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Enregistrement W1493327889

Satisfaction or Business Savvy - Examining the Outcome of New Venture Creation with Respect to Entrepreneurial Characteristics, Expectation, Optimism, Realism, and Pessimism

2011· article· en· W1493327889 sur OpenAlexaboutno aff
Chyi‐Lyi Liang, Paul Dunn

Notice bibliographique

RevueAcademy of Entrepreneurship journal · 2011
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueEntrepreneurship Studies and Influences
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésEntrepreneurshipPessimismOptimismMarketingEconomicsOddsQuarter (Canadian coin)Profit maximizationSmall businessProfit (economics)Net profitBusinessFinanceMicroeconomicsLogistic regressionPsychology
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

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. …

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,010
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,007
Score d'incertitude au seuil0,023

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,010
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0020,001
Science ouverte0,0000,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0070,001

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.

Tête enseignante Opus0,086
Tête enseignante GPT0,289
Écart entre enseignants0,203 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations9
Publié2011
Routes d'admission1
Résumé présentoui

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