The influences of information literacy, internet addiction and parenting styles on internet risks
Abstract
The purpose of this study is to examine how demographics, addiction symptoms, information literacy, parenting styles and internet activities can predict ‘internet risks’. Data were gathered from a probability sample of 718 adolescents and teenagers, aged 9–19 in Hong Kong, using face-to-face interviews. Results show that adolescents who are often targets of harassment tend to be older boys with a high family income. They are targets probably because they spend a lot of time on social networking sites (SNSs) and prefer the online setting. Adolescents who encounter a lot of unwelcome solicitation of personal or private information online tend to be older girls. In information literacy, they are generally very competent with publishing tools but are not structurally literate, especially in understanding how information is socially situated and produced. Implications and recommendations for future research are discussed.
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The low correlation between DL and academic achievement for middle school students may be due to Internet addiction.
“The low correlation between DL and academic achievement for middle school students may be due to Internet addiction.”
From The causal relationship between digital literacy and students’ academic achievement: a meta-analysis (2025) · cites this paper as Leung and Lee, 2012b
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Cheung, C., Yue, X., & Wong, D. S. W. (2014) · Journal of Child and Family Studies · doi:10.1007/s10826-014-0033-2 · Cited by 35 · see citing works
The causal relationship between digital literacy and students’ academic achievement: a meta-analysis
Li, F., Cheng, L., Wang, X., Shen, L., Ma, Y., & Islam, A. Y. M. A. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-04399-6 · Cited by 38 · see citing works
“…sampling encompasses a broader and more diverse population, including individuals with varying educational levels and economic backgrounds (Gubbels et al. 2020; Leung and Lee, 2012a, 2012b; Sabir and Naureen, 2017), which may dilute the correlation between digital literacy and academic performance.”
Behavioral Addictions: Some Developmental Considerations
Derevensky, J. L. (2019) · Current Addiction Reports · doi:10.1007/s40429-019-00257-z · Cited by 33 · see citing works
Toepfl, F. (2013) · European Journal of Communication · doi:10.1177/0267323113511183 · Cited by 33 · see citing works
Unraveling the underlying factors SCulPT-ing cyberbullying behaviours among Malaysian young adults
Balakrishnan, V. (2017) · Computers in Human Behavior · doi:10.1016/j.chb.2017.04.062 · Cited by 32 · see citing works
Gini, G., Marino, C., Xie, J.-Y., Pfetsch, J. S., & Pozzoli, T. (2019) · Cyberpsychology Journal of Psychosocial Research on Cyberspace · doi:10.5817/cp2019-4-1 · Cited by 32 · see citing works
Internet, children and space: Revisiting generational attributes and boundaries
de Almeida, A. N., Delicado, A., Alves, N. D. A., & Carvalho, T. (2014) · New Media & Society · doi:10.1177/1461444814528293 · Cited by 31 · see citing works
Villanueva‐Blasco, V. J., de Valencia, U. I., Bernal, S. S., & de Zaragoza, U. (2018) · Revista de Psicología y Educación - Journal of Psychology and Education · doi:10.23923/rpye2019.01.168 · Cited by 30 · see citing works
Asrese, K., & Muche, H. (2020) · PLoS ONE · doi:10.1371/journal.pone.0238804 · Cited by 30 · see citing works
Yu, L., Recker, M. M., Chen, S., Zhao, N., & Qiu-yan, Y. (2018) · Cyberpsychology Behavior and Social Networking · doi:10.1089/cyber.2017.0503 · Cited by 28 · see citing works
Internet Addiction Impacts on Time Management That Results in Poor Academic Performance
Siddiqi, S., & Memon, Z. A. (2016) · International Conference on Frontiers of Information Technology (FIT) · doi:10.1109/fit.2016.020 · Cited by 27 · see citing works
The Role of Digital Literacy on Online Opportunity and Online Risk in Indonesian Youth
Luthfia, A., Wibowo, D., Widyakusumastuti, M. A., & Angeline, M. (2021) · DOAJ (DOAJ: Directory of Open Access Journals) · doi:10.15206/ajpor.2021.9.2.142 · Cited by 26 · see citing works
Comparing factors related to school-bullying and cyber-bullying
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Cyberbullying: Definitions and Facts from a Psychosocial Perspective
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Hwang, W., Jung, E., Fu, X., Zhang, Y., Ko, K., Lee, S.-A., Lee, Y. M., Lee, S., You, H., & Kang, Y. (2022) · Journal of Child and Family Studies · doi:10.1007/s10826-021-02213-w · Cited by 23 · see citing works
The Family Context in Cybervictimization: A Systematic Review and Meta-Analysis
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Internet Dependence in an Undergraduate Population
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The role of family on internet addiction: A model analysis of co-parenting effect
Sun, Y. (2023) · Cogent Social Sciences · doi:10.1080/23311886.2022.2163530 · Cited by 19 · see citing works
Parent-Child Communication About Educational Aspirations: Experiences of Adolescents in Rural China
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- Authors
- Leung, L., & Lee, P. S. N.
- Affiliations
- Chinese University of Hong Kong (HK)
- ORCID
- Leung, Louis
- Venue
- New Media & Society
- Publisher
- SAGE Publications
- Published
- 2011-08-08
- Volume
- 14
- Issue
- 1
- Pages
- 117-136
- ISSN
- 1461-4448 (ISSN-L)
- 1461-7315
- OpenAlex
- W2147875374
- Cited by
- 273
- References
- 47
- Citations by year
- 2026: 7 · 2025: 7 · 2024: 12 · 2023: 23 · 2022: 27 · 2021: 17 · 2020: 17 · 2019: 30 · 2018: 28 · 2017: 21 · 2016: 22 · 2015: 19 · 2014: 26 · 2013: 11 · 2012: 6
- FWCI
- 46.12
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- New Media & Society (publishedVersion)
- Hong Kong Education Bibliographic Database (submittedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Impact of Technology on Adolescents
- Social Sciences › Arts and Humanities › Literature and Literary Theory › Literacy, Media, and Education
- Physical Sciences › Computer Science › Information Systems › Cybercrime and Law Enforcement Studies
- Keywords
- information literacy, internet addiction, parenting styles, online risks, adolescents, online harassment, social networking sites, demographic predictors, Hong Kong adolescents
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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