What (specifically) differentiates the successful and unsuccessful systems delivery projects (SDPs)
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.
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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 | 0.009 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".