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Abdul Ghofur
Universitas Islam Negeri Walisongo, Indonesia

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Silent Resistance in AI-Driven Marketing: A Qualitative Study of Perceived Manipulation Muhammad Hilmi Labibunnajah; Ninda Fatmawati; Abdul Ghofur
Performance: Jurnal Personalia, Financial, Operasional, Marketing dan Sistem Informasi Vol 33 No 1 (2026): Performance
Publisher : Faculty of Economics and Business Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32424/1.jp.2026.33.1.20101

Abstract

This study aims to explore how consumers perceive perceived manipulation in AI-driven marketing and how such perceptions lead to the emergence of silent resistance. This study employs a qualitative research approach to capture in-depth consumer perceptions and experiences in the context of AI-driven marketing. Data were collected through in-depth interviews with purposively selected informants and analysed using thematic analysis to identify patterns, categories, and emerging themes. The findings reveal that while AI-driven personalisation initially enhances consumer convenience, excessive intensity and accuracy of content exposure lead to discomfort and suspicion. Consumers begin to perceive marketing practices as manipulative when they feel their autonomy in decision-making is being subtly influenced. As a response, consumers engage in silent resistance behaviours such as ignoring content, reducing engagement, and gradually avoiding brands or platforms without expressing explicit complaints. This study contributes to the literature by highlighting the role of perceived manipulation as a psychological mechanism that links AI-driven marketing to implicit consumer resistance. Practically, it suggests that companies should balance personalisation with transparency and ethical considerations to avoid triggering negative consumer perceptions and disengagement. This research offers a novel perspective by uncovering silent resistance as an implicit and underexplored consumer response to perceived manipulation in AI-driven marketing through a qualitative approach.