Using a balanced scorecard to manage corporate social responsibility
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it