A cross-national comparison of public project benefits management practices – the effectiveness of benefits management frameworks in application
Bibliographic record
Abstract
Benefits are the principal reason why an organization may seek to enact change through programmes and projects. The discipline of identification, definition, planning, tracking and realization of benefits is recognized to be instrumental in achieving organizational strategy. In this study, we describe the results of a cross-national comparison of public sector benefits management (BM) practices in Australia, Canada, the UK and the USA. It explores ‘BM practices in action’, considering to what extent ‘espoused’ or ‘mandated’ frameworks are actually practised and perceived by their users. Employing qualitative analysis, semi-structured interview data were analysed from 46 participants with experience in sponsoring, managing and/or reviewing government projects. The results expose considerable variation in the adoption and standardization of BM frameworks from inter and intragovernmental perspectives. We evidence a strong focus on benefits identification across the data set, specifically at the outset (the business case stage seeking project approval) and observe deterioration in focus as the project or programme progresses through the authorization (or assurance) approval gates towards close-out and operations. The results further emphasize the prominence of political interest, leadership buy-in, a benefits-driven culture and a transparent benefits reporting mechanism in the implementation of ‘effective’ BM frameworks.
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 | 0.018 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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 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".