Problem gambling and income as predictors of loot box spending

ABSTRACT

Loot boxes are randomized virtual rewards often purchasable for real money. They have often been compared to gambling activities, and a consistent link between loot box spending and problem gambling symptomatology has been found. We reanalyzed data from 1049 participants across three countries to examine the interaction between yearly income and problem gambling symptomatology on loot box spending. Results evidenced the best model of loot box spending included the combined main effects of income and PGSI, but there was no evidence for an interaction between these factors. Follow-up analysis of the main effect of income indicated greater spending on loot boxes in higher income brackets compared to lower income brackets. Overall, problem gambling symptomatology appears more important than income, but both contribute to loot box spending.

KEYWORDS:

• Gambling
• income
• video games
• expenditure
• loot boxes

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Funding sources

This research was supported by the Marsden Fund Council from Government funding, managed by Royal Society Te Apārangi; MAU1804.

Constraints on publishing

No constraints on publishing were declared by the authors in relation to this manuscript.

Competing interests

No competing interests were declared by the authors in relation to this manuscript.

Preregistration statement

No preregistration was declared by the authors in relation to this manuscript.

Data availability statement

The data described in this article are openly available in the Open Science Framework at https://doi.org/DOI 10.17605/OSF.IO/B87PM.

Open scholarship

This article has earned the Center for Open Science badge for Open Data. The data are openly accessible at https://doi.org/DOI10.17605/OSF.IO/B87PM.

Notes

1. https://www.ato.gov.au/Rates/Individual-income-tax-for-prior-years/

2. https://www.ird.govt.nz/income-tax/income-tax-for-individuals/tax-codes-and-tax-rates-for-individuals/tax-rates-for-individuals

3. https://taxsummaries.pwc.com/united-states/individual/taxes-on-personal-income

4. For further information on the benefits of Bayesian analysis over NHST, we refer interested readers to Wagenmakers et al. (2018).

5. In response to a query by an anonymous reviewer, including gender as a predictor variable did not improve the model fit, BF₁₀ = 0.144.

Additional information

Notes on contributors

Eamon Patrick Garrett

Eamon Garrett is a PhD candidate at the University of Tasmania. His research primarily focusses on gambling-like mechanisms within video games.

James D. Sauer

James D. Sauer is a Senior Lecturer at the University of Tasmania, with an interest in the cognitive and behavioural effects of video gaming and gambling related mechanisms in video games.

Aaron Drummond

Aaron Drummond is a Senior Lecturer at Massey University. He is interested in the way that humans and computers interact, and in particular how digital media such as video games influences human psychology.

Emily Lowe-Calverley

Emily Lowe-Calverley is an Associate Lecturer at the University of Tasmania. Her research interests include social psychology and the effects of interacting with technology, video games, and social media.