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Cited by 272

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

The viability of alternative call centre production models

Batt, Rosemary, Moynihan, Lisa·2002·Human Resource Management Journal

Abstract

In this article we outline three alternative production models and discuss their applicability to call centre management. These include the classic mass production model, the professional service model and the mass customisation model. We then develop a theoretical framework that identifies potential causal links between management practices, workers‚ affective and cognitive reactions, and performance outcomes. We use this framework to review literature on service workplaces in organisational behaviour and HR studies and to assess the empirical evidence regarding these causal explanations. Finally, we present two recent quantitative studies of call centre performance – one that examines affective explanations and one that explores cognitive explanations for the HRperformance link. We conclude with directions for future research.

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Crossref · 10.1111/j.1748-8583.2002.tb00075.x

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Metadata

Authors
Batt, R., & Moynihan, L.
Venue
Human Resource Management Journal
Publisher
Wiley
Published
2002-11
Volume
12
Issue
4
Pages
14-34
ISSN
  • 0954-5395 (ISSN-L)
  • 1748-8583
OpenAlex
W2092208820
Cited by
272
References
5
FWCI
26.04
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: The viability of alternative call centre production models. https://app.cochat.ai/papers/10.1111/j.1748-8583.2002.tb00075.x
The paper
Batt, R., & Moynihan, L. (2002). The viability of alternative call centre production models. Human Resource Management Journal. https://doi.org/10.1111/j.1748-8583.2002.tb00075.x

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.