The dimensions of mobilities: The spatial relationships between corporeal and digital mobilities

Social Science Research 43 (2014) 157–167

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The dimensions of mobilities: The spatial relationships between corporeal and digital mobilities q Sakari Taipale Department of Social Science and Philosophy, University of Jyväskylä, PO Box 35, 40014 University of Jyväskylä, Finland

a r t i c l e

i n f o

Article history: Received 28 January 2013 Revised 2 September 2013 Accepted 11 October 2013 Available online 18 October 2013 Keywords: Mobilities Digital Corporeal Spatial organisation Travelling Finland

a b s t r a c t The aim of this article is to study how the corporeal and digital mobilities are spatially organised in relation to each other in everyday life. The dimensions of mobilities are modelled by using survey data (N = 612) collected from Finland in 2011, Multiple Correspondence Analysis (MCA) and Multiple Regression Analysis (MRA). The results show that the combined use of corporeal and digital means of mobility affect the spatial organisation of mobilities only little. The results indicate that young people and students are more likely to benefit from their mobility in networking activities as they are equipped with a larger variety of mobility means than older people and pensioners. Lastly, women and people living in essentially urban areas are more likely to augment their physical travelling practices by using small-sized digital mobilities than men and people living in rural locations. Ó 2013 Elsevier Inc. All rights reserved.

1. Introduction Recently a number of sociological inquiries have emphasised the complex interdependencies between corporeal and digital mobilities (e.g., Larsen et al., 2006; Kellerman, 2006, 2012; Urry, 2000a, 2007; Elliot and Urry, 2010). Various types of mobilities are said to make up a complex mobility system, and by studying this system it would be possible to better understand people’s needs for travelling in contemporary societies in which daily undertakings, such as work, child care and leisure time activities, are spatially dispersed. Apart from these system-oriented approaches, multiple studies have analysed how information and communication technologies (ICTs) affect the formation of social networks in particular and social interaction in general (e.g., Castells et al., 2006; Carrasco et al., 2008a, 2008b; Mok et al., 2010). The aim of this article is to study how the corporeal and digital mobilities, to borrow Urry’s (2007) phrases, are spatially organised in relation to each other in everyday life. Hereafter, the term ICT will be used when referring to the means of digital mobility; physical movement is used synonymously with corporeal mobility. While previous studies have primarily approached the studied phenomenon by means of qualitative methods, this study adopts a different research strategy: the dimensions of mobilities are explored by using a structured survey and by applying a method of geometric data analysis to the research material collected. Theoretically, the paper builds upon the mobilities paradigm and related studies dealing with how people live their lives today. This paradigm was largely formulated by Urry (2007) who, argues that sociologists have disregarded the role of mobility and communications in the contemporary globalised world. Increased mobility overall brings with it a loosening of the fixed structures of the industrial society (Peters, 2006; Sheller and Urry, 2006; Urry, 2007). However, critical voices remind us that mobility and fixity are historically and geographically perceived differently depending on national spaces (Skeggs, 2004, pp. 48–49). To manage this liquidity Bauman (2000) states that people can make use of multiple means of q

An earlier version of this paper was presented at the 26th Conference of the Nordic Sociological Association, Reykjavik, Iceland, 15–18 August, 2012. E-mail address: sakari.taipale@jyu.fi

0049-089X/$ - see front matter Ó 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.ssresearch.2013.10.003

