What (specifically) differentiates the successful and unsuccessful systems delivery projects (SDPs)
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 Against the backdrop of management, planning, temporary organizations, Shannon–Weaver theory of communication and evaluation theories, the purpose of this research paper is to examine the relative importance of specific project management tasks in the various phases of system delivery projects in distinguishing successful and unsuccessful projects. Design/methodology/approach A survey method was used ( N = 3,129) to collect data from the customers of a major systems delivery project management company operating in the facilities management industry. Logistic regression was used to analyze the capability and relative importance of the tasks in discriminating successful and unsuccessful projects. Findings The results of the paper indicate that three out four installation tasks were among the top three in their ability to differentiate the successful and unsuccessful systems delivery project. Especially critical tasks were “Meeting milestones” and “Allocation of appropriate resources” so that the project could be completed on-time. Relatively less important tasks were “Advice and suggestions regarding the development of specifications for the project” and “Proposal to meet the intent of the company’s specifications” in the proposal phase of the project, and “Resolving warranty issues as defined by the warranty process” in the commissioning phase. Originality/value Previous research has assessed the importance of the various project management phases. This research examines the capability of the more minutiae tasks to distinguish the successful and unsuccessful projects in the various phases of systems delivery projects, i.e. proposal, installation and commissioning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it