Moderating Effects of Business Strategy and Environmental Uncertainty on the Relationship Between Personal Characteristics and Performance of Indonesian SMEs
Bibliographic record
Abstract
This study explores the moderating effects of environmental uncertainty (EU) and business strategy (BS) in order to experimentally investigate the impact of entrepreneurial personal characteristics (EPCs) on the performance of small and medium-sized enterprises (SMEs).The study used a mixed-methods approach, gathering information from 213 SMEs through questionnaires and in-depth interviews.Using the Partial Least Squares (PLS) method, data were evaluated.According to empirical findings, EPCs improve the performance of SME.Additionally, BS modifies the association between EPCs and SME performance.EU, however, deteriorates this connection.These results clarify the debate around the effect of EPCs on the performance of SMEs.By highlighting the favorable impact of EPCs on SME performance and the crucial significance of individual traits and business strategies in SME sustainability in Indonesia, this research can practically inform management policy on future business plans.The report suggests raising environmental management awareness, implementing an environmental orientation plan, and boosting environmental performance and business strategy to improve SME performance.In the context of this study, corporate performance is defined as the degree to which a company meets its operational goals in accordance with its vision and mission.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".