Information System and Firms’ Performance: The Case of Malaysian Small Medium Enterprises
Bibliographic record
Abstract
Information system has been widely used by many corporations to automate existing operations and to improved business activities efficiency. Prior researches have shown that information system adoption help increase firm’s performance and operations efficiency. In Malaysia, Small and Medium Enterprises (SMEs) has been targeted as a mechanism in generating domestic-led investment to stimulate economic development, particularly after the economic crisis that hit the country in 1997. Thus, it is crucial for SMEs to adopt information systems to enhance their business operations capabilities and efficiency. Nevertheless, SMEs limited resources to implement information systems have been one of the critical barriers that hindered the adoption of information systems. Malaysian government had allocated special grants and various initiatives to assist SMEs to assist the adoption of these information systems. Therefore, this present study is to investigate the impact of accounting information systems on firm performance of Malaysian SMEs. Panel data was used to analyse firm’s performance. Results revealed that SMEs adopting accounting information system show significant improvement in performance compared to non-adopters.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".