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Record W2023154699 · doi:10.1504/ijlic.2010.034364

Total innovation management paradigm for SMEs – an empirical study based on SME survey

2010· article· en· W2023154699 on OpenAlexfundno aff
Gang Zheng, Jin Lu, Xiaodan Yu, W.F. Li, Xiu‐Hao Ding

Bibliographic record

VenueInternational Journal of Learning and Intellectual Capital · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsnot available
FundersScience and Technology Program of Zhejiang ProvinceNational Social Science Fund of ChinaNational Natural Science Foundation of ChinaInternational Development Research Centre
KeywordsBusinessKnowledge managementLeverage (statistics)Innovation managementAdaptabilityIndustrial organizationCompetence (human resources)Core competencyQuestionnaireOpen innovationEmpirical researchMarketingManagementComputer scienceEconomics

Abstract

fetched live from OpenAlex

Innovation plays a crucial role for enterprises' sustainable development. Total innovation management, a new innovation paradigm born in the 21st century, not merely emphasises the integration of technological innovation and non-technological innovation, but also includes all innovators and all time-space innovation. The core of TIM, 'three-all and coordination' model, has been applied by many large enterprises who has owned mature conditions for innovation management. Based on more than 200 SMEs survey and a certain number of case studies, this paper implies that the innovation performance in SMEs has something to do with total innovation management competence. However, there are different models of TIM application between large enterprises and SMEs; and how adaptability of TIM pattern leverage innovation capabilities of SMEs, is our task to penetrate.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.042
GPT teacher head0.331
Teacher spread0.289 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations2
Published2010
Admission routes1
Has abstractyes

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