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Paying for loot boxes is linked to problem gambling, regardless of specific features like cash-out and pay-to-win.

Authors :
Zendle, David
Cairns, Paul
Barnett, Herbie
McCall, Cade
Source :
Computers in Human Behavior. Jan2020, Vol. 102, p181-191. 11p.
Publication Year :
2020

Abstract

Loot boxes are items in video games that may be bought with real-world money but contain randomised contents. Due to similarities between loot boxes and gambling, various countries are considering regulating them to reduce gambling-related harm. However, loot boxes are extremely diverse. A key problem facing regulators is determining whether specific types of loot boxes carry more potential for harm, and should be regulated accordingly. In this study, we specify seven key ways that loot boxes may differ from each other: They may involve paid or unpaid openings; give opportunities for cashing out; allow gamers to pay to win; involve the use of an in-game currency; feature crate and key mechanics; show near misses; and contain exclusive items. We then use a large-scale preregistered correlational analysis (n = 1200) to determine if any of these features strengthen the link between loot box spending and problem gambling. Our results indicate that being able to cash out, showing near-misses, and letting players use in-game currency to buy loot boxes may weakly strengthen the relationship between loot box spending and problem gambling. However, our main conclusion is that regardless of the presence or absence of specific features of loot boxes, if they are being sold to players for real-world money, then their purchase is linked to problem gambling. • Preregistered analysis replicates links between loot box spending and problem gambling (η2 = 0.092). • Loot box spending remains linked to problem gambling, regardless of presence or absence of features like cash-out. • Cash out, near-misses, and using in-game currency strengthened links between problem gambling and loot box spending. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
07475632
Volume :
102
Database :
Academic Search Index
Journal :
Computers in Human Behavior
Publication Type :
Academic Journal
Accession number :
140984769
Full Text :
https://doi.org/10.1016/j.chb.2019.07.003