scholarly journals Effect of Emotional Intelligence and Leadership Styles on Risk Intelligent Decision Making and Risk Management

2021 ◽  
Vol 11 (1) ◽  
pp. 71-81
Author(s):  
Jayet Moon

AbstractIn today’s world, uncertainty abounds. It is therefore incumbent on managers to take decisions using unbiased considerations in dealing with organizational risks. Often, risk decisions are replete with assumptions and biases, leading to incorrect decisions. Leaders who apply emotional intelligence (EI) skills are better poised to challenge internal biases and assumptions to improve decision-making, but limited empirical evidence exists that accounts for the nexus between EI, leadership styles and risk perceptions of managers. The purpose of the paper was to explore the relevance of the theory of EI in risk-based decision-making, while comparing various leadership styles. The research adopted a questionnaire survey administered to 173 employed individuals. The research hypotheses analyzed the mediating roles of EI and leadership styles in risk perceptions using ‘t’ statistic and where applicable, Chi-square testing. The results of the analysis confirmed the role of EI in filtering deleterious internal biases and confirmed EI’s presence as a success factor in leadership and decision-making. Transformational leaders are, however, more emotionally intelligent and less biased. These attributes allow for the generation of a suitable risk attitude and enhance risk-intelligent decisions as compared to transactional leaders. This study, while being descriptive, is exploratory in nature and opens pathways for further targeted research based on specific EI abilities or traits and various situational risk attitudes.

2018 ◽  
Vol 7 (4.10) ◽  
pp. 15 ◽  
Author(s):  
Rajat Bhati ◽  
Shubham Saraff ◽  
Chhandak Bagchi ◽  
V. Vijayarajan

Decision Making influenced by different scenarios is an important feature that needs to be integrated in the computing systems. In this paper, the system takes prompt decisions in emotionally motivated use-cases like in an unavoidable car accident. The system extracts the features from the available visual and processes it in the Neural network. In addition to that the facial recognition plays a key role in returning factors critical to the scenario and hence alter the final decision. Finally, each recognized subject is categorized into six distinct classes which is utilised by the system for intelligent decision-making. Such a system can form the basis of dynamic and intelligent decision-making systems of the future which include elements of emotional intelligence.  


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