INTRODUCTION - Web viewEBSCO Publishing : eBook Collection (EBSCOhost) - printed on 6/7/2016 4:41 PM via MASSEY UNIVERSITY AN: 461619 ; Parkin, John.; Cycling and SustainabilityAccount

EBSCO Publishing : eBook Collection (EBSCOhost) - printed on 6/7/2016 4:41 PM via MASSEY UNIVERSITY AN: 461619 ; Parkin, John.; Cycling and SustainabilityAccount: s3027306

Sebastian Bamberg

ABSTRACT

Purpose – To demonstrate where and how transport planners could benefit from insights gained by psychological theory and research.Methodology – Theory-driven narrative review.Findings – An extended version of Ajzen’s (1991) Theory of Planned Behaviour is proposed which could be used for empirically analysing how environmental and psychological factors influence the individual decision to cycle.

CHAPTER 9

UNDERSTANDING AND PROMOTING BICYCLE USE – INSIGHTS FROM PSYCHOLOGICAL RESEARCH

Research implications – The review indicates that taking into accountpsychological insights could considerably increase the understanding of individual bicycle use. Currently research systematically analysing how psychological processes mediate the relation between environmental features and individual cycling is rare.

Practical implications – The review indicates that current interventions for increasing cycling are not very effective. It is assumed that taking into

Cycling and SustainabilityTransport and Sustainability, Volume 1, 219–246 Copyright r 2012 by Emerald Group Publishing Limited All rights of reproduction in any form reservedISSN: 2044-9941/doi:10.1108/S2044-9941(2012)0000001011

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220 SEBASTIAN BAMBERG

account insight from psychological research may help to develop new and more effective interventions for promoting cycling. Furthermore, studies evaluating the effects of these interventions in a methodological acceptable way are urgently needed.

Keywords: Individual decision-making, theory of planned behaviour, habit, values, norms, interventions

INTRODUCTION

For society as well as the individual, cycling has advantages over other modes of transport. For the individual, cycling is a healthy and cheap form

of transport. In congested urban areas cycling is frequently faster than public transport or the car. For society, cycling is good because it emits no

air pollutants, needs little of scarce public space and has relatively cheap infrastructure requirements. However, cycling also has disadvantages,

including the difficulty of carrying loads or getting wet when it rains. Factors such as physical effort and speed also limit the distance that a cyclist

can travel. Due to the health and socio-ecological gains to be realised from increasing the share of bicycle trips, policy-makers are showing increasing

interest in encouraging cycling. However, currently the great majority of people seem not to view cycling as a suitable travel option for them

personally: even in The Netherlands, which has a bicycle-friendly infrastructure and a strong pro-bicycle culture, many people do not cycle in situations when cycling would be a highly appropriate transport mode

(Ministerie van Verkeer enWaterstaat, 2007).

Thus, in order to be able to develop effective policies to encourage cycling, it is essential that we understand better what influences a person’s decision to use, or not to use, the bicycle for a specific trip. This chapter focuses on the contributions of psychological research to this question. The first section provides some arguments, as to why planners could benefit from the use of psychology. In the next section Ajzen’s (1991) theory of planned behaviour (TPB) is introduced, which provides a well-supported model of important psychological factors influencing individual decision-making. In the following sections, this theory is used as the framework for organising the results of research studying the impact of symbolic meaning, environmental concern, values, descriptive and injunctive norms, morality and habit on travel decisions in general, as well as cycling in particular. To summarise,Co

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Understanding and Promoting Bicycle Use 221

an extended TPB model integrating these factors is presented. The last section discusses the findings of a recent systematic review which indicates only very modest effects of interventions to promote cycling. It is assumed that these modest effects reflect deficits in the theoretical understanding of the psychological processes underlying behavioural change. For this reason a revised psychological model focusing on behavioural change processes is presented.

THE RELEVANCE OF PSYCHOLOGY

For a long time transport planners have not seen the necessity to directly model the internal factors underlying the individual decision to travel, preferring instead to focus on more manifest economic factors as measured through a value of time. Especially in relation to cycling, and because of their interest in how objective environmental features directly influence behaviour, it seemed natural to focus on the direct association between specific environmental features and behaviour, without taking into account the complex psychological mechanisms causally mediating this association. As a consequence, the actual decision-making process in transport planning has been frequently treated as a ‘black box’. However, recent reviews of this research approach (see, e.g. Crane, 2000; Ewing & Cervero, 2001; Heinen, van Wee, & Maat, 2010) come to the sobering conclusion that, in general, the direct association of objective environmental features and individual cycling behaviour is low. Correlations are seldom higher than .20–.30, that is to say direct measures of an environmental feature explain about 4–9% of the variance of individual cycling. Even worse is the finding that frequently the empirical associations between specific environmental features and the individual decision to cycle are not stable, that is they vary from sample to sample. For example, Aultman-Hall, Hall, and Baetz (1997) conclude that cyclists do not prefer pedestrian bridges to road bridges when selecting their routes, whereas Stinson and Bhat (2003) suggest that cyclists do prefer pedestrian bridges. According to Moudon et al. (2005) the presence of more bicycle infrastructure does not have a significant effect on cycling levels. However, Dill and Voros (2007) reported that people tend to cycle more often if bicycle paths were available to them, easy to reach and well connected to useful destinations. The relationship between cycling and socio-economic factors is also ambiguous. For example, Dill and Voros (2007) find a positive connection between income and commuting by bicycle; however, Guo, Bhat, and Copperman (2007) report a negative relationship between cycling and

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income, while according to Zacharias (2005), income has no significant effect. From such inconsistencies Heinen et al. (2010) conclude that there is no stable connection between many environmental features and individual behaviour. Obviously individuals in identical situations and in the same socio-economic groups do in fact choose different transport modes. This implies that an individual bases his or her choice less on the objective situation, but more on her or his perception of that situation, and how he or she evaluates this perception. Thus, if we want to understand and predict better the individual decision to cycle or not, we have to directly model the ‘black box’ of individual decision-making, that is to say, we have to deal with internal, psychological factors.

