Social capital, knowledge quality, knowledge sharing, and innovation capability: An empirical study of the Indian pharmaceutical sector
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
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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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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