Managing hybridity: China’s compressed reform trajectory

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ABSTRACT The hybridity of different public management reform patterns has increased in both space and timeframe due to rapid changes in the external environment and internal shifts in values and structures across countries. This study examines this hybridity by analysing the interrelationships among three major management patterns – traditional public administration, New Public Management (NPM), and post-NPM – through macro-causal analysis. The findings highlight China’s compressed reform trajectory, where institutionalisation of the three major patterns occurred almost simultaneously. In contrast, other countries, such as the United Kingdom, the United States, Japan, and South Korea, have followed a longer process, involving the layering of patterns in a linear sequence. The study contributes to a more nuanced understanding of timeframe, efficacy, and challenges in public management reforms of China and transitional economies, particularly highlighting the roles of temporal factors and path dependence.

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‘New Public Managers’ in Europe: Changes and Trends
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New Public Management (NPM) reforms as described in Chapter 1 have affected the profile and functioning of top civil servants. This chapter discusses the combined effect of personnel reforms and the creation of so-called quasi-autonomous organizations. It would seem that as a result of these reforms two different types of ‘new public managers’ have emerged: senior public officials within the central departments and the top executives of quasi-autonomous agencies. We expect a number of differences between these two categories. The main aim of this chapter is to investigate these expectations. To do so, we will use data from two surveys of our own (on the Netherlands and France) and secondary sources on a number of countries (Denmark, Germany, Italy and the United Kingdom).KeywordsCivil ServiceSenior ManagerExecutive AgencySalary ScalePublic Sector ReformThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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