When is a balanced scorecard a balanced scorecard?
Bibliographic record
Abstract
Purpose The Balanced Scorecard (BSC) is widely applied as a performance measurement and strategy implementation tool by organizations. Research has revealed that the term “balanced scorecard” may be understood differently by managers both within as well as across organizations implying that the performance measurement systems implemented in organizations may not be similar to the construct envisioned by Kaplan and Norton. Using Kaplan and Norton's Balanced Scorecard construct as a basis, the paper aims to develop and test a five‐level taxonomy to classify firms' performance measurement systems. Design/methodology/approach A Balanced Scorecard taxonomy is validated using a large sample of professional accountants working in Canadian organizations. Findings The five‐level taxonomy is used to categorize the performance measurement systems of 149 organizations. It is found that 111 organizations' (74.5 percent) performance measurement systems met the criteria to be classified as a Basic Level 1 BSC, while 61 (40.9 percent) organizations have structurally complete Level 3 BSCs, and 36 (24.2 percent) organizations have fully developed Level 5 BSCs. The paper also discusses differences between Level 1 and Level 5 BSC organizations. Research limitations/implications While many researchers assume that organizations' performance measurement systems are similar in implementation level and use, the paper demonstrates that organizations are at different levels of BSC implementation and use, a factor that should be taken into consideration when designing empirical studies to test the efficacy of Kaplan and Norton's BSC. Practical implications The five‐level BSC taxonomy scheme provides managers working with Kaplan and Norton's BSC with a tool to plan their implementation steps and then benchmark their progress towards implementing a fully developed Level 5 BSC. Originality/value In developing and empirically validating a BSC taxonomy, the paper builds on and extends previous research on BSC implementation and its potential implications.
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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 | 0.039 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.019 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".