Collaborative competitors in a fast-changing technology environment: open innovation in environmental technology development in the oil and gas industry
Bibliographic record
Abstract
The open innovation model has been the topic of many studies, but studies of applications of the model in resource–based industries are scarce. In this research, we examine the deployment of the open innovation model in resource–based industries through the case of the Canadian oil and gas sector. Our findings show that the need for technical innovation is rapidly increasing as a result of the nature of the new complex fossil fuel reservoirs the industry is now developing. High research and development costs, long development cycles, resistance to change and high technical risk, we find, are obstacles hindering the development of new innovative technologies. In response to these challenges to innovation, the Canadian industry experimented with a unique model of open innovation by establishing an industry–level organisation. In this study, we explore this emerging model of open innovation and interpret its successes and failures through the use of the theoretical literature. The conceptual model that we derive combines strategic bridging organisational concepts with open innovation ideas to help us understand the building of an innovative technologies network at the industry level.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".