Behavioral Manipulation in Big Data Implementation: Systematic Literature Review

Abstrak

This study examines behavioral manipulation in big data implementation through a systematic literature review of thirty peer-reviewed articles published between 2020 and 2025. The review aims to provide a clear understanding of the mechanisms, impacts, and mitigation strategies related to use of big data to influence human behavior. The PRISMA 2020 framework was applied, starting with 250 identified records; after screening for inclusion and exclusion criteria, 30 studies were selected for full analysis. The results indicate that behavioral manipulation most frequently occurs through algorithmic recommendation systems, price personalization, deceptive interface designs (dark patterns), and data-driven persuasion techniques. These mechanisms were consistently associated with reduced user autonomy, biased decision-making, psychological pressure, and widening social inequalities. Several studies further reveal that algorithmic transparency alone is insufficient to prevent manipulation when users lack meaningful understanding or control over automated systems. The review also identifies emerging mitigation strategies, including dynamic consent mechanisms, independent algorithmic audits, ethical-by-design interfaces, and adaptive regulatory frameworks. However, the findings suggest that such interventions remain fragmented and unevenly implemented across sectors. Approximately 83.3% the reviewed studies conclude that addressing behavioral manipulation of big data requires an integrated response combining technical safeguards, ethical system design, regulatory oversight, and strengthened digital literacy.