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mobility; including but not limited to private cars, trains, buses, and a large number of small electronic appliances (e.g., mobile phones, laptops, tablet PCs and MP3 players). These devices make it possible to overcome distances in communicative and imaginary manners (e.g., Castells et al., 2006; Mok et al., 2010). The focus of this study is on what Kellerman (2012) calls daily spatial mobility: defined by two-way (e.g., journeys to work, school, or shopping and back) rather than one-way movement (e.g., change of residence) that takes place regularly (cf. vacation journeys). To study the relationship between people’s physical movement and ICTs in daily life the article uses nationally representative survey data (N = 612) collected from Finland in 2011. The study is based on a statistical method rooted in the French tradition (Benzécri, 1973), Multiple Correspondence Analysis (MCA), which aims to describe the relationships and distances between two or more categorical variables geometrically. In this, MCA is applied to substantiate, or to oppose or specify, explanatory theories (Le Roux and Rouanet, 2010) and not to predict causal relationships. In this case, it is the theory of mobilities that suggest that there are interconnected systems of mobilities that contribute to the building of networking capacity (or so-called network capital) (Urry 2000a, 2007; Elliot and Urry, 2010; Wellman and Frank, 2001; Quan-Haase et al., 2002). The discovered factorial axes will then be analysed by applying Multiple Regression Analysis (MRA). This method is used to give a clearer picture of the socio-demographic predictors of respondents’ positioning on each axis. Given the motivational basis of this study, Finland serves only as one example in the search for empirical evidence on the theory-related research questions, which will be formulated below. The rest of this article is structured as follows. First, the study is situated in relation to previous literature on mobilities, travelling and ICT usage. Based on this literature review, specific research questions are outlined for the study. This is followed by the presentation of the data, measures and the applied statistical tools. The results of the multivariate analyses are presented before proceeding to a final discussion where the results are juxtaposed with prior knowledge. 2. Literature 2.1. Mobilities paradigm John Urry formulated the mobilities paradigm for the first time at the turn of 21st century. Urry (2000a) posited that societal structuring and transformations are bound up with an idea of what it is to be a member of a given nation-state with certain social rights and duties. Multiple mobilities are transforming the historical subject matter of sociology, that is an individual ‘western’ society with its endogenous social characteristics (Urry, 2000b). The paradigm suggests that the ‘social’ should not be understood as ‘societal’ but rather as ‘mobile’ (Urry, 2003, p. 171). In this new era, mobility should no longer be reduced to social mobility, referring to transformations in individual’s social positions within a given social structure or network and enabled by educational and occupational achievements or meritocratic principles (Kellerman, 2006, p. 9). Along with social mobility, spatial mobility should be considered as a structuring dimension of daily social life (Kaufmann, 2002, and Kaufmann et al., 2004). Social structures and dynamics are, in fact, interdependent with individual’s actual and potential capacity to displace entities, such as people, goods, and information (Kaufmann et al., 2004, p. 745). Ability to move material and immaterial entities affects to individual’s social positions in a given society, just like we have used to think about the impact of education on social mobility. The mobilities paradigm directs the focus of sociology to this greater variety of mobilities and the ways in which they interact, and thus, constitute hybrid mobilities (Urry, 2000a, 2007), and reshape the social structures of contemporary societies and great new inequalities (Kellerman, 2006). The mobility paradigm has made efforts to build a typology of mobilities (Urry, 2007, p. 47; Larsen et al., 2006, pp. 47–48 and Büscher and Urry, 2009, pp. 101–102), which is something new in the social sciences (Cresswell 2010, p. 18). However, the relationships between various modes of mobilities have not yet been geometrically modelled. The most recent and well-established version of the typology consists of five modes of movement. Corporeal mobility, or travelling, refers to the movement of people in terms of their work, family and leisure time as well as to various forms of human migration. The second mode of mobility is physical movement of objects, which refers to exchange of physical items between producers, consumers/ordinary people and retailers. Imaginative mobility occurs through images that are conveyed through media such as radio and television. Virtual mobility makes a stronger reference to movement that takes place in real time, typically on the Internet, and transcends social and geographical distances. Communicative mobility alludes to person-to-person communication. Communicative mobility is performed through such means as postcards, faxes, telephones, emails and the like. These five types of mobility are interdependent categories and coexist in everyday life (Larsen et al., 2006, p. 47). The data used for this study permits us to examine the imaginary, virtual and communicative levels of movement. One mode of mobility, the physical movement of objects, is left outside the analysis for two reasons. First, while other modes of mobility can directly facilitate inter-personal relationships, the physical movement of objects can do so only indirectly. Secondly, the data collected does not contain a proper statistical measure for this modality. 2.2. The dimensions of mobilities Whereas social network researchers have recently paid attention to the possibility of people facilitating interaction by initiating social events within their network by reaching others through ICT (e.g., Carrasco et al., 2008a), the mobility paradigm is more geared towards the analysis of mobility systems: how the physical movement of people and

