La sobrecarga de información percibida por los estudiantes universitarios y su influencia en el síndrome de respuesta inmediata al smartphone durante la pandemia de la COVID-19: Tomando la perspectiva de la personalidad
DOI:
https://doi.org/10.24310/innoeduca.2023.v9i2.15593Palabras clave:
Personalidad, sobrecarga de información percibida, síndrome de respuesta inmediata al smartphone, neuroticismo, extraversiónResumen
La pandemia causada por la COVID-19 ha afectado en gran medida al aprendizaje y a la interacción social de los estudiantes universitarios, provocando emociones negativas de diferentes grados y haciéndoles extremadamente sensibles a la información de los smartphones. Sin embargo, se sabe poco sobre la relación entre la personalidad, la sobrecarga de información percibida (SIP) y el síndrome de respuesta inmediata al smartphone (SIRS) durante el proceso de aprendizaje de los estudiantes en este contexto social de emergencia específico. Por lo tanto, basándose en el modelo de ajuste persona-ambiente, este estudio investigó a 482 estudiantes universitarios de China continental durante la epidemia mediante un enfoque de muestreo de bola de nieve, y analizó la relación entre su personalidad, SIP y SIRS mediante un modelo de ecuaciones estructurales. Los resultados indicaron que los individuos con extraversión y neuroticismo formaron el SIRS a partir de diferentes vías psicológicas. La SIP desempeña un papel mediador parcial entre el neuroticismo y el SIRS y un papel totalmente mediador entre la extraversión y el SIRS. Estos resultados validan la asociación entre la personalidad individual, la SIP y el SIRS en el entorno no convencional y pone de manifiesto la diferencia que existe en la trayectoria psicológica relacionada con el teléfono móvil entre los estudiantes extrovertidos y los neuróticos. Por lo tanto, se recomienda controlar la SIP de forma específica para los individuos con personalidad diferente y guiarlos en el uso racional de los teléfonos móviles durante la epidemia.
Descargas
Métricas
Citas
Aoki, K., Kanoh, H., & Fuse, M. (2017). Trend of immediate response syndrome viewed from visual synchronization task. Procedia Computer Science, 112, 2106-2116. https://doi.org/10.1016/j.procs.2017.08.229
Bawden, D., & Robinson, L. (2008). The dark side of information: overload, anxiety and other paradoxes and pathologies. Journal of Information Science, 35(2), 180-191. https://doi.org/10.1177/0165551508095781
Bianchi, A., & Phillips, J. G. (2005). Psychological predictors of problem mobile phone use. Cyberpsychology & Behavior, 8(1), 39-51. https://doi.org/10.1089/cpb.2005.8.39
Butt, S., & Phillips, J. G. (2008). Personality and self-reported mobile phone use. Computers in Human Behavior, 24(2), 346-360. https://doi.org/10.1016/j.chb.2007.01.019
Cao, W., Fang, Z., Hou, G., Han, M., Xu, X., Dong, J., & Zheng, J. (2020). The psychological impact of the COVID-19 epidemic on college students in China. Psychiatry Research, 287, 112934. https://doi.org/10.1016/j.psychres.2020.112934
Cantero Téllez, R., Romero Galisteo, R. P., & Rodriguez Bailón, M. (2022). Factores personales y docentes relacionados con el estrés percibido por docentes universitarios frente al COVID-19. Innoeduca. International Journal of Technology and Educational Innovation, 8(1), 102–110. https://doi.org/10.24310/innoeduca.2022.v8i1.11920
Chao, M., Xue, D., Liu T., Yang, H. B., & Hall, B. J. (2020). Media use and acute psychological outcomes during COVID-19 outbreak in China. Journal of Anxiety Disorders, 74, 102248. https://doi.org/10.1016/j.janxdis.2020.102248
Chen, C. Y. (2003). Managing perceptions of information overload in computer-mediated communication. Texas A&M University.
Chen, C., Zhang, K. Z., Gong, X., Zhao, S. J., Lee, M. K., & Liang, L. (2017). Understanding compulsive smartphone use: An empirical test of a flow-based model. International Journal of Information Management, 37(5), 438-454. https://doi.org/10.1016/j.ijinfomgt.2017.04.009
Cohen, L., Manion, L., & Morrison, K. (2000). Research methods in education (5th ed.). Routledge Falmer.
Cooper, D. R., & Emory, C. W. (1995). Business research methods. Richard D. Irwin.
Costa, P. T., & McCrae, R. R. (1992). Revised NEO Personality Inventory (NEO PI-R) and NEO Five-Factor Inventory (NEO-FFI): Professional manual. Psychological Assessment Resources Differences, 35, 1285-1292.
