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Cited by 174

The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.

When Do Employees Speak up for Their Customers? A Model of Voice in a Customer Service Context

Open access
Lam, Chak Fu, Mayer, David M.·2014·Personnel Psychology

Abstract

We develop a conceptual model of customer‐focused voice and test it in a hospital setting. Drawing from theory and research on voice, we find that customer orientation and job autonomy are positively associated with customer‐focused voice. In addition, consistent with social information processing theory, these relationships are moderated by service climate, such that a high service climate compensates for the less desirable aspects of employees or their jobs. Finally, we provide evidence for a critical but untested assumption of the voice literature by linking hospital‐level customer‐focused voice to hospital‐level service performance. Results based on data from four unique data sources, provided at varying points in time, and at different levels of analysis demonstrate support for our conceptual model.

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Crossref · 10.1111/peps.12050

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Metadata

Authors
Lam, C. F., & Mayer, D. M.
Venue
Personnel Psychology
Publisher
Wiley
Published
2013-08-23
Volume
67
Issue
3
Pages
637-666
ISSN
  • 0031-5826 (ISSN-L)
  • 1744-6570
OpenAlex
W1813976347
Cited by
174
References
13
FWCI
10.74
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: When Do Employees Speak up for Their Customers? A Model of Voice in a Customer Service Context. https://app.cochat.ai/papers/10.1111/peps.12050
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Lam, C. F., & Mayer, D. M. (2014). When Do Employees Speak up for Their Customers? A Model of Voice in a Customer Service Context. Personnel Psychology. https://doi.org/10.1111/peps.12050

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.