PREDICTIVE ANALYTICS AND EMPLOYEE PRODUCTIVITY IN CONSTRUCTION FIRMS IN SOUTH-SOUTH NIGERIA

Abstract

This study examined the relationship between predictive analytics and employee productivity, specifically focusing on efficiency and continuous improvement, in construction firms in South-South Nigeria. Anchored on the Technology Acceptance Model (TAM), the Diffusion of Innovations Theory, and the Job Demands-Resources (JD-R) model, the study adopted an explanatory cross-sectional survey design. A structured questionnaire was administered to 152 valid respondents drawn from 18 purposively selected construction firms in the South-South geopolitical zone of Nigeria. Data were analyzed using the Spearman Rank Order Correlation Coefficient. The findings revealed a strong and significant positive relationship between predictive analytics and efficiency (r = 0.685, p < 0.05) and between predictive analytics and continuous improvement (r = 0.712, p < 0.05). Both null hypotheses were rejected. These results suggest that the adoption of AI-driven predictive analytics tools substantially enhances operational speed, resource planning, error reduction, innovation, and iterative process improvement among construction workers. The study recommends that construction firms in the region invest in predictive analytics systems integrated with existing project management workflows, supported by targeted digital upskilling programs.