The effect of benchmarking reasons on benchmarking success: An empirical study on public universities
Bibliographic record
Abstract
The aim of this study is to explore benchmarking reasons and their effects on benchmarking success from the perspectives of university managers in different management levels. Six reasons were examined for their role in benchmarking success. These reasons are top management support, university internal assessment, employee participation, benchmarking benefits, benchmarking competitor, and benchmarking partner. Data were gathered by a questionnaire distributed to managers from all levels in public universities. The questionnaire was developed based on related works on benchmarking. Two hundred questionnaires were distributed to the sample members and 167 questionnaires were returned with a response rate of 83.5%. The results indicated university internal assessment is the most influential reason for benchmarking success, followed by benchmarking benefits, benchmarking partner, top management support, and finally, employee participation. It was found that benchmarking competitors had no effect on benchmarking success. Therefore, universities are called for considering such reasons when heading for benchmarking. Researchers also are requested to validate such findings and to explore more reasons for benchmarking success.
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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".