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Record W2412374218 · doi:10.5539/ibr.v9n7p124

Organizational Aesthetic Capability and Firm Product and Process Innovativeness

2016· article· en· W2412374218 on OpenAlexvenueno aff
Derya Dogan, Halit Keski̇n, Ali E. Akgün

Bibliographic record

VenueInternational Business Research · 2016
Typearticle
Languageen
FieldPsychology
TopicCreativity in Education and Neuroscience
Canadian institutionsnot available
Fundersnot available
KeywordsPopularityCognitive dissonanceCompetence (human resources)Knowledge managementBusinessProcess (computing)Dynamic capabilitiesOrganizational learningProduct (mathematics)Perspective (graphical)MarketingPsychologyComputer scienceSocial psychology

Abstract

fetched live from OpenAlex

Taking into consideration the popularity of organizational aesthetics in organizational behavior literature, and adapting dynamic capabilities perspective, we suggest that organizational aesthetic capability is an important competence that enables organizations to cope with the environmental uncertainty. Nonetheless, organizational aesthetic capability is rarely addressed in the technology and innovation management literature. Specifically, we know little about what organizational aesthetic capability is, its ingredients and benefits, and how it works in innovation context. Addressing this particular gap in the literature, this study contributes in two ways. First, we conceptualize organizational aesthetic capability and its sub-dimensions that are alert imagination, to act and defer, awareness of dissonance, analyzing past actions, prefiguring future trajectories, preserve existing modes of operation, willingness to change direction, recognizing symbols in use, and awareness of language. Second, the theoretical framework we proposed highlights the effects of organizational aesthetic capability on product and process innovativeness.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.089
GPT teacher head0.451
Teacher spread0.362 · 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
Published2016
Admission routes1
Has abstractyes

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