نوع مقاله : مقاله پژوهشی
عنوان مقاله English
نویسندگان English
The burgeoning era of smart markets, propelled by pervasive data, artificial intelligence (AI), Internet of Things (IoT), and blockchain technologies, promises unprecedented efficiencies and personalization. However, this transformative potential is critically threatened by an erosion of digital trust, fueled by escalating concerns over data privacy, security breaches, algorithmic opacity, and biased decision-making. Traditional data governance paradigms prove inadequate for navigating the unique complexities of these highly interconnected and autonomous digital ecosystems. This study addresses this pressing challenge by employing a Design Science Research (DSR) methodology to conceptualize, design, and demonstrate a comprehensive Data Governance Framework specifically tailored to foster and sustain digital trust in smart markets.
The developed artifact is a multi-layered framework encompassing: (1) a Strategic & Policy Layer that mandates digital trust as a core organizational imperative, driven by proactive regulatory alignment, transparent data policies, and robust accountability structures; (2) an Operational & Technical Layer that translates policy into practice through Privacy-by-Design and Security-by-Design principles, integrating Explainable AI (XAI), continuous algorithmic bias monitoring, meticulous data quality management, and granular access controls, while addressing the unique governance challenges of DLTs; and (3) an Ethical & Cultural Layer that embeds trust values through ethical AI guidelines, fosters a culture of data stewardship via continuous training, and champions transparent communication and robust data subject rights. The entire framework is underpinned by a principle of continuous monitoring, feedback, and adaptive learning to ensure responsiveness to evolving threats and technologies.
Conceptual scenario analyses, illustrating applications in AI-driven financial services, IoT-enabled smart cities, and blockchain-based supply chains, demonstrated the framework's practical utility in mitigating the "black box" problem, ensuring privacy, and clarifying accountability. Evaluation against criteria such as completeness, internal consistency, alignment with established best practices (e.g., DAMA DMBOK, GDPR, NIST, AI ethics guidelines), and conceptual feasibility confirmed the framework's robustness and problem-solving efficacy. This research significantly contributes to digital trust and data governance theories within the context of smart markets, providing a critical blueprint for businesses and policymakers to responsibly unlock innovation, enhance consumer confidence, and build a sustainable, trustworthy digital economy.
کلیدواژهها English
