The role of artificial intelligence in project management performance: The mediating effects of competence retention and top management support
Bibliographic record
Abstract
This study examines the impact of artificial intelligence (AI) on project management performance, with a focus on the mediating roles of top management support and project management competence retention. A cross-sectional research design was employed, and data were collected from 309 employees using a convenience sampling technique. Conducted within the context of Saudi Arabia’s manufacturing sector, the research aligns with the nation’s Vision 2030 goals of economic diversification and technological advancement. Data analysis was performed using structural equation modeling (SEM) to examine the relationships between the constructs. The results reveal that AI has a significant direct impact on both top management support (β = 0.865) and competence retention (β = 0.827), while also indirectly enhancing project performance through these mediating factors (β = 0.666 and β = 0.471, respectively). Additionally, top management support (β = 0.771) and competence retention (β = 0.507) directly influence project performance. The findings highlight the critical role of AI in improving decision-making, resource allocation, and skill retention, ultimately leading to better project outcomes. The findings have significant implications for organizations and policymakers. Practically, organizations in Saudi Arabia’s manufacturing sector can leverage AI to enhance project outcomes by improving decision-making, resource allocation, and skill retention.
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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 | 0.006 | 0.029 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".