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Varieties of Capital Cities

The Competitiveness Challenge for Secondary Capitals

David Kaufmann

The political and symbolic centrality of capital cities has been challenged by increasing economic globalization. This is especially true of secondary capital cities; capital cities which, while being the seat of national political power, are not the primary economic city of their nation state. David Kaufmann examines the unique challenges that these cities face entering globalised, inter-urban competition while not possessing a competitive political economy.
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David Kaufmann

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Nicola Francesco Dotti

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Edited by Nicola F. Dotti

This book provides theories, experiences, reflections and future directions for social scientists who wish to engage with policy-oriented research in cities and regions. The ‘policy learning’ perspective is comprehensively discussed, focusing on actors promoting ‘policy knowledge’ and interaction among different stakeholders. The book also aims to provide practical insights for policy-makers and practitioners interested in research-based approaches to cities and regions.
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Nicola Francesco Dotti

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Lasse Gerrits and Stefan Verweij

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Lasse Gerrits and Stefan Verweij

We argue that infrastructure projects are complex and that evaluations of such projects need to do justice to that complexity. The three principal aspects discussed here are heterogeneity, uniqueness, and context. Evaluations that are serious about incorporating the complexity of projects need to address these aspects. Often, evaluations rely on single case studies. Such studies are useful because they allow researchers to focus on the heterogeneous, unique, and contextual nature of projects. However, their relevance for explaining other (future) projects is limited. Larger-n studies allow for the comparison of cases, but they come with the important downside that their relevance for explaining single projects is limited because they cannot incorporate heterogeneity, uniqueness, and context sufficiently. The method Qualitative Comparative Analysis (QCA) presents a promising solution to this conundrum. This book offers a guide to using QCA when evaluating infrastructure projects.

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The Evaluation of Complex Infrastructure Projects

A Guide to Qualitative Comparative Analysis

Lasse Gerrits and Stefan Verweij

Infrastructure projects are notoriously hard to manage so it is important that society learns from the successes and mistakes made over time. However, most evaluation methods run into a conundrum: either they cover a large number of projects but have little to say about their details, or they focus on detailed single-case studies with little in terms of applicability elsewhere. This book presents Qualitative Comparative Analysis (QCA) as an alternative evaluation method that solves the conundrum to enhance learning.
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Lasse Gerrits and Stefan Verweij

In this concluding chapter, some of the main issues concerning the evaluation of complex infrastructure projects with QCA are revisited. First, QCA’s capacity to truly capture and study the complexity of the development of infrastructure projects is discussed. QCA’s take on complex causality is relatively static because it does not explicitly integrate the time dimension. Various strategies to integrate time in QCA are discussed, including Temporal QCA (TQCA) and Time-Series QCA (TS/QCA). The different strategies have their strengths and weaknesses and they relate to different research steps (i.e., the case, the calibration, and the comparison) involved in QCA. Second, the deployment of QCA in real-world evaluations and various issues evaluators may run into are discussed. These issues include learning and political accountability, the presentation and visualization of results, and the transfer of lessons learned.

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Lasse Gerrits and Stefan Verweij

We explain and demonstrate how the researcher can identify recurring patterns across cases on the basis of the calibrated data matrix, in a systematic and transparent way. The comparative process in QCA consists of three main steps. First, the calibrated data matrix needs to be transformed into a truth table. In the truth table, the cases are sorted across the logically possible configurations of conditions. Second, the truth table has to be minimized. This is done through the pairwise comparison of truth table rows that are considered to agree on the outcome and differ in their score in but one of the conditions. The result of the minimization is a solution formula. Third, the solution formula needs to be interpreted. Two common issues in the truth table minimization are limited diversity and logical contradictions. We present various strategies for dealing with these issues.