Digital transformation in municipalities for the planning, delivery, use and management of infrastructure assets: Strategic and organizational framework
Bibliographic record
Abstract
As the acceleration of technological development in the built asset industry brings on waves of digital transformation (DT), traditional ways of doing and organizing are being disrupted, especially on the part of large public owners such as municipalities. For these owners, these waves of transformation require constant adaptation as they compete with existing initiatives and embedded legacy practices. This paper presents the results of the second part of a longitudinal research project aimed at framing digital transformation within municipalities to improve urban infrastructure lifecycles. More specifically, the paper presents the results of work undertaken to operationalize, extend and further validate the digital transformation framework that has been developed in part 1 and which is presented elsewhere. The theoretical framework acts as a guide and analysis tool for the digital transformation of municipalities and aims to help them reduce and/or eliminate the barriers and challenges in this digital transformation. To do so, the results from a survey conducted within 44 municipalities and interviews conducted with 13 municipalities of different sizes are presented and discussed through the theoretical framework. The results show that data and information management remain the key issues, especially in a siloed organizational context such as those found within municipalities. Moreover, a significant amount of organizations remain unaware of how to approach digital transformation which in turn leads to disinterest or disengagement in digital transformation, which results in localized or fragmented initiatives. This in turn can cause delays in implementing transformational initiatives and contributes to maintaining a low level of digital maturity. The study also highlights the critical lack of human resources, expertise and appropriate training to support digital transformation.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".