Bibliographic record
Abstract
Purpose The purpose of this study is to examine whether the performance of the compensation system is better explained by the universalist approach or the contingent approach. The paper also attempts to determine the type of fit that yields the most promising gains in terms of perception of performance. Design/methodology/approach Data were collected by questionnaire from 602 large organizations in three countries (Canada, France, and the UK), and from this, five hypotheses were formulated and tested using moderated regression analysis. Findings The study shows that having an optimal relationship among compensation policies (intra‐activity fit) leads to a more efficient compensation system than that obtained following an alignment with business strategies (vertical strategic fit) and with organizational development strategies (internal organizational fit). However, the results suggest that the universalist perspective cannot be rejected. Practical implications Human resources managers should exercise prudence regarding the pairing of compensation policies with various organizational characteristics, particularly those related to compensation management policies, because it is the interaction between compensation policies and their management methods that most influences the perception of performance. Of all these management policies, transparency of salary information seems to be central to the contingency perspective. Originality/value One of the most interesting contributions of this research is the identification of negative alignments that may result in negative performance. The joint application of two compensation policies, which, individually, have a positive influence on performance, can create a negative interaction. Contingency is therefore not always desirable, and prudence is recommended in the types of alignments introduced.
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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".