The Significance of Entrepreneurial Orientation on Firm Performance Through Innovation Capability
Bibliographic record
Abstract
Companies must restructure the resources and capabilities to enhance performance. In a swiftly evolving competitive business environment, these resources and capabilities need to be effectively organized, integrated, and utilized. Consequently, companies should develop an entrepreneurial orientation as a strategy to guide entrepreneurial decisions and activities, focusing on how to leverage resources to convert innovation capabilities into performance. This research investigated the impact of entrepreneurial orientation on innovation capability and firm performance. This study focused on SMEs in Bali, Indonesia, with a sample size of 396. Data were gathered from managers of these SMEs, who served as the research respondents. An online questionnaire was administered through Google Forms and sent via email, resulting in 168 valid responses. Data analysis was performed using SEM PLS, with WarpsPLS 7.0 software. The results indicated a significant impact of entrepreneurial orientation on innovation capability and firm performance. Additionally, innovation capability positively and significantly influenced firm performance. The study also revealed that innovation capability mediates the relationship between entrepreneurial orientation and firm performance. When discussing innovation capabilities, refer to the routine actions involved in configuring capabilities and resources. Enhancing performance requires more than merely altering these capabilities and resources. Entrepreneurial orientation must play a crucial role in intensifying a company's innovation capabilities, which are essential for transforming into firm performance
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".