Exploring temporal dimensions of benefits realisation management in agile IT environments
Bibliographic record
Abstract
This study explored the temporal dimensions of benefits realisation management (BRM) in agile IT project management environments. BRM, focused on aligning strategy with project execution, is inherently temporal, requiring the consideration of past, present, and future outcomes, as well as both short- and long-term benefits. This research explored BRM in agile IT project management through a temporal lens. Adopting a 'time as process' lens, our interest was in how actors collectively negotiate, enact, and interconnect the present, past, and future. Through qualitative interviews and a focus group, we examined how agile methods, specifically Scrum and SAFe, interact with BRM processes across different time perspectives. The findings identify challenges such as (1) limited availability of past project data, (2) neglect of long-term benefits, and (3) lack of harmonisation between past, present, and future considerations in benefits realisation. The paper contributes to project management literature by emphasising the importance of temporal leadership in navigating these challenges and improving the harmonisation of past, present, and future actions in BRM.
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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 | 0.019 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".