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Evaluation of Challenges in the Open Data Services Industry Under Uncertainty: A Methodology Based on Artificial Intelligence and Digital Transformation

Abstract

This paper proposes a novel multi-criteria decision-making (MCDM) framework based on Triangular Linguistic Neutrosophic Cubic Fuzzy Sets (TLNCFSs) integrated with the COBRA method to evaluate strategic alternatives addressing key challenges in the Open Data Services (ODS) industry. In the era of digital transformation and artificial intelligence (AI), open data initiatives face multifaceted obstacles, including regulatory compliance, data interoperability, public engagement, and sustainable governance. To assess effective strategies, this study considers five potential alternatives aligned with AI and digital transformation, evaluated against fourteen critical criteria spanning policy, technical, governance, strategic, economic, and human-centric dimensions. Expert assessments are collected using TLNCFSs to capture uncertainty, indeterminacy, and hesitancy in linguistic evaluations. These assessments are aggregated, defuzzified, and normalized to derive a weighted decision matrix. The COBRA method is then applied to compute distance-based rankings of the alternatives. The proposed framework demonstrates the highest performance for alternatives emphasizing AI-driven automation and scalable cloud-based architectures, whereas the human-centered design approach consistently ranks lowest across all evaluated scenarios. Sensitivity analysis conducted over fourteen weight adjustment cases confirms the model’s robustness and ranking stability. The findings contribute to the advancement of intelligent, uncertainty-aware decision-support tools for digital governance and open data innovation. This research offers practical implications for policymakers and system architects involved in designing resilient and AI-enhanced open data ecosystems.

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