going green? modeling the impact of environmental concerns and perceptions of transportation...

Going Green? Modeling the Impact of EnvironmentalConcerns and Perceptions of Transportation Alternatives

on Decisions to Drive1

Benjamin Gardner2 and Charles AbrahamUniversity of Sussex, UK

A theory-of-planned-behavior-based model of intra-city driving incorporating cog-nitions concerning non-car transportation use, personal and descriptive norms, andthe environment was tested. Participants were 190 residents of a UK city with goodnon-car travel infrastructure. Intention predicted 57% of the variance in behavior. Inaddition, 49% of intention variance was predicted by car-use attitudes, perceivedbehavioral control, descriptive norms, non-car-use attitudes, subjective norms, andpersonal norms. Concern and efficacy for reducing car-related environmental prob-lems were associated with non-car attitudes and personal norms. Results demon-strate the importance of modeling transportation choice on cognitions relating toboth car use and alternative transportation.jasp_600 831..849

Traffic emissions contribute to climate change (Berntsen, 2004) and car-diovascular and respiratory diseases (Peters et al., 2004) and are predomi-nantly attributable to car use (UK Department for Transport [DfT], 2006).Car travel accounts for approximately 60% of all road miles within the U.S.(U.S. Department of Transportation, 2007), and 80% of road travel in theUK (DfT, 2006). In the U.S., private vehicles emit about 175 million tons ofcarbon dioxide per annum (U.S. Environmental Protection Agency, 2002),representing 10% of national carbon dioxide emissions. Similarly, in the UKin 2004, private cars produced about 19.5 million tons of carbon dioxide,accounting for 13% of total UK carbon dioxide emissions (DfT, 2006). OfUK car journeys, 25% cover less than 2 miles (UK Department of theEnvironment, Transport, and the Regions, 2000), and so produce dispropor-tionately more carbon emissions (Saleh, Nelson, & Bell, 1998), and in mostcases could be undertaken using alternative transportation (Goodwin, 1997).

Policies that seek to change behavior via infrastructural modification(e.g., bus priority lanes) or punitive financial measures (e.g., congestioncharging) are likely to be important in providing disincentives for driving (see

1This research was supported by Economic and Social Research Council, UK.2Correspondence concerning this article should be addressed to Benjamin Gardner, Health

Behaviour Research Centre, University College London, 1-19 Torrington Place, London WC1E7HB. E-mail: [email protected]

831

Journal of Applied Social Psychology, 2010, 40, 4, pp. 831–849.© 2010 Copyright the AuthorsJournal compilation © 2010 Wiley Periodicals, Inc.

Transport for London, 2007). Yet such interventions may not change drivers’motivations, and so may relocate rather than reduce driving (Gardner &Abraham, 2007). Driving-reduction initiatives, which alter structures thatfacilitate alternative transportation choices and simultaneously changedriving motivation, are likely to be most effective. Modeling driving motiva-tion is a key step in identifying cognition targets for interventions designed tochange drivers’ motivation. This study presents a model of car-use motiva-tion, taking account of the availability of high-quality alternative transpor-tation options.

The Theory of Planned Behavior

Cognitive antecedents of driving have been modeled using various con-ceptual frameworks, such as the social dilemma perspective (e.g., van Vugt,Meertens, & van Lange, 1995), which highlights the trade-off of personalbenefits and environmental costs, and norm-based models (e.g., Klöckner &Matthies, 2004), which emphasize the role of moral decisions to use environ-mentally friendly transportation. The most widely applied model of modifi-able cognitive antecedents of travel mode choice is Ajzen’s (1991) theory ofplanned behavior (TPB; e.g., Bamberg, Ajzen, & Schmidt, 2003; Bamberg &Schmidt, 1999, 2003). The TPB posits that behavior is most closely deter-mined by intention and perceived control over action (i.e., perceived behav-ioral control or PBC). PBC predicts intention because we do not usuallyintend to do things we think we cannot do. Intention is also determined byattitudes, which measure evaluations of the perceived outcomes of the behav-ior in question (e.g., car use), and subjective norms, which refer to perceivedsocial approval of significant others for the behavior. Intention summatesmotivation to act and is thus conceptually equivalent to preference (Fujii &Gärling, 2003).

The theory is well supported as a model of car-use decisions, typicallyexplaining 50% to 60% of variance in driving intentions and 40% to 60% ofvariance in car use (Bamberg, Ajzen et al., 2003; Bamberg & Schmidt, 2003;Forward, 2004). For example, Bamberg and Schmidt (1999) reported thatTPB cognitions accounted for 68% and 70%, respectively, of variance inintentions to commute by car and car commuting. However, the theory is notexhaustive, and Ajzen (1991) has invited augmentation cognitions wherepredictive utility can be enhanced.

