Literature DB >> 32691198

Online Problem Gambling: A Comparison of Casino Players and Sports Bettors via Predictive Modeling Using Behavioral Tracking Data.

Ivan Ukhov1, Johan Bjurgert2, Michael Auer3, Mark D Griffiths4.   

Abstract

In this study, the differences in behavior between two groups of online gamblers were investigated. The first group comprised individuals who played casino games, and the second group comprised those who bet on sports events. The focal point of the study was on problem gambling, and the objective was to identify and quantify both common and distinct traits that are characteristic to casino and sports problem gamblers. To this end, a set of gamblers from the gaming operator LeoVegas was studied. Each gambler was ascribed two binary variables: one separating casino players from sports bettors, and one indicating whether there was an exclusion related to problem gambling. For each of the four combinations of the two variables, 2500 gamblers were randomly selected for a thorough comparison, resulting in a total of 10,000 participants. The comparison was performed by constructing two predictive models, estimating risk scores using these models, and scrutinizing the risk scores by means of a technique originating from collaborative game theory. The number of cash wagers per active day contributed the most to problem-gambling-related exclusion in the case of sports betting, whereas the volume of money spent contributed the most to this exclusion in the case of casino players. The contribution of the volume of losses per active day was noticeable in the case of both casino players and sports bettors. For casino players, gambling via desktop computers contributed positively to problem-gambling-related exclusion. For sports bettors, it was more concerning when the individual used mobile devices. The number of approved deposits per active day contributed to problem-gambling-related exclusion to a larger extent for sports bettors than casino players. The main conclusion is that the studied explanatory variables contribute differently to problem-gambling-related exclusion among casino players and sports bettors.
© 2020. The Author(s).

Entities:  

Keywords:  Behavioral tracking; Online casino gambling; Online sports betting; Problem gambling; Remote gambling

Mesh:

Year:  2021        PMID: 32691198      PMCID: PMC8364529          DOI: 10.1007/s10899-020-09964-z

Source DB:  PubMed          Journal:  J Gambl Stud        ISSN: 1050-5350


  10 in total

1.  How do gamblers start gambling: identifying behavioural markers for high-risk internet gambling.

Authors:  Julia Braverman; Howard J Shaffer
Journal:  Eur J Public Health       Date:  2010-01-27       Impact factor: 3.367

2.  Gambling and Problem Gambling in Victoria, Australia: Changes over 5 years.

Authors:  Max Abbott; Christine A Stone; Rosa Billi; Kristal Yeung
Journal:  J Gambl Stud       Date:  2016-03

3.  Who Bets on Micro Events (Microbets) in Sports?

Authors:  Alex M T Russell; Nerilee Hing; Matthew Browne; En Li; Peter Vitartas
Journal:  J Gambl Stud       Date:  2019-03

4.  Pop-up messages, dissociation, and craving: how monetary limit reminders facilitate adherence in a session of slot machine gambling.

Authors:  Melissa J Stewart; Michael J A Wohl
Journal:  Psychol Addict Behav       Date:  2012-09-17

5.  Voluntary limit setting and player choice in most intense online gamblers: an empirical study of gambling behaviour.

Authors:  Michael Auer; Mark D Griffiths
Journal:  J Gambl Stud       Date:  2013-12

Review 6.  Online Gambling Addiction: the Relationship Between Internet Gambling and Disordered Gambling.

Authors:  Sally M Gainsbury
Journal:  Curr Addict Rep       Date:  2015-04-11

7.  Testing normative and self-appraisal feedback in an online slot-machine pop-up in a real-world setting.

Authors:  Michael M Auer; Mark D Griffiths
Journal:  Front Psychol       Date:  2015-03-23

8.  The effect of loss-limit reminders on gambling behavior: A real-world study of Norwegian gamblers.

Authors:  Michael Auer; Niklas Hopfgartner; Mark D Griffiths
Journal:  J Behav Addict       Date:  2018-11-12       Impact factor: 6.756

Review 9.  Internet-Based Interventions for Problem Gambling: Scoping Review.

Authors:  Mark van der Maas; Jing Shi; Tara Elton-Marshall; David C Hodgins; Sherald Sanchez; Daniela Ss Lobo; Sylvia Hagopian; Nigel E Turner
Journal:  JMIR Ment Health       Date:  2019-01-07

10.  Demographic, Behavioural and Normative Risk Factors for Gambling Problems Amongst Sports Bettors.

Authors:  Nerilee Hing; Alex M T Russell; Peter Vitartas; Matthew Lamont
Journal:  J Gambl Stud       Date:  2016-06
  10 in total
  6 in total

1.  Using artificial intelligence algorithms to predict self-reported problem gambling with account-based player data in an online casino setting.

Authors:  Michael Auer; Mark D Griffiths
Journal:  J Gambl Stud       Date:  2022-07-19

2.  Consumer Profile Segmentation in Online Lottery Gambling Utilizing Behavioral Tracking Data from the Portuguese National Lottery.

Authors:  Bernardo T Chagas; J F S Gomes; Mark D Griffiths
Journal:  J Gambl Stud       Date:  2021-09-13

3.  Real-Time Tracking Method for Sports Targets in Smart Cities under the Internet+ Background.

Authors:  Jianping Hu; Xinjiang Ye
Journal:  Comput Intell Neurosci       Date:  2022-03-07

4.  Predicting self-exclusion among online gamblers: An empirical real-world study.

Authors:  Niklas Hopfgartner; Michael Auer; Mark D Griffiths; Denis Helic
Journal:  J Gambl Stud       Date:  2022-08-10

5.  Transitioning Between Online Gambling Modalities and Decrease in Total Gambling Activity, but No Indication of Increase in Problematic Online Gambling Intensity During the First Phase of the COVID-19 Outbreak in Sweden: A Time Series Forecast Study.

Authors:  Philip Lindner; David Forsström; Jakob Jonsson; Anne H Berman; Per Carlbring
Journal:  Front Public Health       Date:  2020-09-29

6.  A casino in my pocket: Gratifications associated with obsessive and harmonious passion for mobile gambling.

Authors:  Eoin Whelan; Samuli Laato; A K M Najmul Islam; Joël Billieux
Journal:  PLoS One       Date:  2021-02-24       Impact factor: 3.240

  6 in total

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