Predicting participation and expenditure on distinct gambling-like mechanics: financial engagement, selective problem gambling effects and a gaming disorder null

Purpose Quantitative work on gambling-like mechanics (GLMs) in video games has largely treated them as a uniform category, overlooking differences in reward structure and similarity to traditional gambling. This study examines predictors of expenditure across three disaggregated GLM categories – cosmetic loot boxes (CLB), game-affecting loot boxes (GALB) and other in-game gambling-like content (OGLC). Design/methodology/approach Data were collected via an online survey of adult gamers (N = 587). Three model sets used the same gaming engagement and problem behavior indicators as predictors, each comprising a two-part approach: binary logistic regression on the full sample to predict purchase participation, and OLS linear regression among purchasers to predict expenditure amounts. Findings Financial engagement – expenditure on non-random free-to-play content and battle passes – emerged as the dominant predictor class; gaming frequency and gaming disorder were null throughout. Problem gambling severity predicted participation selectively – most strongly for OGLC – but predicted expenditure amounts in no category. Predictor patterns dissociated between participation and amount decisions in ways aggregated or uniform analytic approaches would have obscured. Practical implications Increased focus should be given to paid game mechanics that closely mimic the visual and esthetic presentation of traditional gambling formats. Engagement systems such as battle passes should be carefully assessed for their association with increased expenditure on GLMs. Attention should be paid to the risks related to purchase entry into GLMs, rather than to expenditure amounts alone. Originality/value This study is among the first to apply a disaggregated GLM taxonomy empirically and to model participation and expenditure amount as distinct outcomes.