Future of work – apprehensions and excitement of management graduates
Bibliographic record
Abstract
Purpose The study explores the perceptions of graduates on their employability and future of work, in light of the challenges thrown upon by new vicissitudes in the work environment. The study intends to assess the preparedness of management graduates in facing the challenges and uncertainties of the actual job market. Design/methodology/approach Semi-structured and informal interviews with 65 management graduates from UK, Canada, Italy and India. The thematic analysis uncovered the concerns and areas to develop for management graduates regarding their future employability perceptions. Findings The authors benefited from a unique opportunity to capture the views and experience of graduates who are undergoing management education during this ambiguous period. Three major themes were developed on how to develop oneself for an ambiguous future of work which includes Psychological strengths, Skill enhancement and Future mindset. The study also unearthed the major focus areas for better employability from a graduate perspective. Practical implications Practical contribution comes from strategies to prepare university graduates for sustainable careers. Study hints at the importance of work experience, adaptability and skill enhancement in meeting the challenges of the future. Originality/value From a global approach this is one among the first studies to look into the graduate perspective of their preparedness for future work.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".