Decision support systems can improve pest-management decisions, yet adoption and sustained use among farmers remain uneven and shaped by perceptions of the technology and attitudes toward uncertainty. This study examines uptake of a wheat pest-management DSS among Italian producers by linking technology-acceptance mechanisms to attribute-based choice behavior. We combine a survey of 239 wheat farmers with a discrete choice experiment in which DSS alternatives vary in annual subscription fee, subscription duration, field-level monitoring stations, forecast lead time, number of pest/disease targets covered, and frequency of on-site scouting visits. We estimate a hybrid two-stage model: Technology Acceptance Model constructs are first recovered via partial least squares structural equation modeling to obtain latent adoption-intention scores; these scores and farmers’ risk aversion are then incorporated as membership covariates in a latent-class conditional logit model of choice. Results support the Technology Acceptance Model, with perceived usefulness and perceived ease of use positively associated with adoption intention. The discrete choice experiment identifies two preference segments: a feature-oriented class that values field-level monitoring, longer lead times, and broader target coverage, and a commitment-averse class that dislikes longer subscription durations and longer forecast horizons. Adoption intention increases the likelihood of belonging to the feature-oriented class, whereas higher risk aversion shifts farmers toward the more cautious segment. The findings provide actionable guidance for DSS design and service bundling—particularly contractual arrangements and feature configurations—tailored to heterogeneous behavioral profiles, thereby improving the effectiveness and scalability of digital advisory tools in crop protection.
Califano et al. (Tue,) studied this question.