THE THEORY OF PLANNED BEHAVIOUR

Because the TPB (Ajzen, 1991) is a well-accepted, empirically supported and relatively parsimonious psychological model of individual decision-making, it provides a good framework for presenting non-psychologists with the results of mobility-related psychological research in a systematic way. The TPB could be viewed as a social psychological variant of the general rational choice approach which is the dominant theoretical foundation of disaggregated travel mode choice modelling (Bamberg & Schmidt, 1998). Fig. 1 depicts the theory in the form of a structural diagram. Behaviour is the manifest, observable response in a given situation with respect to a given target. In the TPB, behaviour is a function of compatible intentions and perceptions of behavioural control. Actual behavioural control refers to the extent to which a person has the skills, resources and other prerequisites needed to perform a given behaviour. Successful performance of the behaviour depends not only on a favourable intention but also on a sufficient level of behavioural control. To the extent that perceived behavioural control (PBC) is an accurate representation of actual behavioural control, it can serve as a proxy of actual control and can be used as a direct predictor of behaviour. Intention is an indication of a person’s readiness to perform a given behaviour, and it is considered to be the immediate psychological antecedent of behaviour. The intention itself is based on attitude towards the behaviour, subjective norm and PBC, with each predictor weighted for its importance in relation to the behaviour and population of interest. Attitude towards a behaviour is the degree to which performance of the behaviour is positively or negatively valued. Attitude towards a behaviour is determined by the total set of accessible behavioural beliefs. Behavioural

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Behavioural Beliefs

Attitude Toward

the Behaviour

Normative Beliefs

Subjective Norm

Intention Behaviour

Control Beliefs

PerceivedBehavioural

Control

Actual Behavioural

Control

Fig. 1. Ajzen’s (1991) Theory of Planned Behavior (TPB).

beliefs link the behaviour of interest to expected outcomes. A behavioural belief is the subjective probability that the behaviour will produce a given outcome. Although a person may hold many behavioural beliefs with respect to any behaviour, only a relatively small number are readily accessible at a given moment. The TPB assumes that the sum of these accessible beliefs – each multiplied by the subjective value of the expected outcome – determines the prevailing attitude towards the behaviour. Subjective norm is the perceived social pressure to engage, or not to engage, in a behaviour. In analogy to attitude, the TPB assumes that subjective norm is determined by the total set of accessible normative beliefs. Normative beliefs refer to the perceived behavioural expectations of such important referent individuals or groups as the person’s spouse, family and friends. It is assumed that the sum of these normative beliefs – each multiplied by the person’s motivation to comply with the different referents – determine the prevailing subjective norm. PBC refers to people’s perceptions of their

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ability to perform a given behaviour. Again the TPB assumes that behavioural control is determined by the total set of accessible control beliefs. Control beliefs have to do with the perceived presence of factors that may facilitate or impede the performance of a behaviour. It is assumed that the sum of these control beliefs – each multiplied by the perceived power of a control factor – determines the prevailing PBC.



The TPB has been applied with success to a wide variety of behavioural domains. Armitage and Conner (2001) conducted a meta-analysis on the efficacy of the TPB involving 185 studies. Of the three TPB constructs, subjective norms were found to be the least predictive, with attitude and PBC explaining a greater amount of the variance. Overall, the TPB accounted for 27% of behaviour and 39% of behavioural intention. Bamberg and Mo ser (2007) conducted a meta-analysis on the efficacy of the TPB in the domain of pro-environmental behaviours. Across 57 independent studies the TPB accounts for 27% of behaviour and 55% of behavioural intention. Gardner and Abraham (2008) found intention explained on average 28% variance in actual car use in a meta-analysis of 23 TPB applications.

Compared with car and public transport use, TPB applications to cycling are still rare. An early study by Bamberg and Schmidt (1992) used the TPB for predicting the intention of a random sample of German first year students (N ¼ 188) to use the bicycle as opposed to the car for trips to their university. This study is interesting because the new students were interviewed directly after their enrolment at the university, that is during their actual decision- making phase. A weakness of the study is the missing of a measure of students’ later actual travel behaviour. Of the interviewed students 63% had the intention to use the car for university trips, 20% to use the bicycle and 17% intended to use public transport or walk. Consistent with these intentions, students reported a much more positive attitude towards car than bicycle use, a stronger subjective norm supporting car use and a higher PBC over car use. As may be seen from Fig. 2, PBC has the strongest impact on the intention to use the car as opposed to the bicycle, followed by attitude and subjective norm. Together attitude, subjective norm and PBC could explain 72% of the variance of students’ intention to use the bicycle, and 62% of the variance of students’ intention to use the car for university trips. Students were also asked to evaluate the importance of nine travel-related attributes as well as their expectation that these consequences were associated with car versus bicycle use for university trips. As may be seen in Table 1, students evaluate flexibility, punctuality and ‘quickness’ as the three most important attributes, and being stress-free, non-polluting and convenient as the least important. As Table 1 also shows, students associate car use for university trips much more strongly with the three important travel attributes than bicycle use.

As expected by the TPB, the sum of the nine belief evaluations multiplied by their probabilities explains 56% (car) and 48% (bicycle) of the variance of the respective attitudes towards car versus bicycle use for university trips. The sum of three normative belief evaluations multiplied by their probabilities for partners, friends and parents explains 52% (car) and 55% (bicycle)Co

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Behavioural Beliefs

r = .75

r = .69

Attitude Toward

the Behaviour

ß = .33

ß = .29 R2 = .62

Normative Beliefs

Control Beliefs

r = .72

r = .74

r = .85

r = .74

Subjective Norm

Perceived Behavioural

Control

ß = .26

ß = .26

ß = .39

ß = .41

Intention

R2 = .72

Fig. 2. Results of a TPB Application to Students’ Intention to Use the Car Versus the Bicycle for University Trips. Source: Bamberg and Schmidt (1992), N ¼ 188. Note: R ¼ bivariate correlations, b ¼ standardized regression coefficients; R2 ¼

explained variance; results for car use above arrows; results for bicycle usebelow arrows.

