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Record W4410873931 · doi:10.11648/j.pbs.20251403.11

Conflict and Harmony in Work and Family: A Bibliometric Perspective on Work-life Balance

2025· article· en· W4410873931 on OpenAlexaboutno aff
Diana Pramudya Wardhani, Achmad Sudiro, Dodi Wirawan Irawanto, Djumilah Hadiwidjojo

Bibliographic record

VenuePsychology and Behavioral Sciences · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsHarmony (color)PsychologyPerspective (graphical)Work–life balanceWork–family conflictWork (physics)Balance (ability)Social psychologyArtificial intelligence

Abstract

fetched live from OpenAlex

This study analyzes publication trends and historical patterns in <i>Work-Family Conflict</i> (WFC) literature using bibliometric analysis. Data were obtained from Scopus, Sinta, and Google Scholar using the keywords <i>work-family conflict, work-life balance,</i> and <i>work stress</i> within the 2020-2025 period. Articles were filtered using <i>Publish or Perish</i> in the fields of management, accounting, psychology, and social sciences. The findings indicate that WFC research has evolved from role conflict conceptualization (1990-2005) to organizational and psychological factors (2006-2018), and the impact of technology and the pandemic (2019-present). Publications have increased significantly since 2020, with the highest contributions from the US, UK, Canada, China, and Australia. Leading journals include the <i>Journal of Vocational Behavior</i>, <i>Journal of Organizational Behavior</i>, and <i>Work & Stress</i>. WFC negatively affects employee well-being, job satisfaction, and family relationships, while also increasing turnover and reducing company productivity. Research gaps remain, particularly in developing countries and in exploring hybrid work models and technology. Future studies should examine labor policies and cultural factors to promote sustainable work-family balance.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.018
Science and technology studies0.0010.003
Scholarly communication0.0000.000
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 head0.117
GPT teacher head0.445
Teacher spread0.328 · 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 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
Published2025
Admission routes1
Has abstractyes

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