Case Study

Based on Maaløe, I will outline the frames for how a case study is defined, including what knowledge such a study could generate. As he states, case studies are characterized as:

• empirical studies of contemporary phenomena that

• exist within the framework of the phenomena's own life and

• do not always meet an obvious, clear interface between the phenomenon and context but which

• require an investigation involving as many data sources as possible.

(Maaløe, 1996, p. 58)³¹

This understanding will be linked to the aeromobilities approach in this thesis, where knowledge is based on the study and understanding of open systems. This is viewed as contextual, as Maaløe states, “Case studies is the method to use, when the interface between the phenomenon and the context is not obvious” (Maaløe, 1996, p. 78)³². A

31 Own translation of Danish text:

• ”Er empiriske undersøgelser af samtidige fænomener”

• ”Foregår inden for rammerne af fænomenernes eget liv”

• ”Møder ikke altid en indlysende klar grænseflade mellem fænomen og kontekst”

• ”Kræver at undersøger inddrager så mange datakilder som muligt.”

(Maaløe, 1996, p. 58]

32 Own translation of Danish text:

“Case-studiet er den metode, man vælger, når græsefalden mellem fænomen kontekst ikke er indlysende klar” (Maaløe, 1996, p. 78].

case study approach focuses on case-specific contextual knowledge in contrast to universal and statistical generalizations that exist within conventional aviation research. This is why my selection of the case study approach is logical in relation to my meta-theoretical position.

The case study approach makes it possible to achieve holistic, in-depth and thorough analysis and interpretation of data and observations (Flyvbjerg, 1991; Flyvbjerg, 2001). This aligns with my approach to look at data through the lenses of materialities and policies. It will be possible to obtain context-dependent knowledge, which should open up to an inductive interpretation (Flyvbjerg, 1991, p. 145). Instead of a single case study, four case studies will be conducted since this will increase the possibility of identifying common characteristics across the cases.

Since my research focuses on a holistic understanding of the drivers behind the production of aeromobilities as imbedded in society, the collection of empirical data based on a field studies seems to be appropriate (I. Andersen, 2014, p. 142). As Andersen points out, the field study has its advantages when you want to understand a certain behavior in contextual settings (I. Andersen, 2014, p. 142). My field studies were conducted at five different locations across Europe. The research, which focuses on understanding the rationalities and causal dynamics within societies, can be challenging since my knowledge and fundamental understanding of local structures and historical development is limited compared to a ‘local’ researchers. This challenges the potential depth of my understanding of the research field, but potentially this will be counter-balanced by the intensity of my research of five different cases. Moreover, I have had the ambition to follow the same structure in each case – based on a theoretical framework I will present below – however during my investigation of my case studies, I realized that each case also have its own life and nature. Therefore, I follow the same structure at each case, but also make space for the case to develop individual.

Selection of Cases

Case selection can be based on different criteria, such as random selection or information-oriented selection, which both have different subcategories of case selection criteria. To obtain as much information and understanding of how societies handle airports, the case studies in this thesis are selected based on the criteria labeled as “extreme/deviant” as a subcategory to the information-oriented selection (Flyvbjerg, 2006) (see also: (Bloch & Lassen, 2016)). This case selection criteria is motivated by the idea that “…the typical or average case is often not the richest in information. Atypical or extreme cases often reveal more information because they activate more actors and more basic mechanisms in the situation studied” (Flyvbjerg, 2006, p. 229).

This strategic selection of cases increases the possibility for generalization across the case studies, and therefore a deeper understanding of strategies and policies behind hub airports (Flyvbjerg, 2001). This is line with the thoughts in critical realism, with the focus on what makes the hub airport possible based on specific context. What elements, components or discourses are present for different developments of hub airport? The studies will focus on European airports since they all operate under relatively comparable framework conditions, which increases the potential of knowledge transfer to the Danish context.

The four European airports that are the basis of my analysis in relation to Copenhagen Airport were selected based on this information-oriented selection strategy (Flyvbjerg, 2006, p. 230) and the “extreme/deviant” case criteria based on their individual characteristics (Flyvbjerg, 2006). Amsterdam and Helsinki Airports were selected because these airports have had significant developments in the number of passengers they serve. Further, Brussels Airport and Zurich Airport were selected due to the especially problematic situations they have been in because of the grounding and bankruptcy of the locally-based network carrier, which caused a temporary but significant drop in traffic. The selected airports, including the Copenhagen airport, are illustrated along with the selection criteria in Table 4 and Figure 10. Each case will be introduced and presented more thoroughly in individual chapters to come.

Table 4: Case airports and selection criteria (Flyvbjerg, 2006). (a) Copenhagen Airport from 2000 to 2017, the level of transfer passengers have decreased by 35% and the relative

transfer passenger share of total passengers has decreased from 47% to 20%. Hub connectivity presently indicates a decrease in hub connectivity from 2007-2017 of 30% (ACI

Europe, 2017a, p. 20) – see further section 5.3. (Other sources: Official airport passenger statistics and (MIDT data) and (SRS seat data)).

Case airports Information oriented selection with focus on 'extreme/deviante' cases Based on Flyvbjerg (Flyvbjerg 2006: 230)

Amsterdam One of the main hub airports in Europe 68,5 mio passengers in 2017

Helsinki Strong development in hub activities between Europe/Asia 18,9 mio passengers in 2017

Brussels Significant passenger drop due to bankruptcy of Sabena in 2001 24,8 mio passengers in 2017 After 14 years passengers level back at index 100

Zurich Significant passenger drop due to grounding of Swiss in 2000 29,4 mio passengers in 2017 After 14 years passengers level back at index 100

Copenhagen Hub activities are declining 

29,2 mio passengers in 2017 Based on Hub connectivity and relative share of transfer passengers(a)

Figure 10 illustrates the locations of the selected airports and their characteristics.

Figure 10: Locations of the four case hub airports and Copenhagen Airport and the selection criteria based on Flyvbjerg (Flyvbjerg, 2006, p. 230).

In order to be able to conduct a knowledge transfer to the Danish context, I have in line with the selected cases; also applied same method approach to the Danish case.

By having the same approach, I will be able to reflect on my findings in relation to the production aeromobilities production at Copenhagen Airport.