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Analysing the antecedents of graduate labour immobility in South Africa

2023· dissertation· en· W7026602420 sur OpenAlex

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Notice bibliographique

RevueBoloka Institutional Repository (North-west University) · 2023
Typedissertation
Langueen
DomaineDecision Sciences
ThématiqueProfessional Masters Programs Analysis
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCasualUnemploymentGovernment (linguistics)Work (physics)Quarter (Canadian coin)Human resourcesVariety (cybernetics)Higher education
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Globally, labour markets are changing at a rapid pace, which has resulted in a decrease as seen in the percentage of employment and stable work conditions. Labour markets play a critical role in the global economy, serving as the mechanism through which individuals offer their skills and services in exchange for employment opportunities. The dynamics of labour markets are influenced by a variety of factors, including economic conditions, technological advancements, government policies, and demographic trends. This wave of change has also affected South Africa, where non-standard employment, such as casual and part-time labour, has increased in addition to the nation's high unemployment rate among youth and graduates as well as the national level by international standards. Higher education qualification has, however, also shown to be no longer a guarantee of employment, much less safe, permanent, and full-time work. With education, skills, and the potential to support the expansion and development of specific sectors and nations, graduate labour immobility offers a valuable human resource pool in today's knowledge-based and fiercely competitive economy. However, the issue is that graduate labour immobility has also had an adverse effect on the economic development of other nations, including South Africa. This is evident in the country's high unemployment rate, which stood at 32.6 percent in the second quarter of 2023. Graduates frequently still struggle to find appropriate work prospects that align with their abilities and future objectives, despite the growing importance of continuing education and skill acquisition. Chapters two and three in this thesis aim to provide a detailed analysis of the antecedents of graduate labour immobility in South Africa, with a specific focus on understanding the antecedent (factors) that contribute to this phenomenon. The study explores various dimensions such as economic, social and individual antecedents (factors) that influence graduates' decisions to remain geographically immobile in their job search and employment choices. Graduate labour immobility in South Africa is a multifaceted phenomenon influenced by various antecedents, encompassing geographical, occupational, and skills-related dimensions. By examining these antecedents comprehensively, policymakers and stakeholders can gain valuable insights into alleviating graduate labour immobility and promoting overall economic development. Furthermore, to predict the potential antecedents that may contribute to labour immobility in South Africa, a multifaceted analysis is crucial. Several key factors could play a significant role in shaping the labour landscape of the country. Economic conditions, such as unemployment rates, income inequality, and the overall health of the job market, would undoubtedly be pivotal elements. Additionally, examining the educational system and its alignment with the demands of the labour market can provide insights into the employability of the workforce. Social factors, including cultural norms, migration patterns, and demographic shifts, may also influence labour immobility. Political considerations, such as government policies, labour laws, and the regulatory environment, should not be overlooked. Moreover, exploring technological advancements and their impact on job automation and skill requirements is essential in understanding the future dynamics of the labour market. Chapter four consists of the research methodology utilised in the study, which employs a mixed-method approach, combining quantitative data analysis and qualitative exploration through participant interviews, to comprehensively examine the factors contributing to graduate labour immobility in the South African context. Key components include socio-economic factors, labour market integration, and the perceptions of graduates regarding their mobility within the workforce. Chapter five consists of the quantitative phase of this research, which involved the development and distribution of a structured questionnaire to a diverse sample of graduates across South Africa. The survey aimed to gather quantitative data on the extent and nature of graduate labour immobility, examining variables such as geographical immobility, occupational immobility, and skills immobility. Statistical analyses, including regression models and correlation studies, were employed to identify significant patterns and relationships within the data. Simultaneously, Chapter six consists of the qualitative phase of the study which gathered data through in-depth interviews with 21 participants, each holding a degree in South Africa. These interviews delved into the subjective experiences and perceptions of graduates, considering the nuanced aspects of labour immobility. Thematic analysis of qualitative data was undertaken to extract rich insights into the underlying factors shaping graduate labour mobility decisions. The findings from the mixed-methods approach revealed a complex interplay of antecedents influencing graduate labour immobility in South Africa. Geographical immobility was influenced by antecedents (factors) such as family ties, housing affordability, and regional economic disparities. Occupational immobility was found to be closely tied to industry-specific demands and career advancement opportunities, while skills immobility was influenced by mismatches between educational qualifications and job requirements. Socio-economic factors emerged as significant determinants of graduate labour immobility, with economic conditions, employment opportunities, and government policies playing pivotal roles. The study underscores the importance of a holistic understanding of the socio-economic landscape in shaping graduate perceptions and decisions related to labour mobility. In conclusion, this research contributes to the existing literature on graduate labour immobility by employing a mixed-methods approach that combines quantitative and qualitative analyses. The integration of diverse data sources provides a nuanced understanding of the antecedents of graduate labour immobility in South Africa, shedding light on the intricate interplay of geographical, occupational, and skills-related factors. The implications of these findings extend to policymakers, educators, and employers, offering valuable insights for the development of strategies to enhance labour market integration and mitigate barriers to graduate mobility in South Africa.

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Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
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 enseignants0,052
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,007
Études des sciences et des technologies0,0010,001
Communication savante0,0000,001
Science ouverte0,0020,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,102
Tête enseignante GPT0,321
Écart entre enseignants0,220 · 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