Incentive mechanisms for managing local subcontractors in international construction projects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it