Still underdetected – Social norms and collective efficacy predict the acceptance of electric vehicles in Germany

Transportation Research Part F 37 (2016) 64–77

Contents lists available at ScienceDirect

Transportation Research Part F journal homepage: www.elsevier.com/locate/trf

Still underdetected – Social norms and collective efficacy predict the acceptance of electric vehicles in Germany Markus Barth ⇑, Philipp Jugert, Immo Fritsche Department of Psychology, University of Leipzig, Germany

a r t i c l e

i n f o

Article history: Received 2 September 2014 Received in revised form 4 November 2015 Accepted 30 November 2015 Available online 4 January 2016 Keywords: Electric vehicle adoption Collective efficacy Norms

a b s t r a c t The role of social identity variables for predicting environmental decisions may often be underdetected by psychological lay people. Applying this to the acceptance of electric vehicles (EVs) in Germany we investigated whether social norms and collective efficacy predict EV acceptance and what psychological laypersons who are either EV experts or EV non-experts think predicts EV acceptance. In preliminary interview studies we explored the beliefs of EV experts and EV non-experts. In a survey study, we then tested whether cost-related advantages and disadvantages were predictive of EV acceptance and whether norms and collective efficacy have independent effects even when controlling for cost-related factors and demographic variables. Results suggest that both EV experts and EV non-experts considered cost-related factors as much more important than social identity processes. However, hierarchical regression analyses of the survey data showed that norms and collective efficacy have equal or even stronger effects on acceptance than cost-related factors. We discuss the theoretical and practical implications of these findings. Ó 2015 Elsevier Ltd. All rights reserved.

1. Introduction Transportation is a major contributor to the global carbon footprint (Hertwich & Peters, 2009). In the face of climate change and dwindling natural resources and as an answer to increased pollution of inner city areas, the interest in alternative fuel solutions such as electric vehicles (EVs) has grown in recent years.1 EVs have become an important part of the political agenda in Europe. For example, the government in Germany has decided to support this new technology with the goal for Germany to become a leading market of electric mobility and that by 2020 there will be at least one million EVs on German streets (Federal Government of Germany, 2010). As Germany is still in an ‘‘early adopter stage” compared to other countries like Norway where EVs are much more common (see Klöckner, Nayum, & Mehmetoglu, 2013), it seems to be of central importance to identify the barriers and also the facilitative factors related to the acceptance of the new technology. Specifically, we were interested in the role of social norms and collective efficacy beliefs as predictors of EV acceptance. Public debates on barriers and opportunities for the ⇑ Corresponding author at: Department of Psychology, University of Leipzig, Neumarkt 9-19, 04109 Leipzig, Germany. Tel.: +49 341 9735968. E-mail address: [email protected] (M. Barth). Although EVs are perceived as a more sustainable alternative to fuel-driven cars, this promise of a cleaner and environmentally friendly mobility choice is bound to additional requirements. For example, the electricity itself would need to be produced in a clean way. Currently, there are considerable regional differences in electricity CO2 intensity (Tran, Banister, Bishop, & McCulloch, 2012) and large potential markets for EVs such as China and Germany still rely heavily on coal-generated electricity. Electric mobility has the potential to become a benefit to the environment but an integrated power-system planning will be one prerequisite to make sure that EVs are indeed the more environmentally responsible choice. As political support has grown as well, there is at least hope that EVs can make a meaningful contribution to fight the negative consequences of climate change. 1

http://dx.doi.org/10.1016/j.trf.2015.11.011 1369-8478/Ó 2015 Elsevier Ltd. All rights reserved.

M. Barth et al. / Transportation Research Part F 37 (2016) 64–77

65

adoption of EVs often seem to focus on personal costs and benefits in terms of financial costs or incentives or worries about usability and convenience issues. However, beyond personal cost-benefit analyses people’s attitudes and decisions are also driven by their perception of the social context and social affiliations. People not only define themselves as isolated individuals but also as members of groups and communities. This is why they do not make their decisions in a social vacuum but are affected by what others think and do (social norms; Cialdini & Trost, 1998) and whether they think that communities or groups can bring about social change, such as establishing sustainable mobility on a collective level (collective efficacy; van Zomeren, Postmes, & Spears, 2008). However, as previous research on social norms and private energy conservation (Nolan, Schultz, Cialdini, Goldstein, & Griskevicius, 2008) suggests, these social psychological predictors of sustainable behavior are underdetected by psychological laypersons. This is why in the present article we aim to test whether social norms and collective efficacy add to the prediction of EV acceptance above and beyond the influence of cost-benefit variables and whether psychological laypersons who are experts (e.g., developers and decision makers) or non-experts on EV are aware of the possible impact of collective cognition. 1.1. Theories on the diffusion of new technologies In his seminal work, Rogers (2003) describes the decision to accept or reject an innovation in society, such as the adoption of EVs, as a social process that can be divided into discrete stages. In the knowledge stage, individuals are exposed to an innovation but are still in need of information. The next stage, persuasion, is characterized by the seeking of related details and positive or negative attributes of the innovation, which should lead to the third stage, decision, where advantages and disadvantages are weighed against each other and the individual decides whether he or she should adopt or reject the innovation. With the addition of a fourth and fifth stage (implementation and confirmation), Rogers’ model describes the full course of the adoption process. In the specific context of EVs in Germany at this time, it can be argued that most people are still in the initial stages, knowledge and persuasion. Therefore, it seems to be important to (a) identify the variables related to EV acceptance and (b) investigate the relative importance of these variables for the acceptance of EVs. We propose that it is not only technical and cost-related information on range, recharge time and purchasing price that determine individual acceptance of EVs but that people also use information from interactions with others that indicate how similar others think and act and whether collective innovation seems feasible. Successful diffusion depends on the adoption of the new technology by more and more people. That means, they need to become interested in it and need to decide to use it. Recently, Klöckner (2014) proposed a stage model to explain EV purchase decisions. He adapted a stage model of behavior change proposed by Bamberg (2013) that includes the pre-decisional stage, the pre-actional stage, the actional stage and the post-actional stage. Individuals move from one stage to the other by forming specific intentions (e.g., a goal intention for transition from the pre-decisional stage to the preactional stage, or a behavioral intention for transition from the pre-actional to the actional stage). Importantly, each type of intention is expected to be influenced by specific variables. Therefore, certain variables can become important at one stage of the process but are no longer influential at another stage. As stated above, Germany is still at the ‘‘early adopter stage” with regard to EV use. It could be argued then, that many potential German EV users are still at the pre-decisional stage. This stage is characterized by the realization that behavior has to be changed (here, mobility behavior) or that it does not need to change. According to Klöckner’s (2014) adapted model, norms play an important role in the formation of goal intentions. This assumption fits a social identity perspective on decision-making processes 1.2. Social identity factors (social norms and collective efficacy) As individuals are interdependent and often experience themselves as members of a group, it is very likely that social identity and group membership are relevant to the adoption of electric vehicles. We define ourselves by identifying with certain social groups (social identity; Tajfel & Turner, 1979). Individuals’ thinking, emotions and behavior usually change when membership in a specific group (e.g., nation, gender, political groups) vs. personal identity becomes salient. Among other reactions, this can lead to discrimination of salient outgroups (Billig & Tajfel, 1973), participation in collective action and social movements (Simon et al., 1998) or changes in health-related behavior (Haslam, Jetten, Postmes, & Haslam, 2009). How people are affected by group membership depends on processes of self-stereotyping where people apply the perceived ingroup prototype (e.g., ‘‘Germans like innovations”) as a description of themselves (‘‘I like innovations”). Perceived ingroup norms inform the perception of what it means to be a group member. When group membership is salient and an individual identifies with that group, s/he is motivated to follow these unspoken rules and customs of her or his group and to act accordingly (e.g. Masson & Fritsche, 2014; Terry, Hogg, & White, 1999; White, Smith, Terry, Greenslade, & McKimmie, 2009). There are different types of norms and all of them could be relevant for the adoption of EVs. When a norm refers to what group members commonly do it is called a descriptive norm (e.g., ‘‘Germans do not drive EVs”; Cialdini, Reno, & Kallgren, 1990). When it refers to what is commonly approved and disapproved within the group it is called an injunctive norm (e.g., ‘‘Germans approve of driving EVs”; Cialdini & Trost, 1998; Smith & Louis, 2009). Social norms could therefore influence the decision to adopt an EV if an individual perceives other group members to be in favor of adoption (injunctive norm). Of course, the perception that very few people use EVs could have the opposite effect and decrease the likelihood of adoption (descriptive norm; Smith et al., 2012).

