The relationship between network centrality and temperament intelligence type in a small innovation network: Exploring the implications for emergent innovation networks.

摘要

This study explores whether centrality in a small emergent innovation network might be related to individual temperament intelligence type when it comes to sharing new and innovative ideas. Using data from a 17-member innovation network devoted to creatively building and improving the Internet, the researcher tested whether centrality and temperament intelligence type within the network might correlate. This study reflects a multidisciplinary approach to research and practice that integrates two important fields of inquiry---social network analysis and personality theory. Combined, the two fields provide a rich analysis of the individual in relation to others in social and organizational networks that cannot be obtained solely through one approach or the other.;Besides employing a social network survey designed to uncover relationships based on new and innovative idea sharing within the network, this study also uses a Five-Factor personality assessment (IPIP-NEO) and converts the assessment results (T-Scores) to a temperament taxonomy (MBTI/Keirsey). This study does not rely solely on correlation analysis, but supplements the quantitative analysis with graphical illustrations (sociograms) to enable the researcher and the reader to gain greater insight into the data.;Due to the extremely homogeneous nature of the innovation network, combined with the small population size, the correlation analysis did not confirm a statistically significant relationship between centrality and temperament intelligence type. However, the supplemental sociograms did suggest a relationship. Also, the temperament analysis of the innovation network members appears to support the temperament research literature regarding which temperament patterns are more inclined to be associated with certain kinds of innovation and entrepreneurship. Further research using social network analysis, temperament analysis, and statistical analysis is recommended.