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Record W2898068153 · doi:10.5430/jms.v9n4p22

How Organizational Culture Affects Employee(s) Work Engagement in the Insurance Industries in Ghana: An Ambidextrous Approach [AAA]

2018· article· en· W2898068153 on OpenAlexvenueno aff
Aphu Elvis Selase, Xinhai Lu, Ekor Sophia Enyonam Abla

Bibliographic record

VenueJournal of Management and Strategy · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsEmployee engagementWork engagementOrganizational cultureBusinessTest (biology)Work (physics)Employee researchOrganisation climateProduct (mathematics)MarketingSample (material)Business administrationPublic relationsPsychologySocial psychologyPolitical scienceEngineering

Abstract

fetched live from OpenAlex

The study assessed how organizational culture affects employee work engagement in the insurance industries in Ghana. A cross sectional survey design was used to purposively sample one hundred and sixty-one (161) employees from two leading insurance companies ambidextrously. The Pearson Product Moment Correlation and Independent t-test were the statistical tools used to test the three hypotheses of the study. The results of the study revealed that, there is a positive significant relationship between organizational culture and employee work engagement. Again it was established that managers are more likely to be engaged on their job than non-managers and gender has no significant influence on engagement levels. The study therefore concluded that, to increase employee work engagement, organizations must adopt a favorable culture. Therefore we recommend that organizations should maintain and sustain favorable culture in order to increase the level of employees work engagement.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.027
GPT teacher head0.239
Teacher spread0.211 · 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 designQualitative
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
Published2018
Admission routes1
Has abstractyes

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