Promoting active mobility behavior by addressing information target groups: The case of Austria

Journal of Transport Geography 83 (2020) 102664

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Promoting active mobility behavior by addressing information target groups: The case of Austria

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Karin Markvicaa, , Alexandra Milloniga, Nadine Haufeb, Maximilian Leodoltera a b

Center for Mobility Systems, AIT Austrian Institute of Technology GmbH, Vienna, Austria Department of Spatial Planning, Vienna University of Technology, Vienna, Austria

A R T I C LE I N FO

A B S T R A C T

Keywords: Active mobility Mobility behavior Social milieus Information target group

Current transport policy objectives aiming to increase active mobility can solely be achieved by changing people's mobility behavior. To arouse interest and influence the decision process of the people, adequate information sources and services as well as appropriate incentives and motivation have to be used, but they have to be target-group specific to reach people more effectively. Influencing factors such as mobility habits, attitudes towards transport modes, shared social norms and values must be considered. Since people resemble in some characteristics, methods from social sciences are applied to identify homogeneous groups of shared mobilityrelated information needs and to extract appropriate group-related arguments to promote active mobility (e.g. health, environment, costs, image, or adventure). The paper describes the methodological approach and the results in form of six comprehensively defined homogeneous target groups derived from 12 qualitative focus groups. Moreover, a survey among a representative sample of 1000 persons in Austria is presented. Based on the outcomes, customized concepts for each specific target group (arguments, information needs, and preferred information channels) have been developed. The concept provides a solid basis for implementing measures to promote active mobility as prerequisite for reaching transport policy objectives.

1. Introduction The current transport legislation on the European level targets a competitive and resource efficient transport system (White paper, 2011) to reach the goals of the 2030 climate & energy framework. These goals aim at lowering the greenhouse gas (GHG) emissions by at least 40% compared to 1990 levels, getting at least 32% of energy from renewables and increasing energy efficiency by at least 32.5% (European Commission 2019). Despite efforts at various levels (politics, stakeholders, etc.), the transport sector continues to contribute significantly to GHG emissions worldwide. In Austria, a share of the transport sector in national GHG emissions of 45.4% excluding emissions trading or 28.8% including emissions trading in 2016 (Umweltbundesamt, 2018a) and a significant increase in fossil fuel sales in 2016 compared to the previous year (Umweltbundesamt, 2018b) show that current challenges cannot be solely solved by implementing more efficient technologies (Binswanger, 2001). This is particularly true because sales of biofuels slumped for the first time in 2016 (−16.3%; Umweltbundesamt, 2018b). The increase in domestic passenger transport in Austria by 3.5% from 2011 to 2014 (Umweltbundesamt, 2016) and the rise of around 5% in the number of

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trucks and buses on the high-ranking road network from 2015 to 2016 (Umweltbundesamt, 2018b) suggest that measures focusing on behavioral change have by far not yet achieved their goal. To achieve goals of efficiency, consistency and sufficiency (Linz, 2004; Fischer and Grießhammer, 2013; Buhl and Acosta, 2016a; Buhl and Acosta, 2016b), active mobility must be promoted, which incorporates walking and cycling. As the infrastructure capacity is limited, active mobility has the potential to contribute to political goals and quality of life (e.g. health) with the features of being carbon-neutral, cheap and more space-saving than other modes of transport (Lähteenoja et al., 2006; Randelhoff, 2015). Nevertheless, active mobility currently has a comparatively low share in cities in many countries (Buehler and Pucher, 2012) although it brings economic advantages. For Austria, the average 5% modal share of cycling accounts for estimated health benefits and respective reduced mortality worth 405 million Euro per year according to the WHO Health Economic Assessment Tool (HEAT). Related health benefits by regular physical activity ‘save’ 412 lives every year (WHO, 2018). Initiating behavior change in mobility does not only require providing the adequate infrastructure, but also the willingness to change individual mobility behavior patterns. The implementation of

Corresponding author. E-mail address: [email protected] (K. Markvica).

https://doi.org/10.1016/j.jtrangeo.2020.102664 Received 26 June 2019; Received in revised form 13 January 2020; Accepted 7 February 2020 0966-6923/ © 2020 Elsevier Ltd. All rights reserved.

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2. Effectiveness of measures promoting active mobility

sustainable forms of behavior and the choice of climate-neutral transport options are fundamentally opposed by several barriers that must be addressed in a targeted manner. Among the most important obstacles are the lack of visibility of options (e.g. e-bike sharing; see Markvica et al., 2017) and insufficient information about the different effects of certain forms of behavior (Haufe et al. 2016). In many cases, people are not informed about alternatives to their common mobility habits (e.g. London Tube Map with Walklines1), or the available solutions and developments are perceived not to meet their needs and thus, they are consequently rejected (Gabrielli et al., 2014). Although the use of climate-neutral mobility options is generally desired and politically demanded, implementation usually fails at the beginning of the communication and diffusion chain. Strategies for initiating sustainable changes in behavior must therefore start at this point. Identifying efficient ways to inform people about alternative options that are presented in an appealing way is thus necessary to address the population. Literature suggests that interventions are significantly more effective targeting specific groups (see Section 2) and that the selection of an appropriate segmentation approach is the key to capture relevant information such as group-specific values, attitudes and the information channels used by these target groups (see Section 3). Hence, mobility behavior must be captured in the social and spatial context (Scheiner, 2009; Dangschat, 2013) to be changed towards a more environmentally friendly mobility (Hunecke, 2015). Comprehensive knowledge of the needs, wishes and preferences of target groups is necessary to address them with suitable arguments and via the right information and communication channels (Markvica et al., 2017). As for now, there is no segmentation of the total population regarding essential attitudes and behavior patterns relevant to mobility available for Austria and therefore target-group-specific interventions lack the basis. Our research concentrates on developing this baseline. A worldwide known milieu-based segmentation approach is altered to fit the mobility context and reveal how social groups communicate about mobility options and perceive them from a customer perspective. The study therefore concentrates on the following scientific question:

