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Res. Lett. 13 (2018) 044025 https://doi.org/10.1088/1748-9326/aaad73 LETTER E-bike trials’ potential to promote sustained changes in car owners mobility habits Corinne Moser1,4 , Yann Blumer2 and Stefanie Lena Hille3 1 Zürich University of Applied Sciences ZHAW, Institute of Sustainable Development, Winterthur, Switzerland 2 Zürich University of Applied Sciences ZHAW, Center for Innovation and Entrepreneurship, Winterthur, Switzerland 3 University of St. Gallen, Institute for Economy and the Environment, St. Gallen, Switzerland 4 Author to whom any correspondence should be addressed. OPEN ACCESS RECEIVED 25 October 2017 REVISED 16 January 2018 ACCEPTED FOR PUBLICATION 7 February 2018 PUBLISHED 4 April 2018 Original content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. E-mail: corinne.moser@zhaw.ch Keywords: long-term impacts, sustainable transport, e-bike, trial, behaviour change, mobility-related habitual associations Supplementary material for this article is available online Abstract Modal shifts hold considerable potential to mitigate carbon emissions. Electric bikes (e-bikes) represent a promising energy- and carbon-efficient alternative to cars. However, as mobility behaviour is highly habitual, convincing people to switch from cars to e-bikes is challenging. One strategy to accomplish this is the disruption of existing habits—a key idea behind an annual e-bike promotion programme in Switzerland, in which car owners can try out an e-bike for free over a two-week period in exchange for their car keys. By means of a longitudinal survey, we measured the long-term effects of this trial on mobility-related habitual associations. After one year, participants’ habitual association with car use had weakened significantly. This finding was valid both for participants who bought an e-bike after the trial and those who did not. Our findings contrast the results of other studies who find that the effect of interventions to induce modal shifts wears off over time. We conclude that an e-bike trial has the potential to break mobility habits and motivate car owners to use more sustainable means of transport. 1. Introduction: The challenge of changing habitual travel behaviour Shifts toward more environmentally friendly transport modes hold considerable potential to mitigate global carbon emissions [1]. Especially in developed coun- tries, cars are still the main mode of transportation, but electric bikes (e-bikes) represent an attractive alterna- tive.This is not onlydue to theirhighenergy and carbon efficiency, but also a variety of other features, including cost savings, health benefits and avoiding traffic con- gestion [2, 3]. While e-bikes may also replace walking or conventional biking, the evidence from the available field studies suggests that purchasing an e-bike results in considerable substitution of car usage [2, 4–10]. However, while e-bike sales have shown rapid growth rates, e-bikes still represent a niche product that appeals mostly to the ‘dark green’ or ‘early adopter’ segments [2, 3, 6, 11–14]. Moving e-bikes from a niche to the mainstream is challenging. One major reason for this is that most travel behaviour is highly habitual [15–17] and gen- erally occurs in stable contexts (including entrenched travel routes and times and established travel purposes, as well as the utilised modes of transportation), making behavioural change difficult [18, 19]. Yet, disruptions of stable contexts have demonstrated a consider- able potential for altering individuals’ mobility-related habits. Examples include highway closures, which may nudge car drivers to try out public transportation [20], or strikes, such as the London Underground strike of 2014,which led to lasting changes inmobility behaviour among about 5% of all affected travellers [21]. In addi- tion, natural disasters, such as hurricanes [22], and personal life events, such as a serious injury [23], qual- ify as disruptions that are sufficiently strong to induce changes in individuals’ mobility