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Public resistance remains a persistent obstacle to the effective implementation of new policy interventions across sectors such as public health, environmental governance, education, and digital regulation. Conventional policy design often prioritizes legal compliance and technical feasibility while underestimating the behavioral, perceptual, and emotional drivers shaping public acceptance. This review paper advances a social marketing–based framework for reducing public resistance to new policy interventions by systematically integrating behavioral science, strategic communication, and stakeholder-centric design principles into the policy cycle. Drawing on interdisciplinary literature from social marketing, behavioral economics, public administration, and political communication, the study synthesizes evidence on how attitudes, perceived costs and benefits, trust in institutions, and social norms influence public responses to policy change. The proposed framework emphasizes audience segmentation, value exchange, co-creation, and adaptive messaging as core mechanisms for aligning policy objectives with citizen motivations. Particular attention is given to the role of framing, credible messengers, and feedback loops in mitigating misinformation, fear, and perceived loss associated with policy transitions. By positioning citizens as active participants rather than passive recipients of policy directives, the framework shifts policy implementation from a compliance-driven model to a relationship-oriented approach. This review contributes theoretically by bridging gaps between social marketing and policy studies, and practically by offering policymakers a structured, evidence-informed guide for designing and communicating policy interventions that are more likely to gain public trust and sustained support. The framework is intended to be adaptable across governance contexts, especially in environments characterized by low institutional trust, rapid social change, or high policy complexity.
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Papers that cite the same references — the relational fallback while embeddings are still backfilling.
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