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electronically-mediated movement are intertwined. According to the mobility paradigm, everyday life is today, largely dependent on people’s movement capacities (Elliot and Urry, 2010, p. 10). As people enact their movement capacities in different times and places, it is argued that daily activities (e.g. paid work, household tasks, leisure) have begun to fragmentize into smaller chunks, which can be in physical or in digital presence (e.g. Hubers et al., 2008 and Schwanen et al., 2008). What is even more important is that these capabilities are not equally distributed within and across societies (Kaufmann et al., 2004). First, as Boltanski and Chiapello (2005, p. 362) highlight, some people’s immobility or more limited travelling practices make possible other people’s higher mobility. Second, due to their costs or poor availability in certain places, ICTs are not at everyone’s disposal. Third, there are people who are cut-off from other’s social circles as they do not see the value of ICT and thus, do not become users (e.g., Chen and Wellman, 2005; Fortunati and Taipale, 2012a; Ling, 2012). By now, sociological research has presented many detailed narratives on how people live their mobile lives and overcome challenges related to restricted mobility and spatially dispersed practices in everyday life (e.g., Elliot and Urry, 2010; Jirón, 2011). A detailed empirical description of how mobilities are spatially organised in relation to each other – in a similar way to Bourdieu’s investigations of the fields of capitals – is still to be carried out (cf. Mok et al., 2010). The first research question of the study is based on this observation: RQ1. What dimensions characterise the spatial organisation of mobilities? In fact, the mobility paradigm gives a good reason to model mobilities as a ‘field of struggles’ consisting of different dimensions. Urry (2007, pp. 194–203 and Elliot and Urry, 2010, pp. 57–63) claims that there is network capital that can be distinguished from cultural and economic capital in Bourdieu’s sense. For Elliot and Urry (2012, p. 11) ‘network capital is largely subjectless, communications-driven and information based network capital,’ whereas ‘cultural and economic capitals are for the most part, built by individuals.’ In fact, like Wellman (2001) several years earlier argued, network capital should perhaps be considered as just one variant of social capital. As a specific form of social capital it might be even more suitable and better applicable in the current world, where social networks are perhaps more individualised and based on person-to-person communications, than the generic term of social capital (e.g. Sik, 1995; Sik and Wellman, 1999; Wellman and Frank, 2001 and Quan-Haase et al., 2002). For Urry (2007, p. 196) network capital alludes to real and potential social relationships that are afforded by mobilities, as well as to people’s capacity to exploit the vast array of mobilities. Quan-Haase et al. (2002) previously applied the same concept of network capital by referring to the potential of communication technologies in managing social relationships in daily life. In light of the mobilities paradigm, however, network capital appears as a resource that is made possible by a wider selection of mobilities not only ICTs. To avoid this friction between the concepts of social and network capital, the term networking capacity that combines the idea of these two distinct capitals is applied hereafter in this study. It results from the unequal distribution of mobilities that networking capacities are unevenly distributed in societies. People with a high level of networking capacity are said to experience a high level of movement (Elliot and Urry, 2010, ch. 5). It is against this background that the idea that sociological inquiries should not be targeted only to the ‘mobility-rich,’ but also on the non-placement of ICTs in people’s everyday lives. All things considered, if we share the idea that life in the contemporary western world is networked by nature and increasingly lived at-a-distance, it is evident that mere physical movement is typically not enough and thus, it needs to be supported by other modes of mobility (Urry, 2007). This leads to the second research question of this study: RQ2. What forms of corporeal and digital mobilities relate to each other in everyday life? Observations from everyday life support the idea that travelling and the use of ICTs are deeply interconnected. When on the move, for example on a train or bus, people habitually manage ‘secondary’ tasks through their mobile phones and laptops. Remote working and parenting, which characterise today’s mobile lifestyles (e.g., Chen and Katz, 2009; Elliot and Urry, 2010, and Madianou and Miller, 2013), are examples of such mundane practices that take place while travelling. However, travel time is also ‘filled’ with less compulsory tasks, such as listening to iPods, playing games, or just browsing and surfing on the Internet in order to kill time. Elliot and Urry (2010, p. 28) have introduced the term ‘miniaturized mobilities’ to describe small electronic devices (such as mobile phones, tablets, and MP3 players), which basically intertwine with the user’s body (e.g., Oksman and Rautiainen, 2003) and are easy to carry while travelling. In terms of the utility of travel time, public transportation allows a more versatile exploitation of these miniaturised digital mobilities than a private vehicle, such as a car or motorcycle, where the driver must concentrate on the driving task itself (Lyons and Urry, 2005). It is also self-evident that the small digital devices are more likely to be interconnected with travelling than bigger and more stationary tools of movement, such as printers and widescreen televisions. Previous literature provides a quite vivid but incoherent picture of how various mobilities are interconnected. There are many ways in which physical journeys and telecommunications influence each other (e.g., Mokhtarian, 2002). In empirically grounded studies, mobile communication tools have sometimes been considered as substitutes for physical travel (e.g., Palen et al., 2001), yet other studies propose that they increase the efficiency of travelling (Ling and Donner, 2009). In contrast to the substitutive and additive effects, Carrasco et al. (2008a) speak to the facilitative effects of ICTs. For example, they argue that e-mails, compared with the telephone, would be more strongly associated with active attempts of reaching out and meeting someone. Furthermore, others also argue that, for instance, the effects of mobile phone usage are roughly evenly balanced between causing, saving and