Environmental Considerations and Personal Moral Norms

In exploring beliefs that may underpin attitudes and intention, trans-portation researchers have tended to focus on personally beneficial

832 GARDNER AND ABRAHAM

outcomes relating to perceived monetary costs, journey times, and flexibil-ity (Bamberg & Schmidt, 1999), but transportation users may also bemotivated by concerns for environmental conservation (van Vugt et al.,1995). Indeed, a number of studies have suggested that environmentalbeliefs may influence travel mode decisions (for an overview, see Gardner &Abraham, 2008), and several types of environmental cognition have beenmeasured: environmental problem awareness (Steg & Vlek, 1997); concernfor the environment (Polk, 2003); perceived severity of the environmentalproblem (Tanner, 1999); perceived threats of environmental damage to theindividual, society, or the biosphere (Collins & Chambers, 2005; deGroot & Steg, 2007; Tanner, 1999); perceived responsibility or feelings ofguilt for the environmental problem (Bamberg, Hunecke, & Blöbaum,2007); perceived utility of car-use reduction for lessening the environ-mental problem (Steg & Sievers, 2000); and belief in one’s ability to exertinfluence over the problem through transportation decisions (Tanner,1999).

Klöckner and Matthies (2004) suggested that environmental beliefsarouse a perceived moral obligation to perform pro-environment actionsand, consequently, impact intentions through personal moral norms, whichhave been shown to contribute variance in travel-mode choice over andabove TPB cognitions (Harland, Staats, & Wilke, 1999). However, the TPBpredicts that concerns about the environment should contribute to generalattitudes toward driving, which represent evaluations of overall outcomes.Indeed, a recent TPB model of intentions to use a public transportationtransfer facility showed that concern for self-relevant consequencesof environmental degradation predicted attitudes toward usingthe facility (de Groot & Steg, 2007). Relationships between environmentalconcerns, personal moral norms, and attitudes have not, however, beenadequately researched in relation to driving.3 Further work must clarifyhow these additional constructs should be used in combination with theTPB.

3A systematic search for studies modeling car-use intentions or behavior conducted inAugust 2006, using online psychology and transport databases (PsycInfo, ScienceDirect, Web ofScience; TRIS Online, National Transportation Library [NTL] Catalog, NTL Digital Reposi-tory) and ancestry searches obtained 137 potentially relevant hits (further details are availablefrom the authors upon request). Only one of these (Bamberg & Schmidt, 2003) measured TPBvariables together with personal norms and environmental concerns in relation to car use.However, the parameters in Bamberg and Schmidt’s predictive model appear to have been fixedsuch that attitudes were perfectly correlated with beliefs relating to speed, comfort, stress, andflexibility, and so it was statistically impossible for environmental concerns to contribute uniquevariance to the explanation of attitudes within their model.

GOAL-DIRECTED ALTERNATIVES AND DRIVING DECISIONS 833

Descriptive Norms

In a revision of the TPB, Ajzen and Fishbein (2005) incorporated descrip-tive norms; that is, the perception that others are engaging in the focalbehavior. Descriptive norms are potentially important determinants ofdriving; for example, Kaiser and Gutscher (2003) observed that a measure ofdescriptive norms relating to refraining from driving in the local city centercorrelated .42 with intention to refrain from driving in the center, and cor-related -.36 with reported city center car use (F. G. Kaiser, personal com-munication, November 17, 2006).

Cognitions Toward Non-Car Use

Behavioral decisions often entail selection among several competing alter-native goal-directed options (Sheppard, Hartwick, & Warshaw, 1988). Traveldecisions are usually motivated by journey needs (Gardner & Abraham,2007); hence, non-car transportation options represent behavioral alterna-tives. Real-world transportation decisions are likely to be informed bychoices between available options (cf. Abraham & Sheeran, 2003), but con-ventional TPB operationalizations focus on one behavioral option and somay fail to capture the process of selection between alternatives (Fishbein &Ajzen, 1975). Taking account of beliefs about alternatives, therefore, mayenhance the predictive utility of the TPB (cf. Ajzen & Fishbein, 1969), buthow best to incorporate choice in the TPB is unclear.

Fishbein, Ajzen, and Hinkle (1980) measured each construct in relation totwo behavioral alternatives, and calculated differential measures of eachconstruct by subtracting, e.g., attitude scores on one option from attitudescores on the specified alternative. Ajzen and Fishbein (1969) suggestedranking the favorability of scores on constructs relating to available alterna-tives, and Abraham and Sheeran (2003) proposed measures that contrast onealternative with another. These operationalizations of choice processes arelikely to improve the predictive utility of the TPB (see Sheppard et al., 1988),but may conceal information regarding perceptions of each behavioraloption. For example, do people choose to drive because of the appeal of caruse, or the unattractiveness of non-car travel? Such insights are likely to beimportant in locating targets for driving-reduction campaigns.

We suggest that incorporating cognitions toward alternatives—that is,attitudes, norms, and PBC surrounding aggregated non-car traveloptions—as independent predictors over and above cognitions toward thefocal behavior may offer a more informative means of integrating choice intothe TPB in this domain. For example, Mann (2004) found that a TPB model


of public transportation use intentions was significantly enhanced by ameasure of PBC over car use. Furthermore, attitudes, subjective norms, andPBC relating to not driving tend to yield stronger correlations with drivingintentions and behavior than do cognitions focused on car use (Gardner &Abraham, 2008). However, no published evidence is available to assess therelative contributions of TPB cognitions relating to car use and to non-cartransportation using multivariate methods.

