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11 July 2018

22 Pages

Engaging in Pro-Environment Travel Behaviour Research from a Psycho-Social Perspective: A Review of Behavioural Variables and Theories

and
1
EA 7284 AVENUES, Sorbonne Universités, Université de Technologie de Compiègne, 60203 Compiègne, France
2
Interuniversity Department of Regional and Urban Studies and Planning, Politecnico di Torino, 10125 Torino, Italy
*
Author to whom correspondence should be addressed.

Abstract

This paper aims to review variables and behavioural theories originating from social and environmental psychology as applied to transport research, to better understand decision-making mechanisms, information processing and modal choice. The first section provides an overview of the main psycho-social variables which explain behaviour and, notably, pro-environment behaviour. The analysis shows the relations among variables, highlighting some potential cause-effect mechanism or, at least, the influence that such variables can have on behaviour. Furthermore, the strengths and weaknesses of using psycho-social variables to predict travel behaviour are discussed. Such analysis feeds the section related to the behavioural theories. These are reviewed with a focus on potential application to transport sector, showing the would-be added value of introducing a socio-psychological approach in the current vision, focused on stochastic models based on maximisation of personal utility. To this end, attention is paid to the data collection and analysis, basic for any models and even more challenging to collect when they deal with personal characteristics of individuals. Finally, the concept of attitude and intention is discussed, opening the doors between disciplines to overcome the attitude-behaviour gap.

1. Introduction

Transport is known to be the second source of greenhouse gas emissions (GHG) and the sector which relies the most heavily on petrol consumption worldwide [1]. To tackle such problems but also to provide greater accessibility to the workplace, places of leisure, shopping areas and so forth, to improve our quality of life and environmental conditions, a transport policy paradigm shift occurred nearly 25 years ago. In fact, even though social scientists and environmental psychologists began to study travel behaviour in the 70’s, only since the 90’s has a large amount of research been carried out to better understand modal choice from a socio-psychological point of view and to use this knowledge to try to “pull people out of their cars”.
This paper aims at giving an extensive vision of the state of the art of behavioural theories originating from social and environmental psychologists and applied to transport research in order to better understand the mechanisms behind the pro-environment travel behaviour. The strengths and weaknesses of such theories will be discussed as related to their ability in forecasting mobility habits with attention to the modal choice.
The first objective is understanding the determinants of travel behaviour, starting from the psycho-social variables that are considered key in the decision-making mechanism of individuals when choosing a transport mode. To this end, attention is paid to the variables which quantitatively explain behaviour and, notably, pro-environment behaviour (Section 2) and these are analysed in terms of their reciprocal relations and how they potentially influence the behaviour, showing the strengths and weaknesses of their use in transport sector.
The second objective, dealt with in Section 3, focuses on the individuation of the most significant behavioural theories found in the relevant literature in the last 20 years and which mainly use the variables defined in Section 2.
The third objective tackles the critical analysis of the role of variables (data), how they are analysed as well as interlinked in the models predicting the behaviour and is dealt with in Section 4.
Thanks to the outcome of the analyses presented in Section 2, Section 3 and Section 4 that shows the key issue of the gap between attitudes/intentions and behaviour, the discussion ends with some proposals to try to pave the way for the redefinition of the concept underlying the attitude-behaviour or intention-behaviour gap to better forecast travel behaviour and provides recommendations for further research.

3. Review of Main Behavioural Theories Explaining Behaviour and Pro-Environment Behaviour

The psycho-social variables described in Section 2 may contribute to explaining individual behaviour and, as shown in Figure 1 and reported in Table 2, some of them could be easily and successfully used to forecast travel behaviour, improving its prediction. However, measuring the variables better fitting the transport sector it is not a trivial task, especially since overlapping concepts and ambiguous definitions are often encountered, leading to contradictory results. This holds true when trying to understand and predict human decision-making processes: in fact, entanglement, reciprocal moderations or enhancement, or nonlinear relationships between factors and behaviour have to be taken into account.
Table 2. Overview of selected behavioural theories.
As regards the transport field, in the early 90’s, a shift was observed from the empirical correlation between variables and behaviour towards the construction of theories, psycho-social constructs and causal processes generating the behaviour [67].
A large number of theoretical frameworks have been developed over the years in order to capture the factors leading to decision-making or behavioural choices. We selected the most relevant theories that have been used to more broadly explain pro-environment behaviours, and, for some of them, to specifically expound modal choice or modal shift from the environmental psychology literature. Such theories are based on correlational evidence and, as such, have similar properties. We can define three different groups of theories in behavioural research, namely (1) individual-focused theory of decision-making; (2) individual-focused theory of behavioural change; (3) community-focused theory. In Table 2 the overview of such theories is presented, describing them and providing the related references while Section 3.1, Section 3.2 and Section 3.3 analyse them in relation to the psycho-social variables they use. Section 3.4 finally presents the strengths and weaknesses of the application of such theories to the transport sector.

