The role of GoJek and Grab sharing economy platforms and management accounting systems usage on performance of MSMEs during covid-19 pandemic: Evidence from Indonesia
Bibliographic record
Abstract
This study investigates the influence of MSME actors' characteristics on the use of sharing economy, management accounting system, and financial performance during the Covid-19 Pandemic. Based on a questionnaire survey obtained from 167 respondents, we hypothesize and find that age and non-formal education have a positive effect on the use of the sharing economy. MSMEs that are managed by actors at a young age tend to use the sharing economy to maintain their business. In addition, MSMEs’ leaders that receive non-formal education acquire additional business knowledge encouraging them to use the sharing economy. Furthermore, the use of the sharing economy has a positive effect on Management Accounting Systems usage. Finally, Management Accounting Systems usage has a positive effect on the financial performance. The results of this study provide useful insights into the design of effective MSMEs' mentoring systems and support the Indonesian government program toward empowering MSMEs.
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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 | 0.001 | 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) | 0.003 | 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".