Evaluating risk in the innovation projects of small firms
Bibliographic record
Abstract
A model developed for a risk assessment instrument to be used by entrepreneurs, their advisors and financial backers is presented. By modelling the entire lifecycle of an innovation project, and a variety of intrinsic and managerial risks of the project, we created an 'expert system' that serves both to evaluate and mitigate risk. Many innovations are proposed and carried out by individual entrepreneurs and small firms. Many regions, including Montreal, Quebec and Ottawa owe them their economic rebirth. The success of entrepreneurial firms spurred an entire body of literature dealing with the inability of large firms to innovate (see Dougherty and Heller 1994; Christensen 1997; Leifer et al. 2001). Yet, entrepreneurs complain about the lack of adequate financing for innovation. On the one hand, banks rely heavily on personal guarantees, require physical assets as collateral, and have difficulty valuing intangible assets such as ideas, knowledge, competencies and even patents (Julien, St-Pierre & Beaudoin 1996). On the other hand, venture capitalists are accused of herding behavior, leading to waves of over-financing in certain areas leaving other areas hungry for funds (Robbins-Roth 2001), and of making trust in the entrepreneur and the management team the main criterion for the financing decision (Knight, 1994; Zopounidis 1994). Most complaints center on the inability of financial institutions to assess the likelihood that an innovation project will be a successful. Banks and traditional financial institutions, used to deal with more mature or larger businesses, place entrepreneurial innovation projects outside the risk range with which they are comfortable (Levratto, 1994) and rely on collateral to prevent adverse selection by the entrepreneurs who seek funding. Venture capitalists and capital providers with higher risk tolerances have more technical competencies required to evaluate the innovation and reduce the information asymmetry. However, many dysfunctions have been revealed about the way they assess projects (Julien et al. 1996), including the paradoxical tendencies to make poorer predictions when they had more information (Zacharakis and Meyer 2000) and to give insufficient weigh to technical issues as a source of project failure (Fries and Guild 2002). Hence, a reliable tool for assessing the prospects of entrepreneurial innovation projects would be of significant value, particularly in the context of the Knowledge Economy. A team of researchers was commissioned by Canada Economic Development to produce a computerized tool for the assessment of risk in such projects. This paper details the model of risk that underlies the web-based tool, the measurement approach and the structure of the tool. The paper begins with a theoretical background on the evaluation of risk in entrepreneurial innovation projects. Then, we outline the methods used to develop and test the questionnaire. The following section introduces the model of risk and discusses how it was implemented in the tool. Next, we discuss the sections and subsections of the questionnaire. A conclusion section closes our argument.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".