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changing car trips (Ling and Haddon, 2003, p. 253). A more comprehensive picture of how ownership and use of various mobilities are related to each other is still to be painted. Even if the idea of hypothesis setting is not well in line with the chosen research method, as the MCA method only aims to reveal features and latent relationships between variables in the data (Contarello and Sarrica, 2007, p. 1019), some results can be considered to be more likely than others. What can be expected is that, especially portable ICTs, cluster with various modes of travelling. They have the capacity to augment the mobile capacities of the individual, yet to a lesser extent in the case of a car driver rather than of a user of public transportation. In contrast, the more stationary and larger sized ICTs, such as desktops, printers, video recorders or digital television receivers, are less likely to group together with the physical modes of travelling. The third research question turns the attention from mobilities to socio-demographics and how they are related to the spatial organisation of mobilities. RQ3. Are socio-demographic factors, such as gender, age, education, economic activity, household income and the degree of urbanisation, associated with the identified dimensions of mobilities? Like the other two research questions, this one is principally exploratory owing to the limited amount of prior empirical research, especially from the studied country, Finland. Regarding the possession and use of ICTs, previous literature shows that gender gaps in the use of digital communication technologies have diminished (Robbins and Turner, 2002; Fortunati, 2008). In Finland, in 2003 it was already noted that young girls had surpassed young boys in the use of certain ICT and mobile services, such as calling by mobile phone and sending SMS, and that they used the Internet to the same degree as boys (e.g., Kangas and Kuure, 2003; Oksman and Turtiainen, 2004; Taipale, 2013). Concerning age, young people have adopted and use more ICTs, including the Internet and email, than older age groups (Statistics Finland, 2012). The possession of various ICTs is positively linked to a higher level of education as well (e.g., Fortunati and Taipale, 2012a, 2012b). In terms of geography, the possession of desktop computers is not highly differentiated in Finnish households (average possession rate, 44%), yet tablet PCs are more common, on average, in a capital region (14%) than in the country (8%). Regarding income, on average, in households with a monthly net income of more than 3000 Euros, desktop computers are more common (58%) and in households earning more than 5000 Euros a month, tablet PCs (14%) are more common than in the country (Statistics Finland, 2012). As a large part of all the miniaturised mobilities are relatively inexpensive today, earnings may be a poor differentiation in this respect. In general, previous studies from Finland and abroad, hint that most urban city centres, as well as surrounding middle-size and small municipalities where families are made up of well-educated adults who have children and who commute, may contain the most active ICT users (e.g., Taipale, 2009, and Fortunati and Taipale, 2012b). Socio-demographics are also associated with different forms of physical travel. Regarding gender, the National Travel Survey 2010–2011 from Finland shows that in the country the average number of trips per day for women and men has evened out at 2.9 for both genders. In Finland, men still drive cars more often, while women are more typically car passengers and more frequent users of public transportation, especially trains and buses, than are men. Men also travel longer distances and spend more time travelling than do women. Regarding age, younger respondents, especially those under 35, use public transportation most frequently. Finally, the same survey also reveals that, while the use of private cars is most common in sparsely populated rural areas, the use of public transportation and walking are most common practices in conurbations (Finnish Transport Agency, 2013). 3. Material and methods 3.1. Data and methods The data (N = 612) was collected from Finland in May–June 2011. The mode of data collection was a structured postal survey. The Population Register Centre of Finland carried out the nationally representative sampling. The sampling frame consisted of 15–65 year-old Finnish-speaking citizens covering all geographical regions of the country. The survey was pre-tested with 10 individuals. Weighted data is used to correct skewed gender and age distributions that result from the higher non-response rate of 25–34 year-old men and 35–44 year-old women when compared with other groups. A response rate of 24.4% was reached without reminders. The design of the questionnaire benefitted mainly from two earlier survey studies. The majority of the questions were adopted from the ‘Telecommunication and Society in Europe’ telephone survey that was carried out in Italy, Spain, Germany, France and the United Kingdom in 2009. This survey was a partial replica of another survey conducted in the same countries in 1996 (Fortunati, 1998). Another survey used was conducted by the Department of Communication Studies at the University of Michigan in 2005 by Mike Traugott, Thomas Wheeler and Rich Ling (On the Move, 2006). Although similarities with these surveys make cross-national comparisons possible, these two surveys do not include the indicators of travelling required and applied in this study. In terms of methods, the article utilises Multiple Correspondence Analysis (MCA) and Multiple Regression Analysis (MRA). As opposed to the idea of confirming or rejecting hypotheses, the first and foremost aim of MCA is to reveal how the data is organised. Moreover, MCA is particularly suitable for categorical data and makes it possible to visually present the relations

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between multiple variables. Its equivalent for continuous variables is Principal Component Analysis (Greenacre, 1984; Contarello and Sarrica, 2007; Le Roux and Rouanet, 2010). The matrix of Respondents  Mobility Categories is the subject of detailed analysis. The analyses used in this article were executed with the optimal scaling procedure of the SPSS 18 programme. In the MCA, gender, age, education level, respondent’s main activity and the degree of urbanisation were used as supplementary variables (sometimes called illustrative variables). These socio-demographic variables do not contribute to the building of the factorial plane but considerably enrich the interpretation of results. Multiple regression analysis is utilised to find out how socio-demographics predict respondents’ positioning on each factor. In this connection, tolerance and the variance inflation factor (VIF) (1/Tolerance) values are examined to detect multicollinearity. If VIF exceeds the critical value 10 (tolerance, correspondingly, is less than 0.1) further investigations are required (e.g., Gross, 2003, p. 304). Finally, the Adjusted R squared is used to measure the proportion of variation in the dependent variables and accounted for the explanatory variables in the MCA.

