El efecto condicional indirecto de la expectativa de rendimiento en el uso de Facebook, Google+, Instagram y Twitter por jóvenes
DOI:
https://doi.org/10.4185/RLCS-2017-1181Palabras clave:
TIC, medios sociales, innovación, adopción, usos, jóvenes, expectativa de rendimientoResumen
Estudios anteriores han encontrado una relación entre el grado en que las personas perciben que una tecnología ayudará a mejorar su desempeño (expectativa de rendimiento) y el uso de dicha tecnología, pero existe poca investigación que compruebe los mecanismos y condiciones por los que este efecto opera en la adopción de medios sociales. Metodología: Se encuestaron 502 estudiantes de Colombia y se realizó un análisis de mediación moderada. Resultados y conclusiones: Se encuentran altas tasas de adopción (68%) de los medios sociales populares (Facebook, Google+, Instagram, Twitter). Consistente con la Teoría Unificada de Aceptación y Uso de Tecnología (UTAUT), el efecto condicional indirecto de la expectativa de rendimiento sobre el uso de los medios sociales resultó un predictor relevante con pesos de hasta 0,53. Este efecto estuvo mediado por la intención de uso y en algunos casos moderado por la edad y sexo.
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Agarwal, R. y Karahanna, E. (2000). Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24(4), 665–694. DOI: https://doi.org/10.2307/3250951
Akbulut, Y. y Günüç, S. (2012). Perceived Social Support and Facebook Use among Adolescents. International Journal of Cyber Behavior, Psychology and Learning, 2(1), 30-41. DOI: https://doi.org/10.4018/ijcbpl.2012010103
Akram, M. S. y Albalawi, W. (2016). Youths' Social Media Adoption: Theoretical Model and Empirical Evidence. International Journal of Business and Management. 11(2), 22-30 DOI: https://doi.org/10.5539/ijbm.v11n2p22
Arcila, C., Calderín, M. y Aguaded, I. (2015). Adoption of ICTs by Communication Researchers for Scientific Diffusion and Data Analysis. El Profesional de la Información, 24(5). DOI: https://doi.org/10.3145/epi.2015.sep.03
Aydn, B. y Volkan Sar, S. (2011). Internet addiction among adolescents: the role of self-esteem. Procedia Social and Behavioral Sciences, 15, 3500–3505. DOI: https://doi.org/10.1016/j.sbspro.2011.04.325
Ben, M. (2016). Futuro Digital Colombia 2016. Trabajo presentado en IAB Day Colombia. Agosto 28,
Bringué, X. y Sádaba, C. (2008). Generación Interactiva en Iberoamérica. Niños y adolescentes frente a las pantallas. Retos educativos y sociales. Barcelona: Ariel, Colección Fundación Telefónica.
Bringué, X. y Sádaba, C. 2011. Menores y redes sociales. Madrid: Foro generaciones interactivas.
Cázares, A. (2010). Proficiency and attitudes toward information technologies' use in psychology undergraduates. Computers in Human Behavior, 26(5), 1004-1008. DOI: https://doi.org/10.1016/j.chb.2010.02.015
Cheung, C. M., Chiu, P. Y. y Lee, M. K. (2011). Online social networks: Why do students use Facebook? Computers in Human Behavior, 27, 1337–1343 DOI: https://doi.org/10.1016/j.chb.2010.07.028
Cheung, C. M. y Lee, M. K. (2010). A theoretical model of intentional social action in online social networks. Decision Support Systems, 49, 24–30 DOI: https://doi.org/10.1016/j.dss.2009.12.006
Claggett, J. L. y Goodhue, D. L. (2011). Have IS researchers lost bandura's self-efficacy concept? A discussion of the definition and measurement of computer self-efficacy. Trabajo presentado en 44th Hawaii International Conference on System Sciences (HICSS). DOI: https://doi.org/10.1109/HICSS.2011.219
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Hillsdale, NJ: Erlbaum.
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297-334. DOI: https://doi.org/10.1007/BF02310555
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319-339. DOI: https://doi.org/10.2307/249008
Davis, F. D., Bagozzi, R. P. y Warshaw, P. R. (1992). Extrinsic and Intrinsic Motivation to Use Computers in the Workplace. Journal of Applied Social Psychology, 22(14), 1111- 1132. DOI: https://doi.org/10.1111/j.1559-1816.1992.tb00945.x
Duggan, M. (2015). Mobile Messaging and Social Media – 2015. Recuperado de http://www.pewinternet.org/2015/08/19/mobile-messaging-and-social-media-2015/
Faul, F., Erdfelder, E., Buchner, A. y Lang, A. (2009). Statistical power analysis using G*Power 3.1: Tests for correlation and regression analysis. Behavior Research Methods, 41(4), 1149-1160 http://dx.doi.org/10.3758/BRM.41.4.1149 DOI: https://doi.org/10.3758/BRM.41.4.1149
Faul, F., Erdfelder, E., Lang, A. & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175-191, http://dx.doi.org/10.3758/BF03193146 DOI: https://doi.org/10.3758/BF03193146
Hall, D. y Mansfield, R. (1995). Relationships of Age and Seniority with Career Variables of Engineers and Scientists. Journal of Applied Psychology, 60(2), 201-210. DOI: https://doi.org/10.1037/h0076549
Hayes, A. (2005). Statistical Methods for Communication Science. Mahwah, NJ: Lawrence Erlbaum Associates. ISBN: 978-0805854879
Hayes, A. (2013). Introduction to mediation, moderation, and conditional process analysis: A regression based approach. New York: Guilford Press.