The Present Study

Building on the evidence that the TPB provides a useful model of car use,this study seeks to explore whether three groups of additional measures couldenhance the capacity of the model to predict driving motivation. These aremeasures of (a) environmental concern and control; (b) descriptive andpersonal norms concerning transportation mode choice; and (c) attitudes,norms, and PBC focusing on non-car transportation modes. To test themodel in its entirety, we included a measure of behavior (for a discussion ofthe limitations of the TPB for predicting travel choice behavior, see Verplan-ken, Aarts, van Knippenberg, & Moonen, 1998). Hence, we tested fourhypotheses. Note that we used a cross-sectional design, so the word “predict”is used here in a statistical sense to indicate relationships between dependentand independent variables.

Hypothesis 1. Following from TPB predictions, (a) intentionand car-use PBC will predict car use; and (b) car-use PBC,attitudes, and subjective norms will predict intention to drive.

Hypothesis 2. Attitudes, subjective norms, and PBC toward notusing a car to travel will predict intention to drive, over andabove car-use TPB cognitions.

Hypothesis 3. Personal moral norms and descriptive norms willpredict intention, over and above car-use and non-car-useattitudes, subjective norms, and PBC.

Hypothesis 4. Environmental considerations (i.e., problemawareness, environmental concern, efficacy for reduction of theenvironmental problem) will predict car-use and non-car-useattitudes, and personal norms.

Method

A cross-sectional survey was employed to explore these relationshipsbecause car-use behavior has been shown to be stable over short periods of


time. For example, Bamberg, Ajzen et al. (2003) found that self-reportedcar-use measures taken 1 year apart correlated at .72, and Mann (2004)observed that proportions of car use over 1 week correlated .87 with the samemeasure taken 1 week later. Thus, in the absence of interventions, we wouldexpect cross-sectional and prospective prediction of driving to yield near-identical results.

Procedure and Participants

Car drivers living in a small UK city (Brighton and Hove) were recruited.The city has an excellent public transportation infrastructure: Extensivebicycle lanes provide safe routes throughout much of the city; bus services arefrequent, with some routes operating 24 hours per day; and eight trainstations offer good suburban links. All bus and train services offer discountedtravel to senior transportation users and season ticket holders. We focused onparticipants from a city with good public transportation services becausenon-car transportation is a feasible alternative for these participants’ intra-city journeys, so barriers to non-car travel are more likely to be psychologicalthan situational.

Questionnaires were sent by mail with a prepaid addressed envelope to cardrivers, who were invited to participate in a travel feedback interventionprogram. This yielded 85 responses. The same questionnaire was also postedon a website, and a hyperlink was e-mailed to staff and students at a localuniversity. E-mail recipients were encouraged to forward the e-mail to others,who in turn were asked to forward the e-mail to others. This recruited anadditional 105 current drivers residing in Brighton and Hove. All partici-pants were entered into a £50 (~$90 US) prize draw.

Overall, 190 participants (115 females, 73 males, 2 of unspecified gender)were entered into the analysis. Participants’ ages ranged from 18 to 86 years(M = 36.9 years, SD = 18.2). A power analysis with a significance criterion of.05, presupposing medium effect sizes for a maximum of 11 potential predic-tors, indicated that a sample of 123 participants would yield an 80% chanceof detecting real effects.

Measures

Car use. Car use was measured using the following items: “In the lastweek, how many of your journeys within Brighton and Hove were madeusing? (a) a car (including taxis); and (b) other transportation modes (e.g.,bus, train, bike, walking)?” The choices were all journeys, most journeys, some


journeys, few journeys, and no journeys. The latter item was recoded so thatcar use was indexed as the proportion of car to non-car travel (r = .80,p < .001). Values greater than .50 indicate greater proportions of car use,while values less than .50 indicate greater non-car transportation mode use.

Unless otherwise stated, cognition measures employed 7-point responseoptions ranging from 1 (strongly disagree) to 7 (strongly agree). The itemswere coded so that high scores indicate stronger reporting of the measuredcognition. The items were designed in accordance with TPB questionnaireconventions (Conner & Sparks, 1996), or were derived from reliable opera-tionalizations in past travel mode research (e.g., Bamberg, Ajzen et al., 2003;Bamberg, Rölle, & Weber, 2003; Collins & Chambers, 2005; Nilsson &Küller, 2000; Verplanken et al., 1998).

Intention to use a car was measured using four items. Sample itemsare “Next week, I intend to use a car for most of my journeys withinBrighton and Hove,” and “I plan to make most of my journeys withinBrighton and Hove next week without using my car” (reverse-scored;a = .91).

Attitude toward using a car and attitude toward using non-car modes wereboth measured using items introduced with the stem “Making most of myjourneys within Brighton and Hove next week [by car/without using my car]would be . . . ,” which was followed by the anchors very bad–very good andvery unattractive–very attractive (car, r = .58, p < .001; non-car, r = .62,p < .001).

