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Record W3094623565 · doi:10.5267/j.ac.2020.10.011

Relationship between saving and investment pattern and orientation towards finance among working women in the universities of Saudi Arabia

2020· article· en· W3094623565 on OpenAlexvenueno aff
Wardah Abdulrahman Bindabel, Ansa Salim

Bibliographic record

VenueAccounting · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
FundersPrince Sattam bin Abdulaziz University
KeywordsScale (ratio)Construct (python library)Investment (military)FinancePopulationOrientation (vector space)BusinessGeographyPolitical scienceDemographySociologyComputer science

Abstract

fetched live from OpenAlex

The main objective of the study was to find out the relationship between saving and investment pattern and orientation towards finance among the working women at the universities of Saudi Arabia. Orientation towards finance (ORTOFIN) is one's attitude towards effectively managing financial activities. This attitude is backed by individual behavior toward financial management. ORTOFIN scale was made as a construct to measure the behavioral dispositions of individuals that are connected to their behavior patterns towards finance and orientations. The data was collected using the ORTOFIN scale which was constructed and used in the European population as well as validated using standard procedures into the Asian population. The present study concentrates on the working women at the Universities of Saudi Arabia. The data collected from 192 women employees of different Universities in Saudi Arabia. This study states there is a significant positive relationship among the saving and investment pattern and orientation towards finance among the working women at the universities of Saudi Arabia. The finding of the study revealed Financial Management Behavior act as a major contributor to the orientation towards finance and the factor of personnel planning is another significant contributor towards ORTOFIN.

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 score0.008
Threshold uncertainty score0.349

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 head0.026
GPT teacher head0.219
Teacher spread0.193 · 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

Citations10
Published2020
Admission routes1
Has abstractyes

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