The elephant in the room: Leveraging dynamic capabilities to bridge innovation performance, failure, and learning from failure
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Which strategic capabilities simultaneously drive innovation and learning from failures (LFF)? We address this question with two objectives. First, synergies between innovation performance, failures, and LFF strategies are assessed. Second, heterogeneities in the determinants of these processes are explored to identify common and specific predictors for each process. Based on a sample of 436 Canadian SMEs and drawing on the dynamic capabilities theory, we developed an original framework that disentangles the sensing, seizing, and reconfiguring capabilities. The econometric exercise revealed that complementarities between innovation failures and LFF and among LFF strategies emerge through complex interactions. Results show nuances regarding levels of microfoundation capabilities, such as those for seizing when managing innovation and LFF. This study provides practical insights for managers on improving innovation performance and capitalizing on unconventional solutions, such as previous failures. We discuss findings along with their theoretical and practical implications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it