66

M. Barth et al. / Transportation Research Part F 37 (2016) 64–77

There are two other conceptualizations of norms relevant in this context. Provincial norms refer to the influence that behavior of others can have on our decisions when those others occupy a comparable setting (Goldstein, Cialdini, & Griskevicius, 2008). Therefore, people in similar life conditions or people who are physically proximate can influence our actions as their behavior may inform us about what is appropriate in these situations, and they may do this also when we do not conceive of them as members of our own group (‘‘do as the Romans do when you are in Rome”, Goldstein et al., 2008). On the other hand, subjective norms describe the perceived expectations of significant other individuals (Ajzen, 1991). The opinions of family members and friends who are important to the self can have an impact on individual behavior. Subjective norms differ from provincial norms in that significant others need not to share the same living conditions (e.g., to live close by) to have an influence on behavior. Applied to the context at hand, acceptance might be increased when EVs are positively evaluated by members of the ingroup (e.g., one’s own nation; this would be the injunctive norm) or by close friends (the subjective norm) and when EVs are actually used by people of one’s own group, such as the inhabitants of people’s own city, the citizens of their own nation or the members of their own socio-cultural groups (the descriptive norm) or when EVs are driven by people living in the same street (the provincial norm). Klöckner (2014) proposed that social norms influence goal intentions in the pre-decisional stage. Following this argument, we would also assume that social norms can be particularly influential in the early stages of the decision-making process or in the early stages of diffusion. Surprisingly though, with some exceptions like Klöckner’s work, norms have not received much attention in the context of EV adoption, and if at all, they rarely have been addressed directly or in a differentiated way. Axsen, Orlebar, and Skippon (2013) found that social negotiation (i.e. discussing EVs with others) can change individual perceptions of EVs and that participants were influenced by social interaction (see also Axsen & Kurani, 2011). Graham-Rowe et al. (2012) reported that low social desirability of EV-use could serve as a potential barrier to adopt an EV. In line with this assumption, Jansson (2011) found that adopters and non-adopters of EVs differ on personal and social norms. One recent study found that subjective and personal norms were positively related to the intention to use alternative fuel vehicles (Petschnig, Heidenreich, & Spieth, 2014). It has also been suggested that the more people think that the adoption of an EV will have positive outcomes for their social status, the more likely they are to adopt (Noppers, Keizer, Bolderdijk, & Steg, 2014). Although most of the cited research worked with one or two normative constructs, there has been no research that incorporated all four types of norms, descriptive, injunctive, subjective and provincial, in the context of EV adoption. Controlling for the effects of all norms at the same time will make it possible to test whether all norms or just some of them have independent effects on EV adoption. This would also have important consequences for the development of strategies to increase EV acceptance via normative influence as the effects of descriptive norms and injunctive norms would need to be changed in fundamentally different ways. Bringing about social change such as establishing clean and sustainable traffic systems is usually not the work of single individuals but depends on the behavior of collectives. This is why individuals often perceive personal helplessness when considering to engage in actions (e.g., buying or using EVs) to foster societal innovation (e.g., establishing green mobility in the industrialized world). Recent research suggests that humans’ ability and propensity to think in terms of ‘‘We”, and thus to invoke social identity, may help to overcome personal paralysis by providing a sense of collective efficacy. Collective efficacy can be defined as the belief that the ingroup is capable of affecting important aspects of its environment (see van Zomeren et al., 2008). It should foster individuals’ actions towards collective goals by increasing their perception that their personal behavior is a movement towards collective change (Jugert et al., in preparation). Accordingly, a meta-analysis revealed that collective efficacy increases people’s collective action intentions (van Zomeren et al., 2008). Homburg and Stolberg (2006) found that pro-environmental behavior was not so much predicted by the amount of personal efficacy (i.e. the belief that one can solve a task personally) but by the amount of perceived collective efficacy (i.e. the belief that groups of people are efficacious in solving tasks). It seems that being a member of a group changes our beliefs about what we can achieve. Even though we ourselves cannot solve pressing problems such as climate change we may feel that as a group we have the power to make a difference. Using an EV could thus be framed as a personal contribution to a superordinate collective goal of becoming a more sustainable society and the intention to use EVs should be stronger if there is the perception that the in-group is capable of achieving this goal. The relationship between collective efficacy and acceptance of EVs has not been tested yet. It is our assumption that collective efficacy beliefs, just as social norms, will have an influence on the formation of goal intentions in the early stages of the process. Even though empirical evidence points to the importance of social norms and collective efficacy beliefs, these factors are usually not in the focus of the public discussion and commonly underestimated. As Nolan et al. (2008) were able to show, normative influence led to the biggest changes in conservation behavior but the normative information used by the authors was at the same time rated as least motivating of all the information materials the authors employed in their study. In other words, more energy was conserved after reading about other community members’ behavior than after appealing to the participants’ self-interest (save money by conserving energy) or their environmental concern (save energy because it is good for the environment) although the residents of the investigated area thought that the normative information had nothing to do with them conserving energy. Ignoring the importance of social norms and collective efficacy for the acceptance of EVs could result in less than optimal strategies aimed at increasing the acceptance of EVs. To counter this development, it is necessary to call attention to the relevance of norms and efficacy beliefs in the context of EV adoption.

M. Barth et al. / Transportation Research Part F 37 (2016) 64–77

67

1.3. Personal factors With regard to non-social predictors, there has been a rise in scientific research on EV adoption (e.g. Axsen et al., 2013; Franke, Neumann, Bühler, Cocron, & Krems, 2012; Klöckner et al., 2013). It has been found that functional aspects of EVs like range and recharging time are often seen as drawbacks with potentially negative effects for acceptance (Axsen et al., 2013; Bunch, Bradley, Golob, Kitamura, & Occhiuzzo, 1993; Chéron & Zins, 1997; Graham-Rowe et al., 2012; Hidrue, Parsons, Kempton, & Gardner, 2011). Franke and Krems (2013) found the perceived limited driving range of an EV to be an obstacle to the adoption of EVs. Experience and daily practice were able to reduce this discomfort with limited range considerably. Hackbarth and Madlener (2013) concluded that conventional vehicles will maintain their dominance in the market as long as driving range and problems with the charging infrastructure have not been resolved. In line with this argument, Potoglou and Kanaroglou (2007) proposed that reduced monetary costs and purchase tax relieves would increase adoption rates of alternative-fuel vehicles. Ziegler (2012) suggested that taxation of gasoline as well as subsidization of alternative energy sources would promote EV adoption (see also Krupa et al., 2014). In addition, both the generosity and type of tax incentives are linked to adoption (Gallagher & Muehlegger, 2011). These findings underline the specific focus on personal cost-benefit related factors that is prevalent in research on acceptance of EVs. Recent studies have questioned the success of monetary incentives to facilitate adoption (Beresteanu & Li, 2011; Chandra, Gulati, & Kandlikar, 2010). Consequently, research has been broadened by a number of publications and additional variables have been explored with regard to their effect on acceptance of EVs. For example, there are positive effects of personal experience with EVs and personal environmental concern (Jensen, Cherchi, & Mabit, 2013). The importance of personal experience has found additional support in other works. Experience can significantly reduce range anxiety (Franke & Krems, 2013). Other perceived barriers are affected by experience as well, e.g. battery issues and the low noise level (Bühler, Cocron, Neumann, Franke, & Krems, 2014, see also Brand, Petri, Haas, Krettek, & Haasper, 2013; Cocron & Krems, 2013). Adoption of EVs becomes more likely, the less negative people evaluate the disadvantages of EVs and the more they feel able to drive an EV (Bockarjova & Steg, 2014). Personal experience addresses both issues. In addition to personal experience, evaluations of the attributes of an EV as environmentally friendly were also positively related to adoption indicators (Noppers et al., 2014). In line with Klöckner’s (2014) argument, we would assume that not all of the variables discussed above are of equal importance in all stages of the decision-making process. Cost factors like the purchasing price could become more important in the actional stage while social norms or evaluations of the sustainable nature of the EV have more influence in earlier stages. As our study investigates the specific German situation, we decided to concentrate on variables that would be relevant in the initial stages, such as, for instance, social norms. In the German context, we assume that the diffusion of EVs is still in an early stage. Potential adopters will therefore not have enough information on EVs or will seek positive or negative evaluations to form a decision (Rogers, 2003). Social groups and relevant others can be a source of information. Especially in the pre-decisional stage, normative influence should be strongly related to goal intentions (Klöckner, 2014). Although there have been a few studies on the effect of norms on EV adoption (e.g. Petschnig et al., 2014), recent work on EV adoption suggests country specific differences in the effects of adoption-related variables. Therefore, the effects of social norms on the acceptance of EVs need to be replicated in the German context. In addition, there has been no research that investigated the specific effects of all normative variables at the same time. This paper seeks to investigate the effects of norms and collective efficacy perceptions on the acceptance of EVs in the form of goal intentions in the early stages of the diffusion process in Germany. By using qualitative and quantitative data, we hope to contribute to a better understanding of both personal and social predictors of EV adoption intentions and their relative importance. To gauge whether public discourse accurately represents the actual associations of personal and social factors with EV adoption goals, we compare psychological laypersons’ perceptions of important predictors of EV adoption with the prediction results of a survey study measuring possible predictors and indicators of EV adoption intentions. Building upon the theoretical and empirical work we have discussed in this section, we hypothesize that normative influence is underdetected by psychological laypersons. We also assume that norms and collective efficacy are significant predictors of goal intentions independent of cost-related variables. 2. Preliminary studies To elucidate psychological laypersons’ perceptions of what factors determine EV adoption, we conducted two preliminary interview studies with EV experts and EV non-experts. We asked them about the advantages and disadvantages of EV use in Germany, relevant forms of private EV use in the future, and the factors that the interviewees deemed to be the most central for acceptance of EVs. Besides assessing psychological laypersons’ perceptions of determinants of EV adoption we also wanted to use the data for replicating and validating results of previous studies on EV acceptance in the German context and to identify the most important private EV use scenarios and possible predictors to be included in the following questionnaire study. For the purpose of this study, we defined experts as persons working in a field that is directly linked to EVs or EV related areas (e.g., the economy, politics, etc.). We were interested in the perspectives of both EV experts’ and EV non-experts’ point of view for two reasons. First, EV experts are much closer to decision-making processes concerning the future of EVs in Germany. As such, their opinions on EVs might very well be related to the strategies that are planned to facilitate the diffusion of

