AI-Driven Matching Platforms and Seasonal Labor Allocation

Evidence from Agribusiness Enterprises in the South-West Region of Cameroon

Abstract

This study evaluated the association between AI-driven matching platform adoption and seasonal labor allocation efficiency and agribusiness productivity in the South-West Region of Cameroon. A cross-sectional comparative survey design was employed, with data collected from 207 agribusiness enterprises using structured questionnaires. Independent samples t-tests and multiple regression analysis were utilized to test the research hypotheses. The findings revealed that platform adopters demonstrated significantly higher labor allocation efficiency scores (M = 4.11) compared to non-adopters (M = 2.75), and achieved superior productivity outcomes across crop yield and labor productivity indicators. Regression analysis indicated that platform adoption explained 23.8% and 31.2% of the variance in labor allocation efficiency and agribusiness productivity respectively, with both effects being statistically significant (p < 0.05). The study recommends that agribusiness enterprises prioritize platform adoption and leverage platform data analytics for strategic labor planning to enhance productivity.