Background: The expansion of big data analytics and artificial intelligence has transformed digital markets, social media platforms, and public services into complex ecosystems centered on data extraction, personalization, and predictive modeling. Organizations increasingly rely on user generated data to fuel innovation, refine marketing strategies, and optimize service delivery. However, this shift has intensified longstanding concerns about privacy, surveillance, and information asymmetries. Existing literature has explored the privacy paradox, privacy calculus, information privacy concerns, and the structural features of big data business models, yet an integrated theoretical framework that connects these perspectives across private and public domains remains underdeveloped.

Purpose: This study develops a comprehensive multiperspective framework that synthesizes big data business model theory, privacy calculus, personalization research, social media engagement studies, and public sector data governance scholarship. The aim is to explain how organizations design and justify data driven personalization strategies while individuals negotiate privacy risks through cognitive, emotional, and contextual mechanisms. The paper also examines the tension between innovation and data protection across commercial platforms, influencer economies, and AI driven public services.

Method: The study employs an integrative theoretical methodology grounded in structured literature synthesis. Core constructs are derived from established scales and models of information privacy concern, transparency, personalization, social media engagement, and data sharing. The framework is developed through conceptual triangulation, comparing findings from marketing, information systems, public administration, and behavioral economics.

Results: The analysis reveals that personalization acceptance is mediated by perceived relevance, transparency, trust, contextual norms, and reward structures. The so called privacy paradox emerges as a function of bounded rationality, information asymmetry, and affective decision making rather than irrational inconsistency. In digital markets, loyalty programs, influencer marketing, and live streaming commerce amplify data exchange by embedding profiling within social connectedness and experiential engagement. In public services, AI driven solutions face similar privacy trade offs but are shaped by institutional trust and civic expectations. A revised privacy calculus model is proposed, incorporating structural business model incentives and contextual platform affordances.

Conclusion: The study advances theory by integrating business model analysis with individual level privacy behavior, bridging commercial and public sector contexts. It highlights the need for transparent data governance, participatory design, and value sensitive personalization strategies. Future research should empirically test the proposed framework across cultures and regulatory environments to assess the sustainability of data driven ecosystems.