A decision-analytic framework for policy design under resource constraints: Application to sustainable agricultural technology adoption

Designing effective policy interventions for sustainable agricultural technology adoption under resource constraints remains a persistent challenge. Existing research identifies socioeconomic, institutional, and behavioral determinants of adoption, but rarely provides quantitative mechanisms to translate barriers into coherent, cost-sensitive policy strategies. This study develops a hybrid Multi-Attribute Decision-Making (MADM) framework integrating the Best-Worst Method (BWM) and Quality Function Deployment (QFD) with 0–1 linear programming model, an approach not previously applied in national agricultural policy contexts. BWM derives the relative importance of adoption barriers; QFD translates these into strategy contribution values, serving as objective-function coefficients identifying optimal portfolios across eleven resource-capacity scenarios. Results indicate ease of use and maintenance, and efficient, productive and compatible technology are the most influential adoption barriers, while government support, input support, and positive attitude towards technology emerge as the most relevant strategies for Bangladesh. Under severe resource constraints, positive attitude towards technology and ecosystem capacity building forms the most cost-efficient portfolio. Distinct from comparable hybrid frameworks, every ranking and portfolio is validated through a three-part sensitivity analysis, including 2000 Monte Carlo perturbations, confirming this ranking is stable (Kendall’s tau = 0.978) and identifying a robust core retained regardless of input uncertainty. The QFD roof matrix further identifies strategy pairs whose joint implementation may yield cost and time savings, offering an interpretive layer alongside the optimized portfolio. This robustness testing, applied within a domain where this integrated framework has not previously been used, provides policymakers a transparent, replicable, resource-aware tool for prioritizing adoption interventions, adaptable to diverse development contexts.