Analyzing the interrelationships of evaluation indicators in the open data services industry's efforts toward digital transformation: A novel group decision-making approach

Abstract

The open data services industry is critical in advancing artificial intelligence (AI), particularly as businesses navigate digital transformation. This transformation is essential for enhancing AI-driven decision-making, fostering innovation, and improving operational efficiency. This study aims to identify and analyze the interrelationships among key evaluation indicators to optimize performance within the open data services industry. Using data collected from expert interviews and a combined Delphi method with the interval single-valued trapezoidal neutrosophic DEMATEL (DIN-DEMATEL) technique, we establish influence relationships and prioritize the weights of these indicators. Our analytical approach provides a structured evaluation of how criteria interact and influence one another. The findings highlight “resource utilization efficiency,” “innovation-driven productivity,” and “financial performance and sustainability” as the three most influential indicators in the overall evaluation system. These indicators drive efficiency, foster innovation, and ensure financial stability, forming the foundation for operational excellence and strategic growth. The findings provide a clear framework for businesses aiming to enhance their competitiveness through digital transformation while advancing the theoretical understanding of interrelationships within the open data services industry. The study offers actionable recommendations for companies to target improvements in these areas, enhancing competitiveness and resilience in digital transformation.