Performance‐based rewards and innovative behaviors
Bibliographic record
Abstract
This study investigates the effects of two internal factors, performance‐based rewards and employee perceptions of human resource (HR) strength, and one external factor, country‐level uncertainty avoidance, on employee innovative behaviors. Drawing on situational strength theory, we first hypothesize performance‐based rewards will positively relate to innovative behaviors, and second, this relationship is stronger when employees understand the wider Human Resource Management (HRM) system as intended by management, referred to as HR strength. Finally, we assess the effect of uncertainty avoidance on the relationship between performance‐based rewards and innovative behaviors. Three‐level data from 1,598 employees and 186 managers in 29 organizations across 10 countries showed both employee perceptions of HR strength and uncertainty avoidance of a country that differentially influence the relationship between performance‐based rewards and innovative behaviors. However, a significant relationship between performance‐based rewards and innovative behaviors was not found. This study offers novel insights into how organizations can use internal factors in a systematic manner to promote innovative behaviors in their workplace, and highlights the limitations of sustaining innovative behaviors in countries characterized by high levels of uncertainty avoidance.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".