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Optimizing the distribution of outcomes across multiple levels of subcontracting in construction projects

S. Mahdi Hosseinian (Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran)
Amirhomayoun Jaberi (Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran)

Engineering, Construction and Architectural Management

ISSN: 0969-9988

Article publication date: 29 September 2023

109

Abstract

Purpose

Although outcome sharing in construction is a well-established concept in the literature, there is still an ongoing debate on the most effective approach for distributing project outcomes between an owner and downstream contracting parties (DCPs). To address this issue, this paper aims to investigate an optimal framework for distributing project outcomes among various levels of subcontracting in construction projects. The framework includes contractors, subcontractors, sub-subcontractors and other related parties.

Design/methodology/approach

To formulate the optimization problem, the principal–agent model is utilized. The theoretical development is validated through an experiment conducted with employees from road construction companies.

Findings

When distributing outcomes among various levels of subcontracting, the sharing should be determined by their contribution to the outcome, effort costs, level of outcome uncertainty and risk preference.

Originality/value

This paper expands on the existing principal–agent theory by incorporating multiple levels of agents, transforming the conventional view of outcome sharing among downstream subcontracting levels into testable hypotheses and well-defined concepts. The paper has practical implications for industry practitioners seeking to effectively allocate benefits and costs throughout a project's subcontracting chain.

Keywords

Citation

Hosseinian, S.M. and Jaberi, A. (2023), "Optimizing the distribution of outcomes across multiple levels of subcontracting in construction projects", Engineering, Construction and Architectural Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/ECAM-05-2023-0439

Publisher

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Emerald Publishing Limited

Copyright © 2023, Emerald Publishing Limited

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