Algorithmic persuasion in AI personalization: the role of moral disengagement in shaping ethical judgment and brand responses

Purpose AI-driven personalization is commonly framed as a tool for enhancing relevance and engagement. This study advances a process-based perspective by conceptualizing algorithmic persuasion as a morally consequential form of interactive influence. Specifically, it examines how AI persuasion intensity activates moral disengagement, shapes ethical judgment and ultimately influences brand trust and brand avoidance. Design/methodology/approach A multi-method design was employed, combining survey data from 451 consumers analyzed using PLS-SEM, qualitative interviews for interpretive elaboration and a controlled experiment involving 306 participants to establish causal inference and examine boundary conditions. Findings The findings reveal a sequential moral process through which intensive AI-driven persuasion increases moral disengagement, weakens ethical judgment, and subsequently shapes divergent brand responses. The structural model supports all hypothesized relationships, while the experiment demonstrates that AI transparency constrains disengagement and attenuates the indirect effects of algorithmic persuasion. These findings indicate that AI personalization operates not only through perceived relevance, but also through moral rationalization. Originality/value This study contributes to interactive marketing research by reframing AI-driven personalization as a conditional moral process rather than a purely technological capability. It introduces moral disengagement as a core pre-evaluative mechanism underlying algorithmic persuasion and identifies transparency as a critical condition governing its activation. By integrating structural, interpretive, and causal evidence, the study provides a more nuanced explanation of how consumers morally interpret AI-driven influence in digital marketplaces.