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Record W2649902482

Leading Innovation Change in Today's Competitive Environment

2015· article· en· W2649902482 on OpenAlexaboutno aff
Miguel Orta, Edel Lemus

Bibliographic record

VenueNSUWorks (Nova Southeastern University) · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Capital and Performance Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsCreativityChief executive officerCompetitive advantageElement (criminal law)Foundation (evidence)BusinessOrganisation climateMarketingManagementOrganizational cultureBusiness environmentOfficerPublic relationsKnowledge managementEconomicsPolitical scienceComputer scienceBusiness administration
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this article is to explore the existing relationship between leaders and today’s competitive environment and innovation. The three strategies that leaders (as cited in Cumming, 1998) can use to create a climate that encourages innovation are (1). The foundation of creativity, (2). The application of a new idea and (3). The applicability of a successful concept. Legrand and Weiss (2011) reveal that 80% of the leaders in the organization believe that innovation is important for the organization's future success. Bill Gates, the former Chief Executive Officer (CEO) of Microsoft clearly believed and understood that innovation is important element for organizational success. Innovation is the engine for growth for all businesses in the 21 century. Innovation and creativity (as cited in Angle, 1989) shared similar characteristics. Corporate culture and a climate that encourages innovation are two factors that are often found within the leadership literature. Therefore, a leader needs to understand the applicability of the living system theory among individuals and organizations by reshaping the social progress of the business world as noted by Vancouver (1996).

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.767
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.061
GPT teacher head0.220
Teacher spread0.160 · 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.

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
Published2015
Admission routes1
Has abstractyes

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