Misaligned expectations in public sector innovation: differences between citizens and public servants
Bibliographic record
Abstract
Public sector innovations often fail if they do not meet citizens’ expectations. However, little is known about how well public servants understand these expectations. This study identifies a perception gap between citizens and public servants regarding innovation characteristics, which are specific attributes of public sector innovations that shape citizen support and legitimacy. Using Q-methodology with Swiss citizens and public servants, we identify four distinct citizen groups: result-centric, trust-centric, certainty-centric, and cost- and rule-of-law-centric. Each group emphasizes different characteristics, such as ease of use, efficiency, trialability, and trust. By contrast, public servants perceive only three homogenized citizen groups – customer-centric, trust-centric, and result-centric – overlooking expectations related to democratic participation and co-creation. This mismatch risks undermining the legitimacy and adoption of innovations. The study advances a citizen-centred view of innovation characteristics, highlights the importance of citizen heterogeneity, and provides practical guidance on designing innovations that align with diverse citizen expectations.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".