The “good workplace”
Bibliographic record
Abstract
Purpose The purpose of this paper is to take a serious look at the relationship between joint consultation systems at the workplace and employee satisfaction, while at the same time accounting for the (possible) interactions with similar union and management-led high commitment strategies. Design/methodology/approach Using new, rich data on a representative sample of British workers, the authors identify workplace institutions that are positively associated with employee perceptions of work and relations with management, what in combination the authors call a measure of the “good workplace.” In particular, the authors focus on non-union employee representation at the workplace, in the form of joint consultative committees (JCCs), and the potential moderating effects of union representation and high-involvement human resource (HIHR) practices. Findings The authors’ findings suggest a re-evaluation of the role that JCCs play in the subjective well-being of workers even after controlling for unions and progressive HR policies. There is no evidence in the authors’ estimates of negative interaction effects (i.e. that unions or HIHR negatively influence the functioning of JCCs with respect to employee satisfaction) or substitution (i.e. that unions or HIHR are substitutes for JCCs when it comes to improving self-reported worker well-being). If anything, there is a significant and positive three-way moderating effect when JCCs are interacted with union representation and high-involvement management. Originality/value This is the first time – to the authors’ knowledge – that comprehensive measures of subjective employee well-being are being estimated with respect to the presence of a JCC at the workplace, while controlling for workplace institutions (e.g. union representation and human resource policies) that are themselves designed to involve and communicate with workers.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".