- Demographic, Behavioural and Normative Risk Factors for Gambling Problems Amongst Sports Bettors
- Is there a need for personal gambling licences? - Janne Nikkinen,
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Musick and Christopher E. Authors: Jacob A. Jordaan and Bogdan Dima. Authors: Giovanni Piumatti.
Demographic, Behavioural and Normative Risk Factors for Gambling Problems Amongst Sports Bettors
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Open Choice — What is this? Impact Impact factor: 1. Exploratory pairwise comparisons were then conducted via a series of 6 Mann-Whitney U tests. The results of these comparisons are reported below as Table 4.
Is there a need for personal gambling licences? - Janne Nikkinen,
The Spearman rank correlation matrix for loot box spending, other microtransaction spending, and problem gambling categorisation was computed. This is shown below as Table 5. The same analysis was then conducted on the relationship between raw problem gambling scores i.
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The Spearman rank correlation matrix for these variables is displayed below as Table 6. We then conducted a series of exploratory analyses to investigate whether the relationship between other microtransactions and problem gambling was significantly weaker than the relationship between loot box spending and problem gambling. In order to assess the difference between these relationships, they were entered into statistical analyses which allowed the testing of interactions between different kinds of spending and problem gambling severity.
A mixed-model ANOVA was first conducted, with problem gambling non problem gamblers, low-risk gamblers, moderate-risk gamblers, problem gamblers as a between-participants factor and type of spending loot box spending, spending on in-game items other than loot boxes as within-participants factor.
We therefore ran a follow-up nonparametric analysis whose assumptions did not require data to be normally distributed. More specifically, an Aligned Rank Transform test, analogous to a nonparametric ANOVA, was conducted according to [ 24 ], with problem gambling non problem gamblers, low-risk gamblers, moderate-risk gamblers, problem gamblers as a between-participants factor and type of spending loot box spending, spending on in-game items other than loot boxes as within-participants factor.
Results indicated a significant effect of problem gambling categorisation F 3, The results of this study provide further evidence of a potentially important relationship between problem gambling and loot box spending. Individuals who did not have gambling problems spent significantly less money on loot boxes than those who were problem gamblers, or at risk of problem gambling. However, when it comes to analysing differences between other subgroups, it is interesting to note that whilst differences were observed between non problem gamblers and both low-risk, moderate-risk, and problem gamblers, no significant differences were observed within these groups.
Our results gave no evidence to support the idea that low risk, moderate risk, and problem gamblers differed from each other in terms of their spending on loot boxes, in contrast to [ 21 ].
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There are several reasons why this might be the case. Firstly, it may be the case that real differences exist in the world, but our sample was not large enough to observe these effects.
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It is worth noting that in this dataset, for instance, only 56 moderate risk gamblers formed part of the sample. It is possible that there are other explanations for this lack of an effect.
Differences are thought to exist between individuals who are categorised as ether low-risk, moderate-risk, and problem gamblers. For example, problem gamblers have been observed to gamble more, and have higher levels of debt than moderate-risk gamblers[ 26 ]. However, as noted in [ 23 ], demographic and behavioural differences between low and moderate risk categories are often small and statistically insignificant.
Indeed, in some studies, these classification categories are collapsed into each other e. Further studies are needed with larger numbers of gamers that fall into these categories in order to determine whether the lack of an effect observed here between these subgroups is simply a Type II error, or whether it really does represent a meaningful facet of the relationship between loot box spending and problem gambling amongst gamers.
It is important to note that the overall relationship between problem gambling and loot box spending that was observed is of small-to-medium size. This suggests that the relationship between problem gambling and loot box spending may be comparable in strength to the relationship between problem gambling and known risk factors in the gambling literature. Interestingly, other factors that relate to technology use have been estimated to have similarly strong links with problem gambling. For example, exposure to gambling content in social media has been shown to share a moderate-strength relationship with problem gambling[ 30 ], as has engagement with simulated gambling in digital and social media [ 31 ].
It may therefore be the case that the links between loot boxes and problem gambling that we see here are not an isolated phenomenon. They may instead be a single indicator of a phenomenon, as new forms of communication and entertainment technology allow audiences easy access to novel gambling-like and gambling-related experiences. Though the picture of loot box spend is consistent, the complexity of the microtransaction spend indicates how important it is to gather more data in this area. Exploratory follow-up analyses painted an inconsistent picture: An aligned rank transform revealed a significant interaction between types of spending and problem gambling, indicating that the relationship between other microtransactions spending and problem gambling is inferior to the relationship between loot box spending and problem gambling.
