New approaches in project performance evaluation techniques
Bibliographic record
Abstract
Purpose The purpose of this paper is to describe a Doctor of Project Management research study including summary of the literature review, the application of the combination of case study, survey and theory building research methodologies, key research findings and potential areas for future research. Design/methodology/approach The research investigates the reasons for the limited adoption of earned value management (EVM) as a project performance evaluation technique. It proposes new extensions to this technique that will be beneficial to project management practitioners. The multifaceted research approach incorporates the following elements: a review of previous and current literature on EVM; a survey of project management practitioners on their practices and attitudes towards EVM; analysis of the known challenges of the EVM technique; development of techniques to address and resolve the EVM challenges; consolidation of those techniques into a single framework and implementation model; and validation of that framework and model through multiple methods. Findings The research confirms that EVM can be greatly enhanced and simplified though three key initiatives: include the cost assurance (i.e. risk transfer) provided by procurement contracts; measure project achievement and progress on the completion of each phase, rather than monthly; and combine the above into a simplified, single model. Originality/value This paper provides practitioners with an insight into how EVM can be enhanced and applied in project management organisations. In particular, the integrated PAVA technique should be particularly useful to projects using the rolling wave approach, as its recognition of phases provides a framework for short‐ and long‐term planning.
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.
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 | 0.087 | 0.157 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".