In the literature, there are numerous examples of studies assessing the effectiveness of measures for promoting active mobility (e.g. Crawford and Garrard, 2013; Norwood et al., 2014; PASTA Consortium, 2017). In general, interventions for increasing the share of non-motorized transport comprise a variety of ‘hard’ (physical) and ‘soft’ (nonphysical) measures (Magginas et al., 2018). The line between hard and soft measures is often not drawn strictly as this “seems to be primarily of academic interest” (OECD, 2004: 13). Evidence for the effectiveness of specific measures is hard to find, as interventions are often part of combined strategies to achieve different goals, e.g. reduce traffic congestion, improve health, decarbonize transport, etc. These goals are not necessarily accompanied by controlled evaluation studies. Given the need to increase active mobility, which is expressed in many urban development plans (Koszowski et al., 2019; Cirianni et al., 2018), it is important to identify the most appropriate measures for different circumstances to provide guidance for planners and decision makers in achieving their transport policy goals and obtain the highest possible return of investment. Conclusions drawn from several systematic reviews of related studies mainly provide the following insights: 1) Evidence for statistically significant positive effects of hard measures, such as community design, infrastructure availability and infrastructure quality, is inconsistent (Cao et al., 2006; Handy et al., 2006; Ogilvie et al., 2007; Forsyth and Krizek, 2010). Many studies indicate that the built environment can influence people's travel behavior, even when controlling for self-selection effects (Cao et al., 2006; Handy et al., 2006). In some cases, it is shown that the effectiveness of physical measures on walking and/or on cycling is strongly dependent on the general community layout as well as the availability and quality of the infrastructure. While the availability of infrastructure and (to some extent) its quality is important for cycling, for walking community design features are more relevant. The reviewed studies suggest that the complexity of factors influencing mobility behavior change makes it difficult to extract distinct cause-effect relations. Forsyth and Krizek (2010) report that the evidence does not back up even some seemingly obvious assumptions. For example, while distance is relevant for pedestrians, some are willing to walk significantly further than planning rules expect, infrastructure such as footpaths are not equally necessary for all, and attractive aesthetics have a much smaller effect than assumed. The review studies draw the conclusion that the success of hard measures imposing changes in the physical and economic environment depends on a variety of (local) factors and needs to be thoroughly planned for each case.

What information target groups or clusters can be identified, localized and described for Austria to enable the promotion of active mobility in a target-group-specific manner? The study aims at identifying groups that have homogeneous mobility behavior patterns and need specific information or respond particularly to specific arguments for behavioral change (e.g. health, environment, costs, image, experience). The occurrence of the groups is extrapolated to the population and spatially located in Austria. Furthermore, the groups are extensively described using common characteristics to make the target groups addressable for future developments and to enable targeted inclusion in research and development projects. Specific concepts for mobility interventions and campaigns can be drawn from the description of the target groups as milieu-related motives can be utilized for the promotion. Based on a comprehensive discussion of the effectiveness of different approaches to promote walking and cycling in Section 2, as well as related work concerning methods for target group segmentations and milieu-based modeling in Section 3, the paper describes the methodology designed for this approach combining both qualitative-interpretative and quantitative-statistical methods in Section 4. Section 5 presents the results of the recently completed empirical analysis. In Section 6, the opportunities and restrictions arising from the results are discussed and summarized in the concluding Section 7.

2) Soft measures such as campaigns, education or social marketing show significant, but moderate effects (Forsyth and Krizek, 2010; Bird et al., 2013). Only few programs have been robustly evaluated or observed for a longer period, but the systematic reviews show that soft measures are promising to a certain extent, as many examples of studies report measurable effects. However, the size of the effect is often low and dependent on a variety of factors, which require more research efforts. The design of campaigns might be relevant, but it is still unclear which characteristics of an intervention are most important, as there have been equal effects shown for quite minimal educational campaigns as well as more elaborate interventions. Moreover, such measures are not equally effective for everyone; it seems that campaigns and social marketing reach mainly persons already prone to change. The characteristics and specification of target groups appear to be highly relevant for the success of soft measures.

1 “London Tube Map with Walklines: sometimes it's quicker to walk”: http:// rodcorp.typepad.com/rodcorp/2003/10/london_tube_map.html (5/8/2019)

2

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3) The highest statistically significant effect is observed in ‘tailored’ interventions targeting motivated groups and/or groups with specific requirements (Forsyth and Krizek, 2010; Ogilvie et al., 2007).

groups or gender. The combination of household variables, age, work status and income is, for example, used to identify different life cycles or life stages (Haustein and Hunecke, 2013; Jäger, 1989). Sociological research on the social stratification in modern societies has shown that the complexity of social activities cannot be explained satisfactorily by socio-demographic variables alone (Haustein and Hunecke, 2013). Attitudinal variables have been introduced to explain and understand individual mobility behavior in more depth and to segment the population into meaningful groups (Haustein and Hunecke, 2013). The life style and milieu-oriented approach was first introduced into mobility research at the end of 1990s (Hinkeldein et al., 2015). Among the attitude-based segmentations, two basic classes exist: the mobility-specific approach resp. life-style approach and the holistic approach resp. milieu approach (Haufe and Dangschat, 2016):

Following the previous insight, the reviews state that soft measures are particularly successful when targeting specific motivational factors, e.g. people in life changing situations (e.g. moving to a new area), who have to adopt to new circumstances, people with low income, who are sensitive to pricing strategies, or individuals with limited access to driving (little self-confidence or no driving license), who need alternatives. Ogilvie et al. (2007: 9) conclude that “different types of people may respond to different approaches, tailored to their psychological characteristics or life circumstances”. This does not only affect the motives influencing people's behaviors (e.g. financial, environmental, self-fulfillment aspects), but also the channel via a message is communicated (e.g. by a personal device, an authority or the social peer group). Hard and soft measures are frequently combined to get better results in changing mobility behavior (Laine et al., 2018; Bamberg et al., 2011). They are especially present in terms of ‘banning the car’ as described in Laine et al. (2018). Further examples are the Lüneburg transdisciplinary research project ‘Energy Transition & Mobility’ which is based on push and pull measures already in use in various cities and proposes a reciprocal combination (Opel and Schomerus, 2014) and Brannigan et al. (2018) promoting this approach in terms of cycling projects. However, there is very little data available to provide information on the impact of a combination. To sum up, the analysis performed in the meta reviews suggests that the effectiveness of an intervention can be significantly increased by targeting specific groups. However, there are different approaches how to segment target groups, which can have consequences on the appropriateness of selected interventions.

• Mobility-specific approaches, which are further known as life style •

approaches, are based on attitudes and preferences in relation to mobility. They focus on the analysis of orientations and behaviors, which are relevant for this field. Types are elaborated from the observed field of action. Holistic approaches are known as milieu approaches and relate to the idea that spheres of activity are influenced by general values, beliefs and viewpoints. They do not relate to a specific field of action and the distinction between types is founded on fundamental values and attitudes in everyday life.

