The role of sustainability control systems in translating CSR into performance in Iran
Bibliographic record
Abstract
Purpose This study aims to explore the extent to which companies rely on sustainability management control systems (SMCS) to translate corporate social responsibility (CSR) into superior performance building upon the premise of the natural resource orchestration perspective. Design/methodology/approach Data were collected based on a survey data set from 118 Chief Financial Officers of publicly listed companies in Iran. The theoretical model was tested using partial least squares structural equation modeling (PLS-SEM, SmartPLS 3.0) as a method that enjoys minimum demands concerning normality assumptions and sample size. Findings The findings support the full mediation effect of SMCS on the relationship between CSR and organizational performance. This implies that CSR affects performance only through the mediating role of SMCS. Practical implications The central premise in the proposed theoretical framework is that the utilization of proper management control mechanisms (i.e. SMCS) can help the organization to better synchronize, measure and manage – i.e. “orchestrate” – the social, environmental and economic impacts, and this, in turn, leads to improved organizational performance. Originality/value To the best of the authors’ knowledge, this is the first study of its kind, building on a unique synthesis of the agency cost perspective and resource orchestration theory, to introduce the “natural resource orchestration” approach for examining the intervening role of SMCS between CSR and organizational 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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".