The Relationship Between Unlearning and Innovation Ambidexterity with the Performance of New Product Development Teams
Bibliographic record
Abstract
Abstract Previous research has suggested that unlearning is not linked to performance improvements in a team setting. Further, unlearning may have deleterious effects on performance outcomes because when it happens, teams are likely to lose the way they perform tasks and the reasons for their operational existence. In contrast, this study predicts that teams can conduct exploitative and exploratory activities in a balanced manner predicated on unlearning practices to improve new product development (NPD) performance. We hypothesized that while unlearning allows NPD teams to balance exploitative and exploratory learning activities, simultaneous yet balanced exploitation and exploration at high levels, namely innovation ambidexterity, links unlearning practices to NPD performance. This occurs by providing task-relevant knowledge for the replacement of outdated routines and beliefs during NPD processes. Data were collected from 198 NPD teams (i.e., 464 individual participants). The examination of ordinary least squares regression-based path analyses revealed that innovation ambidexterity mediates the relationship of unlearning with NPD performance, operationalized as product development speed, cost, and product success. Overall, this study shows that the unlearning-performance relationship occurs through simultaneous exploitative and exploratory learning activities in a balanced manner.
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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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".