patterns. While external disruptions often occur in a sud- den and random manner, many behaviour-change programmes use the same principle. They deliber- ately introduce contextual changes to promote a shift toward more sustainable behaviour. In the mobility © 2018 The Author(s). Published by IOP Publishing Ltd https://doi.org/10.1088/1748-9326/aaad73 https://orcid.org/0000-0001-8071-3681 https://orcid.org/0000-0003-0224-4538 http://crossmark.crossref.org/dialog/?doi=10.1088/1748-9326/aaad73&domain=pdf&date_stamp=2016-03-30 http://creativecommons.org/licenses/by/3.0 http://creativecommons.org/licenses/by/3.0 mailto:corinne.moser@zhaw.ch https://doi.org/10.1088/1748-9326/aaad73 Environ. Res. Lett. 13 (2018) 044025 field, providing people with the option of experiencing alternative modes of transportation seems promising in breaking deep-rooted mobility habits, especially if these opportunities co-occur with contextual changes in individuals’ private lives (e.g. moving) [24, 25]. For instance, the results of previous research suggest that providing a free travel card for public transportation to habitual car drivers can trigger significant changes in modal choices toward more efficient modes of trans- portation [26–31]. Yet, longitudinal analyses that assess the long-term effect of these interventions are scarce, and those that exist suggest that for most participants, the effects of the interventions start wearing off after the end of the intervention [27, 28, 30]. While most of the available interventions focus on the switch from cars to public transportation, there have also been three studies on e-bikes [10, 11, 32]. These studies showed that trying out an e-bike for two to four weeks is a promising approach to breaking participants’ mobility habits, resulting, inter alia, in a higher willingness to purchase an e-bike [11], lower habitual association with car use directly after the trial [32] and interest in using e-bikes more often in the future [10]. However, none of the previous studies pro- vided a longitudinal assessment of whether the context disruption caused by an e-bike trial is strong enough to induce long-term shifts in participants’ mobility- related habits. This is the main objective of the present study. 2. Method 2.1. Intervention design The annual Bike4Car programme in Switzerland seeks to break car drivers’ habitual behaviour. In this programme, organised by a Swiss environmental non- governmental organisation (NGO), car owners are offered a free trial of an e-bike over a 2 week period in exchange for their car keys. In 2015 Bike4Car was implemented in collaboration with bike retailers mak- ing e-bikes available to the participants; the Swiss Federal Office of Energy, which supported the pro- gramme with an intense national ad campaign (TV, internet and posters); and 32 cities responsible for local promotion. Between May and September 2015, 1854 car owners participated in Bike4Car. After the end of the programme, participants were offered a coupon to purchase an e-bike for a reduced price. Reductions var- ied by retailer. The largest participating retailer offered a reduction of 500 CHF (approx. 425 Euro), covering around 20%–25% of the price of an e-bike. By Novem- ber 2015 10% of participants used their coupon to buy an e-bike. 2.2. Data collection The following analysis is based on a longitudinal series of two online surveys of all participants of the 2015 Bike4Car programme. The organising NGO sent the link to the first questionnaire by email to participants immediately after they signed up for the trial. Between May and July 2016, about one year after the start of the programme, all participants were asked to fill out a follow-up questionnaire. To ensure a sufficiently high response rate, email reminders were sent to the participants in each study wave. As an incentive for par- ticipation, all respondents were entered into a lottery for attractive e-bike- or bike-related prizes sponsored by the programme partners. Questionnaires were avail- able in German, Italian and French, which are