<![CDATA[Objective criterion weighting plays an important role in multi-criteria decision-making (MCDM), particularly when decision outcomes depend on how differences among criteria are represented. However, conventional objective weighting approaches may emphasize specific statistical characteristics of the decision matrix without explicitly considering criterion heterogeneity as an integrated concept. This study proposes Heterogeneity-Informed Value Emphasis (HIVE) Weighting, a Heterogeneity-Informed Value Emphasis approach, for objective criterion weighting in MCDM. The method incorporates the mean, standard deviation, and relative variability of normalized criterion values to construct a heterogeneity-based measure of criterion importance. The resulting weights are then integrated with established MCDM methods to examine their applicability in alternative evaluation. A comparative analysis is conducted against Entropy, WENSLO, LOGSTA, CRITIC, and MEREC, followed by evaluations using the SAW and MOORA methods. Sensitivity analysis is further performed by varying criterion weights across multiple perturbation scenarios to examine the stability of the resulting rankings. The findings indicate that HIVE generates a more balanced representation of criterion importance than several comparative weighting approaches while preserving compatibility with different MCDM aggregation procedures. The ranking results obtained through SAW and MOORA show consistent patterns, while the sensitivity analysis indicates that the overall ranking structure is relatively stable under changes in criterion weights. These results demonstrate that HIVE provides an interpretable and flexible framework for incorporating criterion heterogeneity into objective weighting. The proposed approach offers a basis for further research on heterogeneity-aware weighting across diverse MCDM datasets and decision-making contexts, supporting more reliable and transparent weighting practices in complex decision environments.]]>
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