Integrated Reporting and Financial Performance: Empirical Evidences from Bahraini Listed Insurance Companies
Bibliographic record
Abstract
In Middle Eastern countries, Integrated Reporting concept is gaining momentum and companies are adopting it in non-standardize way however, it is not mandatory by law. The current study is aimed at exploring among five listed insurance companies in Bahrain and its effects on their financial performance (Return on Assets assumed). Content, descriptive and linear regression analyses were employed to analyze the collected data over a period of four years from 2012 to 2015. The research findings suggested that there is a wide variation of companies’ compliance with , and the use of non-uniform disclosure formats. The content elements whose level of disclosures appeared to improve include the external environment and organizational overview, governance, and outlook, while there is a decreasing level of disclosures that are witnessed for risk and opportunities. The business model, strategy and resource allocation have a positive and significant relationship with Return on Assets (ROA), while risk and opportunities and performance elements negatively, but significantly related to ROA. This research will help the policy makers, regulators, investors, companies, researchers and analysts to understand the importance of . Further, it provides the broad understanding and application of to the researchers, academicians and students communities.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".