the three official languages of Switzerland. As almost no partic- ipants chose the Italian option, the following analyses focus on the German and French questionnaires only. 2.3. Sample The responses used for the analyses came from N = 405 participants who fully completed the pre-trial questionnaire. Compared to the overall participation in the Bike4Car programme (N = 1854), this corre- sponds to a response rate of 22%. Moreover, N = 300 participants completed the follow-up questionnaire (response rate = 16%). The responses used for the analyses in this paper come from the N = 144 partici- pants who completed both the pre-trial and follow-up questionnaires (combined response rate = 8%, see supplementary materials A for further details avail- able at stacks.iop.org/ERL/13/044025/mmedia). Table 1 provides an overview of the samples. It shows that, compared with the Swiss population [33–35], well- educated men were overrepresented among the survey participants. In addition, more than half of participants lived in households with two or more cars indicating that the programme reaches a target group with a real potential for mobility-related energy savings. The sam- ple characteristics of the participants were comparable in the pre-trial and follow-up questionnaires. 2.4. Questionnaires Mobility-related habitual associations. All question- naires included the response frequency measure that Verplanken and colleagues [36] developed, which Thøgersen and Møller [28] also used. They listed nine typical mobility-related situations and asked participants to choose themeansof transport that spon- taneously came to mind for each one. These situations are described on a rather general level and participants are asked for spontaneous reactions. This is why Ver- planken et al [36] argue that participants’ reactions draw on ‘pre-existing schemas or scripts about mode choice in general’ (36: 290) which are dominated by habits. Although authors claim that this instrument does measure habits [36, 28], it does not measure actual behaviour but rather habitual associations. The following nine situations were taken from Thøgersen and Møller [28] and adapted slightly to better fit the Swiss context: ‘picking someone up from the railway station’, ‘visiting a friend in the closest city’, ‘visiting the mountains with friends for a day’, ‘commuting to 2 http://stacks.iop.org/ERL/13/044025/mmedia Environ. Res. Lett. 13 (2018) 044025 Table 1. Sociodemographic characteristics of the sample compared to the Swiss population. Sociodemographic characteristics Swiss population statistics Pre-trial (N = 405) Follow up (N = 300) Pre-trial and follow up (N = 144) Male 50% [33] 65% 70% 72% Mean age (SD) 42.1 [33] 43.3 (10.5) 43.9 (10.4) 43.6 (10.7) University degree 27% [34]a 57% 56% 54% Vocational training 38% [34]a 29% 32% 31% 0 car in household 22% [35] 2%b 2%b 1%b 1 car in household 49% [35] 44% 45% 43% 2 or more cars in household 29% [35] 54% 53% 56% a Education level of permanent population in Switzerland between 25 and 65 years old [34]. b Although car owners were the programme’s target group, interested people who did not own a car were not excluded from the trial. Table 2. Mean sum scores of means mobility-related habitual associations for the pre-trial questionnaire and a representative Swiss sample. Means (M) and standard deviations (SD). Sum score Pre-trial M (SD), (N = 405) Representative sample M (SD), (N = 1476) t (df), p-value Effect size r Car 4.32 (2.00) 3.47 (2.63) 8.53 (404), p<.001∗∗∗ .39 Bicycle 1.70 (1.56) 0.75 (1.33) 12.25 (404), p<.001∗∗∗ .52 By foot 1.18 (1.15) 2.46 (1.60) −22.46 (404), p<.001∗∗∗ .75 Train 0.95 (0.95) 1.25 (1.32) −6.27 (404), p<.001∗∗∗ .30 E-bike 0.30 (0.96) 0.11 (0.55) 3.92 (404), p<.001∗∗∗ .19 Bus/tram 0.22 (0.56) 0.72 (1.25) −18.10 (404), p<.001∗∗∗ .67 Motorcyclea 0.21 (0.62) — — — Other 0.09 (0.52) 0.21 (0.72) −4.84 (404), p<.001∗∗∗ .23 Notes: Sum scores are between 0 and 9, with 9 signifying the most pronounced habitual association related to specific means of transport. a For the representative sample [37], no ‘motorcycle’ option was included. *** p<.001. One-sample t-tests (two-tailed). work’, ‘doing sports’, ‘going for a walk in the forest’, ‘going shopping in the closest supermarket’, ‘going to the closest post office’ and ‘visiting somebody in the countryside’. Participants could choose from a list of seven options, including car, motorcycle, train, bus/tram, bicycle, e-bike and walking (see supplemen- tary materials B for further details). The number of times participants mentioned each means of transport was taken as an indicator of participants’ mobility- related habitual associations. For each participant, a sum score for each chosen means of transport was calculated, with possible scores of 0–9. E-bike purchase. The follow-up questionnaire asked participants if they or a member of their house- hold had bought an e-bike since the end of the programme. In the responses, 117 participants (39%) stated that they had not purchased an e-bike, 50 (17%) reported that they intended to buy an e-bike in the upcoming months and 133 (44%) indicated that they had bought an e-bike. 2.5. Statistical analyses All questionnaireswerematched for analyses. Statistical analyses were carried out using the Software IBM SPSS Statistics 24. They included repeated measures analysis of variance (ANOVAs), paired-samples t-tests (two- tailed) and one-sample t-tests (two-tailed). 3. Results: Long-term impacts of the trial on mobility-related habitual associations Of all modes of transportation, participants displayed the strongest initial (i.e. pre-trial) habitual associations with cars, followed by bicycles and walking. Partic- ipants in the e-bike trial reported stronger habitual associations with car, bike and e-bike use compared to a representative sample of the average Swiss pop- ulation (see table 2). This data has been collected in a separate survey among a sample that is representative to theSwisspopulationwith respect to characteristics such as gender, age, educational level and income [37]. The observed differences between both samples are another indicator that the programme reached a relevant target group. Table 3 displays the mean sum scores for the different means of transport reported in the pre- trial and follow-up questionnaires. After one year, participants showed significantly weaker habitual asso- ciations with car and motorbike use and significantly stronger habitual associations with e-bike use com- pared to the associations displayed in the pre-trial questionnaire. This means that the average number of times that participants mentioned cars and motorbikes dropped significantly one year after participating in the programme, while the number of times participants mentioned e-bikes increased significantly. Next, we analysed whether there were differences in the observed shifts of habitual associations between those participants who bought an e-bike after the trial (i.e. buyers, n = 53) and those participants who had not purchased an e-bike (i.e. non-buyers, n = 91). Table 4 displays the respective mean sum scores of habitual associations for buyers and non-buyers. For habitual associations with car use, the repeated- measures ANOVA showed a significant main effect of time, F(1) = 14.53, p<.001, 𝜂 𝑝 2 =.09; this indicated that participants had a weaker habitual association with 3 Environ. Res. Lett. 13 (2018) 044025 Table 3. Comparison between the mean sum scores of mobility-related habitual associations in the pre-trial and follow-up questionnaires. Means (M) and standard deviations (SD). Sum score Pre-trial M (SD), (N = 144) Follow-up M (SD), (N = 144) t (df), p-value Effect size r Car 4.26 (1.99) 3.74 (1.91) 3.54 (143), p<.001∗∗∗ .28 Bicycle 1.81 (1.55) 1.69 (1.63) 0.99 (143), p =.32 .08 By foot 1.12 (1.14) 1.22 (1.14) −1.19 (143), p =.24 .10 Train 0.95 (0.87) 0.99 (1.00) −0.45 (143), p =.65 .04 E-bike 0.31 (1.02) 0.90 (1.50) −4.70 (143), p<.001∗∗∗ .37 Bus/tram 0.19 (0.52) 0.21 (0.51) −0.28 (143), p =.78 .02 Motorcycle 0.26 (0.70) 0.15 (0.57) 2.45 (143), p =.02∗ .20 Other 0.06 (0.26) 0.10 (0.39) −1.30 (143), p =.20 .11 Notes: Sum scores are between 0 and 9, with 9 signifying the most pronounced habitual association related to specific means of transport. *** p<.001. * p<.05 (paired-samples t-tests, two-tailed). 