Predicting Innovation Capability through Knowledge Management in the Banking 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
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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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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