Facilitating a team culture: a collaborative balanced scorecard as an open reporting system
Bibliographic record
Abstract
The balanced scorecard (BSC) allows firms to place importance on both financial and nonfinancial performance measures in four perspectives for developing and implementing corporate strategy and performance evaluation. The BSC literature however provides minimal insight on how to set targets, how to weigh measures when evaluating managers and the firm, and how to resolve conflicts that arise in the BSC process. Researchers have attempted to fill these gaps using two contending approaches. In particular, Datar et al. (2001) uses an agency model to select the optimal set of weights and more recently Herath et al. (2009) develop a mathematical programming–based collaborative decision model to find the optimal (or approximately optimal) set of target and weights considering inputs from two parties. In this article, we apply the Herath et al. (2009) model to a detailed BSC example. We demonstrate how the collaborative BSC model can be implemented in Microsoft Excel by practitioners to minimize BSC conflicts. Finally, we discuss how the model facilitates alignment and a culture of open reporting (information sharing) around the BSC that is necessary for its effective implementation.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 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".