Predicting construction schedule overruns during COVID-19 using ordinal logistic regression

Abstract

Purpose

The effects of the COVID-19 outbreak on the construction industry were formidable and far-reaching, as the construction sector is a major contributor to the gross domestic product (GDP), which balances various sectors of the global economy, and to infrastructure growth, which is a primary gauge of a nation’s advancement. The outbreak led to workforce disruptions, worker deficits, dwindling efficiency, elongated project durations, and scarce opportunities for training and mentorship, and despite endeavors to mitigate these challenges, construction timelines experienced significant interruptions. Various researchers have pinpointed contributing elements, but few have constructed a predictive model to gauge the degree of impact.

Design/methodology/approach

Therefore, this research intends to fill by introducing an ordinal logistic regression method to forecast the impacts of a pandemic or other similar type of crisis. To achieve this, an online survey was developed and distributed to collect the perceptions of the construction engineers and managers about the diverse contributors to the exceeding project timelines during the COVID-19 pandemic outbreak.

Findings

Findings from this study indicate that financial liquidity, modifications to original plans, delays in securing governmental clearances, and a shortage of competent labor have medium-to-high levels of impact on project schedules.

Originality/value

This study will furnish decision-makers with crucial knowledge that will give them the tools to refine their strategies and judiciously allocate resources to overcome the unique hurdles encountered by various construction segments and will enhance the industry's capability to respond more effectively to challenges inherent in this type of crisis.

Citation

Adepu, N., Kermanshachi, S., Pamidimukkala, A. and Nwakpuda, E. (2024), "Predicting construction schedule overruns during COVID-19 using ordinal logistic regression", International Journal of Building Pathology and Adaptation, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJBPA-11-2023-0161