Emotional intelligence and leadership in organizations: A meta-analytic test of process mechanisms

The present study—employing psychometric meta-analysis of 92 independent studies with sample sizes ranging from 26 to 322 leaders—examined the relationship between EI and leadership effectiveness. Overall, the results supported a linkage between leader EI and effectiveness that was moderate in nature (ρ = .25). In addition, the positive manifold of the effect sizes presented in this study, ranging from .10 to .44, indicate that emotional intelligence has meaningful relations with myriad leadership outcomes including effectiveness, transformational leadership, LMX, follower job satisfaction, and others. Furthermore, this paper examined potential process mechanisms that may account for the EI-leadership effectiveness relationship and showed that both transformational leadership and LMX partially mediate this relationship. However, while the predictive validities of EI were moderate in nature, path analysis and hierarchical regression suggests that EI contributes less than or equal to 1% of explained variance in leadership effectiveness once personality and intelligence are accounted for. ^

Emotional Intelligence and Leadership in Organization: A Meta-analytic Test of Process Mechanisms

The present study – employing psychometric meta-analysis of 92 independent studies with sample sizes ranging from 26 to 322 leaders – examined the relationship between EI and leadership effectiveness. Overall, the results supported a linkage between leader EI and effectiveness that was moderate in nature (ρ = .25). In addition, the positive manifold of the effect sizes presented in this study, ranging from .10 to .44, indicate that emotional intelligence has meaningful relations with myriad leadership outcomes including effectiveness, transformational leadership, LMX, follower job satisfaction, and others. Furthermore, this paper examined potential process mechanisms that may account for the EI-leadership effectiveness relationship and showed that both transformational leadership and LMX partially mediate this relationship. However, while the predictive validities of EI were moderate in nature, path analysis and hierarchical regression suggests that EI contributes less than or equal to 1% of explained variance in leadership effectiveness once personality and intelligence are accounted for.

Actionable pattern analysis in computing and business intelligence systems

With advances in science and technology, computing and business intelligence (BI) systems are steadily becoming more complex with an increasing variety of heterogeneous software and hardware components. They are thus becoming progressively more difficult to monitor, manage and maintain. Traditional approaches to system management have largely relied on domain experts through a knowledge acquisition process that translates domain knowledge into operating rules and policies. It is widely acknowledged as a cumbersome, labor intensive, and error prone process, besides being difficult to keep up with the rapidly changing environments. In addition, many traditional business systems deliver primarily pre-defined historic metrics for a long-term strategic or mid-term tactical analysis, and lack the necessary flexibility to support evolving metrics or data collection for real-time operational analysis. There is thus a pressing need for automatic and efficient approaches to monitor and manage complex computing and BI systems. To realize the goal of autonomic management and enable self-management capabilities, we propose to mine system historical log data generated by computing and BI systems, and automatically extract actionable patterns from this data. This dissertation focuses on the development of different data mining techniques to extract actionable patterns from various types of log data in computing and BI systems. Four key problems—Log data categorization and event summarization, Leading indicator identification , Pattern prioritization by exploring the link structures , and Tensor model for three-way log data are studied. Case studies and comprehensive experiments on real application scenarios and datasets are conducted to show the effectiveness of our proposed approaches.