A dimensional analysis of stakeholder assessment of project outcomes
Bibliographic record
Abstract
Driven by an interest in developing a deeper understanding of stakeholder interests, this study undertakes a dimensional analysis of how different stakeholders assess project outcomes. Most importantly, in our analysis, we take into consideration the largely unaccounted-for conceptual difference between project success and project failure. Data were collected over a 2-year period (between 2013 and 2015) from 1631 project stakeholders in nine countries. We analyzed the survey data using three-way Multidimensional Scaling. We found that most project stakeholders tend to be more specific in their assessment of project success than when assessing project failure. We also found that most stakeholders attached maximal and different levels of importance to different dimensions of project outcomes. In particular, we found that when assessing project ‘success’, project stakeholders appear more focused on project effectiveness. On the other hand, when assessing project ‘failure’, project stakeholders appear more focused on efficiency. Understanding how stakeholders assess and prioritize project outcomes is of particular interest to project managers as it enables them develop a clearer understanding of individual interests of various stakeholders. For stakeholders themselves, such an understanding helps limit possible disruptions to the project emanating from contesting decisions made by the project manager.
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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 | 0.016 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".