Table 2.

Key research gaps and corresponding methodological contributions.

Focus area	Research gap	Proposed solution
Integration of subjective and behavioral dimensions	Most existing decision-making frameworks primarily emphasize numerical indicators, while human-related aspects such as expert judgment, behavioral tendencies, and strategic intuition are often insufficiently addressed.	The proposed RANCOM module explicitly incorporates expert evaluations, allowing behavioral preferences and professional insights to be systematically integrated alongside quantitative data.
Weighting of financial and strategic criteria	Many prior studies depend either on purely subjective weighting schemes or fully data-driven techniques, which may result in biased or unstable weight distributions.	The LOPCOW method is employed to derive objective weights by analyzing data variability and informational contribution, thereby improving consistency while still supporting informed decision-making.
Preference modeling and alternative ranking	Conventional EPROMETHEE-II approaches often show limited responsiveness to nonlinear relationships and minor preference differences among competing alternatives.	An enhanced EPROMETHEE-II mechanism is adopted to better capture preference intensity, leading to more reliable, transparent, and meaningful ranking results.
Portfolio optimization and risk-responsive management	Traditional portfolio selection models frequently overlook ESG considerations, adaptive investment behavior, and the growing complexity of modern financial assets.	The proposed framework evaluates investment alternatives in a comprehensive manner, enabling risk-aware and ESG-integrated portfolio optimization under dynamic market conditions.
Modeling and managing uncertainty	Uncertainty arising from expert assessments and fluctuating market environments is not adequately represented in many existing decision models.	Intuitionistic fuzzy Z-number (IFZN) modeling is utilized to better reflect uncertainty and reliability in expert opinions, thereby strengthening the robustness of the evaluation process.
Decision support in evolving financial contexts	Several available decision-support tools lack flexibility and fail to adapt to rapidly changing financial and economic environments.	By combining objective weighting, expert judgment, and uncertainty handling, the proposed decision-support framework offers an adaptive and context-aware solution for complex financial decision-making.