PNS-WINGS-AHP–EWM Integrated Framework for Multi-Criteria Decision Analysis: An Application-Oriented Decision Framework in Digital Agriculture

Vollständiger Abstract

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Digital-agriculture development increasingly requires multi-criteria decision analysis (MCDA) approaches to evaluate complex and interrelated influencing factors. However, existing methods often face difficulties in representing uncertain expert judgments, capturing inter-factor influence structures, and integrating complementary weighting information. To address these challenges, this study develops an integrated Pythagorean Neutrosophic Sets (PNS)–Weighted Influence Non-linear Gauge System (WINGS)–Analytic Hierarchy Process (AHP)–Entropy Weight Method (EWM) framework for digital-agriculture factor evaluation. Specifically, PNS is employed to represent uncertain linguistic assessments through truth, indeterminacy, and falsity information; WINGS is applied to identify the direction and intensity of expert-perceived influence relationships among factors; and a coordinated weighting mechanism integrates AHP-based preference information with entropy-based cross-expert dispersion information. The proposed framework is applied to a digital-agriculture case to identify key influencing factors and determine development priorities. The results indicate that Economic Development Level (W1) ranks first in the final composite priority assessment, followed by Digital Technology Level (W6) and Digital Infrastructure (W5). Furthermore, the WINGS analysis reveals the perceived influence structure among factors and identifies Digital Infrastructure (W5) as the strongest net-influencing factor within the network. Comparative analyses suggest that the proposed framework provides a systematic representation of uncertainty, influence relationships, and complementary weighting information for digital-agriculture factor evaluation. This study highlights the application potential of integrating established MCDM techniques to support complex agricultural decision-making problems.