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Volume 3, Issue 4, 2025

Abstract

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Enhancing logistics performance has been widely recognized as a critical pathway for accelerating economic development in emerging economies. In this context, a rigorous and objective assessment of national logistics performance remains essential. Accordingly, an integrated multi-criteria decision-making (MCDM) framework based on the Skewness Impact Through Distributional Evaluation (SITDE) method and the Multi-Attributive Border Approximation Area Comparison (MABAC) method was proposed for the systematic evaluation of logistics performance across the Emerging Seven (E7) economies. Within this framework, criterion weights were objectively derived using the SITDE method by capturing the distributional characteristics and skewness effects inherent in logistics performance indicators, thereby minimizing subjectivity in the weighting process. Subsequently, the MABAC method was employed to rank countries by quantifying their distances from criterion-specific boundary approximation areas. The empirical analysis focused on the E7 countries—China, India, Brazil, Russia, Indonesia, Mexico, and Türkiye—using the Logistics Performance Index (LPI) indicators obtained from the World Bank database. The results demonstrated that timeliness emerged as the most influential determinant of overall logistics performance. Among the E7 countries, China was identified as exhibiting the highest logistics performance, whereas Russia recorded the lowest performance level. Notably, Türkiye was ranked second, despite its comparatively lower level of economic development relative to several other E7 economies. The robustness and stability of the proposed SITDE–MABAC framework were further confirmed through comprehensive sensitivity and comparative analyses. Beyond methodological advancement, the findings offer important managerial, policy-oriented, and region-specific insights, providing evidence-based guidance for policymakers and logistics practitioners seeking to enhance logistics efficiency, resilience, and international competitiveness in developing economies.

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As geopolitical competition intensifies and risks of global technological decoupling rise, the Digital Silk Road (DSR) is undergoing a strategic transition from the hard connectivity of physical infrastructure toward the soft connectivity of software ecological collaboration. Utilizing quarterly high-frequency indicators from the GitHub Innovation Graph (2020Q1–2025Q3), this study empirically examines the evolution of software collaboration networks between China and Belt and Road Initiative participating countries. We introduce the concept of digital gravity shift—a structural reorientation of innovation based on collaboration density and network resilience—to extend traditional innovation gravity theories. The findings reveal that: First, a significant digital gravity shift has occurred; unlike the stagnating Group of Seven (G7)-centric pathways, internal collaboration within the DSR exhibits a unique U-shaped resilience, where geopolitical shocks have paradoxically catalyzed the reorganization of innovation paths. Second, the collaboration model is transforming from unidirectional technology spillover toward bidirectional reciprocal symbiosis, signaling the maturation of digital social capital and mutual dependency within the Global South. Third, the substance of collaboration has achieved a qualitative leap from surface-level tasks to core system engineering, uncovering a leapfrog catch-up mechanism driven by the lower entry barriers of open-source modularity. This research provides granular empirical evidence for an emerging multipolar innovation landscape and offers strategic insights for mitigating technological fragmentation and enhancing national innovation resilience in the post-decoupling era.

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Energy supply selection has become a crucial component of organizational strategy, as firms strive to balance sustainability, reliability, and cost efficiency under uncertain market and policy conditions. This study develops a strategic decision-support framework that integrates type-2 fuzzy logic with the Combined Compromise Solution (CoCoSo) method to evaluate alternative energy supply options. The hybrid model addresses the ambiguity inherent in expert judgments by employing type-2 fuzzy sets and prioritizes competing alternatives through the CoCoSo ranking process. Six evaluation criteria—cost, reliability, maintenance, environmental impact, supply stability, and policy support—were defined based on expert consultation. The proposed framework was applied to an industrial case study, demonstrating its capacity to manage conflicting objectives and deliver a transparent, rational ranking of energy alternatives. Sensitivity analysis confirmed the robustness of the results. The findings provide actionable insights for decision-makers and policymakers seeking data-driven strategies to enhance sustainable energy planning and operational efficiency.
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