Factors Contributing to Sustainable Growth Performance in Indonesian SMEs: The Role of Business Incubators
Bibliographic record
Abstract
Sustainable growth performance is crucial for the development and success of small and medium-sized enterprises (SMEs), particularly in the context of business incubators.Companies must put into practice a number of measures that will promote enterprise sustainable growth performance in order to accomplish this.This study intends to investigate the impact of numerous factors, including external networks, entrepreneurial learning, and the innovation process, on the sustainable growth performance of SMEs.This study also examines how the innovation process mediates the relationship between the sustainable growth performance of an enterprise's development, entrepreneurial learning, and external networks.To understand the impact of each element, we use quantitative approaches in surveys by providing questionnaires to respondents via online platforms.Out of 320 SMEs chosen by purposive sampling technique with several criteria, 290 SMEs in the Province of DKI Jakarta, Indonesia, returned the questionnaire completely and the data can be analyzed further (90% response rate).We use structural equation modeling to process the data with AMOS 7 software.The results of this study show that the innovation process is positively influenced by external networks and entrepreneurial learning, and it positively influences sustainable growth performance of SMEs.Between external network and entrepreneurial learning, the one with stronger influence to innovation process is entrepreneurial learning, although both are found to be significant.Furthermore, this study also proven that innovation process acts as the mediator in the influence of external network and entrepreneurial learning on sustainable growth performance of SMEs.Ultimately, this study has been demonstrated that SMEs who join business incubators can maximize their potentials to innovate and obtain sustainable growth performance through utilizing their external network and carrying out entrepreneurial learning.
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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 | 0.002 | 0.005 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".