Predicting Innovation Capability through Knowledge Management in the Banking Sector
Bibliographic record
Abstract
Purpose: The purpose of this study was to investigate the effects of knowledge management on innovation capability in the banking sector. Research methodology: Cross-sectional research design was employed in this study as it supports the use of questionnaire for data collection. Fifteen deposit money banks constitute the accessible population. Questionnaire was used as an instrument for data collection. A sample size of 272 was drawn from the overall population of 920. Overall, 259 staff participated in the study. Demographic characteristics of participants were analysed with frequency distribution while linear regression was used to analyse formulated hypotheses with the aid SPSS. Findings: This study found that knowledge management has significant positive effects on innovation capability. Research limitations: The research limitation is associated with cross-sectional survey and geographical scope. Future studies should employ longitudinal survey that support data collection for a year. Secondly, future studies should be carried out in other countries other than Africa. Practical implications: The implication of the finding is that managers and directors of banks should encourage knowledge management practices in their workplaces as this has proven by this study to improve innovation capability in terms of marketing innovation capability, product innovation capability and process innovation capability. Originality/Value: There is no research that has investigated the effects of knowledge management on innovation capability. Thus, this study provides new insight on promoting innovation capability through knowledge management.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".