Social capital, knowledge quality, knowledge sharing, and innovation capability: An empirical study of the Indian pharmaceutical sector
Bibliographic record
Abstract
Management of technology and innovation is a topic that has been subjected to a lot of discussions by the academicians and practitioners alike. Furthermore, researchers have emphasized the importance of the role that knowledge management/knowledge sharing can play in promoting innovation in an organization. The purpose of this paper is to evaluate the role of social capital and knowledge sharing in achieving innovation capability of an organization. It also discusses the role that knowledge quality might play in fostering the innovation capability of an organization. The basic research model was developed based on an in‐depth review of the extant literature and subsequently tested based on survey data collected from 97 senior executives across multiple pharmaceutical organizations in India. The findings of the partial least squares structural equation modeling indicated that knowledge quality and explicit and tacit knowledge sharing had a significant effect on innovation capability of pharmaceutical organizations in India. It further highlighted that although relational and cognitive social capital play a significant role in improving the quality of shared knowledge among the employees, structural social capital did not have a significant role to play. The findings of this study are expected to aid the pharmaceutical sector to understand the role that knowledge sharing might play in achieving its innovation capability and design knowledge management strategies accordingly.
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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 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".