Table 1. German Students’ (N ¼ 188) Evaluation of Nine Travel Related Consequences and Their Perceived Association with Car Versus

Bicycle Use for University Trips.

Evaluation of Travel-Related Attributes

Perceived Probability These Consequences are Associated with y

Car Bicycle

M M M

Flexible 2.45 1.64 0.68Punctual 2.34 0.98 0.08Quick 2.27 1.35 -0.51Safe (accident) 2.27 -0.48 -1.61Cheap 2.14 -0.21 2.22Safe (crime) 2.14 1.62 -1.57Stress-free 1.91 -0.59 0.82Non-polluting 1.89 -1.78 2.71Convenient 1.02 1.92 -1.51

Note: M ¼ mean, range of all scales is from -3 to þ 3.

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of the variance of the respective subjective norms, and the sum of two control beliefs (ownership of a car/bicycle and trip distance) explains 72% (car) and 55% (bicycle) of the variance of the PBC over using the car versus bicycle for university trips.

Without reference to the TPB framework, a considerable number of studies have analysed the direct impact of perceived consequences (behavioural beliefs) on bicycle use (for a review see Heinen et al., 2010). In these studies safety is also often mentioned as a reason not to cycle. The low costs of cycling in comparison to the costs of other forms of transport as well as travel time and effort are other important consequences influencing bicycle use.

Within the context of obesity prevention, de Bruijn, Kremers, Schaalma, van Mechelen, and Brug (2005) applied the TPB to understand factors influencing adolescents’ (n ¼ 3,859; mean age 15) decisions to use the bicycle for transportation. In this study, attitude (b ¼ .39), subjective norm (b ¼ .12) and PBC (b ¼ .33) predict 49% of the variance of intention to cycle, and intention and PBC explain 29% of the variance of behaviour. The behavioural, normative and control beliefs were not assessed in this study.

In a third study, de Bruijn, Kremers, Singh, van den Putte, and van Mechelen (2009) used the TPB for modelling adults’ cycling to work

(n ¼ 317, mean age 42). Together intention (b ¼ .20) and PBC (b ¼ .28) explain 25% of the variance of behaviour. The authors do not report the results of a regression analysis but only the correlations

between attitude (r ¼ .53), subjective norm (r ¼ .29) and PBC (r ¼ .77) with intention. The behavioural, normative and control beliefs were not assessed in this study. The first conclusion from the studies presented

above is that the TPB provides a useful psychological model for assessing the kinds of consequences people associate in a specific situation with

cycling (behavioural, control and normative beliefs), and how internal factors (attitude, subjective norm, PBC and intention) mediate the impact of

these perceived consequences on behaviour. Furthermore, the available results indicate that PBC has the strongest impact on a person’s intention

to cycle followed by attitude and subjective norm. However, on average the studies explain 20–30% of the variance of behaviour and 50% of the

variance of intention,indicating that the TPB may not be a sufficient model.

EXTENDING THE BELIEF BASE OF ATTITUDES

In the literature, attitude is understood as the degree to which a person has a favourable or unfavourable evaluation of an object or behaviour in

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question. As a social psychological variant of the rational choice theory, the TPB assumes that the attitude towards a specific transportation option reflects primarily the perceived quality of its instrumental function, that is, transporting people from one place to another. Indeed, the above reported study supports the assumption that instrumental beliefs have a strong impact on attitudes towards different transportation options. However, empirical studies indicate that instrumental behavioural beliefs alone seldom explain more than 50% of the attitudinal variance. As a consequence researchers have explored beliefs other than instrumental beliefs that contribute to the formation of peoples’ attitudes towards travel options. The starting point was insights from consumption research (e.g. Allen, 2002; Dittmar, 1992) that the validation of a product or service is not only influenced by instrumental beliefs but also by emotional function, and this is related to pleasure and relaxation, and symbolic meaning. The symbolic function can, in turn, be subdivided into two components: the person’s position or social status and their expression of personal identity and values. Especially for the car, this multi-functionality has been intensively researched (e.g. Ennis & Zanna, 2000; Hunecke, 2000; Lois & Lo pez-Sa´ ez, 2009; Steg, 2005; Steg & Tertoolen, 1999; Steg, Vlek, & Slotegraaf, 2001). Summarising the respective research Gatersleben (2007) concluded that the affective appraisal of a car depends on instrumental as well as symbolic aspects.

A second conclusion, therefore, is that there is growing evidence that the focus of the TPB on the instrumental aspects of travel options is too narrow. For many people, behaviours as car users also seem to have a strong symbolic meaning, and this allows them to express their personal identity and values. Interventions aiming to increase cycling should take these symbolic aspects of car use, as well as symbolic aspects of cycling, into account.

