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Record W2987616518 · doi:10.5430/ijfr.v11n1p206

Female Directors, Family Ownership and Firm Performance in Jordan

2019· article· en· W2987616518 on OpenAlexvenueno aff
Zaid Saidat, Claire Seaman, Mauricio Silva, Lara Al‐Haddad, Zyad Marashdeh

Bibliographic record

VenueInternational Journal of Financial Research · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsStock exchangePanel dataBusinessContext (archaeology)Ordinary least squaresAccountingDemographic economicsSample (material)Regression analysisEconomicsFinanceEconometrics

Abstract

fetched live from OpenAlex

This study examines the impact of female directors on the financial performance of family and non-family Jordanian firms. A sample of 103 Jordanian public firms listed on Amman Stock Exchange for the time period 2009-2015 was selected. The study had a quantitative approach and used a panel data methodology. The data analysis was conducted using Ordinary Least Square Regression. ROA and Tobin’s Q were deployed as measurement of financial performance. The appointment of female directors does not have any significant impact on the financial performance of family firms. However, with regard to non-family firms, female directors appeared to have a negative impact on the performance of these firms. The impact of female directors on family firm performance merits further research in the context of different countries and cultures. Appointments based on qualifications and expertise is more likely to have a positive impact. Jordan is an under-researched area where the impact of female directors on the firm performance would merit further research. Differentiating between the impact of female directors on family and non-family firms would also merit further research, especially in the context of the conditions under which they are appointed.

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)0.000
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 score0.099
Threshold uncertainty score0.303

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.057
GPT teacher head0.310
Teacher spread0.252 · 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

Citations20
Published2019
Admission routes1
Has abstractyes

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