Collective Bargaining and Perceived Fairness: Validating the Conceptual Structure
Bibliographic record
Abstract
The aim of this study is to conceptualize the “perceived fairness in the context of collective bargaining” and empirically validate its internal structure. This concept refers to employees’ justice perceptions formed during the collective bargaining process (the process of determining the employees’ working conditions when they are unionized). Drawing on the organizational justice literature, we derive a conceptualization and formulate hypotheses regarding the convergent, discriminant and predictive validity of the concept’s dimensionality. The study was conducted among faculty at a Canadian university, where the collective bargaining process took nearly two years to complete. Using confirmatory factor analyses and hierarchical regressions, we find support for discriminant, convergent, and predictive validity. The results show that the new conceptualization includes eight distinct dimensions, combining the two sources of (in)justice (employer and union) and the four types of justice perceptions: procedural, distributive, relational (interpersonal) and informational justice. Indeed, employees clearly distinguish eight justice dimensions, which have a differential effect on their attitudes: trust in the employer and satisfaction with the union. Moreover, collective bargaining is an allocation process which encourages employees to participate actively. Because such participation might entails costs (energy, time, loss of money), employees are likely to form their justice perceptions based on not only elements from the structural model (Leventhal, 1980) and the process control model (Thibaut and Walker, 1975), but also two new justice elements: the usefulness of actions (the probability that actions force the employer to improve their offer to the expected level) and the profitability of actions (cost-benefits ratio). Opening the black box of collective bargaining through the concept of fairness is the first step in order to understanding the attitudinal and behavioural consequences of collective bargaining after employees have returned to work.
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 | 0.036 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".