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Record W2787538109 · doi:10.5430/ijba.v9n2p9

Differences in Work Values by Gender and Generation: Evidence from Egypt

2018· article· en· W2787538109 on OpenAlexvenueno aff
Maha Ahmed Zaki Dajani

Bibliographic record

VenueInternational Journal of Business Administration · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsReligiosityPsychologyConfirmatory factor analysisScale (ratio)PrestigePopulationSocial psychologyWork (physics)Structural equation modelingDemographyDemographic economicsStatisticsSociologyGeographyMathematicsEconomics

Abstract

fetched live from OpenAlex

This research was built on the previous research titled “The Mediating Role of Work Values in the Relationship between Islamic Religiosity and Job Performance: Empirical Evidence from Egyptian Public Health Sector” (2017) and continued to examine work values differences based on gender and generation. A quota sampling procedure was used to survey (400) participants in (10) public Egyptian hospitals. The positive response rate of the target population was (83.75%). Work values were measured using Lyon Work Values Survey (LWVS) revised 25-item scale to assess four types of work values, namely, instrumental values, cognitive values, social/altruistic values, and prestige values. It also ranked the importance of each of these 25-items according to gender and age. A confirmatory factor analysis, using AMOS 20, was conducted to confirm the factor structure of the used scale on the target population. The Findings revealed that there exists a similarity on the high importance of instrumental work values to both males and females, in all age groups. Dissimilarities are more apparent among other types of work values based on gender and generations. These results suggested that understanding work values differences based on these two demographic factors have a significant impact on the improvement of human resources practices and the development of management theory.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.347

Codex and Gemma teacher scores by category

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

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 head0.055
GPT teacher head0.277
Teacher spread0.222 · 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 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

Citations7
Published2018
Admission routes1
Has abstractyes

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