An Assessment Methodology for Predicting the Success of Technological Enterprises
Date
2008-07-15T16:12:52Z
Authors
Abbas, Abeer Al-Hassan
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Abstract
Every year thousands of students and budding entrepreneurs participate in technical business plan competitions in over 44 institutions in the US alone. The stakes are high: over $4 million dollars of prize money and hundreds of millions of dollars of venture capital are available. The evaluation process takes an average of 4 months to complete as the technical innovation, business plan, and personal traits of the entrepreneur are put to the test. How do each of these factors contribute to a winning proposal? Which is more important? How does the entrepreneur’s personality contribute to the success or failure of a proposal? My research investigates the importance of these three criteria (technical value, business management plan, and personality) in the ranking of technical business plans. Business plan evaluations were collected from five separate university competitions. The statistical significance of incorporating the Jung’s Personality Trait as a measure of entrepreneurial capability was estimated and is presented. The research findings suggest that the technical innovation is most significantly related to the proposal ranking followed by the management concept and personality type of the entrepreneur. A multiple-linear regression model was developed to predict the rankings of business plan competitions using personality type, business value and technical value as the independent variables. The inclusion and exclusion criteria were specified in terms of a threshold value of the F statistic. The criteria for removal was set at 0.1 and for entry was set at 0.05. The coefficient of multiple correlation (adjusted R2) for the best predictive model was 0.939 for a regression through the origin. The predictive model was used to estimate the semifinalist rankings and to select finalists for a business plan competition whose data was not used in the model development. Predicted rankings were highly correlated with observed rankings (R2=0.88) and the model perfectly selected the top four finalists from a group of seven semi-finalists.
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Keywords
Technical Entrepreneurship, Technological Entrepreneurship, Innovative Technology, Business Plan Competition Evaluation, Technological Venture Evaluation