An exploratory study of factors affecting undergraduate employability
Bibliographic record
Abstract
Purpose The current study was conducted to increase our understanding of factors that influence the employability of university graduates. Through the use of both qualitative and quantitative approaches, the paper explores the relative importance of 17 factors that influence new graduate employability. Design/methodology/approach An extensive review of the existing literature was used to identify 17 factors that affect new graduate employability. A two‐phase, mixed‐methods study was conducted to examine: Phase One, whether these 17 factors could be combined into five categories; and Phase Two, the relative importance that employers place on these factors. Phase One involved interviewing 30 employers, and Phase Two consisted of an empirical examination with an additional 115 employers. Findings Results from both the qualitative and quantitative phases of the current study demonstrated that 17 employability factors can be clustered into five higher‐order composite categories. In addition, findings illustrate that, when hiring new graduates, employers place the highest importance on soft‐skills and the lowest importance on academic reputation. Research limitations/implications The sectors in which employers operated were not completely representative of their geographical region. Practical implications The findings suggest that, in order to increase new graduates’ employability, university programmes and courses should focus on learning outcomes linked to the development of soft‐skills. In addition, when applying for jobs, university graduates should highlight their soft‐skills and problem‐solving skills. Originality/value This study contributes to the body of knowledge on the employability of university graduates by empirically examining the relative importance of five categories of employability factors that recruiters evaluate when selecting new graduates.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".