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Identifying the predictors of employee health and satisfaction in an NPM environment : testing a comprehensive and non-linear demand-control-support model

Noblet, Andrew and Rodwell, John 2009, Identifying the predictors of employee health and satisfaction in an NPM environment : testing a comprehensive and non-linear demand-control-support model, Public management review, vol. 11, no. 5, pp. 663-683.

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Title Identifying the predictors of employee health and satisfaction in an NPM environment : testing a comprehensive and non-linear demand-control-support model
Author(s) Noblet, Andrew
Rodwell, John
Journal name Public management review
Volume number 11
Issue number 5
Start page 663
End page 683
Publisher Routledge
Place of publication London, England
Publication date 2009
ISSN 1471-9045
1471-9037
Keyword(s) employee well-being
job satisfaction
job stress
new public management
law enforcement
Summary The Demand-Control-Support (DCS) model is investigated in the context of police officers working within an organization that has relatively widespread uptake of New Public Management (NPM) practices. A survey of 479 police officers from two geographic regions was undertaken and the results indicate that the DCS offers a simple, yet powerful, framework for identifying the conditions to be managed in an NPM-oriented environment. Job control and work-based support predict all four target variables, strengthening the view that decision-making latitude and support from supervisors and colleagues represent critical resources for promoting the well-being, satisfaction and commitment of public sector employees.
Language eng
Field of Research 150311 Organisational Behaviour
Socio Economic Objective 910405 Public Sector Productivity
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2009, Taylor & Francis
Persistent URL http://hdl.handle.net/10536/DRO/DU:30020752

Document type: Journal Article
Collections: Faculty of Business and Law
Deakin Graduate School of Business
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Created: Tue, 27 Oct 2009, 17:30:07 EST by Lee-Ann Stones