Experiences of Playscan: Interviews with users of a responsible gambling tool

Online gambling, encompassing a wide variety of activities and around-the-clock access, can be a potential risk factor for gamblers who tend to gamble excessively. Yet, the advent of online gambling has enabled responsible gambling (RG) features that may help individuals to limit their gambling behaviour. One of these features is RG tools that track gamblers' behaviour, performs risk assessments and provides advice to gamblers. This study investigated users' views and experiences of the RG tool Playscan from a qualitative perspective using a semi-structured interview. The tool performs a risk assessment on a three-step scale (low, medium and high risk). Users from every risk category were included. Twenty interviews were carried out and analysed using thematic analysis. Two main themes with associated sub-themes were identified: "Usage of Playscan and the gambling site" and "Experiences of Playscan". Important experiences in the sub-themes were lack of feedback from the tool and confusion when signing up to use Playscan. These experiences counteracted positive attitudes that should have promoted usage of the tool. Providing more feedback directly to users is a suggested solution to increase usage of the RG tool.

Usage of a Responsible Gambling Tool: A Descriptive Analysis and Latent Class Analysis of User Behavior

Gambling is a common pastime around the world. Most gamblers can engage in gambling activities without negative consequences, but some run the risk of developing an excessive gambling pattern. Excessive gambling has severe negative economic and psychological consequences, which makes the development of responsible gambling strategies vital to protecting individuals from these risks. One such strategy is responsible gambling (RG) tools. These tools track an individual's gambling history and supplies personalized feedback and might be one way to decrease excessive gambling behavior. However, research is lacking in this area and little is known about the usage of these tools. The aim of this article is to describe user behavior and to investigate if there are different subclasses of users by conducting a latent class analysis. The user behaviour of 9528 online gamblers who voluntarily used a RG tool was analysed. Number of visits to the site, self-tests made, and advice used were the observed variables included in the latent class analysis. Descriptive statistics show that overall the functions of the tool had a high initial usage and a low repeated usage. Latent class analysis yielded five distinct classes of users: self-testers, multi-function users, advice users, site visitors, and non-users. Multinomial regression revealed that classes were associated with different risk levels of excessive gambling. The self-testers and multi-function users used the tool to a higher extent and were found to have a greater risk of excessive gambling than the other classes.