Evaluating the Interrelationships Among Key Stakeholders’ Performance in Achieving Project Success
Bibliographic record
Abstract
Poor performance of key stakeholders contributes to project problems such as delays, cost overruns, inferior quality, work accidents, etc.Many studies have evaluated the correlation between stakeholders and project success; however, no research has been conducted to evaluate the interrelationships among their performances, i.e how one stakeholder's performance affects the other(s) and ultimately determines the project's success.This study aims to fill this gap.A conceptual model was developed using the technique of Structural Equation Modeling (SEM) and then was tested upon empirical data from a field survey of 273 experienced practitioners on construction projects in Indonesia representing the owners (27%), the designers (15%), the supervisors (17%), and the contractors (41%).The results show that all key stakeholders have important roles in the project's success, although their contributions vary.The owner has a positive effect on all other stakeholders' performance, the designer only has a significant influence on the supervisor's performance, the supervision consultant only has a significant effect on the contractor's performance and the contractor has the greatest direct influence on the success of the project.This finding enriches the literature on stakeholder management of construction projects, especially in developing countries which is currently still sparse.
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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".