3.1. Individual-Focused Theories of Decision-Making

Individual-focused theories of decision-making represent the largest part of psycho-social studies applied to transport research; among the most significant are the Theory of Planned Behaviour [15], the Theory of Interpersonal Behaviour [25], the Norm Activation Theory [13], the Value-Belief-Norm theory [49,76] (Table 2).
The Theory of Planned Behaviour (TPB) is most likely the best-known behavioural theory in transport research, environmental psychology and health related studies. It is, in turn, acclaimed for its great success and remarkable predictive power in empirical studies [77] or profoundly criticised and utterly disregarded [78]. Through years of widespread applications in many fields, it has been modified and many variations exist in literature; researchers have added more variables to try to more accurately predict future behaviour: anticipated emotions [79], perceived mobility needs [80], personal norms [34]. Furthermore, Conner and Armitage [81] proposed to add six variables to the initial model: (1) belief salience; (2) locus of control, that builds up the perceived behavioural control together with self-efficacy; (3) moral norms; (4) self-identity, that expresses the recognition of the potential and qualities of the individual to cover his/her societal role; (5) affective beliefs, anticipated emotions or regret; (6) past behaviour/habit.
Trying to reinforce a theory explaining reasoned (or planned) behaviour by adding “habits”—a non-deliberative variable—risks becoming an auto-destructive idea. It is clear from Figure 2 that such additions would lead to a model closer to the Theory of Interpersonal Behaviour (TIB) than to the TPB. In fact, the Theory of Interpersonal Behaviour (TIB) by Triandis [25] shows great similarity to Ajzen’s TPB; both theories aim to explain intention to engage in a certain behaviour and to perform such a behaviour. In TIB, however, intention is not only driven by personal cognition (subjective norms [SN] and attitude [ATT]) but also by emotions (affective constructs), social norms and self-identity (social constructs). Moreover, habits play a direct role in explaining behaviour; Triandis [25] argues that automatic performance of a behaviour decreases the level of conscious control over such behaviour. Finally, whereas in the TIB the presence of objective external restriction in performing a behaviour has a direct effect, the TPB assumes a subjective representation of those factors influencing intention.
Figure 2. Similarities and differences among individual-focused theories of decision-making.
The Norm-Activation Theory (NAT) [13] was first developed as a model for explaining altruistic behaviour. The rationale is that pro-social behaviour depends on the activation of personal moral norms, which are triggered once individuals expect a negative outcome to a given situation (problem awareness [PA] and adverse consequences [AC]) and when they believe their action may have a role in reducing this threat (ascription of responsibility [AR]). It seems that there is confusion among researchers about how to interpret the causal relationship between the variables: at least three approaches have been proposed in the literature to deal with such variables: (1) the relationship between Personal Norms (PN) and Behaviour is moderated by Problem Awareness (PA) and Ascription of Responsibility (AR) [90,91]; (2) Problem Awareness (PA) influences Ascription of Responsibility (AR), which in turn influences Personal Norms (PN) and PN influence behaviour [32,49,92,93]; (3) both Problem Awareness (PA) and Ascription of Responsibility (AR) influence PN, while PN, in turn, influence behaviour [67,94]. The first interpretation refers to a moderator model, while the other two interpretations imply a causal or mediation model. In a series of studies, De Groot and Steg [95,96] compared all three models and found most consistent support for the second one, which is represented in Figure 2. Moreover, they adopted an experimental design that supports a causal relationship between the variables.
More recently, Stern proposed to include values and worldviews in Schwartz’s Theory [76]; he constructed the Value-Belief-Norm Theory (VBN) to explain environmentally significant behaviour through a causal chain from stable general values and beliefs to specific behavioural norms. The idea is that a specific behaviour will comply with a subjective norm only if it is not inconsistent with one’s own personal values.

