When more information is not enough: AI-enabled advisory and coordinated farming decisions under uncertainty among smallholder farmers in the Mekong Delta

Abstract

Despite the rapid expansion of digitally mediated and increasingly AI-enabled agricultural advisory systems, more information does not necessarily lead to more coordinated farming decisions under uncertainty. This study reconceptualizes AI-enabled advisory as a coordination-support mechanism and examines how it is associated with coordinated decision-making through three behavioural capacities: understanding system interactions, operational coordination capability, and adaptive complexity management. Using a survey dataset of 386 smallholder rice farmers from An Giang, Tra Vinh, and Ca Mau provinces in Vietnam's Mekong Delta, the study applies partial least squares structural equation modelling (PLS-SEM). The results show that AI-enabled advisory is positively associated with all three behavioural capacities, while adaptive complexity management has the strongest association with coordinated decision-making capability (β = 0.33, p < 0.001). It also represents the strongest indirect pathway between AI-enabled advisory and coordinated decision-making (β = 0.135, p < 0.001). These findings suggest that the value of AI-enabled advisory lies not simply in providing more information, but in strengthening farmers' capacities to interpret system interdependencies, coordinate operational activities, and adapt decisions under changing environmental conditions. By reframing AI-enabled advisory as a coordination-support mechanism, the study provides a process-oriented explanation of how digitally mediated advisory contributes to coordinated farming decisions in complex agricultural systems.

The authors' abstract, as published at the source. Social Sciences & Humanities Open, 2026 · DOI ↗