The Balanced Scorecard: The Effects of Assurance and Process Accountability on Managerial Judgment
Bibliographic record
Abstract
The balanced scorecard is one of the major developments in management accounting in the past decade (Ittner and Larcker 2001). Lipe and Salterio (2000) find that managers ignore one of the key scorecard features, the inclusion of measures that are unique to the strategic objectives of a business unit, when making performance evaluation judgments. This study identifies and tests two approaches to reducing this “common measures bias.” We examine whether increasing effort via invoking process accountability (i.e., requiring managers to justify to their superior their performance evaluations) and/or improving the perceived quality of the balanced scorecard measures (i.e., via an independent third-party assurance report on the balanced scorecard) increases managers' usage of unique performance measures in their evaluations. Results suggest that either the requirement to justify an evaluation to a superior or the provision of an assurance report on the balanced scorecard increases the use of unique measures in managerial performance evaluation judgments. Implications for theory and practice are discussed.
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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 | 0.048 | 0.248 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".