Research on Voluntary Travel Behaviour Change

Abstract

With greater policy emphasis now given to travel demand management (TDM), research to date has shown that a desired shift in transport modes to alleviate environmental and health issues has not yet been made (Banister 2011). Evidence suggests that although many interventions which aim at Voluntary Travel Behaviour Change (VTBC) and policies have been implemented, the private car is still the primary mode of transport (Pachauri et al. 2014, J. Rogelj 2018) mainly because travel behaviours have not changed (Banister 2011). Travel behaviour is a topic of interest in a rising number of studies whilst, it is indicated that making an impact to it can be even more beneficial than other interventions such as changes in the built environment or, regulations to deter from car use (Anable 2005, Brög et al. 2009, Banister 2011). To better understand VTBC and how this study approaches it, research into how these behaviours are formed and, how they can be affected, is reviewed in this section. Theories and models used to understand and explain travel behaviours are compared, in order to better understand previous work in the field; and the rationale adopted later in the methodology of this study.

Travel behavior models and encouraging voluntary change of transport mode selection

Voluntary Travel Behaviour Change (VTBC) programs are becoming increasingly prominent among the policy options for achieving less motorized travel choices (Chatterjee, Bonsall 2009, Pangbourne, Masthoff 2016). VTBC programs are interventions that aim to address travel behaviours of people by appealing to norms and, other elements that form behaviours (Brög et al. 2009). VTBC programs use theories to explain behaviours that are evaluated and evolved by using models that aim through analysis to give a purposeful representation of reality and then messages in order to affect them (Pangbourne, Masthoff 2016).

Table 1 (Adjei, Behrens 2012)

Rational choicetheory

Rational choice theory (RCT) proposes that consumers try to maximize their utility by calculating the monetary values and benefits of choices available to them (Simon, Herbert A. 1955, Browning et al. 1999, Glimcher et al. 2005, Adjei, Behrens 2012). It is also known as utility maximization theory and was firstly used

in microeconomic theory. The term ‘rationality’ has been found in studies over the years (Yang, Lester 2008, Adjei, Behrens 2012) with two general meanings. The first meaning regards the process through which an outcome is reached. The second which is not so commonly found focuses on the outcome and not the process to reach used to reach the outcome. An event is deemed as rational if it maximizes utility to the person. RCT uses the latter definition of rationality.

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In RCT, the assumption that individuals have a comprehensive knowledge of the variousalternatives available to them and have the capability of ranking them according to their utilities or not, is made (Simon, Herbert A. 1955). The alternative that is deemed the most useful is then selected. Rational choice theories are regarded to be individualistic, thus choices made by individuals are presumed to not be affected by choices made by others.

The fundamental assumptions of RCT have been criticized in numerous studies with the main argument that individuals don not have capability of being informed about every alternative and that also many times cannot perform the comparison (Jackson 2005). Changes have therefore been constructed to the classical rational choice theories in efforts to direct some of these restrictions. In particular, Simon in 1957 broke the principle of ‘bounded rationality’ in an endeavor to address this limitation of the classical rational choice theory. He argued that individuals do not experience the capability to either develop or solve the complex problems required for even a reasonable approximation of objective rational behavior(Simon, Herbert Alexander 1957). An individual only behaves rationally utilizing a simplified model of the real world by making trade-offs. Instead of ‘maximizing’, the individual looks for an alternative that is ‘satisficing’ (Adjei, Behrens 2012) this entails the accumulation of less information and less computation compared to classical rational choice theory.

RCT has dominated the conventional transport planning and modelling, in so far as it provides the theoretical underpinning of the modal split and trip assignment phases of the four-step demand forecasting model (Adjei, Behrens 2012).

Prospect theory

Kahneman and Tversky (Kahneman, Tversky 2013) criticized expected utility theories such as the RCT for being unhelpful when it comes to making conclusions in which the result is unsure (e.g. a route choice decision in the context of volatile congestion and uncertain travel time). They therefore advanced prospect theory (PT) as an alternative example for explaining how decisions are made which affect some level of doubt.

