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A machine learning approach for predicting critical factors determining adoption of offsite construction in Nigeria

Godoyon Ebenezer Wusu (Big Data and Innovation Lab, University of Hertfordshire, Hatfield, UK)
Hafiz Alaka (Big Data and Innovation Lab, University of Hertfordshire, Hatfield, UK)
Wasiu Yusuf (Big Data and Innovation Lab, University of Hertfordshire, Hatfield, UK)
Iofis Mporas (Hertfordshire Business School, University of Hertfordshire, Hatfield, UK)
Luqman Toriola-Coker (Yaba College of Technology, Yaba, Nigeria)
Raphael Oseghale (Department of Management, Leadership and Organization, Hertfordshire Business School, University of Hertfordshire, Hatfield, UK)

Smart and Sustainable Built Environment

ISSN: 2046-6099

Article publication date: 12 December 2022

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Abstract

Purpose

Several factors influence OSC adoption, but extant literature did not articulate the dominant barriers or drivers influencing adoption. Therefore, this research has not only ventured into analyzing the core influencing factors but has also employed one of the best-known predictive means, Machine Learning, to identify the most influencing OSC adoption factors.

Design/methodology/approach

The research approach is deductive in nature, focusing on finding out the most critical factors through literature review and reinforcing — the factors through a 5- point Likert scale survey questionnaire. The responses received were tested for reliability before being run through Machine Learning algorithms to determine the most influencing OSC factors within the Nigerian Construction Industry (NCI).

Findings

The research outcome identifies seven (7) best-performing algorithms for predicting OSC adoption: Decision Tree, Random Forest, K-Nearest Neighbour, Extra-Trees, AdaBoost, Support Vector Machine and Artificial Neural Network. It also reported finance, awareness, use of Building Information Modeling (BIM) and belief in OSC as the main influencing factors.

Research limitations/implications

Data were primarily collected among the NCI professionals/workers and the whole exercise was Nigeria region-based. The research outcome, however, provides a foundation for OSC adoption potential within Nigeria, Africa and beyond.

Practical implications

The research concluded that with detailed attention paid to the identified factors, OSC usage could find its footing in Nigeria and, consequently, Africa. The models can also serve as a template for other regions where OSC adoption is being considered.

Originality/value

The research establishes the most effective algorithms for the prediction of OSC adoption possibilities as well as critical influencing factors to successfully adopting OSC within the NCI as a means to surmount its housing shortage.

Keywords

Acknowledgements

This research did not receive any form of grant or funding from the public, commercial or not-for-profit organizations.

The authors thank Balogun Habeeb, Olu-Ajayi Razaq, Egwim Emeka and Sulaimon Ismail (of Big Data Technologies and Innovation Laboratory) for their support and input on the model development. The authors have no conflict of interest to disclose.

Citation

Wusu, G.E., Alaka, H., Yusuf, W., Mporas, I., Toriola-Coker, L. and Oseghale, R. (2022), "A machine learning approach for predicting critical factors determining adoption of offsite construction in Nigeria", Smart and Sustainable Built Environment, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/SASBE-06-2022-0113

Publisher

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Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

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