In this study, we developed a modified version of the CRiteria Importance Through Inter-criteria Correlation (CRITIC) method, namely the Distance Correlation-based CRITIC (D-CRITIC) method. The usage of the method was illustrated by evaluating the weights of five smartphone criteria. The same evaluation was repeated using four other objective weighting methods, including the original CRITIC method. The results from all the methods were further analyzed based on three different tests (i.e., the distance correlation test, the Spearman rank-order correlation test, and the symmetric mean absolute percentage error test) to validate D-CRITIC. The tests revealed that D-CRITIC could produce more valid criteria weights and ranks than the original CRITIC method since D-CRITIC yielded a higher average distance correlation, a higher average Spearman rank-order correlation, and a lower symmetric mean absolute percentage error. Besides, additional sensitivity analysis indicated that D-CRITIC has the tendency to deliver more stable criteria weights and ranks with a larger decision matrix. The research has contributed an alternative objective weighting method to the area of multi-criteria decision-making through a unique extension of distance correlation. This study is also the first to propose the idea of a distance correlation test to compare the performance of different criteria weighting methods.

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Organic waste disposal in landfills has created various environmental issues, such as greenhouse gas emissions and leachate. Awareness of this issue has resulted in diverting landfill to compost. Thus, there is a need to develop an analytical tool to select the best composting technology. Therefore, this paper reviews a range of assessment steps designed to evaluate specific sustainability criteria (environmental, social, economic, and technical) for organic waste management to select the most suitable composting technology. Due to the complexity of conflicting criteria and alternatives in composting technology, a multi-criteria decision-making (MCDM) technique is suggested to ensure the quality of the decision-making process. As an additional benefit, the synthesis results via the MCDM tool will be more credible when seeking validation by stakeholders.

In helping consumers to make a wise decision in purchasing a personal computer (PC), the use of multi-criteria decision analysis methods is a way to provide ranking of the attributes of PCs and to construct the computer preference index (CPI). This paper employs the Rank Ordered Centroid (ROC) method to do the ranking. The findings reveal that the most important attribute is the CPU, followed by the hard drive, the price, the memory card, the warranty, the size, the screen resolution, the Ethernet, the weight and the DVD. Whilst, the CPI is constructed by using a Multi-Criteria Decision Making (MCDM), called the Simple Additive Weighting (SAW) method. Analysis of data from 25 PCs from four brands, Toshiba, Dell, HP Compaq and Acer, presents the CPI. The resulted CPI shows that two HP Compaq models, Presario V3632 and V3653 are preferred most, while, Acer Aspire 4920-5AA0516MI is the least preferred.

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