Effective KPI development using environment-based design (EBD) methodology: A case study of airline KPI system
Bibliographic record
Abstract
Background Key Performance Indicators (KPIs) are crucial for guiding employees towards organizational goals and providing stakeholders with insights into goal achievement. Developing effective KPIs is particularly challenging for startups due to dynamic environments and limited resources, often requiring extensive domain expertise. Methods This study proposes an effective KPI development process using the Environment-Based Design (EBD) methodology. The approach systematically converts an organization's mission statement into KPIs through three steps: generating questions and answers, identifying a performance network, and extracting KPIs. It employs tools like the Recursive Objective Model (ROM) and structured question-asking techniques to aid knowledge acquisition and performance network integration. Results The methodology was validated using Flybe's case, Europe's largest regional airline. Two graduate student designers with no aviation experience developed KPIs that were comparable to those in Flybe's 2018 Annual Report, while also addressing additional operational aspects like route attractiveness. Conclusion The EBD guided approach reduces reliance on prior domain knowledge, making KPI development accessible to non-experts and enhancing repeatability for experts. Although the study is limited to the airline industry and English-speaking countries, it demonstrates the potential for broader application. Future research should explore the approach in diverse organizational contexts and cultural settings to further validate its effectiveness and adaptability.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".