Contractor–Subcontractor Relationships with the Implementation of Emerging Interorganizational Technologies: Roles of Cross-Project Learning and Pre-Contractual Opportunism

Abstract

This article examines how the successive implementation of emerging interorganizational technologies influences a contractor’s selection of relationships with subcontractors and explores how system inefficiency is generated in this process. With technology implementation in successive projects, both contractor and subcontractor could gain obvious benefits from cross-project learning while partnering is employed. However, these benefits also provide the subcontractor with more opportunities to hold up the contractor; in this situation, the competitive relationship seems superior, as it could better curb the subcontractor’s pre-contractual opportunism. A model is established to simulate how these two adversarial effects collectively influence the contractor’s relationship selection if it aims to minimize its total subcontracting cost. It is shown that technology implementation could result in a more stable contractor–subcontractor relationship whether through providing the contractor with stronger incentives to employ partnering or via increasing the contractor’s possibility to successively select the same subcontractor in the competitive mechanism, however, employing cost-based mechanisms will induce system inefficiency in terms of increasing the supply chain’s operation cost, and this inefficiency will be more obvious as cross-project learning becomes more pronounced. It is suggested that some social factors also be relied on to better seize the opportunities provided by technology implementation.

Keywords:

• contracts
• construction project management
• economics
• procurement management

Acknowledgment

Part of this article was written during the first author’s visit to the University of Manchester. The authors would like to thank Professor Graham Winch for his kind invitation and the China Scholarship Council for its sponsorship. The authors are also grateful to Dr. Bin Xin and Wen Yue at the University of Manchester for their help in data processing.