Does one project success measure fit all? An empirical investigation of Brazilian projects
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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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 | 0.033 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".