Causal Relationships of Readability Risks in Construction Contracts

Issues related to readability risks in contracts could exacerbate conflict, claim and dispute occurrences in construction projects. Determination of root causes of readability risks by defining casual relationships in construction contracts is essential to improve contract documentation and enable successful risk management. This paper aims to differentiate net causes from net effect factors of readability risks in construction contracts. Most significant readability risks in construction contracts were analyzed using fuzzy decision-making trial and evaluation laboratory (DEMATEL) method, which is known for its wide implementation in similar problems. Root cause degree (RCD) diagram was drawn to illustrate the differentiation of these factors by adopting maximum mean de-entropy (MMDE) algorithm. Analysis results indicated that poor grammar use, legal terminology, visual representation, and negative language were the major underlying cause factors; while lengthy document, use of abbreviations, scope complexity, controversial uses, repetitions, and ambiguous words were the net effect factors. The results are expected to improve readability of contract documents, which would contribute to more effective risk management and better allocation of project resources.

Causal Relationships of Readability Risks in Construction Contracts

Issues related to readability risks in contracts could exacerbate conflict, claim and dispute occurrences in construction projects. Determination of root causes of readability risks by defining casual relationships in construction contracts is essential to improve contract documentation and enable successful risk management. This paper aims to differentiate net causes from net effect factors of readability risks in construction contracts. Most significant readability risks in construction contracts were analyzed using fuzzy decision-making trial and evaluation laboratory (DEMATEL) method, which is known for its wide implementation in similar problems. Root cause degree (RCD) diagram was drawn to illustrate the differentiation of these factors by adopting maximum mean de-entropy (MMDE) algorithm. Analysis results indicated that poor grammar use, legal terminology, visual representation, and negative language were the major underlying cause factors; while lengthy document, use of abbreviations, scope complexity, controversial uses, repetitions, and ambiguous words were the net effect factors. The results are expected to improve readability of contract documents, which would contribute to more effective risk management and better allocation of project resources.

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