Crow, A. J. D. (2019). Associations Between Neuroticism and Executive Function Outcomes: Response Inhibition and Sustained Attention on a Continuous Performance Test. Perceptual and Motor Skills, 126(4), 623-638.
https://doi.org/10.1177/0031512519848221
De Pascalis, V., Sommer, K., & Scacchia, P. (2018). Extraversion and behavioural approach system in stimulus analysis and motor response initiation. Biological Psychology, 137, 91-106. https://doi.org/10.1016/j.biopsycho.2018.07.004
De-Sola Gutiérrez, J., Rodríguez De Fonseca, F., & Rubio, G. (2016). Cell-phone addiction: A review. Frontiers in Psychiatry, 7, 175. https://doi.org/10.3389/fpsyt.2016.00175
Doucet, C., & Stelmack, R. M. (2000). An event-related potential analysis of extraversion and individual differences in cognitive processing speed and response execution. Journal of Personality and Social Psychology, 78(5), 956-964. https://doi.org/10.1037/0022-3514.78.5.956
Ellwart, T., Happ, C., Gurtner, A., & Rack, O. (2015). Managing information overload in virtual teams: Effects of a structured online team adaptation on cognition and performance. European Journal of Work and Organizational Psychology, 24(5), 812-826. https://doi.org/10.1080/1359432x.2014.1000873
Elmer, T., Mepham, K., & Stadtfeld, C. (2020). Students under lockdown: Comparisons of students' social networks and mental health before and during the COVID-19 crisis in Switzerland. PLOS ONE, 15(7), e0236337. https://doi.org/10.1371/journal.pone.0236337
Eppler, M. J., & Mengis, J. (2004). The concept of information overload: A review of literature from organization science, accounting, marketing, MIS, and related disciplines. Information Society, 20(5), 325-344. https://doi.org/10.1080/01972240490507974
Eysenck, H. (1985). Behaviourism and Clinical Psychiatry. International Journal of Social Psychiatry, 31(3), 163–169. https://doi.org/10.1177/002076408503100301
French, J. R., Caplan, R. D., & Van Harrison, R. (1982). The mechanisms of job stress and strain (Vol. 7). J. Wiley.
Fu, S., Li, H., Liu, Y., Pirkkalainen, H., & Salo, M. (2020). Social media overload, exhaustion, and use discontinuance:
Examining the effects of information overload, system feature overload, and social overload. Information Processing &Amp; Management, 57(6), 102307. https://doi.org/10.1016/j.ipm.2020.102307
Galván Orozco, A., López Pérez, O., Chávez López, J. K., & Contreras López, E. X. (2022). Entorno virtual de aprendizaje: las redes sociales para aprender en la universidad. Innoeduca. International Journal of Technology and Educational Innovation, 8(1), 91–101. https://doi.org/10.24310/innoeduca.2022.v8i1.12340
Gjoreski, M., Kolenik, T., Knez, T., Luštrek, M., Gams, M., Gjoreski, H., & Pejović, V. (2020). Datasets for Cognitive Load Inference Using Wearable Sensors and Psychological Traits. Applied Sciences, 10(11), 3843. https://doi.org/10.3390/app10113843
Gray, J. A. (1981). A critique of Eysenck's theory of personality. In H.J. Eysenc, (Ed.), A model for personality (pp. 246-276). Springer. https://doi.org/10.1007/978-3-642-67783-0_8
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis. Pearson new international edition.
Hong, H., & Kim, H. J. (2020). Antecedents and Consequences of Information Overload in the COVID-19 Pandemic. International Journal of Environmental Research and Public Health, 17(24), 9305. https://doi.org/10.3390/ijerph17249305
Horwood, S., & Anglim, J. (2018). Personality and problematic smartphone use: a facet-level analysis using the Five Factor Model and HEXACO frameworks. Computers in Human Behavior, 85, 349-359. https://doi.org/10.1016/j.chb.2018.04.013
Hou, M., & Cheng, J. (2021). The Role of Social Networks in Mobile Phone Use among Pedestrians: A Pilot Study in China. Sustainability, 13(1), 420. https://doi.org/10.3390/su13010420
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
Hwang, M. Y., Hong, J. C., Tai, K. H., Chen, J. T., & Gouldthorp, T. (2020). The relationship between the online social anxiety, perceived information overload and fatigue, and job engagement of civil servant LINE users. Government Information Quarterly, 37(1), 101423. https://doi.org/10.1016/j.giq.2019.101423
Igarashi, T., Motoyoshi, T., Takai, J., & Yoshida, T. (2008). No mobile, no life: Self-perception and text-message dependency among Japanese high school students. Computers in Human Behavior, 24(5), 2311-2324. https://doi.org/10.1016/j.chb.2007.12.001
Islam, A. N., Laato, S., Talukder, S., & Sutinen, E. (2020). Misinformation sharing and social media fatigue during COVID-19: An affordance and cognitive load perspective. Technological Forecasting and Social Change, 159, 120201. https://doi.org/10.1016/j.techfore.2020.120201
Kanoh, H. (2016). Analysis of the immediate response syndrome for university students. Information Processing Society of Japan SIG Technical Report, 167(3), 1-7.