0 1 2 3 4 5 6 7 8 9 Pre-trial Follow-up M ea n ha bi tu al a ss oc ia tio ns w ith c ar u se 0 1 2 3 4 5 6 7 8 9 M ea n ha bi tu al a ss oc ia tio ns w ith e -b ik e us e Pre-trial Follow-up E-bike buyers (n = 53) Non-buyers (n = 91) E-bike buyers (n = 53) Non-buyers (n = 91) Figure 1. Change in habitual associations with car use and e-bike use over time for buyers and non-buyers of e-bikes. Main effects of time and purchase behaviour and their interaction on habitual associations with car use (left side) and e-bike use (right side; N = 144). Table 4. Comparison of mean scores of mobility-related habitual associations in the pre-trial and follow-up questionnaires for buyers and non-buyers. Means (M) and standard deviations (SD). Sum score Buyers (n = 53); M (SD) Non-buyers (n = 91); M (SD) Pre-trial Follow-up Pre-trial Follow-up Car 3.85 (1.69) 3.04 (1.13) 4.51 (2.12) 4.14 (2.14) E-bike 0.42 (1.28) 2.06 (1.73) 0.24 (0.83) 0.23 (0.79) Notes: Sum scores are between 0 and 9, with 9 signifying the most pronounced habitual association related to specific means of transport. car use one year after Bike4Car (see table 3 for M and SD). Furthermore, the significant main effect for e- bike purchase, F(1) = 9.14, p<.01, 𝜂 𝑝 2 = .06, indicated that on average, over both time points, habitual asso- ciations with car use were less pronounced for e-bike buyers compared to non-buyers. The interaction effect between the two variables time and e-bike purchase was not statistically significant, F(1), = 2.12, p =.15, 𝜂 𝑝 2 =.02 (see figure 1). This suggests that the pro- grammehada long-termeffectonparticipants’ habitual associations with car use, regardless of whether they would go on to purchase an e-bike. For habitual associations with e-bike use, we found a significant main effect of time, F(1) = 52.43, p<.001, 𝜂 𝑝 2 =.27, as well as a significant main effect for e-bike purchase, F(1) = 39.94, p<.001, 𝜂 𝑝 2 =.22. These main effects were further qualified by a significant interac- tion effect between the two variables time and e-bike purchase, F(1), = 53.85, p<.001, 𝜂 𝑝 2 =.28. This finding indicates that only participants who bought an e-bike after the programme exhibited increased habitual asso- ciations with e-bike use one year later. For non-buyers, habitual associations with e-bike use stayed practically the same over time (see table 4 and figure 1). 4. Discussion and conclusions In line with previous research [11, 15, 16, 21, 24, 26– 27], our study findings indicate that disruptions of individuals’ mobility context may trigger changes in habitual travel choices. Bearing in mind that our study did not measure actual habits but rather habitual asso- ciations it provides strong evidence that exchanging one’s car keys for an e-bike for just a few weeks influ- ences long-term habitual associations with car usage, and that this change persists even a year after the end of the intervention. This contrasts the findings of other studies who find that the effect of interventions wears off over time [27, 28, 30]. While this decrease in habit- ual associations with car use was most pronounced for participants who did buy an e-bike following the trial, 4 Environ. Res. Lett. 13 (2018) 044025 participants who did not change their mobility con- text displayed a significant long-term shift away from car use as well. Furthermore, it is noteworthy that this shift in habitual associations could be observed after a winter season has passed; which is usually cold, rainy and sometimes even snowy in Switzerland, and thus not ideal for riding a bike—electric or not. We can point to several plausible explanations for the observed persistence of the intervention’s effect mobility-related habitual associations. One is the strength of the habit disruption induced by the programme, as participants were required to hand over their car keys for