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Record W4389145594 · doi:10.4236/ti.2023.144018

Impact of Knowledge Management on Firms’ Innovation Performance

2023· article· en· W4389145594 on OpenAlex

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueTechnology and Investment · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Capital and Performance Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessIndustrial organizationKnowledge managementMarketingProcess managementComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.750
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.022
GPT teacher head0.256
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it