An exploratory study of project success with tools, software and methods
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the relationships between project delivery success factors, project management tools, software, and methods. Design/methodology/approach A statistical analysis was undertaken using data from a survey from a purposive sample of 150 participants across three countries (Australia, Canada and the UK). The findings were used to consider the relationships between project success factors, project management tools, software, and methods. Findings The findings reveal certain insights in the use of tools and methodologies. Of all the variables measured, the number of project management tools used and the number of risk tools used showed the highest direct correlation. It was therefore surmised that the use of tools from one of these categories is often coincident with the use of tools from the other category. Also, the use of project management tools exhibited less variability as compared to use of information communication technology support tools and risk management tools. In addition, use of formal project management methods exhibited less variability than use of formal decision‐making methods. Therefore, it is suggested that use of project management tools and methods is more consistent across the organizations studied, as compared to other tools and methods. Originality/value This paper extends the survey findings of an international 2011 study and sheds light on the use of project management and related tools and methods.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".