A project manager's level of satisfaction in construction logistics
Bibliographic record
Abstract
Customer satisfaction and continuous improvement are the fundamental goals of construction logistics. While much research has been focusing on exploring the relationship between the contractors and the ultimate customers, known as the owner, to improve the understanding of the significance of customer satisfaction, the need to examine the relationship between material suppliers and contractors is highly in demand. The purpose of this study is to extend the framework for construction material logistics in customer satisfaction from owner to project manager level. This paper examines how construction logistics affect a project manager's level of satisfaction. A survey established the general importance that a project manager must place on the construction logistics. Accordingly, the most significantly correlated factors in customer satisfaction were obtained from a project manager's point of view. Two hundred twenty-three experienced project managers provided valuable data to the study. Five important factors related to satisfaction were found through interviews with project managers and a literature review. These included personnel, material flow, schedule adherence, contractor's organization, and information flow. The study results suggest that material flow and information flow are worthy of the most attention. Satisfying the above factors will greatly improve the construction logistics that will, as a result, immensely increase the project manager's level of satisfaction.Key words: construction logistics, customer satisfaction, project manager, survey.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".