Relationship Between Bank-Specific Characteristics and Web-Based Disclosures of the Commercial Banks in India
Bibliographic record
Abstract
This article examines the extent of web utilization as a tool for the disclosure of corporate information on the website and exhibits the association between bank-specific attributes such as size, age, profitability, market discipline, listing status, leverage, foreign ownership, and type of sector in relation to the web disclosures of 87 public, private, and foreign sector Indian commercial banks. To achieve the objective, a checklist index of 143 items of information was developed. To examine the hypotheses of the study, a panel regression model was estimated on the data of 87 Indian commercial banks. Panel regression results indicate that size, market discipline (CAR), profitability, listing status, type of ownership, and type of sector have a significant relationship with the level of web disclosure, and banks are more likely to use the websites to disclose information. On the contrary, age, leverage, and market discipline (NPA) have insignificant relationship with the web-based disclosure level, and Indian banks have not shown any relationship with the disclosure score. The study will help the managers to meet the actual and potential informational needs of the investors; for the investor, it will help to assess investment decisions in a better way.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".