While mobility-style approaches include mobility-related variables (e.g. Götz et al., 2002; Hunecke et al., 2010), milieu approaches are based on general values and attitude variables alone. In recent years, the use of ‘pure’ fundamental attitude-based segmentations to promote environmentally sustainable transport has significantly increased. An often-used milieu model is the market segmentation of the Sinus Institute (e.g. Sinus, 2013; INTEGRAL/T-Factory, 2014). The so-called Sinus-Milieus are based on fundamental values and everyday attitudes towards work, family, leisure, money and consumption (Barth and Flaig 2013). Sinus-Milieus aim to explain specific attitudes and methods of behavior for each milieu on causal-analytical grounds. According to Sinus, value attitudes and mental predispositions that are the result of a person's individual and social development, which have a major effect on behavior. The use of the Sinus-Milieus for scientific analysis has been criticized because of its lack of information disclosure. However, studies show valuable results, so that it is common to use the Sinus-Milieus in the field of mobility and sustainability research despite this disadvantage (Gröger et al., 2011). Deeper insights into mobility behavior gained by connecting mobility patterns to attitudes and values of certain groups have been one of the main advantages of the attitude−/value-based approach. Hence, the attitudinal based approach provides starting-points for interventions to alter individual mobility behavior to increase sustainability. Nevertheless, knowledge on the evolvement of attitudes towards different transport modes and on braking habits addressing specific attitudes and values is missing so far. Thus, this study focusses on mobility related information and the communication of mobility options within social groups. Gaining insights on the opinion-forming process leading to the image of transport modes and the group-specific information retrieval, strategies can be developed using attitude-related motives to encourage specific groups to alter their mobility behavior.

3. Target group segmentation 3.1. Segmentation approaches Segmentation approaches in transport research are commonly used to analyze daily travel behavior. Furthermore, transport providers and municipalities use segmentation approaches to target interventions in favor of sustainable transport modes. Different segmentation approaches exist in the field of travel behavior, e.g. to detect specific target groups or segment a population into homogeneous groups (Prillwitz and Barr, 2008). There are four basic classes of variables mostly focused in mobility research: travel behavior, spatial variables, socio-demographic and socio-economic variables and attitude-based approaches. In the travel behavior approach, the population segments are defined by their actual behavioral patterns which are, for example, characterized by trip frequency, mode choice and trip purpose (Kutter, 1973). With a focus on walking, Ausser et al. (2013) identified walkingtypes based on a combination of frequency and attractiveness of walking. The geographical approach focusses on the aspects of the residential location of people and therefore differentiates between urban, suburban and rural areas. National travel surveys tend to use this approach to describe the mode choice within the spatial context, considering the related opportunities and restrictions. Other studies concentrate on the settlement structure of specific study areas to examine its influence on travel behavior (Krizek and Waddell, 2002) or take the qualities of locations into account such as the ‘accessibility’ (Geurs and Van Wee, 2004) or ‘walkability’ (Madsen et al., 2013). An easy, in practice applicable, segmentation approach in transport research is based on socio-demographic and socio-economic variables. Official statistics are often used as data base for these segmentations. The most common socio-demographic categorizations are based on age

3.2. Milieu-based modeling To describe – explain and predict – user-specific preferences and actions, there is a need for a differentiated theory of action, which allows a mapping of the dynamics of social changes. Within sociology, differing behavior has mostly been explained by the means of the social class- and stratification-concepts in the past (e.g. Giddens, 1984; Herkommer, 1975; Geißler, 1990). Since World War II, growing income 3

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kilometer (statista, 2019a). In 2018, around 58.3% of Austria's population lived in cities, i.e. municipalities with more than 10,000 inhabitants (statista, 2019b). Since most parts of Austria are rather rural, small towns outweigh the big ones. There is only one major city in Austria and that is the City of Vienna with approximately 1.9 million inhabitants. There are four other bigger cities (Graz, Linz, Salzburg and Innsbruck), which have between 100,000 and 200,000 inhabitants and are therefore a lot smaller than the capital city Vienna. The lowest density with only about 59 inhabitants per square kilometer can be found in Carinthia (statista, 2019a). The study intended to cover different spatial conditions. During data collection it was differentiated between the following sizes of communities considering the specific position of the Austrian capital:

and educational levels have led to modern societies with high standards of living. Within these societies a subsequent change of the social structures has reduced the importance of classic inequalities (Hradil, 1999). Applied within the field of behavior, this classic approach now comes across major problems. It is argued that these difficulties are partly due to a change of the amount of personal choice, which an individual has within the society. In modern societies, behavior is now largely free from restraints and expectations. Behavior can therefore no longer be explained as the result of social class- and stratificationconcepts or socio-economic variables, but more as an expression of personal value patterns, cultural attitudes or socio-cultural variables. Socio-economic structures are no longer primarily responsible for behavior but remain as boundaries, within which personal choice is possible (Bögenhold, 2001). With this strong reduction of the importance of classic socio-economic variables and the rising expression of personal values and cultural attitudes, concepts based solely on socio-economic variables are not able to sufficiently explain inequalities and the resulting behavior. New theories are needed, which replace or supplement the existing concepts of social class and stratification with the element of personal choice. Socio-cultural variables like social milieu have thus finally been introduced to explain and understand individual behavior in more depth and to segment the population into meaningful groups (Dangschat and Mayr, 2012). Hradil argued that life goals or fundamental values and everyday attitudes define the behavior of people and presented a three-stage model: social status, social milieu, and social action. By social milieu he understood a group of people who have the same external conditions of life and/or inner attitudes, out of which common lifestyles form (typical behavior pattern) (Hradil, 2009). For transport research this means that the milieu or fundamental values and everyday attitudes determine the typical behavior pattern of mobility and the observable mobility behavior (Fig. 1). Socio-economic structures are no longer primarily responsible for behavior but remain as boundaries. Therefore, this study focuses on mobility related information, or how social groups communicate about mobility options. By understanding the origins of ‘images’ of different mobility options and by analyzing group-specific habits of information retrieval, it is possible to develop strategies utilizing milieu-related motives for persuading specific groups to change their mobility habits.

