Heterogeneous trajectories in digital financial usage intensity: evidence from the Netherlands

Abstract

This study employs Latent Class Growth Modelling (LCGM) and quantile regression to analyse the trajectories and heterogeneous determinants of digital payment usage intensity (DPU) using data from the Netherlands (2019–2023). LCGM identifies distinct usage trajectories, guiding the quantile regression analysis by determining the number of quantiles to examine. The findings reveal that the baseline DPU declines across quantiles, indicating reduced unexplained variation among high-intensity users. Gender effects show minimal differences at lower quantiles but become significantly positive for females at higher quantiles. Tertiary education and financial knowledge play essential roles at lower quantiles, emphasising their importance in supporting low-intensity users, but their influence diminishes as usage intensity increases. These results imply that targeted interventions for low-intensity users should focus on financial literacy and education, while efforts for high-intensity users may benefit from enhancing usability and engagement with digital payment systems.