Analysis of dating app classification and predictors of dating app addiction based on user experience factors

Dating apps now re-engineer relationship formation, sometimes fostering addictive use that disrupts daily life and harms platform reputations. Sometimes, however, they encourage addictive behavior that disrupts daily life and damages the reputation of the platforms. This study integrates psychological addiction models with uses-and-gratifications research to construct an empirical framework for predicting excessive dating app usage behavior. First, we classify services as either location-based or group-based, then build a 12-factor user experience (UX) model to identify potential addiction drivers. A survey measured these UX factors alongside a 20-item Dating Apps Addiction Related Scale. Multiple regression analysis revealed that self-optimality, usability, selectivity, casual sex, and self-fulfillment significantly increase addiction scores for location-based apps. For group-based apps, selectivity, agglomerativity, identity, and social barrier emerge as key predictors. We interpret these results through the lens of the Interaction of Person–Affect–Cognition–Execution (I-PACE) model, Griffiths’s six-component syndrome of behavioral addiction, and classical human–computer interaction (HCI) theory. We hypothesize that instant proximity rewards accelerate compulsive loops in location-based platforms while community validation prolongs engagement in group-based contexts. These findings contribute to theoretical development by connecting specific UX design elements to addiction mechanisms. They also provide designers, regulators, and users with actionable guidance to promote a healthier digital dating ecosystem.