Sustainability Reporting and Management Control System: A Structured Literature Review
Bibliographic record
Abstract
The purpose of this paper is to contribute to the management accounting literature by reviewing how previous studies conceptualised the relationship between sustainability reporting and management control systems, analysing the main themes and discussing potential future developments of the sustainability reporting and management control systems (SRMCS) research agenda. This study builds on the structured literature review method by categorising and synthesising 15 years of research into the topic “sustainability reporting and management control”. Approximately 500 relevant articles were identified in the first round of searching Google Scholar and Scopus with the selected keywords, but after filtering and manual assessment, 45 articles were selected for the full review. Coding reliability was maintained with the K-alpha test. Our findings divulge that the researcher looks at the management control and the sustainability reporting agenda with just one eye. They either focus on management control or sustainability reporting. Very little research focuses on relationships. In addition, from the methodological point of view, we found that qualitative case studies and interviews dominate the field, together with commentary papers. We proposed a framework showing a complex and multifaceted relationship (a spider diagram) to conceptualise the synthesis of the literature. This framework is intended as a blueprint for the relationship between sustainability reporting and management control in order to design and redesign a company’s internal strategies on management control systems (MCS).
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.
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 | 0.018 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.041 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".