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This Author published in this journals
All Journal Journal of Advanced Sciences and Mathematics Education Smart Society: Community Service and Empowerment Journal
Naning Retnowati
Politeknik Negeri Jember, Indonesia
Humanities Chemistry Education Languange, Linguistic, Communication & Media Mathematics Physics Social Sciences Other

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Geofencing-based data-driven workforce analytics framework using causal modeling for operational efficiency in vocational agribusiness systems Dyah Kusuma Wardani; Naning Retnowati; Paramita Andini; Mohammad Edwinsyah Yanuan Putra; Dhanang Eka Putra
Journal of Advanced Sciences and Mathematics Education Vol. 6 No. 1 (2026): Journal of Advanced Sciences and Mathematics Education
Publisher : CV. FOUNDAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/jasme.v6i1.659

Abstract

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. 
Digital Transformation of Labor Management in Vocational Agribusiness: An ADDIE-Based Evaluation of Efficiency and Accountability in Teaching Factory Units Dyah Kusuma Wardani; Naning Retnowati; Paramita Andini; Mohammad Edwinsyah Yanuan Putra; Dhanang Eka Putra
Smart Society Vol. 6 No. 1 (2026): Smart Society
Publisher : FOUNDAE (Foundation of Advanced Education)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/smartsoc.v6i1.929

Abstract

Background: The growing integration of digital technologies into agribusiness has reshaped organizational practices, yet labor management in many vocational Teaching Factory (TEFA) units continues to rely on paper-based procedures. Such practices frequently lead to attendance discrepancies, delays in payroll administration, and limited supervisory control. In response to these challenges, this study developed a digital labor-management system intended to strengthen operational efficiency and accountability in vocational agribusiness settings.. Methods: Adopting a research and development approach grounded in the ADDIE framework, the study combined system development with a stepped-wedge quasi-experimental evaluation. The research was conducted over twelve weeks in four TEFA units at Politeknik Negeri Jember and involved 25 casual workers and eight supervisors. The proposed platform integrated geolocation, geofencing, real-time productivity logging, and managerial dashboards. Data were analyzed using descriptive statistics and difference-in-differences models with fixed effects, while usability and technological readiness were assessed through the System Usability Scale (SUS) and Technology Readiness Level (TRL). Findings: The implementation of the system increased labor productivity by 13.4%, reduced cost-to-serve by 9.7%, shortened payroll-processing time by 41%, and lowered worker lateness by 48%. In addition, the accountability index improved by 0.88 standard deviations, indicating substantial gains in transparency and administrative performance. Conclusion: The findings suggest that digital labor management can enhance both efficiency and accountability, providing a practical foundation for the modernization of workforce administration in vocational agribusiness. Novelty/Originality of this article: This study offers an integrated framework that combines geolocation-based technology, the ADDIE model, quasi-experimental evaluation, and standardized usability and technology-readiness assessments within the context of vocational agribusiness labor management.
Co-Authors Andini, Paramita Dyah Kusuma Wardani Mohammad Edwinsyah Yanuan Putra Putra, Dhanang Eka