3.2. Individual-Focused Theories of Behavioural Change

The approach, focused on behavioural change, includes three main theories: the TransTheoretical Model, the Stage model of Self-Regulated Behavioural Change and the Protection-Motivation Theory (Table 2).
Firstly developed in the field of health-related behavioural changes, such as refraining from smoking, doing exercise, adopting a low-fat diet or using condoms, the TransTheoretical Model (TTM) is a model of intentional change, introducing the temporal dimension of change: while behavioural change was previously analysed as an event, the TTM recognises it as a process involving five stages—Pre-contemplation, Contemplation, Preparation, Action and Maintenance—and different processes, or activities, in which people engage to overcome difficulties encountered through the stages. These activities involve, for example, emotions, cognition, behaviour, social support or information gathering. The theory states that the effectiveness of a process/activity depends on an individual’s position towards change; it is considered as a theory of ideal change, due to strong criticism towards the arbitrary threshold of stages [97], the nonlinear patterns of progression through stages [98,99] and the lack of effectiveness of interventions using TTM framework [100]. For a detailed discussion about the pros and cons of the model, refer to Armitage [101]. The TTM, as a model of voluntary behavioural change, has been rightly applied to consumer behaviour [102]. Bamberg [103] adapted the TTM to develop the Stage model of Self-Regulated Behavioural Change (SSBC), sometimes referred to as MaxSem model [104,105], for proposals on personal travel plans focusing on car-use. Interestingly enough, Bamberg [103] retained four stages, by combining preparation and action, that are identified from constructs taken from both Ajzen’s TPB and Shwartz’s NAT. This approach is in line with Armitage and Arden [106], although they argued in favour of TPB over TTM in furnishing tools for behavioural change. Bamberg et al. [22] support their voluntary behavioural change approach, which is obviously limited in range for a systemic change; however, they point out and we share their view, that stronger and wider empirical evidence is required to prove the efficiency of this model promoting sustainable transport. The Protection-Motivation Theory (PMT) [87] sets out a framework to develop and evaluate persuasive communication as well as a social cognition model to predict health behaviour. The origins of the theory lay in the study of the persuasive impact of fear on attitude and behaviour [107] initially undertaken through the Health Belief Model, from which PMT derives. The theory postulates that behavioural intention—here named protection motivation—to perform the recommended behaviour is assessed through a process of both threat and coping appraisal. Threat is supposedly determined by the assessment of both the severity of the threat and the likelihood of being directly affected by it. On the other hand, the coping appraisal is driven by the evaluation of the efficacy of the recommended behaviour on limiting the threat and individuals’ ability to perform the recommended behaviour. Kim, Jeong and Hwang [108] used the PMT together with Fishbein and Ajzen’s TRA to predict pro-environmental behaviour with relative success.
Figure 3 shows the similarities among the three theories related to the behavioural change, highlighting how the TTM and SSBC are very close to each other, aggregating the stages in a different way while PMT gives fear (and hence, emotions) as well as threat and coping appraisal as the main motivators of behaviour. All those theories consider self-efficacy as an important factor, at the end of the stages for TTM and SSBC (maintenance and post-actional) and as precursor of behaviour for PMT, more in line with community-focused theories (SCogT, see Section 3.3).
Figure 3. Similarities and differences among individual-focused theories of behavioural change.

3.3. Community-Focused and Social Interaction Theories

The Social Cognitive Theory (SCogT) [88] rejects the assumption that a behaviour is led by a stimuli-response phenomenon, making people act in a mechanical way, like for example, computing information to choose the best alternative in accordance to self-stated rules of decision-making. According to Bandura, people are seen as agents interacting among themselves, having expectations about the future, being influenced by environmental considerations and prompted to self-reinforcement and adaptive behaviour. People learn by doing, by observing others and they can modify the reality; thus, the human mind is “generative, creative, proactive and reflective, not just reactive” ([88] p. 157). According to SCogT, the most important influential factor of behaviour is self-efficacy, or confidence in their own capacity to attain a goal. This goal is defined thanks to outcome expectations (at different levels) and socio-structural factors (the environment that can facilitate or impede a given achievement). It is then assumed that the goal, expected outcome and self-efficacy itself can predict behaviour. No studies have been found applying SCogT either in the transport sector or in pro-environmental behaviour, while a lot of research encourages the use of SCogT framework as a theory-based intervention to promote healthy behaviour.
Festinger’s Social Comparison Theory (SCompT) [89] states that when someone has no hint about how to judge or to act, (s)he will compare him/herself with most similar individuals and tend to adapt and reduce the gap between his/her own and others’ behaviour. This is a direct implication of descriptive social norms, viewed as a dynamic process towards an asymptotic goal instead of a static injunction of what should be done.
The common element in the theories described in Figure 4 (explaining the comparison with other individuals) is represented by the “environment” in SCogT theory and by “evaluation” in SCompT theory. In the community-focused theories, there emerges an aspect common to the individually-focused theories: this is that all theories refer to individuals, since they all deal with “personal characteristics”.
Figure 4. Similarities and differences among community-focused and social interaction theories.
Moreover, in individual-focused theories, the comparison with other individuals is implicitly included in the variable “norms”. In fact, “social descriptive norms” are based upon observations of “what people do”, which implies people watch others and act on the basis of what they do. Table 1 (grey section related to norms) shows the example of Iranian students acknowledging that, after observing other individuals on the bus, they changed their own behaviour, in regard to personal norms, adapting them to those of the others, “according to the rule”: if somebody of a status higher than my own uses the bus, I should do likewise. They modified their perception of bus use in their own country, where just poor people and the immigrants resort to public transport.

