Contributory role of dynamic capabilities in the relationship between organizational learning and innovation performance
Bibliographic record
Abstract
Purpose The direct impact of organizational learning (OL) on organizational performance has been studied over the past two decades. However, how OL contributes to organizational innovation still remains under-researched. Based on the knowledge-based view of the firm and dynamic capability theory, we developed a theoretical framework in order to empirically examine how OL offers organizations the essential tools for creating dynamic capabilities (DCs), which pave the way for innovation performance (IP). Design/methodology/approach The authors apply a time-lagged, multisource and survey-based research designed to test the proposed model in the pharmaceutical industry where knowledge is a source of innovation. The data collected from companies operating in such an industry were analyzed by utilizing hierarchical regression analysis to explore how OL could lead to IP through DC. Findings The results indicated that OL is positively, significantly associated with DCs, as well as its dimensions of learning, integrating and reconfiguring capabilities. The findings showed that these capabilities are significant predictors of innovation performance. In addition, the findings revealed that innovation culture significantly moderates the relationship between DCs and innovation performance. Originality/value By dedicating more time and resources, managers can reinforce dynamic capabilities as a strategic tool to generate new knowledge and distribute it across the organization, which can go a long way toward boosting innovation performance in the pharmaceutical industry. This study offers researchers and practitioners invaluable insights into how effective OL can enhance firm-level innovation performance through dynamic capabilities.
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.
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 | 0.022 |
| 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 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".