Usage of a Responsible Gambling Tool: A Descriptive Analysis and Latent Class Analysis of User Behavior
Usage of a Responsible Gambling Tool: A Descriptive Analysis and Latent Class Analysis of User Behavior
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DOI：
10.1007/s10899-015-9590-6
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发表时间：
2016-09-01
影响因子：
2.4
通讯作者：
Carlbring, Per
中科院分区：
文献类型：
--
作者：
Forsstrom, David;Hesser, Hugo;Carlbring, Per
赌博是世界各地常见的消遣方式。大多数赌徒可以从事赌博活动而不会产生负面后果，但有些人则面临着形成过度赌博模式的风险。过度赌博会产生严重的负面经济和心理后果，这使得制定负责任的赌博策略对于保护个人免受这些风险至关重要。其中一种策略是负责任赌博 (RG) 工具。这些工具跟踪个人的赌博历史并提供个性化反馈，可能是减少过度赌博行为的一种方法。然而，该领域缺乏研究，而且对这些工具的使用知之甚少。本文的目的是描述用户行为，并通过进行潜在类别分析来调查是否存在不同的用户子类。对9528名自愿使用RG工具的在线赌徒的用户行为进行了分析。网站访问次数、进行的自我测试和使用的建议是潜在类别分析中包含的观察变量。描述性统计显示，该工具的功能总体上初次使用率较高，重复使用率较低。潜在类别分析产生了五种不同的用户类别：自我测试者、多功能用户、建议用户、网站访问者和非用户。多项回归显示，不同类别与不同的过度赌博风险水平相关。自测者和多功能用户使用该工具的程度较高，被发现比其他类别有更大的过度赌博风险。
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.