Big data adoption in public infrastructure projects - contrasting perceptions to conceptualise organisational tensions
Bibliographic record
Abstract
Research on projects has highlighted big data as a tool to better understand project characteristics and its dynamics. To maximise these benefits, organisations collaborate to create common big data repositories. However, public infrastructure projects do not seem to have adopted this technological innovation. Based on a pluralist perception of organisational effectiveness, this paper aims to explain how project actors' differing perceptions affect big data adoption. We identify and classify the perceived benefits and challenges related to adopting big data. Findings highlight that perceptions vary as a function of the organisation's position in public projects, but also of its organisational values. This research conceptualises perceptions of big data adoption, identifying three specific organisational tensions - learning, performing, and organising - all of which underlie an overarching belonging tension. This paper underscores the need for collaborative management of these tensions to fully leverage big data's potential, improve decision-making, and enhance project management practices.
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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 | 0.028 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.001 | 0.003 |
| 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".