The Mediating Role of E-Commerce Adoption in the Relationship Between Government Support and SME Performance in Developing Countries
Bibliographic record
Abstract
In developing countries, SMEs contribute significantly to GDP and employment (up to 33% and 45%, respectively). Governments in developing countries such as Indonesia take initiatives to provide support to MSMEs through facilities and regulations to improve their performance. Previous studies lack theoretical consensus on the relationship between government support (e.g., incentives, training, regulatory changes, technology facilitation) and SME performance. This study uses a quantitative approach. An online survey of 1514 SMEs was conducted for this study. A valid sample of 402 SMEs was collected for this study. This study investigates the government-supported SME performance relationship and explores the mediating role of e-commerce adoption on the government-supported SME performance relationship. The hypotheses were tested using the partial least squares (PLS) approach with the help of SmartPLS 3.2.8 software. This study demonstrates the mediating role of ecommerce marketplace adoption in the relationship between government support and SME performance. The findings provide new insights into the role of government in driving SME performance (p < 0.00). This study can have implications for determining government policies to improve the performance of SMEs. This study explains the need for government policies to encourage SMEs to adopt e-commerce. In addition, the government can improve facilities for SMEs to make it easier to adopt the e-commerce marketplace.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".