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

J Gambl Stud. 2016 Sep;32(3):889-904. doi: 10.1007/s10899-015-9590-6.


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.

Keywords: Decrease gambling; Initial high usage; Latent class analysis; Low repeated usage; Responsible gambling tool; User behavior.

MeSH terms

  • Adult
  • Attitude to Health
  • Behavior, Addictive / psychology*
  • Female
  • Gambling / psychology*
  • Health Behavior*
  • Humans
  • Internet
  • Male
  • Middle Aged
  • Personality*
  • Risk
  • Socioeconomic Factors
  • Young Adult