THE ROLE OF ENVIRONMENTAL CONCERN

Since the 1970s the relationship between environmental concern and the performance of pro-environmental behaviours has been a constant theme of research. In the meantime it has become widely accepted that this direct relation is only weak. On average, the empirical correlation between environmental concern and the performance of a specific behaviour is about.20–.30 (e.g. Eckes & Six, 1994). From the viewpoint of the TPB, this weak correlation is not surprising: environmental concern is not an attitude towards a behaviour, but an attitude towards a social issue. Thus, despite my great concern about the consequences of global climate change, because

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of low behavioural control (e.g. flying is the only possible travel option), I might decide to use the aeroplane for visiting an international climate protection conference. As a consequence, the TPB views environmental concern not as a direct, but as an indirect determinant of specific behaviours. As a general attitude, environmental concern provides a heuristic for how to judge behavioural options, that is, it should influence what kind of behavioural, normative and control beliefs a person generates in a situation towards a specific behavioural option. A study by Bamberg (2003) confirms that environmental concern is strongly associated (r ¼ .63) with thebehavioural beliefs students connect with switching to a ‘green’ electricitysupplier. However, when controlling the effect of the TPB constructs on intention, environmental concern has no additional direct effect on their intention. That is to say, the TPB constructs completely mediate the relation between environmental concern and intention (see Gardner & Abraham, 2010, for similar results). Bamberg (2003) found another mechanism through which environmental concern may influence behavioural intentions. The individual degree of environmental concern moderates the impact of subjective norm and PBC on intention: for people with high environmental concern, PBC has the strongest impact on intention, and for people with low environmental concern, subjective norm exceeds the stronger impact on intention.

A third conclusion from the literature, therefore, is that environmental concern is probably better viewed not as a direct but as an indirect determinant of cycling: it may have an impact on the kind of beliefs people associate with cycling in a specific situation and how they validate these beliefs. Furthermore, because of their general motivation to behave in an environmentally friendly way, people with a high degree of environmental concern seem to focus more on potential barriers (PBC) preventing cycling, whereas people with a low degree of environmental concern focus more on what important reference persons expect them to do. Thus, a campaign aiming to increase cycling may be more effective when using different arguments for people with high as opposed to low environmental concern.

THE ROLE OF VALUES

Values are ‘y desirable goals varying in importance, and which serve as guiding principles in the life of a person or other social entity’ (Schwartz, 1992, p. 21). Because of their trans-situational nature, the relation between values and specific behaviour should be even more indirect than that ofCo

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environmental concern and specific behaviour. Indeed, Schultz (2000) views values as one important psychological foundation, from which environmental concern develops. He built on findings by Stern and Dietz (1994) that identified three types of values regarding the environment involving concern for self, concern for others and concern for ecology. Schultz (2001) repli- cated these findings and named these three orientations egoistic, altruistic and biospheric values (see also de Groot & Steg, 2008, 2009). In a study on the effects of park-and-ride systems on public transport use, de Groot and Steg (2007) sought to identify the place of the three value orientations within the TPB framework. The authors found that egoistic, altruistic and biospheric value orientations were all related to attitude towards the use of the public transport. However, altruistic and biospheric values provided the most stable basis for pro-environmental behaviour. Furthermore, after controlling for the effect of the TPB constructs, the three value orientations did not have a significant effect on intention.

A fourth conclusion is that very abstract trans-situational orientation values probably exert only a very indirect influence on cycling via their impact on general attitudes and situational beliefs. However, they may be important for the understanding of the relation between objective socio- economic factors and cycling. Socio-economic variables like gender, age, education, religion or employment may be associated with differences in value orientation. In the context of transport interventions, taking into account value orientation may be useful when developing sub-group-specific communication strategies.

EXTENDING THE BELIEF BASE OF PBC

In the TPB, the PBC construct is used for assessing a person’s overall judgement of how easy or difficult the performance of a behaviour will be. In the context of mobility behaviour, the perceived overall behavioural control probably depends on four factors, namely trip distance, availability of a car or bicycle, transport infrastructure and personal living circum- stances. Research on PBC over different transportation options has mainly focused on beliefs related to the first three of these aspects. The effects of the personal living situation are seldom explicitly addressed. However, socio- demographic variables, which reflect the actual living situation (e.g. household size, income) are important predictors of mobility behaviour and should thus not be neglected (Gatersleben, Steg, & Vlek, 2002; Poortinga, Steg, & Vlek, 2004). For this reason Haustein and Hunecke

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(2007) introduce the concept of perceived mobility necessities (PMN) for directly assessing how the actual living situation influences individual travel mode choice. If one perceives one’s own life as requiring a high level of mobility, the private car is likely to be by far the most often used mode of transportation, whereas alternative modes will most likely be used less often. PMNs are, on the one hand, determined by actual demands, resulting from having a job or children, for instance. On the other hand, personal differences regarding the perception of the same demands are expected. While for some people, having children may result in the perceived necessity to be always available as a driver, others are able to live with children in a car-free household. In their study, Haustein and Hunecke reported a correlation of .40 between PMN and overall PBC.

A fifth conclusion is that the new concept of PMN provides an interesting theoretical approach to integrate the impact of the actual living situations into the TPB framework. In the context of cycling, the concept could be used for analysing how the perceived mobility needs resulting from a person’s actual living situation influences a person’s perceived control over bicycle use for a specific trip. It could be assumed that PMN are influenced not only by objective socio-economic factors but also by a person’s degree of environmental concern, as well as value orientations.

RECONSIDERING THE ROLE OF SUBJECTIVE NORM

In the TPB, the construct of subjective norm refers to the perceived social pressure and expectations of others for an individual to engage in a behaviour. In meta-analyses, subjective norms have had the weakest predictive power of the three factors, resulting in the argument that it is the least important part of the TPB (e.g. Armitage & Conner, 2001). Cialdini, Reno, and Kallgren (1990), however, argued that one reason for their weak predictive power may be the one-sided conception of social norms within the TPB framework. Cialdini et al. propose to differentiate between two types of norms: injunctive norms and descriptive norms. Injunctive norms refer to what ought to be done. Injunctive norms are essentially the subjective norms described in the TPB. Descriptive norms refer to what people actually do, and are not included in the TPB model. Cialdini (2007) noted that injunctive and descriptive norms relate to different motivations. Obedience to injunctive norms is motivated by the fear of social exclusion and sanctions, descriptive norms guide behaviour by

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their informational function: by providing information on how most people behave in a specific situation, and without much of their own reasoning, people can use descriptive norms as orientation for what behaviour is appropriate in a specific situation. In a series of studies, Cialdini and colleagues (e.g. Kallgren, Reno, & Cialdini, 2000) also clarified an important prerequisite necessary for norms having an impact on individual behaviour. Norms only affect behaviour when they are salient at the time of the action. This finding regarding norm salience may account for the dis- crepancies in the literature on the usefulness and predictive ability of subjective norms.

