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Record W1972517914 · doi:10.5430/ijba.v4n6p82

Innovation in the Global Age: Implications for Business and Management in the Knowledge Economy

2013· article· en· W1972517914 on OpenAlexvenueno aff
Anshuman Prasad, Pushkala Prasad

Bibliographic record

VenueInternational Journal of Business Administration · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicUniversity-Industry-Government Innovation Models
Canadian institutionsnot available
Fundersnot available
KeywordsKnowledge economyGlobalizationRestructuringDigital economyOpen innovationBusinessProcess (computing)Tacit knowledgeDigital RevolutionKnowledge managementEconomicsMarketingEconomyPolitical scienceMarket economyComputer science

Abstract

fetched live from OpenAlex

The current era of globalization is characterized by far-reaching shifts in the economy as well as in a variety of other spheres of human activity. The present article focuses upon innovation, an activity that is viewed as crucial for business success in today’s knowledge economy, and which seems to be in the middle of an extensive process of global restructuring. The article begins with an overview of the nature of the knowledge economy, highlighting in the process the significance of the digital revolution, the rising knowledge-intensity of economic activities, the changing relationship between services and manufacturing, and the growing importance of tacit knowledge. Next, the article examines some of the major structural changes that are transforming the field of innovation in a rapidly globalizing knowledge economy, and while so doing draws attention, in particular, to certain changes in scholarly understanding of the innovation process, the rise of considerable innovation activity outside the traditional centers of innovation, and the ongoing globalization of innovation. Finally, the concluding section of the article discusses some implications of these developments for business and management.

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 score0.877
Threshold uncertainty score0.666

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
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 head0.041
GPT teacher head0.291
Teacher spread0.249 · 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

Citations1
Published2013
Admission routes1
Has abstractyes

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