Much more work is needed first to understand the structure of the data in order to determine which more sophisticated modelling approaches might be appropriate to its analysis. In particular, based on our data here we might propose a generalized linear model of the spend data that could then be used as the foundation for future studies and their analysis.
This further opens up the opportunity for Bayesian analysis that can consider robust parameter estimation of such models. It would be premature to do such analysis on our current data because until this study, we had no conception of what the data might look like and therefore what might be suitable models. An additional note must be made about the sample that was used in this study. An unusually large number of participants in this study identified as problem gamblers. Overall, individuals from within our participant sample scored 8 or higher on the PGSI.
This indicates a much higher level of problem gambling in our sample than in the population at large, in which problem gambling is relatively rare [ 23 ]. It seems likely that the prevalence of problem gamblers in our sample is due to the data collection method we employed: In this study, we recruited participants via Amazon Mechanical Turk, a popular microwork platform.
It may be the case that microworkers are more likely to suffer from problem gambling than the general population. This point is supported by previous research on gambling amongst microworkers. For example, in [ 32 ], researchers examined the usefulness of the Crowdflower microwork platform for recruiting individuals with gambling problems. The data collection method we employed here may therefore have allowed us a good opportunity to study the behaviours of problem gamblers who are also gamers.
Similarly, previous studies on loot boxes have seen a large gender imbalance amongst participants: In [ 15 ], only 5. By contrast, in the sample used here, almost a third of participants identified as female. Again, the difference between these samples may be due to the data collection method employed. The fact that similar patterns of results were replicated in this study despite the different composition of the sample under test suggests that the link between problem gambling and loot box spending may generalise widely across different groups of gamers. Further work is necessary to confirm that this is the case.
Finally, it is key to note that the specific methodology followed during this study itself carries inherent limitations. It may be the case that these estimates are imprecise, and that taking a direct measure of actual spending would allow a more precise evaluation of the strength of any link between problem gambling and loot box spending. Additionally, this work is primarily concerned with the replication of previous work on links between the amount that individuals spend on loot boxes and their problem gambling severity. However, all of these factors may be of importance when it comes to any relationship between loot box spending and problem gambling.
This research provides further evidence of a potentially important link between problem gambling and the amount that individuals spend on loot boxes. It directly addresses the limitations of previous research, in which a similar link was seen in an unblinded and self-selected sample. This research replicates that relationship and suggests that it remains in existence even when a sample is unaware of the fact that research concerns loot boxes and gambling, and have not self-selected into a loot box-related study.
However, it is key to note that the causal direction of this relationship is unclear. It may be the case that loot boxes cause individuals to become problem gamblers. It may also be the case that pre-existing gambling problems cause individuals to spend more money on loot boxes. They would instead be providing an opportunity for games companies to exploit serious pre-existing psychological problems amongst their customers for massive monetary gains.
The correlational nature of this study makes it impossible to determine which of these pictures of the effects of loot boxes is true.
However, regardless of the direction of causality, the games industry faces a crisis of conscience. Industry bodies such as the ESRB and IGEA are finding it increasingly difficult to claim in good faith that there is little evidence of a link between problem gambling and loot box use. Loot boxes are a novel phenomenon, and game developers may understandably be wary of the association of their products with gambling.
We strongly believe that this encompasses the need for continual reflection on the potential effects of loot boxes on the behalf of industry stakeholders. It is our view based on the findings of this study that ratings agencies should consider incorporating additional parental advisories into games that persist in featuring loot boxes. In [ 34 ], King and Delfabbro outline a broad variety of different ways that loot box related harm may be mitigated by employing social responsibility measures.
In light of the results seen here, we believe that many of the suggestions that are suggested in that document are appropriate. Most importantly, we follow them in their suggestion that appropriate content descriptors are added to games that feature loot boxes. We recommend that games with loot boxes are restricted to players of legal gambling age. It is also our opinion that the severity of the link seen here suggests that relevant authorities should seriously consider restricting access to loot boxes as if they were a form of gambling.
Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Abstract Loot boxes are items in video games that contain randomised contents and can be purchased with real-world money. Introduction Loot boxes are a profitable mechanism in modern video games that shares significant psychological and structural similarities with gambling.
Loot boxes are big business Loot boxes are items in video games that players can buy with real-world money, but which, when opened, contain randomised contents.
Loot boxes share psychological and structural features with gambling.