Emotional Intelligence: Problems With Conceptualization

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Author(s): Nishtha Sharma (Kalinga University, India)and Shinki Pandey (Kalinga University, India)
Copyright: 2024
Pages: 20
Source title: Machine and Deep Learning Techniques for Emotion Detection
Source Author(s)/Editor(s): Mritunjay Rai (Department of Electrical and Electronics Engineering, Shri Ramswaroop Memorial University, India)and Jay Kumar Pandey (Department of Electrical and Electronics Engineering, Shri Ramswaroop Memorial University, India)
DOI: 10.4018/979-8-3693-4143-8.ch003

Keywords: Information Science Reference / Knowledge Management and Library Science / Machine Learning / Psychology

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Abstract

Emotional intelligence, though captivating, faces challenges due to the lack of a unified definition and measurable aspects. This ambiguity hinders research and development of interventions. This chapter explores these complexities. Current models like Goleman's, emphasizing social skills and self-awareness, and Mayer and Salovey's, focusing on cognitive processes, exemplify the variation. Each model has strengths and weaknesses, highlighting the need for a more cohesive approach. Measuring EI is another hurdle. Self-report measures face social desirability bias, while observer ratings lack objectivity. Performance-based assessments offer promise, but may not generalize to real-world situations. A multi-method approach that combines these methods is recommended. Cultural variations further complicate the picture. Emotional expression and expectations differ significantly, making existing models potentially biased. Culturally sensitive measures and interventions are crucial.

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