They suggested that people attempt to avoid issues which they are uncertain about when making a choice. They perform this by giving more weight to alternatives with greater certainty of outcome than others. Kahneman and Tversky showed that when gains and losses associated with choice options are made more explicit, loss aversion is set off, as the emotion of loss is more potent than the emotion of gain (Adjei, Behrens 2012).

The theory includes two stages the editing-evaluation and, the decisionmaking procedure. In the editing stage the different choices are organized and reformulated to simplify them for valuation. In the evaluation stage the individual, then assesses the choices by applying decision weights and subjective values. The option with the highest value is selected.

The use of PT in travel behavior studies has concentrated more on travel and arrival times than modal choice, because of the high level of precariousness in the former (Adjei, Behrens 2012).

Habit formationtheory

The phenomenon of habituation and automatic behavior has long been grounded in several domains, including biology and social psychology (Adjei, Behrens 2012). Even though habit may not involve much deliberation, the conduct is still heading towards the accomplishment of certain ends. It is reasoned that the more repetitious an activity becomes, the stronger the formation of habit and the less deliberation (Bamberg et al., 2003).

Gärling and Axhausen (2003) indicate that, if the situation remains unchanged, repeating choice after first time deliberation may be more rational or more appropriate than the maximal rationality of RCT. They contend, nevertheless, that it may not be considered rational (in the narrow sense) if the same choice is fixed even when the situation changes, at which time the choice may be believed as strongly habitual and cannot be modified easily by small changes in conditions (Adjei, Behrens 2012).

Habitual choice behavior requires little or no deliberation over the various options available. Inducing a deliberate choice-making process has been seen to be a step towards breaking habit. Measures include the provision of information about alternatives, creation of awareness, provision of incentives and disruptions in car use. The provision of information about alternatives may not be effective in changing strong habits as compared to other measures such as disruption of traffic flow, as information on alternatives is not considered when making such choices (Adjei, Behrens 2012). It is thus imperative to recognize the point of habit when formulating policies aimed at changing habitual choices.

Habit formation theories have received increasing attention in travel behaviour studies over the past decade. Some authors argue habits developed in past behaviour are a better predictor of behaviour than attitudes (Triandis, 1977, Ouellette and Wood, 1998).

The theories of reasoned action and plannedbehaviour

The theory of reasoned action (TRA) was developed to explain and predict volitional behaviours (Fishbein and Ajzen, 1975). TRA advances that a person’s behavior can be anticipated by the strength of intention – thus intention is the immediate determinant of action (Ajzen, 1985, Fishbein and Ajzen, 1975, Ajzen, 1991). Intentions are in turn influenced by attitudes toward behavior (which are a function of behavioral beliefs) and subjective norms (a function of normative beliefs).

Ajzen (1985, 1991) argued most behaviors depend, to some degree at least, on elements such as time, money, skills and the cooperation of others (collectively representing actual behavioral control), and these factors may not forever be under the ascendency of the individual. This implies that TRA cannot be applied to anticipate these types of behaviors.

As an extension to TRA, the theory of planned behavior (TPB) was introduced by Ajzen (1985) to anticipate non-volitional behavior (i.e. The decision-maker does not possess perfect command of all factors determining the selection). As a third determinant of intention (in addition to attitude and subjective norm) he introduced perceived behavioural control. Perceived behavioural control refers to the perceived difficulty or ease of performing behaviour. He argued intentions in the strict sense can only anticipate a person’s effort to perform the behavior and not necessarily the real carrying out of the behavior. The ability of intention to predict attempted behavior, but not actual behavior, means that in that respect may be factors beyond the person’s control preventing it. As a latest addition to what appears to be a continuous advance of the TPB to predict actual behavior, actual behavioral control has also been included in addition to perceived behavioral control (Ajzen and Fishbein, 2005).

TPB has been employed in various travel behavior studies since its inception – ranging  from public transport use (e.g. Bamberg and Schmidt, 1999) to road crossing (e.g. Evans and Norman, 1998). It has been used in explaining and measuring the influence of attitudes and beliefs on behaviour.

