Integrating service design principles and information technology to improve delivery and productivity in public sector operations: The case of the South Carolina DMV
Bibliographic record
Abstract
Abstract One relatively unanswered question regarding operational efficiency and effectiveness is whether and how public sector or government operations can employ service strategy and design concepts to deal with the conflicting objectives of minimizing expenditures while providing for an increasing number of “causes” [Haywood‐Farmer, J., Nollet, J., 1991. Service Plus: Effective Service Management, G. Morin Publisher, Quebec]. In this paper, we argue that the mechanism that permits or enables simultaneous success on these dimensions in public sector operations is information technology applied in conjunction with a unified set of service operations concepts. To demonstrate this contention, we employ an adaptation of the Goldstein et al. [Goldstein, S.M., Johnston, R., Duffy, J., Rao, J., 2002. The service concept: the missing link in service design research? Journal of Operations Management 20 (2), 121–134] service planning design framework, taking issue with some interpretative aspects of their strategic model. The modified planning framework was applied to an initiative in South Carolina state government to improve operations and technology deployment at the Department of Motor Vehicles (DMV). The detailed and ongoing case study illustrates the utility of a broad service‐based, IT‐enabled approach to designing a government service, while simultaneously demonstrating that operational service alignment is the key to avoiding results that have long been labeled a dilemma in the public sector.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".