Evaluating Inspiring Factors and Obstacles in the Start-Up of Owned Agri-Preneurial Businesses: Underlying Evidence from South Africa
Notice bibliographique
Résumé
Contemporary economies worldwide appreciate the underlying importance of service sectors, culminating in sectorial growth and satisfactory performance of agri-preneurship. This study is premised on two simple explanations that cascaded into two hypothetical questions: (i) Are there inspiring factors for individuals to start up their own agri-preneurship business? (ii) Are there factors hindering individuals from starting up their own agri-preneurship business? These two hypothetical constructs triggered four primary arguments established in the study. This study was conducted in Mbombela local Municipality in category ‘B’, which is made up of the Ehlanzeni District, Mpumalanga South Africa. From a population of 1021 farmers, 458 samples of respondents were obtained and considered to be realistic for the study. The study embraced the mixed research approach, and the field survey was done between the months of February 2022 and June 2022. Descriptive statistics, involving the frequency count and percentages, were used to determine the motives and obstacles of starting up one’s own business. The hypothesis was assessed by employing multinominal logistic regression to determine the relationship between demographic variables and predictor variables. The findings revealed that potential agri-preneurs are affected by an array of inspiring factors in the decision-making process. Further, the results revealed that agri-preneurial spirit is inhibited by numerous challenges. In the shared view of respondents, there was a portmanteau of obstacles impeding the commencement of one’s own business. The results also showed that, in the self-realization variant, age (p < 0.035) and marital status (p < 0.033) were found to be correlated and statistically significant as an inspiration for setting up an agri-preneurship business, while the self-satisfaction variant specified marital status (p < 0.001) to be significant and to negatively (β −1.564) influence inspiration for agri-preneurship. In the independence variant category, two crucial variables, level of formal education (p < 0.005) and farm experience (p < 0.007), were found to be correlated and statistically significant in determining agri-preneurial decisions. This study has several policy propositions: the government and allied stakeholders must consider the motives that inspire potential investors in agriculture. The government must also oversee the reasons for young and old agri-preneurs leaving agri-businesses because of failure. This study underscores the need to subjectively analyze agri-preneurial inspiring factors and obstacles to performance. The contributions of agri-preneurial businesses to economic growth and poverty alleviation justify the need for a concerted effort to motivate potential investors. Another pertinent contribution of the study is highlighting the obstacles of agri-preneurs to enable the government to support and prepare responsive, innovative, and resilient agri-preneurs in South Africa. This paper recommends that starting up one’s own agri-preneurial business is a good idea for abating hunger and unemployment.
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 | 0,004 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,003 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 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 ».