Organizational Agility as a Mediator between Project Management, Innovation, and Environmental Sustainability Practices on Project Success in the Construction Industry
Bibliographic record
Abstract
This study investigates how project management practices, innovation initiatives, and environmental sustainability practices influence project success, with a focus on the mediating role of organizational agility in the construction industry. While previous research has examined these factors individually, few studies have explored their combined impact on project outcomes within a mediated framework. A quantitative research design was adopted, and data were collected from project managers and organizational leaders using structured questionnaires. Structural equation modeling (SEM) was applied to test the hypothesized relationships and mediation effect. The results indicate that project management practices, innovation initiatives, and environmental sustainability practices positively affect project success, and organizational agility significantly mediates these relationships, enhancing their overall impact. The study contributes original insights by integrating environmental sustainability and innovation in a mediated model, emphasizing organizational agility as a critical mechanism for achieving successful project outcomes. These findings provide practical guidance for managers and policymakers to improve project performance by combining structured management practices, innovative approaches, and sustainability considerations in the construction industry.
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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 | 0.004 | 0.014 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 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".