• ≤ 5000 inhabitants • ≤ 20,000 inhabitants • ≤ 50,000 inhabitants • > 50,000 inhabitants • Vienna While Vienna represents the metropolitan area, Graz and Linz are typical urban areas. Small towns and rural areas can be found throughout Austria (e.g. Ried im Innkreis as typical small-city region with < 20,000 inhabitants). Further information on the sample characteristics can be found in Section 4.4. 4.2. Current population segmentation Sinus-Milieus, which have been described in Section 3.1, are the starting point of our research. These social milieus are available for different countries and are based on surveys performed on a regular basis. Based on these comprehensive surveys, k-means clustering is performed to identify clusters common values and attitudes. Social milieus have been investigated since the year 2001 and have been kept up-to-date ever since. The current version of the Sinus-Milieus in Austria from the year 2014 consists of 10 clusters of persons with homogenous values and attitudes, which are called ‘social milieus’. Fig. 2 illustrates the position of the milieu in the Austrian society according to social situation and basic orientation as well as their occurring in the general population. The higher a milieu is in this chart, the higher are education, income and occupation group. The position on the value axis (horizontal) marks the respective formative basic orientation, i.e. the social guiding values dominant in a certain historical epoch and the mentalities derived from them. The 10 milieus that are the starting point of our research will be briefly characterized:

4. Methodology 4.1. Spatial location Austria has a population of around 8.9 million inhabitants (early 2019) and a population density of 105.6 inhabitants per square

• Conservatives (6%): milieu in the traditional area; characterized by • • • •

Fig. 1. Theoretical construction to explain mobility behavior. 4

a high ethic of responsibility; strongly influenced by Christian values; high appreciation of education and culture; critical of current social developments Traditionals (13%): focused on security, order and stability; skeptical or hostile to changes; strong roots in the old petty bourgeois world, the traditional working-class culture and the traditional rural milieu; overrepresented among old people Established (9%): performance-oriented elite with traditional anchoring; high level of professional awareness; pronounced ethos of responsibility Post-materialists (9%): ‘cosmopolitan social critics’; well educated above average; wide range of interests; critical attitude towards globalization; often involved in environmental or social issues; drawn towards sustainability in everyday life High Achievers (9%): characterized by efficiency, personal responsibility and individual success have top priority; flexible and globally oriented; high level of business and IT competence

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Fig. 2. Sinus-Milieus in Austria (INTEGRAL, 2014).

• Digital Individualists (8%): mentally and geographically mobile; • • • •

balanced gender ratio and eight participants each. Focus groups composed of individual members that “presumably share some common views” (Yin, 2011: 141). Moderately sized groups have the advantage of creating an atmosphere encouraging honesty (Powell and Single, 1996). Literature suggests between six and ten persons participating in a focus group that are strangers to each other (Powell and Single, 1996). Our focus groups were used to “compare the groups' reactions to the same concepts” (Nassar-McMillan and Borders, 2002: 2). In our case, this referred to mobility behavior and attached communication and information needs. The participants were recruited after their affiliation to the 10 Sinus-Milieus. In the composition of the groups, it was ensured that all Sinus-Milieus were equally represented in the focus groups except for the ‘Traditionals’ since this type has limited mobility due to higher age and attitudinal requisitions. Thus little evidence was expected for this group out of the qualitative phase. The composition of the focus groups in line with the regional context resulted in the following Sinus-Milieu combinations:

optimally networked both online and offline; constantly on the lookout for new experiences and ‘projects’; good education and skills; very young milieu and therefore on the rise New Middle Class (14%): mainstream ready to perform and adapt; striving for professional and social establishment; preference towards secure and harmonious conditions; usually ‘wait-and-see’ mentality Adaptive-Pragmatists (12%): pronounced life pragmatism; ‘adapters’ of new trends; willingness to perform on the one hand and desire for fund and entertainment on the other hand; imitate behavior of other milieus; overrepresented among young people Consumption oriented (9%): consumer-oriented lower class; strives for participation; marked by pronounced feelings of disadvantage, fear of the future and resentment; effort to keep pace with the lifestyle and consumer standards of the middle class Escapists (11%): momentary, experience-hungry lower middle class; search for fun and entertainment while at the same time denying the conventions of the majority society

1) Metropolitan (Vienna): ‘Established’ and ‘Conservatives’, ‘Digital Individualists’, ‘Escapists’ and ‘Consumption Oriented’, ‘High Achievers’, ‘Post-materialists’ 2) Urban (Graz): ‘Established’ and ‘Conservatives’, ‘Digital Individualists’, ‘Escapists’ and ‘Consumption Oriented’, ‘High Achievers’, ‘Adaptive-Pragmatists’ and ‘New Middle Class’ 3) Small town/rural (Ried): ‘Established’, ‘Adaptive-Pragmatists’, ‘New Middle Class’, and ‘Post-materialists’

4.3. Data collection and analysis The methodological approach consisted of quantitative and qualitative methods which were based on the results from a desk research on classifications of mobility target groups and the systematic preparation of mobility-related information categories (see Fig. 3). 4.3.1. Qualitative survey For the qualitative approach, an interview guideline for focus groups was developed covering various questions related to current mobility patterns, attitudes towards transport modes and their image, willingness to change behavior, information needs, affinity to certain arguments and campaigns, familiarity with different mobility information services, campaigns and new mobility options. Potential of mobility-related information in relation to different target groups were examined in the context of focus groups with representatives of different social milieus of the introduced population segmentation. Therefore 12 focus groups in the three mobility areas metropolitan (Vienna), urban (Graz) and small town/rural (Ried) took place with a

Building on the findings of the focus groups, six hypothetical clusters could be identified. These clusters where used to generate indicators and accordingly statements (following the Sinus-Milieu approach) that enable the quantification of the clusters. All in all, 32 statements have been developed to identify the information clusters. All of them have been used in the quantitative study. 4.3.2. Quantitative survey A questionnaire on mobility behavior, needs and attitudes including all 32 statements was designed and performed by a combination of computer assisted web interviewing (CAWI) and computer assisted 5

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Fig. 3. Methodological background.

information cluster. Accepting iterative adjustments of the centroids within the clustering algorithm showed minor changes in the second decimal place. This confirms the hypotheses about the information clusters' constitutions. As this is a hypothesis-based approach, we have assumed a given number of clusters. All clusters show considerable size, which justifies their relevance and confirms the hypothesis of six different information clusters. Voxco was used as survey software, SPSS24 for additional calculations and multivariate evaluation, and Gesstabs as tabulation software for data preparation.

Table 1 Demographics for the qualitative and quantitative survey. Focus groups (N = 96)

Online and telephone survey (N = 1000)

Sex Male Female

48% 52%

49% 51%

Age 14–29 yrs 30–44 yrs 45–59 yrs ≥ 60 yrs.

