Best-Worst Scaling in Agricultural Research: A Review of Methods and Applications

Understanding stakeholder preferences is essential for designing effective agricultural policies, promoting technology adoption, and aligning market strategies with consumer demand. Best–Worst Scaling (BWS) is a robust stated preference method that captures preferences by choosing the most and least important attributes within a choice set. This review highlights the statistical foundations of BWS, including Random Utility Theory, and common analytical models such as Multinomial Logit, Latent Class Analysis, Random Parameter Logit, and Hierarchical Bayesian frameworks to estimate preference heterogeneity. A bibliometric analysis of BWS applications in agricultural research highlights increasing adoption, publication trends, and leading contributors in the field. The findings reveal that BWS provides actionable insights into consumer and farmer preferences, informing product development, policy formulation, and sustainable decision-making, and demonstrates its growing relevance as a rigorous tool for evidence-based agricultural research.