<![CDATA[Background: Digital transformation in agribusiness increasingly adopts geospatial and IoT-based monitoring technologies, yet most applications emphasize asset tracking or simulation-based modeling rather than empirically validated workforce performance evaluation. Existing analytical studies often rely on structural influence modeling without integrating real-time labor data and causal inference methods. This gap is particularly visible in vocational agribusiness systems, where digital governance initiatives remain underexplored from a rigorous quantitative perspective. Aims: This study develops and empirically validates a geofencing-based, data-driven workforce analytics framework using causal modeling to assess operational efficiency and governance outcomes in vocational agribusiness production units. Method: A quasi-experimental stepped-wedge design was implemented across four Teaching Factory units over 12 weeks. Real-time geospatial attendance logs were integrated with production and payroll data to construct a worker-level panel dataset. Treatment effects were estimated using a Difference-in-Differences model with worker and time fixed effects. Robustness checks included parallel trend diagnostics, placebo tests, and alternative specifications. Results: Digital workforce monitoring significantly improved performance. Labor productivity increased by 13.4%, cost-to-serve decreased by 9.7%, payroll processing time declined by 41%, and lateness was reduced by 48%. The Accountability Index improved by 0.88 standard deviations. Robustness analyses confirmed the stability of these effects. Conclusion: Geofencing-based digital monitoring functions as an operational optimization mechanism rather than merely a compliance tool. The proposed framework provides scalable, data-driven evidence for improving workforce governance in labor-intensive agribusiness systems. ]]>
