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Record W3021035743

The Relationship between Female Representation at Strategic Level and Firm's Competitiveness: Evidences from Cargo Logistic Firms of Pakistan and Canada

2017· preprint· en· W3021035743 on OpenAlexaboutno aff
Adnan ul Haque, Riffat Faizan, Antje Cockrill

Bibliographic record

VenueRePEc: Research Papers in Economics · 2017
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicBelt and Road Initiative
Canadian institutionsnot available
Fundersnot available
KeywordsAutonomySnowball samplingDiversity (politics)Nonprobability samplingRepresentation (politics)Style (visual arts)Demographic economicsSample (material)Gender diversityStratified samplingDynamismBusinessMarketingEconomicsPolitical scienceManagementDemographySociologyGeographyMathematics
DOInot available

Abstract

fetched live from OpenAlex

The comparative study investigates the impact of various attributes such as feminine leadership style, gender diversity, and autonomy linked with the female representation at strategic level on the performance and competitiveness of the Cargo Logistic Firms in the contrasting economies; Pakistan and Canada. Previous studies offered limited insight due to unidimensional while this study takes multivariate approach, considering; variable of interest examined in terms of economies and gender. Cross-sectional research design following semi-structured questionnaire circulated among targeted audience by combining of stratified (probability) and convenience, purposive, and snowball (non-probability) sampling technique at layers of management. The combined sample size is 631 employees. The results showed that females prefer more flexible leadership style in comparison to males. Organisations having high gender diversity and female representation at strategic level are more progressive and innovative. Interestingly, in developing economies, rapid career growth chances are higher for females. Females are more people oriented while male are more task-oriented. Males have high desire for autonomy at workplace.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.277
Threshold uncertainty score0.898

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.328
GPT teacher head0.381
Teacher spread0.053 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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