PBC over car use and PBC over non-car use were each measured using twoitems. The car-use items were “I am able to control whether I use a car formost of my journeys in Brighton and Hove next week,” and “I have nocontrol over whether I use a car for most of my journeys in Brighton andHove next week” (r = .47, p < .001). The non-car-use items were “I am able tocontrol whether I make most of my journeys in Brighton and Hove next weekwithout using a car,” and “I have no control over whether I make most of myjourneys in Brighton and Hove next week without using a car” (r = .61,p < .001).

Subjective norms relating to car use and subjective norms relating to non-caruse were measured using matched single items. Those items are “If I use a carfor most of my journeys within Brighton and Hove in the next week, mostpeople who are important to me would approve,” and “If I make most of myjourneys within Brighton and Hove in the next week without using a car,most people who are important to me would approve.”

Descriptive norm for car use was measured using the following items:“Most people who are important to me use a car for most of their journeyswithin the city,” and “How do the people that are important to you makemost of their journeys within the city?” Responses for the latter item were


rated on a scale ranging from (others never use a car) to (others always use acar; r = .70, p < .001). Descriptive norm for non-car use was not measuredbecause it was assumed that the perception that others mostly use cars wasequivalent to the belief that others mostly do not use non-car transportation.

Personal norm for non-car use was measured using three items (e.g., “Nomatter what other people do, I feel it is right to use my car as little aspossible”; a = .76). Personal norms for car use were not measured becausepersonal norms are morally guided (Schwartz, 1977) and, unlike non-cartravel, car use is unlikely to be undertaken for moral purposes.

Environmental problem awareness was measured using two items. Thoseitems are “Car use causes environmental problems,” and “Cars are bad forthe environment” (r = .54, p < .001).

Environmental concern was measured using two items. Those items are “Iam concerned about problems such as air pollution, noise, and energy use,”and “I am worried about environmental problems such as air pollution,noise, and energy use” (r = .65, p < .001).

Perceived control over (car-related) environmental problem reduction(PCE) was measured using two items. Those items are “Through my trans-port decisions, I can make a difference to the environment,” and “How Ichoose to travel does not affect the environment” (r = .55, p < .001).

Results

Sample Homogeneity

Participants in the postal group reported more car use (M = 3.57,SD = 1.18) than did the Internet-based group (M = 3.10, SD = 1.26),t(186) = 2.62, p = .01; and group differences were observed on five otherstudy variables (intention, car PBC, non-car PBC, car subjective norm,descriptive norm), but these group differences were largely attributable toage differences. Participants who were recruited by mail were significantlyolder (range = 22–86 years; M = 52.5 years, SD = 15.1) than participants whowere recruited online (range = 18–70 years; M = 24.4 years, SD = 8.0),t(119.94) = 16.37, p < .001. Dividing the combined dataset at the median age(i.e., 30 years) achieved homogeneity within each age subset, leaving fewdifferences between recruitment groups. Thus, the two groups were otherwisehomogeneous across measures.

Descriptive Statistics and Correlations

Table 1 shows that mean scores fell around the midpoint for most vari-ables. Participants used cars more than non-car modes (M = 0.58) but also


Tab

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held strong pro-environmental views (all Ms > 5.45). With the exception ofPBC (car use PBC, M = 4.84; non-car use PBC, M = 4.80), mean TPB cog-nition scores favored non-car use (attitude, M = 4.50; subjective norm,M = 4.59) more than car use (attitude, M = 3.94; subjective norm, M = 4.06).

Table 1 also shows that most variables significantly correlated with car-use intentions in the expected direction. Intention to drive was the strongestcorrelate of car use (r = .76, p < .001). Car-use PBC was unexpectedly nega-tively correlated with car use (r = -.33, p < .001) and intention (r = -.37,p < .001), and car-use and non-car-use PBC were highly positively correlated(r = .76, p < .001).

A principal components analysis extracted a single factor that explained68.1% of variance in the four PBC questionnaire items, and onto which eachitem loaded strongly (minimum item loading = .72). Both car-use and non-car-use PBC measures, therefore, were likely to have been underpinned by asingle latent variable relating to perceived transportation choice. Descriptivenorms for car use were also unexpectedly negatively associated with behavior(r = -.24, p = .001) and intention (r = -.28, p < .001).

Hypothesis 1

A multiple regression model shows that intention and car-use PBCexplained 57% of variance in car-use behavior, F(2, 185) = 124.18, p < .001;but only intention was a significant predictor (b = .73, p < .001; car-use PBC,b = -.07, p = .21). Hypothesis 1a was thus partially supported.

Regressing intentions to drive onto car-use attitudes, subjective norms,and PBC showed that attitudes (b = .36, p < .001) were the strongest predic-tor, PBC had an unexpected negative effect (b = -.31, p < .001), and subjec-tive norms had no effect (b = .09, p = .17). The model rerun excludingsubjective norms explained 28% of the variance in intention (R2 = .28), F(2,182) = 35.88, p < .001; and confirmed the predictive utility of both car-useattitudes (b = .38, p < .001) and PBC (b = -.32, p < .001). Thus, mixedsupport was observed for Hypothesis 1b.

