Bibliographic record
Abstract
Purpose Traditional performance measurement models and frameworks fail to take into account the intricacies and specificity of service businesses. The important characteristics of services, role of employees and partners, important of measures and concurrent production and delivery need to be incorporated into the framework. This paper seeks to address these issues. Design/methodology/approach The research followed a case‐based methodology using semi‐structured interviews. Literature review and case‐based methodology led to the conception of initial deployment framework. Findings Existing scorecards do not emphasize the deployment aspects of the scorecard and overlook trade‐offs and benchmarking decisions. Practical implications The scorecard provides guidance for successful deployment. The framework incorporates the importance of service innovation and role of employees and partners into the scorecard. Relative decision trade off and benchmarking are an integral part of the deployment process. Originality/value The two founding blocks of the scorecard are value maximization theory proposition and Six Sigma methodology. The service scorecard supports stakeholders that drive business performance thus ensuring accountability, innovation and collaboration. The scorecard offers a set of measures that builds upon existing measures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".