Employees perceptions of non‐monetary recognition practice and turnover: Does recognition source alignment and contrast matter?
Bibliographic record
Abstract
Abstract Nonmonetary recognition originates from various sources (distal and proximal) and research has yet to examine the interplay among them. Results of a 2‐year time‐lagged study (N = 221), employing polynomial regression and response surface analysis, revealed that when distal organisational nonmonetary recognition is aligned with recognition from proximal sources, employees had lower turnover intentions and, indirectly, were less likely to quit 2 years later. For the most part, these relationships do not differ significantly based on the level at which alignment of distal and proximal recognition occurs. In terms of contrasts, when distal recognition exceeds the level of proximal recognition from the supervisor, turnover intentions are higher. For other proximal sources (co‐workers, physicians and patients), turnover intentions were higher irrespective of the type of contrast. This study adds to the strategic HRM literature by showing that contrasts between distal and proximal recognition undermine HR practice perception and employees' organisational attachment.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".