Increasing productivity and sustainability of corporate performance by using management control systems and intellectual capital accounting approach
Bibliographic record
Abstract
The purpose of this article is to provide an overview of the literature covering the area of management control systems (MCS) and intellectual capital accounting approach in logistics and related these concepts to sustainability of corporate performance. Management control system (MCS) is system in companies which gathers and uses information to assess the performance of diverse company resources like human, physical, financial aspects of the companies. The application of a management control system in the field of quality management is found to be useful in explaining what changes are necessary to maintain high quality levels. The other useful method, for assessing the performance of diverse companies' resources like intangible assets is intellectual capital accounting approach. Intellectual capitals are intangible assets that create value for business units and are one of the main factors in creating competitive advantages for companies. Attention and focus on intellectual capitals in organizations and companies are one of the fundamental segments in value chain in the direction of value creation, and measurement and accurate disclosure of intellectual capital make managers and stakeholders successful in conducting the organization. The current study develops necessitates of using these methods for reach to corporate sustainability and sustainable development in companies.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".