the two-week duration of the trial. Hence, participants could not rely on their cars for commuting, shopping or leisure activities; instead, they had to organise their day-to-day activities around their e-bikes. Most studies that offer participants free use of public transportation as an alternative to cars [26, 27] may not have been able to provide a strong enough disruption, as they do not require participants to completely forgo the use of their cars. Furthermore, while habitual car drivers may have some misconcep- tions about public transportation [20], most people in Switzerland have experience with using it, which makes it improbable that they are positively surprised by a trial. In contrast, since it is still a niche mode of transportation, most participants may not have any previous experience with riding an e-bike. Hence, dur- ing the two-week trial, participants may have had novel, first-hand experiences of the benefits of e-bikes, including health benefits, time savings or the realisa- tion that steep slopes—a key barrier to conventional cycling [2, 3, 11, 32]—are much less of a challenge than they may have expected. In this study itwasnot possible to trackparticipants’ actual travel behaviour over time. This is an important direction for future research using for example track- ing devices and travel diary studies. Still, the observed shifts of participants’ mobility-related habitual associa- tionshint that e-bike trials hold a considerable potential in terms of promoting sustained energy and carbon efficient travel behaviour. Thus, policy-makers should consider supporting programmes that enable people to experience the benefits of novel means of trans- port directly. Creating options for such experiences has the potential for promoting sustainable mobility behaviour. Furthermore, such measures may also be useful in inducing behaviour change related to the use of other energy-related services. Acknowledgments This research project is part of the National Research Programme ‘Managing Energy Consumption’ (NRP 71) of the Swiss National Science Foundation (SNSF). Further information on the National Research Pro- gramme can be found at www.nrp71.ch. The project is also part of the Swiss Competence Center for Research in Energy, Society and Transition (SCCER CREST), Work Package 2 Change of Behaviour, for further information see www.sccer-crest.ch/. We would like to thank myblueplanet for their cooperation in develop- ing the questionnaires, for managing the data collection and for sharing insights into the programme Bike4Car. ORCID iDs Corinne Moser https://orcid.org/0000-0001-8071- 3681 Yann Blumer https://orcid.org/0000-0003-0224- 4538 References [1] Sims R et al 2014 Transport Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change ed O Edenhofer et al (Cambridge: Cambridge University Press) [2] Fishman E and Cherry C 2016 E-bikes in the mainstream: reviewing a decade of research Transport Rev. 36 72–91 [3] Popovich N, Gordon E, Shao Z, Xing Y, Wang Y and Handy S 2014 Experiences of electric bicycle users in the Sacramento, California area Travel. Behav. Soc. 1 37–44 [4] SFOE 2014 Verbreitung und Auswirkungen von E-Bikes in der Schweiz [distribution and impacts of e-bikes in Switzerland] (Bern: Swiss Federal Office of Energy) [5] Fyhri A, Sundfør H B and Weber C 2016 Effect of subvention program for e-bikes in Oslo on bicycle use, transport distribution and CO2 emissions (Effekt av tilskuddsordning for elsykkel i Oslo på sykkelbruk, transportmiddelfordeling og CO2 utslipp) (Oslo: Institute of Transport Economics) [6] Jones T, Harms L and Heinen E 2016 Motives, perceptions and experiences of electric bicycle owners and implications for health, wellbeing and mobility J. Trans. Geogr. 53 41–9 [7] Fyhri A and Fearnley N 2015 Effects of e-bikes on bicycle use and mode share Transport. Res. D Transport Environ. 