Kanoh, H. (2017). Issues of Online Communication and Immediate Response Syndrome. International Journal of Social Science and Humanity, 7(6), 350-357. https://doi.org/10.18178/ijssh.2017.7.6.847
Kanoh, H., & Chou, D. (2018). The Relationship Between Immediate Response Syndrome and the Expectations Toward Artificial Intelligence and Robots in Taiwan. Journal of Psychology Research, 8(1), 20-25. https://doi.org/10.17265/2159-5542/2018.01.003
Kim, J. H., Jung, S. H., Ahn, J. C., Kim, B. S., & Choi, H. J. (2020). Social networking sites self-image antecedents of social networking site addiction. Journal of Psychology in Africa, 30(3), 243-248. https://doi.org/10.1080/14330237.2020.1767932
Kleinmuntz, D. N., & Schkade, D. A. (1993). Information displays and decision processes. Psychological Science, 4(4), 221-227. https://doi.org/10.1111/j.14679280.1993.tb00265.x
Komarraju, M., Karau, S. J., & Schmeck, R. R. (2009). Role of the big five personality traits in predicting college students' academic motivation and achievement. Learning and Individual Differences, 19(1), 47-52. http://dx.doi.org/10.1016/j.lindif.2008.07.001
Kuss, D. J., & Griffiths, M.D. (2011). Online social networking and addiction - a review of the psychological literature. International Journal of Environment Research, 8, 3528-3552. https://doi.org/10.3390/ijerph8093528
Lang, A. (2000). The limited capacity model of mediated message processing. Journal of Communication, 50(1), 46-70. https://doi.org/10.1111/j.14602466.2000.tb02833.x
Lau, R. S., & Cheung, G. W. (2010). Estimating and comparing specific mediation effects in complex latent variable models. Organizational Research Methods, 15(1), 3-16. https://doi.org/10.1177/1094428110391673
Lee Y. K., Chang, C. T., Lin, Y., & Cheng, Z. H. (2014). The dark side of smartphone usage: Psychological traits, compulsive behavior and technostress. Computers in Human Behavior, 31, 373-383. https://doi.org/10.1016/j.chb.2013.10.047
Lei, Z. Q., Zhang, M. L., Shi, C. S., & Cheng, Q. L. (2018). Research on the Influence Mechanism of Information Overload of University Users on Library Performance-Based on Empirical Research of Moderating Effect Model of Information Literacy and Knowledge Structure. 2018 International Conference on Management Science & Engineering(ICMSE). https://doi.org/10.1109/icmse.2018.8745290
Li, X., & Chan, M. (2021). Smartphone uses and emotional and psychological well-being in China: the attenuating role of perceived information overload. Behaviour & Information Technology, 41(11), 2427-2437. https://doi.org/10.1080/0144929X.2021.1929489
Liu, S., Lithopoulos, A., Zhang, C. Q., Garcia-Barrera, M.A., & Rhodes, R.E. (2021). Personality and perceived stress during COVID-19 pandemic: Testing the mediating role of perceived threat and efficacy. Personality and Individual Differences, 168, 11035. https://doi.org/10.1016/j.paid.2020.110351
Lu, X., Watanabe, J., Liu, Q., Uji, M., Shono, M., & Kitamura, T. (2011). Internet and mobile phone text-messaging dependency: Factor structure and correlation with dysphoric mood among Japanese adults. Computers in Human Behavior, 27(5), 1702-1709. https://doi.org/10.1016/j.chb.2011.02.009
Luu Duc Huynh, T. (2020). The COVID-19 risk perception: A survey on socioeconomics and media attention. Economics Bulletin, 3(1), 758–764. https://doi.org/10.17632/wh9xk5mp9m.3
Mathews, A. (1990). Why worry? The cognitive function of anxiety. Behaviour research and therapy, 28(6), 455-468.
