Moderating role of innovation culture in the relationship between organizational learning and innovation performance
Bibliographic record
Abstract
Purpose Different studies have analyzed the relationship between organizational learning (OL) and innovation performance (IP). However, the question of how innovation culture (IC) affects the relationship between OL and IP remains unexplored. This study aims to examine the impact of IC on the relationship between OL and various dimensions of IP, including product, process and objective innovation. Design/methodology/approach A research model was developed and performed based on the relevant literature in the field of OL, IC and IP. The hypotheses are tested with the data collected from companies operating in an intensive knowledge-based industry. Findings Based on the results of 625 questionnaires completed by pharmaceutical companies, OL activities and IC can result in product and process innovation. However, this relationship was not supported for the objective innovation. Furthermore, in terms of the moderating role of IC in the relationship between OL and IP dimensions, the results were significant. Practical implications The findings help to gain a better understanding of how organizational commitment by creating a culture for innovation can help to maximize the benefits of continuous OL in product and process innovation. Originality/value Considering the three aspects of IP, it is the first survey of the contribution of OL in firms’ IP with considering the moderating role of IC. The proposed model would enrich the relevant literature and provide us with better understanding how OL contributes to the IP.
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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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 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".