68

M. Barth et al. / Transportation Research Part F 37 (2016) 64–77

EVs in Germany. Comparing their assessment of causal factors with the results of a quantitative survey study regressing EV acceptance on multiple predictors would thus allow for estimating the accuracy of decision making strategies. Second, we wanted to investigate whether EV experts and non-experts differ in their assessment of the most important factors determining the acceptance of EVs. 2.1. Method In May and June 2013 we contacted nine German experts from various fields and asked for a personal interview on the acceptance of EVs. As EV experts we were looking for interviewees whose professional duties are directly linked to EVs. We included one representative of an international energy supplier, one representative of a local public utility company, one representative of a local car sharing provider, two scientists who did research on EVs (in the fields of psychology and ecology), the representative of a regional corporation in the automobile sector, the representative of a local car dealer, the representative of a national association for the promotion of EVs and a member of the Saxon State Parliament. We guaranteed each interviewee full anonymity. We offered to conduct the interviews face to face or via telephone if a personal meeting could not be arranged. With one exception, the interviews were digitally recorded and took between 50 and 65 min. One interviewee was uncomfortable with being recorded. In this case, we took notes of all of the answers. All interviewees had personal experience with an EV. The central part of the interview concerned all the factors that the experts perceived to be predictive of the acceptance of EVs and which of those factors they deemed most relevant. Importantly, we did not offer examples, but were interested in the factors that came to the experts’ minds. That is, we wanted to identify the most salient factors that were related to the acceptance of EVs in the experts’ point of view. For use in later studies, we also asked the experts to indicate the most likely scenario of private EV use. In later parts of the interview, we also discussed other variables and potential ways to facilitate the diffusion of EVs in Germany. For the sake of brevity, we will not report the results from these portions of the interviews and concentrate on the aspects central to the topic of this paper. For the second part, we conducted short interviews (8–15 min) with 33 persons from the general public recruited in a major German city in September and October 2013. As Germany is believed to be in the early stages of EV adoption (e.g. Bühler et al., 2014), we concentrated on interviewees who fit the general profile of early adopters of EVs. Recent research describes early adopters as relatively young, environmentally aware and living in metropolitan areas (e.g. Hackbarth & Madlener, 2013; Rolim, Baptista, Farias, & Rodrigues, 2014; Ziegler, 2012). Krupa et al. (2014) propose that marketing campaigns should target left-leaning, environmentally concerned customers. Therefore, we approached participants in a meeting of a local group of environmental activists as well as customers of a local store that sells environmental friendly products. In addition, we also asked visitors of a family event of the local energy supplier to participate. At this event, an EV was exhibited and visitors had the opportunity to experience this new technology firsthand. We also interviewed students on campus. The complete sample of 33 persons consisted of 10 ‘‘green” interviewees, 10 students and 13 visitors of the family event. Although this sample is very selective, we believe that it covers those individuals that are most likely to become interested in EVs in this early stage of diffusion in the German context. Nevertheless, we acknowledge the limitation, that a more general sample might have yielded different results. We guaranteed anonymity to all our participants. We informed them of the topic of the interview and asked for their consent to record the whole interview for later analysis. Central to this paper, in one part of the interview, we asked what factors would increase the chances of them driving an EV and what would make it harder for them to do so. At the end of the interview, we asked for the most important influence on the decision to use EVs. Once again, we were interested in the factors that are most salient to the interviewees and we did not offer suggestions in this part of the interview. In later parts of the interview, we discussed additional topics related to EVs and mobility in general. We confine ourselves to report only the results directly related to the topic of this paper. All interviewees were thanked for their participation and there was time for questions regarding the study. All interviews were transcribed verbatim and analyzed. This was done in a stepwise procedure with independent ratings by three raters in a first step and all subsequent analyses done in group discussions. Employing qualitative content analysis (Mayring, 2010), we systematically reduced the content of the interviews by employing paraphrasing (i.e. the shortening and rephrasing of the material), generalization to a common level of abstraction (i.e. summarizing interview units on a lower level into a new superordinate category) and by reducing the content through elimination of categories with similar meaning. The resulting categorical system was unambiguously applicable to all interview data. In the final step, frequencies of the different categories were counted to identify the most common answers. 2.2. Results All results are summarized in Table 1. Both experts and non-experts agreed that the most positive attributes of EVs are the environmentally friendly nature of the new technology and the fact that EVs are less expensive in maintenance than regular vehicles. On the other hand, the high purchasing price as well as the perceived limited range of EVs and the missing infrastructure were the disadvantages that were most often mentioned. There was also agreement that the purchasing price was the most important factor related to adoption of EVs with range as the second most important factor. Importantly, neither EV experts nor EV non-experts named norms or efficacy beliefs as important predictors of acceptance. Instead,

69

M. Barth et al. / Transportation Research Part F 37 (2016) 64–77 Table 1 Results from the preliminary interview studies. Question

Category

Experts (n = 9)

Non-experts (n = 33)

Increase acceptance

Sustainable technology Lower maintenance

7 (78%) 6 (67%)

18 (55%) 8 (24%)

Decrease acceptance

Purchasing price Limited range Missing infrastructure

8 (89%) 7 (78%) 5 (56%)

13 (39%) 12 (36%) 13 (39%)

Most important

Purchasing price Range

6 (67%) 4 (44%)

21 (64%) 9 (27%)

Note: For each category, the absolute frequencies from both interviewee groups are shown with relative values in parentheses. In the interest of brevity, only the most frequent categories have been included in the table.

