The Management of Financial Disclosure on Corporate Websites: A Conceptual Model
Bibliographic record
Abstract
This paper addresses the impact of Internet financial reporting (IFR) on financial accounting theory by incorporating it in the general Gibbins, Richardson, and Waterhouse 1990 (GRW) disclosure-management framework. The GRW model assumes that the firm has a relatively stable process of disclosure management. This process varies between two positions: one, ritualistic, and the other one, opportunistic. These dimensions can coexist in the same firm but, on average, the policy of a firm will either be more ritualistic or opportunistic. Our survey of the financial information disclosed in traditional financial reporting (TFR) as compared to websites disclosures of a random sample of Canadian companies documents a significant difference between TFR and IFR as well as a wide variability among the sample firms in their use of IFR content, format and technology. We interpret this variability in the incremental difference of IFR over TFR, as an indication that a firm's ritualistic or opportunistic behaviour under IFR is not different from its behaviour under TFR. Thus, the adapted GRW (1990) conceptual model appears to have the potential to support future research in the management of financial disclosure on corporate websites.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".