Forging a Stronger Academic-Practitioner Partnership–The Case of Net Promoter Score (NPS)
Bibliographic record
Abstract
This research examines the issues that impede the development of robust academic analysis of popular managerial practices and metrics using the case of the Net Promoter Score/System (NPS). The issues identified include limited academic access to the original methodology/data underlying a managerial practice, different vocabularies used by academics and practitioners, the inability of academics to disprove unfalsifiable claims, and the lack of any precise specification of theory. Finally, academics’ failure to pay detailed attention to dependent variables leads to confused messages being sent to managers. We offer recommendations to overcome these issues.
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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 | 0.123 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.027 |
| Scholarly communication | 0.019 | 0.022 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".