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Work–Life Balance Programs and Organizational Profitability: Existence, Availability and Usage

2019· article· en· W2964838337 on OpenAlexaff
Duckjung Shin, Jackson E. I. Enoh

Bibliographic record

VenueAcademy of Management Proceedings · 2019
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsProfitability indexBusinessOrganizational commitmentOrganizational performanceBalance (ability)RevenueWork (physics)Human resourcesWork–life balanceInvestment (military)MarketingEconomicsFinanceManagementPsychologyOrder (exchange)

Abstract

fetched live from OpenAlex

This paper investigates the relationship between work–life balance programs (WLBPs) and organizational outcomes. First, to balance the human cost approach that emphasizes the costs associated with WLBPs and the human investment approach that supports the positive benefits associated with WLBPs, we examine the effect of WLBPs on organizational profitability (revenues minus expenditures per employee). Second, we extend the discussion on the job demands-resources model by considering how and why WLBPs, a type of organizational job resources, can mitigate non-job demands such as personal health and wellness, and family duties and responsibilities. Specifically, we test the moderating role of the availability and usage of WLBPs in the relationship between WLBPs and organization profitability. Our findings support that these two dimensions of organizational job resources (availability and usage) influence organizational profitability. The availability of WLBPs was positively associated with organizational profitability, while the usage of WLPBs was negatively associated with organizational profitability. The availability of WLBPs significantly moderate the WLBPs-profitability relationship, and the usage of WLBPs marginally significantly moderate the WLBPs-profitability relationship. We discuss the theoretical and practical implication of the findings.

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)0.002
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 score0.038
Threshold uncertainty score0.813

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.040
GPT teacher head0.357
Teacher spread0.316 · 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
Published2019
Admission routes1
Has abstractyes

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