15% 50% 30% 5%

23% 26% 26% 25%

Size of residential area ≤ 50,000 inhab. 25% > 50,000 inhab. 33% Vienna 42%

69% 10% 21%

4.4. Sample characteristics The survey sample consists of persons from focus groups and survey participants. The gender ratio is balanced in the sample in both qualitative and quantitative survey. While the survey shows that the different age groups are roughly equally represented, significantly more 30–44-year-olds were interviewed among the focus groups (see Table 1). Five focus groups took place in Vienna, four in Graz and three in Ried. Participants of the focus groups in Graz and Ried tended to live in the agglomeration or towns nearby instead of the city itself. To get a more accurate picture in terms of the spatial distribution in Austria, the quantitative survey had a stronger focus on persons living in towns with less than 50,000 inhabitants. By performing a combination of focus groups and a quantitative survey based on extensive literature review it was ensured that not only the current mobility patterns and the willingness to use certain mobility offers were gathered but also background information such as the capability, the competences, the household equipment and the available transport services in the living environment.

telephone interviewing (CATI). This was essential to reach both, people who are used to computers and the Internet and those who are not, as for example, elderly people. One thousand respondents were interviewed (750 CAWI and 250 CATI) for about 30–40 min. The survey sample was chosen to be representative for the Austrian population. Due to cost reasons (namely a budget cut by the funding organization), the sample was reduced to the smallest possible. The market research partner Integral decided on 1000 interviews as case numbers in the individual clusters would be too small otherwise. Based on results from the qualitative study, a disproportional spatial distribution of the information clusters was suspected. For this reason, it was decided on a biased sample size distribution, requiring reweighting for data analysis. Finally of the 32 statements the eight most informative - in terms of expected variation over the different information clusters - were selected. A so-called “hypothesis-supported” segmentation used to confirm hypotheses that arose from the focus groups about the existence and constitution of the information clusters. Hence, based on the results from the qualitative methods, initial centroids for each of the six information clusters were formulated. A centroid is described by the population's average answers to the eight statements, artificially biased according to the hypotheses by adding a bias-term. This bias-term equals a shift between −1 and + 1 multiplied by the standard deviation of the statement's answers of the whole population. The magnitude and direction (plus or minus) of the shift results from the qualitative analysis. To assign respondents to the predefined centroids, a k-means clustering was performed, whereas each centroid represents one

5. Results The result section presents the findings in depth. First, general insight on the clusters characteristics is provided. Based on that, additional information is provided for each cluster in terms of mobility style and motivation, current mode choice and potential for behavioral change, information needs and arguments as well as socio-demographic data. Combining the results from the qualitative and quantitative survey originated in six information clusters: 1) Spontaneous – On the Go, 2) Highly Informed Sustainability, 3) Efficiency-oriented Information Pickers, 4) Interested Conservatives, 5) Low Demand, 6) Digital 6

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Fig. 4. Resulting information clusters and their relation to Sinus-Milieus.

different transport options and have a positive attitude towards all transport modes and sharing concepts. The ‘Efficiency-oriented Information Pickers’ cluster is stable on routines but does many nonroutine trips with a positive attitude towards driving and walking and resentments towards public transport and cycling due to perceived inefficiency resp. lack of fun. The ‘Interested Conservatives’ cluster is positive towards driving and cycling, stable in behavior but open minded. These persons regard walking as too time consuming and have only limited interest in sharing concepts. People belonging to the cluster ‘Low Demand’ are drawn to cars (and want to use their own), use the public transport only for commuting or short distances and regard active mobility as leisure activity. For the cluster ‘Digital Illiterates’, the car is an object of desire but very cost intense. They have fixed patterns and they are not interested in sharing concepts.

Illiterates. Fig. 4 shows the information groups in their relation to the Sinus-Milieus as the segmentation basis and indicates the distribution of the clusters in the Austrian population. In the following, the six clusters identified will be briefly characterized: 1) Spontaneous – On the Go (6%): has a high basic orientation and a high social status; can be found among the Sinus-Milieus of the ‘High Achievers’ and ‘Digital Individualists’ and partially the ‘Adaptive-Pragmatists’; is very mobile and flexible 2) Highly Informed Sustainability (17%): has a moderate basic orientation and a middle to high social status; resembles mainly the milieus of the ‘Established’, ‘Post-Industrialists’ and partially the ‘High Achievers’ and ‘New Middle Class’; is ecology-minded combined with a high information demand 3) Efficiency-oriented Information Pickers (16%): with varying social status and high basic orientation; can be found among the ‘High Achievers’, ‘Post-materialists’, ‘Adaptive-Pragmatists’, ‘Escapists’ and the ‘Digital Individualists’ milieu; is very organized in the uptake of information 4) Interested Conservatives (35%): average social status and a low to medium basic orientation; resembles the ‘New Middle Class’ and to a limited level the ‘Post-materialists’, the ‘Established’, the ‘AdaptivePragmatists’, the ‘Conservatives’, the ‘Consumption Oriented’ and the ‘Traditionals’; its need for information is mediocre 5) Low Demand (16%): has a low social status and an average basic orientation; matches the ‘Consumption Oriented’, ‘AdaptivePragmatists’, ‘New Middle Class’ and ‘Escapists’ milieus; has a low requirement for information 6) Digital Illiterates (10%): with a low social status and basic orientation; represented by the ‘Traditionals’ milieu as well as the ‘New Middle Class’ and the ‘Consumption Oriented’; is overwhelmed by digital media

5.2. Current mode choice and potential for behavioral change The current mode choice pattern for the six information clusters shows certain preferences among the groups. The lowest share of cycling usage among all six clusters and the second highest share of public transport usage and walking can be found among the persons assigned to the ‘Spontaneous – On the Go’ cluster. The cluster ‘Highly Informed Sustainability’ walks very often but nonetheless has a big share of car. A tendency towards car use could also be identified for the ‘Efficiencyoriented Information Pickers’ cluster who uses all transport modes. The most frequently used transport modes among the ‘Interested Conservatives’ cluster are car and bicycle. Car use is very dominant among the ‘Low Demand’ cluster. The cluster ‘Digital Illiterates’ on the other hand walks very often and cycles on a regular basis. Table 3 points out the preferences towards certain modes of transport for each cluster as well as the most frequent combinations used. Among all clusters, a willingness to change transport mode of 49% was detected. These 49% consist of 22% willing to change in general (core potential) and 27% willing to change when appropriate information is given (additional potential). Within the group of current car drivers, the willingness to change towards walking is 15% (7% core potential and 8% additional potential). From car to cycling it is 20% with same values for core and additional potential. The willingness to change from car to public transport is 23% (9% core potential and 14%

