<p>Interval-valued intuitionistic fuzzy sets (IvIFSs) provide an effective way for modeling uncertainty and vagueness by representing membership and non-membership values in the form of intervals. This paper proposes a new correlation coefficient measure (CCM) for IvIFSs which is developed by modified formulations of deviation and covariance which captures the maximum variation between fuzzy variables. The theoretical properties of the proposed measure are examined to ensure its validity and reliability. An algorithm based on the proposed CCM is introduced for solving multi-criteria decision-making (MCDM) problems. To demonstrate its practical utility, the proposed measure is applied to an investment decision problem which involves multiple investors and sectors. The results show that the proposed CCM consistently recognizes the most suitable sector for each investor. A comprehensive comparative analysis of the proposed measures has been conducted with some existing measures to evaluate its performance. The results of the comparative analysis have been shown using heat map and comparative bar chart. The findings confirm that the proposed CCM provides improved discrimination and stable ranking performance compared to existing IvIFS coComplementrrelation measures.</p>