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Early detection items and responsible gambling features for online gambling

Author

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  • Joerg Haefeli
  • Suzanne Lischer
  • Juerg Schwarz

Abstract

Early detection is an effective building block for the prevention of problem gambling. This study aims to identify communication-based indicators for gambling-related problems in the setting of online gambling. In the framework of a semi-structured interview, customer service employees of three online gambling operators were surveyed, to identify indicators in customer correspondence could be used as a predictor for gambling-related problems. In a confirmatory part of the study, we investigated to what degree these indicators are able to predict problem gambling in a prospective empirical design. An optimally parsimonious log-linear model, was able to correctly predict 76.6% of the cases. Discussed in the light of this evidence, communication-based indicators could constitute an effective component of early detection. Due to the fact that the internet offers optimal conditions for consistent monitoring and objective analysis, the suggested predictive model could be combined with other models, relying on the analysis of gambling behaviour.

Suggested Citation

  • Joerg Haefeli & Suzanne Lischer & Juerg Schwarz, 2011. "Early detection items and responsible gambling features for online gambling," International Gambling Studies, Taylor & Francis Journals, vol. 11(3), pages 273-288, December.
    • Handle: RePEc:taf:intgms:v:11:y:2011:i:3:p:273-288
    DOI: 10.1080/14459795.2011.604643
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    Cited by:

    1. Patrycja Michalska & Anne Chatton & Louise Penzenstadler & Paweł Izdebski & Emilien Jeannot & Olivier Simon & Magali Dufour & Lucien Rochat & Suzanne Lischer & Yasser Khazaal, 2020. "Perspective of Internet Poker Players on Harm-Reduction Strategies: A Cross-Sectional Study," IJERPH, MDPI, vol. 17(23), pages 1-16, December.
    2. Maris Catania & Mark D. Griffiths, 2021. "Understanding Online Voluntary Self-Exclusion in Gambling: An Empirical Study Using Account-Based Behavioral Tracking Data," IJERPH, MDPI, vol. 18(4), pages 1-11, February.

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