Building absorptive capacity in a mega-project program alliance: Learning to mitigate rework
Bibliographic record
Abstract
Collaborative procurement forms such as program alliancing can create a burgeoning environment for absorptive capacity to materialize, enabling learning and rework to be mitigated. However, little is known about the learning routines and practices enabling program alliances to tackle their rework effectively. As a result, this has stymied best practices that can be used to reduce rework from being made available to other construction organizations. This paper fills this void by addressing the following research question: How does a program alliance develop its absorptive capacity to learn and mitigate its rework? We use an illustrative case study approach to draw on the practices of a transport mega-project (>AU19 billion) delivered using a series of program alliances to address our research question. We reveal how one of its program alliances utilized its absorptive capacity to assimilate and apply new knowledge to manage errors and mitigate rework. Additionally, we unearth the presence of desorptive capacity, as the alliance exploited its error knowledge and transferred it to others as part of an incentivization scheme manufactured by the client authority to stimulate learning and continuous improvement within the project. The knowledge gleaned from the program alliance case examined in this paper provides an opportunity for organizations to learn how to deal with errors and rework, which has been absent in the literature.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".