Evidence map›Paper›PMID 32023306›Full record

ArticlePloS one2020

A hybrid approach using entropy and TOPSIS to select key drivers for a successful and sustainable lean construction implementation.

Gholamreza Dehdasht, M Salim Ferwati, Rosli Mohamad Zin, Nazirah Zainul Abidin

Abstract read
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Gholamreza DehdashtSchool of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai, Malaysia.ORCID 0000-0002-2344-0020
M Salim FerwatiDepartment of Architecture and Urban Planning, College of Engineering, Qatar University, Doha, Qatar.ORCID 0000-0002-0530-4180
Rosli Mohamad ZinSchool of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai, Malaysia.
Nazirah Zainul AbidinSchool of Housing, Building and Planning, Universiti Sains Malaysia, Penang, Malaysia.ORCID 0000-0002-8152-007X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Successful implementation of the lean concept as a sustainable approach in the construction industry requires the identification of critical drivers in lean construction. Despite this significance, the number of in-depth studies toward understanding the considerable drivers of lean construction implementation is quite limited. There is also a shortage of methodologies for identifying key drivers. To address these challenges, this paper presents a list of all essential drivers within three aspects of sustainability (social, economic, and environmental) and proposes a novel methodology to rank the drivers and identify the key drivers for successful and sustainable lean construction implementation. In this regard, the entropy weighted Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was employed in this research. Subsequently, an empirical study was conducted within the Malaysian construction industry to demonstrate the proposed method. Moreover, sensitivity analysis and comparison with the existing method were engaged to validate the stability and accuracy of the achieved results. The significant results obtained in this study are as follows: presenting, verifying and ranking of 63 important drivers; identifying 22 key drivers; proposing an MCDM model of key drivers. The outcomes show that the proposed method in this study is an effective and accurate tool that could help managers make better decisions.

Indexed as

Construction IndustryEntropySustainable Development

Identifiers

PMID32023306
PMCPMC7001944

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.