Post-Harvest Loss Reduction in Perishable Crops: Task-Technology Fit and Emotion-Driven Acceptance of On-Farm Transport Robots

As global food security challenges escalate and post-harvest losses in perishable crops remain a critical pressure point, on-farm transport robots have emerged as a promising sustainable solution for transforming farm-to-storage logistics systems and reducing agricultural waste. However, farmer acceptance of robotic transport technologies remains heterogeneous and represents a critical barrier to achieving widespread adoption of these sustainable agricultural innovations. Existing research has yet to integrate task-technology fit (TTF), anticipated emotions, and anthropomorphism into a unified theoretical framework for understanding sustainable agricultural technology adoption. Drawing on TTF theory and the model of goal-directed behavior, this study proposes a comprehensive model integrating anticipated emotions as mediators and robot anthropomorphism as a moderator. We surveyed 320 farmers and employed PLS-SEM to test our hypotheses. Results indicate that farm transport task complexity, farmer technology readiness, and robot transport functionality significantly strengthen TTF (β = 0.136, 0.358, 0.382, respectively; all p < 0.01). TTF drives acceptance intention through a dual-path emotional mechanism: directly enhancing positive expectancy emotions (β = 0.411, p < 0.001) while reducing negative expectancy emotions (β = −0.150, p < 0.05). Crucially, higher anthropomorphism levels diminish both emotional mediation paths (β = −0.053 and β = −0.027, both p < 0.01), establishing important boundary conditions for sustainable agricultural technology design. These findings suggest that reducing post-harvest losses requires prioritizing functional consistency over overly anthropomorphic designs in agricultural robots, thereby promoting the development of agricultural technologies that are both emotionally resonant and highly functional.