Protecting Consumer Autonomy from AI-Personalized Dark Patterns on E-Commerce Platforms in Indonesia
Keywords:
Artificial Intelligence, Consumer Autonomy, Dark Patterns, E-Commerce, PersonalizationAbstract
Purpose - This study examines how Indonesian e-commerce consumers experience and interpret AI-personalized dark patterns and how these practices affect consumer autonomy, trust, consent, perceived manipulation, digital vulnerability, and resistance.
Methodology - A qualitative interpretive design involved 24 active e-commerce consumers selected purposively in Indonesia. Data were collected from November 2025 to January 2026 through semi-structured in-depth interviews supported by scenario-based interface elicitation and analyzed using reflexive thematic analysis.
Findings - Six interconnected themes emerged: conditional acceptance of personalization, limited awareness of algorithmic influence, urgency and scarcity pressure, unequal effort in consent and refusal, declining trust after perceived manipulation, and consumer strategies for restoring autonomy. Personalization was accepted when it improved relevance while preserving control, but was viewed negatively when it became opaque, repetitive, difficult to refuse, or restrictive of deliberation.
Implications - Consumer protection should address both visible interface features and the algorithmic processes determining how personalized persuasion is delivered. E-commerce platforms should strengthen transparency and meaningful user control, while Indonesian policymakers need clearer guidance for adaptive digital choice architectures.
Originality - This study conceptualizes AI-personalized dark patterns as an adaptive process of digital influence and integrates consumer autonomy, digital choice architecture, digital vulnerability, privacy, and trust within the Indonesian e-commerce context.
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