Igartua, J. J. (2006). Métodos Cuantitativos de Investigación en Comunicación. Madrid: Editorial Bosch.
Jackson, C. M., Chow, S. y Leitch, R. A. (1997). Toward an understanding of the behavioral intention to use an information system. Decision Sciences, 28(2), 357-389. DOI: https://doi.org/10.1111/j.1540-5915.1997.tb01315.x
Kemp, S. (2017). Digital, Social and Mobile in 2017. We are social, January 2017 Recuperado de https://wearesocial.com/blog/2017/01/digital-in-2017-global-overview
Lin, K. Y. y Lu, H. P. (2011). Why people use social networking sites: An empirical study integrating network externalities and motivation theory. Computers in Human Behavior, 27, 1152–1161 DOI: https://doi.org/10.1016/j.chb.2010.12.009
Mac Callum, K. y Jeffrey, L. (2013). The influence of students' ICT skills and their adoption of mobile learning. Australasian Journal of Educational Technology, 29(3), 303-314. DOI: https://doi.org/10.14742/ajet.298
Macía-Sepúlveda, F. (2010). Validez de los tests y el análisis factorial: nociones generales, Ciencia y Trabajo, 12(35), 276-280.
Mander, J. (2016). GWI Social Summary. Recuperado de https://www.globalwebindex.net/hubfs/Reports/GWI_Social_-_Q1_2016_Summary.pdf
Minton, H. L. y Schneider, F. W. (1980). Differential Psychology. Waveland Press, Prospect Heights, IL.
Notley, T. (2009). Young People, Online Networks, and Social Inclusion. Journal of Computer-Mediated Communication, 14, 1208-1227. DOI: https://doi.org/10.1111/j.1083-6101.2009.01487.x
Pérez-Gil, J., Chacón, S. y Moreno, R. (2000). Validez de constructo: El uso de análisis factorial exploratorio-confirmatorio para obtener evidencias de validez. Psicothema, 12(2), 442-446.
Porter, L. (1963). Job Attitudes in Management: Perceived Importance of Needs as a Function of Job Level. Journal of Applied Psychology, 47(2), 141-148 DOI: https://doi.org/10.1037/h0041677
Quinlan, S., Gummer, T., Roßmann, J. & Wolf, C. (2017). ‘Show me the money and the party!’–variation in Facebook and Twitter adoption by politicians. Information, Communication & Society, 1-19. DOI: https://doi.org/10.1080/1369118X.2017.1301521
Subrahmanyam, K. y Lin, G. (2007). Adolescents on the net: internet use and wellbeing. Adolescence, 42(168), 659-677
Valkenburg, P. M. y Peter, J. (2007). Preadolescents and Adolescents Online Communication and their Closeness to Friends. Developmental Psychology, 43, 267-277. DOI: https://doi.org/10.1037/0012-1649.43.2.267
Venkatesh, V., Morris, M. G., Davis, G. B. y Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. DOI: https://doi.org/10.2307/30036540
Venkatesh, V., Thong, J. y Xu, X. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36(1), 157-178 DOI: https://doi.org/10.2307/41410412
Wang, N. y Yongqiang, S. (2015). Social influence or personal preference? Examining the determinants of usage intention across social media with different sociability. Information Development (2015): 0266666915603224.
Yuki, T. y Marchant, I. (2014). El Estado de Social Media en América Latina. [Presentación de PowerPoint]. Recuperado de https://www.comscore.com/esl/Insights/Presentations-and-Whitepapers/2014/The-State-of-Social-Media-in-Latin-America-and-the-Metrics-that-Really-Matter
Zhang, P., Aikman, S. N. y Sun, H. (2008). Two Types of Attitudes in ICT Acceptance and Use. Journal of Human Computer Interaction, 24(7), 628–648. DOI: https://doi.org/10.1080/10447310802335482
Zheng, R. y Cheok, A. (2011). Singaporean Adolescents’ Perceptions of On-line Social Communication: An Exploratory Factor Analysis. Journal of Educational Computing, 45(2), 203-221. DOI: https://doi.org/10.2190/EC.45.2.e