Using a balanced scorecard to manage corporate social responsibility
Bibliographic record
Abstract
Abstract This conceptual paper aims to tie together the insights from the body of research on corporate social responsibility (CSR) and management accounting and control systems to investigate a model in which performance measurement systems (PMSs) can play a role in translating socially responsible initiatives into enhanced performance. The underlying assumption of the “fit‐as‐mediation” approach signifies that company practices can play a role in the determination of the structure and implementation of particular managerial processes, and this, in turn, may support information processing and lead to desirable results within organizations. Synthesizing theory from performance measurement and CSR, the paper's analysis and discussions elucidate how the implementation of an overarching PMS, that is, sustainability balanced scorecard (BSC), could translate the knowledge‐related factor, that is, CSR, into enhanced performance. The proposed model may inspire a new research agenda to show how socially responsible or sustainability initiatives are managed and measured in organizations and how they are properly aligned with specific managerial processes to deliver real value. Although the importance of CSR and its wide implications has long been appreciated in the literature, there still remains a paucity of information concerning the importance of particular managerial processes, for example, PMS, whereby organizations can translate their CSR into enhanced performance. This paper, therefore, seeks to bridge this gap by proposing a conceptual model in which an integrated PMS, that is, sustainability BSC, comes to play a role in the association between CSR and corporate performance.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".