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Record W2900536378 · doi:10.5267/j.msl.2018.10.013

The effective role of work environment and its influence on managerial innovation

2018· article· en· W2900536378 on OpenAlexvenueno aff
Salah A. Alabduljader

Bibliographic record

VenueManagement Science Letters · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicBusiness and Economic Development
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)BusinessKnowledge managementProcess managementWork environmentComputer scienceIndustrial organizationBusiness administrationWork performance

Abstract

fetched live from OpenAlex

Trying to create an innovative organization means to have employees who are innovative, within the internal structure of the organization. This paper aimed at examining the influence of work environ-ment on managerial innovation within the insurance sector in Kuwait. A quantitative tool, self-administered questionnaire, was adopted and distributed among165 managers who worked for 21 insurance companies in Kuwait. The results indicate that among the chosen variables representing the dimensions of the work environment, the most influential dimension was facilities. This reveals that it was the influence of facilities that provided a healthy work environment for the employees who enhanced their abilities to present their best performance and be innovative. The study suggests that organizations should pay extra attention to certain factors of employee appraisal such as incentives, appreciation and recognition, that may, in turn, help increase the degree of managerial innovation. In addition, it is suggested to examine the influence of work environment on the leaders’ ethical orientations within the insurance sector in Kuwait.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.003
GPT teacher head0.172
Teacher spread0.168 · 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

Citations6
Published2018
Admission routes1
Has abstractyes

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