The adoption of open innovation to address environmental challenges in a process-oriented industry
Bibliographic record
Abstract
While the benefits to applying the open innovation model are numerous, scholars have yet to address how this model can be applied effectively to boost process innovation in process-oriented industries. In this paper we extend the open innovation model both theoretically and practically by identifying the boundary conditions that motivate firms and the approaches that have been implemented in practice in applying this model in a process-oriented industry. Using the upstream oil industry, as a representative of process-oriented industries, we explore how firms in this industry have evolved to apply open innovation practices over time to deal with the industry’s challenges. In our investigation, we first explored why these firms adopted the model. We argue that institutional forces represented the primary motivators to adopting open innovation in order to respond to the social and environmental concerns faced by the industry. Second, we explored how firms evolved practices to gain both the benefits of open innovation while overcoming its challenges. In our study of the industry’s evolutionary history in applying the open innovation model, we demonstrate that a variant of an innovation intermediary was a necessary governance organization to address problems of adopting open innovation in this industry. We argue that the lessons learned from the experiments of the upstream oil industry in regards of adoption of the open innovation model can be helpful to other industries, particularly other process-oriented industries, which seek to effectively employ innovation intermediaries.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".