Identification and analysis of enterprise risks in the open product innovation: the case of Volkswagen Brazil
Bibliographic record
Abstract
Purpose The problem statement is how to identify and analyze the corporate risks involved in the relationships with external agents involved in the open product innovation process (OPIP)? Seeking to extend this investigation, the purpose of this paper is to analyze the enterprise risks identified in corporate relations with external agents of the OPIP. This study proposes the systematization of the process of identification and analysis of the enterprise risks involved in the process of open product innovation. Design/methodology/approach The case explored in this study is the OPIP of Volkswagen do Brasil (VWB), one of the most important subsidiaries of the Volkswagen Group. Criteria were selected to both assessing corporate relations with external agents of the open innovation of VWB and analyzing the enterprise risks identified in these relations. Data collection included interviews with management-level professionals engaged in the OPIP activities and technical visits to a VWB’s industrial plant. Findings Results demonstrate that the enterprise risks mostly affecting the OPIP have a critical impact on the manufacturing process and initial sales of the new product. Originality/value The originality of the study focuses on the proposal of a systematization of how to identify and analyze the corporate risks involved in the process of open product innovation. The study focuses on the theoretical frontier on the open innovation and enterprise risk management (ERM) in the open innovation process.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".