The role of inbound and outbound open innovation on firm performance in environmental turbulence era: Mediating of product and marketing innovation
Bibliographic record
Abstract
Open innovation has been identified as two dimensions, the flow of knowledge obtained from outside and processed within the organization and has a role as a key business responsiveness to prevent any risks that will be faced. From this knowledge flow is a successful approach to new product development featuring outbound and inbound knowledge that is managed with the aim of getting out of the bounds of risk. Therefore, this study investigates and explains the clausal relationship between the variables used, such as inbound and outbound open innovation, product innovation, marketing innovation, firm performance, and environmental turbulence as moderating variables. This study uses a quantitative approach and designs a questionnaire that has been distributed to 115 SMEs owner / managers as a sample. In the process of formal data collection, a random sample was used in this study which was distributed to the owner / manager of SMEs. While the processing, analysis, and hypothesis testing process of this study uses PLS-SEM which is a statistical tool for applying all data scales, does not require many assumptions, and confirms relationships. The findings in this study indicate that what has a positive and significant effect is the relationship of inbound open innovation to product innovation, product innovation to marketing innovation, marketing innovation to firm performance. In addition, the moderating effect of environmental turbulence in a positive way is only product innovation on firm performance. Further explanation of the implications of the findings has been discussed and confirmed.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".