The Role of the Management Control System in Supporting ESG-Focused Transformation in Financial Intermediaries: A Case Study of an Italian Bank
Bibliographic record
Abstract
This paper investigates how environmental, social and governance (ESG) issues are declined within the management control system (MCS), focusing on an Italian bank. Deepening the role that management control tools can play in supporting banks in facing the challenges posed by the ESG-focused transformation is pivotal. This is even more important if considering that national and international supervisors are engaged in disseminating a series of initiatives to promote the adoption of practices focused on sustainability issues among intermediaries: on this topic, for example, in April 2022, the Bank of Italy issued a first document of Supervisory Expectations on Climate and Environmental Risks, containing non-binding indications regarding the integration of climate and environmental risks into governance and control systems, business model and corporate strategy, organizational system and operational processes, in the risk management system and in the market disclosure of supervised banking and financial intermediaries. Due to the explorative nature of this research, the work is based on the qualitative analysis of an in-depth case study (Eisenhardt & Graebner, 2007; Miles et al., 2014). The selected case is an Italian bank with many recognitions over time about its engagement on ESG-focused transformation. Our investigation aims to deepen the role of MC tools in promoting sustainable development and realizing economic, social, and environmental purposes. This study can be considered original for two reasons. First, it contributes to extending the literature regarding the role of MCS in supporting the implementation of sustainable strategies, with a focus on banks. Second, it unveils the benefits and critical areas of the MCS in sustainable-oriented management.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".