Identifying Potentially Effective Preventive Measures of Game Addiction on Children and Adolescents: A Literature-Based Discovery (LBD) Approach

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Abstract

To help us go through the vast, multidisciplinary literature on game addiction, this study makes use of a knowledge extraction technique called Literature-Based Discovery (LBD) to identify potentially effective but under-studied measures that help prevent the negative effects of online game addiction among children and adolescents. LBD was implemented using association rule mining to generate a list of preventive measure-negative effect pairs (rules), which are then evaluated using the ‘support’ metric. This helped us identify preventive measures that have received relatively less research attention, and one such preventive measure is peer education. Using meta-analysis, we computed the average effect size of peer education and showed that it is comparable to other popular preventive measures in terms of effectiveness. This study which involves a total of 876 articles demonstrates generally that LBD can be a useful research method to discover promising but under-studied research topics in the existing literature.
Original languageEnglish
Title of host publicationPACIS 2022 Proceedings
PublisherAssociation for Information Systems
Number of pages16
Publication statusPublished - Jul 2022
EventPacific Asia Conference on Information Systems (PACIS) 2022 - Virtual Conference
Duration: 5 Jul 20229 Jul 2022
https://aisel.aisnet.org/pacis2022

Conference

ConferencePacific Asia Conference on Information Systems (PACIS) 2022
Abbreviated titlePACIS 2022
Period5/07/229/07/22
Internet address

Keywords

  • Literature-Based Discovery
  • online game addiction
  • preventive measures
  • adolescents

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