An expert diagnosis system for the benchmarking of SMEs' performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The purpose of this paper is to show that benchmarking allows SMEs to improve their operational performance. Design/methodology/approach The paper presents a fully implemented expert diagnostic system which evaluates on a benchmarking basis the performance of SMEs. Findings The research results with hundreds of SMEs show that benchmarking allows them to improve their operational and financial performance thus confirming the usefulness of benchmarking for SMEs, especially since traditional performance models for large enterprises do not apply well to SMEs. Research limitations/implications Based on data mining techniques, future work should allow us to significantly extend our knowledge on SMEs, and further improve our evaluation model of SME performance. Practical implications Practitioners and researchers should pay more attention to benchmarking as a valuable performance evaluation tool, not only for large businesses, but for SMEs as well. Originality/value The paper highlights the development and use of a benchmarking‐based “360‐degrees” performance evaluation system for SMEs.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it