You are here:

Determinants of digital inequality in universities: the case of Ecuador

, Universidad Tecnica Particular de Loja ; , Universitat Oberta de Catalunya

Journal of e-Learning and Knowledge Society Volume 11, Number 3, ISSN 1826-6223 e-ISSN 1826-6223 Publisher: Italian e-Learning Association


The digital divide was initially defined by socioeconomic variables, mainly the level of family income, but now it focuses on how the Internet is used and is called digital inequality. In the case of universities, recent studies have pointed to the existence of patterns that are dependent on a variety of socioeconomic variables. This article analyses the effect that the level of family income, gender and age of students from five Ecuadorian universities has on Internet use for academic activities and entertainment purposes. In the procedure applied to a sample of 4,697 students, factor analysis was used to reduce the data, and multivariate logistic regression was used to estimate the relationships. The results show that the higher the level of family income, the better the technology use for academic activities. Regarding entertainment, the level of income does not determine the intensity of technology use, though it does determine the types of tool that students use. With reference to gender, men have a greater tendency to use technology for entertainment, but there is no difference between genders when it comes to academic uses.


Torres-Diaz, J. & Duart, J. (2015). Determinants of digital inequality in universities: the case of Ecuador. Journal of e-Learning and Knowledge Society, 11(3),. Italian e-Learning Association. Retrieved February 16, 2019 from .


