aceptación Tecnológica. Profesorado. Revista de Currículum y Formación del Profesorado, 18(1), 431-445.
  • Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results. (Tesis Doctoral). Sloan School of Management, Massachusetts.
  • 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
  • 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
  • De Marchis, G. P. (2012). La validez externa de las encuestas en la web. Amenazas y su control. Estudios sobre el Mensaje Periodístico, 18, 263-272. doi:10.5209/rev_ESMP.2012.v18.40980
  • Diamantopoulos, A., & Winklhofer, H. M. (2001). Index construction with formative indicators: An alternative to scale development. Journal of Marketing Research, 38(2), 269-277.
  • Falk, R. F., & Miller, N. B. (1992). A primer for soft modeling. Akron, Ohio: The University of Akron.
  • Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley Publishing Company.
  • Fornell, C., & Larcker, D. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. doi:10.2307/3151312
  • George-Palilonis, F., & Filak, V. (2009). Blended Learning in the visual communications classroom. Student reflections on a multimedia course. Electronic Journal of e-Learning,7(3).
  • Graham, C. R. (2006). Blended learning systems. Definition, current trends, and future directions. En C. J. Bonk and C. R. Graham (Eds.), The handbook of blended learning. Global perspectives, local designs. San Francisco: Pfeiffer.
  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2009). Multivariate Data Analysis. New Jersey: Prentice Hall.
  • Hussein, Z. (2017). Leading to intention: The role of attitude in relation to Technology Acceptance Model in e-learning. Procedia Computer Science, 105, 159-164. doi:10.1016/j.procs.2017.01.196
  • Imbernón, F. (Ed.) (2008). Análisis y propuestas de competencias docentes universitarias para el desarrollo del aprendizaje significativo del alumnado a través del e-learning y el b-learning en el marco del EEES. Recuperado de: http.//tecnologiaedu.us.es/nweb/htm/pdf/EA20070049_Dr_Francisco_ Imbernon.pdf.
  • Karahanna, E. & Straub, D. W. (1999). The psychological origins of perceived usefulness and ease-of-use. Information & Management. 35(4), 237-250. doi:10.1016/S0378-7206(98)00096-2
  • Khechine, H., Lakhal, S., Pascot, D., & Bytha, A. (2014). UTAUT model for blended learning: the role of gender and age in the intention to use webinars. Interdisciplinary Journal of E-Learning and Learning Objects, 10, 33-52
  • Lee, Y. C. (2006). An empirical investigation into factors influencing the adoption of e-learning system. Online Information Review, 30(5), 517-541. doi:10.1108/14684520610706406
  • 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
  • Lin, W., & Wang, Ch. (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
  • Marakarkandy, B., Nilay, Y., & Dasgupta, C. (2017). Enabling internet banking adoption: An empirical examination with an augmented Technology Acceptance Model (TAM). Journal of Enterprise Information Management, 30(2), 263-294. doi:10.1108/JEIM-10-2015-0094
  • Martin García, A. V., García del Dujo, Á., Muñoz Rodríguez, J. M. (2014). Factores determinantes de adopción de blended learning en educación superior. Adaptación del modelo UTAUT. Educación XX1, 17(2), 217-240. doi:10.5944/educxx1.17.2.11489
  • Morán, L. (2012). Blended-learning. Desafío y oportunidad para la educación actual. EDUTEC, Revista Electrónica de Tecnología Educativa, 39. Recuperado de: www.edutec.es/revista/index.php/edutec-e/article/download/371/108
  • Motaghian, H., Hassanzadeh, A., & Moghadam, D. K. (2013). Factors affecting university instructors' adoption of web-based learning systems: case study of Iran. Computers & Education, 61, 158-167. doi:10.1016/j.compedu.2012.09.016
  • Nunnally, J. (1978). Psychometric Theory. New York: McGraw-Hill.
  • Owston, R., York, D., & Murtha, S. (2013). Student perceptions and achievement in a university blended learning strategic initiative. Internet and Higher Education, 18, 38-46. doi:10.1016/j.iheduc.2012.12.003
  • Padilla-Meléndez, A., del Aguila-Obra, A. R., & Garrido-Moreno, A. (2013). Perceived playfulness, gender differences and technology acceptance model in a blended learning scenario. Computers & Education, 63, 306-317. doi:10.1016/j.compedu.2012.12.014
  • Roby, T., Ashe, S., Singh, N., Clark, C. (2013). Shaping the online experience: How administrators can influence student and instructor perceptions through policy and practice. The Internet and Higher Education, 17, 29–37. doi:10.1016/j.iheduc.2012.09.004
  • 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, 247-256. doi:10.1016/j.compedu.2013.09.016
  • S. Taylor, S. & Todd, P. (1995). Assessing IT usage: The role of prior experience. MIS Quarterly, 19, 561-570. doi:10.2307/249633
  • Tran, K. N. N. (2016). The adoption of blended e-learning technology in Vietnam using a revision of the Technology Acceptance Model. Journal of Information Technology Education: Research, 15, 253-282.
  • 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), 1–13. doi:10.1080/1360144X.2013.786720
  • Tselios, N., Daskalakis, S., & Papadopoulou, M. (2011). Assessing the acceptance of a blended learning university course. Educational Technology & Society, 14(2), 224–235.
  • 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., & Bala, H. (2008). Technology Acceptance Model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. doi:10.1111/j.1540-5915.2008.00192.x
  • Wu, B., & Chen, X. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior, 67, 221-232. doi:10.1016/j.chb.2016.10.028
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