Governance-Bounded Algorithmic Design Levers Shaping Digital Consumer Behavior: A Systematic Review And Layered Model

Algorithmic design levers in digital platforms shape attention, trust, and consumer decisions to adopt, continue, or resist platform mediated services. Evidence on interface signals, personalization, frictionless transactions, gamification, and governance is synthesized, treating governance and culture as boundary conditions for welfare outcomes including transparency, privacy, and perceived agency. Following PRISMA 2020, Scopus was searched for English language journal articles published from 2016 to 2025; records were screened using predefined criteria, yielding 70 peer reviewed studies. Thematic synthesis and vote counting were used to integrate heterogeneous contexts and measures. Findings converge on four domains: signal architecture and attention capture; personalization and recommender systems; frictionless transactions and gamified journeys; and social commercial convergence with governance. Across domains, levers operate through cognitive, affective, and social mechanisms, including perceived usefulness, enjoyment, trust, fairness, social proof, and perceived control. A governance bounded layered model is proposed to link levers to mechanisms and behavioral and welfare outcomes, and gaps are identified in construct standardization, interaction effects, and causal evidence. Future research should prioritize longitudinal and field designs and develop shared welfare and fairness metrics.