People, service and trust: is there a public sector service value chain?
Bibliographic record
Abstract
This article reviews the evidence for the existence of a ‘public sector service value chain’, offering a new way of thinking about what Bouckaert and his colleagues have called the micro-performance approach to improving trust and confidence in public institutions (Bouckaert et al., 2002). In particular, the article focuses on the role of service delivery in enhancing citizen trust and confidence. But it does so in the context of a broader model, one that links service delivery to other important aspects of management performance, especially people management. The article refers to this model as the ‘public sector service value chain’, drawing on work by Heskett and others in the private sector (Heskett et al., 1994, 1997). The article reviews evidence for links between employee engagement (satisfaction and commitment) and client satisfaction in the public sector, and between public sector client satisfaction and citizen trust and confidence. The article identifies the five main ‘drivers’ of service satisfaction in the public sector, and reviews both purported ‘drivers’ of employee engagement as well as data documenting the influence service delivery appears to have on citizens’ trust and confidence in Canada. The article outlines a forward research agenda, to identify the drivers of staff satisfaction and commitment, as well as drivers of trust and confidence in public institutions, and to determine whether the proposed links in the ‘public sector service value chain’ can be empirically validated.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".