Construction output modelling: a systematic review

Oshodi, Olalekan S. and Edwards, David J. and Lam, Ka Chi and Olanipekun, Ayokunle O. and Aigbavboa, Clinton O. (2020) Construction output modelling: a systematic review. Engineering, Construction and Architectural Management, 27 (10). pp. 2959-2991. ISSN 1365-232X

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Official URL: https://doi.org/10.1108/ECAM-03-2019-0150

Abstract

Purpose: Construction economics scholars have emphasised the importance of construction output forecasting and have called for increased investment in infrastructure projects due to the positive relationship between construction output and economic growth. However, construction output tends to fluctuate over time. Excessive changes in the volume of construction output have a negative impact upon the construction sector, such as liquidation of construction companies and job losses. Information gleaned from extant literature suggests that fluctuation in construction output is a global problem. Evidence indicates that modelling of construction output provides information for understanding the factors responsible for these changes. Methodology: An interpretivist epistemological lens is adopted to conduct a systematic review of published studies on modelling of construction output. A thematic analysis is then presented, and the trends and gaps in current knowledge are highlighted. Findings: It is observed that interest rate is the most common determinant of construction output. Also revealed is that very little is known about the underlying factors stimulating growth in the volume of investment in maintenance construction works. Further work is required to investigate the efficacy of using non-linear techniques for construction output modelling. Originality: This study provides a contemporary mapping of existing knowledge relating to construction output and provides insights into gaps in current understanding that can be explored by future researchers.

Item Type: Journal Article
Keywords: Construction output, Forecasting, Modelling, Systematic review, Text mining
Faculty: Faculty of Science & Engineering
Depositing User: Lisa Blanshard
Date Deposited: 25 Aug 2020 10:19
Last Modified: 09 Sep 2021 16:06
URI: https://arro.anglia.ac.uk/id/eprint/705801

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