An effect‐cause‐effect analysis of project objectives and trade‐off assumptions
Bibliographic record
Abstract
Purpose This paper endeavors to critically examine the trade‐offs among project objectives and their underlying assumptions. Design/methodology/approach Effect‐cause‐effect (ECE) methodology of theory of constraints (TOC) has been applied to examine the assumptions behind successfully managing business projects. Findings The essence of discussion in this paper leads towards the realization that a possibility exists for time, cost and quality objectives to be pursued collectively in a project management environment. Research limitations/implications This paper evaluates to what extent trade‐offs among project objectives actually exist and explores the possibility of their co‐existence in a project management environment. This realization can significantly impact the project trade‐off models in existing literature. Originality/value Time, cost and quality have been recognized to be important objectives to successfully complete a project and several studies have acknowledged the necessity to address their trade‐offs. However, most of these studies have taken the trade‐offs for granted without critically examining the assumptions behind such trade‐offs. The present paper fills that gap by applying ECE approach of TOC to examine project management trade‐off assumptions. There‐in lies the value of the current paper.
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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 | 0.071 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".