The DOI matches its Crossref record.
Profiles of commitment: An empirical test
Abstract
Prior research has demonstrated the importance of distinguishing among foci and bases of commitment. Foci of commitment are the individuals and groups to whom an employee is attached, and bases of commitment are the motives engendering attachment. This study uses distinctions among foci and bases of commitment to develop four profiles of commitment, and examines the extent to which differences in these patterns predict other variables. Cluster analysis of 440 employees suggests the following profiles: (1) The Locally Committed (employees who are attached to their supervisor and work group), (2) the Globally Committed (who are attached to top management and the organization), (3) the Committed (who are attached to both local and global foci), and (4) the Uncommitted (who are attached to neither local nor global foci). The profiles are differentially related to intent to quit, job satisfaction, prosocial organizational behaviors, and certain demographic and contextual variables. Implications of these findings for theory and practice are discussed.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
A person‐centered approach to commitment research: Theory, research, and methodology
Meyer, J. P., & Morin, A. J. S. (2016) · Journal of Organizational Behavior · doi:10.1002/job.2085 · Cited by 487
Bisbe, J., Batista-Foguet, J.-M., & Chenhall, R. (2007) · Accounting, Organizations and Society · doi:10.1016/j.aos.2006.09.010 · Cited by 422
Perreira, T. A., Morin, A. J. S., Hebert, M., Gillet, N., Houle, S. A., & Berta, W. (2018) · Journal of Vocational Behavior · doi:10.1016/j.jvb.2017.12.004 · Cited by 130
Does the Job Satisfaction—Job Performance Relationship Vary Across Cultures?
Ng, T. W. H., Sorensen, K. L., & Yim, F. H. K. (2009) · Journal of Cross-Cultural Psychology · doi:10.1177/0022022109339208 · Cited by 123
McInerney, D. M., Ganotice, F. A., King, R. B., Marsh, H. W., & Morin, A. J. S. (2015) · Teaching and Teacher Education · doi:10.1016/j.tate.2015.08.004 · Cited by 95
Metadata
- Authors
- Becker, T. E., & Billings, R. S.
- Venue
- Journal of Organizational Behavior
- Publisher
- Wiley
- Published
- 1993-03
- Volume
- 14
- Issue
- 2
- Pages
- 177-190
- ISSN
- 0894-3796 (ISSN-L)
- 1099-1379
- OpenAlex
- W1969785372
- Cited by
- 402
- References
- 0
- FWCI
- 3.04
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
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.
