A dynamic capabilities view of employability
Bibliographic record
Abstract
Purpose – The increasing demand for post-secondary education, and the ongoing difficulty students’ face in securing appropriate work upon program completion, highlight the importance of an enhanced understanding of employability resources for university graduates. Just as organizations achieve a strategic advantage from resources and dynamic capabilities (DCs), university graduates can similarly apply these principles and tactics to be competitive in the job market. The purpose of this paper is to ask the question: how can new graduates enhance their competitive advantage when entering the employment market? To address this question the authors propose to adopt the DCs framework to analyze the competitive advantage of a graduate and argue that university graduates can take specific steps to enhance their own competitive advantage in the labor market. Design/methodology/approach – An extensive review of the existing human resource and strategic management literature was used to develop a conceptual DCs model of employability. The core dimensions of the conceptual model were refined using 26 one-on-one interviews with employers of new university graduates. This study concludes by recommending specific empirical and experimental research to further test the model. Findings – The results from the qualitative study identified the importance of four specific resources that university graduates should possess: intellectual, personality, meta-skill and job-specific. In addition, the authors suggest that integrated DCs are crucial for enhancing the value of these individual resources. Both pre-graduate application and the construction of personal narratives are essential signals that university graduates can mobilize individual resources in a complementary and strategic manner, in real-world settings, to maximize value. Research limitations/implications – This is an exploratory study and is designed as a foundation for future empirical and experiential research. Practical implications – The findings suggest that, in order to increase employability, university students need to assume a DCs view of competitive advantage. As a result, students need to reflect on both their intrinsic and learned resources to create a systematic competitive advantage that is valued, rare and difficult to replicate or substitute. Social implications – This paper challenges students to assume a holistic view of education by recognizing education extends far beyond a classroom. Therefore, differentiation and value creation is reflected in the synthesis and application of both intrinsic and learned resources. Originality/value – The integration of strategic management and human resource literature is a unique theoretical approach to explore the drivers of graduate employability.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".