3.2. Measures Corporeal travelling measures were drawn from previous studies, which asked: ‘How often do you use the following types of transport for any kind of journey?’ (Stradling, 2003, p. 104). The following modalities were included in the battery: car driver, car passenger, motorbike, bus, train, taxi, bicycle and walking (at least 10 min).1 The answer choices were: (1) most days; (2) once or twice a week; (3) about once a fortnight; (4) about once a month; (5) several times a year; (6) about once a year or less; and (7) never. The answer categories were divided into two, indicating frequent (answer choices 1 and 2) and infrequent (choices 3–7) use of the mobility in question. Dummy recoding was chosen as it produces a sufficient number of responses to each recoded answer category.Digital mobilities that comprise digital tools of imaginative, virtual, and communicative movement were investigated with two sets of questions. The first question was: ‘Which of the following types of equipment or services do you have in your home (whether belonging to you or another household member)?’ Respondents were asked to answer ‘yes’ or ‘no’ regarding the following twelve devices: desktop, laptop, tablet PC, Internet connection, (digital) television, digital camera, video camera, printer, radio, DVD player, video recorder and digital TV receiver that can record programmes.2 The first four modalities are considered as tools of virtual mobility, although they also make possible person-to-person-communication; the remaining devices serve as channels for imaginative mobility. The second question was: ‘Which of the following types of equipment do you own personally?’ Respondents were asked, once again, to answer either ‘yes’ or ‘no’ in relation to the following appliances: mobile phone, iPod, MP3 player (other than iPod), games console and e-book reader. For the analysis, iPods and other MP3 players were combined into one variable, and e-book readers were excluded from further analysis due to the small number of users (N = 4). The mobile phone is primarily a medium of communicative mobility, although it also allows movement on the levels of imagination and virtuality, while MP3 players and game consoles enable travelling on the level of imagination.

3.3. Supplementary variables Gender was measured with fixed answer categories: 1 = ‘Male’ (45.6%) and 2 = ‘Female’ (54.6%). Age was determined by asking for respondents’ year of birth, which was then recoded into full years (Range = 15–74, M = 41.75, SD = 14.19), and further divided into three categories (24 years or less, 25–44, 45 years or more). Education level was measured as the highest completed level of education, in accordance with the International Standard Classification of Education (ISCED97). The ISCED scale was recorded in three new categories to ensure a sufficient number of responses to each: 1 = ‘Low education’ (ISCED levels 1–2; 15.3%), 2 = ‘Middle education’ (ISCED levels 3, 4 and 5b; 53.2%), and 3 = ‘High education’ (ISCED levels 5a and 6, 31.5%). The indicator for main economic activity was derived from the question ‘What describes your current situation best’: 1 = ‘Employee’ (65.7%), 2 = ‘Housewife/-husband’ (4.3%), 3 = ‘Unemployed’ (4.2%), 4 = ‘Pensioner’ (11.8%) or 5 = ‘Student’ (14.0%). The level of urbanisation was measured based on the size of the place of abode as estimated by the respondents and was recorded in three categories as outlined by the OECD: essentially rural (less than 5000 inhabitants, 9.0%), relatively rural (5,001–100,000 inhabitants, 55.7%), and essentially urban (more than 100,000 inhabitants, 35.3%). Household income was measured by asking ‘What is the total annual income of your household, that is, the approximate total of all salaries, pensions and other revenues?’ Answer categories ranging from 1 = ‘Less than 20,000 Euros’ to 8 = ‘More than 80,000 Euros’ were supplied (M = 4.35, SD = 2.34, Range = 1–8).

1 As flying is not an everyday practice for the majority of Finns, and modes of public transportation such as metro or trams are only available in the capital city of Helsinki, these were not investigated in the questionnaire. 2 On September 1 2007, television broadcasting was digitalised in Finland and all TV sets must now either include an integrated digital receiver or a separate digital receiver ‘box’ attached to it. For this reason, the questionnaire included a measure only for such digital TV viewers which have an additional recoding function, and thus have capacity to separate television users from each other.

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Table 1 Correspondence analysis. Factor

Algebraically negative pole Non-possession of digital mobility tools Type of mobility

First Inertia 13.0%

No Digital Camera No Internet No DVD No Printer No Digital TV Recorder No Video Recorder No Desktop No Radio CarDb No TV No Video Camera No Laptop

Coord. 1.611 2.355 1.638 .979 .688 .588 .591 1.587 .645 1.265 .351 .664

Algebraically positive pole Sedentary means of digital mobility mobility Ctra

Type of mobility

Coord.

Ctr.

9.6 9.2 8.9 8.7 6.5 4.6 4.6 4.4 4.0 3.2 2.6 2.4

Digital TV Recorder Video Camera Printer Video Recorder Desktop

.499 .658 .371 .406 .392

4.6 4.4 3.2 3.1 2.9

13.4 9.1 7.2 7.2 6.3 4.2 2.6 2.4

No Laptop No Internet No MP3 Player No Digital Camera +CarD Bus Bicycle

.996 1.771 .471 .901 .303 .263 .288

8.6 8.2 6.0 4.7 3.4 2.9 2.6

Digitally augmented travelling Second Inertia = 8.2%

+Bus 1.174 CarD .778 +Train 1.696 MP3 Player .570 No TV 1.398 +Bicycle .446 Laptop .240 No Video Recorder .331 [Age 24 or less; students; 20,000 Euros or less]

Traditional travelling

[Age 45 or more; pensioners]

c

Categories of supplementary variables, which have computational contributions greater than 2.2, are presented in square bracket. a Only categories that have contributions greater than the average (Ctr > 2.2) were selected for the interpretation (see also Endnote 1). b A plus (+) sign before the label refers to the frequent (weekly) and a minus ( ) sign to the infrequent (less than weekly) use of corporeal mobility in question.

