Research On Measurement Of Contract Functions And Governance Of Contract Violations In Construction Projects

Due to the long duration,high uncertainty,and high asset specificity of construction projects,contract violations frequently arise.Construction companies need to design more appropriate contracts to effectively prevent contract violations and once violations occur,apply appropriate enforcement practice to address violations.The governance process for contract violations can be divided into contract design to prevent violations and contract enforcement to handle violations.This study scrutinizes how both can be used to effectively govern contract violations.This study classifies construction contracts into control,coordination and adaptation from the functional perspective and uses this classification as an analytical framework to manually encode construction contracts sentence by sentence;and uses the manual encoding results as a training set,and develops a machine learning model to portray construction contracts based on the Ro BERTa pre-training model.Next,based on this model,actual construction contracts are analyzed and combined with a questionnaire approach to survey the personnel on the projects corresponding to these contracts;and the Qualitative Comparative Analysis is used to find the matching pattern between contracts and project characteristics.The results show that at different levels of asset specificity and uncertainty,different contract functions need to be used in conjunction under different governance frameworks(transactional,hybrid or relational governance frameworks)to prevent contract violations.In terms of contract enforcement,based on attribution theory,this study explores the impact of inter-organizational trust on contract enforcement.The empirical results show that goodwill trust mitigates the severity of contract enforcement by reducing relational risk perception,while the path from competence trust,to performance risk perception,and then to contract enforcement is not supported empirically;on the other hand,this study explores the alternative mechanism of contract enforcement —reputation enforcement— and the enforcement decision process(violation-perceptionenforcement),and the results show that letter violations and violations of tacit agreement lead to more severe contract enforcement and reputation enforcement than violations of unilaterally assumed obligations,and relational risk perception mediates the above relationship,while the former two types of violations result in no significant difference in enforcement severity.This study develops a machine learning model,which provides a more accurate and efficient tool for empirical researchers to portray and measure construction contracts;clarifies the alignment between different contract functions and project characteristics in governing project risk and violations,which contributes to resolving existing contradictions about the relationship between contracts,project characteristics and behavior,and provides advice to practitioners on how to design more effective contracts.This study constructs the conceptual framework of enforcement decisions,that is,“violations-risk perception-enforcement”,and portrays the conception of reputation enforcement in the construction industry,which clarifies the role of contract enforcement and reputation enforcement in governing project risks and violations,and providing a reference for practitioners to make appropriate enforcement decisions and thus,mitigate disputes.