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SBIR Phase I: Neurodynamic Models for Identifying and Optimizing Entrepreneurial Teams

$165,000FY2012TIPNSF

The Learning Chameleon, Inc., Marina Del Rey CA

Investigators

Abstract

This Small Business Innovation Research (SBIR) Phase I project will develop multi-dimensional neurodynamic models of the cognitive organizations of teams that span zero history to proven entrepreneurial teams (ET), with the purpose of developing a neurophysiologic instrument to rate a team's entrepreneurial aptitude. This continuum will be developed using an established business training task that will be performed by zero history teams, student teams participating in the Edson Entrepreneurial Initiative within the Venture Catalyst at ASU and established entrepreneurial teams recruited either from the Venture Catalyst or from recent NSF SBIR awardees. Neurodynamic models will be generated using EEG technologies and protocols previously developed for high fidelity military training activities. These models dynamically follow the engagement and workload of each member of the team as well as the entire team and will be customized for studying entrepreneurial teams. By exploring the neurological functioning of ET more informed theory may be produced to better understand and predict which teams are likely to become sustained ET. Furthermore, by developing quantitative measures / models that reflect team experience / efficiency we may begin to develop training approaches to accelerate and test the development of the ET skillset. The broader impact/commercial potential of this project lies in its generality. The metrics and modeling approaches will be easily customized for other business and non-business (i.e. education, training and / or military) related team activities enhancing the commercial potential of the product. The proprietary neurodynamic assessment system will be marketed to corporate training / coaching programs, financial backers who wish to decrease uncertainty in ventures, and entrepreneurial organizations who wish to optimize their performance.

View original record on NSF Award Search →