From interactive control to IT project performance: examining the mediating role of stakeholder analysis effectiveness
Bibliographic record
Abstract
Purpose This paper aims to unpack the relationship between the interactive use of project control systems (PCS) and project performance by examining the role of stakeholder analysis effectiveness in enacting this relationship. A conceptual framework was developed based on the stakeholder theory and the levers of control framework. Design/methodology/approach Partial least square-structural equation modelling analysis was conducted on the cross-sectional questionnaire data collected from 109 information technology (IT) projects. Findings The interactive use of PCS enables project managers to effectively deal with the stakeholders-related uncertainty, and stakeholder analysis effectiveness partially mediates the positive relationship between the interactive use of PCS and IT project performance. Originality/value This study extends the project control literature by explaining the positive relationship between the interactive use of PCS and project performance. The findings contribute to the stakeholder analysis literature by operationalizing the stakeholder analysis effectiveness construct and identifying it as a new mediator between the interactive use of PCS and project performance.
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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 | 0.020 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".