Hypothesis 2

To test Hypothesis 2, we added non-car-use attitudes and subjectivenorms as a block at a second step in the preceding trimmed regression ofintention. Non-car-use PBC was not entered because of problems of col-linearity with car-use PBC. Adding these two variables caused a significantincrement in explained variance (R2 = .44), F(4, 180) = 29.37, p < .001


(DR2 = .16, DF = 26.00, p < .001); and both non-car attitudes (b = -.37,p < .001) and subjective norms (b = -.16, p = .01) had significant effects.Additionally, car-use PBC (b = -.26, p < .001) and attitudes (b = .17, p = .01)remained predictive. Non-car TPB cognitions thus significantly improved thepredictive utility of a TPB model comprised of cognitions toward car use,generally supporting Hypothesis 2.

Hypothesis 3

Adding personal and descriptive norms together at a third step in thepreceding regression of intention significantly enhanced the model (R2 = .49),F(6, 178) = 25.01, p < .001 (DR2 = .05, DF = 8.19, p < .001). Both added vari-ables made unique contributions to the model, supporting Hypothesis 3, butwhile personal norm had an expected negative impact on intentions (b = -.21,p = .001), the negative impact of descriptive norms was unanticipated(b = -.13, p = .02). Participants were less motivated to travel by car whereothers were also expected to drive, perhaps because of expected congestionproblems (see van Vugt et al., 1995). Within this model, car-use PBC(b = -.26, p < .001), non-car-use attitude (b = -.26, p < .001), car-use atti-tudes (b = .13, p = .04), and non-car subjective norms (b = -.13, p = .03)remained predictive.

Hypothesis 4

To test Hypothesis 4, we ran three regression procedures using environ-mental concern, problem awareness, and PCE entered as predictors in ablock. Together, the three variables explained only 6% of the variance inattitudes toward car use, F(3, 183) = 3.93, p = .01; and none were individuallypredictive (minimum p = .14). Within a model trimmed to include onlyenvironmental concern and PCE ( ps = .14 in the previous model), neitherpredictor was significant (environmental concern, b = -.13, p = .10; PCE,b = -.15, p = .07).

Environment-related cognitions explained 15% of the variance in non-car-use attitudes, F(3, 184) = 10.67, p < .001. Within this model, PCE was asignificant predictor (b = .22, p = .01), and there was a marginal effect ofenvironmental concern (b = .14, p = .09), but problem awareness did notpredict non-car attitudes (b = .10, p = .26). In a subsequent model thatexcluded problem awareness, both PCE (b = .27, p = .001) and environ-mental concern had significant effects (b = .17, p = .03; R2 = .14), F(2,185) = 15.35, p < .001.


Together, the three environment-related variables explained 29% of thevariance in personal norm, F(3, 185) = 25.61, p < .001; but only PCE (b = .27,p = .001) and environmental concern (b = .24, p = .001) were significantlypredictive (problem awareness, b = .13, p = .10). A model excluding problemawareness retained the proportion of explained variance (R2 = .28), F(2,186) = 36.71, p < .001, (PCE, b = .34, p < .001; environmental concern,b = .28, p < .001). Therefore, Hypothesis 4 was generally supported.

A Model of Car-Use Motivation

Figure 1 shows a path model, constructed on the basis of our (trimmed)regression analyses, which summarizes our findings. Participants were,unsurprisingly, more likely to have traveled by car over the past week ifthey had intended to do so. Participants were more likely to intend to travelby car if they perceived less control over car use; held positive attitudestoward car use and negative attitudes toward non-car use; experienced lesssocial pressure not to drive; perceived non-car use to be a normative behav-ior among significant others; and did not hold a personal norm for non-caruse. Non-car-use attitudes and personal norm were each associated withperceived control over environmental problem reduction and environmen-tal concern.

Figure 1. Path model with standardized regression coefficients. All paths significant at p < .05.PBC = perceived behavioral control; PCE = perceived control over environmental problemreduction.


Discussion

The present study provides the first TPB-based model of car use to includeboth cognitions relating to driving and those relating to alternative transpor-tation, as well as environment-related beliefs. The study focused on local carjourneys among residents living in a city with excellent non-car transporta-tion facilities, and thus produced a model of motivations to drive in thecontext of high-quality alternative transportation opportunities. Car use wasdetermined by intention, which, in turn, was predicted by attitudes, PBC anddescriptive norms for car use; and attitudes, subjective norms, and personalnorms for non-car use. The findings support the predictive utility of the TPBin this domain (Bamberg & Schmidt, 1999, 2003) and demonstrate how themodel could be extended to provide a more comprehensive representation ofdriving-related cognitions.

Cognitions toward non-car transportation contributed to the explanationof car-use intentions and behavior after controlling for car-use cognitions,demonstrating that measures of beliefs toward driving may not sufficientlyencompass perceptions of transportation alternatives (Abraham & Sheeran,2003; Ajzen & Fishbein, 1969). While further work using larger samples isneeded to establish the robustness of our findings, our results suggest that inaddition to decreasing the appeal of driving, driving-reduction campaignsshould address the perceived unattractiveness of alternative transportation.This may be particularly important because drivers often hold misconcep-tions regarding non-car travel (Gardner & Abraham, 2007), for example,tending to overestimate public transportation travel times (Fujii & Kitamura,2003).

