An innovative negotiation framework for resolving complex construction conflicts and disputes has been developed in this research. The unique feature of the proposed negotiation framework is that it takes into account the attitudes of the decision makers, which is an important human factor in construction negotiation at both the strategic and tactical levels of decision making. At the strategic level, the Graph Model for Conflict Resolution (GMCR) technique has been systematically employed as a method of determining the most beneficial strategic agreement that is possible, given the competing interests and attitudes of the decision makers. At the tactical level, a previously agreed-upon strategic decision has been analyzed in depth using utility functions in order to determine the trade-offs or concessions needed for the decision makers to reach a mutually acceptable resolution of the negotiation issues. A real-life case study of a brownfield construction negotiation has been used to illustrate how the proposed methodology can be applied and to demonstrate the importance and benefits of incorporating the attitudes of the decision makers into the negotiation process to better identify the most feasible resolutions. The proposed attitude-based negotiation framework constitutes a new systems engineering methodology that will assist managers in tackling real-world controversies, particularly in the construction industry. The negotiation framework has been implemented into a convenient negotiation decision support system that automates the proposed negotiation methodology. The research is expected to improve negotiation methodologies for construction disputes, thereby saving significant amounts of time and resources. The proposed methodology may also assist decision makers in overcoming the challenges of conventional negotiation processes because the incorporation of the attitudes of the decision makers results in a more accurate identification of tradeoffs, greater recognition of the level of satisfaction of the decision makers, and enhanced generation of optimum solutions.

An innovative negotiation framework for resolving complex construction conflicts and disputes has been developed in this research. The unique feature of the proposed negotiation framework is that it takes into account the attitudes of the decision makers, which is an important human factor in construction negotiation at both the strategic and tactical levels of decision making. At the strategic level, the Graph Model for Conflict Resolution (GMCR) technique has been systematically employed as a method of determining the most beneficial strategic agreement that is possible, given the competing interests and attitudes of the decision makers. At the tactical level, a previously agreed-upon strategic decision has been analyzed in depth using utility functions in order to determine the trade-offs or concessions needed for the decision makers to reach a mutually acceptable resolution of the negotiation issues. A real-life case study of a brownfield construction negotiation has been used to illustrate how the proposed methodology can be applied and to demonstrate the importance and benefits of incorporating the attitudes of the decision makers into the negotiation process to better identify the most feasible resolutions. The proposed attitude-based negotiation framework constitutes a new systems engineering methodology that will assist managers in tackling real-world controversies, particularly in the construction industry. The negotiation framework has been implemented into a convenient negotiation decision support system that automates the proposed negotiation methodology. The research is expected to improve negotiation methodologies for construction disputes, thereby saving significant amounts of time and resources. The proposed methodology may also assist decision makers in overcoming the challenges of conventional negotiation processes because the incorporation of the attitudes of the decision makers results in a more accurate identification of tradeoffs, greater recognition of the level of satisfaction of the decision makers, and enhanced generation of optimum solutions.