Satisfaction or Business Savvy - Examining the Outcome of New Venture Creation with Respect to Entrepreneurial Characteristics, Expectation, Optimism, Realism, and Pessimism
Notice bibliographique
Résumé
INTRODUCTION In the most recent global turmoil of the financial market, entrepreneurs continue to struggle to secure funding and market opportunities. According to the latest research released by the Small Business Administration, the first three quarters of 2009, small businesses accounted for almost 60 percent of the net job losses, with the greatest losses in the first quarter. By the third quarter, net small firm job losses were one-third what they had been in the first quarter.' (SBA, 2011) Interestingly, the American Express Open Small Business Monitor found that 55 percent of entrepreneurs were optimistic about the future of their businesses in September 2009, up 10 percent from earlier in the year' (SBA, 2011) This situation brings up a myth of entrepreneurship that many researchers have yet to identify: why do entrepreneurs start and stay in business even when the economic environment is against the odds of success? It is typically assumed that people engage in entrepreneurship because there are profits to be made. In the traditional school of economic way of thinking, we assume the creation of value-added goods and services should lead to profit maximization. Furthermore, the decisions and actions related to profit maximization should be positively correlated with higher utility for individuals who are making the decisions. In contrast to this view, this paper argues that entrepreneurship is more adequately characterized as a beyond-profit-seeking activity. Evidence from some has shown that entrepreneurship does quite generally not pay in monetary terms (Baron & Shane, 2005; Hey, 1984, Petrakis, 2005; De Meza and Southey, 1996; Coelho and De Meza, 2006; Brocas & Carrillo, 2004; Puri and Robinson, 2004; Simon and Houghton, 2002; Benz, 2006). However the literature lacks empirical studies to examine how entrepreneurs reflect on the outcomes of new venture creation with respect to financial reward and personal satisfaction. This article focuses on understanding if being an entrepreneur is truly rewarding because it entails substantial non-monetary benefits, like greater autonomy, broader skill utilization, and the possibility to pursue one's own ideas. We have introduced an innovative framework to examine entrepreneurs' reflection after starting and running their new ventures linking to 5 factors: entrepreneurial characteristics, expectation, optimism, realism, and pessimism There are three reasons for us to choose these five factors as the core of the paper. First, optimism, realism and pessimism are newly introduced to entrepreneurship studies in recent years, and there is a lack of understanding what these factors mean to entrepreneurs and how they impact decisions. Second, many researchers have argued against the idea that we should pay more attention to how entrepreneurs are made, not who entrepreneurs are. Several studies have confirmed the importance of recognizing the differences between optimism, realism, and pessimism and other entrepreneurial characteristics (references will be added later due to authors' identities). There is a need to further examine the levels of effects of optimism, realism, and pessimism on entrepreneurs and their decisions in venture creation. Most of the studies have emphasized on pre-venture psychology and extraordinary circumstances that drive people to become entrepreneurial. We understand the rate of failure is high among new ventures in the first 1-3 years of establishment. What we don't know enough, is how entrepreneurs manage to survive beyond the objective of profit maximization. Very limited information exists to verify how entrepreneurs feel after they start the business, given business outcomes and personal satisfaction. Thirdly, entrepreneurial decision-making is a complex process. We agree that a positive cash flow implies a happy business. We have learned from much of the literature regarding separated issues about characteristics of entrepreneurs, entrepreneurial decision making and behavior, and reactions of entrepreneurs while facing challenges and barriers. …
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».