Effects of knowledge management practices on innovation in SMEs
Bibliographic record
Abstract
The management of knowledge assets is crucial for gaining competitive advantage and has a huge strategic importance for the firms. Knowledge management has become one of the emerging fields in today’s research world and has turned out to be a major concern for the organizations as it plays a crucial role in the growth and development of the organization. Knowledge management is a new concept that is why it is gaining increased attention among small and large organizations. In this study three important knowledge management practices are discussed and the necessary insights regarding knowledge management processes and their positive impacts within an organization are provided. The study also brings forth the relationships which knowledge management processes have with radical innovation in small and medium enterprises. The explanatory method of research and quantitative type of research to test the hypothesis of research was used to carry out the study and survey type was involved by using questionnaire. The knowledge management and innovation instruments were adapted from previous researchers. This study’s target population consisted of small and medium-sized enterprises that included service sectors in Quetta, Balochistan. A convenient sampling was applied to collect the necessary data from SMEs. A total of 850 firms were communicated and requested to participate in this survey but 300 (35.6% response rate) accepted to fill out the survey questionnaire. The study utilized structural equation modelling to examine numerous complex cause and effect relationships between variables. The results indicate a positive association between Knowledge management processes and radical innovation. The positive link between Knowledge management processes and innovation indicates the importance and value of knowledge management in achieving competitive advantage through 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 | 0.003 | 0.022 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".