The impact of sharing economy platforms, management accounting systems, and demographic factors on financial performance: Exploring the role of formal and informal education in MSMEs
Bibliographic record
Abstract
This study analyzes the effectiveness of sharing economy platforms and management accounting systems (MAS) on the financial performance of Micro, Small, and Medium Enterprises (MSMEs) in Malang City, Indonesia, by considering the moderating effect of demographic factors such as gender, age, and business tenure. The investigation also examines the impact of formal and informal education on financial performance, positing that practical training yields greater financial improvement than theoretical schooling. This research examines 234 MSMEs using structural equation modeling (SEM) with SmartPLS and employs path analysis to investigate the impact of sharing economy platforms on MAS, as well as its consequences for financial performance. The results indicate that sharing economy platforms and MAS have a significant effect on financial performance. Informal education has a significant effect on sharing economy platforms and MAS, whereas formal education has a negative effect on financial performance. Demographic factors were observed to have a significant moderating effect on the path from MAS to financial performance. This study introduces the Adaptive Financial Capability Model (AFCM), a novel framework that uniquely integrates adaptive learning derived from informal education with demographic factors. By bridging practical training with contextual variables, such as gender, age, and business tenure, the AFCM provides an original perspective on enhancing financial management and technology adoption within 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 | 0.002 |
| 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".