5.1. Mobility style and motivation For each cluster, the mobility style and motivation as well as attitude towards transport modes are presented in Table 2. The ‘Spontaneous – On the Go’ and ‘Highly Informed Sustainability’ clusters use 7

Journal of Transport Geography 83 (2020) 102664 Object of desire, costs often too high Very bad image, captive riders Rather positive, lack of fun Critical, too slow but more fun than cycling No Rather not Limited

Drawn to it, fun factor Positive, to avoid crowded public transport

Cars are like second skin, often two cars Used only for commuting and short distances Leisure activity leisUre activity Positive, but tries to avoid driving, no insistence on ownership but should be available Positive, but concerns (aggressive atmosphere)

Positive, only given up if to tedious Usage only for pragmatic causes (commuting) Positive, modern and calming Critical, too time consuming

Security, costs, stability Costs, planning, stability

Fixed patterns Habitual behavior

Stable behavior, but open minded Pragmatic, reliability, novelty

Different options, mobile, stable on routine trips Efficiency, rationality, planning Car enthusiasts, strongly use car-sharing Critical, perceived as inefficient Critical, lack of fun Positive, preferred over cycling Limited

In addition to the current mode choice and attitude towards certain transport modes, the information need as well as appropriate arguments and offers were questioned. Table 4 shows that the information requirement and the willingness to change decreases constantly from the first cluster to the last cluster. The convincing arguments cover topics from fun, creativity, flexibility, individuality, rationality, efficiency, social responsibility and sustainability to costs and health. Appropriate apps only reach four out of six groups since the ‘Low Demand’ cluster and the ‘Digital Illiterates’ cluster do not use apps at all while campaigns reach all information needs groups. The average number of information sources used is highest for the ‘Highly Informed Sustainability’ cluster, which uses nearly twice the average (3.6 in the sample). This means that this cluster can be reached via different sources, whereas the ‘Digital Illiterates’ cluster hardly uses any information channel and is therefore difficult to address. 5.4. Sex, age and spatial distribution

Yes

Even though demographic characteristics alone are not sufficient to characterize the clusters. They are essential to gain a better understanding of the clusters' lifestyle. Table 5 gives an overview of the demographic data and spatial distribution of the six information target groups. Outstanding statistical results (more common than total of respondents or less common than total of respondents) are highlighted by bold print. The sex ratio within the groups is balanced except for the clusters ‘Efficiency-oriented Information Pickers’ and the ‘Digital Illiterates’. The ‘Efficiency-oriented Information Pickers’ cluster has a larger share of males (60%) than females (40%) and the ‘Digital Illiterates’ cluster has a much smaller share of males (28%) than females (72%). Within the ‘Spontaneous – On the Go’ cluster, the average age is significantly lower (39 years) than the population sample average (47 years) which is explained through technophilia as a driving force. People older than 59 years are overrepresented within the ‘Interested Conservatives’ cluster. In terms of spatial localization, a concentration on urban areas can be identified in the case of the ‘Spontaneous – On the Go’ cluster. The spatial distribution of the ‘Highly Informed Sustainability’ cluster equates to the sample average somewhat in favor of larger cities. This means that the major part lives in municipalities with less than 50,000 inhabitants (66% versus 70% in the sample) but slightly more people than average live in cities with more than 50,000 inhabitants (34% versus 30% in the sample). The ‘Efficiency-oriented Information Pickers’ cluster is slightly underrepresented in municipalities with less than 5000 inhabitants (34% versus 42% in the sample) but on the contrary overrepresented in the capital city (30% versus 21% in the sample). The ‘Interested Conservatives’ cluster can be found throughout Austria but is significantly underrepresented in the capital city (14% versus 21% in the sample). The ‘Low Demand’ cluster can also be found throughout Austria but is overrepresented in municipalities with 20,000–50,000 inhabitants (13% versus 8% in the sample). The major part of the ‘Digital Illiterates’ cluster can be found in more rural areas with less than 20,000 inhabitants (70% versus 62% in the sample) while it is underrepresented in the capital city (16% versus 21% in the sample).

Interest in sharing concepts

Attitudes towards public transport Attitudes towards cycling Attitudes towards walking

Attitudes towards driving

Motivation

Responsibility, sustainability, awareness

All modes used (especially active modes) Mobility style

Mobile, flexible, not trapped in routines Efficiency, flexibility, experience Positive, flexible, no insistence on ownership Positive, reasonable, but not individual Positive, but not trendy Positive, efficient and fast on short distances Yes

Low demand Interested conservatives Highly informed sustainability Spontaneous – on the go

Table 2 Mobility style, motivation, attitudes towards transport modes and interest in sharing concepts for the identified information target groups.

additional potential). In addition, the willingness to use active modes instead of public transport is 14% (8% core potential and 6% additional potential). The willingness to change towards other modes is highest among the clusters ‘Spontaneous – On the Go’ and ‘Highly Informed Sustainability’. Only 7% of the persons assigned to within Cluster ‘Digital Illiterates’ are prepared to change their transport mode. 5.3. Information needs and arguments

Efficiency-oriented information pickers

Digital illiterates

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Table 3 Mode choice and potential for behavioral change of the information target groups (N = 1000). Spontaneous – on the go

Highly informed sustainability

Efficiency-oriented information pickers

Interested conservatives

Low demand

Digital illiterates

Dominant transport mode (used more than once a week) Walking 65% 70% Cycling 14% 27% PT 44% 47% Motorcycle 3% 3% Car 68% 65%

53% 16% 38% 4% 68%

59% 24% 23% 3% 70%

41% 19% 17% 3% 75%

60% 20% 18% 0% 52%

Frequent combinations PT/walking Car/walking Car and two other modes Solely by car ø number of transport modes

24% 15% 8% 29% 2.0

17% 16% 11% 20% 2.2

11% 16% 8% 29% 1.8

9% 20% 10% 29% 1.9

7% 10% 7% 47% 1.6

10% 12% – 25% 1.7

Willingness to change towards From car to walking From car to cycling From PT to walking/cycling Overall willingness

other modes 5% 7% 14% 20%

10% 15% 16% 31%

7% 6% 8% 16%

7% 12% 6% 18%

6% 8% 4% 13%

2% 4% 2% 7%

Highest values are highlighted in bold print.