Attitudes toward different transportation modes appeared to have differ-ent underlying beliefs: non-car-use attitudes were influenced by environment-related cognitions, but attitudes toward driving were not. Appeals toenvironmental concerns, therefore, may have little impact on perceptions ofcar use, which previous studies suggest may summate utilitarian and affectivebeliefs regarding meeting personal journey needs (Gardner & Abraham,2007; Steg, 2005). Indeed, environmental concern and PCE explained only14% of the variance in non-car attitudes, so environmental beliefs are likelyto compete with journey-oriented perceptions in the formation of attitudestoward non-car transportation.

The strong effect of personal moral norms on intention concurs withprevious suggestions that car use has a moral dimension not captured byutility-based cognitions (Harland et al., 1999; but see Bamberg & Schmidt,2003). Our results also echo previous findings that driving is seen as morallyrelevant when individuals are concerned about its ecological consequences(Klöckner & Matthies, 2004).


Our results support an extension of the normative component of the TPB,but we found an ironic effect of descriptive norms not envisioned by TPBtheorists (e.g., Ajzen & Fishbein, 2005): Participants were more likely tointend to drive when they expected others not to drive. These findings echothe results from a sample of individuals with preferences for positive self-oriented outcomes over socially beneficial consequences, who van Vugt et al.(1995) found were more likely to prefer to drive when the majority of othertransportation users were expected to use public transportation, so as tobenefit from less road congestion. Conversely, and in line with TPB predic-tions (Ajzen & Fishbein, 2005), van Vugt et al. showed that participants withprosocial value orientations tended to choose public transportation as aresult of strengthened perceived moral duty to use public transportationwhen others also chose to do so. Thus, social value orientations might mod-erate the effects of descriptive norms on intentions. Further research isneeded to clarify the motivational role of descriptive norms in real-worldsettings, both in isolation and in light of potential interaction with socialvalue priorities.

Car-use PBC negatively correlated with intentions and behavior: Partici-pants who felt less able to exert control over their car use were more likely todrive. PBC has previously been analyzed into self-efficacy (i.e., confidence inone’s ability to initiate behavior; Bandura, 1997) and perceived controllability(which relates to perceived external constraints; Ajzen, 2002; Trafimow,Sheeran, Conner, & Finlay, 2002). A single latent variable underpinned carand non-car PBC measures, suggesting that PBC tapped perceptions of thechoice and availability of non-car alternatives, addressing controllability,rather than self-efficacy. In other words, lower PBC scores may have reflecteda perceived lack of availability of non-car transportation, prompting greatercar use and, conversely, higher PBC indicated greater perceived choice, andso was associated with less car use and greater public transportation use.Hence, the car-use PBC measure performed counter to TPB predictions. Wewere unable to model car-use PBC adequately on any other variables, so thebeliefs underpinning participants’ control perceptions were unknown. Thelack of a direct effect of PBC on behavior might signify that participants helddistorted views of their actual control over travel-mode choice becausecontrol perceptions are likely to influence behavior where they accuratelyreflect actual control (Ajzen, 1991).

Limitations of the present study should be acknowledged. It might beargued that participants took part in our study primarily because they heldstrong views on transportation issues, and so may not adequately representtransportation users more generally. However, we observed considerablevariation in cognition scores, demonstrating representation of a wide spec-trum of attitudes, beliefs, and values.


Self-reported driving may underestimate true levels of car use (Jakobsson,2004), but we sought to minimize journey recollection problems by measuringproportions of car use, rather than the number of journeys made. Also, ourcognition measures related to future behavior, so they did not correspond withthe behavior measure, which related to past behavior. However, previousresearch has suggested that travel mode choice is a stable behavior (Bamberg,Ajzen et al., 2003; Mann, 2004), so longitudinal data are unlikely to haveproduced significantly different results, given strong correlations between pastand future travel mode choice behavior observed in previous studies.

The present study modeled cognitive antecedents of driving, but furtherwork is needed to establish whether these cognitions can predict intentionand behavior change. Moreover, rational models of planned behaviorassume that motivation is sufficient to guide action, but counterintentionalhabits may represent a potent unanticipated barrier to the transition of travelmode motivations into non-driving behavior (Klöckner, Matthies, &Hunecke, 2003; Verplanken et al., 1998). In stable decisional contexts, habitswill concur with intentions but dominate decisions (Aarts, Verplanken, & vanKnippenberg, 1998), so habit effects may be concealed by high intention–behavior correlations. A recent simulation study (Fife-Schaw, Sheeran, &Norman, 2007), however, suggested that, at least for reasoned behavior,modifying TPB cognitions may be sufficient to change intentions and action.

