Digital innovation strategies in the public sector
Bibliographic record
Abstract
Despite the increasing attention on digital innovations in the private sector, little is known about digital innovation strategies in the public sector. This knowledge gap is growing as public sector employees are increasingly embracing new digital tools and ideas. Drawing on rich qualitative data from practitioners in 25 cities across 18 countries, this study analyzes the digital innovation strategies pursued in the public sector, with specific attention placed on digital orientation and the foci of value creation activities. Extending the OECD's Observatory of Public Sector Innovation (OPSI) framework, we identify four distinct digital innovation strategies in the public sector: enhancement-oriented, anticipatory, adaptive, and persistent. Our findings reveal that enhancement-oriented and persistent strategies are the most prevalent, reflecting a strong focus on internal value creation through process optimization and long-term organizational change. In contrast, adaptive and anticipatory strategies are less common. We find a near-equal prevalence of incremental and transformational goals, indicating balanced strategic orientation. Our findings also suggest that practitioners often employ multiple strategies, reflecting the multifacetedness of driving digital innovation in the public sector. We provide valuable insights into various activities linked to the four identified innovation strategies, ending with a comprehensive discussion of our findings, conclusions, study limitations, and future research directions.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".