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Record W3149011689 · doi:10.5267/j.msl.2021.3.002

Knowledge management capability as an absorptive for driving innovation: The role of type of innovation

2021· article· en· W3149011689 on OpenAlexvenueno aff
Muhamad Robith Alil Fahmi, Edy Yulianto

Bibliographic record

VenueManagement Science Letters · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Capital and Performance Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessCompetitive advantageKnowledge managementContext (archaeology)Absorptive capacityResource (disambiguation)Product innovationProcess (computing)Service (business)MarketingIndustrial organizationComputer science

Abstract

fetched live from OpenAlex

Knowledge-Based View as an intangible resource for the company will become the knowledge capability it possesses. Particularly in the context of SMEs in developing countries like Indonesia, SMEs have a big role in contributing to the country's economy. Therefore, knowledge capability is a resource that must be owned by SMEs that should be able to encourage adopting this type of innovation. In accordance with the basis of Knowledge-Based View, this knowledge will have an impact on company performance and its competitive advantage through the types of innovations that have been adopted. The quantitative method was used by distributing questionnaires totaling 120 SMEs in Indonesia and the data were processed using PLS-SEM. This study has a hypothesis that the relationship between knowledge management capability has a positive and significant effect on firm performance, as well as the mediating role of the type of innovation. The results in this study indicate that knowledge management capability does not have a significant effect on firm performance. However, the relationship between knowledge management capability shows that it has a significant effect on marketing, product, process, and service innovation. Discussions related to these results are also explained by implication factors in this study.

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.

How this classification was reachedexpand

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: Empirical
Teacher disagreement score0.777
Threshold uncertainty score0.423

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.009
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.023
GPT teacher head0.267
Teacher spread0.244 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2021
Admission routes1
Has abstractyes

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