E-Filing Behaviour among Academics in Perak State in Malaysia
Bibliographic record
Abstract
Perak State government in Malaysia has been promoting an Internet tax filing called electronic filing as part of its e-government initiative. Starting in year 2006, Malaysia Inland Revenue Board (IRBM) has launched the e-filing method for individual taxpayers and from that point of time, Malaysia’s citizens are provided with the option to choose their tax filing method either in the way of manual tax-filing method or e-filing method. This study focuses on the Perak State academics’ intention and behavior to adopt e-filing tax system. The target population for this study is academic staff in Perak State in Malaysia. 116 usable questionnaires were collected from three public institutions and two private institutions of higher learning in Perak State in Malaysia and the data analyzed through the SPSS. The findings show that perceived use of use, perceived usefulness, perceived security, and perceived credibility do influence the Perak State academic’s e-filing adoption intention. However, perceived service and information quality has not influenced their e-filing adoption intention. This study provides several important implications for building and promoting effective e-filing system by the IRBM.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".