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Research Trend on the Use of IT in Digital Addiction: An Investigation Using a Systematic Literature Review

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Listed:
  • Flourensia Sapty Rahayu

    (Program Studi Sistem Informasi, Universitas Atma Jaya Yogyakarta, Daerah Istimewa Yogyakarta 55281, Indonesia)

  • Lukito Edi Nugroho

    (Departemen Teknik Elektro dan Teknologi Informasi, Universitas Gadjah Mada, Daerah Istimewa Yogyakarta 55281, Indonesia)

  • Ridi Ferdiana

    (Departemen Teknik Elektro dan Teknologi Informasi, Universitas Gadjah Mada, Daerah Istimewa Yogyakarta 55281, Indonesia)

  • Djoko Budiyanto Setyohadi

    (Magister Informatika, Universitas Atma Jaya Yogyakarta, Daerah Istimewa Yogyakarta 55281, Indonesia)

Abstract

Despite the negative role of IT in digital addiction development, IT may have a positive role in dealing with digital addiction. The present study undertakes a systematic literature review to explore the state of play and the trend regarding the use of IT in digital addiction research. Using predefined keywords, the Scopus database was searched for relevant literature published from 2017 to 2020. The initial search found 1655 papers. Six stages of study selection were completed using a set of inclusion and exclusion criteria. The study selection and quality assessment process were applied, then 15 papers were selected for further review. The results show that addiction detection using IT is the most researched topic in digital addiction research. The most commonly used IT in the selected studies are AI methods and biosignal recording systems. Various approaches in detection, prevention, and intervention are suggested in the selected studies. The advantages and limitations of each approach are discussed. Based on these results, some future research directions are suggested.

Suggested Citation

  • Flourensia Sapty Rahayu & Lukito Edi Nugroho & Ridi Ferdiana & Djoko Budiyanto Setyohadi, 2020. "Research Trend on the Use of IT in Digital Addiction: An Investigation Using a Systematic Literature Review," Future Internet, MDPI, vol. 12(10), pages 1-23, October.
    • Handle: RePEc:gam:jftint:v:12:y:2020:i:10:p:174-:d:430788
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    References listed on IDEAS

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    1. Jingyun Choi & Mi Jung Rho & Yejin Kim & In Hye Yook & Hwanjo Yu & Dai-Jin Kim & In Young Choi, 2017. "Smartphone dependence classification using tensor factorization," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-12, June.
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