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All Journal Journal of Economic Development and Village Building
Ako, Eyong
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Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Environmental Science Industrial & Manufacturing Engineering

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Human-AI Hybrid Workplace Optimization and Productivity in Labor-Intensive Agricultural Operations in Centre Region of Cameroon: An Empirical Study Ako, Eyong
Journal of Economic Development and Village Building Vol. 4 No. 2 (2026): Journal of Economic Development and Village Building ( Issue in Progress )
Publisher : Politeknik Siber Cerdika Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59261/jedvb.v4i2.101

Abstract

The agricultural sector in Sub-Saharan Africa faces severe labor shortages and productivity stagnation, with Cameroon's Centre Region experiencing particular challenges including 15% workforce decline over five years and only 1.5% annual productivity growth. This study investigated the relationship between human-AI hybrid workplace optimization and productivity in labor-intensive agricultural operations in Cameroon's Centre Region. A cross-sectional survey of 138 agricultural operators across 15 enterprises examined how human-AI collaboration intensity influences operational productivity and how hybrid workplace optimization affects workforce outcomes. Findings revealed significant positive relationships between collaboration intensity and productivity, with AI-human task sharing showing the strongest correlation (r = 0.485, p < 0.001). Hybrid workplace optimization significantly predicted workforce outcomes (R² = 0.379), with training and skill development emerging as the strongest predictor (β = 0.242, p = 0.003). This study contributes empirically to Socio-Technical Systems Theory by demonstrating that human-AI collaboration intensity is a measurable predictor of productivity outcomes in Central African agricultural contexts, extending previous research primarily focused on developed economies. The findings provide practical guidance for Cameroonian agri-businesses, cooperatives, and extension services seeking to optimize human-AI collaboration through targeted investments in training, organizational support, and clear role definition, while acknowledging infrastructural and capacity-building challenges. Given the cross-sectional design, causal relationships cannot be definitively established; future longitudinal research is recommended.
Worker-Side Labor Matching Platforms: An AI Tool for Agri-Workers Employed by Agribusinesses in Northwest Cameroon Ako, Eyong
Journal of Economic Development and Village Building Vol. 4 No. 1 (2026): Journal of Economic Development and Village Building
Publisher : Politeknik Siber Cerdika Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59261/jedvb.v4i1.105

Abstract

This study designed, developed, and evaluated a worker-side AI-powered labor matching platform for agricultural workers in post-conflict Northwest Cameroon, where the Anglophone Crisis has disrupted informal labor networks. While existing agricultural platforms prioritize farm managers or buyers, this study introduces a worker-centric approach placing agricultural workers as primary users and provides the first empirical evaluation of such a platform in a post-conflict African setting. A concurrent mixed-methods design was employed across 10 agribusinesses in Santa, Ndop, and Bamenda, tracking 350 workers (175 women, 175 men) over six months through surveys, interviews, focus groups, and usability testing. Difference-in-differences and panel regression analyses were used to assess impact. Voice-enabled, offline-capable platforms were significantly more accessible than text-based alternatives: 73% preferred voice interfaces and 87% required offline capability. Platform users experienced significant improvements: +1.0 additional days worked weekly, +2,400 CFA higher wages, and reduced wasted travel by 23 percentage points (p<0.001). Agribusinesses benefited through reduced labor shortages (-1.9 days monthly), lower hiring costs (-16,400 CFA), and decreased harvest waste (-7 percentage points). Worker-centric platforms can reduce information asymmetry when designed with voice interfaces, offline capability, multilingual support, and integration with informal networks. Policymakers and technology developers should prioritize inclusive design and rural onboarding.
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