Effects of Contractual Governance on IT Project Performance under the Mediating Role of Project Management Risk: An Emerging Market Context
Bibliographic record
Abstract
In this study, we explore the impact of contractual governance (CG) on project performance (PP) under the mediation of project management risk (PMR). Contractual governance influences favorably IT projects performance in an emerging market context where the IT sector is growing. The principal-agent theory is used to build a research model that schedules project governance and IT project risk management. Data were collected from 295 IT professionals and the response rate was 73.75%. Smart PLS was employed to test proposed relationships. The findings postulate a strong causal relationship between the CG, PP and PMR. Fundamental elements (FE), change elements (CE), and governance elements (GE) have a significant positive relationship with project management risk (PMR), and PMR positively affects PP. Additionally, PMR mediates the relationship of FE, CE and GE with PP. Overall, the results of the study provide pragmatic visions for IT industry practitioners and experts, but the unscheduled risk to the IT industry may bring enormous harm. Consequently, effective and well-structured governance in a strategic way tends to improve the project performance by monitoring and managing both project risk and quality. In addition, the study empirically supports the significant impacts of project governance dimensions i.e., fundamental elements, change elements and governance elements on project management risk and project performance. It also guides researchers and adds value to the project performance-related literature by filling the gap.
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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 | 0.009 | 0.024 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".