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Exploring social and moral dynamics around efficiency in government outsourcing

Fisher, D. (2022). Exploring social and moral dynamics around efficiency in government outsourcing. (Unpublished Doctoral thesis, City, University of London)


Efficiency is an idea that comes in many guises. This thesis is a qualitative investigation that considers the various meanings this word inhabits in different contexts: in a strategy document, during a disruption, in a public inquiry, by an occupational group, in the moral dimension and as a dark practice. Each theoretical lens that is cast onto the uses and understandings of efficiency makes unique contributions to a variety of literatures in business and management studies. My first article contributes to both commensuration and sense-making literatures by detailing how assumptions around what is commensurable with efficiency can break down and be repackaged through disruption. My second article contributes to literatures on embodiment, bio- power and governmentality. In this paper I identify how efficiency plays on a worker’s body and mind, detailing how perceived individualized energy trade-offs of train drivers are mediated by technology and organizational design. This article inextricably links understandings of efficiency with wider society - initiating a discussion about normative dimensions of efficiency. This is explored in article three by theoretically considering what a moral efficiency would look like in government outsourcing in the UK. Finally, article four contributes to wrongdoing literatures by detailing how efficiency-cost cutting as a practice sits alongside a broader range of practices used to raid the state and convert public assets into private wealth.

Publication Type: Thesis (Doctoral)
Subjects: H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
Departments: Bayes Business School > Bayes Business School Doctoral Theses
Bayes Business School > Management
Doctoral Theses
[thumbnail of Fisher_thesis_2022 PDF-A.pdf] Text - Accepted Version
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