Financial structures and their impact on project financial performance: Funding sources, and sustainability, empirical study
Bibliographic record
Abstract
This paper explores the critical influence between financial structures and project financial performance, focusing on the impact of funding sources and sustainability considerations in Saudi production projects. The financial structure of a project, encompassing the mix of debt and equity financing, significantly influences its profitability, liquidity, and long-term viability. This paper examines various funding sources available to project managers, including equity, debt. Furthermore, it underscores the importance of integrating sustainability principles into project financial structures, highlighting how neglecting environmental, social and governance (ESG) factors can negatively affect project outcomes. By understanding the implications of different financial structures and incorporating sustainability considerations, project managers can optimize resource allocation, and ensure the long-term success of their projects. The study relied on a questionnaire to collect data from a sample of administrators and accountants in Saudi production projects using a descriptive analytical approach. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results of the study indicated a positive impact of long-term loans on profitability and sustainable liquidity. It also indicated a positive impact of equity on sustainable profitability and a negative impact on sustainable liquidity in Saudi production projects.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".