Theory of interpersonalbehaviour

Like TPB, the theory of interpersonal behaviour (TIB) also defines intention as one of the influential factors of behaviour. Unlike the TPB however, Triandis (1977) also brings into consideration habit when explaining or predicting behavior. Triandis (1977) proposed habit, intention and facilitating conditions as the three determinants of behaviour in a ranking order. Habits and intentions interact with environmental elements that either help or inhibit behavior. He argued the stronger the habit, the less the effect of intentions on behaviour, and vice versa.

The frequency of past conduct is applied as a determinant for habit. Intentions are determined by position, social factors and affection. As with Fishbein and Ajzen’s TRA and TPB, attitude is preceded by belief and evaluation of outcome (representing the deliberative nature of humankind), while social factors (the subjective norm in TRA and TPB) are specified by the norms, roles and self-concept (representing the extent to which revered individuals and club can affect behavior). Unlike TRA and TPB, TIB considers affection

as a third determinant of intentions. This exemplifies the extent to which the person enjoys or dislikes the behavior.

Despite the greater predictive ability of the TIB over TPB as demonstrated by Bamberg and Schmidt (2003), TIB has received comparatively less attention in the travel behavior field. Bamberg and Schmidt (2003) suggest that TIB may gain increased recognition in this area due to the insufficiencies of TPB in explaining social behaviors and an increasing recognition of substance abuse as a major agent in travel behavior.

Norm activationtheory

The norm activation theory (NAT) developed by Schwartz (1977 as quoted in, Bamberg and Schmidt, 2003) proposes personal norms as the determinant of pro-social conduct. The hypothesis was posited to explain altruistic behavior (Darnton, 2008, Wall et al., 2008). Personal norms are made through an adaptation of societal norms (Klöckner and Matthies, 2004). These personal norms are said to be triggered just when the individual becomes aware of the effects of his or her conduct and involves responsibility for them. (Wall et al., 2008)

Schwartz and Howard (1981 as cited in, Klöckner and Matthies, 2004) developed four- stages through which normative decisions are made: attention, motivation, evaluation and denial stages. Hence there is the need for awareness to act, which should be consistent with one’s personal norms, leading to a motivation for behavioral modification. This is then accompanied by an evaluation of the costs and benefits of enacting the various choices. After evaluation, the alternative with the highest utility is chosen. If no clear decision is made, the fourth stage of denial is executed, at which stage the moral component of the decision-making process is either altered or entirely removed. The process is repeated until a choice is made.

There have been mixed results in the application of NAT in travel behaviour studies. For instance, Bamberg and Schmidt (2003) found no significant relations between moral beliefs and car utilization, while Wall et al (2007) found evidence of personal norms informing car use reduction goals.

Cognitive dissonancetheory

Cognitive dissonance theory (CDT) proposes that a person will try attaining consonance between two cognitions if they conflict with each other (e.g., knowledge about his or her behaviour, and the environment) (Festinger, 1957). In efforts to achieve consonance, either of the two cognitions (e.g., behavioural or environmental) would need to be changed. Yet because of the lack of dominance over one’s environment most of the time, it is a lot easier for the individual to change the behavioral cognition to reflect the environmental cognition.

In modifying the behavioral cognition, the individual may either alter the behavior itself or may try to gain more information to buttress the behavior. For instance, later on becoming aware of the unsustainable nature of auto use, a habitual car user may desist from employing the car or may seek contrary information about its benefits to come to terms with using the car. Festinger pointed out that, even though people pursue the reduction of dissonance, it may endure because of the troubles which may be encountered while changing either the behavior, or the knowledge about the behavior.

While the application of CDT in travel behavior studies is seldom explicitly acknowledged, it could be contended that it is implicit in studies that use information as a way of shifting the attitudes of decision-shapers. In such cases the new information may be discordant with the decision-maker’s cognition, causing behavioral change.

Stages of changemodel

The stages of change model (SCM) – one of the constructs of a trans-theoretical model of behavioural changes developed by Prochaska and DiClemente (1986) to assess a person’s prospects of changing behaviour – posits six stages through which behavioural changes occur. These are the pre-contemplation, contemplation, preparation, action, maintenance and termination points.

