Impact of Knowledge Management on Firms’ Innovation Performance
Bibliographic record
Abstract
Purpose: To provide empirical evidence to explore the impact of knowledge management (KM) on the innovation performance of listed manufacturing firms in Ghana. Manufacturing firms are threatened by the absence of internal competitive expertise and external challenges related to varied institutional settings. Design/Methodology/Approach: Data were collected using 110 questionnaire surveys sent out to senior managers from a cross-section of manufacturing industries. A total of 1140 usable questionnaires survey were returned representing a 100 percent response rate. The hypotheses and assumptions in the form of mail survey, secondary data, and direct surveillance were established using structural equation modelling. Findings: How a firm acquires knowledge, disseminates it and finally its responsiveness toward knowledge management influence on firm innovation performance was tested using developed hypotheses based on theoretical and research framework. The quantitative survey approach was chosen to evaluate the significance of each hypothesis. Empirical evidence asserts that a knowledge management capability firm uses resources efficiently to be innovative and significantly positive in performance. All three KM elements: knowledge acquisition, knowledge dissemination, and responsiveness to knowledge have a significant positive relationship to firm innovation performance. Research Limitations/ Implications: The sample used slightly under-represented smaller firms and was not entirely characteristic of manufacturing industry segments. Data were also collected in Ghana so the study needs a broader replication in different contexts and or countries with longitudinal studies. Practical Implications: This paper presents manufacturing firms in a developing economy, Ghana intending to substantiate knowledge management and innovation performance implementation in an emerging economy and latecomer development to unravel its impact on listed manufacturing firms in Ghana. Knowledge management is incorporated in numerous firms and necessitates a business instance to defend program outlay to contrivance knowledge management behaviours and practices. This paper provides sustenance for the importance of knowledge management to augment both technological (ICT-based) and human resource (organizational) innovation execution that will bring benefit to manufacturing firms in Ghana’s innovation performance. Originality/Value: This paper is amongst the first to find empirical results to back the role of knowledge management within manufacturing firms. Additionally, the aligning of knowledge management as a coordinative instrument is also of significant input to our discernment in this area.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".