4. Results The results of the MCA modelling of the spatial relationship between people’s physical and digital mobilities are presented in Table 1. Two axes are interpreted whose variances (inertia values) are 13.0% and 8.2%.3 Mobility categories that contribute more than the average contribution4 (Ctr > 2.2) were selected for the interpretation. The factorial plane consisting of Axis 1 and 2 is illustrated in Fig. 1. In response to RQ1, Table 1 shows the main dimensions which contribute most to the spatial organisation of mobilities. Axis 1 opposes the categories that indicate ‘Non-possession of digital mobility tools’ to those referring to the possession of ‘Sedentary means of digital mobility.’ Altogether, 16 categories with a contribution higher than the average (Ctr > 2.2) are located on each side of the axis. This factor separates people who have taken up the least portable ICTs, such as desktop computers, printers, video recorders and digital TV receivers that can record programmes, from those who have chosen not to acquire ICTs. The latter have somewhat categorically not appropriated a large number of ICTs, such as an Internet connection, DVD players, desktops and even television. Additionally, these people combine the infrequent use of a private car with their lifestyle. On the opposite pole of the axis are those who have adopted some older, larger and less portable ICTs. These include those mobilities which allow imaginative travelling through video (e.g., video camera, video recorder, digital television receiver with a recoding function), but also desktop computers and printers that can serve as a channel for person-to-person communication. It is worth keeping in mind that some widely penetrated portable ICTs, such as the mobile phone, do not differentiate between the two opposites poles of the first axis. Hence, it would also be unfair to claim that the non-possession of ICTs would mean complete rejection of all digital appliances or should be understood as a sign of being a digitally excluded or technological refusnik (Selwyn, 2003). Instead, the non-adoption of the latest digital technologies, and the deployment of only some older ICTs as well, should rather be understood so that the structures of the information society also make it possible to act otherwise (Orlikowski, 1992, and Selwyn, 2003). The second axis is formed by two opposite poles: ‘Traditional travelling’ and ‘Digitally augmented travelling.’ It is characteristic of the ‘Traditional travelling’ pole that major ICTs – the Internet and laptop – are not used. Some more ecological means of physical travelling, such as bus and bicycle, are infrequently used as well. Instead, mobility is largely organised 3 The choice of number of axes to be interpreted is guided by the eigenvalues, the cumulated modified rates, and especially the interpretability of axes (Le Roux and Rouanet, 2010, p. 51). 4 The absolute contribution of a point to a dimension is the proportion of inertia (variance) explained by the point. The sum of the contributions of the points to each factor is equal to 100. Only points with considerable value (i.e. >100/n where n is the number of points) are discussed.

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Fig. 1. Two-dimensional factorial plane of mobilities. Note: A plus (+) sign before the label refers to the frequent (weekly) and a minus ( ) sign to the infrequent (less than weekly) use of corporeal mobility in question. Regarding digital forms of mobility, a prefix ‘‘No’’ refers to non-possession and the absence of prefix to the ownership of digital tool in question (i.e. NoDTVRec = Non-owners of digital television receiver with a recoding function).

around the frequent use of private cars. At the opposite pole of this axis are those who combine public forms of transportation, such as train and bus, and bicycle with the use of mobile digital appliances, such as MP3 players and a laptop. Interestingly, the non-possession of television is located at this pole, which can indicate that laptops are increasingly used to receive television broadcasts and a separate TV set is not necessarily required (Fortunati and Taipale, 2012b). In response to RQ2, the MCA reveals that physical travelling and the possession of ICTs are related to each other, although not in a very complex manner. Regarding the first axis, ICTs and people’s physical mobility are basically not interwoven at all. It is primarily the negative pole of the second axis, entitled ‘Digitally augmented travelling,’ which reflects the interconnected nature of corporeal mobility and ICTs. As was anticipated, it is exactly the miniaturized, portable ICT tools, in this case MP3 players and laptops, which are associated with physical travelling. Instead, it is some other small-sized gadgets, such as mobile phones and tablet computers, which are not bound to the physical modes of movement in any particular way. Regarding mobile phone, this probably stems from its high ownership rate (96.9%, Statistics Finland, 2012) and from the fact that it is always carried along. Tablet computers, on the other hand, are only now becoming more and more popular (Statistics Finland, 2012). The third research question (RQ3) was formulated to seek answers to whether or not socio-demographic factors are associated with the identified dimensions of mobility. Results indicate that none of the supplementary variables are associated with the first axis. The results of regression analyses, that were executed to further study how socio-demographic (supplementary) variables are associated with the two axes, are more illustrative in this respect. As Table 2 shows, regarding the first axis, high household income seems to be connected to the possession of sedentary ICTs, whereas low income is more typical among people who have not adopted the non-portable ICTs. Another clearly significant predictor is the degree of urbanisation of the place of the respondent’s abode. People living in essentially urban locations, quite surprisingly, do not take up sedentary ICTs as commonly as people living in essentially rural areas that serves as a reference group in the analysis. Taken altogether, the findings indicate that the conventional socio-demographic variables are not very strongly connected with everyday lifestyles, which are either opposed to the adoption of the latest technologies of digital mobility or based on the use of sedentary and older tools of digital mobility. The second axis provides more information regarding RQ3. The supplementary variables show that the most mature respondents (aged 45 or more), and hence also pensioners, are the most typical users of the traditional means of travelling. This is well in line with the previous research findings that indicate, for example, that older generations consider it more important to have their own car than younger generations (Sandqvist, 2002). The second axis also points out that the youngest group of respondents (24-year-olds or less), and, consequently, students, more often than other activity groups (e.g., employees, the unemployed, pensioners or housewives/husbands), who use more public transport and fewer private cars, and who augment their physical travelling practices by using easily transportable and small-sized mobilities, such as laptops

