Operational Impact of Service Innovations in Multi‐Step Service Systems
Bibliographic record
Abstract
Service quality is an important attribute that is used to characterize many service systems. In this study, we examine a service system with two consecutive steps that have shared resources. The service process consists of a base service (first step in the process) followed by a second step that adds additional value. We first look at a social surplus maximizing service provider (SP) who decides the optimal service capacity and re‐optimizes in response to changes in the speed of service of the first step due to local innovations. Our main objective is to explore using simple and stylized models, the effect on the service system of local innovations in step 1 that decrease this step's service times. We find that the effect of such innovations can sometimes lead to the worsening of certain critical service quality measures when SPs are monopolists. Next, using a model of competition, we find that this effect continues to hold in settings where SPs compete for arrivals. Our results have interesting consequences for many service systems and may help explain some of the unintended effects of service innovations reported in the literature.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".