Top

13-11-2024 | Original Paper

Assessing the Risk of Online Gambling Products: A Replication and Validation of Behavioural Markers of Harm Using the Problem Gambling Severity Index

Authors: Paul Delfabbro, Jonathan Parke, Maris Catania

Published in: Journal of Gambling Studies

Abstract

In this paper, we investigate the validity of objective operator data as proxy indicators for riskier gambling as based upon an independent self-report measure. Such work is important to strengthen the validity of gambling research involving objective behavioral indicators of harm used to detect higher risk gambling or product choices. To address these aims, a total of 21,464 individual customers from a single international operator completed the Problem Gambling Severity Index (PGSI). These data were then mapped to measures of player gambling activity and a range of objective behavioural markers of harm. The results confirmed that people scoring 8 + on the PGSI were found to have higher levels of gambling involvement (participation, days active and expenditure) on a range of gambling products, with differences generally larger for casino than wagering activities. Importantly, this group was also more likely to have a higher incidence of behavioural markers of harm (e.g., declined deposits). The data allowed for the replication of a previous study using such markers to detect differences in product risk, but further validated their use in a variety of analytical contexts by showing a concordance between self-reported and objective risk measures.

A ‘game show’ is a broad term for a gambling product that combines a live presenter with some variation of a of a casino or slot product. The purpose of the live presenter is to enhance the social expereince of the product by narrating the action and interacting with players (usually through an online chat function).

A reverse withdrawal capability permits players to cancel a pending cash withdrawal from their betting account to their bank account (which can sometimes take more than 48 h) and return the funds into the betting account to permit more gambling.

A payment provider or bank may block a deposit if they suspect fraudulent activity. Deposit behaviour which might be indicative fraudulent activity can include repeated deposits in a short period of time, deposits at unusual times, increasing sizes of deposits, deposits which may be very large in size or any other deposit activity which may be considered erratic or out of character. We considered that this marker is relevant because deposit behaviour by at-risk or disordered gamblers could show similar characteristics.

Bonus page visits involve looking for free bets or chips, or money to top-up the account balance which may be given as loyalty bonus or incentive for play. These are often subject to wagering requirements before account withdrawals are permitted.

Auer, M., & Griffiths, M. D. (2013). Voluntary limit setting and player choice in most intense online gamblers: An empirical study of gambling behaviour. Journal of Gambling Studies, 29(4), 647–660. https://doi.org/10.1007/s10899-012-9332-yCrossRefPubMed

Auer, M., Malischnig, D., & Griffiths, M. (2014). Is “pop-up” messaging in online slot machine gambling effective as a responsible gambling strategy? Journal of Gambling Issues, 29(10), 1–10. https://doi.org/10.4309/jgi.2014.29.3CrossRef

Auer, M. M., & Griffiths, M. D. (2015). The use of personalized behavioral feedback for online gamblers: an empirical study. Frontiers in Psychology, 6, 1406. https://doi.org/10.3389/fpsyg.2015.01406CrossRefPubMedPubMedCentral

Auer, M. M., & Griffiths, M. D. (2016). Personalized behavioral feedback for online gamblers: A real world empirical study. Frontiers in Psychology, 7, 1875. https://doi.org/10.3389/fpsyg.2016.01875CrossRefPubMedPubMedCentral

Auer, M., Hopfgartner, N., & Griffiths, M. D. (2020). The effects of voluntary deposit limit-setting on long-term online gambling expenditure. Cyberpsychology, Behavior, and Social Networking, 23(2), 113–118. https://doi.org/10.1089/cyber.2019.0202CrossRefPubMed

Auer, M., & Griffiths, M. D. (2022a). Attitude towards deposit limits and relationship with their account-based data among a sample of german online slots players. Journal of Gambling Studies. https://doi.org/10.1007/s10899-022-10155-1CrossRefPubMedPubMedCentral

Auer, M., & Griffiths, M. D. (2022b). The relationship between structural characteristics and gambling behaviour: An online gambling player tracking study. Journal of Gambling Studies. https://doi.org/10.1007/s10899-022-10115-9CrossRefPubMedPubMedCentral

Auer, M., & Griffiths, M. D. (2022c). Using artificial intelligence algorithms to predict self-reported problem gambling with account-based player data in an online casino setting. Journal of Gambling Studies. https://doi.org/10.1007/s10899-022-10139-1CrossRefPubMedPubMedCentral

Auer, M., & Griffiths, M. D. (2022d). An empirical attempt to operationalize chasing losses in gambling utilizing account-based player tracking data. Journal of Gambling Studies. https://doi.org/10.1007/s10899-022-10144-4CrossRefPubMedPubMedCentral

