Factores que afectan la intención de continuar utilizando sistemas de aprendizaje electrónico: un estudio con servidores públicos federales
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Este estudio tuvo como objetivo examinar cuales son los reflejos de las fallas cognitivas cuando los servidores públicos federales usan Internet en la satisfacción del diseño de contenido, en la satisfacción del diseño de la interfaz y de estos en el valor de utilidad percibido y en la intención de continuar usando los sistemas y -aprendizaje utilizado en cursos de formación. Con este fin, se realizó una investigación descriptiva, con un enfoque cuantitativo y con datos recopilados de una encuesta aplicada a 50 funcionarios de un hospital universitario federal ubicado en la región del Medio Oeste de Brasil. Para probar las hipótesis, se aplicó la técnica de ecuaciones estructurales. Entre los resultados, se observó que el valor de utilidad percibido afecta positivamente la intención de continuar usándolo, sin embargo, no fue posible confirmar que la falla cognitiva en Internet afecte negativamente la satisfacción del individuo con el diseño de contenido y el diseño de la interfaz. El sistema de aprendizaje electrónico, y esa satisfacción con el diseño de contenido y el diseño de la interfaz afectan positivamente el valor de utilidad percibido. En tiempos de limitaciones financieras, especialmente en el sector público, el aprendizaje electrónico permite que la capacitación llegue a varias fuerzas de trabajo dispersas geográficamente, convirtiéndose en herramientas populares para facilitar los procesos de enseñanza y aprendizaje que permiten un aprendizaje flexible.
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ABUBAKAR, A. M. et al. Knowledge management, decision-making style and organizational performance. Journal of Innovation & Knowledge, v. 4, n. 2, p. 104-114, 2019.
ADMIRAAL, W.; LOCKHORST, D. E-learning in small and medium-sized enterprises across Europe: attitudes towards technology, learning and training. International Small Business Journal, v. 27, n. 6, p. 743-767, 2009.
AHMED, H. M. S. Hybrid e-learning acceptance model: learner perceptions. Decision Sciences Journal of Innovative Education, v. 8, n. 2, p. 313-346, 2010.
AJZEN, I.; FISHBEIN, M. Understanding attitudes and predicting social behavior. In Understanding attitudes and predicting social behavior. Prentice Hall. 1980.
AL-FRAIHAT, D.; JOY, M.; SINCLAIR, J. Evaluating e-learning systems success: an empirical study. Computers in Human Behavior, v. 102, p. 67-86, 2020.
AL-SAMARRAIE, H.; TEO, T.; ABBAS, M. Can structured representation enhance students’ thinking skills for better understanding of e-learning content? Computers & Education, v. 69, p. 463-473, 2013.
ARBAUGH, J. B.; DURAY, R. Technological and structural characteristics, student learning and satisfaction with web-based courses: an exploratory study of two on-line MBA programs. Management Learning, v. 33, n. 3, p. 331-347, 2002.
BROADBENT, D. E. et al. The cognitive failures questionnaire (CFQ) and its correlates. British Journal of Clinical Psychology, v. 21, n. 1, p. 1-16, 1982.
CHENG, B. et al. Acceptance of competency-based workplace e-learning systems: effects of individual and peer learning support. Computers & Education, v. 57, n. 1, p. 1317-1333, 2011.
CHIU, C. M.; WANG, E. T. Understanding Web-based learning continuance intention: the role of subjective task value. Information & Management, v. 45, n. 3, p. 194-201, 2008.
CHO, V.; CHENG, T. E.; LAI, W. J. The role of perceived user-interface design in continued usage intention of self-paced e-learning tools. Computers & Education, v. 53, n. 2, p. 216-227, 2009.
CONNOLLY, T.; STANSFIELD, M.; HAINEY, T. Towards the development of a games-based learning evaluation framework. In: CONNOLLY, T.; STANSFIELD, M.; BOYLE, L. Games-based learning advancements for multi-sensory human computer interfaces: techniques and effective practices. [S.l.]: IGI Global, 2009. p. 251-273.
CHIU, C‐M.; CHIU, C‐S.; CHANG, H‐C. Examining the integrated influence of fairness and quality on learners’ satisfaction and Web‐based learning continuance intention. Information systems journal, v. 17, n. 3, p. 271-287, 2007.
DANIEL, D. B.; WOODY, W. D. E-textbooks at what cost? Performance and use of electronic v. print texts. Computers & Education, v. 62, p. 18-23, 2013.
DAVIS, F. D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, v.13, n.3, p. 319-340, 1989.
ECCLES, J. S. Subjective task value and the Eccles et al. model of achievement-related choices. Handbook of Competence and Motivation, cap.7, 1 Ed., p. 105-121, 2005.
FORNELL, C.; LARCKER, D. F. Structural equation models with unobservable variables and measurement error: algebra and statistics. Journal of Marketing Research, v. 18, n. 3, p. 382-388, 1981.
GARDINER, M. M.; CHRISTIE, B. Applying cognitive psychology to user-interface design. New York: John Wiley & Sons, 1987.
HAIR JUNIOR, J. F. et al. Primer on partial least squares structural equation modeling (PLS-SEM). Los Angeles: SAGE, 2014.