Our study assumed that non-car travel was a viable alternative to drivingfor our participants, but drivers may face constraints that necessitate car use,such as the need to make multipurpose trips, or driving for business purposes(see O’Fallon, Sullivan, & Hensher, 2004). We sought to reduce situationalconstraints by undertaking our study in a city with excellent services andinfrastructure to support non-car travel. This minimizes the possibility thatnegative views of non-car travel reflected real service deficiencies, as opposedto psychological reactions, but also limits the validity of our findings to areaswith poor public transportation services.

Nonetheless, our results point toward potential psychological obstacles tocar-use reduction, which may remain in the presence of accessible, high-quality alternative options, and thus the importance of cognitions relating tonon-car alternatives and of pro-environmental cognitions in shifting drivingmotivations for optional car journeys. Best practice is likely to require adop-tion of infrastructural or legislative strategies in tandem with persuasivepsychological strategies to target such cognitions.

References

Aarts, H., Verplanken, B., & van Knippenberg, A. (1998). Predictingbehavior from actions in the past: Repeated decision making or


a matter of habit? Journal of Applied Social Psychology, 28, 1355–1374.

Abraham, C., & Sheeran, P. (2003). Implications of goal theories for thetheories of reasoned action and planned behavior. Current Psychology,22, 264–280.

Ajzen, I. (1991). The theory of planned behavior. Organizational Behaviorand Human Decision Processes, 50, 179–211.

Ajzen, I. (2002). Perceived behavioral control, self-efficacy, locus of control,and the theory of planned behavior. Journal of Applied Social Psychology,32, 665–683.

Ajzen, I., & Fishbein, M. (1969). The prediction of behavioral intentions in achoice situation. Journal of Experimental Social Psychology, 5, 400–416.

Ajzen, I., & Fishbein, M. (2005). The influence of attitudes on behavior. In D.Albarracín, B. T. Johnson, & M. P. Zanna (Eds.), Handbook of attitudesand attitude change: Basic principles (pp. 173–221). Mahwah, NJ:Lawrence Erlbaum.

Bamberg, S., Ajzen, I., & Schmidt, P. (2003). Choice of travel mode in thetheory of planned behavior: The roles of past behavior, habit, and rea-soned action. Basic and Applied Social Psychology, 25, 175–187.

Bamberg, S., Hunecke, M., & Blöbaum, A. (2007). Social context, personalnorms, and the use of public transportation: Two field studies. Journal ofEnvironmental Psychology, 27, 190–203.

Bamberg, S., Rölle, D., & Weber, C. (2003). Does habitual car use not leadto more resistance to change of travel mode? Transportation, 30, 97–108.

Bamberg, S., & Schmidt, P. (1999). Regulating transport: Behaviouralchanges in the field. Journal of Consumer Policy, 22, 479–509.

Bamberg, S., & Schmidt, P. (2003). Incentives, morality, or habit? Predictingstudents’ car use for university routes with the models of Ajzen, Schwartz,and Triandis. Environment and Behavior, 35, 264–285.

Bandura, A. (1997). Self-efficacy: The exercise of control. New York:Freeman.

Berntsen, T. (2004). Klimaeffekter av utslipp fra transport [The effect oftransport emissions on the climate]. Cicerone, 4, 8–9. Retrieved January21, 2006, from www.cicero.uio.no/fulltext.asp?id=3032&lang=en

Collins, C. M., & Chambers, S. M. (2005). Psychological and situationalinfluences on commuter-transport-mode choice. Environment and Behav-ior, 37, 640–661.

Conner, M., & Sparks, P. (1996). The theory of planned behaviour andhealth behaviours. In M. Conner & P. Norman (Eds.), Predicting healthbehaviour: Research and practice with social cognition models (pp. 121–162). Buckingham, UK: Open University Press.


de Groot, J., & Steg, L. (2007). General beliefs and the theory of plannedbehavior: The role of environmental concerns in the TPB. Journal ofApplied Social Psychology, 37, 1817–1836.

Fife-Schaw, C., Sheeran, P., & Norman, P. (2007). Simulating behaviourchange interventions based on the theory of planned behaviour: Impactson intention and action. British Journal of Social Psychology, 46, 43–68.

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: Anintroduction to theory and research. Reading, MA: Addison-Wesley.

Fishbein, M., Ajzen, I., & Hinkle, R. (1980). Predicting and understandingvoting in American elections: Effects of external variables. In I. Ajzen &M. Fishbein (Eds.), Understanding attitudes and predicting social behavior(pp. 173–195). Englewood Cliffs, NJ: Prentice Hall.

Forward, S. (2004). The prediction of travel behaviour using the theory ofplanned behaviour. In T. Rothengatter & R. D. Huguenin (Eds.), Trafficand transport psychology (pp. 481–492). Amsterdam: Elsevier.

Fujii, S., & Gärling, T. (2003). Application of attitude theory for improvedpredictive accuracy of stated preference methods in travel demand analy-sis. Transportation Research Part A, 37, 389–402.

Fujii, S., & Kitamura, R. (2003). What does a one-month free bus ticket doto habitual drivers? An experimental analysis of habit and attitudechange. Transportation, 30, 81–95.

Gardner, B., & Abraham, C. (2007). What drives car use? A grounded theoryanalysis of commuters’ reasons for driving. Transportation Research PartF, 10, 187–200.

