The Distinguishing Coefficient (ξ) is an important parameter of Grey Relational Analysis (GRA), a flagship multi-criteria decision making (MCDM) model of Grey System Theory, an intelligent and multifaceted field developed by Chinese scientists in 1980s. However, the scholars widely assume ξ = 0.5. The current study questions this practice. Also, some scholars have argued that the variation in ξ doesn’t influence the ranking of the factors through GRA. On contrary, the study demonstrates, the variation in ξ can influence the ranking. This has been shown through a case involving primary data concerning the perceived relative importance of Project Management Knowledge Areas (PMKAs). This study is significant for the analysts of uncertain systems, represented by grey or fuzzy systems, who intend to use GRA for intelligent multi-criteria decision making. It encourages ξ – driven sensitivity analysis of GRA model before interpreting the results. The study reveals, by tailoring the value of ξ a point can be achieved where the ranking obtained through GRA can be made most comparable to the other MCDM methods. For comparative analysis of the GRA based results the study deployed three other MCDM techniques; Analytic Hierarchy Process, Best Worst Method and Simple Additive Weighting.

Contractors in the international marketplace are experiencing more severe political risks than ever before. They have also in response endeavored to mitigate the high degree of political risk exposure in order to survive. It is important however to identify and manage the critical variables affecting political risks associated with international construction projects. Relatively few studies of political risks, particularly in the context of international projects, have been carried out. The objective of this paper is to examine how international contractors attempt to identify and manage the critical variables which affect the level of political risks when they venture out of their home countries. 85 variables contributing to the level of political risks encountered by international contractors were identified in this paper. Based on the findings from an international survey on political risks, this study reveals that the variables, such as degree of stability of the government, project desirability to the host country, policy uncertainty, racism and xenophobia, and unfavorable attitude towards foreign businesses are the most significant variables. Several recommendations are also provided for international contractors in this paper. This research will assist international contractors to manage the political risk factors when expanding overseas.