3.4. Strengths and Weaknesses of Application to Transport Sector of Behavioural Theories

The individual-focused theories of decision-making represent an alternative to the classical economic approach to decision-making based on utility maximisation. TPB and TIB have both strengths and weaknesses, the latter having already been analysed adding new constructs in the model. The adaptations of TPB in the transport sector are several as discussed in Section 3.1. A development of TPB and TIB, based on alternative theoretical concepts and psychometrics of attitudes, intentions and norms may produce interesting insight. The best use of these theories in the transport sector lies in the modal choice where the use of attitudinal variables could largely improve modal choice models. Furthermore, the collection of data related to users’ attitudes would be even more important in market segmentation studies, providing more elements for a better forecast of modal choice. Instead, NAT and VBN are operationally too simplistic (or too focused on pro-social behaviour) to explain modal choice.
The individual-focused theories of behavioural change are largely empirical and originate from health-related studies. They postulate that change is driven by the level of risk or threat that could occur in a do-nothing situation, integrating knowledge and values into the change process. The usefulness of these frameworks of behavioural change for the forecast of modal shift is low, also in the long-term. The main problem of the application to the transport sector lies in the absence of perceived risk related to the modal choice, in the short-term and in the inability to imagine the consequences (in the distant future) related to the modal choice. Even though there is an increasing amount of information available and several campaigns have been launched to raise awareness about the health risks associated with air pollution (and notably by transport), particularly when the pollutants’ thresholds are exceeded (frequent in some EU countries during the winter season), transport is not perceived as an ailment. Thus, only interventions enforced by law prevent people from using the car.
Conceptually powerful, the community-focused and social interaction theories represent the social formation of reality. They could be useful in a research-action context, or participatory intervention on a defined social and cultural context (e.g., living labs, educational contexts). Their application to the transport sector could be related to the analysis of large-scale behavioural change. In fact, transport entails social as well as individual issues, the latter generating the incentive to induce the social change. For example, if a person often uses the car, (s)he can understand that (s)he contributes to pollution, damaging others as well as her/himself; if the others often use the car, as (s)he does, (s)he realises that they damage her/himself, as well as themselves. This situation where no-one wins could spur a sort of reciprocal control and social comparison that could induce a (slow) behavioural change.
Table 3 presents a final synthesis of potential advantages and shortcomings of the above theories.
Table 3. Synthesis of advantages and shortcomings of the selected behavioural theories.