HONG, J. C. et al. Internet cognitive failure relevant to users’ satisfaction with content and interface design to reflect continuance intention to use a government e-learning system. Computers in Human Behavior, v. 66, p. 353-362, 2017.
HSU, C. L.; LU, H. P. Why do people play on-line games? An extended TAM with social influences and flow experience. Information & Management, v. 41, n. 7, p. 853-868, 2004.
KLEM, L. Structural equation modeling. In: GRIMM, L. G.; YARNOLD, P. R. (Ed.). Reading and understanding more multivariate statistics. Washington, DC: American Psychological Association, p. 227-260, 2006.
LEE, Y. H.; HSIEH, Y. C.; MA, C. Y. A model of organizational employees’-learning systems acceptance. Knowledge-Based Systems, v. 24, n. 3, p. 355-366, 2011.
LIN, C. Y., HUANG, C. K.; ZHANG, H. Enhancing employee job satisfaction via e-learning: the mediating role of an organizational learning culture. International Journal of Human-Computer Interaction, v. 35, n. 7, p. 584-595, 2019.
LIU, Z. Reading behavior in the digital environment: changes in reading behavior over the past ten years. Journal of Documentation, v. 61, n. 6, p. 700-712, 2005.
LOHR, L. L. Designing the instructional interface. Computers in Human Behavior, v. 16, n. 2, p. 161-182, 2000.
MARLER, J. H.; DULEBOHN, J. H. A model of employee self-service technology acceptance. In: MARTOCCHIO, J. (Ed.). Research in personnel and human resources management. Bingley: Emerald, 2005. p. 137-180.
MARTIN, A.; MADIGAN, D. (Ed.). Digital literacies for learning. London: Facet, 2006.
ONG, C. S.; LAI, J. Y.; WANG, Y. S. Factors affecting engineer’s acceptance of asynchronous e-learning systems in high-tech companies. Information & Management, v. 41, n. 6, p. 795-804, 2004.
PITUCH, K. A.; LEE, Y. K. The influence of system characteristics on e-learning use. Computers & Education, v. 47, n. 2, p. 222-244, 2006.
REISETTER, M.; LAPOINTE, L.; KORCUSKA, J. The impact of altered realties: implications of on-line delivery for learners’ interactions, expectations, and learning skills. International Journal on e-learning, v. 6, n. 1, p. 55-80, 2007.
RINGLE, C. M.; SILVA, D.; BIDO, D. S. Modelagem de equações estruturais com utilização do SmartPLS. Responsibilities and Rights Journal, v. 2, n. 2, p. 121-139, 2014.
SALAS, E. et al. Emerging themes in distance learning research and practice: some food for thought. International Journal of Management Reviews, v. 4, n. 2, p. 135-153, 2002.
SAN-MARTÍN, S. et al. The determinants of teachers’ continuance commitment to e-learning in higher education. Education and Information Technologies, n.1, v.1, p. 1-21, 2020.
SANTOS, E. A. et al. Ferramentas informais em contextos formais: aplicação de um modelo para avaliar a aceitação de tecnologias móveis entre professores universitários. Revista Gestão & Tecnologia, v. 19, n. 4, p. 117-137, 2019.
SCHMIDT, K. H.; NEUBACH, B.; HEUER, H. Self-control demands, cognitive control deficits, and burnout. Work & Stress, v. 21, n. 2, p. 142-154, 2007.
STAATS, H.; KIEVIET, A.; HARTIG, T. Where to recover from attentional fatigue: an expectancy-value analysis of environmental preference. Journal of Environmental Psychology, v. 23, n. 2, p. 147-157, 2003.
SUTCLIFFE, A. G.; KURNIAWAN, S.; SHIN, J. E. A method and advisor tool for multimedia user interface design. International Journal of Human-Computer Studies, v. 64, n. 4, p. 375-392, 2006.
VAUGHAN, K.; MACVICAR, A. Employees’ pre-implementation attitudes and perceptions to e-learning. Journal of European Industrial Training, v. 8, n. 5, p. 400-413, 2004.
VEKIRI, I. Information science instruction and changes in girls' and boy's expectancy and value beliefs: In search of gender-equitable pedagogical practices. Computers & Education, v. 64, p. 104-115, 2013.
VENKATESH, V. Determinants of perceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, v. 11, n. 4, p. 342-365, 2000.
VENKATESH, V.; DAVIS, F. D. A theoretical extension of the technology acceptance model: four longitudinal field studies. Management Science, v. 46, n. 2, p. 186-204, 2000.
WHELAN, E.; ISLAM, A. N.; BROOKS, S. Cognitive control and social media overload. 2017. Disponível em: <https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1266&context=amcis2017>. Acesso em: 07 set. 2019.
WHELAN, E.; ISLAM, A. N.; BROOKS, S. Is boredom proneness related to social media overload and fatigue? A stress-strain-outcome approach. 2020. Disponível em: <https://www.researchgate.net/publication/339467710_Is_boredom_proneness_related_to_social_media_overload_and_fatigue_A_stress-strain-outcome_approach>. Acesso em: 07 set. 2019.
WICKENS, C. D. Cognitive factors in display design. Journal of the Washington Academy of Sciences, v.83, n.4, p. 179-201, 1993.
WOODS, D. D.; HOLLNAGEL, E. Joint cognitive systems: patterns in cognitive systems engineering. Boca Raton, FL: CRC, 2006.
