Experiences to Voluntarily Adopt Malaysian Business Reporting System MBRS: A Case Study of SMPs
Bibliographic record
Abstract
The Companies Commission of Malaysia (SSM) has established the eXensible Business Reporting Language (XBRL) which is the Malaysian Business Reporting System (MBRS). This study examines the technological, organisational and environmental factors influencing the usage of MBRS among the practitioners. Using interview as the data collection among 12 respondents which are practitioners from selected Corporate Secretaries fom small medium practices (SMPs). Data from interview has analysed based on descriptive coding and pattern coding that developed by Technological, Organisational and Environmental (TOE) theory using the Atlas.ti. The findings of this study indicates seven (7) technological factors which are assurance for data quality, relative advantage and the availability of regulator’s platform and system, limited tools and software, compability of format, compatibility of content and how the mTool could provide ease of use to the corporate secretary. In related to organisational factors, There are seven (7) challenges that can be considered discovered from organisational which are challenge to face attitude of preparers, limited practitioners that have own sufficient skills and knowledge, limited capable resources and preparers to manage the MBRS. In addition, there are six (6) environmental factors which are the technical support from regulator, the provision of incentive that should be given to the practitioners or SMPs, the effective strategies for promotion and educate practitioners method of voluntary submission. However, the lack of readiness on the use MBRS among trading partners and other stakeholder involvement would also challenge the adoption of MBRS. Therefore, this TOE factors would be important to practitioners to be ready on the enforcement of MBRS.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".