4. Discussion

It is unlikely that scientists will one day be able to untangle the complexity of the human mind: (a) taking into account all possible factors prompting one person to act in a certain way; (b) taking into account, at the individual level, both endogenous variables (from real-time information gathering to lifelong memories and unconscious decision-making processes) and exogenous variables (from particular weather conditions to normative demands).
However, several methodological tools and models have been developed to forecast human behaviour, as shown in Section 3, mainly based on psychological theories. Looking at Figure 1 and Table 1, by reporting the main psycho-social variables influencing behaviour and, notably, pro-environment behaviour, we can observe that all those variables are used in both individual-focused and community-focused theories (Table 2, Figure 2, Figure 3 and Figure 4); the difference is the way in which they are considered in relation to the individual. If we think that human behaviour (and notably pro-environment travel behaviour) is related both to individual and social characteristics and that taking decisions may breed an interaction with others (social aspect) and prompt potential change, the models should bring all these different issues together to better predict behaviour. An approach like the one shown in Figure 1, integrating the variables related both to the individual and to the social interaction in one framework, could perform well in transport because, in travel-related choices, not only is the individual side significant but, likewise, the social one (e.g., interaction with others when using public transport instead of a personal car). However, so far, the models most commonly used to forecast travel behaviour are stochastic (discrete choice models), based on theory of the maximum utility that relies on individual benefit (individualism and eco-centrism). Furthermore, such models, calculating the probability that an event will happen (and, hence, that an individual makes a certain choice), are based on the observation of the occurrence of such event and they are neither able to understand the motivations behind the users’ choice, nor to include psycho-social variables.
In psychology, the theory is needed to build measures of subjective mental states in response to questions about or observation of body movements, drawings and so forth. Furthermore, when constructing a theory based upon subjective psychological constructs, narratives are necessary to give sense to data because one can subjectively refer his/her own experience to what those narratives express. Thus, in order to support a theory, not only are data required: definitions have to be precise, concepts should be intelligible and cause and effects should be coherent with one’s own pace of thinking. The development of a theory is not independent from the construction of psycho-social variables, as some of them are constructed ad hoc in order to support the narratives.
The complexity of interaction between alleged independent variables is largely underestimated. The causal chain that leads to decision-making, or behavioural intention, is hypothesised by the formulation of the theory and—the majority of studies being observational—our certainty about the direction of causality is inexistent. Thus, regularity models of causation—based on observational phenomena and data—whose first theorist was the philosopher David Hume (An Enquiry into Human Understanding, 1751), have been abandoned in favour of counterfactual models. Those models define causation in terms of a comparison of both observable and unobservable events. Structural equation modelling (SEM) is one of prominent approaches to causal inference using counterfactuals, based on the work of Judea Pearl [109].
Hidalgo and Sekhon [110] notice that modern advocates of structural equation modelling (SEM) argue that it “does not explicitly take into account knowledge about the mechanisms linking background, independent and dependent variables” (p. 209). Thus, SEM does not model causality in relation to experiments but sets up a causal model of the analysed relationships through a system of structural functions so that “hypothetical interventions should be explicitly and formally related to the causal mechanisms under study”. In this way, the use of SEM as a method to derive causality from observational data could be misleading because the aim of the SEM is to support a narrative to explain the causality and too much is left to the researcher’s assumptions, often insufficiently discussed. Feedback loops or non-linear relationships are mostly unexplored. Can we really claim that a positive attitude towards cycling will lead someone to ride rather than being a consequence of it? The question is not new: Bandura [111] regretted that most psychodynamic processes are inferred by the behavioural response they ought to explain.
In psychology, the measurement of psycho-social constructs, such as attitudes, norms, affects and so forth, is mainly performed under the paradigm of Classical Test Theory. Practically, as it is assumed that internal states of mind are unobservable, the variables of interests are determined through the use of questionnaire items: a latent factor that will capture the covariance of different items is constructed. However, the validity of measures of unobservable variables depends on the strength of their concept and on the ability of items—carrying their own measurement error—to reflect a specific psychological construct without accidentally measuring an outside variable. Applying complex correlational models (such as the SEM) to data necessarily leads to the multiplicative invalidity problem [112], where relationships among unobserved variables may be illusions created by the multiplication of measures errors.
In several studies, inferences are generally of poor quality and, pretty often, only reflect the researcher’s thoughts due to the way questions are asked. Some issues closely related to correct data interpretation are given here as explicit: we lack univocal definitions of psychological constructs; we often lack conformity between measures and behaviour (Ajzen’s compatibility principle); many common modulating factors, both internal (ambiguity of ecological attitudes) and external (peer pressure, context such as costs, built environment and normative system) are not accredited by the theories; when measuring past behaviour to predict future behaviour, are we sure we are not measuring the same thing ? This aspect also raises the problem of the questions which arise when looking ahead, because the literature clearly shows a lack of longitudinal (before/after) studies. Are we aware of the mathematical implications of our model and statistical analysis? Too often questions of mutual causality and the validity of linear response are overlooked.