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Table 2 Hierarchical regression models for Axes 1 and 2 (Beta, sig). Axis 1 ( ) Non-possession of digital mobility tools – Sedentary means of digital mobility (+) Gender (ref = male)

.029

Axis 2 ( ) Digitally augmented travelling – Traditional travelling (+) .151***

Age (ref = 24 years or less) – 25–44 years .007 – 45 years or more .065

.105** .313***

Education (Ref. low) – Medium – High

.009 .109

.131* .042

Main activity (Ref.=employed) – Housewife/ .038 houseman – Unemployed .009 – Pensioner .043 – Student .040

.004 .048 .088* .164**

Degree of urbanisation (ref. essentially rural) – relatively rural .004 – essentially urban .183** Household income .432***

.006 .218** .034

Adjusted R2 N

.281 539

.215 539

*

p < .05; p < .01; *** p < .001. **

and MP3 players. The correspondence analysis hint that low household income (20,000 Euros or less) could also be linked with ‘Digitally augmented travelling’ practices. Regression analysis presented in Table 2 confirms the findings of the MCA which showed that younger people and students often take more advantage of digitally augmented travelling, while older adults and especially pensioners stick to travelling by a private car. In addition, the regression model reveals the difference between men and women, and the difference between people living in urban and rural locations. Interestingly, it is more common for women and people living in essentially urban locations to augment their physical travelling practices with digital tools such as MP3 players and laptops. This can be understood against the fact that women are, on average, less affluent and more dependent on public transportation than men. In addition, women use more ICTs in order to cope with childcare and to coordinate family calendars (e.g., Schwanen et al., 2008; Fortunati and Taipale, 2014); as such, traditional travelling by private car still manifests itself as a masculine practice (Urry, 2007). Contrary to the MCA, regression modelling does not detect a relationship between ‘Digitally augmented travelling’ practices and low household income. 5. Discussion The empirical results presented above are now discussed in light of the mobility paradigm and especially one of its main theses; various mobilities are tightly interconnected and thus, consequently add to one’s networking capacity (Urry, 2000a, 2007; Elliot and Urry, 2010). The dimensions of mobilities, as they emerge in this study, provide relatively little support for the interconnectedness thesis. It seems that mobilities are organised first and foremost according to whether people have adopted at least some non-transferrable means of digital mobility or they have not taken up any new ICT (Axis 1). Mobilities are only secondarily organised around the dimension that emphasise the simultaneous use of various means of physical travelling and ICTs (Axis 2). These findings point to the main mobility gap in Finland, namely that it is not between ‘haves’ and ‘have nots’ (cf. Norris, 2001 and DiMaggio et al., 2004), but rather the one between ‘have a little’ and ‘have nots.’ Furthermore, there is no reason to believe that these mobility ‘have a littles’ and ‘have nots’ would be in that social position involuntarily. Rather, they may perceive that a larger set of technical tools, combined with more modes of physical travelling, would not response to their individual needs for daily spatial mobility. Their daily life is perhaps not so much influence by the ‘unsteerable’ juggernaut travelling through space, as Anthony Giddens (1990) has famously described the conditions of modernity. Instead, their life styles are most likely steadier, more predictable, slower in tempo, and would thus, benefit less from the use of the newest ICTs that are often described as vehicles for controlling this juggernaut of modernity. Having said this, the non-possession of ICTs and keeping ICTs in isolation from physical modes of travelling, appears as a life style choice. For mobility, ‘have a littles’ and ‘have nots,’ it may be a part of their life politics. They remain faithful to older, solitary and stationary communication technologies, as this choice is still possible for them within the institutional and governmental structures of the current Finnish information society.

