An exploratory study of project success with tools, software and methods
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 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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 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