Negotiating Individual Employment Relations, Evidence from four Dutch Organizations
Bibliographic record
Abstract
Our purpose is to assess the actual experiences of companies in the context of individualised employment relationships. We have three questions: (1) what issues of the employment relationship can be individually negotiated in organizations? (scope); (2) what issues of the employment relationship are individually negotiated in organizations? (actual use); (3) what are the advantages and disadvantages of negotiations according to employees and managers?; We conducted four case studies in Dutch companies in different sectors (telecom, insurance, manufacturing and consultancy). The data were collected in a total of 69 semi-structured interviews with line managers, HR managers and shop floor employees. We focused on five topics of the employment relation: contract, working hours, wages, development and performance. We found that the scope for negotiation differs according to topic: there is considerable scope with regard to working hours, development and contract and little scope with regard to wages and performance goals. However, employees and supervisors use the scope for negotiating only for working hours and to a lesser extent development. On other topics negotiations hardly take place (e.g. contract) or only under specific conditions (e.g. performance goals in non-routine processes). Furthermore, we found that employees and managers perceive both advantages and disadvantages of negotiations. Considering the (dis) advantages our conclusion is that there must be an optimum in the scope and use of negotiating the employment relationship in order to serve conditions as fairness, fit, cost effectiveness and extra-role behaviour.Our paper provides empirical data on how individualised employment relations take place in practice. It offers insight in different companies on the scope for, the actual use of and the effects of individual negotiations on different aspects of the employment relationship.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".