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

Version 2 2024-06-04, 12:22
Version 1 2019-12-27, 11:45
journal contribution
posted on 2024-06-04, 12:22 authored by Argaw GurmuArgaw Gurmu, CS Ongkowijoyo
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.

History

Journal

Journal of Construction Engineering and Management

Volume

146

Article number

ARTN 04019115

Pagination

1 - 13

Location

Reston, Va.

ISSN

0733-9364

eISSN

1943-7862

Language

English

Publication classification

C1 Refereed article in a scholarly journal

Issue

3

Publisher

ASCE-AMER SOC CIVIL ENGINEERS