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Record W4210457687 · doi:10.7202/1085568ar

Employees’ Attitude Toward Women Managers in the UAE: Role of Socio-Demographic Factors

2022· article· en· W4210457687 on OpenAlexvenueno aff
Mohammed Abdul Nayeem

Bibliographic record

VenueJournal of Comparative International Management · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSocioeconomic Development in MENA
Canadian institutionsnot available
Fundersnot available
KeywordsMarital statusPsychologyContext (archaeology)PerceptionSocial psychologyExploratory researchSocial learning theorySample (material)Applied psychologySociologyPopulationSocial scienceGeographyDemography

Abstract

fetched live from OpenAlex

This study aims to understand and examine the relationship between socio-demographic factors and the attitudes toward women managers in the context of the United Arab Emirates (UAE). This research paper is an exploratory research and we have taken into consideration a basic framework to understand the relationship that exists among different factors responsible for different perceptions and attitudes toward women managers. The literature review has focused on the various overarching perspectives of women managers and the attitudes formed by different societal actors. The paper draws mainly from the Social Learning Theory of Albert Bandura (1977). A sample of 213 employees was drawn to understand the relationship. The respondents were administered the well-established (Women as Managers Survey) WAMS questionnaire to get responses on various dimensions. Multivariate Regression Analysis using SPSS 26.0 version was applied to analyze the data.
 
 The findings of the study are parallel to previous works such that age, work experience, and educational level found support to the hypothesized relationship whereas gender and marital status did not. Limitations and future direction along with practical implications and recommendations are also given to draw suitable inferences.

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)0.002
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.199
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.039
GPT teacher head0.336
Teacher spread0.297 · 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 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

Citations3
Published2022
Admission routes1
Has abstractyes

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