cost-related variables were most prominent. This result is in line with our hypothesis that normative influence is underdetected by psychological laypersons. As most likely scenarios for private use, the experts identified a scenario were an EV was bought and privately owned as well as a scenario were the EV was used as part of a car sharing pool (both scenarios were identified by six experts). 2.3. Discussion We conducted two preliminary interview studies to investigate the current German perspective on advantages and disadvantages of EVs. In line with previous research (Bühler et al., 2014; Egbue & Long, 2012; Sierzchula, Bakker, Maat, & van Wee, 2014), cost-related factors like purchasing price and limited range were the most frequently mentioned disadvantages of EVs. At the same time, those two factors were stated to be the most important factors related to acceptance of EVs. Interestingly, there was no real difference between EV experts and EV non-experts in their point of view. Although they were better informed about the subject, experts did not seem to have a markedly different perspective on the current situation in Germany than non-experts. The results seem to suggest that strategies like tax incentives (Gallagher & Muehlegger, 2011) or subsidization (Ziegler, 2012) could increase acceptance of EVs in Germany. At the same time, Sierzchula (2014) proposed that targeted education and experience programs might be a cheaper way than subsidies. Research on the effects of prolonged experience with EVs has shown that daily practice is related to less range anxiety (Franke & Krems, 2013; Rauh, Franke, & Krems, 2015). In addition, experience also increased positive attitudes towards EVs and the intention to recommend them to others (Bühler et al., 2014). Therefore, it seems plausible that there are other variables that are related to acceptance of EVs besides cost-related factors like purchasing price. These variables might be as strongly related to acceptance and yet they are not as salient to perception. Especially in the German early adopter stage, other ways should be explored to promote acceptance. For example, individuals in the pre-decisional stage (Klöckner, 2014) form their goal intentions under the influence of social norms. Norms have already been found to be positively related to the intention to use EVs (Petschnig et al., 2014). Nevertheless, the results from our interviews do not suggest that norms or other social psychological variables are perceived as important factors related to acceptance. This is in line with our hypothesis that psychological laypersons are not aware of the influence of norms and it also replicates findings from research on energy conservation (Nolan et al., 2008). In their study, Nolan et al. found that normative influences are usually rated as least influential on individual behavior although they actually had the strongest effect. Although undetected by our interviewees, norms could be utilized to increase acceptance and to help form a goal intention of driving EVs. Consequently, we wanted to build on previous research (Klöckner, 2014; Petschnig et al., 2014) and investigate the relation of various types of norms on the acceptance of EVs. In our next study we intended to use the results from the preliminary interview studies to include the factors that our interviewees stated and to add social identity variables such as norms and collective efficacy (Homburg & Stolberg, 2006). By controlling for the cost-related variables that figured in the interviews, this will allow us to test for the independent relations of the social identity variables to the acceptance of EVs. 3. Questionnaire study The goal of this study was to investigate the relative strength of the relationship between acceptance of EVs in the early stages of diffusion in Germany and factors we derived from the interviews of the preliminary studies and from social psychological literature (Cialdini et al., 1990; Goldstein et al., 2008; Homburg & Stolberg, 2006). Specifically, we wanted to test if norms and collective efficacy are independently related to acceptance when cost/benefits-related factors are controlled for. For exploratory reasons, we also included two scenarios of EV use, a buying scenario and a car sharing scenario which were described as the most likely scenarios of EV use in Germany by the experts interviewed before. Although this study concentrates on variables that are related to the formation of a general goal intention to use an EV, specific contexts of EV may not only change acceptance and willingness to adopt but may also imply different predictor sets or relative predictor weights.

70

M. Barth et al. / Transportation Research Part F 37 (2016) 64–77

3.1. Method 3.1.1. Participants This study was conducted online in April 2014. As in the preliminary studies, we chose to aim at the parts of the population from which early adopters will most likely come. A link to the study was made available to students of a long-distance university who participated for credit points. Additionally, we used the crowdsourcing platform WorkHub to gather a portion of the data. This allowed us to broaden the demographic background of our participants by specifically targeting fulltime employed persons. The link was also advertised in different online forums. As an incentive, participants were entered into a lottery with gift coupons for an online store and a test drive with an EV as prizes. The sample consisted of 601 participants. We omitted cases where participants had not entered any data (n = 7). As participants who took much longer than average most likely were interrupted while working on the questionnaire and as we had no means to control for what they did during the break, we decided to exclude participants who took more than two standard deviations above the mean for completion (n = 12). We also did not include 34 participants who finished the questionnaire in an unreasonably fast way (2 SD below average). This resulted in a final sample size of N = 548 (M Age = 32.91, SD = 9.30, min = 17, max = 73, 54.3% female, 1 person chose an alternative gender). 261 participants (47.6%) were students with 99 of them being employed (i.e. they were not full-time students but studied in their leisure time). In sum, 386 (70.4%) participants were employed (either full-time or part-time). In our sample, the distributions of gender and age was roughly representative of potential EV buyers in Germany. A recent study on trends related to car purchases in Germany supported this assumption (Aral Aktiengesellschaft., 2013), showing that an equal amount of women and men (26% each) intended to buy a new car within the next 18 months. Additional support for an equal distribution of gender in our study came from a study on general mobility trends among young adults in Germany (Kuhnimhof, Buehler, Wirtz, & Kalinowska, 2012). According to this study, gender differences in car travel have largely disappeared among younger Germans. Concerning the average age of our participants, results from the Aral study also suggested that persons younger than 39 years are more likely to buy a new car. In addition, Egbue and Long (2012) argued that technology enthusiasts are most likely coming from an age range of 18–44 and that this group is a vital part of the category of early adopters. Importantly, our study did not intend to investigate buying behavior but the intention to use or try out an EV. From the perspective of behavioral change models (Bamberg, 2013; Klöckner, 2014), we investigated the pre-decisional stage and the formation of a goal intention, not the actional stage. 3.1.2. Procedure After a short introduction, we asked our participants for some basic demographic data (age, gender, household size, household income) and they also indicated if they needed to commute to work. We also asked about their personal experience with EVs (e.g., if they had ever driven an EV or how much knowledge they had about the new technology). 95 participants had previously driven an EV. Scenarios of EV use. We introduced each participant to one of two different scenarios in which EVs could become relevant in the future. We did this to specify the more general acceptance measure (e.g., I would like to use an EV). On the one hand, we described a scenario in which they owned their own EV. In the other scenario, they were using EVs in the context of a car sharing deal. Participants were randomly assigned to one of the two scenarios (for the buying scenario, n = 282; for the sharing scenario, n = 266). Both scenarios gave very basic information on the costs related to this scenario and other relevant data. A translation of both scenarios can be found in the Appendix. Following this, participants responded to various statements, which served as measures of the different possible predictors of acceptance.2 Unless otherwise noted, all items were measured with Likert scales ranging from 1 to 7 (with 7 meaning absolute agreement with the statement). Items were aggregated into scales by computing their mean. The questionnaire was written in German, the examples given below are translations by the authors. Personal costs. Three items covered the reservations about EVs that we had found in the preliminary studies. ‘‘I think EVs are too expensive.”, ‘‘In my view, recharging the batteries of an EV takes too long.” and ‘‘The range of an EV is not far enough for me,” a = .64. Personal benefits. As lower maintenance costs and sustainability were the two most frequent advantages in our interview studies, we included two items for maintenance costs (‘‘The maintenance of EVs is less expensive.” and ‘‘Considering all costs, driving an EV is no more expensive than driving a conventional car.”, r = .24, p < .001) and one item for sustainability (‘‘EVs are environmentally friendly”). Social norms. Injunctive norms (e.g., ‘‘Most of the people in my region approve of the use of EVs.”) were measured with three items, a = .89. We also measured descriptive norms (e.g., ‘‘There are already people in my region who drive an EV,” a = .75,

2 Please note that these measures were taken from a bigger survey that touched on different aspects related to mobility. We will only report the measures relevant to the research at hand for purposes of brevity.

71

M. Barth et al. / Transportation Research Part F 37 (2016) 64–77

three items), provincial norms (e.g., ‘‘Most of the people living in my street would love to drive an EV,” a = .82, three items) and subjective norms (e.g., ‘‘Most of the people that are important to me approve of driving an EV,” a = .83, three items). Collective efficacy. We used six items to measure collective efficacy (e.g., ‘‘As inhabitants of this region we can do much to noticeably reduce CO2 emissions together,” a = .89). General and scenario specific acceptance of EVs. Three items measured the general acceptance of EVs: ‘‘I would like to use an EV”, ‘‘I will not use an EV under any circumstances” (R) and ‘‘I intend to do a test-drive with an EV in the next months,” a = .72. In addition to the items on the general acceptance of EVs, participants were subsequently asked if they would use an EV in the context of the scenario they had been given (i.e. if they wanted to buy an EV or if they wanted to drive an EV via car sharing). Although these intentions were phrased more specifically than the acceptance items, they are not representing a different stage in the decision-making model and should be relevant at the early German stage of diffusion. Klöckner (2014) measured goal intention with a similar question (‘‘I intend to buy an EV”). After they had responded to all the items, all participants were thanked and debriefed. 3.2. Results 3.2.1. Demographic variables and acceptance Participants reported an average of 2.33 persons in their households. On average, participants reported an income between 1500 and 3000 €. 52.2% of the participants commuted to work. When we correlated age with general acceptance, there was a weak positive relation, r = .20, p < .001). There was only a marginally significant correlation between household income and acceptance, r = .08, p = .08. We found a significant effect of gender on acceptance, F(1, 542) = 20.57, p < .001, g2 = .04. Men (M = 4.79, SD = 1.54) were more likely to accept EVs than women (M = 4.23, SD = 1.35). No other demographic variables had an effect on acceptance. 3.2.2. Experience and knowledge Participants who had the opportunity to test-drive an EV in the past reported a stronger acceptance of EVs (M = 5.69, SD = 1.37) than those with no personal experience (M = 4.23, SD = 1.36), F(1, 539) = 90.57, p < .001, g2 = .14. Perceived personal knowledge about the new technology was also positively related to acceptance. The better participants believed their knowledge about EVs to be, the stronger was their acceptance, r = .42, p < .001. 3.2.3. Predictors of acceptance Please refer to Table 2 for intercorrelations of all predictors and their means and standard deviation. Intercorrelations between the norm variables were only moderate and intercorrelations of the items within a norm scale were higher than between scales. Accordingly, we concluded that discriminant validity was sufficient and treated all four norm scales as separate variables. We used hierarchical regression analyses to test the unique predictive power of our measures. To control for demographic variables, we entered age, gender, personal knowledge and previous experience in a first step. Following this, we entered cost-related disadvantages and personal benefits. In the final step, we added all norm scales (injunctive norm, descriptive norm, provincial norm, and subjective norm) and collective efficacy. Please see Table 3 for an overview of all regression coefficients. We found significant increases in R2 for each step. The final model accounted for 54% of the variance. All variables significantly predicted acceptance with two exceptions. Personal experience via test-drive only reached marginal significance. Descriptive norms were not significantly related to acceptance of EVs.