A sixth conclusion is that the understanding of how descriptive norms influence behavioural decision-making may have important implications for cycling promoting campaigns. Cialdini (2007) argues that information provided as a descriptive norm about ‘normal’ behaviour, for example, focusing on the extent of unwarranted car use, could have detrimental influences on interventions. By drawing people’s attention to how common and widespread car use is, the campaign normalises it, making people more likely to engage in this undesired behaviour due to its perception as the descriptive norm. Instead, Cialdini (2007) argues that cycling promotion campaigns should emphasise the extent to which people are cycling, thus establishing a positive descriptive norm. The findings by Kallgren et al. (2000) also have implications for the design of norm-oriented interventions to encourage cycling. They indicate that norms of any kind will only have an effect on behaviour if they are salient at the time of decision.

CYCLING AS A PRO-SOCIAL, MORAL BEHAVIOUR

An even more radical re-conception of the role of normative factors within the TPB framework was stimulated by a line of research which views the use of non-motorised transport instead of the car as an example of an altruistic, pro-social behaviour. Researchers who share this view often use the Norm- Activation Model (NAM; Schwartz, 1977) as a theoretical framework. The basic premise of the NAM is that moral or personal norms are direct determinants of pro-social behaviour. Schwartz (1977) conceived personal norms not as perceived external social pressure, but as internalised behavioural standards. Perceiving a discrepancy between internalised behavioural standards and intended or actual behaviour should elicit a strong feeling of the obligation to bring this behaviour in line with internalised standards. Behaving in line with internalised personal norms is self-rewarding: When

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fulfilling personal norms, people experience positive emotions like pride and satisfaction. A number of studies provide evidence that personal norms contribute to the explanation of travel mode choice (e.g. Abrahamse, Steg, Gifford, & Vlek, 2009; Hunecke, Blo baum, Matthies, & Ho ger, 2001; Wall, Devine-Wright, & Mill, 2007). The formation as well as activation of a personal norm is probably based on the interplay of cognitive, emotional and social factors (e.g. Bierhoff, 2002). In the field of pro-environmental behaviour the awareness of and knowledge about environmental problems are probably important cognitive preconditions for developing personal norms. Attribu- tion processes seem to be a second important cognitive process contributing to the development of personal norms. The internal attribution of a harmful behaviour often triggers emotional reactions, namely guilt feelings (e.g. Weiner, 1995). Guilt is defined as a ‘painful feeling of regret that is aroused when the actor actually causes, anticipates causing, or is associated with an aversive event’. (Ferguson & Stegge, 1998, p. 20). Guilt is an important pro- social emotion because it results in a felt obligation (personal norm) to compensate for the caused damage (Baumeister, 1998). Feelings of guilt are also closely related with social norms. A perceived mismatch between one’s own behaviour and injunctive social norms leads to feelings of guilt (Baumeister, 1998). Besides their impact on feelings of guilt, injunctive social norms also directly contribute to the development of personal norms. They deliver the standards for what behaviour an important social reference group views as right or wrong behaviour in a specific context. If an individual internalises these standards, they provide the content of her or his personal norms.

Acknowledging that most cases social behaviours are best viewed as a mixture of self-interest and pro-social motives, in the last few years a number of researchers have proposed to combine the TPB and NAM (Bamberg & Schmidt, 2003; Harland, Staats, & Wilke, 1999; Manstead, 2000; Wall et al., 2007). Fig. 3 presents graphically an integration proposed by Bamberg, Hunecke, and Blo hbaum (2007). As can be seen, besides attitude and PBC, instead of subjective norm, personal norm is conceptua- lised as a third direct determinant of intention. The integrative model ascribes a more indirect role to subjective norms. In line with the above discussion on the informational influence of descriptive norms, it is assumed that people frequently follow social norms not because they fear social pressure, but because they use social norms as information about what behaviour is appropriate. Descriptive social norms may not only provide information about whether a specific behavioural option is morally right or wrong, but also whether it is beneficial or easy to perform. Fig. 3 depicts the

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Problem awareness

.43.34

Perceived responsibility

.30

.44

.23.13

.15

R2 = .20

Social norm

.32

Feelings of guilt

.19

.27

.18

.19.27

.25

R2 = .11

Perceived behavioural

control

.26

R2 = .31

Attitude

.30

.15.31

.29

.29

R2 = .52Behavioural

intention

.52R2 = .27

Behaviour

R2 = .18 .20 R2 = .57Personal

norm

R2 = .58

Fig. 3. Results of Path Analysis of Meta-Analytically Pooled Correlations (Single- Headed Arrows Represent Causal Paths, Double-Headed Arrows Represent

Correlations). Source: Adapted from Bamberg and Mo¨ ser (2007).

results of a model test conducted by Bamberg and Mo ser (2007). For this purpose meta-analytically pooled correlations of the 9 model constructs obtained from 57 independent samples are used as input for a meta- analytical structural equation model (MASEM, see Becker, 2000). As can be seen from the pooled data in Fig. 3, intention accounts for 27% of the variance in behaviour. Furthermore, intention mediates the effects on behaviour of the other constructs. PBC, attitude and personal norm have independent effects on intention. Together they explain 52% of the variance in intention. Feelings of guilt, social norm, responsibility and problem awareness all have significant effects on personal norm. Together they explain 58% of the variance in personal norm. Social norm has a direct effect on PBC and attitude in addition to its direct and indirect (via feelings of guilt) effects on personal norm. There is also a direct effect of feelings of guilt on attitude. Problem awareness has a direct effect on responsibility, feelings of guilt, social norm and personal norm.

