Assessing symptoms of excessive SNS usage based on user behavior: Effective factors associated with addiction components

Ploypailin Intapong, Saromporn Charoenpit, Tiranee Achalakul, Michiko Ohkura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Excessive Social Networking Site (SNS) usage leads to negative consequences that are associated with addiction. We assessed the symptoms of excessive SNS usage by studying user behavior in SNSs. Understanding how people behave in them helps detect and identify the symptoms of excessive SNS usage. In our previous studies, we developed a data collection application and experimentally collected data from undergraduate students in Thailand. We employed modified versions of Internet Addiction Test (IAT) and Bergen Facebook Addiction Scale (BFAS) for measuring SNS addiction. Our results identified the differences between excessive and normal users. In this article, we clarified the factors associated with addiction components reflected by IAT and BFAS question items. Our analytic results indicated the effective factors for addiction components.

Original languageEnglish
Title of host publicationProceedings of 2018 5th International Conference on Business and Industrial Research
Subtitle of host publicationSmart Technology for Next Generation of Information, Engineering, Business and Social Science, ICBIR 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages137-142
Number of pages6
ISBN (Electronic)9781538652541
DOIs
Publication statusPublished - 2018 Jun 20
Event5th International Conference on Business and Industrial Research, ICBIR 2018 - Bangkok, Thailand
Duration: 2018 May 172018 May 18

Other

Other5th International Conference on Business and Industrial Research, ICBIR 2018
CountryThailand
CityBangkok
Period18/5/1718/5/18

Keywords

  • Addiction components
  • SNS
  • SNS Addiction
  • Social Networking Site

ASJC Scopus subject areas

  • Business, Management and Accounting (miscellaneous)
  • Artificial Intelligence
  • Computer Science Applications
  • Industrial and Manufacturing Engineering
  • Social Sciences (miscellaneous)
  • Signal Processing
  • Information Systems and Management
  • Management Science and Operations Research

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  • Cite this

    Intapong, P., Charoenpit, S., Achalakul, T., & Ohkura, M. (2018). Assessing symptoms of excessive SNS usage based on user behavior: Effective factors associated with addiction components. In Proceedings of 2018 5th International Conference on Business and Industrial Research: Smart Technology for Next Generation of Information, Engineering, Business and Social Science, ICBIR 2018 (pp. 137-142). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICBIR.2018.8391181