Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the use of the Balanced Scorecard (BSC) in universities. Initially directed toward profit‐oriented businesses, the BSC has since been adopted by many non‐profit organisations with seemingly diverse objectives. A number of primarily publicly‐funded universities and institutions, which are part of these universities, also embarked upon this strategic management approach. They soon discovered that the classical BSC approach and, for that matter, a modified approach suited for non‐profit organisations had to be further modified to suit their unique circumstances. As universities struggle to adapt the BSC approach to fit their needs, questions have been raised whether BSC is an appropriate strategic management tool for universities. Design/methodology/approach Through a critical review of the discourse surrounding this issue and BSC of 30 universities around the globe, this article examines the use of BSC in universities. Findings It was found that concerns regarding suitability of this approach for universities are not only serious but most universities, by nature or circumstances, are ill positioned to mobilise substantial resources, lay the necessary groundwork and develop systems in order to benefit from this initiative. It was found that universities, which did adopt BSC, have diverse expectations, understanding and implementation strategies. Lack of understanding, an unclear success rate, slower new adoptions and subsequent abandonment of this approach by some of the universities suggest that the BSC approach may have failed to meet expectations. Originality/value To date little has been written on the use and suitability of the balanced scorecard in universities.
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 | 0.073 | 0.174 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 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".