6. Discussion

and attitudes of the six clusters identified, concrete measure can be proposed for each cluster. There are a variety of measures taken to promote active mobility in different countries. For Austria, this is particularly true in terms of cycling as there are not only campaigns targeting current shortages but also competitions on a national level to encourage cycling on the commute.3 Whether interventions to promote active mobility are promising depends on several factors – target group specific intervention is one of them. It has been mentioned earlier that the effectiveness of an intervention can be significantly increased by targeting specific groups (e.g. Forsyth and Krizek, 2010). Looking at the classic segmentation approaches, that means using information on travel behavior, spatial location, socio-demographic or socio-economic data for the design of the intervention so far. The life style and milieu-oriented approach offers an increased focus on information needs and communication channels, which stems from market research, and thus enables a more targeted addressing of the group in focus. This counteracts the moderate effects of campaigns, education or social marketing identified by Forsyth and Krizek (2010) and Bird et al. (2013) as these measures are not equally effective for everyone. Tailored intervention gives the opportunity to assess the effectiveness on the behavior of groups or clusters beforehand, which saves time and money. Furthermore, research points out that life-changing situations are a promising starting point for interventions as individuals moving to new homes and people who change their residential area or working environment tend to question mobility habits (MOBILSERVICE, 2004). Especially younger age groups are flexible users due to the probability of changes in their private situation (USEmobility, 2011). This makes them an interesting target group for behavior change interventions in the mobility domain. To use this window of opportunity, it is essential to speak their language and act through the right information channels. For Austria, that means that the background information on the six clusters such as the touchpoints, expectation on communication, style, elements and arguments for cycling and walking are valuable input for the design of interventions. Even though, the popularity of communication channels changes over time (especially social media). The findings are a promising starting point for interventions. It is particularly easy to update the cluster information as they correlate with the Sinus-Milieus, which are revised every couple of years.

In this study six information target groups are identified, which can be addressed for promoting active mobility behavior. The qualitative and quantitative analysis of mobility patterns, information retrieval habits and social values and the respective generation of information clusters form the basis for identifying successful strategies to encourage active mobility behavior. 6.1. Target group segmentation advantages compared to other approaches Different approaches to segmentation are used depending on the discipline. Individualization as a megatrend (Gemünden and Schoper, 2015) requires a new approach to target group segmentation as needs, values and preferences come to the fore. The individual use of solutions is becoming increasingly important, making a target-group-oriented offer essential. The Sinus-Milieus respond to this development in society, as they focus their attention on personal factors and, for example, more on the emotional level. This fits very well the fact that successful product placements and services are advertised more strongly via associated values. The focus groups in combination with the quantitative surveys have shown that the type formation under the guise of the Sinus-Milieus reflects the population of Austria to a sufficient degree. It has been found that the mobility needs of individual groups are very strongly connected with underlying value concepts and ideas, from which it was deduced that this level can be used for an address. This type of classification has already been helpful in terms of follow-up research performed in Austria (e.g. Smarter Together in Vienna2). At the same time, these findings have so far only been used in Austria and would have to be transferred to other countries to test the segmentation concept. 6.2. Active mobility measures and acceptance among clusters The measures that can be taken to influence mobility behavior range from nudges (i.e. influencing human behavior without imposing prohibitions and/or mandatory measures or economic incentives) to information provision, legal and/or tax law requirements, monetary punishments/rewards, prizes and competitions to calculation examples and training sessions. As can be seen, the range of potential ‘hard’ and ‘soft’ measures is broad. Based on the insight on the preferences, needs 2

“We are Smarter Together”: https://www.smarter-together.eu/ (5/8/2019)

3

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No use of apps Classic campaigns Non No use of apps No particular Park and ride systems No particular No particular Cycling infrastructure (taking a bicycle in) public transport Useful information, environment Environmental responsibility Car- and bike- sharing, alternative transport modes, cycle infrastructure, pooling

Costs Costs, health Efficiency, health, costs Rationality, sustainability, costs, health

‘Hard’ measures as the sole action can encounter misunderstanding and resistance among the citizens affected. A combination of ‘hard’ and ‘soft’ measures is therefore more promising to make the underlying argument easier to understand for the target group. In particular, restrictive measures require a combined approach to ensure enforcement. The example of the ‘mobility account’ illustrates this particularly well as the introduction, which aims at a moderate and conscious use of mobility, could have serious consequences for the realities of life of the population. This can hardly be implemented without an appropriate communication strategy in Austria as acceptance among the population is currently not sufficient to the extent required. 6.3. Promising starting points for interventions We found that some clusters are more promising target groups than others: ‘Spontaneous – On the Go’, ‘Highly Informed Sustainability’, ‘Efficiency-oriented Information Pickers’ and partially the ‘Interested Conservatives’. To reach them via ‘soft’ measures, the following information is valuable: The playful, digital and creative-individualistic access via social media channels and below the line would be an appropriate communication strategy for mobility behavior change, which especially focuses on those people resembling the ‘Spontaneous – On the Go’ cluster in its values and attitudes. In terms of life-changing situations, a mobility game to explore the new residential area and connect to other people would be an opportunity to get these people not only interested in mobility related decision making but also engaged. Integrating environmental and social aspects, appeals and arguments as well as individualistic would in addition make it interesting for those resembling the ‘Highly Informed Sustainability’ cluster in most characteristics. The ‘Efficiency-oriented Information Pickers’ cluster primarily changes its mobility behavior because of changing circumstances, which can be restrictions or additional costs that make the mobility behavior inefficient. The ‘Interested Conservatives’ cluster is responsive to mobility alternatives to become a role model for others and be socially accepted. Both clusters are practical and do not appreciate a demanding communication style. To reach both, communication of mobility offers in a fact-based manner concentrating on efficiency aspects would be the most successful way to address them.

Fun, creativity, flexibility, individuality Creative, gamified Funny, playful Car-sharing, alternative transport modes, pooling, park and ride systems

6.4. Spatial aspect within the clusters and implication for planning There are few large cities in Austria. This goes hand in hand with infrastructural prerequisites, which poses challenges to the population in terms on their day to day life. The bond to motorized individual transport is therefore still very dominant in some areas. Despite different spatial conditions, the identified clusters can be found in different region types. The findings make it obvious that some types or clusters are more focused on urban areas than others. However, due to the importance of the mindset, these people can also be found in rural areas. Urban types are the ‘Spontaneous – On the Go” cluster and the ‘Efficiency-oriented Information Pickers' cluster, which are very flexible types of persons and therefore enjoy a variety of different mobility options. The ‘Interested Conservative’ type on the other hand is more strongly represented in rural areas. The geographical reference of the clusters is important for the development of measures as the basic infrastructure and the associated possibilities are different. The current data situation allows a rough classification by region. Where possible, the reliability of measures should be increased by carrying out additional surveys revealing the distribution of clusters by region. As mobility behavior is largely determined by local influence factors (e.g. infrastructure, culture), the transferability of the results to other countries is severely restricted. There is a need for research here at various levels, especially since the approach to the role of the state is

Highest values are highlighted in bold print.

