Notice bibliographique
Résumé
Purpose This article aims to investigate motivations, success factors, problems, and business‐related stress of entrepreneurs in small‐ and medium‐sized enterprises and relates them to the success of the Chinese entrepreneurs. Design/methodology/approach A total of 196 entrepreneurs in Beijing, Shanghai, and Guangzhou were randomly selected for a survey, which was analyzed to determine motivations, success factors, problems, and business‐related stress by gender. Ordered logit models were applied to motivation and success factors. Findings Results showed that 68 percent were male and 32 percent female. The average age of the entrepreneurs was about 32 years old and time devoted to their business was almost 45 hours per week. Of the total respondents, 56 percent were married and 44 percent single. When asked to indicate their motives for business ownership, these entrepreneurs suggested that increasing income, becoming their own boss, and to prove that they can succeed were the most important reasons. Reputation for honesty, providing good customer services, and having good management skills were reported to be necessary conditions for business success. Friendliness to customers and hard work were also critical for high‐performance enterprises. Among the problems encountered by entrepreneurs, unreliable/undependable employees were the most critical. Intense competition and lack of management training also proved to be great challenges for Chinese entrepreneurs. Practical implications Policy makers can strengthen its small business entrepreneurs by promoting the factors that lead to entrepreneurs' success, such as the ability to manage personnel and management skills through business outreach services provided by universities, government agencies, and nonprofit organizations. In addition, the government has the ability to simplify the tax system, and reduce payroll taxes. Technical assistance in areas such as market research, human resources management, and technological support should be provided to small business owners. Originality/value This study applied to Chinese entrepreneurs in addition to an extensive analysis of the factors that affect motivations, success, problems, and business stress.
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 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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,002 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».