A dynamic capability view of career adaptation: an exploratory study
Bibliographic record
Abstract
Purpose Disruptive forces, such as the global pandemic and technological innovation, are leading to growing labor uncertainty. For organizations, being able to adapt is a key skill for employees, while adapting to different employment contexts is increasingly essential for career success. This study leverages career adaptability theory and integrated dynamic capabilities to isolate skills enabling career adaptation. Design/methodology/approach A qualitative study was conducted to develop a skills codebook using a Delphi technique to converge on career adaptation skills, which was validated against leading meta-skills frameworks and a purposeful sample of 15 occupational competency models. Findings The codebook phase identified 24 distinct meta-skills in 6 clusters: problem-solving, self-reliance, collaboration, communication, core literacies and core workplace skills. Findings confirmed that most of the skills identified by the experts were also present across the meta-skills frameworks. Research limitations/implications This study highlights research opportunities, including a recommendation to extend the codebook by conducting a large sample empirical study of occupational competency models. Practical implications Adaptive individuals remain attractive in the job market. With the proposed framework, individuals can systematically reflect on ways to develop career adaptation skills. Other stakeholders should support the development of skills that facilitate an individual's capacity to adapt to diverse employment contexts. Originality/value This study contributes to resolving the debate on skills contributing to career adaptation by combining the career adaptability theory and integrated dynamic capabilities, to produce a harmonized meta-skills codebook including labels, definitions and synonyms. This study validates the codebook against leading skills frameworks and occupational competency models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".