The effect of product innovation on business performance during COVID 19 pandemic
Bibliographic record
Abstract
Management research maintains business performance of local products during the pandemic, requiring organizations to act effectively and efficiently in managing resources based on relevant knowledge. Efforts are needed to sustain local product business, maintain the key to organizational success based on knowledge sharing and innovation in improving business performance so that local products remain sustainable. This research was analyzed quantitatively using SEM-AMOS statistical tools, in 300 local product business actors in Central Kalimantan and Bali. The proactive antecedents of market orientation were obtained significantly positive encouraging mediation role of knowledge sharing by 51% and innovation by 63% on business performance. Activities that are managed so that superior products become management activities with significant positive Planning, Organizing, Actuatingand Controlling (POAC) carried out by mediating positive impacts to maintain local product business. The novelty of this research is a conceptual model based on Knowledge Based View (KBV) to increase local product business in two provinces affected by Covid-19. The implication of this research is to encourage business actors to synergize with market orientation into relevant information to identify changes and needs, as well as to encourage knowledge sharing and innovation in improving business performance of local products that are in accordance with consumer needs.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".