36 45–52 [8] Winslott Hiselius L and Svensson Å 2017 E-bike use in Sweden—CO2 effects due to modal change and municipal promotion strategies J. Clean. Prod. 141 818–24 [9] Cherry C R, Yang H, Jones L R and He M 2016 Dynamics of electric bike ownership and use in Kunming, China Transport. Policy 45 127–35 [10] Cairns S, Behrendt F, Raffo D, Beaumont C and Kiefer C 2017 Electrically-assisted bikes: potential impacts on travel behaviour Transport. Res. A Policy Prac. 103 327–42 [11] Fyhri A, Heinen E, Fearnley N and Sundfør H B 2017 A push to cycling—exploring the e-bike’s role in overcoming barriers to bicycle use with a survey and an intervention study Int. J. Sustain. Trans. 11 681–95 [12] Wolf A and Seebauer S 2014 Technology adoption of electric bicycles: a survey among early adopters Transport. Res. A Policy Prac. 69 196–211 [13] Seebauer S 2015 Why early adopters engage in interpersonal diffusion of technological innovations: an empirical study on electric bicycles and electric scooters Transport. Res. A Policy Prac. 78 146–60 [14] Wüstenhagen R, Markard J and Truffer B 2003 Diffusion of green power products in Switzerland Energy Policy 31 621–32 [15] Wood W, Tam L and Witt M G 2005 Changing circumstances, disrupting habits J. Pers. Soc. Psychol. 88 918 [16] Verplanken B and Roy D 2016 Empowering interventions to promote sustainable lifestyles: testing the habit discontinuity hypothesis in a field experiment J. Environ. Psychol. 45 127–34 5 http://www.nrp71.ch http://www.sccer-crest.ch/ https://orcid.org/0000-0001-8071-3681 https://orcid.org/0000-0001-8071-3681 https://orcid.org/0000-0001-8071-3681 https://orcid.org/0000-0003-0224-4538 https://orcid.org/0000-0003-0224-4538 https://orcid.org/0000-0003-0224-4538 https://doi.org/10.1080/01441647.2015.1069907 https://doi.org/10.1080/01441647.2015.1069907 https://doi.org/10.1080/01441647.2015.1069907 https://doi.org/10.1016/j.tbs.2013.10.006 https://doi.org/10.1016/j.tbs.2013.10.006 https://doi.org/10.1016/j.tbs.2013.10.006 https://doi.org/10.1016/j.jtrangeo.2016.04.006 https://doi.org/10.1016/j.jtrangeo.2016.04.006 https://doi.org/10.1016/j.jtrangeo.2016.04.006 https://doi.org/10.1016/j.trd.2015.02.005 https://doi.org/10.1016/j.trd.2015.02.005 https://doi.org/10.1016/j.trd.2015.02.005 https://doi.org/10.1016/j.tranpol.2015.09.007 https://doi.org/10.1016/j.tranpol.2015.09.007 https://doi.org/10.1016/j.tranpol.2015.09.007 https://doi.org/10.1016/j.tra.2017.03.007 https://doi.org/10.1016/j.tra.2017.03.007 https://doi.org/10.1016/j.tra.2017.03.007 https://doi.org/10.1080/15568318.2017.1302526 https://doi.org/10.1080/15568318.2017.1302526 https://doi.org/10.1080/15568318.2017.1302526 https://doi.org/10.1016/j.tra.2014.08.007 https://doi.org/10.1016/j.tra.2014.08.007 https://doi.org/10.1016/j.tra.2014.08.007 https://doi.org/10.1016/j.tra.2015.04.017 https://doi.org/10.1016/j.tra.2015.04.017 https://doi.org/10.1016/j.tra.2015.04.017 https://doi.org/10.1016/s0301-4215(02)00147-7 https://doi.org/10.1016/s0301-4215(02)00147-7 https://doi.org/10.1016/s0301-4215(02)00147-7 https://doi.org/10.1037/0022-3514.88.6.918 https://doi.org/10.1037/0022-3514.88.6.918 https://doi.org/10.1016/j.jenvp.2015.11.008 https://doi.org/10.1016/j.jenvp.2015.11.008 https://doi.org/10.1016/j.jenvp.2015.11.008 Environ. Res. Lett. 13 (2018) 044025 [17] Gärling T and Axhausen K W 2003 Introduction: habitual travel choice Transportation 30 1–11 [18] Danner U N, Aarts H and de Vries N K 2008 Habit vs. intention in the prediction of future behaviour: the role of frequency, context stability and mental accessibility of past behaviour Br. J. Soc. Psychol. 47 245–65 [19] Verplanken B, Aarts H, van Knippenberg A and Moonen A 1998 Habit versus planned behaviour: a field experiment Br. J. Soc. Psychol. 37 111–28 [20] Fujii S, Gärling T and Kitamura R 2001 Changes in drivers’ perceptions and use of public transport during a freeway closure Environ. Behav. 33 796–808 [21] Larcom S, Rauch F and Willems T 2017 The benefits of forced experimentation: striking evidence from the London underground network (Oxford: University of Oxford Department of Economics) [22] Kaufman S, Qing C, Levenson N and Hanson M 2012 Transportation During and After Hurricane Sandy (Rudin Center for Transportation NYU Wagner Graduate School of Public Service) [23] Musselwhite C B A et al 2016 Breaking the habit: does fracturing your wrist change your travel and driver behaviour? Transport. Res. F Traffic Psychol. Behav. 