https://doi.org/10.1016/00057967(90)90132-3
Matz, S. C., Appel, R. E., & Kosinski, M. (2020). Privacy in the age of psychological targeting. Current Opinion in Psychology, 31, 116-121. https://doi.org/10.1016/j.copsyc.2019.08.010
McCrae, R. R., & John, O. P. (1992). An introduction to the Five-Factor Model and Its Applications. Journal of Personality, 60(2), 175-215. https://doi.org/10.1111/j.1467-6494.1992.tb00970.x
Misra, S., & Stokols, D. (2011). Psychological and health outcomes of perceived information overload. Environment and Behavior, 44, 737-759. https://doi.org/10.1177/0013916511404408
Oulasvirta, A., Rattenbury, T., Ma, L., & Raita, E. (2011). Habits make smartphone use more pervasive. Personal and Ubiquitous Computing, 16(1), 105-114. https://doi.org/10.1007/s00779-011-0412-2
Panda, A., & Jain, N. K. (2018). Compulsive smartphone usage and users' ill-being among young Indians: Does personality matter? Telematics and Informatics, 35(5), 1355-1372. https://doi.org/10.1016/j.tele.2018.03.006
Peterson, R. A., & Kim, Y. (2013). On the relationship between coefficient alpha and composite reliability. Journal of Applied Psychology, 98(1), 194-198. https://doi.org/ 10.1037/a0030767
Prasitratanaporn, T. (2010). Information overload among professionals in Thailand. Journal of Information Technology Impact, 10(3), 171-200.
Prowse, R., Sherratt, F., Abizaid, A., Gabrys, R. L., Hellemans, K. G. C., Patterson, Z. R., & McQuaid, R. J. (2021). Coping With the COVID-19 Pandemic: Examining Gender Differences in Stress and Mental Health Among University Students. Frontiers in Psychiatry, 12(439). https://doi.org/10.3389/fpsyt.2021.650759
Przybylski, A. K., Murayama, K., DeHaan, C. R., & Gladwell, V. (2013). Motivational, emotional, and behavioral correlates of fear of missing out. Computers in Human Behavior, 29(4), 1841-1848. https://doi.org/10.1016/j.chb.2013.02.014
Ragu-Nathan, T. S., Tarafdar, M., Ragu-Nathan, B. S., & Tu, Q. (2008). The consequences of technostress for end users in organizations: Conceptual development and empirical validation. Information systems research, 19(4), 417-433. https://doi.org/ 10.1287/isre.1070.0165
Rathore, F. A., & Farooq, F. (2020). Information Overload and Infodemic in the COVID-19 Pandemic. Journal of the Pakistan Medical Association, 70(5), 162-165. https://doi.org/10.5455/JPMA.38. PMID: 32515403
Roberts, J.A., Pullig, C., & Manolis, C. (2015). I need my smartphone: A hierarchical model of personality and cellphone addiction. Personality and Individual Differences, 79, 13-19. https://doi.org/10.1016/j.paid.2015.01.049
Roos, J. M., & Kazemi, A. (2018). Personality traits and Internet usage across generation cohorts: Insights from a nationally representative study. Current Psychology, 40(3), 1287-1297. https://doi.org/10.1007/s12144-018-0033-2
Russell, E., & Woods, S. A. (2020). Personality differences as predictors of action-goal relationships in work-email activity. Computers in Human Behavior, 103, 67-79. https://doi.org/10.1016/j.chb.2019.09.022
Schmitt, J. B., Debbelt, C. A., & Schneider, F. M. (2017). Too much information? Predictors of information overload in the context of online news exposure. Information, Communication & Society, 21(8), 1151-1167. https://doi.org/10.1080/1369118X.2017.1305427
Shirish, A., Srivastava, S. C., & Chandra, S. (2021). Impact of mobile connectivity and freedom on fake news propensity during the COVID-19 pandemic: a cross-country empirical examination. European Journal of Information Systems, 30(3), 322-341. https://doi.org/ 10.1080/0960085X.2021.1886614
Soucek, R., & Moser, K. (2010). Coping with information overload in email communication: Evaluation of a training intervention. Computers in Human Behavior, 26(6), 1458-1466. https://doi.org/10.1016/j.chb.2010.04.024
Stachl, C., Hilbert, S., Au, J. Q., Buschek, D., De Luca, A., Bischl, B., & Bühner, M. (2017). Personality traits predict smartphone usage. European Journal of Personality, 31(6), 701-722. https://doi.org/10.1002/per.2113
Taber, K. S. (2017). The use of Cronbach's Alpha when developing and reporting research instruments in science education. Research in Science Education, 48(6), 1273-1296. https://doi.org/10.1007/s11165-016-9602-2
Tan, W-K., Hsiao, Y-J., Tseng, S-F., & Chan, C-L., (2018). Smartphone application personality and Its relationship to personalities of smartphone users and social capital accrued through use of smartphone social applications. Telematics and Informatics, 35(1), 255-266. https://doi.org/10.1016/j.tele.2017.11.007
Tosun, L.P., & Lajunen, T., (2010). Does Internet use reflect your personality? Relationship between Eysenck's personality dimensions and Internet use. Computers in Human Behavior, 26(2), 162-167. https://doi.org/10.1016/j.chb.2009.10.010
van Deursen, A. J., Bolle, C. L., Hegner, S. M., & Kommers, P. A. (2015). Modeling habitual and addictive smartphone behavior. Computers in Human Behavior, 45, 411–420. https://doi.org/10.1016/j.chb.2014.12.039
Vásquez, M.-S., Nuñez, P. ., & Cuestas, J. (2023). Teachers’ Digital Competences in the context of COVID-19. A quantitative approach. Pixel-Bit. Revista De Medios Y Educación, (67), 155–185. https://doi.org/10.12795/pixelbit.98129
Vorderer, P. Hefner, D., Reinecke, L., & Klimmt. C. (2017). Permanently Online Permanently Connected Living and Communicating in a POPC World. Routledge.
