Social entrepreneurship as a tool for sustainable development in the townships in KwaZulu-Natal province
Notice bibliographique
Résumé
Globally, social entrepreneurs have been identified as major contributors to solving social problems such as unemployment, poverty and inequality. They can also be considered a catalyst to economic transformation as a result of their significant contribution to a country’s Gross Domestic product (GDP) and have been identified as a crucial part of any country that aspires to achieve sustainable development. In countries, such as the United Kingdom, United States, Canada, France and Australia, growth in social value creation attributable to social entrepreneurs has strengthened, indicating any predicated or identified economic, social and environmental challenges may be mitigated or addressed by their inputs. This indicates beyond reasonable doubt that social entrepreneurs have an important role to play in any country and, it can also be argued, more especially in developing countries such as South Africa. The ineffective use and low contribution of this phenomenon to address the many socio-economic challenges affecting South Africa have been a significant concern to the South African government, academies, investors and policy makers, leading to a call for additional support for social entrepreneurship emergence and growth in the country. The National Development Plan (NDP) considers small businesses such as social enterprises a key component of any inclusive growth strategies and envisions by 2030, 90 percent of new jobs will be created by micro, small and medium enterprises. Research on factors affecting social entrepreneurs’ growth and their contribution to sustainable development in the country has seen various extensive studies in this field, nonetheless, a serious research gap remains regarding an in-depth understanding of those characteristics and factors that critically influence social entrepreneurship and improve their contribution to sustainable development. Furthermore, theoretical models of these factors are lacking. The current study, therefore, covers a critical research area with the potential to improve the present state of South African social entrepreneurship, with specific reference to social entrepreneurship within the townships in KwaZulu-Natal (KZN). The study aimed to identify the critical factors affecting social entrepreneurship as a tool for sustainable development in the KZN townships and to propose a prototype model to improve its effective contribution, since the contribution level currently experienced in the social sector is poor, which is attributed to various significant challenges faced by social entrepreneurs in South Africa. Research was thus conducted in three townships within the KZN Province, employing a quantitative research method, where the study population consisted of 90 social entrepreneurs. A non-probability, convenience sampling technique was adopted, while a closed-ended questionnaire was used to collect the primary data for the study. Inferential and descriptive statistical analysis of the data was undertaken using SPSS (version 27.0) software. The findings of the study indicate several challenges severely affect social entrepreneurs, who are constantly faced with significant barriers that affect their performance, impacting on social entrepreneurs’ innovativeness and creativity that could promote and improve their contribution to sustainable development. The study identified environmental factors to have a significant impact on social entrepreneurs’ contribution to sustainable development, while society’s perception, social networking, social impact measurement and access to financial resources were also identified as catalysts to social entrepreneurs’ performance. Key insights are provided into both the theoretical and practical implications of social entrepreneurs’ contribution to sustainable development. In addition, an extensive range of recommendations is outlined and a theoretical framework proposed for those factors that social entrepreneurs, government officials, and policy makers can understand to help develop remedies for current challenges affecting social entrepreneurs
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 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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 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 ».