Exploring the Causal Relationship of Emotional Intelligence to Clergy Leadership Effectiveness
R. Michael Oney
There is a crisis of leadership effectiveness within the church. If present trends continue, the impact of the American church will continue to lose vital influence within society. This ecclesiastical crisis of the American church served as the impetus to exploring clergy leadership effectiveness and emotional intelligence (EI) competencies. Notably, EI has received a great deal of research attention as a means of developing exceptional leaders in regard to organizational performance variables. However, research exploring the predictive relationship between EI in clergy leadership and individual church performance variables has been substantially lacking. This research explored the predictive abilities of EI in clergy as measured through the Bar-On EQ-i on five performance variables of their local churches. The five performance variables assessed were Sunday morning attendance, water baptisms, Holy Spirit baptisms, and Sunday school attendance within the U.S. Assemblies of God denomination. Data were collected from a sample of 136 senior pastors. Logistic regression was the dominant form of analysis used to assess the predictive abilities of Clergy EI on church performance variables. The results revealed that EI is not a statistically significant predictor of the ecclesiastical performance variables of Sunday morning attendance, water baptisms, Holy Spirit baptisms, and Sunday school attendance. The logistic regression model indicated a marginal predictive relationship between clergy EI and conversions. Implications for future research are discussed. Specific attention is given to exploring the value and means of defining clergy leadership effectiveness including theological considerations. The most salient results of this research are that EI is not a strongly predictive construct relative to certain ecclesiastical performance variables and that the EI of clergy related to facilitating conversions is a predictive quality.
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