Awareness and adoption readiness of machine learning technology in the construction industry of a developing country: A case of Nigeria.

Loya, S.O., Ele, E., Awodele, I.A., Sofolahan, O. and Omoboye, O. 2024. Awareness and adoption readiness of machine learning technology in the construction industry of a developing country: A case of Nigeria. ITEGAM-JETIA. 10 (47), pp. 50-62. https://doi.org/10.5935/jetia.v10i47.1072

TitleAwareness and adoption readiness of machine learning technology in the construction industry of a developing country: A case of Nigeria.
TypeJournal article
AuthorsLoya, S.O., Ele, E., Awodele, I.A., Sofolahan, O. and Omoboye, O.
Abstract

This study investigated the awareness of the Nigerian construction organisations on some identified ML application areas, and the readiness of the organisations to adopt ML learning in the identified application areas. A comprehensive Literature review was undertaken to identify the application areas of ML, then, a well-structured questionnaire was developed and used to gather relevant data from construction professionals using the snowball sampling method via electronic means. 143 valid responses were obtained, and the gathered data were analysed using arrays of descriptive and inferential analytical tools. The study revealed that the critical applications areas of ML with higher awareness level and adoption readiness in Nigeria are (1) Health and Safety prediction and management, (2) Waste management, (3) Prediction of and management of construction costs, (4) Risk Management, (5) Structural Health Monitoring and Prediction, and (6) Building Life-Cycle assessment and management. Further, a significant statistical difference was observed between the opinions of the participants regarding the awareness and adoption readiness of the various ML application areas. This study identified critical application areas of ML where the awareness and adoption readiness are very high, thus, signalling the preparedness of the Nigerian construction industry (NCI) to embrace ML to drive sustainable construction.

KeywordsAwareness, Adoption readiness, Machine learning, Construction industry, Sustainability.
JournalITEGAM-JETIA
Journal citation10 (47), pp. 50-62
ISSN2447-0228
Year2024
PublisherGalileo Institute of Technology and Education of the Amazon (ITEGAM)
Accepted author manuscript
File Access Level
Open (open metadata and files)
Publisher's version
License
CC BY 4.0
File Access Level
Open (open metadata and files)
Digital Object Identifier (DOI)https://doi.org/10.5935/jetia.v10i47.1072
Web address (URL)https://itegam-jetia.org/journal/index.php/jetia/en/article/view/1072
Publication dates
Published28 Jun 2024

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