“What Matters More for Entrepreneurship Success? A Meta-Analysis Comparing General Mental Ability and Emotional Intelligence in Entrepreneurial Settings”, 2020-11-03 (; backlinks; similar):
Using meta-analysis, we investigate the extent to which General Mental Ability (GMA) and Emotional Intelligence (EI) predict entrepreneurial success. Based on 65,826 observations, we find that both GMA and EI matter for success, but that the size of the relationship is more than twice as large for EI. Our study contradicts and adds important contextual nuance to previous meta-analyses on performance in traditional workplace settings, where GMA is considered to be more critical than EI. We also contribute to the literature on cognitive and emotional intelligence in entrepreneurship.
Managerial Summary: While previous studies have shown General Mental Ability (GMA, cognitive intelligence) to be more important for success compared to Emotional Intelligence (EI) in traditional workplace settings, we theorize that EI will be more important in entrepreneurial contexts. Entrepreneurship is an extreme setting with distinct emotional and social demands relative to many other organizational settings. Moreover, managing an entrepreneurial business has been described as an “emotional rollercoaster.” Thus, on a relative basis we expected EI to matter more in entrepreneurial contexts and explore this assumption using a meta-analysis of 65,826 observations. We find that both GMA and EI matter for entrepreneurial success, but that the size of the relationship is more than twice as large for EI.
…The dominant meta-analytic paradigm in entrepreneurship is psychometric meta-analysis (2004). However, we did not choose this procedure for 2 reasons. First, the chief advantage of psychometric meta-analysis is the ability to correct for statistical artifacts such as unreliability and range restriction. In our data, a large percentage of the samples did not report the needed information to make these corrections locally and the global corrections via artifact distributions with the limited number of samples that reported necessary information would likely have been strongly influenced by second order sampling error.