38 83–93 [24] Verplanken B, Walker I, Davis A and Jurasek M 2008 Context change and travel mode choice: combining the habit discontinuity and self-activation hypotheses J. Environ. Psychol. 28 121–7 [25] Strömberg H, Rexfelt O, Karlsson I C M and Sochor J 2016 Trying on change—trialability as a change moderator for sustainable travel behaviour Travel Behav. Soc. 4 60–8 [26] Abou-Zeid M and Ben-Akiva M 2012 Travel mode switching: comparison of findings from two public transportation experiments Transp. Policy 24 48–59 [27] Fujii S and Kitamura R 2003 What does a one-month free bus ticket do to habitual drivers? An experimental analysis of habit and attitude change Transportation 30 81–95 [28] Thøgersen J and Møller B 2008 Breaking car use habits: the effectiveness of a free one-month travelcard Transportation 35 329–45 [29] Thøgersen J 2009 Promoting public transport as a subscription service: effects of a free month travel card Transport Policy 16 335–43 [30] Matthies E, Klöckner C A and Preißner C L 2006 Applying a modified moral decision making model to change habitual car use: how can commitment be effective? Appl. Psychol. 55 91–106 [31] Abou-Zeid M, Witter R, Bierlaire M, Kaufmann V and Ben-Akiva M 2012 Happiness and travel mode switching: findings from a Swiss public transportation experiment Transport Policy 19 93–104 [32] Moser C, Blumer Y and Hille S L 2016 Getting started on a car diet: assessing the behavioural impacts of an e-bike trial in Switzerland Proceedings of the 2016 International Energy Policies and Programmes Evaluation Conference (Amsterdam: IEPPEC) [33] SFSO 2016 Population: Keyfigures 2014 (Neuchâtel: Swiss Federal Statistical Office) [34] SFSO 2017 Bildungsstand der Bevölkerung 2016 [Level of Education of the Swiss Population 2016] (Neuchâtel: Swiss Federal Statistical Office) [35] SFSO 2017 Verkehrsverhalten der Bevölkerung [Mobility Behaviour of the Swiss Population] (Neuchâtel: Swiss Federal Statistical Office) [36] Verplanken B, Aarts H, van Knippenberg A and van Knippenberg C 1994 Attitude versus general habit: antecedents of travel mode choice1 J. Appl. Soc. Psychol. 24 285–300 [37] Moser C, Cometta C and Frick V 2016 How Do Different Residential Consumer Groups React towards Monetary and Uncon ventional Non-monetary Incentives to Reduce Their Electricity Consumption? (Ittigen: Swiss Federal Office of Energy) 6 https://doi.org/10.1348/014466607x230876 https://doi.org/10.1348/014466607x230876 https://doi.org/10.1348/014466607x230876 https://doi.org/10.1111/j.2044-8309.1998.tb01160.x https://doi.org/10.1111/j.2044-8309.1998.tb01160.x https://doi.org/10.1111/j.2044-8309.1998.tb01160.x https://doi.org/10.1177/00139160121973241 https://doi.org/10.1177/00139160121973241 https://doi.org/10.1177/00139160121973241 https://doi.org/10.1016/j.trf.2016.01.008 https://doi.org/10.1016/j.trf.2016.01.008 https://doi.org/10.1016/j.trf.2016.01.008 https://doi.org/10.1016/j.jenvp.2007.10.005 https://doi.org/10.1016/j.jenvp.2007.10.005 https://doi.org/10.1016/j.jenvp.2007.10.005 https://doi.org/10.1016/j.tranpol.2012.07.013 https://doi.org/10.1016/j.tranpol.2012.07.013 https://doi.org/10.1016/j.tranpol.2012.07.013 https://doi.org/10.1007/s11116-008-9160-1 https://doi.org/10.1007/s11116-008-9160-1 https://doi.org/10.1007/s11116-008-9160-1 https://doi.org/10.1016/j.tranpol.2009.10.008 https://doi.org/10.1016/j.tranpol.2009.10.008 https://doi.org/10.1016/j.tranpol.2009.10.008 https://doi.org/10.1111/j.1464-0597.2006.00237.x https://doi.org/10.1111/j.1464-0597.2006.00237.x https://doi.org/10.1111/j.1464-0597.2006.00237.x https://doi.org/10.1016/j.tranpol.2011.09.009 https://doi.org/10.1016/j.tranpol.2011.09.009 https://doi.org/10.1016/j.tranpol.2011.09.009 https://doi.org/10.1111/j.1559-1816.1994.tb00583.x https://doi.org/10.1111/j.1559-1816.1994.tb00583.x https://doi.org/10.1111/j.1559-1816.1994.tb00583.x