Materials & methods

While the subsequent section will illustrate this step-wise process in some detail, the reader is invited to turn towards the papers for a full account of the data generation and analysis procedures.

Qualitative Pre-studies (for more detail, see Paper I)

As a first step, two semi-guided focus groups were conducted. They were used as a rather exploratory means of testing existing and generating new hypotheses with respect to RQ1.

Compared to classical interviews, these discussions allowed for an integration of broader perspectives within a limited survey preparation time. Furthermore, the thought-provoking group dynamics arising from interactive discussion settings can provide relevant additional insights, which is desirable considering the exploratory nature of the topic at hand (see Kühn & Koschel, 2011). The discussions thematised attitudes towards ‘Sommerfrische’ destinations and the factors influencing tourism decisions in response to climate change and heatwaves.

A qualitative content analysis based on Mayring (2000) was performed using the verbatim transcripts of both focus groups. This method seemed suitable since the analytical goal was to explore possibly relevant additional influence factors by reducing the multiplicity of the material to a number of manageable categories. It was not, as done within grounded theory or critical realist approaches, to build a comprehensive theory grounded in the data or conduct an in depth- analysis of mechanisms underlying behavioural choices (Kühlmeyer et al., 2020). To scrutinize the researcher’s own subjectivity, the coding scheme, which was developed in an iterative open coding process, was critically discussed with project partners who observed the focus groups (Kühn & Koschel, 2011). The results were ultimately used for a review of the perception of rural

‘Sommerfrische’ travels (see Paper I) and the survey development.

Quantitative survey development

The core dataset, which is the basis for most of the papers included in this dissertation, is derived from a quantitative online-survey among 877 Viennese citizens. The survey covered a range of themes, including (i) general travel patterns, preferences and motives (ii) previous trips to rural summer destinations in Austria, (iii) attitudes towards these destinations (based on an extended TPB model), (iv) perceptions of heat and personal adaptation measures, (v) desired tourism and transport offers in such rural Austrian destinations, (vi) sociodemographics, and (vii) available and typically used mobility tools. The recruitment of participants was carried out by an online panel provider, which allowed to create a representative sample for the Viennese population by age and gender. The sample description of participants can be found in Papers II and III. After cleaning and preparing the dataset, a descriptive analysis of the final data was conducted.

The survey data were used for the analysis of sociodemographic, attitudinal, and motivational factors influencing tourism choices (see Papers II & III). To also analyse the influence of supply- side factors, different data mining strategies were exploited in order to collect and annotate information on each destination visited by one or more participants (see categories 4 and 5 in Table 4). The complete dataset including trip and destination features was the basis for the analysis of spatial travel patterns as well as the tourism destination and transport mode choices (see Papers IV & V). Table 4 below (a modified version of Table 2 from Paper V) illustrates the

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final dataset consisting of the survey data (covering categories 1, 2 and 3) and the annotated trip and destination data (covering categories 4 and 5).

Table 4: Data generated through survey (groups 1- 3) and data mining (groups 4-5)

Determinant dimensions and variables Source

1 - Person and household characteristics

gender, age, education, income, location, heat exposure, travel patterns mobility tools: cars, bikes, PT reduction cards

Viennese survey 2 - Travel motivations

e.g. relaxing, sports, time with family, escape the city Viennese survey 3 - Situational characteristics

e.g. trip duration, travel party, chosen accommodation Viennese survey 4 - Attributes of car and PT trips

travel time, changes, service intervals, PT category etc. VAO 5 - Destination features

in-destination mobility offers and regional connectivity by bus/train tourism facilities in 60-minute walking distance

‘Scotty’ (ÖBB) search, OSM queries, manual search Analysis of factors influencing urban-rural destination choice (see Papers II and III)

The first analytical step involved the analysis of descriptive data and a segmentation analysis (see Paper II), which seemed suitable given the increasingly heterogeneous preferences and travel patterns of travellers visiting nature-based destinations (Derek et al., 2019; Smith et al., 2019).

For the segmentation of travellers, a-priori (theory-driven or common-sense) or a-posteriori (data- driven, explorative) approaches exist (Dolnicar, 2008). In this study, both methods were tried, but as suggested by previous research (see Finsterwalder & Laesser, 2013), sociodemographic variables seem to have less explanatory value when explaining travel behaviour. In consequence, travel motives were used for the segmentation analysis. The Principal Component Analysis of all motive-related variables produced three core motive groups, which were each described with regard to socio-demographic, behavioural and attitudinal variables.

The second analytical step includes a destination choice model based on the Theory of Planned Behaviour (see Paper III). Due to the importance previous research has attributed to the social influences of peers and media on tourism decisions, the coverage of these themes seemed important, especially when studying tourism choices under the influence of climate change (Sirakaya & Woodside, 2005; Karl, 2018). Since conventional discrete choice models (such as Pröbstl‐Haider & Haider, 2013) often disregard innate personality features and their persuasibility by social groups, the TPB was considered a suitable theoretical framework. As a result, an extended TPB model was developed that accounts for factors relevant to the study context (see Conner & Armitage, 1998 for an account of extensions used in previous TPB models). For this study, social norms, the media image of rural Austrian destinations, past behaviour and perceived heat stress were included as additional constructs. Two latent dependent variables were defined:

the behavioural intention to visit such destination (i) in general and (ii) in case of increasing urban heat waves. Confirmatory factor analysis was applied to all 48 TPB-related items before developing the Structural Equation Model (SEM) in an iterative process to analyse the strength and direction of the causal relationships between the constructs. Different fit indices were used to evaluate the change in model fit and explained variance of the behavioural intention produced by the additional construct (see Hu & Bentler, 1995; Lam & Hsu, 2006).