Table 2 Intercorrelations of the measures used in the questionnaire study. 1 1 2 3 4 5 6 7 8 9

General Acceptance Costs Maintenance Sustainable Subjective norms Descriptive norms Injunctive norms Provincial norms Collective efficacy

M (SD) ** ***

p < .01. p < .001.

2

3

4

5

6

7

8

9

–

.36*** .32*** –

.30*** .19*** .26*** –

.62*** .36*** .30*** .30*** –

.29*** .17*** .11** .00 .28*** –

.51*** .26*** .24*** .23*** .57*** .39*** –

.44*** .23*** .17*** .16*** .47*** .28*** .59*** –

.45*** .27*** .25*** .32*** .45*** .23*** .39*** .32*** –

4.59 (1.36)

4.30 (1.32)

5.46 (1.45)

4.79 (1.25)

4.27 (1.73)

4.07 (1.17)

3.35 (1.20)

5.01 (1.30)

.39***

–

4.48 (1.47)

72

M. Barth et al. / Transportation Research Part F 37 (2016) 64–77 Table 3 Hierarchical regression on general acceptance, final model. Predictor

B

Gender Age Experience Knowledge Cost-related disadvantages Lower Maintenance costs Sustainability Injunctive norm Descriptive norm Provincial norm Subjective norm Collective efficacy

ß .21 .01 .25 .11 .14 .08 .08 .12 .01 .11 .35 .15

t 2.11* 2.63** 1.72a 3.40** 3.82*** 2.17* 2.31* 2.27* 0.39 2.40* 7.26*** 3.81***

.07 .08 .07 .14 .13 .07 .08 .10 .01 .09 .29 .13

Note: Gender was coded 1 = men and 2 = women. Experience was coded 1 = has no personal experience and 2 = has personal experience. R2 = .21 for Step 1. DR2 = .16 for Step 2 (p < .001). DR2 = .17 for Step 3 (p < .001). a p < .10. * p < .05. ** p < .01. *** p < .001.

Table 4 Hierarchical regression for the buying scenario and for the sharing scenario (final model). Predictor

Buying scenario B

Gender Age Experience Knowledge Cost-related disadvantages Lower Maintenance costs Sustainability Injunctive norm Descriptive norm Provincial norm Subjective norm Collective efficacy

Sharing scenario ß

.14 .01 .10 .15 .19 .27 .06 .02 .01 .27 .27 .20

t .04 .04 .02 .16 .13 .19 .05 .01 .01 .18 .18 .14

B 0.74 0.85 0.38 2.50* 2.68** 3.78*** 0.94 0.15 0.24 3.04** 2.71** 2.67**

ß .01 .03 .53 .05 .26 .10 .09 .27 .04 .21 .12 .24

t .00 .16 .11 .06 .21 .07 .08 .18 .04 .14 .09 .17

0.05 2.89** 1.67 0.81 3.38** 1.22 1.32 2.52* 0.66 2.14* 1.24 2.66**

Note: For the buying scenario R2 = .20 for step 1. DR2 = .19 for step 2. DR2 = .10 for step 3. For the sharing scenario R2 = .05 for step 1. DR2 = .13 for step 2. DR2 = .11 for step 3. * p < .05. ** p < .01. *** p < .001.

3.2.4. Exploratory analyses We conducted separate hierarchical regression analyses for the buying scenario and for the car sharing scenario. We used the same stepwise procedure as for the general acceptance scale. Please refer to Table 4 for an overview of all the regression coefficients. The inclusion of the cost-related variables and of the norm variables and collective efficacy significantly increased R2 for both the buying and the sharing scenario. However, there were some differences between those scenarios concerning the relationship between the predictors and the specific intention to use an EV in that scenario. Personal knowledge, maintenance costs and the subjective norm were only significantly related to the intention to buy an EV. Age and injunctive norms were uniquely related to the intention to use an EV via car sharing. In both scenarios, costrelated disadvantages, the provincial norm and collective efficacy were significantly related to intentions. Most importantly, as hypothesized, social identity variables predicted EV acceptance above and beyond personal cost-benefit factors in all analyses. 3.3. Discussion The results of this study were in line with previous findings that cost-related disadvantages like purchasing costs and limited range are negatively related to EV acceptance (Krupa et al., 2014) and that the environmental attributes of an EV as well as the fact that EVs are less cost intensive concerning their maintenance are positively related to acceptance (Noppers et al., 2014). Importantly, when we added norms and collective efficacy, we found effects for them as well, even when we controlled for the effects of the cost/benefit-related variables and for the effects of demographic variables. This was in line with our initial assumption. Although the interviewees in the preliminary studies were primarily thinking of personal costs and

