Customer influence on value delivery in agricultural product development: the Brazilian farmers' perspective

Abstract

This study aims to quantify the value delivery attributes of an innovative model of grain drying machine, focusing on its potential to enhance sustainability and efficiency in Brazilian agriculture. This study employs a mixed-method approach, integrating statistical inference and machine learning techniques to evaluate product attributes that maximize value perception. Artificial neural networks and descriptive statistics were used to determine the relative importance of these attributes in delivering value to the customer from a survey with 135 Brazilian farmers. Results indicate that farmers prioritize silo handling, drying capacity and automation, while artificial neural networks effectively capture preference patterns for sustainable agricultural technologies. The study demonstrates that data-driven modeling can enhance the precision of value delivery assessments, optimize product-market fit and reduce uncertainty in the adoption of agricultural innovations. Future research should explore dynamic models that incorporate real-time behavioral data to expand the analysis to more diverse agricultural ecosystems. This research advances the application of artificial intelligence in agricultural decision-making, offering a novel framework for real-time value assessment of product development. By integrating choice experiments with machine learning, this study pioneers an adaptive methodology that refines agricultural product innovation based on continuous market feedback.