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Inter-organizational unfairness in the construction industry

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  • M. Loosemore
  • B. Lim

Abstract

There are numerous examples of unfair inter-organizational business practices in the construction industry. Conflict and confrontation, corruption, bid-shopping, insecurity of payment and supply chain exploitation are just some examples which have been documented over several decades in many countries. There have been numerous initiatives to make the construction industry a fairer business environment, but these have been largely developed in a conceptual vacuum. Consequently, few advances have been made in making the industry a fairer place to do business. To address the lack of theory in this area and provide a conceptual foundation for future improvement, theories of organizational justice were used as the basis for a survey of 135 consultants, contractors, subcontractors and suppliers from across the Australian construction supply chain. The findings reveal that mainstream theories of justice may need refinement and reorganization to be applied to a construction industry context. Furthermore, in contrast to much previous research, the results indicate that levels of interpersonal, social and informational justice are high within the Australian construction industry. However, they also show that more can be done to improve levels of procedural and distributive justice, particularly in relation to subcontractors and suppliers in the construction supply chain. Many of these findings are transferable to other countries which are culturally, contractually and organizationally similar to the Australian construction industry.

Suggested Citation

  • M. Loosemore & B. Lim, 2015. "Inter-organizational unfairness in the construction industry," Construction Management and Economics, Taylor & Francis Journals, vol. 33(4), pages 310-326, April.
  • Handle: RePEc:taf:conmgt:v:33:y:2015:i:4:p:310-326
    DOI: 10.1080/01446193.2015.1057193
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    Cited by:

    1. SeyedAli Ghahari & Cesar Queiroz & Samuel Labi & Sue McNeil, 2021. "Cluster Forecasting of Corruption Using Nonlinear Autoregressive Models with Exogenous Variables (NARX)—An Artificial Neural Network Analysis," Sustainability, MDPI, vol. 13(20), pages 1-20, October.

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