M. Barth et al. / Transportation Research Part F 37 (2016) 64–77

73

benefits (such as, for instance, those related to usability, monetary, or environmental issues) as factors affecting EV acceptance, our results suggest that there are additional effects at play, involving perceptions of collective norms and efficacy. These social identity predictors were of at least equal weight. Building on previous work on the effects of norms on EV acceptance (Petschnig et al., 2014), we found significant and independent effects for a variety of norms related to what others value and what is perceived to be accepted within the community. Surprisingly though, descriptive norms (what others actually do) were not significantly related to the acceptance of EVs. Research from other contexts has usually found descriptive norms to be a very important determinant of individual behavior (Nolan et al., 2008). We will further discuss this unexpected finding in Section 4. There were also positive relations between acceptance and personal experience as well as with personal knowledge about EVs. This result is in line with previous findings on the effects of personal experience (Skippon & Garwood, 2011). Experience is related to a decrease in the perception of the disadvantages of EVs (Bühler et al., 2014) and it predicts less range anxiety (Rauh et al., 2015). Our results add evidence that experience can have positive effects on the formation of goal intentions to use an EV above and beyond the parallel (and positive) effect of mere knowledge. The causal direction of the relationship between experience or knowledge on the one side and acceptance on the other is less clear. Gaining experience and knowledge about the new technology could dispel previous misinformation and prejudice which would lead to a more positive attitude and more acceptance. At the same time, greater acceptance could also lead to an increased motivation to search for additional information. It is also plausible that both variables influence each other and the relationship is bidirectional. Additional research is needed to better understand this relation. We also found initial evidence that EV use in different contexts might necessitate specifically tailored strategies to facilitate the acceptance of the new technology. For the cost/benefit factors, buying intentions were more strongly influenced by the lower maintenance costs of EVs than car sharing intentions. This seems plausible since maintenance costs are only relevant to owners of an EV, as they would not be responsible for the car’s maintenance in a sharing scenario. Of importance for understanding social normative processes in both scenarios of EV use, it also turned out that people seem to adhere to different kinds of norms when it comes to personal buying or collective sharing intentions. Approval of EVs by single important individuals, such as close friends, seems to be important when a car is permanently purchased, but not in a sharing scenario. On the other hand, injunctive norms (i.e. what are the perceived rules in the community) were only related to sharing intentions but not to buying. Sharing economies are a relatively recent social phenomenon. Whether or not car sharing is perceived personally efficient (e.g., the availability of a close-meshed grid of sharing stations) depends on the expected participation rates in the community. Also, sharing is a genuinely collective endeavor. This may be why the perception of a greater societal consensus that sharing is approved of, but not the opinions of personally significant others, determine sharing intentions. Further research is necessary to validate these speculations. Although the differences between scenarios were interesting, we need to address the limitations of the approach we employed in this study. Buying an EV and using an EV via car sharing are two very different situations. More importantly, the intention to use an EV in these scenarios is not only related to attributes of the EV but also to specific attitudes towards car ownership or towards a car sharing model. Therefore, a comparison of both scenarios only emphasizes that specific contexts can strengthen or weaken the relation between one variable and a goal intention. In addition, we worked with hypothetical decisions. Replicating our results in situations were real decisions are made such as the real purchase of an EV (as in Klöckner, 2014) would be an important step to validate our findings. In a similar vein, although we have tried to make sure that our sample appropriately represents the group of potential EV users, future research should aim to increase the quality of the sample composition. This could mean to only include persons who intend to buy a new car in the foreseeable future or to consider other factors indicative of the relevant phase in the stage model. 4. General discussion The aim of this paper was to explore the current perspective of potential EV users from Germany on electric mobility and to identify predictors of a general goal intention to use EVs. Specifically, we were interested in the predictive power social identity variables, such as social norms and collective efficacy, have above and beyond considerations of personal costs and benefits. Replicating previous work (Byrne & Polonsky, 2001; Hidrue et al., 2011), we found that cost-related variables are related to the acceptance of EVs. Importantly, cost-related variables were also most frequently cited when our interviewees thought about predictors of acceptance. In line with the literature, high purchasing prices, the limited range, the missing infrastructure and the long recharging of the batteries were negatively related to intentions to use an EV. Some of those disadvantages are technological in nature and future generations of EVs will probably be able to recharge faster and will be equipped with batteries that double the range of a current EV. On the other hand, it has been proposed that new mobility policies should build around financial incentives and the development of infrastructure (Gallagher & Muehlegger, 2011; Krupa et al., 2014). These strategies will certainly have an effect on the acceptance of EVs and they might become even more important the closer an individual is to buying an EV. Nevertheless, our results suggest that the early goal intention is not only related to these variables. Over and above personal cost/benefit variables, people’s perceptions of ingroup norms and collective efficacy also contributed to the explanation of goal intentions. Obviously, people do not live and decide in a social vacuum. Particularly in the early stages of deciding for or against adopting socio-technological innovations, such as EV, people need social validation.

74

M. Barth et al. / Transportation Research Part F 37 (2016) 64–77

This is provided by referring to what is accepted within both those broader collectives that define people’s social identity, expressed in injunctive norms (Cialdini & Trost, 1998; Smith & Louis, 2009), and more personalized relationships, represented by subjective norms (Ajzen, 1991). The additional effect of provincial norms (Goldstein et al., 2008) may indicate that beyond social identity and interpersonal relations people adopt their personal EV acceptance to the inferred attitudes of other people who live under similar conditions. However, given the correlational nature of the present findings, future, experimental, work is warranted to cleanly disentangle the three norm processes’ effects on the acceptance of sociotechnological innovations. Interestingly, descriptive norms had no effect on the goal intention to use an EV. This seems to contradict some findings on the importance of descriptive norms on individual behavior (Cialdini et al., 1990; Smith & Louis, 2009; Smith et al., 2012; White et al., 2009). The lack of any descriptive norm effect can be explained in terms of the specific context of technological innovations. In contrast to behavior that has been investigated in the literature on descriptive norms, electric vehicles are a very recent phenomenon. Their lacking presence on today’s roads might not be perceived to be diagnostic for social disapproval as it would be the case with well-established behavior, such as using busses. People may discount descriptive norm information as a source for their own decisions, because they do not think that the current situation is diagnostic of the future development of the EV market. If diagnosticity can explain the absence of an effect of descriptive norms, this leads to an interesting question. Is there some kind of threshold, a moment in the diffusion process when the absence of EVs on Germany’s streets becomes diagnostic and will then decrease personal EV acceptance? As soon as EVs are no longer conceived as novel or when a newly developed infrastructure will not be matched by a notable increase in number of users, descriptive norm information could become an obstacle to the formation of a positive goal intention. Perceptions of collective efficacy also had an effect on the intention to use an EV. Being part of an efficacious collective can increase willingness to adopt seemingly pro-environmental innovations in personal transportation. This adds to initial results by Homburg and Stolberg (2006) who found private action intentions against city pollution to be determined by perceived collective efficacy rather than personal efficacy. In fact, research on collective action has identified collective efficacy as one of the central determinants of people’s decision to engage in political action towards collective goals (Van Zomeren et al., 2008). Likewise, considering the adoption of EVs can be understood as a private way to advance a collective goal, such as the reduction of pollution and the use of fossil fuels. Collective efficacy might be an indispensable ingredient for turning perceptions of personal helplessness in the face of global environmental crises into personal action plans. Otherwise, the attempt to personally solve the problems that have arisen through the behavior of collectives would seem futile. Similar to other approaches like targeted education and experience programs (Franke et al., 2012; Sierzchula, 2014), focusing on strategies that employ social norms and collective efficacy could effectively complement other, economical or technological intervention efforts, such as subsidies. As adopters and non-adopters of EVs have been found to differ on social norms (Jansson, 2011), it is possible that norm-focused strategies could also have an effect in later stages of the adoption process. Intervention programs may increase the perception of positive EV adoption norms by fostering EV communication on the level of communities, but may also employ classical marketing approaches, by spreading normative messages, such as positive polling results or pro-EV statements of national authority figures. At the same time, individual EV adoption decisions might be framed as being part of a collective endeavor. Messages, such as, for instance, ‘‘Germany is going for the mobility revolution”, would take into account that global or even regional environmental problems caused by conventional individualized traffic (e.g., carbon emission, air pollution) can never be solved by individuals but need concerted action of collectives. Whereas focusing on personal pro-environmental action capabilities might often leave people with a sense of personal helplessness, addressing collective, joint action would strengthen perceptions of efficacy and thus the intention to act. As Germany is still in an early stage of diffusion, we concentrated on goal intentions. Goal intentions form early in the decision making process (Bamberg, 2013; Klöckner, 2014) and do not necessarily or directly lead to actual behavior, such as buying an EV or joining an EV car sharing initiative. Therefore, we did not collect data on actual behavior. Nevertheless, it is important to understand how goal intentions form in the first place, as the intention to use an EV is an important precondition for any further decision-making concerning the specific brand one would like to buy or the date, place and time one intends to purchase the car or to become an EV car sharing customer. Therefore, additional work is necessary to identify the variables strongly related to the actual act of purchasing an EV. Due to the nature of our study, there are some limitations concerning the results. Correlational designs only allow for rejecting causal hypotheses and to determine the maximal strength of a possible effect of predictors on the outcome variable but they do not give final certainty about the direction of the effect. To establish causality, future research should concentrate on experimental designs where one or more of the predictors described in this paper are manipulated to explore their effects on acceptance of EVs. This would also allow for the investigation of more complex processes, such as mediation and moderation models. In addition, the effects of social norms and collective efficacy need to be tested in the other stages of the decision-making process with real behavior instead of hypothetical behavior as the dependent variable. Finally, the sample recruitment of our studies has to be critically evaluated. We intended to include participants whose profile matched the profile of typical early adopters. Building on findings from the literature (e.g. Hackbarth & Madlener, 2013; Ziegler, 2012), we concentrated on young, left-leaning individuals with an affinity for technological innovations. Therefore, it is possible that our results cannot be generalized to others sub-populations. Furthermore, the opportunity for a free test drive with an EV as part of the prize pool may have led to self-selection of the sample and to a disproportionately high representation of possible early adopters of EV. At the same time, as the diffusion of EVs in Germany is in its early stages, it is plausible that