Wegmann, E., Oberst, U., Stodt, B., & Brand, M. (2017). Online-specifific fear of missing out and Internet-use expectancies contribute to symptoms of Internet-communication disorder. Addictive Behaviors Reports, 5, 33-42. https://doi.org/10.1016/j.abrep. 2017.04.001
West, R., Michie, S., Rubin, G. J., & Amlot, R. (2020). Applying principles of behaviour change to reduce SARS-CoV-2 transmission. Nature Human Behaviour, 4(5), 451-459. https://doi.org/10.1038/s41562-020-0887-9
Whelan, E., Islam, A. K. M. N., & Brooks, S. (2020). Is boredom proneness related to social media overload and fatigue? A stress-strain-outcome approach. Internet Research, 30(3), 869-887. https://doi.org/10.1108/intr-03-2019-0112
Xiao, L., & Mou, J. (2019). Social media fatigue-Technological antecedents and the moderating roles of personality traits: The case of WeChat. Computers in Human Behavior, 101, 297-310. https://doi.org/10.1016/j.chb.2019.08.001
Yu, T., & Richardson, J. C. (2015). Examining reliability and validity of a Korean version of the Community of Inquiry instrument using exploratory and confirmatory factor analysis. Internet and Higher Education, 25, 45-52. https://doi.org/10.1016/j.iheduc.2014.12.004
Zhan, Z., Huo, L., Yao, X., & Zhong, B. (2021a). China's Formal Online Education under COVID-19: Actions from Government, Schools, Enterprises, and Families. Routledge. https://www.doi.org/10.4324/9781003188261
Zhan, Z., Li, Y., Yuan,X., & Chen, Q. (2021b). To be or not to be: Parents’ willingness to send their children back to school after the COVID-19 outbreak. The Asia-Pacific Education Researcher, 31, 589-600. https://doi.org/10.1007/s40299-021-00610-9
Zhan, Z., Wei, Q., & Hong, J.C. (2021c). Cellphone addiction during the COVID-19 outbreak: How online social anxiety and cyber danger belief mediate the influence of personality. Computers in Human Behavior, 121, 106790.
https://doi.org/https://doi.org/10.1016/j.chb.2021.106790
Zhang, S. W., Zhao, L., Lu, Y. B., & Yang, J. (2016). Do you get tired of socializing? An empirical explanation of discontinuous usage behaviour in social network services. Information & Management, 53(7), 904-914. https://doi.org/10.1016/j.im.2016.03.006
Zhu, J. M. (2020). Epidemic information dissemination and management in the era of media. People Network. http://yuqing.people.com.cn/n1/2020/0323/c209043-31644615.html
Descargas
Publicado
Cómo citar
Número
Sección
Licencia
Derechos de autor 2023 Innoeduca. International Journal of Technology and Educational Innovation
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
Todos los contenidos publicados por Innoeduca. International Journal of Technology and Educational Innovation están sujetos a la licencia de Creative Commons Reconocimiento-No comercial- SinObraDerivada 4.0 Internacional, cuyo texto completo se puede consultar en https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode. Por lo tanto, la copia, distribución, comunicación pública, obras derivadas y el uso comercial de los contenidos están permitidas siempre que la fuente y el autor del texto se citen.
Es responsabilidad de los autores obtener los permisos necesarios de las imágenes que están sujetas a derechos de autor.
Este obra está bajo una licencia de Creative Commons Reconocimiento-NoComercial-SinObraDerivada 4.0 Internacional.