Chapter 4 - Outlining the research concept

29 Analysis of mobility patterns and combined mode and destination choice (see Papers VI-V) In tourism and transport literature, actual and perceived/cognitive accessibility are mentioned as key constraints to travels by PT (Prideaux, 2009; Schirpke et al., 2018; Le-Klähn, 2019). These constraints can hardly be studied using psychological models such as the TPB with their focus on personality rather than destination features (see Sparks & Pan, 2009). Instead, existing studies focussing on accessibility often use GIS-based approaches to investigate the PT network connectivity of specific tourist regions (Tomej & Liburd, 2020). Since this thesis does not focus on single destinations but on factors influencing modes choices for tourism trips, a trip-based approach was applied.

Using both the survey and annotated data, an exploratory spatial analysis was performed (see Paper IV) to explore the spatial distribution of travel patterns. The first step included a comparison of car-free and car-owning households with respect to the desire and perceived ability to visit rural Austrian destinations, showing a similar travel interest but different perceived travel abilities. Car-free households feel more constrained in their travels, resulting in a need to adjust either their travel modalities, used transport modes (e.g. PT, carsharing) or visited destinations.

To analyse spatial travel patterns of PT travellers and the characteristics of those destinations visited without a car, destinations were first clustered by their respective share of car-free arrivals based on the Viennese survey data. For this purpose, a two-step cluster analysis was performed, identifying three distinct groups, which were then used for a k-means cluster analysis (Dolnicar, 2008). The three groups included destinations predominantly visited (i) by PT (n=25), (ii) by car (n=175), and (iii) by both transport modes alike (n=112). Based on the cluster variable, all three groups were characterized regarding available tourism and transport infrastructures (mean-value comparison) and their spatial distribution and clustering throughout Austria (hotspot analysis).

Following this visualisation of spatial patterns of mode and destination choices, an inferential analysis was performed to investigate their causal relationships. Therefore, a multinomial logit model (MNL) was developed to analyse the causal relationships between mode and destination choices and the variables influencing both. In many cases, mode choices are modelled using DCM (working with disaggregate stated preference data) whereas destination choices are often modelled using spatial interaction models (working with aggregate, revealed preference data) (see Guo et al., 2012; Landauer et al., 2014). There are doubts about the alignment of stated behaviour with actual behaviour, which is why revealed data were preferred over stated data in this study.

The MNL allows to jointly model destination and mode choices using revealed preference data at the disaggregate level of individual trips, which is a particular strength of this approach. Given the large number of alternatives (295 destinations times two mode choices) and the large number of candidate model predictors (341 variables from all five dimensions of Table 3), an ex-ante screening of candidate variables was done using semi-partial correlations. The high correlation between their t-values with those of the final model predictors suggest this to be a suitable selection method. Assuming a dependency between different choice alternatives, a nested structure was tried, but it did not improve the model fit.

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Q-methodology (see Paper VI)

In the last step, a Q-methodology study was undertaken to study automobility in the context of urban-rural tourism trips in New Zealand (see Paper VI). The purpose of this study was to provide a more holistic perspective on the narratives evolving around leisure or tourism-related mobility practices, complementing and contrasting the insights generated throughout the previous papers on the Austrian context. To take these previous insights consideration while also exploring unknown perceptions of transport options for tourism trips, as well as reflections upon personal choice motivators in an open yet structured manner, Q-methodology seems suitable (Stergiou &

Airey, 2011). As an inherently mixed method, it asks a diverse set of participants to rank state- ments by their level of agreement (Watts & Stenner, 2012). The more quantitative sorting process is then complemented by a qualitative post-sorting interview that aims to delve into the reasoning behind the sorting choices and depict possible inconsistencies and hidden choice motivators.

Drawing from insights of classical mode choice models, studies on tourism mobilities and mobility cultures, a set of 47 statements on mode choice influences were compiled. They covered normative statements on (i) transport and tourism infrastructure and supply, (ii) travel behaviour, (iii) transport policy, (iv) public discourses, (v) instrumental car use motives, (vi) symbolic- affective car use motives, (vii) additional trip aspects, and (viii) motivations for leisure trips. To diversify the group of participants as emphasized by Q-researchers (Watts & Stenner, 2012), selection criteria were defined and different communication channels used to find the total of 25 interview partners. Once entered, the quantitative Q data were analysed by means of a by-person factor analysis that is at the core of Q-methodology (Watts & Stenner, 2012). After completing the quantitative analysis, the results were contrasted with and complemented by the qualitative information gained through the post-sorting interviews and analysed by means of a content analysis. This helped reducing the risk of inadvertently overlooking any pertinent choice factors.