Gardner, B., & Abraham, C. (2008). Psychological correlates of car use: Ameta-analysis. Transportation Research Part F, 11, 300–311.

Goodwin, P. B. (1997). Mobility and car dependence. In T. Rothengatter &E. Carbonell Vaya (Eds.), Traffic and transport psychology (pp. 449–464).Oxford, UK: Pergamon.

Harland, P., Staats, H., & Wilke, H. A. M. (1999). Explaining proenviron-mental intention and behavior by personal norms and the theory ofplanned behavior. Journal of Applied Social Psychology, 29, 2505–2528.

Jakobsson, C. (2004). Accuracy of household planning of car use: Compar-ing prospective to actual car logs. Transportation Research Part F, 7,31–42.

Kaiser, F. G., & Gutscher, H. (2003). The proposition of a general version ofthe theory of planned behavior: Predicting ecological behavior. Journal ofApplied Social Psychology, 33, 586–603.

Klöckner, C. A., & Matthies, E. (2004). How habits interfere with norm-directed behavior: A normative decision-making model for travel modechoice. Journal of Environmental Psychology, 24, 319–327.


Klöckner, C. A., Matthies, E., & Hunecke, M. (2003). Problems of opera-tionalizing habits and integrating habits in normative decision-makingmodels. Journal of Applied Social Psychology, 33, 396–417.

Mann, E. (2004). Cognitive and affective antecedents of commuter transportmode choices. Unpublished doctoral dissertation, University of Sussex,UK.

Nilsson, M., & Küller, R. (2000). Travel behaviour and environmentalconcern. Transportation Research Part D, 5, 211–234.

O’Fallon, C., Sullivan, C., & Hensher, D. A. (2004). Constraints affectingmode choices by morning car commuters. Transport Policy, 11, 17–29.

Peters, A., von Klot, S., Heier, M., Trentinaglia, I., Hörmann, A., Wich-mann, H. E., et al. (2004). Exposure to traffic and the onset of myocardialinfection. New England Journal of Medicine, 351, 1721–1730.

Polk, M. (2003). Are women potentially more accommodating than men to asustainable transportation system in Sweden? Transportation ResearchPart D, 8, 75–95.

Saleh, W., Nelson, J. D., & Bell, M. G. H. (1998). Modelling vehicle emis-sions using the TEMIS program. Part 1: enhance of the TEMIS frame-work (Proceedings of the Institution of Mechanical Engineers, Part A).Journal of Power and Energy, 212, 197–204.

Schwartz, S. H. (1977). Normative influences on altruism. In L. Berkowitz(Ed.), Advances in experimental social psychology (Vol. 10, pp. 221–279).San Diego, CA: Academic Press.

Sheppard, B. H., Hartwick, J., & Warshaw, P. R. (1988). The theory ofreasoned action: a meta-analysis of past research with recommendationsfor modifications and future research. Journal of Consumer Research, 15,325–343.

Steg, L. (2005). Car use: Lust and must. Instrumental, symbolic, and affectivemotives for car use. Transportation Research Part A, 39, 147–162.

Steg, L., & Sievers, I. (2000). Cultural theory and individual perceptions ofenvironmental risks. Environment and Behavior, 32, 248–267.

Steg, L., & Vlek, C. (1997). The role of problem awareness in willingness-to-change car use and in evaluating relevant policy measures. In T. Rothen-gatter & E. Carbonell Vaya (Eds.), Traffic and transport psychology:Theory and application (pp. 465–475). Oxford, UK: Pergamon.

Tanner, C. (1999). Constraints on environmental behavior. Journal ofEnvironmental Psychology, 19, 145–157.

Trafimow, D., Sheeran, P., Conner, M., & Finlay, K. A. (2002). Evidencethat perceived behavioural control is a multidimensional construct:Perceived control and perceived difficulty. British Journal of Social Psy-chology, 41, 101–122.


Transport for London. (2007). Central London congestion charging: Impactsmonitoring (5th annual report). London: Author.

UK Department for Transport. (2006). Transport trends: 2006 edition.Retrieved June 5, 2007, from www.dft.gov.uk/162259/221412/190425/220778/Trends_2006_FINAL_v5.pdf

UK Department of the Environment, Transport, and the Regions. (2000).Transport Statistics Bulletin. National Travel Survey: 1997/99 update.London: Stationery Office.

US Department of Transportation. (2007). National transportation statistics.Retrieved June 5, 2007, from www.bts.gov/publications/national_transportation_statistics/pdf/entire.pdf

US Environmental Protection Agency. (2002). Inventory of U.S. greenhousegas emissions and sinks: 1990–2000. Washington, DC: Office of Atmo-spheric Programs.

van Vugt, M., Meertens, R. M., & van Lange, P. A. M. (1995). Car versuspublic transportation? The role of social value orientations in a real-lifesocial dilemma. Journal of Applied Social Psychology, 25, 258–278.

Verplanken, B., Aarts, H., van Knippenberg, A., & Moonen, A. (1998).Habit versus planned behavior: A field experiment. British Journal ofSocial Psychology, 37, 111–128.


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