Does Information Overload of Annual Reports Matter?
Bibliographic record
Abstract
Nowadays, information overload is an increasing concern and has become an alarming issue. Bursa Malaysia requires all PLCs to have corporate disclosures in their annual reports in order to cultivate good corporate governance. However, annual report readability issues are evident and poor annual report readability is a common occurrence in Malaysia. Thus, this paper seeks to empirically investigate the association between information overload issues, annual readability and financial performance of Malaysian PLCs. Secondary data consisting of 85 PLCs from the years 2015 to 2017 were used. The results have revealed that the information overload issues, i.e. too many disclosures for each company, negatively affect the companies’ financial performance. Firms with annual reports that are easier to read with ideal readability have better financial performance. Not only that, fewer information overload issues tend to be encountered when the annual reports have good readability levels. Future studies are suggested to include primary data as well as non-listed companies for comprehensive coverage and generalization. Policy makers are encouraged to create minimum disclosure requirements which address the information gap between informed and uniformed investors. In addition, with developments in technology, advanced smartphone applications can be developed for investors to conveniently access the financial information of 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 | 0.014 | 0.196 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".