Decision pathways in mobile-app shopping: a Throughput Model study of online consumers

Purpose Mobile shopping environments increasingly rely on artificial intelligence (AI) to personalise recommendations, curate product information and support purchase decisions. Yet, existing consumer decision-making models maintain a linear, stage-based logic that fails to capture how AI-enabled interfaces compress, extend or reconfigure the pathways consumers actually follow. This study aims to use the Throughput Model (TPM) to conceptualise and empirically validate distinct algorithmic decision pathways through which perception, information search and evaluative judgement interact to shape purchase intentions in mobile-app shopping contexts. Design/methodology/approach Survey data were collected from Gen Z consumers with recent mobile-app shopping experience. The study applies partial least squares structural equation modelling and mediation analysis to test the TPM and estimate one-step, two-step and three-step decision pathways in AI-enabled mobile commerce. Findings Perception, information search and judgement each exert significant effects within the model, with judgement emerging as the central integrative mechanism linking upstream inputs to purchase intention. These results demonstrate that AI-enabled mobile interfaces simultaneously support impulsive, efficiency-driven and deliberative decision architectures. Originality/value To the best of the authors’ knowledge, this study provides the first empirical operationalisation of the TPM in mobile-app commerce, reconceptualising purchase decisions as coexisting algorithmic pathways rather than a single sequential route. It offers marketers a diagnostic framework for segmenting consumers by decision architecture and designing adaptive AI-enabled shopping experiences accordingly.