View References & Citations Map


  1. Bartlett, J., Kotrlik, J., & Higgins, C. (2001), Organizational Research: Determining Appropriate Sample Size in Survey Research. Information Technology, Learning, and Performance Journal, 19(1), 43–50.
  2. Castaño-Muñoz, J., Duart, J.M., & Sancho-Vinuesa, T. (2013), The Internet in face-toface higher education: Can interactive learning improve academic achievement?. British Journal of Educational Technology. Doi:10.1111/bjet.12007Cea,M.A.(2005),Laexterior i zac ión de la xeno fob ia. Revista E spaño la de Investigaciones Sociológicas, 112(5), 197–230.
  3. Chou, C., Condron, L., & Belland, J.C. (2005), A review of the research on Internet addiction. Educational Psychology Review, 17(4), 363–388. Doi:10.1007/s10648005-8138-1
  4. Cochran, W. (1977), Sampling techniques (3rd ed.). New York: John Wiley& Sons.
  5. Díaz De Rada, J.V. (2002), Técnicas de análisis multivariante para investigación social y comercial. Madrid: RAMA.
  6. DiMaggio, P., & Hargittai, E. (2001), From the ‘Digital Divide’ to ’Digital Inequality’: Studying Internet Use As Penetration Increases. New Jersey: Princeton University.
  7. DiMaggio, P., Hargittai, E., Celeste, C., & Shafer, S. (2004), From unequal access to differentiated use: A literature review and agenda for research on digital inequality. In K. Neckerman (Ed.), Social inequality (pp. 355–400). New York: Citeseer.
  8. DLINHE. (2011), Digital Literacy in Higher Education. URL: from (accesed 10th March 2013)
  9. Duart, J.M., & Lupiáñez-Villanueva, F. (2005), E-strategies in the introduction and use of information and communication technologies in the university. RUSC. Universities and Knowledge Society Journal, 2(1).
  10. Falk, J.H., & Needham, M.D. (2013), Factors contributing to adult knowledge of science and technology. Journal of Research in Science Teaching, 50(4), 431–452.
  11. Graham, R. (2008), The Stylization of Internet Life?: Predictors of Internet Leisure Patterns Using Digital Inequality and Status Group Perspectives. Sociological Research Online, 13(5).
  12. Gutierrez, L., & Gamboa, L. (2010), Determinants of ICT Usage among Low-Income Groups in Colombia, Mexico, and Peru. The Information Society: An international Journal, 26(1), 346–363.
  13. Hargittai, E. (2002), Second level digital divide: Differences in people’s online skills. First Monday, 7(4). Doi:10.5210/fm.v7i4.942.
  14. Huang, W.H.D., Hood, D.W., & Yoo, S.J. (2013), Gender divide and acceptance of collaborative Web 2.0 applications for learning in higher education. Internet and Higher Education, 16, 57–65. Doi:J.iheduc.2012.02.001 Joiner, R., Gavin, J., Brosnan, M., Cromby, J., Gregory, H., Guiller, J., Moon, A. (2012), Gender, internet experience, internet identification, and internet anxiety: A ten-year followup. Cyberpsychology, Behavior and Social Networking, 15(7), 370–372.
  15. Junco, R., & Cotten, S. (2011), Perceived academic effects of instant messaging use. Computers& Education, 56(2), 370–378. Doi:10.1016/J.compedu.2010.08.020
  16. Junco, R., Merson, D., & Salter, D.W. (2010), The effect of gender, ethnicity, and income on college students’ use of communication technologies. Cyberpsychology, Behavior and Social Networking, 13(6), 619–27.
  17. Kim, J., LaRose, R., & Peng, W. (2009), Loneliness as the cause and the effect of problematic Internet use: The relationship between Internet use and psychological well-being. Cyber psychology and Behavior, 1 2 , 4 5 1 – 4 5 5. D o i : 1 0. 1 0 8 9 / cpb.2008.0327
  18. Kubey, R.W., Lavin, M.J., & Barrows, J.R. (2001), Internet use and collegiate academic performance decrements: Early fíndings. Journal of Communication, 51(2), 366–382.
  19. Leung, L., & Lee, P. (2012), Impact of Internet Literacy, Internet Addiction Symptoms, and Internet Activities on Academic Performance. Social Science Computer Review, 30(4), 403–418.
  20. Livingstone, S., Bober, M., & Helsper, E. (2005), Inequalities and the digital divide in children and young people’s internet use: findings from the UK Children Go Online project. London UK: London School of Economics and Political Science.
  21. Livingstone, S., & Helsper, E. (2007), Gradations in digital inclusion: children, young people and the digital divide. New Media& Society, 9(4) , 671–696.
  22. López-Pérez, M.V., Pérez-López, M.C., Rodríguez-Ariza, L., & Argente-Linares, E. (2013), The influence of the use of technology on student outcomes in a blended learning context. Educational Technology Research and Development, 61(4), 625– 638. Doi:10.1007/s11423-013-9303-8
  23. Markovíc, S., & Jovanovíc, N. (2012), Learning style as a factor which affects the quality of e-learning. Artificial Intelligence Review, 38, 303–312.
  24. PIC. (2003), Internet Catalonia Project. URL: (Accesed 20th April 2013)
  25. Selwyn, N. (2010), Degrees of Digital Division: Reconsidering Digital Inequalities and Contemporary Higher Education. RUSC. Universities and Knowledge Society Journal, 7(1).
  26. Shunglu, S., & Sarkar, M. (1995), Researching the Consumer. Marketing and Research, 23(2).
  27. Torres-Diaz, J.C., & Infante-Moro, A. (2011), Desigualdad digital en la universidad: Incidencia de los ingresos en los usos de Internet en Ecuador. Comunicar, 37(2).
  28. Torres-Diaz, J.C., Morocho, M., & Guamán, J. (2010), La educación virtual en Ecuador. In C. Rama & J.L. Pardo (Eds.), La educación superior a distancia: Miradas diversas desde Iberoamérica (pp. 76–91). Madrid: INTEVED.
  29. Wittwer, J., & Senkbeil, M. (2008), Is students’ computer use at home related to their mathematical performance at school?. Computers& Education, 50(4), 1558–1571.

These references have been extracted automatically and may have some errors. If you see a mistake in the references above, please contact

View References & Citations Map

Cited By

  1. Inclusive digital online environments as a device for pedagogic differentiation: a taxonomy proposal

    Angélica Monteiro & Carlinda Leite

    Journal of e-Learning and Knowledge Society Vol. 12, No. 4 (Sep 27, 2016)

These links are based on references which have been extracted automatically and may have some errors. If you see a mistake, please contact