Does one project success measure fit all? An empirical investigation of Brazilian projects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The purpose of this research is to identify and accumulate knowledge on the existing developments on project success measures. The authors aim to contribute to this debate by providing both researchers and project management professionals with reliable contemporary project success criteria that permit generalization for a proper assessment regardless of the type and context of the project. Design/methodology/approach Data were collected from 264 Brazilian project managers from a range of industries, sectors of activities and business areas with different levels of experience. Data analysis was performed using the R software package. Findings In this research, the authors propose a general performance measure of project success where different projects can grade differently using the same scale. The data analysis validated five constructs of the developed model in the Brazilian setting. Originality/value Most of the actual project success measures used in project management literature have been tested in a specific industry or sector. Combinations of the type of project, industry, sector, project nature, stakeholders and other variables make each project unique. Thus, any effort to find a context-specific tool of measure will be an endless endeavor. To fill this gap, more general project success criteria need to be explored to offer a common point of comparison between projects. This is the motivation of the present study.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it