Purpose The purpose of this paper is to investigate the mechanism by which uses and gratification (U&G) constructs predict continuance intention to play (ContInt) the augmented reality game Pokémon Go (PG), through multiple serial mediation technique, with enjoyment and flow as mediators. The model also integrates other motivational factors specific to PG, namely, network externality and nostalgia and investigates the process by which they influence ContInt through players’ inherent need-to-collect animated monsters and online community involvement, respectively. Design/methodology/approach The model was tested using 362 validated responses from an online survey of PG players in Malaysia. Partial least squares structural equation modeling was used to analyse the data. The predictive relevance of the model was tested via partial least squares-Predict. Findings ContInt is influenced through various mechanisms. Enjoyment is the most important mediator, mediating three U&G predictor constructs (achievement, escapism, challenge and social interaction) and the outcome ContInt. Flow did not have any influence on ContInt unless coupled with enjoyment as a serial mediator. Network externality and nostalgia were found to only influence ContInt through mediators, online community involvement and need-to-collect Pokémon Monsters, respectively. Overall, the results show evidence of four indirect-only mediation paths and one complementary partial mediation path. Originality/value Provides support for an integrated model incorporating psychological, social and gaming motivational factors. While most other studies focus on direct relationships, we focus on indirect relationships through multiple sequential mediation analysis, following the recent modern mediation analysis guidelines. Contrary to previous findings, flow was not an important factor in predicting ContInt for gaming and nostalgia does not link directly to ContInt.