Popularizing a Management Accounting Idea: The Case of the Balanced Scorecard
Bibliographic record
Abstract
Abstract We explore how the Balanced Scorecard (BSC), as a management accounting technique, was developed and marketed as a general management practice. Drawing on actor network theory (ANT), we analyze interviews with key actors associated with theBSC, insights gained from attendingBSCtraining workshops, and other documentary evidence to construct a history of theBSC. Our historical analysis offers theoretical tools to understand how the various features of the accounting technique were translated and transformed, that is, shaped and solidified. This translation entailed processes of modification, labelling, framing, and specification of abstract categories and cause‐effect relations. We also examine the networks and associations that both shape the form of theBSCand mobilize the interests of various constituencies around it to produce what can be regarded as a global management technology. Finally, we highlight the strategies and actions used to maintain control of this technique through its continuous reinvention, and, by doing so, we emphasize the idea of strategic agency.
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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 | 0.022 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.014 | 0.038 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".