2020
DOI: 10.1108/ecam-10-2019-0564
|View full text |Cite
|
Sign up to set email alerts
|

Efficiency estimation and reduction potential of the Chinese construction industry via SE-DEA and artificial neural network

Abstract: PurposeThe objective of this study is to evaluate the overall technical efficiency, labor efficiency, capital efficiency and equipment efficiency of 30 Chinese construction sectors to foster sustainable economic growth in the construction industry.Design/methodology/approachThis study employed the super-efficiency data envelopment analysis (SE-DEA) and artificial neural network model (ANN) to evaluate the industrial performance and improvement potential of the Chinese regional construction sectors from 2000 to… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
4
0

Year Published

2021
2021
2023
2023

Publication Types

Select...
5

Relationship

0
5

Authors

Journals

citations
Cited by 8 publications
(4 citation statements)
references
References 60 publications
(64 reference statements)
0
4
0
Order By: Relevance
“…Azadeh et al (2015) incorporated artificial neural networks (ANNs), genetic algorithms (GAs), and multivariate analysis to develop a flexible algorithm for the optimisation of machinery productivity. Most recently, Yuan et al (2020) evaluated industrial productivity and its improvement potential using a super-efficiency data envelopment analysis (SE-DEA) and ANN. For the random forest category, Liu et al (2018) focussed on the improvement of construction productivity by adopting a generalised additive model (GAM) and random forest to access the impact of outdoor ambient environmental factors on construction productivity.…”
Section: Research Areas and Methodologiesmentioning
confidence: 99%
“…Azadeh et al (2015) incorporated artificial neural networks (ANNs), genetic algorithms (GAs), and multivariate analysis to develop a flexible algorithm for the optimisation of machinery productivity. Most recently, Yuan et al (2020) evaluated industrial productivity and its improvement potential using a super-efficiency data envelopment analysis (SE-DEA) and ANN. For the random forest category, Liu et al (2018) focussed on the improvement of construction productivity by adopting a generalised additive model (GAM) and random forest to access the impact of outdoor ambient environmental factors on construction productivity.…”
Section: Research Areas and Methodologiesmentioning
confidence: 99%
“…The construction industries in 31 regions of Chinese mainland are selected as research subjects. To evaluate their safety efficiency under the strategy of safety priority, the paper constructs an indicators system of inputs, desirable outputs, and undesirable outputs based on the literature summary and the reality of construction industry (Kang et al, 2020;Yang et al, 2022;Yuan et al, 2020;Zhou et al, 2019;Zhang et al, 2021). The specific indicators are shown as follows.…”
Section: Data Description and Analysis Framework Designmentioning
confidence: 99%
“…The findings concluded that DEA and ANN with genetic algorithm is the best approach to predict performance with 94% accuracy. Yuan et al (2020) studied the overall efficiency of Chinese construction companies by incorporating super efficiency DEA-ANN to identify and predict improvement potential. The study concluded that sustainability competitiveness exists within Indian hospitality industry.…”
Section: Literature Reviewmentioning
confidence: 99%