Factors Influencing Knowledge and Persuasion of Financial Regulators in the XBRL Adoption Process: The Technological Perspective
Bibliographic record
Abstract
This study examined the factors influencing XBRL adoption process among the Malaysian regulators in the financial reporting environment. Specifically, this study aims to examine the drivers and challenges in the knowledge and persuasion phase faced by the Malasyain regulators in their adoption process of XBRL. This study relied on two frameworks namely, the adoption process framework with specific focus on knowledge and persuasion, and the TOE framework focusing on technological context. Using a qualitative approach, this study found that within the technological context, relative advantage and trialability were the drivers in the knowledge and persuasion phase. This study also found that the regulators were aware of XBRL and had made efforts to understand the XBRL taxonomy. However, the regulators were needed to develop the internal capability of their organisations since different regulators have shown different factors during the knowledge and acquisition phase that is necessary for XBRL adoption. The findings in this study serve as guidelines to other regulators in Malaysia and other countries that have plans to adopt XBRL.
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 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".