Predicting Construction Labor Productivity Based on Implementation Levels of Human Resource Management Practices

Abstract

The prediction of the odds of achieving higher or lower productivity as compared to some baseline productivity is one of the important steps to consider while analyzing labor productivity in construction projects. The objective of this research is to build a logistic regression model that can be used to estimate the productivity of building projects based on the levels of planning or implementation of human resource management practices. Quantitative data were collected from 39 contractors who worked on multistory building projects completed between 2011 and 2016. Correlation analysis was carried out and the associations between productivity, human resource management (HRM) practices, company profiles, and project characteristics were investigated. Logistic regression analysis was conducted to develop the probability-based labor productivity prediction model. Project delay is found to be negatively correlated with HRM practices, whereas company size is positively associated with HRM practices. A scoring tool to measure the levels of HRM practice implementation on building projects was developed. On that basis, a logistic regression model of HRM practices and productivity was built. This study contributes to the body of knowledge by proposing a tool that can be used to assess the odds of having high productivity based on the implementation levels of HRM practices on a certain building project.