M. Barth et al. / Transportation Research Part F 37 (2016) 64–77

75

early attempts to facilitate the adoption of EVs will target the specific audience of early adopters. Our results offer several variables that could serve as the foundation for such strategies, such as both personal cost/benefit and social identity factors. After supportive boundary conditions have been successfully implemented, it would be necessary to test whether the same strategies are useful to target a broader audience. In conclusion, we propose that collective processes such as collective efficacy and normative influence should receive more attention in research on EV acceptance and use in the future. Our findings underline the importance of going beyond the obvious personal cost/benefit-related factors and think about creative ways to utilize social identity effects to incentivize the use of EVs, especially in the early stages of diffusion. Although they might be under-detected, processes linked to people’s social identity will constantly shape our experiences and decision-making. They can become powerful tools to shape the future of mobility. Acknowledgements This research was supported by a grant from the Federal Ministry of Economics and Technology [16SBS002D]. We like to thank our partners at the Stadtwerke Leipzig AG, the Fraunhofer Institute for East- and Middle European Studies as well as the Engineering Unit at the Leipzig University of Applied Sciences for their support. We further thank Martha Pfuch, Theresa Kral, Alexandra Hildebrandt, Lisa Opitz and Julian Scharmacher for their help with the acquisition and preparation of the data. Appendix A. Scenario information for buying and car sharing (translated) A.1. Buying scenario You bought an electric car. The purchasing price was 40% higher than the cost of a comparable petrol-driven car. Because of the lower maintenance, the initial price disparity offsets after about 10 years. Daily routes to work, school or shopping can be run by the EV with its range of 150 km. You have to charge the vehicles’ battery regularly. For this purpose you can use a terminal in your own garage or public charging stations for example at light posts in your living area. Charging stations can also be found on parking lots of large companies or shopping centers. While you are working or staying at home over night, the battery can be charged completely. As the owner of an EV you get more favorable conditions to rent a petrol-driven car for long distances. Instead of renting, maybe there are two cars in your household, a conventional one and an electric one. A.2. Car sharing scenario As a customer of a car sharing provider you are using an EV only when needed. Car sharing is an attractive solution especially for people who rarely drive by car and who do not drive more than 20,000 km per year. In this case, up to 2000 € are saved in comparison to an owned car. If more than 20,000 km are driven, car sharing is more expensive than using a purchased vehicle. An EV provides a range of 150 km. For longer distances you have the possibility to rent a petrol-driven car out of the sharing-pool, or to use alternative means of transportation like trains. Public transportation services already provide more favorable conditions for customers of car sharing. At every time you can check online where a vehicle close by is free for you to use. The electric car is being charged when you return it to its carport. If you use the vehicle for a longer period of time, there are public charging stations for example at light posts in your living area or on parking lots of shopping centers. In addition to the little efforts for charging, you have almost no expenditure of maintenance. The car sharing provider undertakes all essential technical services, so you have no additional costs for repair and upkeep. References Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. http://dx.doi.org/10.1016/07495978(91)90020-T. Aral Aktiengesellschaft. (2013). Aral Studie – Trends beim Autokauf [Aral study – Trends in car purchasing]. Retrieved from . Axsen, J., & Kurani, K. S. (2011). Interpersonal influence in the early plug-in hybrid market: Observing social interactions with an exploratory multi-method approach. Transportation Research Part D: Transport and Environment, 16(2), 150–159. http://dx.doi.org/10.1016/j.trd.2010.10.006. Axsen, J., Orlebar, C., & Skippon, S. (2013). Social influence and consumer preference formation for pro-environmental technology: The case of a U.K. workplace electric-vehicle study. Ecological Economics, 95, 96–107. http://dx.doi.org/10.1016/j.ecolecon.2013.08.009. Bamberg, S. (2013). Changing environmentally harmful behaviors: A stage model of self-regulated behavioral change. Journal of Environmental Psychology, 34, 151–159. http://dx.doi.org/10.1016/j.jenvp.2013.01.002. Beresteanu, A., & Li, S. (2011). Gasoline prices, government support, and the demand for hybrid vehicles in the United States. International Economic Review, 52(1), 161–182. http://dx.doi.org/10.1111/j.1468-2354.2010.00623.x. Billig, M., & Tajfel, H. (1973). Social categorization and similarity in intergroup behaviour. European Journal of Social Psychology, 3(1), 27–52. http://dx.doi. org/10.1002/ejsp.2420030103. Bockarjova, M., & Steg, L. (2014). Can Protection Motivation Theory predict pro-environmental behavior? Explaining the adoption of electric vehicles in the Netherlands. Global Environmental Change, 28, 276–288. http://dx.doi.org/10.1016/j.gloenvcha.2014.06.010. Brand, S., Petri, M., Haas, P., Krettek, C., & Haasper, C. (2013). Hybrid and electric low-noise cars cause an increase in traffic accidents involving vulnerable road users in urban areas. International Journal of Injury Control and Safety Promotion, 20(4), 339–341. http://dx.doi.org/10.1080/17457300.2012.733714.

