SELECTING EFFICIENT DIGITAL TRANSFORMATION STRATEGIES IN PUBLIC ADMINISTRATION: A NOVEL FRACTAL FUZZY-BASED DECISION-MAKING PERSPECTIVE

Digital transformation is increasingly viewed as a central mechanism for improving efficiency and service quality in public administration, yet public organizations often face difficulties in systematically prioritizing digital reform initiatives. The main challenge lies in evaluating alternative strategies under conditions of limited resources, institutional constraints, and uncertainty. Although the existing literature provides extensive discussions on digital government and public sector innovation, it offers relatively limited guidance on how decision makers can objectively compare and rank digital transformation strategies using integrated and transparent analytical frameworks. In response to this gap, this study aims to develop a comprehensive decision-making model to support the prioritization of digital transformation strategies that enhance public sector efficiency. The proposed hybrid framework integrates the COWEB technique for determining criteria importance, the RATGOS approach for ranking strategic alternatives, and generalized fractal fuzzy sets to more effectively manage ambiguity in expert judgments. Evaluation criteria and strategic alternatives are identified through a detailed literature review and expert consultations. The results indicate that implementation complexity is the most influential criterion, while the adoption of AI based public service applications represents the most effective strategy. The study provides original methodological contributions and practical insights to support feasible, consistent, and sustainable digital transformation decisions in public administration.