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Article
Publication date: 2 February 2024

Pushkar Pushp and Faisal Ahmed

The discourse on global value chains (GVC) is undergoing a transformation in terms of its conceptualisation, theorisation and pragmatic applications. Today, the production systems…

Abstract

Purpose

The discourse on global value chains (GVC) is undergoing a transformation in terms of its conceptualisation, theorisation and pragmatic applications. Today, the production systems have become more complex as global economic order continues to witness marked geo-economic manoeuvring. Thus, the direction of discourse on GVC ought to move from mere theoretical propositions toward becoming more evidence based. There have been recent studies that have used the governance and upgrading propositions by Gary Gereffi and others to seek quantitative evidence. This study aims to decipher the quantitative discourse on GVC and to set the emerging and future research agenda.

Design/methodology/approach

Through a systematic literature review, the authors first analyse the quantitative studies on GVC carried out during the last two decades. The authors then outline a future research agenda and examine a few relevant modelling techniques that could potentially be used to solicit newer evidence in GVC research.

Findings

The authors categorise the quantitative discourse on GVC into three crucial themes, namely, GVC framework, GVC participation and position, environmental aspects and regionalisation in GVC. The most commonly used quantitative techniques are gravity model, panel data estimation, structural decomposition analysis and computable general equilibrium modelling.

Originality/value

This paper contributes to the GVC discourse in two ways. Firstly, the authors argue that the theoretical frameworks within the GVC discourse should be complemented by evidence-based quantitative studies. Secondly, the authors suggest potential modelling techniques that can be used on the emerging and future research agenda.

Details

Critical Perspectives on International Business, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1742-2043

Keywords

Article
Publication date: 11 October 2023

Chinthaka Niroshan Atapattu, Niluka Domingo and Monty Sutrisna

Cost overrun in infrastructure projects is a constant concern, with a need for a proper solution. The current estimation practice needs improvement to reduce cost overruns. This…

Abstract

Purpose

Cost overrun in infrastructure projects is a constant concern, with a need for a proper solution. The current estimation practice needs improvement to reduce cost overruns. This study aimed to find possible statistical modelling techniques that could be used to develop cost models to produce more reliable cost estimates.

Design/methodology/approach

A bibliographic literature review was conducted using a two-stage selection method to compile the relevant publications from Scopus. Then, Visualisation of Similarities (VOS)-Viewer was used to develop the visualisation maps for co-occurrence keyword analysis and yearly trends in research topics.

Findings

The study found seven primary techniques used as cost models in construction projects: regression analysis (RA), artificial neural network (ANN), case-based reasoning (CBR), fuzzy logic, Monte-Carlo simulation (MCS), support vector machine (SVM) and reference class forecasting (RCF). RA, ANN and CBR were the most researched techniques. Furthermore, it was observed that the model's performance could be improved by combining two or more techniques into one model.

Research limitations/implications

The research was limited to the findings from the bibliometric literature review.

Practical implications

The findings provided an assessment of statistical techniques that the industry can adopt to improve the traditional estimation practice of infrastructure projects.

Originality/value

This study mapped the research carried out on cost-modelling techniques and analysed the trends. It also reviewed the performance of the models developed for infrastructure projects. The findings could be used to further research to develop more reliable cost models using statistical modelling techniques with better performance.

Details

Smart and Sustainable Built Environment, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2046-6099

Keywords

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