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Record W4376874298 · doi:10.6007/ijarbss/v13-i5/16739

Elationship between Work-life Balance and Work Performance among Working Women in West Malaysia

2023· article· en· W4376874298 on OpenAlexaff
Rohaizah Abd. Latif, Davinderpal Singh, Pradip Menon, Saamalla Ramurudek, T. Baskaran, B Raman

Bibliographic record

VenueInternational Journal of Academic Research in Business and Social Sciences · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsRoyal Bank of Canada
Fundersnot available
KeywordsWork–life balanceData collectionWork (physics)Balance (ability)Job satisfactionProductivityPsychologyCorporationOperations managementSocial psychologyBusinessEngineeringStatisticsMathematicsEconomicsEconomic growth

Abstract

fetched live from OpenAlex

The purpose of the study is to examine the relationship between women’s work-family balance and work performance among working women in West Malaysia. With the purpose of accomplishing the aforesaid objectives, a survey is carried out within West Malaysia among 11 states in West Malaysia. A quantitative technique is used for data collection. For this purpose, 395 questionnaire was distributed to working women from different working organisations and 384 questionnaires were returned back. Statistical package for social sciences (SPSS) were used for data analysis by assuming ANOVA, coefficient, correlation, normality, reliability, and regression analysis testings. Hence, the study found a statistically significant positive relationship between work-family and job performance among working women. It also found that a significant positive relationship exists between work-family balance dimension and job performance. Further, satisfaction balance is identified as the major contributor to job performance. It is anticipated that improving the work performance of women will not only increase organizational productivity but will also boost financial performance. As a result, the corporation and female employees have recommended that individuals focus on their engagement and satisfaction balance to improve their productivity or job performance.

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)0.015
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 score0.016
Threshold uncertainty score0.690

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.197
GPT teacher head0.433
Teacher spread0.236 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

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