Methodological principles for assessing the role of emotional intelligence in management activities

Abstract

Relevance of the study. The rapid expansion of artificial intelligence in management, especially in situations marked by crisis and uncertainty, calls for a reassessment of the human factors underpinning decision-making. While algorithmic tools improve analytical capacity, they remain limited in addressing ethical considerations, social dynamics, and emotional contexts. This brings renewed attention to emotional intelligence as a key condition for ensuring the adequacy, legitimacy, and societal acceptance of managerial decisions, and highlights the need for its structured evaluation within modern management systems. Aim of the study. The purpose of the article is to develop and justify methodological principles for assessing the role of emotional intelligence in management activities, with a focus on its interaction with AI-supported decision-making in conditions of digital transformation and instability. Methods. The research is based on a combination of theoretical generalization, comparative analysis, and quantitative modeling. A multiplicative approach is applied to determine the integrated effect of emotional intelligence on decision quality. The model incorporates three interrelated elements: the level of development of emotional intelligence components, the functional influence of these components on managerial tasks, and their relative importance within a specific management context. Correlation analysis and expert-based weighting are used to support the structure of the model. Results. The study presents a methodological model that enables a quantitative assessment of the contribution of emotional intelligence to managerial decision-making. The findings indicate that the influence of individual components is not uniform: empathy and communication skills demonstrate the strongest impact due to their direct role in interaction, trust-building, and legitimization of decisions. The proposed framework allows for adaptation to different management contexts, linking individual competencies with situational requirements. At the same time, the results confirm that emotional intelligence mitigates the limitations inherent in algorithm-based decision support. Conclusions. The results of the study support the inclusion of emotional intelligence as a core element in the evaluation of managerial effectiveness, particularly in environments where artificial intelligence is actively used. The proposed approach provides a practical tool for improving decision quality, strengthening human-centered management, and maintaining institutional trust. Overall, effective management in complex conditions requires a balanced integration of analytical capabilities and emotional competencies.