A hybrid decision‑analytic framework recommends that government support, input subsidies and attitude‑building are the most cost‑efficient strategies to boost agricultural technology uptake in Bangladesh, with portfolio rankings robust to extensive input uncertainty. The approach gives policymakers transparent, budget‑aware portfolios and highlights complementary strategy pairs, but relies on expert inputs and lacks empirical outcome validation.

Main Finding

A novel hybrid Multi-Attribute Decision-Making (MADM) framework — combining the Best‑Worst Method (BWM), Quality Function Deployment (QFD), and a 0–1 linear programming optimizer — produces transparent, cost‑sensitive portfolios of policy interventions to promote sustainable agricultural technology adoption. Applied to Bangladesh across eleven resource‑capacity scenarios, the framework identifies (1) the most influential adoption barriers and (2) cost‑efficient, robust intervention portfolios (including strategy pairs with complementarities). The ranking and portfolios are robust to input uncertainty (2000 Monte Carlo perturbations; Kendall’s τ = 0.978).

Key Points

• Novel integration: BWM → QFD → 0–1 linear programming to map barriers → strategy contribution values → optimized binary portfolios under budget/resource constraints.
• Principal barriers (most influential): ease of use & maintenance; technology efficiency/productivity; technology compatibility with existing practices.
• Top recommended strategies for Bangladesh: government support, input support, and fostering positive attitudes toward technology.
• Under severe resource constraints: the most cost‑efficient portfolio emphasizes positive attitude toward technology plus ecosystem/capacity building.
• QFD roof matrix reveals strategy pairs with potential joint cost/time savings (complementarities), giving interpretive guidance beyond the optimizer’s output.
• Robustness and validation: three‑part sensitivity analysis including 2000 Monte Carlo perturbations; rankings stable (Kendall’s τ = 0.978) and a robust core set of strategies persists across perturbations.
• Policy relevance: produces transparent, replicable, resource‑aware prioritization for national policy contexts; adaptable to other development settings.

Data & Methods

• Methods combined:
  • Best‑Worst Method (BWM) to elicit and weight the relative importance of adoption barriers (expert judgments).
  • Quality Function Deployment (QFD) to translate barrier weights into strategy contribution values; these values become objective‑function coefficients.
  • 0–1 linear programming to select binary portfolios of strategies that maximize aggregate contribution subject to resource‑capacity (budget) constraints.
• Experimental design:
  • Implemented across eleven resource‑capacity scenarios (varying budget/implementation capacity).
  • Optimization produces portfolios for each scenario; QFD roof matrix used to identify complementary strategy pairs.
• Validation:
  • Three‑part sensitivity analysis (one component being 2000 Monte Carlo perturbations of input weights/coefficients).
  • Stability assessed with Kendall’s tau (0.978), and identification of a robust core of strategies that remain selected despite input uncertainty.
• Application context: national policy setting for Bangladesh (survey/expert elicitation implied for BWM weights and strategy cost/capacity estimates).

Implications for AI Economics

• Methodological precedent: demonstrates how hybrid MADM + integer optimization can translate qualitative/behavioral barriers into quantitatively optimal, budget‑constrained policy portfolios — a template applicable to technology diffusion problems in AI economics (e.g., public support for AI adoption in agriculture, health, or manufacturing).
• Policy targeting under scarcity: shows how to prioritize interventions when fiscal or administrative capacity is limited — useful for economic models of technology adoption that need tractable, implementable policy levers.
• Complementarity detection: QFD roof matrix identifies strategy pairs with joint benefits; analogous techniques could detect complementarities across AI policies (training + subsidies, regulation + standards) to improve cost‑effectiveness.
• Robustness and transparency: the strong sensitivity testing (Monte Carlo, rank stability) is a model for evidence standards in AI policy design — helps guard against brittle recommendations from expert‑elicited inputs.
• Extensions and opportunities:
  • Replace or augment expert elicitation with data‑driven estimates (field trials, observational adoption data, or causal impact estimates) to reduce subjective bias.
  • Integrate dynamic/adaptive optimization (multi‑period budgeting, learning) or stochastic programming to account for evolving AI technology and feedback effects on adoption.
  • Use automated/AI tools for large‑scale sensitivity analysis, scenario generation, and visualization to support policymaker decision interfaces.
• Caveats for application: dependence on expert judgments and accurate cost estimates; transferability requires recalibration to local institutional and economic conditions.