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Stefan Verweij and Christian Zuidema

Using a complexity lens has not gained widespread attention for explaining why certain planning processes succeed and others fail. This chapter responds by proposing Qualitative Comparative Analysis (QCA) as a method for analyzing the performance of spatial planning processes, taking into account the perceived complexity of the contextual environments encountered. This proposal is rooted in a contingency perspective. Contingency perspectives assume that, for spatial planning approaches and organizational formats to perform well, they should be adapted to the environment in which they intend to operate; i.e., a degree of ‘fit’ is required. The perceived complexity of the contextual environments is among the key arguments contingency studies propose as informing fit. QCA follows this argument, but also responds to some of its main critiques. First, QCA can help avoid a deterministic perspective on complexity informing fit, as it assumes complexity as being conceptualized by stakeholders involved in the planning process. Second, QCA can help overcome a reductionist perspective and its simplistic reliance on a pairwise analysis of relations between isolated environmental variables and isolated organizational or decision-making variables. QCA approaches ‘fit’ from a more holistic perspective, by analyzing the performance of spatial planning processes as configurations of alternative spatial planning approaches, organizational forms, and contextual environments. The result is an understanding of ‘fit’ following what QCA considers ‘complex causality’, which is manifested in configurations of various interacting and interdependent conditions. Next to being able to analyze relationships between the performance of spatial planning processes and the perceived complexity of the contextual environment, by using the notion of ‘complex causality’ QCA also allows for analyzing spatial planning processes as complex adaptive systems.

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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

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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Lasse Gerrits and Stefan Verweij

We explain why it is important to research specific cases and how exactly cases are to be understood and studied using QCA. Cases allow the researcher to account for the heterogeneity, uniqueness, and contextuality of projects. Whereas the term ‘case’ is often used indiscriminately, in QCA it is a clearly defined and important building block. In QCA, cases are conceptualized as configurations of conditions. This configurational nature highlights the complexity of the case. Cases can be researched in two principal ways: case-driven and theory-driven. The case-driven route is decidedly grounded in empirical material, with the boundaries and aspects of cases being constructed during the empirical research process. In the more theory-driven route, the boundaries and aspects of cases are defined by prior theories. Both routes constitute dialogues between data and theory. The chapter explains the concrete research steps involved in both routes.

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Lasse Gerrits and Stefan Verweij

We explain and demonstrate how the selected cases have to be prepared for the actual comparison. This involves a serious effort with regard to the interpretation of the case materials. In QCA, this process of interpreting data is guided by calibration, where raw (qualitative) case data are transformed into quantitative values. Calibration is important because it systematizes interpretation and makes it transparent. There are three principle types of calibration in QCA: crisp-set QCA, fuzzy-set QCA, and multi-value QCA. We explain and demonstrate the different types of calibration using real examples. We also discuss good practices that will help the researcher in making sound decisions when calibrating. The calibration results in a calibrated data matrix, which forms the input for the formal comparison in QCA. Having completed this chapter, the researcher will be able to start the comparison.

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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.

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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