Validation issues of a performance management system for design: three case studies
Bibliographic record
Abstract
Purpose This study aimed to discuss issues related to the process for validating a performance management system for design (PMSD) in three product development companies. Design/methodology/approach The use of multifunctional groups becomes important because it favors viewing the organization as a whole, thereby reducing existing gaps between segments of the company. To support this study, focus group research was used. Findings Viewing design as a resource that contributes to increased competitiveness offers companies benefits, such as improved performance measurement. This measurement is based on indicators and, to be useful, an indicator system should stimulate the company's interest. In addition, the present study made it possible to conclude that the validation process is essential in preimplementation stages because validation allows the PMSD to be adapted to bring it closer to the reality of companies, thus increasing the chances of success during the implementation stage. Originality/value Validation of the metrics from the perspective of senior management enabled critical analyses of the applicability of the PMSD, as well as its suitability and approximation to the reality of businesses, by selecting the most relevant data.
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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 | 0.141 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".