Browse by title

You are looking at 1 - 10 of 1,458 items :

Clear All
You do not have access to this content

Decision-Making for Sustainable Transport and Mobility

Multi Actor Multi Criteria Analysis

Edited by Cathy Macharis and Gino Baudry

Multi-Actor Multi-Criteria Analysis (MAMCA) developed by Professor Cathy Macharis enables decision-makers within the sectors of transport, mobility and logistics to account for conflicting stakeholder interests. This book draws on 15 years of research and application during which MAMCA has been deployed to support sustainable decisions within the transport and mobility sectors.
This content is available to you

Edited by Cathy Macharis and Gino Baudry

This content is available to you

Cathy Macharis and Gino Baudry

You do not have access to this content

Air Transport Security

Issues, Challenges and National Policies

Edited by Joseph S. Szyliowicz and Luca Zamparini

The growing number of terrorist attacks throughout the world continues to turn the interest of scholars and governments towards security issues. As part of the Comparative Perspectives on Transportation Security series, this book provides a multidisciplinary analysis of the security challenges confronting air transportation. The first part encompasses the industry’s characteristics and the policy, economic and regulatory issues shaping the security environment. The second provides a comparative analysis of security policies and practices in several key countries.
This content is available to you

Joseph S. Szyliowicz and Luca Zamparini

You do not have access to this content

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.

You do not have access to this content

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.

You do not have access to this content

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.

You do not have access to this content

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.

You do not have access to this content

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.