Evaluating the role of social capital, tacit knowledge sharing, knowledge quality and reciprocity in determining innovation capability of an organization
Bibliographic record
Abstract
Purpose Knowledge sharing has become an integral part of organizations’ business strategies, along with aiding organizations to grow and innovate in the market, and gain competitive advantage. This paper aims to concentrate on the role of tacit knowledge sharing in fostering innovation capability of an organization. Specifically, the study considers social capital (relational, cognitive and structural) as an important precursors to tacit knowledge sharing, which in turn, influences innovation capability of an organization. The study further discusses the role that knowledge reciprocation plays in successful tacit knowledge sharing. The relation between knowledge quality and innovation capability is also discussed in the paper. Design/methodology/approach The investigation started with a review of extant literature in the field of knowledge sharing and innovation to derive a set of constructs. A set of hypotheses was developed based on the identified constructs, which was subsequently validated through a primary survey based on a structured questionnaire on a sample size of 190 respondents from the Indian industrial domain. The survey responses were subsequently analysed using the statistical technique of structural equation modeling and conclusions were drawn from the findings. Additionally, careful attention was paid in eliminating the common method bias, which is often associated with a primary survey. Findings A set of six hypotheses were derived based on the identified constructs and were subsequently validated. While validating the hypotheses, it was observed that while knowledge reciprocity, relational social capital and cognitive social capital was positive associated with tacit knowledge sharing, structural social capital did not have a significant effect on the same. Additionally, it was also observed that both tacit knowledge sharing and the quality of knowledge were positively associated with innovation capability. Practical implications The present day business marked by intense competition requires firms to be more aware of their innovative capabilities. Effective sharing of knowledge or information can be deemed as a vital component in achieving this objective. Organizations that practice and nurture innovation activities can use the findings of the current study as a part of their knowledge management strategy. In addition to using the explicit knowledge, which are structured in nature, organizations can also start using tacit knowledge to harness their innovation potential – and the findings from the current study can act as a motivational tool for them to do so. Originality/value Although there is a growing body of literature concerning the role of knowledge management in innovation, there still a dearth in discussing the role of tacit knowledge sharing in exploiting the innovation capability of an organization. The main discussion of this paper brings together a set of important constructs that exhibits the significant role that tacit knowledge sharing plays in determining the innovation capability of an organization. Furthermore, it tries to marry the concepts of social capital and tacit knowledge sharing with innovation capability, therefore adding significantly to the body of literature in knowledge management as well as innovation.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".