Balancing the scorecard through academic accounting research: opportunity lost?
Bibliographic record
Abstract
Purpose As one of the co‐authors of the first academic research papers published in a major accounting research journal on the subject of the balanced scorecard, the author was asked to reflect on his experiences with research in this area over the last 12 years since that first paper was published. The purpose of this paper is to do this through personal reflection and a literature review. Design/methodology/approach Employing personal reflection and a literature review, the author examines three issues: what motivated him to start this research program; the way the research program unfolded and its unintended consequences; and finally some reflections on the academic research enterprise as it is practised in North America that are reflected by the unintended consequences. Findings The author looks at the differences in psychology research traditions and how they shaped the research program on the balanced scorecard into an attempt to “debias” a problem rather than to bring strong human information processing theory to bear on how the scorecard dealt with some of the issues in its application. The author suggests that these different focuses explain how management accounting behavioral researchers lost an opportunity to have greater impact in the development of this performance tool. Originality/value The paper questions and documents limitations that arise from the reliance on an underlying psychology paradigm that focuses on human limitations, rather than one focused on aiding humans to perform better. It suggests that greater research contributions in management accounting could be obtained if more researchers focused on how management accounting information can be developed that takes advantage of human information processing strengths.
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 | 0.110 | 0.182 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.026 | 0.028 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".