High 5.9

Willingness to change Average number of information sources Arguments

Appropriate apps Appropriate campaigns Specific information interest

On-trip, mobile information, apps Information requirements

Role models, social responsibility Hardly using apps Responsibility, fitness Cycling infrastructure, (supraregional) public transport

Middle 3.0 Middle 4.5 High 6.1

No demand for information, reduced mobility Close to zero 0.7 Little demand for information, mainly routine trips Close to zero 2.5 Pre-trip information, online or print, hardly apps Frequent new trips, high demand, new media Pre-trip, multiple information sources

Digital illiterates Spontaneous – on the go Spontaneous – on the go

Table 4 Selection of findings for the development of persuasion strategies for the identified information target groups (N = 1000).

Interested conservatives Efficiency-oriented information pickers Highly informed sustainability

Low demand

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Table 5 Structure of the information target groups (N = 1000). Spontaneous – on the go

Highly informed sustainability

Efficiency-oriented information pickers

Interested conservatives

Low demand

digital illiterates

Sex Male Female

52% 48%

50% 50%

60% 40%

48% 52%

49% 51%

28% 72%

Age 14–29 yrs 30–44 yrs 45–59 yrs ≥ 60 yrs.

32% 37% 25% 6%

40% 27% 20% 13%

30% 29% 31% 11%

18% 21% 26% 35%

19% 36% 28% 18%

5% 13% 26% 56%

Size of residential area ≤ 5000 inhab. 23% ≤ 20,000 inhab. 14% ≤ 50,000 inhab. 2% > 50,000 inhab. 17% Vienna 43%

46% 16% 4% 12% 22%

34% 20% 6% 11% 30%

45% 22% 10% 9% 14%

43% 17% 13% 6% 20%

44% 26% 5% 8% 16%

behavior patterns in Austria and provide sufficient information on the population regarding mobility behavior, needs, attitudes and information retrieval. The main advantage of the attitude and value-based approach we used is that it enables target-group specific interventions, in this case to foster active mobility. The results can be regarded as extension of the current Sinus-Milieu approach since they especially target mobility in every aspect including information collection and perception as well as the opinion-forming process which affects the image of certain transport modes. To capture spatial differences and address the suspected disproportional spatial distribution of the clusters, the research was done in metropolitan, urban and town/rural areas in Austria. It was found that all six clusters can be found throughout Austria, which highlights the importance of the mindset and puts the infrastructure requirements into perspective. The gained insights from developing mobility information clusters are not only important for transport infrastructure providers and municipalities, but also for policy makers since the clusters can be regarded as basis for developing encouragement strategies to foster active mobility and to allocate resources properly (Markvica et al., 2016). As policy interventions have more weight on European level, it is desired to broaden the view of the current research by gaining knowledge of mobility behavior clusters in other European countries. Sinus-Milieus are available for several countries. Hence, the approach can be easily reproduced in other countries.

different (e.g. Nadin and Stead, 2008) and therefore the orientation of the measures to promote active mobility must alter. 6.5. Research potential on the European scale A main research topic would be to understand both the general determinants of mobility transitions in different settings (local spatial structure and mobility options, social practices and norms, etc.) as well as the mechanisms that initiate successful change processes (either in bottom-up initiatives or in institutionalized settings such as e.g. living labs). Mobility behavior is largely determined by local influence factors, e.g. local infrastructure, mobility options, and social norms and culture. By connecting mobility change initiatives from different countries and different regions therein and comparing collected data from mobility change processes, it would be possible to distinguish local parameters of behavior change and initiative developments from independent and generalizable parameters (cognitive, affective, emotional, social, political, technological etc.). This knowledge is essential for developing effective strategies for fostering mobility change initiatives, which can be implemented in other European cities. Currently, there are already a handful of alternative performers, such as bottom-up initiatives as well as experimentally institutionalized instruments like urban living labs, which are taking alternative action by the making of supportive coalitions for sustainable mobility transition on the European level (Voytenko et al., 2015). These sustainable mobility performers are independently from each other producing and co-producing valuable experience and insights into the patterns of behavioral change. The characteristic of those innovative performers is hardly observed (Schuurman et al., 2013) nor is the knowledge about behavior change translated effectively between these performers nor are they recognized enough as relevant means to stimulate critical mass movements in the sense of collective change in mobility behavior above the local scale. For behavioral change towards active mobility it would be a feasible approach to establish a cross-national platform, where behavioral change should be put at the heart of a research and exchange process between sustainable mobility performers. This enables the development strategies for sustainable mobility transition.

Declaration of competing interest On behalf of all authors, the corresponding author states that there is no conflict of interest. Acknowledgements This work is part of the scientific project “pro:motion” supported by the Austrian Federal Ministry for Transport, Innovation and Technology. We gratefully thank the whole project team, especially Beatrix Brauner (Sensor Marktforschung) for leading the qualitative research on hypothetical information clusters, Martin Mayr and Karin Bauer (INTEGRAL Marktforschung) for leading the quantitative analysis, and Jens Dangschat (Vienna University of Technology), Norbert Sedlacek and Irene Steinacher (Herry Consult) for their contributions to this work.

7. Conclusions According to our research question “What information target groups or clusters can be identified for Austria to enable the promotion of active mobility in a target-group specific manner?” we found that six information target groups or clusters could be identified for Austria. These clusters enable us to get a deeper understanding of mobility

Author contributions The authors confirm contribution to the paper as follows: study 11

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conception and design: AM; desk research: KM, AM, NH, Herry Consult; survey preparation: KM, AM, NH, INTEGRAL Marktforschung, Sensor Marktforschung, Herry Consult; survey and data collection: INTEGRAL Marktforschung; survey data validation: ML; focus group preparation: KM, AM, NH, Sensor Marktforschung, INTEGRAL Marktforschung Herry Consult; focus group execution: Sensor Marktforschung; analysis and interpretation of the results: KM, AM, NH; elaboration of communication strategies: KM, AM, NH; draft manuscript preparation: KM, AM; manuscript review: NH, ML. All authors reviewed the results and approved the final version of the manuscript.

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