Enabling management control in improving the performance of SMEs
Bibliographic record
Abstract
This study aimed to investigate the effect of management control system enabling with capabilities that can improve the performance of SMEs. This study was conducted among the managers of SMEs of local food products in Banten Province, Indonesia. The number of respondents in this study was 85 SME managers. This study used structural equation modeling as an analytical tool and PLS Smart software to process the data. The findings of this study show that there was a positive effect of the use of enabling management control system (MCS) on creativity; there was a positive and significant effect of the use of Enabling MCS on cost efficiency; there was a positive and significant effect of the use of Enabling MCS on performance; there was a positive and significant effect of creativity on innovation; there was a positive and significant effect of cost efficiency on performance; and finally there was a positive and significant effect of innovation on performance. The implication of this study is that it can provide a choice of control system for SME management, which up to now still uses conventional control system. The use of Enabling MCS can create a capability for the managers of SMEs of local food products to win the competition. This is due to the uniqueness of Enabling MCS that can increase creativity and also cost efficiency.
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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 | 0.003 | 0.009 |
| 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".