Managing urban infrastructure assets in the digital era: challenges of municipal digital transformation
Bibliographic record
Abstract
Purpose The purpose of this study is to frame digital transformation (DT) within municipalities to improve the life cycles of urban infrastructure. Design/methodology/approach The study provides the results from a systematic review of the literature on concepts of DT and its implications for municipalities, barriers and challenges to DT, as well existing DT frameworks for municipalities and their built assets. This literature review leads to the development of a DT framework to help cities conduct a planned and federated DT beforehand. Then, workshops are conducted with two major Canadian municipalities. Findings The results of these studies point to the need for a dedicated DT framework for municipalities because of their particular context and their role and proximity to citizens. The theoretical framework develops 22 elements, which are divided among 6 categories. Through its application, the framework helps to identify and target the predominant issues hindering the DT of municipalities, specifically “legacy practices” and “data management.” Research limitations/implications Limitations include limited experimental conditions and small sample size. Further work is needed to validate the framework. Other approaches are advocated to complement the data collection and analysis to generate more convincing results. Practical implications The theoretical framework was validated through two case studies on two large Canadian municipalities. Social implications Municipalities maximize the value they provide to citizens and to be at the forefront of resilience and sustainability concerns. The use of technology, digital processes and initiatives helps cities to improve planning, optimize works and provide better services to citizens. Originality/value The framework is original in that it specifically aligns assets management with DT in a municipal context.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.012 | 0.020 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".