A seventh conclusion is that, as with most pro-environmental behaviours, the decision to cycle probably reflects a mixture of self-interest and pro- social motives. Most people want to see themselves as a socially responsible person who contributes to the benefit of their community. With their focus on self-related health and financial benefits, many cycling campaigns neglect this pro-social motive. Thus, framing cycling more as an opportunity to bring actual behaviour more in line with important personal standards may be an effective motivational strategy, especially when pro-social motives areCo

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combined with self-interest-related arguments. In line with this expectation, a study by Abrahamse et al. (2009) found that self-interest-related variables explained 52% of current car use intention; however, variables reflecting pro-social considerations explained a larger amount (24%) of variance of car reduction intention than self-interest-related variables (18%). Research findings also indicate that increasing the awareness of negative socio- ecological consequences associated with car use as well as the individual accountability for these harms provide effective strategies for making personal norms salient.

THE ROLE OF HABIT IN TRAVEL BEHAVIOUR

When considering travel behaviour, the TPB ignores another important aspect of this behaviour, its repetitive nature. That is, if on one day a person cycles in order to achieve a specific goal (e.g. reaching her/his working place), he/she is likely to use the experiences of that behaviour in a decision concerning a similar opportunity to cycle on a subsequent day. In other words, the decision to cycle is strongly influenced by experiences gained from previous cycling events. By presuming that behaviour produces feedback that influences subsequent attitudes and perceptions of social norms and control, Ajzen (1991) acknowledges that previous behaviour may influence later behaviour. Moreover, with increased practice, PBC should become a better representation of actual control, that is it should be directly related to behaviour. Thus, within the confines of the TPB, the impact of past behaviour on later behaviour is mediated by attitudes, social norms, control and intentions to execute the behaviour.

However, it may be questioned whether individuals always go through such a contemplative decisional process as described by the TPB, when they make the same decisions again and again. Triandis (1980) suggests that deliberate intentions may become irrelevant in guiding behaviour when the behaviour has been performed repeatedly in the past and has become habitual, that is Triandis hypothesises that intention and habit interact in the prediction of later behaviour: when behaviour is performed many times, one does not need to weigh pros and cons or to check on one’s attitudes and behavioural control in order to arrive at a choice. Instead subsequent behaviour may be associated with, and automatically triggered by, the specific situational cues that normally precede it. Indeed, studies that have included a measure of past behavioural frequency suggest that in the case of habitual behaviours frequency of past behaviour accounts for more of the

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variability in the prediction of actual behaviour than intentions (Aarts, 1996; Ouellette & Wood, 1998; Verplanken, Aarts, Van Knippenberg, & Van Knippenberg, 1994).

For examining the impact of habit on cycling, de Bruijn et al. (2009) include a measure of habit strength (Verplanken & Orbell, 2003) besides the TPB variables in their study. As expected, those who cycled more had higher values for this measure. Compared with the model using only intention and PBC as predictors, a regression model including habit strength as an additional predictor of cycling significantly increases the explained behavioural variance (from 25% to 33%). Furthermore, after controlling the effect of habit strength, the effects of intention and PBC are no longer statistically significant. As proposed by Triandis, de Bruijn et al. (2009) add, in their last step, an intention and habit strength multiplicative interaction term to the regression equation. This analysis indicates a significant main effect of habit strength (b ¼ .35) as well as a significant intention and habit strength multiplicative interaction term (b ¼ .33). Adding the interaction term results in a significant increase of explained behavioural variance (from 33% to 37%). Decomposing the interaction term by simple slope analyses showed that intention was a significant predictor of cycling at low (b ¼ .67) levels of habit strength, but it was an insignificant predictor (b ¼ .10) at high levels of habit strength. These results provide empirical support for Triandis’s assumption that deliberate intentions become irrelevant in guiding the decision to cycle when it has been performed repeatedly in the past and has become habitual.

But what precisely are habits? Psychologists conceptualise habits as the learning of sequences of acts that have become automatic responses to specific situations, which may be functional in order to satisfy specific needs, or to obtain certain goals (Ronis, Yates, & Kirscht, 1989; Triandis, 1980). Habits are characterised by a goal-directed type of automation; habitual behaviours are instigated by a specific goal-directed state of mind in the presence of stimulus cues. Satisfactory experiences with a behaviour enhance the tendency to repeat the same course of action, mainly because the behaviour becomes more strongly associated with the goal one initially wished to attain (e.g. a link between cycling and feeling fit). Conversely, dissatisfaction weakens the link between behaviour and goal, decreasing the probability that a person will continue the behaviour. Habitual behaviour may be conceived of as mentally represented structures in which a certain situation is strongly associated with the goal that is chronically pursued in that situation (the concept of schemas or scripts; Abelson, 1981). The habitual chosen type of behaviour can then automatically and immediately

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be activated on the instigation of the related behavioural goal (Bargh, 1989; Bargh & Gollwitzer, 1994). Thus, when a person nearly always cycles from home to work, that goal will eventually become automatically activated within that situation.

The eighth conclusion is that habits may be characterised as strong mental links between specific behaviours and positive expectations associated with those behaviours, and which may be activated automatically by environmental cues. Because strong habits lead to a kind of ‘tunnel vision’ (Verplanken & Wood, 2006), habitual behaviours are difficult to influence by informational campaigns aiming to increase people’s motivation or intention to cycle: people with strong habits expect prior experiences to repeat, and as a result, they do not easily detect environmental changes (e.g. Fujii, Ga rling, & Kitamura, 2001). They also search less extensively for information about behavioural alternatives and for information about the performance context itself. In addition, their search tends to be biased towards confirming the habitual option (e.g. Verplanken, Aarts, & Van Knippenberg, 1997). Finally, strong habits are associated with simple, shallow decision rules. Essentially, people with strong habits possess motivational and informational biases that reduce the likelihood that they will receive and favourably evaluate new, counter-habitual information (e.g. Verplanken et al., 1997). Furthermore, even if interventions are successful in increasing a person’s deliberative intention to cycle, in the case of habitual non-cyclists the enactment of this intention may be blocked by automatically activated habits.