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Furthermore, given that they voluntarily choose this kind of a life trajectory, it is worth asking how damaging the new social stratifications produced by the unequal distribution of mobilities ultimately are (cf. Kellerman, 2006). For mobility ‘have a littles’ and ‘have nots,’ the limited networking capacity may be versatile enough to permit relatively wide-ranging social networks and manage their individual daily needs of mobility. They can be content with less networking capacity and with experiencing a lower level of movement. When it comes to the concept of networking capacity, the study hints that it is especially young people and students who possess the most versatile reservoir of mobilities, and who could thus be most likely to increase their networking capacity. The results also indicate than women are at the cutting-edge with regard to the combined use of corporeal mobilities and ICTs. Such use is likely to serve women’s role as the maintainers of the domestic sphere. Also urban environments that provide infrastructure supportive of different modes of travelling (e.g., trains, buses, bicycles) proved to be one important condition for digitally augmented travelling. In contrast, people aged 45 years or more and pensioners are those who most often seem to stick to the use of private cars, and simultaneously do not take up the novel means of digital mobility, such as laptops, the Internet and digital cameras. In addition, this older age group is more likely to reside in rural rather than urban locations (Axis 2). These findings are consistent with prior studies showing older people’s higher attachment to private cars, younger people’s higher and rural dweller’s lower adoption rates of newest ICTs (e.g., Sandqvist, 2002; Finnish Transport Agency, 2013 and Statistics Finland, 2012). Hence, the results of this study suggest that the most noticeable gap in the distribution of networking capacity could be between the young (students) and older citizens (pensioners), although the division between rural and urban matters as well. The interconnectedness thesis would thus be less applicable when trying to understanding daily spatial mobility of older people living in rural and semi-rural environments. It is particularly interesting to see that low household income is not an obstacle to the mixed use of ICT and people’s travelling in Finland. Some previous results are supportive of this finding. For instance, Dieleman et al. (2002) found a low influence of income level on the choice between modes of physical transport. Although people with higher incomes might have more capacity to physically travel and socialise with other (Carrasco et al., 2008b), the results of this study rather indicate that people choose the means of travelling and communication which fit their own needs and lifestyle. Another explanation for this finding can be found in social obligations and physical constrains. Kaufmann (2002) relates these to mobile lifestyles. Young and urban people are free to travel more spontaneously, and in Finnish cities distances are relatively short and easy to overcome. Also, public transportation is better available in urban locations. Furthermore, young urbanites also have fewer family commitments that would restrict their travel and would put pressures on time allocation. Without such pressures, they are more able to expeditiously respond to the large amount of received emails and SMSs as well as to hang out on the Internet (e.g., Kellerman, 2006, p. 35) than adults. This said, as Kaufmann (2002, p. 19) states, we cannot be sure that mobile people have more freedom in the way they live their daily lives, even if they would theoretically have a higher probability to experience more mobility (Elliot and Urry, 2010). More inquiries, especially those that are qualitative in nature, are required to understand how social commitments influence the use of multiple mobilities, but also to investigate the motivations behind the non-adoption of multiple means of mobility. The results also contribute to the well-known concept of ‘networked individualism’ presented by Barry Wellman and his colleagues (e.g., Wellman et al., 2003), which indicates that personal and portable communication technologies, such as the Internet and mobile phones, are transforming communities by making them ever less reliant on fixed places and groups. On the contrary, the sense of community is increasingly based on person-to-person relations that are by nature, networked (Wellman, 2001; Quan-Haase et al., 2002, and Elliot and Urry, 2010). The two-dimensional analysis of the spatial organisation of mobilities presented here hints that young students and especially women, who may lack economic capital, combine a larger range of corporeal modes of transportation with some very individual tools of communication (e.g., laptop, MP3 player), something that gives them an advantage in engineering their own social life and social relationships.

6. Conclusions This study investigated how the corporeal and digital mobilities are spatially organised in relation to each other in everyday life. This was done by analysing the dimensions of mobilities that were created by using survey data and Multiple Correspondence Analysis, and then further analysed by means of Multiple Regression Analysis. The results show that the combined use of corporeal and digital means of mobility have relatively little effect on the spatial organisation of mobilities. The study has certain limitations that must be taken into account when interpreting the results. First of all, the applied measures of digital mobility are indicative of possession of ICTs, not of use. By investigating the actual use of these mobilities (for example, the frequency, purpose and context of use) with appropriate data, it should be possible to provide a more nuanced picture of how mobilities are organised in relation to each other in everyday life. Secondly, the study was based on data collected from one country, Finland. Cross-national explorations are required to find out whether mobilities are organised in relation to each other differently elsewhere. Thirdly, the MCA turns our attention to those mobilities that are located at the opposite ends of the axes, and consequently what remains in between is easily neglected. This applies, for instance, to the mobile phones that are surely much more complexly interwoven with other forms of mobility than this study tells us.

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Funding This study is part of a postdoctoral research project funded by the Academy of Finland (Project ID: 137446).

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