Balanced scorecard design preferences according to subjects' knowledge and expertise
Bibliographic record
Abstract
This study is motivated by the need to further explore judgmental bias within the balanced scorecard (BSC). The focus is not only within its use as a performance measurement tool with bonus and incentive implications (Lipe and Salterio, 2000) but more importantly with its design stages which are little explored yet. This study evaluates if differences in the frequency of use of measures in each perspective of the BSC are affected by any of the following three elements: 1) the subjects' accounting knowledge (Dilla and Steinbart, 2005); 2) the subjects' professional expertise (Dilla and Steinbart, 2005); 3) the BSC purpose of use (Malina and Selto, 2001). This study asks four groups of subject to design a BSC and although the results reported do not belong to a pure laboratory experiment, the systematic analysis of its results provides a clear positive answer to Dilla and Steinbart (2005) alternative explanations. The results show that accounting trained subjects and professionals exhibit a different understanding and design of the BSC, however, there are no significant differences in the measures used at the design phase when the BSC is designed for compensation or strategy implementation purposes.
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How this classification was reachedexpand
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 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".