Effectiveness of bonus and penalty incentive contracts in supply chain exchanges: Does national culture matter?
Bibliographic record
Abstract
In this study, we investigate the impact of national culture on the effectiveness of bonus and penalty incentive contracts in supply chain exchanges. We conducted laboratory experiments in Canada, China, and South Korea, involving transactional exchanges in which suppliers were presented with either bonus or penalty contracts. Then we compared suppliers’ contract acceptance, level of effort, and shirking across national cultures. Our findings reveal critical cultural influences on contract effectiveness. We show that although acceptance of bonus contracts is comparable across cultures, suppliers from Canada, a national culture considered low in power distance and high in humane orientation, exhibit lower acceptance rates of penalty contracts. In addition, we find evidence that suppliers associated with collectivist cultures exert more effort and shirk less in bonus contracts but these relationships also are more complex. When we compare contract effectiveness across bonus and penalty contracts within a given cultural setting, we find in all three countries greater acceptance of bonus contracts than penalty contracts. Also, after contracts are accepted, bonus contracts are more successful in China because suppliers exert greater effort and shirk less under bonus contracts than penalty contracts. However, in Canada and South Korea, the results of accepted contracts for both penalty and bonus contracts are nearly indistinguishable.
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".