At the pre-contemplation stage, decision-makers have no design of switching behavior as they are not cognizant of the troubles connected with their behavior or are in a state of defense. Through the supply of information and social pressures, decision-makers may become aware of the effects of their behavior (Frasier et al., 2001). Decision- makers then start contemplating behavioral changes, at which stage the benefits and prices of various options are seen. Later on the decision-makers become aware of the costs and benefits of change, they prepare for behavioral change by making action plans. In the planning phase, some behavioral changes may be observed (Frasier et al., 2001, Darnton, 2008). The action program for behavioral change is then transported out at the next stage where behavioral changes can be overtly observed. The next stage – maintenance – may be considered very significant in the design of behavioral interventions, especially when dealing with habitual behavior. At this level, the decision- maker tries to avoid a reversion to past conduct. Keeping the context inside which the behavioral change occurred is therefore significant for the new behavior to become habitual. The new behavior then alters personal norms and the temptation to regress to past undesired behavior becomes minimal.

SCM has been employed in developing behavioral interventions. Nkurunziza et al (2012) for example argue that segmenting decision makers, according to the different stages can aid in defining more targeted and efficient travel behavior interventions (in this instance, cycling in Dar Es Salaam).

Self-perceptiontheory

Self-perception theory (SPT) was proposed to provide an alternative explanation to the phenomena leading to Festinger’s CDT (Bem, 1972, Bem, 1967). In SPT, an individual discovers or amends his or her attitudes, emotions, and other interior states by watching his or her conduct and experience.

Contrary to CDT, and most other behavioral theories, SPT is counterintuitive as behavior is taken to precede attitude. In SPT, an individual’s attitude towards a particular behavior may change after enacting the behavior in query. Jackson (2005) contends that while the assumption of behavior preceding attitude may not always be valid, SPT can complement Festinger’s CDT. Ray (1972) identifies experiments involving forced behavioral changes as examples of where SPT had greater explanatory power than CDT. In these types of experiments, participants are usually not persuaded by information (the case of CDT) or by other sorts of incentives into changing behavior, but are instead pushed into acting so.

Similar to CDT, the explicit use of SPT in travel behavior studies has been restricted. Still it is implicit in interventions where individuals are prepared to have certain travel choices aimed at altering their posture (e.g. The issuance of free public bus tickets [Fujii and Kitamura, 2003], or the closing of a freeway forcing drivers to utilize public transport [Fujii et al., 2001, Fujii et al., 2001, Fujii and Kitamura, 2003]).

Goal settingtheory

The goal setting theory (GST) advanced by Latham and Locke (1991, Locke et al., 1981) proposes that human behaviour is motivated by conscious purpose, which is in turn regulated by the decision-maker’s goals. GST focuses on the execution of behaviors – in other words, on why some people do better than others when given the same knowledge and ability. GST states that the most uncomplicated and most direct motivational explanation for why some people perform better than others is that they have different performance goals (Latham and Locke, 1991 p. 213).

Two principal ingredients in setting goals – content and intensity – are considered to define the point of execution. Capacity can also be divided into how specific and difficult the set destination is. People with more specific and challenging goals are considered to deliver higher performance towards goal attainment than those with either specific, but unchallenging, vague, but challenging, vague but unchallenging, or no goals (Locke et al., 1981). Vaguely formed goals lead to lower performance, but with higher individual satisfaction results – e.g. People with a ‘do your best’ goal may be met with any achievement compared to people with specific finishes. Also more challenging goals are considered to result in better performance than soft ones, yet though they are seldom made. They must nevertheless be ready inside an individual’s capacity for easy acceptance of goals. Strength determines the clarity and commitment of the person to goal accomplishment. Factors affecting commitment to a goal include setting achievable and appropriate goals. The more people perceive a goal as achievable and appropriate, the higher their commitment to making the goal. In short, for better performance in behavioural change, the goal should be specific, challenging, achievable and appropriate.