Blanco, C., Blaszczynski, A., Clement, R., Derevensky, J., Goudriaan, A. E., Hodgins, D., van Holst, R. J., Ibanez, A., Martins, S., Moersen, C., Molinaro, S., Parke, A., Peren, F., Petry, N., & Wardle, H. (2013). Assessment tool to measure and evaluate the risk potential of gambling products, ASTERIG: A global validation. Gaming Law Review and Economics, 9, 635–642.CrossRef

Braverman, J., & Shaffer, H. J. (2012). How do gamblers start gambling: Identifying behavioural markers for high-risk internet gambling. The European Journal of Public Health, 22(2), 273–278.CrossRefPubMed

Braverman, J., Laplante, D. A., Nelson, S. E., & Shaffer, H. J. (2013). Using cross-game behavioral markers for early identification of high-risk internet gamblers. Psychology of Addictive Behaviors, 27(3), 868–877. https://doi.org/10.1037/a0032818CrossRefPubMed

Braverman, J., Tom, M.A., & Shaffer, H.J. (2014). Accuracy of self-reported versus actual online gambling wins and losses. Psychological assessment, 26(3), 865–877. https://doi.org/10.1037/a0036428CrossRefPubMed

Browne, M., Delfabbro, P., Thorne, H. B., Tulloch, C., Rockloff, M. J., Hing, N., Dowling, N. A., & Stevens, M. (2023). Unambiguous evidence that over half of gambling problems in Australia are caused by electronic gambling machines: Results from a large-scale composite population study EGMs cause over half of gambling problems. Journal of Behavioral Addictions, 12, 182–193.CrossRefPubMedPubMedCentral

Catania, M., & Griffiths, M. D. (2021). Understanding online voluntary self-exclusion in gambling: An empirical study using account-based behavioral tracking data. International Journal of Environmental Research and Public Health, 18(4), 2000. https://doi.org/10.3390/ijerph18042000CrossRefPubMedPubMedCentral

Chen, Z., Doekemeijer, R. A., Noel, X., & Verbruggen, F. (2022). Winning and losing in online gambling: Effects on within-session chasing. PLoS ONE, 17(8), e0273359. https://doi.org/10.1371/journal.pone.0273359CrossRefPubMedPubMedCentral

Delfabbro, P., King, D. L., Browne, M., & Dowling, N. A. (2020). Do EGMs have a stronger association with problem gambling than Rracing and casino table games? Evidence from a decade of Australian prevalence studies. Journal of Gambling Studies, 36(2), 499–511. https://doi.org/10.1007/s10899-020-09950-5CrossRefPubMed

Delfabbro, P., & Parke, J. (2021). Empirical evidence relating to the relative riskiness of scratch-card gambling. Journal of Gambling Studies, 37(3), 1007–1024. https://doi.org/10.1007/s10899-021-10033-2CrossRefPubMed

Delfabbro, P.H., Parke, J., Catania, M., & Chikh, K. (2023). Behavioural markers of harm and their potential in identifying product risk in online gambling. International Journal of Mental Health and Addiction.

Dowling, N., Smith, D., & Thomas, T. (2005). Electronic gaming machines: Are they the ‘crack cocaine’ of gambling? Addiction, 100, 33–45.CrossRefPubMed

Dragicevic, S., Tsogas, G., & Kudic, A. (2011). Analysis of casino online gambling data in relation to behavioural risk markers for high-risk gambling and player protection. International Gambling Studies, 11(3), 377–391. https://doi.org/10.1080/14459795.2011.629204CrossRef

Dragicevic, S., Percy, C., Kudic, A., & Parke, J. (2015). A descriptive analysis of demographic and behavioral data from Internet gamblers and those who self-exclude from online gambling platforms. Journal of Gambling Studies, 31(1), 105–132.CrossRefPubMed

Ferris, J., & Wynne, H. (2001). The Canadian Problem Gambling Index: Final Report. Ottawa: Canada.

Forrest, D., & McHale, I. (2022). Patterns of play. Technical report 2: Account data stage. London: GambleAware.

