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Record W3190642898 · doi:10.4018/jgim.20211101.oa41

The Impacts of Knowledge Management Practices on Innovation Activities in High- and Low-Tech Firms

2021· article· en· W3190642898 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Global Information Management · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsUniversity of Saskatchewan
FundersShenzhen UniversityNational Taiwan UniversityDepartment of Industrial and Systems Engineering, Hong Kong Polytechnic UniversityMinistry of Education, IndiaNational Research Foundation of KoreaCivil Aviation University of ChinaDeakin UniversityKyung Hee UniversityNational Research FoundationHong Kong Polytechnic UniversitySouth China Normal UniversityUniversity of WarwickUniversity of Electronic Science and Technology of ChinaCity University of Hong Kong
KeywordsHigh techBusinessInnovation managementProduct innovationIntellectual propertyEmpirical researchIndustrial organizationNew product developmentAffect (linguistics)Dual (grammatical number)Product (mathematics)Empirical evidenceMarketingKnowledge managementComputer science

Abstract

fetched live from OpenAlex

This paper presents an empirical study on how knowledge management practices and innovation sources affect product innovation performance, among the 152 manufacturers in the low- and high- tech industries in China. The results indicate that external innovation sources are positively correlated with innovation activities and new product performance. Intellectual Property (IP) and knowledge management practices (KMP) are positively correlated with innovation activities, and KMP is positively correlated with innovation sources. The dual effect of KMP shows its indispensable effect on the new product development for both high-tech and low-tech firms, but for low-tech firms, such effect is relatively weak. This empirical study shows that IP management is critical to high-tech but not low-tech firms. We also found that, for innovation activities, low-tech depends on the external sources of innovation whilst high-tech firms do not.

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)0.001
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: none
Teacher disagreement score0.874
Threshold uncertainty score0.542

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.272
Teacher spread0.258 · 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