Incentive mechanisms for managing local subcontractors in international construction projects
Bibliographic record
Abstract
In the domain of international construction projects, managing supply chains presents distinct challenges due to the intricate task of coordinating multiple stakeholders within diverse cultural, economic, and regulatory contexts. Prior research highlights the significance of incentive mechanisms and collaborative procurement practices, yet the effectiveness of these strategies in augmenting performance for international projects has not been extensively examined. This investigation explores the effects of financial and non-financial incentives on the performance of global construction projects, with a focus on the mediating role of cooperation. Data were gathered from Chinese international contractors and local suppliers and subsequently analyzed using Structural Equation Modeling. The results reveal two separate pathways of influence. Firstly, financial incentives exhibited substantial direct and indirect impacts on project performance; secondly, while non-financial incentives did not directly affect project performance, they significantly impacted cooperation levels, which in turn mediated the relationship between non-financial incentives and project performance. This study provides essential perspectives for managing international construction projects, emphasizing the critical need for the combined application of financial and non-financial incentives to foster cooperation and ultimately achieve superior project outcomes.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".