Understanding Job Quality Within the European Economic Regimes: The Mediating Role of HR Strategies
Bibliographic record
Abstract
Job quality has gained recognition as a critical objective amidst the rise in non-standard job contracts and declining employer commitment to employee development. Despite efforts by EU countries to enhance job quality, significant cross-national disparities persist. Institutional theory posits that a country’s institutional regime influences job quality (Holman, 2013) due to differences in industrial relations and production systems shaped by national, political, and historical compromises (Davoine et al., 2008). Understanding how institutional regimes affect job quality is essential for effectively implementing initiatives in new contexts. Using Amable’s (2003) categorization of regimes—social democratic, continental, liberal, and southern European regimes— and manager responses from the Third European Company Survey (ECS; 2013), this study addresses three objectives: identifying cross-national variation in job quality among regimes, (2) examining the relationship between organizations’ HR strategies and job quality, and (3) assessing whether HR strategies mediate institutional influences on job quality differences. Results indicate that organizations in distinct regimes incorporate varying levels of commitment and development strategies into their HR practices. Contrary to expectations, firms in continental regimes utilized these strategies most, followed by liberal, social democratic, and southern European regimes. Organizations employing commitment HR strategies exhibit higher levels of compensation, employee-controlled work organization, engagement, and development opportunities. Finally, commitment HR strategies mediate the relationship between economic regime and job quality, underscoring the role of institutional structures in shaping job quality through HR strategies.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".