76

M. Barth et al. / Transportation Research Part F 37 (2016) 64–77

Bühler, F., Cocron, P., Neumann, I., Franke, T., & Krems, J. F. (2014). Is EV experience related to EV acceptance? Results from a German field study. Transportation Research Part F: Traffic Psychology and Behaviour, 25, 34–49. http://dx.doi.org/10.1016/j.trf.2014.05.002. Bunch, D. S., Bradley, M., Golob, T. F., Kitamura, R., & Occhiuzzo, G. P. (1993). Demand for clean-fuel vehicles in California: A discrete-choice stated preference pilot project. Transportation Research Part A: Policy and Practice, 27(3), 237–253. http://dx.doi.org/10.1016/0965-8564(93)90062-P. Byrne, M. R., & Polonsky, M. J. (2001). Impediments to consumer adoption of sustainable transportation. International Journal of Operations & Production Management, 21(12), 1521–1538. http://dx.doi.org/10.1108/EUM0000000006293. Chandra, A., Gulati, S., & Kandlikar, M. (2010). Green drivers or free riders? An analysis of tax rebates for hybrid vehicles. Journal of Environmental Economics and Management, 60(2), 78–93. http://dx.doi.org/10.1016/j.jeem.2010.04.003. Chéron, E., & Zins, M. (1997). Electric vehicle purchasing intentions: The concern over battery charge duration. Transportation Research Part A: Policy and Practice, 31(3), 235–243. http://dx.doi.org/10.1016/S0965-8564(96)00018-3. Cialdini, R. B., Reno, R. R., & Kallgren, C. A. (1990). A focus theory of normative conduct: Recycling the concept of norms to reduce littering in public places. Journal of Personality and Social Psychology, 58(6), 1015–1026. http://dx.doi.org/10.1037/0022-3514.58.6.1015. Cialdini, R., & Trost, M. R. (1998). Social influence. Social norms, conformity, and compliance. In D. T. Gilbert, S. T. Fiske, & G. Lindzey (Eds.), The handbook of social psychology. Boston: Oxford University Press. Cocron, P., & Krems, J. F. (2013). Driver perceptions of the safety implications of quiet electric vehicles. Accident Analysis & Prevention, 58, 122–131. http://dx. doi.org/10.1016/j.aap.2013.04.028. Egbue, O., & Long, S. (2012). Barriers to widespread adoption of electric vehicles: An analysis of consumer attitudes and perceptions. Energy Policy, 48, 717–729. http://dx.doi.org/10.1016/j.enpol.2012.06.009. Franke, T., & Krems, J. F. (2013). Interacting with limited mobility resources: Psychological range levels in electric vehicle use. Transportation Research Part A: Policy and Practice, 48, 109–122. http://dx.doi.org/10.1016/j.tra.2012.10.010. Franke, T., Neumann, I., Bühler, F., Cocron, P., & Krems, J. F. (2012). Experiencing range in an electric vehicle: Understanding psychological barriers. Applied Psychology, 61(3), 368–391. http://dx.doi.org/10.1111/j.1464-0597.2011.00474.x. Gallagher, K. S., & Muehlegger, E. (2011). Giving green to get green? Incentives and consumer adoption of hybrid vehicle technology. Journal of Environmental Economics and Management, 61(1), 1–15. http://dx.doi.org/10.1016/j.jeem.2010.05.004. Goldstein, N. J., Cialdini, R. B., & Griskevicius, V. (2008). A room with a viewpoint: Using social norms to motivate environmental conservation in hotels. Journal of Consumer Research, 35(3), 472–482. http://dx.doi.org/10.1086/586910. Graham-Rowe, E., Gardner, B., Abraham, C., Skippon, S., Dittmar, H., Hutchins, R., & Stannard, J. (2012). Mainstream consumers driving plug-in batteryelectric and plug-in hybrid electric cars: A qualitative analysis of responses and evaluations. Transportation Research Part A: Policy and Practice, 46(1), 140–153. http://dx.doi.org/10.1016/j.tra.2011.09.008. Hackbarth, A., & Madlener, R. (2013). Consumer preferences for alternative fuel vehicles: A discrete choice analysis. Transportation Research Part D: Transport and Environment, 25, 5–17. http://dx.doi.org/10.1016/j.trd.2013.07.002. Haslam, S. A., Jetten, J., Postmes, T., & Haslam, C. (2009). Social identity, health and well-being: An emerging agenda for applied psychology. Applied Psychology, 58(1), 1–23. http://dx.doi.org/10.1111/j.1464-0597.2008.00379.x. Hertwich, E. G., & Peters, G. P. (2009). Carbon footprint of nations: A global. Trade-Linked Analysis. Environmental Science & Technology, 43(16), 6414–6420. http://dx.doi.org/10.1021/es803496a. Hidrue, M. K., Parsons, G. R., Kempton, W., & Gardner, M. P. (2011). Willingness to pay for electric vehicles and their attributes. Resource and Energy Economics, 33(3), 686–705. http://dx.doi.org/10.1016/j.reseneeco.2011.02.002. Homburg, A., & Stolberg, A. (2006). Explaining pro-environmental behavior with a cognitive theory of stress. Journal of Environmental Psychology, 26(1), 1–14. http://dx.doi.org/10.1016/j.jenvp.2006.03.003. Jansson, J. (2011). Consumer eco-innovation adoption: Assessing attitudinal factors and perceived product characteristics. Business Strategy and the Environment, 20(3), 192–210. http://dx.doi.org/10.1002/bse.690. Jensen, A. F., Cherchi, E., & Mabit, S. L. (2013). On the stability of preferences and attitudes before and after experiencing an electric vehicle. Transportation Research Part D: Transport and Environment, 25, 24–32. http://dx.doi.org/10.1016/j.trd.2013.07.006. Jugert, P., Greenaway, K. H., Barth, M., Büchner, R., Eisentraut, S., & Fritsche, I. (2015). Collective efficacy increases pro-environmental intentions when social identity is salient. (submitted for publication). Klöckner, C. A. (2014). The dynamics of purchasing an electric vehicle – A prospective longitudinal study of the decision-making process. Transportation Research Part F: Traffic Psychology and Behaviour, 24, 103–116. http://dx.doi.org/10.1016/j.trf.2014.04.015. Klöckner, C. A., Nayum, A., & Mehmetoglu, M. (2013). Positive and negative spillover effects from electric car purchase to car use. Transportation Research Part D: Transport and Environment, 21, 32–38. http://dx.doi.org/10.1016/j.trd.2013.02.007. Krupa, J. S., Rizzo, D. M., Eppstein, M. J., Brad Lanute, D., Gaalema, D. E., Lakkaraju, K., & Warrender, C. E. (2014). Analysis of a consumer survey on plug-in hybrid electric vehicles. Transportation Research Part A: Policy and Practice, 64, 14–31. http://dx.doi.org/10.1016/j.tra.2014.02.019. Kuhnimhof, T., Buehler, R., Wirtz, M., & Kalinowska, D. (2012). Travel trends among young adults in Germany: Increasing multimodality and declining car use for men. Journal of Transport Geography, 24, 443–450. http://dx.doi.org/10.1016/j.jtrangeo.2012.04.018. Masson, T., & Fritsche, I. (2014). Adherence to climate change-related ingroup norms: Do dimensions of group identification matter? European Journal of Social Psychology, 44(5), 455–465. http://dx.doi.org/10.1002/ejsp.2036. Mayring, P. (2010). Qualitative Inhaltsanalyse: Grundlagen und Techniken (11., aktual., überarb. Aufl). Beltz Pädagogik. Weinheim: Beltz. Nolan, J. M., Schultz, P. W., Cialdini, R. B., Goldstein, N. J., & Griskevicius, V. (2008). Normative social influence is underdetected. Personality and Social Psychology Bulletin, 34(7), 913–923. http://dx.doi.org/10.1177/0146167208316691. Noppers, E. H., Keizer, K., Bolderdijk, J. W., & Steg, L. (2014). The adoption of sustainable innovations: Driven by symbolic and environmental motives. Global Environmental Change, 25, 52–62. http://dx.doi.org/10.1016/j.gloenvcha.2014.01.012. Petschnig, M., Heidenreich, S., & Spieth, P. (2014). Innovative alternatives take action – Investigating determinants of alternative fuel vehicle adoption. Transportation Research Part A: Policy and Practice, 61, 68–83. http://dx.doi.org/10.1016/j.tra.2014.01.001. Potoglou, D., & Kanaroglou, P. S. (2007). Household demand and willingness to pay for clean vehicles. Transportation Research Part D: Transport and Environment, 12(4), 264–274. http://dx.doi.org/10.1016/j.trd.2007.03.001. Rauh, N., Franke, T., & Krems, J. F. (2015). Understanding the impact of electric vehicle driving experience on range anxiety. Human Factors: The Journal of the Human Factors and Ergonomics Society, 57(1), 177–187. http://dx.doi.org/10.1177/0018720814546372. Rogers, E. M. (2003). Diffusion of innovations (5th ed). New York: Free Press. Rolim, C. C., Baptista, P. C., Farias, T. L., & Rodrigues, O. (2014). Electric vehicle adopters in Lisbon: Motivation, utilization patterns and environmental impacts. European Journal of Transport and Infrastructure Research, 14, 229–243. Sierzchula, W. (2014). Factors influencing fleet manager adoption of electric vehicles. Transportation Research Part D: Transport and Environment, 31, 126–134. http://dx.doi.org/10.1016/j.trd.2014.05.022. Sierzchula, W., Bakker, S., Maat, K., & van Wee, B. (2014). The influence of financial incentives and other socio-economic factors on electric vehicle adoption. Energy Policy, 68, 183–194. http://dx.doi.org/10.1016/j.enpol.2014.01.043. Simon, B., Loewy, M., Stürmer, S., Weber, U., Freytag, P., Habig, C., ... Spahlinger, P. (1998). Collective identification and social movement participation. Journal of Personality and Social Psychology, 74(3), 646–658. http://dx.doi.org/10.1037/0022-3514.74.3.646. Skippon, S., & Garwood, M. (2011). Responses to battery electric vehicles: UK consumer attitudes and attributions of symbolic meaning following direct experience to reduce psychological distance. Transportation Research Part D: Transport and Environment, 16(7), 525–531. http://dx.doi.org/10.1016/j. trd.2011.05.005.

M. Barth et al. / Transportation Research Part F 37 (2016) 64–77

77

Smith, J. R., & Louis, W. R. (2009). Group norms and the attitude-behaviour relationship. Social and Personality Psychology Compass, 3(1), 19–35. http://dx.doi. org/10.1111/j.1751-9004.2008.00161.x. Smith, J. R., Louis, W. R., Terry, D. J., Greenaway, K. H., Clarke, M. R., & Cheng, X. (2012). Congruent or conflicted? The impact of injunctive and descriptive norms on environmental intentions. Journal of Environmental Psychology, 32(4), 353–361. http://dx.doi.org/10.1016/j.jenvp.2012.06.001. Tajfel, H., & Turner, J. C. (1979). An integrative theory of intergroup conflict. In W. G. Austin & S. Worchel (Eds.), The social psychology of intergroup relations (pp. 33–47). Monterey, CA: Brooks/Cole. Terry, D. J., Hogg, M. A., & White, K. M. (1999). The theory of planned behaviour: Self-identity, social identity and group norms. British Journal of Social Psychology, 38(3), 225–244. http://dx.doi.org/10.1348/014466699164149. Tran, M., Banister, D., Bishop, Justin D. K., & McCulloch, M. D. (2012). Realizing the electric-vehicle revolution. Nature Climate Change, 2(5), 328–333. http:// dx.doi.org/10.1038/nclimate1429. van Zomeren, M., Postmes, T., & Spears, R. (2008). Toward an integrative social identity model of collective action: A quantitative research synthesis of three socio-psychological perspectives. Psychological Bulletin, 134(4), 504–535. http://dx.doi.org/10.1037/0033-2909.134.4.504. White, K. M., Smith, J. R., Terry, D. J., Greenslade, J. H., & McKimmie, B. M. (2009). Social influence in the theory of planned behaviour: The role of descriptive, injunctive, and in-group norms. The British Journal of Social Psychology/the British Psychological Society, 48(Pt 1), 135–158. http://dx.doi.org/10.1348/ 014466608X295207. Ziegler, A. (2012). Individual characteristics and stated preferences for alternative energy sources and propulsion technologies in vehicles: A discrete choice analysis for Germany. Transportation Research Part A: Policy and Practice, 46(8), 1372–1385. http://dx.doi.org/10.1016/j.tra.2012.05.016.