Gainsbury, S. M., Abarbanel, B., & Blaszczynski, A. (2020). The relationship between in-play betting and gambling problems in an Australian context of prohibited online in-play betting. Frontiers in Psychiatry, 11, 574884. https://doi.org/10.3389/fpsyt.2020.574884CrossRefPubMedPubMedCentral

LaPlante, D. A., Nelson, S. E., & Gray, H. M. (2014). Breadth and depth involvement: Understanding Internet gambling involvement and its relationship to gambling problems. Psychology of Addictive Behaviors, 28(2), 396–403. https://doi.org/10.1037/a0033810CrossRefPubMed

Luquiens, A., Dugravot, A., Panjo, H., Benyamina, A., Gaïffas, S., & Bacry, E. (2019). Self-exclusion among online poker gamblers: Effects on expenditure in time and money as compared to matched controls. International Journal of Environmental Research and Public Health, 16(22), 4399. https://doi.org/10.3390/ijerph16224399CrossRefPubMedPubMedCentral

Luquiens, A., Tanguy, M.-L., Benyamina, A., Lagadec, M., & Aubin, H.-J., Reynaud, M. (2016). Tracking online poker problem gamblers with player account-based gambling data only. International Journal of Methods in Psychiatric Research, 25(4), 333–342.CrossRefPubMedPubMedCentral

McAuliffe, W. H. B., Louderback, E. R., EdsonLaPlante, T. C. D. A., & Nelson, S. E. (2022). Using “markers of harm” to track risky gambling in two cohorts of online sports bettors. Journal of Gambling Studies, 38(4), 1337–1369. https://doi.org/10.1007/s10899-021-10097-0CrossRefPubMed

Nelson, S. E., Edson, T. C., Louderback, E. R., Tom, M. A., Grossman, A., & Laplante, D. A. (2022). Changes to the playing field: A contemporary study of actual European online sports betting. Journal of Behavioral Addictions. https://doi.org/10.1556/2006.2021.00029CrossRef

Newall, P. W., Russell, A. M., & Hing, N. (2021). Structural characteristics of fixed-odds sports betting products. Journal of Behavioral Addictions, 10, 371–380. https://doi.org/10.1556/2006.2021.00008CrossRefPubMedPubMedCentral

Newall, P. S., & Cortis, D. (2021). Are sports bettors biased towards longshots, favorites, or both? A literature review. Risks, 1, 1–9.

Parke, A., & Parke, J. (2019). Transformation of sports betting into a rapid and continuous gambling activity: A grounded theoretical investigation of problem sports betting in online settings. International Journal of Mental Health and Addiction, 17(6), 1340–1359.CrossRef

Parke, J., Parke, A., & Blaszczynski, A. (2016). Key issues in produced-based harm minimisation. The Responsible Gambling Trust. https://doi.org/10.13140/RG.2.2.30894.10560CrossRef

Percy, C., França, M., Dragičević, S., & d’AvilaGarcez, A. (2016). Predicting online gambling self-exclusion: An analysis of the performance of supervised machine learning models. International Gambling Studies, 16(2), 193–210. https://doi.org/10.1080/14459795.2016.1151913CrossRef

Perrot, B., Hardouin, J.-B., Grall-Bronnec, M., & Challet-Bouju, G. (2018). Typology of online lotteries and scratch games gamblers’ behaviours: A multilevel latent class cluster analysis applied to player account-based gambling data. International Journal of Methods in Psychiatric Research, 27(4), e1746. https://doi.org/10.1002/mpr.1746CrossRefPubMedPubMedCentral

Perrot, B., Hardouin, J.-B., Thiabaud, E., Saillard, A., Grall-Bronnec, M., & Challet-Bouju, G. (2022). Development and validation of a prediction model for online gambling problems based on players’ account data. Journal of Behavioral Addictions, 11(3), 874–889. https://doi.org/10.1556/2006.2022.00063CrossRefPubMedPubMedCentral

Price-Waterhouse Coopers (2017). Remote gambling research. Interim report on Phase II. London: GambleAware

Russell, A. M. T., Hing, N., Browne, M., Li, E., & Vitartas, P. (2019). Who bets on micro events (microbets) in sports? Journal of Gambling Studies, 35(1), 205–223.CrossRefPubMed

Xuan, Z., & Shaffer, H. (2009). How do gamblers end gambling: Longitudinal analysis of internet gambling behaviors prior to account closure due to gambling related problems. Journal of Gambling Studies, 25(2), 239–252. https://doi.org/10.1007/s10899-009-9118-zCrossRefPubMed

Title: Assessing the Risk of Online Gambling Products: A Replication and Validation of Behavioural Markers of Harm Using the Problem Gambling Severity Index
Authors: Paul Delfabbro
Jonathan Parke
Maris Catania
Publication date: 13-11-2024
Publisher: Springer US
Published in: Journal of Gambling Studies
Electronic ISSN: 1573-3602
DOI: https://doi.org/10.1007/s10899-024-10363-x

Podcast | Sexual health in older age

Springer Medicine

Assessing the Risk of Online Gambling Products: A Replication and Validation of Behavioural Markers of Harm Using the Problem Gambling Severity Index

Abstract

Podcast | Sexual health in older age

Springer Medicine

Abstract

Please log in to get access to this content