Development of Integrated Decision Support Framework for Payment Management and Negotiation in Construction Projects
Bibliographic record
Abstract
Effective cash flow management has become of increasing importance for the success of projects and involved participants given the wide payment-related problems combined with the relatively high financial risks encountered in the construction industry worldwide. The power asymmetry in the conventional top-down payment decision-making process is a key contributor to an imbalanced cash flow management culture with adverse impacts that cascade down the construction chain. Unfortunately, previous studies mostly addressed cash flow and payments from a single participant perspective and did not fully extend the developed approaches to model and support a joint decision-making process, engaging the different participants affecting financial operations. Other jurisdictional efforts include the enactment of prompt payment legislation for the regulation of payment practices which also require investigations of their efficacy and micro/macro impacts. In light of this, this research proposes an adaptive decision support framework for evaluating, managing, and negotiating payments in construction projects while incorporating the individual and collective financial roles of key participants (owner, contractor, and subcontractors). The framework is comprised of three modules. The first module for data acquisition represents the input data for the project under study. The second module focuses on the simulation, analysis, and negotiation of payment and financial arrangements by integrating agent-based simulation, data envelopment analysis, and game theory, respectively. The third module finally sets the basis for informed joint decision-making, providing recommendations for improved performance. The framework modules support a multi-level study of the project performance while capturing the driving force of participants in negotiating mutually rewarding payment arrangements. Furthermore, the research establishes two formulations for the joint decision-making process under single and multiple contractors along with the owner and respective subcontractors. The framework implementation is demonstrated using an illustrative case study project to assess varying conditions of payment cycles, status, advance payments, and interest rates. It is also tested using a real-life complex project to assess its applicability and benefits. Finally, this research can be utilized as a valuable resource for industry practitioners and jurisdictions to collaborate towards enhanced contractual arrangements that alleviate the financial risks encountered in the construction industry, serving the economy at large.
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.
How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".