In the travel behavior field, GST has been employed in some car use reduction studies (e.g. Loukopoulos et al., 2004, Loukopoulos et al., 2006), and employed extensively in several Japanese Travel Feedback Programmes based on individualised communication and hedonic feedback (see Gärling and Fujii, 2006). (Adjei, Behrens 2012)

Figure 1(Van Acker et al. 2010)

 In 2003 Bamberg and Schmidt in their study set out to determine whether incentives, morality or habit are the greatest influences in forming behaviors and, to do that, they compared NAT, TPB and Triandis model (Bamberg, Schmidt 2003) by running all of them to examine the same samples.nning all of them to analyze the same samples. In their study, the comparison between the three suggests that TPB and Triandis better predict and influence behaviours towards choosing a mode of transport (Bamberg, Schmidt 2003).

 In 2007 Wall et al. In their study evaluated different models and adopted a combination of NAT and TPB in order to evaluate whether environmental issues are a driver to change mode of transport (Wall et al. 2007) whilst, their studies were limited in a university environment they were able to effectively combine the two theories in one model. Despite the inability to generalize their results at a national level (United Kingdom), their results showed promise (Wall et al. 2007) in affecting behaviours.

 Donald et al in 2014 reconfirm the findings by conducting a wide research using an extension of TPB that has been derived by reviewing a lot of other extensions (Donald et al. 2014). In their study, Donald et al suggested that an element of TPB the Perceived Behavioural Control (PBC) shows the greatest correlation to change in behaviours when choosing transport mode.

Table 2 (Adjei, Behrens 2012)

The theory of planned behaviour

The theory of planned behaviour (TPB) (Ajzen, 1991) has been utilized to explain and predict numerous transport-related behaviours, with studies concerning personal car use and public transport in the Netherlands, as well as the UK; bus use in Germany; awareness of transport issues in the UK; and the introduction of bus passes for students in Canada (Donald et al., 2014).

Figure 1 (Ajzen, 1991 the original model of the theory of planned behaviour

1

The theory of planned behaviour is used to predict behaviour as an outcome by utilizing three main elements that form the intention of changing it: Attitude; Subjective Norm and Perceived behavioural control (PBC) (Ajzen, 1991), defined as follows:

• Attitude – The degree to which a person has a positive or negative evaluation of the behaviour examined.
• Subjective norms – The belief about whether people whose opinions an individual cares about approve or disapprove of the behaviour.
• Perceived behavioural control – A person’s perception of the level of difficulty to perform a behaviour.

Measuring attitude, subjective norms, and perceived behavioural control (PBC) is used by TPB to predict behavioural intentions (Ajzen, 1991). PBC depends on a personal evaluation of whether a behaviour is accepted by one’s peers or not, and the perception of control over performing the behaviour. With regard to commuting to work, a person will use a particular transport mode if their evaluation of it is positive (attitude), they perceive social pressure to use it (subjective norm) and believe they are able to choose this mode (PBC). The TPB evaluates the causes of behaviour (Donald et al., 2014). The TPB can, therefore, be considered as a series of hypotheses linking behaviour to intentions, intentions to attitudes, norms, and control, and these components to behavioural, normative, and control beliefs (Ajzen, 1991; Donald et al., 2014).

Extensions of the theory of planned behaviour in transport

Figure 2 Donald et al. (2014) have proposed an extension of TPB to better predict the travel mode choice an individual would make.

In Figure 2, the extension proposed by (Donald et al., 2014) is depicted. Apart from the elements in the original model, there are some additions, including moral norm; descriptive norm; environmental norm and, introducing habit as shown in Figure 2 whilst being considered as influential as intention in the forming of a behaviour. The results of their study show that the TPB variables they chose were good predictors of commuters’ transport mode choice habits and intentions and that they are enhanced by the inclusion of the additional variables depicted. Results also suggest that the role the additional variables had varies by transport mode type. The most important variable for predicting intentions in this study was PBC, which supports the findings of other researchers. For instance, Wall et al. (2007), found that PBC and moral norm were amongst all variables the ones best suited to predict driving intention. Harland et al. (1999) found PBC to be the highest predictor of intention of modal choice of transport alternative to a car.

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