neccton GmbH, Davidgasse 5, 7052, Muellendorf, Austria.
International Gaming Research Unit, Psychology Department, Nottingham Trent University, 50 Shakespeare Street, NG1 4FQ, Nottingham, UK.
J Gambl Stud. 2023 Mar;39(1):265-279. doi: 10.1007/s10899-022-10115-9. Epub 2022 May 13.
Structural characteristics of games have been regarded as important aspects in the possible development of problematic gambling. The most important factors along with individual susceptibility and risk factors of the individual gambler are the structural characteristics such as the speed and frequency of the game (and more specifically event frequency, bet frequency, event duration, and payout interval). To date, the association between structural characteristics and behavior has not been studied in an online gambling environment. The present study investigated the association between structural characteristics and online gambling behavior in an ecologically valid setting using data from actual gamblers. The authors were given access to data from a large European online gambling operator with players from Germany, Austria, UK, Poland, and Slovenia. The sample comprised 763,490 sessions between November 27, 2020 and April 15, 2021 utilizing data from 43,731 players. A machine learning tree-based algorithm with structural characteristics and session metrics explained 26% of the variance of the number of games played in a session. The results also showed that only 7.7% of the variance in the number of bets placed in a session was explained by the game's structural characteristics alone. The most important structural characteristic with respect to the number of games played in a session was the event frequency of the game followed by the maximum amount won on a single bet in a session.
游戏的结构特征被认为是导致赌博问题产生的重要因素之一。除了个体易感性和风险因素外,最重要的因素还包括游戏的速度和频率(具体而言,是事件频率、下注频率、事件持续时间和支付间隔)。迄今为止,在在线赌博环境中,还没有研究过结构特征与行为之间的关系。本研究在生态有效的环境中,利用实际赌徒的数据,调查了结构特征与在线赌博行为之间的关系。作者获得了一家大型欧洲在线博彩运营商的数据,该运营商的玩家来自德国、奥地利、英国、波兰和斯洛文尼亚。该样本包括 2020 年 11 月 27 日至 2021 年 4 月 15 日期间的 763,490 个会话,涉及 43,731 名玩家的数据。使用基于结构特征和会话指标的机器学习树状算法解释了会话中玩游戏次数的 26%的方差。结果还表明,仅游戏的结构特征就能解释会话中下注次数的 7.7%的方差。对于会话中玩游戏的次数而言,最重要的结构特征是游戏的事件频率,其次是会话中单次下注的最大金额。