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Employee work–life balance outcomes in Ireland: a multilevel investigation of supervisory support and perceived organizational support
Abstract
This article examines how employee perceptions of supervisory and organizational support for work–life balance, in addition to the number of work–life balance programmes available, predict a number of work–life balance outcomes including role conflict, job satisfaction, family satisfaction and turnover intentions in a sample of large private and public sector organizations in Ireland. The attitudes of HR managers towards work–life balance programmes are also explored. To account for the nested structure of the data, analyses were conducted using hierarchical linear modelling. We found that perceptions of work–life supportiveness as measured at the HR manager and immediate supervisor levels affect employee uptake of work–life programmes, employee work–life balance outcomes and turnover intentions.
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“Research Proposition 8(a-d): Supervisor support will be positively related to HRM (AI) , (a) initiation, (b) adoption, (c) routinisation, and (d) extension.”
From AI-augmented HRM: Antecedents, assimilation and multilevel consequences (2023) · cites this paper as McCarthy et al., 2013
In the citing paper
“Research Proposition 8(a-d): Supervisor support will be positively related to HRM (AI) , (a) initiation, (b) adoption, (c) routinisation, and (d) extension.”
From AI-augmented HRM: Antecedents, assimilation and multilevel consequences (2023) · cites this paper as McCarthy et al., 2013
In the citing paper
“Research Proposition 8(a-d): Supervisor support will be positively related to HRM (AI) , (a) initiation, (b) adoption, (c) routinisation, and (d) extension.”
From AI-augmented HRM: Antecedents, assimilation and multilevel consequences (2023) · cites this paper as McCarthy et al., 2013
In the citing paper
“Research Proposition 8(a-d): Supervisor support will be positively related to HRM (AI) , (a) initiation, (b) adoption, (c) routinisation, and (d) extension.”
From AI-augmented HRM: Antecedents, assimilation and multilevel consequences (2023) · cites this paper as McCarthy et al., 2013
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Cited by
AI-augmented HRM: Antecedents, assimilation and multilevel consequences
Prikshat, V., Malik, A., & Budhwar, P. (2023) · Human Resource Management Review · doi:10.1016/j.hrmr.2021.100860 · Cited by 200
Metadata
- Authors
- McCarthy, A., Cleveland, J. N., Hunter, S., Darcy, C., & Grady, G.
- Affiliations
- Ollscoil na Gaillimhe – University of Galway (IE)
- Pennsylvania State University (US)
- The Pennsylvania State University
- National College of Ireland (IE)
- Corresponding author
- McCarthy, Alma
- Venue
- The International Journal of Human Resource Management
- Publisher
- Informa UK Limited
- Published
- 2012-08-13
- Volume
- 24
- Issue
- 6
- Pages
- 1257-1276
- ISSN
- 0958-5192 (ISSN-L)
- 1466-4399
- OpenAlex
- W2150253885
- Cited by
- 139
- References
- 0
- Citations by year
- 2026: 2 · 2025: 10 · 2024: 16 · 2023: 12 · 2022: 14 · 2021: 18 · 2020: 11 · 2019: 10 · 2018: 10 · 2017: 15 · 2016: 7 · 2015: 10 · 2014: 2 · 2013: 2
- FWCI
- 14.77
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The International Journal of Human Resource Management (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Work-Family Balance Challenges
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Employee Performance and Management
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Job Satisfaction and Organizational Behavior
- Keywords
- work-life balance, supervisor support, perceived organizational support, HR managers, role conflict, job satisfaction, family satisfaction, turnover intention, employee perceptions, Ireland
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.