MODELLING THE DECISION TO CYCLE WITHIN AN EXTENDED VERSION OF THE TPB

Fig. 4 presents an extended version of the original TPB which integrates the research results discussed in the previous sections. The model views value orientations, knowledge, perceived social norms and general attitudes as indirect behavioural determinants. Together with perceived features of the objective environment and past experiences, these constructs provide the informational basis from which people derive the behavioural and control beliefs they associate with cycling. Personal norm, PBC and attitude summarise the evaluative trend of these beliefs. Intention reflects the conscious weighting of the perceived total costs, benefits and collective implications associated with cycling. In new decision situations, intention is the only direct determinant of behaviour. Repetition of the same decision in

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Cyclinghabit

Awareness of negativePerceived descriptive and consequences caused byinjunctive social normsown car useconcerning cycling

General attitudes(e.g. environmental concern)

Objectiveenvironmental features

Behavioural controlrelated beliefs (e.g. trip distance, features of traffic system, perceived mobility necessities)Beliefs concerning

the instrumental function and symbolic meaning of cycling

Perceived obligationto cycle (personal norm)

Overall perceivedbehavioural control over cyclingAttitude toward

cycling

Intention to cycle

Cycling

Socio-economic Factors

Value orientations



Fig. 4. A Extended TPB Model for Modelling the Decision to Cycle or Not to Cycle.

a specific situation, however, makes it more and more habitual, that is the respective behaviour could be automatically activated by situational cues.

PROMOTING BEHAVIOURAL CHANGE

The last section summarises psychology’s progress in understanding internal factors influencing peoples’ current decision to cycle or not. However, from

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a policy-related viewpoint, the central challenge is not to understand why people currently cycle or do not cycle, but how to motivate non-cyclists – especially current car users – to consider this travel option. In the last few decades, a great variety of interventions have been developed for this purpose (for an overview, see Pucher, Dill, & Handy, 2010). In a recent systematic review, Yang, Sahlqvist, McMinn, Griffin, and Ogilvie (2010) summarise the results of studies evaluating the effectiveness of such interventions. Screening thousands of references, these authors find only 25 methodologically acceptable controlled evaluation studies, of which, however, only 2 are true randomised controlled trials. Of these 25 studies, 6 studies concern interventions designed primarily to promote cycling. Sixteen studies evaluate one particular approach – individualised marketing of walking, cycling and public transport – in different locations. One study assessed an intervention targeted specifically at children. In their synthesis, Yang et al. (2010) come to the conclusion that available studies point to very limited benefits from initiatives that were intended to encourage cycling. The six interventions designed primarily to promote cycling increased the annual number of such trips by 0–3.4%, and the 16 studies evaluating the effects of professional cycle marketing resulted in an average of only eight additional cycle trips per person per year. Moreover, it was generally unclear whether the initiatives had recruited new cyclists, or whether existing cyclists and pedestrians had merely made small increases in their activities.

One explanation for these sobering results may be that the evaluated interventions are based on an inadequate understanding of the psychological processes underlying behavioural change. Most currently used intervention approaches assume that the negative perception of cycling is the main barrier preventing people from switching to the bicycle. As a consequence these interventions focus on improving the objective conditions for cycling as well as communicating these improvements. However, this approach neglects one central feature of travel behaviour: its strong habitual nature. As discussed above, even if information-oriented campaigns may be successful in increasing people’s intention to cycle, strong habits may block the actual enactment of this intention. Thus in the context of behavioural change, breaking the habit of car use is a central challenge for effective interventions. Furthermore, currently used intervention approaches probably underesti- mate the cognitive, motivational and volitional difficulties habitual car users experience when actually trying to change their behaviour. Psychological research indicates that for them, behavioural change programmes are a long way from requesting the successful transition through different stages (e.g. Gatersleben & Apleton, 2007; Prochaska & DiClemente, 1984). WithinCo

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Emotions anticipatedwith goal progress

Salient social norms

Felt obligation to fulfil personal standards Goal intention Behavioural

intention Implementationintention

New habit

Negative affect

Perceived responsibility

Perceived goalfeasibility

Perceivedbehavioural control over different behavioural change

strategies

Cognitive planningabilities

Skills to cope withimplementation problems

Skills to resisttemptation

Skills to recover from relapsePerceived negativeAttitude towardsconsequences ofdifferent behaviouralown behaviourchange strategies

Pre-Contemplation Contemplation Preparation/Action Maintenance



each of these stages, the person is confronted with a specific task (Achziger & Gollwitzer, 2008): in the first stage habitual car users must consciously re- evaluate their current behaviour. If they come to the conclusion that they want to change their current behaviour (e.g. reduce their car use), in the second stage they have to select a suitable behavioural strategy for reaching this change goal (e.g. walking or cycling instead of car use). If they decide to test cycling, in the third stage they have to plan the steps necessary for actually implementing this behavioural strategy (e.g. organising a bicycle, finding a good cycling route, finding solutions for where to park the bicycle, how to deal with bad weather conditions). In the fourth stage they have to evaluate their experiences with the new behaviour. If these experiences do not fulfil their expectations, they have to modify their behavioural strategy. If the experiences are satisfying, they may develop a new cycling habit.

