Evaluating risk in the innovation projects of small firms
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,015 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».