User Awareness on the Implementation of Digital Service Tax in Malaysia
Bibliographic record
Abstract
The development of information technology and rising internet usage has fueled the expansion of digital-based services across the globe. The government started charging a six percent digital service tax (DST) to foreign digital service providers (FSPs) on 1st January 2020. In its first year of operation, this tax brought in more than RM400 million for the government. Digital Service Tax is only imposed on taxable digital services that are provided to consumers in Malaysia. Although the implementation of DST has been previously announced and the act has been enacted, there is a possibility that users have low awareness due to lack of information and unclear about the purpose of DST implementation. Moreover, a lack of understanding of the law may be a factor in the consumers' need for more awareness. Thus, this study aimed to examine factors influencing user awareness on the implementation of DST in Malaysia. This study applied a quantitative research method where questionnaires were distributed through Google forms among individual users in Klang Valley. A total of 63 respondents have been received, and the Statistical Package for Social Sciences (SPSS) has been used to analyse the data. According to this study, approximately 50% of the respondents learned about DST from television, radio, Internet and websites. Furthermore, the relationships between self-attitude, economic factors and user acceptance towards user awareness have been tested. Nevertheless, only economic factors and user acceptance have a significant positive influence towards the user awareness in the implementation of DST in Malaysia. Disseminating DST information to all users will increase their understanding and inculcate positive self-attitude towards acceptance of new tax policy. Therefore, the result of the study will assist the regulator, particularly the tax authority in finding a solution that could increase the level of tax awareness among the public and users.
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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 | 0.002 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".