The role of innovation capability in mediation of COVID-19 risk perception and entrepreneurship orientation to business performance
Bibliographic record
Abstract
The purpose of this study is to explain how innovation capability influences business performance by mediating the effect of Covid-19 risk perception and entrepreneurial orientation. This study's population consists of MSMEs in Bali's tourism and creative industries. Purposive sampling was utilized to choose 90 managers of MSMEs in the tourist and creative economy sectors. Path Analysis with SEM-PLS was employed as the analytical approach. The findings revealed that the COVID-19 Risk Perception had a negative and significant impact on business performance, but the Entrepreneurship Orientation had a positive and significant impact. Furthermore, COVID-19 Risk Perception had a negative and significant impact on Innovation Capability; while the Entrepreneurship Orientation had a positive and significant effect on Innovation Capability; and Innovation Capability had a positive and significant impact on business performance. In addition, Innovation Capability was able to mediate the influence of COVID-19 Risk Perception and Entrepreneurship Orientation on business performance. Therefore, it is important for MSMEs in the tourism and creative economy sectors in Bali to improve their Entrepreneurship Orientation so that they are able to build higher innovation capabilities in order to improve business performance in facing the risks of the COVID-19 pandemic.
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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 | 0.009 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".