In line with this reasoning, Bamberg (in press) has recently proposed the Stage Model of Self-regulated Behavioural Change, which explicitly concep- tualises behavioural change as transition through the time-ordered sequence of the following four qualitatively different stages: pre-contemplation, contemplation, preparation/action and maintenance (see Fig. 5). Each of the four stages of change is characterised by the attempt to solve one of the tasks described above, as well as the problems people typically encounter when trying to solve them. Furthermore, the model postulates that the transition through the stages is marked by three critical transition points: the formation of a goal intention marks the transition from the pre-contemplative to the contemplative stage, the formation of a behavioural intention marks the transition from the contemplative stage to the preparation/action stage

Fig. 5. The Stage Model of Self-Regulated Behavioural Change. Source: Bamberg, Fujii, Friman, and Ga¨ rling (2011).

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and the formation of an implementation intention marks the transition from the preparation/action stage to the maintenance stage.

As can be seen from Fig. 4, the model assumes that in the pre- contemplative stage habitual car users start to consciously reflect on their current behaviour, when they become aware (e.g. through talks with important reference people or through the media) that their personal car use contributes to damaging the environment. If people accept their personal responsibility, they will experience negative emotions such as feelings of guilt. The internal attribution of responsibility may also raise peoples’ concern about what ‘important others’ might expect them to do (making social norms salient). Together, negative emotion and social concerns raise self-awareness, a psychological state in which people automatically compare their actual behaviour with important self-standards (e.g. Scheier, Fenig- stein, & Buss, 1974). The stronger the perceived discrepancy between these self-standards and current behaviour, the stronger should be the feelings of obligation to change this behaviour (activated personal norm). The activation of a personal norm is accompanied by the expectation of positive feelings (pride, satisfaction) resulting from bringing behaviour more in line with the personal norm. If personal norm and anticipated positive emotions are strong, and the perceived feasibility of changing current behaviour is high, a goal intention is formed which marks the transition into the next contemplation stage.

In the second contemplative stage, a person considers the personal consequences (attitude) associated with alternative behavioural options as well as the perceived difficulty in performing them (behavioural control). The behavioural intention, marking the transition between the contemplation and preparation/action stages, results from the person’s weighing of the pros and cons of different behavioural options for reaching the change goal, as well as the perceived difficulty of performing these options.

Once a behavioural intention is formed, the person enters the preparation/action stage. The main task of this stage includes initiating the actions necessary for enacting new behavioural intentions. For this purpose plans have to be made for when and where to act to complete the intended new behaviour. Here cognitive planning abilities and skills to cope with real or anticipated implementation problems are relevant variables. At the end of this stage an implementation intention (e.g. ‘Tomorrow I will check my bicycle and the next day I will use it at 7:30 am for my trip to the university.’) is formed and the behaviour is actually performed.

The final stage – maintenance – comprises stabilising the changed behaviour and implementing new behavioural routines or habits based on the

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changed behaviour. The task in this stage consists of coping with unpleasant experiences with the new behaviour and the resulting temptation to fall back to old behaviour. For this purpose skills to resist this temptation and – if a relapse happens – skills to recover and re-establish the new behaviour are necessary.

One important practical implication of viewing behavioural change as a transition through a sequence of different stages is that, instead of one single intervention for all people, specific intervention packages should be matched to the needs and barriers of people in specific stages. Interventions targeting people at earlier stages of change are likely to be more successful if they concentrate on providing information that can increase both problem awareness and perceived personal responsibility. Interventions designed to activate supporting social and personal norms are probably also important at this stage. People who have formed a goal intention need information about the availability as well as the pros and cons of different behavioural alternatives. People who have already formed a behavioural intention probably benefit most from interventions supporting the implementation

Table 2. Examples of Stage-Tailored Intervention Approaches.

Stage of Change Intervention Strategies

Pre-contemplation – Intervention type I: Make social and personal norms salient (e.g.Goldstein & Cialdini, 2007)

– Intervention type II: Enhance problem awareness and self-focus (e.g. Prochaska, Redding, & Evers, 2002)

– Intervention type III: Enhance goal setting and goal commitment (e.g. McCalley & Midden, 2002)

Contemplation – Intervention type IV: Provide information about the pros and cons of different behavioural alternatives and enhancing perceived behavioural control (e.g. Fishbein & Ajzen, 2010)

Preparation/action – Intervention type V: Support behavioural planning (e.g.Gollwitzer, 1999)

Maintenance – Intervention type VI. Provide behavioural feedback (e.g. McCalley & Midden, 2002)

– Intervention type VII: Prevent the temptation to relapse (e.g. Marlatt & Donovan, 2005)

General strategies – Provide social support (e.g. Hogan et al., 2002)– Change objective context conditions (see chapter Bolderdijk,

Lehman & Geller)

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and initiation of this intention, such as detailed behavioural planning. Table 2 presents intervention techniques discussed in the literature for influencing the stage-specific change mechanisms postulated by the stage model of self-regulated behavioural change.

GENERAL CONCLUSION

This chapter has argued that systematically researching the internal factors influencing a person’s decision to cycle is a prerequisite not only for the empirical prediction of cycling behaviour but also for developing effective cycling promotion interventions. With both the extended TPB and the Stage Model of Self-regulated Behavioural Change, psychology offers empirically supported theoretical frameworks planners can use for this purpose. However, empirical applications of these frameworks to cycling are still rare. Future studies should focus on the following aspects: using the extended TPB model for exploring the relationship between objective environmental features and cycling and how this relationship is moderated by general attitudes and values and using the stage model of self-regulated behavioural change as a basis for the development of theory-driven interventions. Finally, and most importantly more methodologically sound studies (randomised control trials) should be conducted to evaluate the intervention effects on cycling.

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INTRODUCTION - Web viewEBSCO Publishing : eBook Collection (EBSCOhost) - printed on 6/7/2016 4:41 PM via MASSEY UNIVERSITY AN: 461619 ; Parkin, John.; Cycling and SustainabilityAccount