Personalization without progress: Gen Z, AI, and the missed marketing opportunity in foodservice apps

This study examines how Generation Z consumers interpret and evaluate AI-driven personalization in mobile food applications. Drawing on 30 qualitative interviews with Canadian participants aged 18–24, the analysis develops and empirically grounds a mid-range theoretical model of AI-enabled food personalization as a relational process rather than a purely technical optimization tool. Findings reveal that personalization is experienced as efficient yet static, often reinforcing prior behaviors while failing to adapt to evolving identities, emotional contexts, or health aspirations. Trust in AI emerged as conditional and dynamically calibrated, shaped by perceived transparency, commercial intent, and alignment with users’ future-oriented goals. Participants were receptive to personalization that signaled recognition and autonomy support, but skeptical of opaque or commercially driven nudges. By extending the personalization paradox into digitally mediated food environments and integrating commitment-trust theory with algorithmic transparency and identity alignment mechanisms, the study advances theory on AI-mediated engagement in foodservice marketing. Practical implications highlight the importance of transparency signaling, identity-responsive discovery, autonomy-supportive wellness integration, and affective calibration in designing relationally meaningful personalization systems.