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.
Intellectual Merit A weakness in the market is the lack of quantitative models of team organization and function. Having such models could better inform why some teams function better than others: Are certain teams more cognitively flexible and able to more rapidly enter and exit organized neurophysiologic states? Can these abilities be taught, and if so, how? Longitudinal extensions of these models could help validate training doctrines, predict teamwork breakdowns, suggest routes for teams to regain their rhythm once lost, or apply adaptations in real-time. The goal of our Phase I research was to develop neurodynamics systems to begin to understand, at a quantitative level, the neurobiology of effective Entrepreneurial Teams (ET), diagnosing difficulties some teams have in becoming effective ET and predicting which teams are likely to be successful ET. To develop such models we collected neurophysiologic (EEG) measures of Engagement and Workload from twenty-three 5-person student and Entrepreneurial Teams (ET) while they performed business case history team discussions. From these data we developed information and organization-centric neurodynamic models spanning this range of expertise with the purpose of developing a scale(s) to rank a team’s aptitude. The framework is information centric in the sense that raw EEG measures are converted into Neurodynamic Symbols (NS) that each second shows the cognitive levels for each team member and the team as a whole. The NS data streams generated during teamwork have structure and within that structure statistical regularities exist which identify ‘interesting periods’ relevant to the team and teamwork. This framework forms the core of our innovation and is quantitative in that the level of proficiency of a team is specified in bits of information (see attached figure). This model was easily able to distinguish novice teams which had near random neurodynamics organizations from the more expert-teams which were more flexibly organized from a cognitive perspective. Broader Impacts Although collaborative learning is often regarded as a core competency of today’s society few systems exist for the quantitative description and study of teamwork, especially with complex and real-world tasks. The range of disciplines and activities expected to be impacted by our products are large and the research is expected to have significant societal impact. Our information – organization modeling systems are located at the intersection of collaborative learning, psychometrics, complexity theory and neurobiology and the resulting principles and applications will have foundational impacts on each of these disciplines. Figure Legend: The x-axis plots low / high levels of neurodynamics entropy and relates them to the cognitive organization of the team ranging from rigid to random. These scales parallel team flexibility with randomly organized teams being more (too) flexible than rigid teams. NS entropy is expressed either numerically, or visually in transition matrices. The y-axis represents the team performance / outcomes. Ideally teams want to be in the ‘sweet spot’ of cognitive organization having sufficient organization to efficiently complete the task, but also having the flexibility to respond to unforeseen problems.