Chorus, Molin and Van Wee [113], clearly showed in their meta-analysis that the expected effects of travel information are below expectations although they may be effective in some specific cases. Hunecke, Haustein, Grischkat and Böhler [114] used a hierarchical regression analysis to assess the effects of psychological variables when socio-demographic and infrastructures are controlled. They concluded that, in terms of ecological impact, the measured psychological variables were responsible for an additional 14% of explained variance (reaching 60% of total variance); this clearly shows that our understanding of the effect of psychological factors on travel behaviour is very low and we probably still miss key factors that could improve our understanding.
However, behavioural research points out the existing gap between the measured attitude, the measured behavioural intention and the observed behaviour. This inconsistency between what people say and what people do is referred to as the attitude-behaviour gap [115,116] or the intention-behaviour gap [117,118], demonstrating the volatility of the concepts of attitude or intention. Mostly attributed to information deficit, which refers to the level of knowledge about behavioural consequences or, in a more tangible way, to the existence and to the characteristics of possible alternatives, such a gap poses a real problem for any policy maker. When attitudes and behaviour are inconsistent, which is called cognitive dissonance [119], individuals are more likely to change their attitudes instead of behaviour, in many ways [120]. Tertoolen, Van Kreveld and Verstraten [121] showed that making the gap between attitudes and behaviour more obvious could entail undesirable consequences defined as social dilemmas and the unwillingness of people to change their habits.
Moreover, there is clear confusion about measuring psychological constructs at a specific level (e.g., attitudes towards the use of public transport to commute) or at a general level (e.g., attitudes towards alternatives to cars for mobility needs). Taking the example of NAT applications, some researchers measured the various model variables on a general level [49,92], while others measured them on a specific level [33,93,122]. Pronello and Camusso [35] used both specific and general attitudes to segment the market.
Adding complexity in the behavioural theories will not, arguably, solve the gap between attitude and behaviour, while a deeper understanding of travel behaviour requires a redefinition of the concepts (attitude and habits) and a comprehension of how such concepts and psychological constructs are understood. In an article recalling a historic debate, Kaiser, Byrka and Hartig [123] stated that “the attitude-behaviour gaps are empirical chimeras” (p. 351). They give a paradigmatic answer to many of the problems cited above using Campbell’s definition of “behavioural disposition” [124]. Within this concept, attitude and behaviour are ideally perfectly connected through a “behavioural disposition”, making the “blurred” causal relationship unnecessary. Attitude towards a given object is only person-dependent and reflects itself through a set of behaviours transitively ordered according to the level of difficulty (cost) to perform them: in practice, attitudes are measured by means of what people do, not of what they say. With such a Campbellian measure of attitudes, as explained by Kaiser and Byrka [125] (p. 26), “there is no room for hypocrisy”: people put their general attitudes into specific attitude-relevant practices and differences in people’s general attitudes can be derived from their attitude-relevant behaviour. Indeed, we can consider that a bike commuter shows a higher behavioural disposition to cycling than someone who states that (s)he is feeling good, feels loved and is encouraged by peers to use the bike. This implies that answers to a given set of behaviours, defined by the researcher, are direct projections of latent attitude on real behaviours. Our logic in every-day actions is constructivist: our beliefs, knowledge, habits, desires are derived from our past experience, learning skills and capacity to adapt. As argued by Smith and Conrey [126], social psychologists should be interested in studying theories through the lens of interactions amongst individuals, instead of expressing them as relationships among variables. Despite success in a wide variety of fields, social simulations are far from obtaining global consensus in the scientific community. However, they seem a useful and promising tool that social scientists need to describe and understand the real-world.
Concerning habits, Schwanen, Banister and Anable [127] initiated a philosophical discussion about our understanding of how those habits are developed and perceived. It is doubtful whether past-behaviour (or habit strength) properly explains future behavioural intention. However, unsurprisingly, past behaviour has been successfully used as a predictor of both behavioural intention and behaviour itself, at least when the context is stable [73,128] and may represent the main obstacle to behavioural change [26,129]. However, Schwanen et al. [127] (p. 524) observed that “both dependent and independent variables may well be measuring one and the same thing—A general tendency to perform the behaviour in question”. Taleb [130] showed how past facts (historical time series) are not good predictors of the future and pointed out the fragility of our knowledge, so that predictions based on past knowledge seem by far less useful than a continuous adaptation and adjustment to unpredictable events.

5. Recommendations and Conclusions

To improve the prediction ability of models, it is necessary to investigate the hidden motivations and to understand what the variables to analyse and, hence, the data to collect are. But human beings are complex and too many variables interact in their choice processes; furthermore, they are social beings and this fact cannot be ignored when trying to predict their choices particularly when related to their travels. Their trips characterise them as social beings (altruism) or as individuals (egoism) and the choice of transport mode, for example the car, can enhance their individualistic side (boost of ego) while public transport puts them in relation with others, confronts them with others and induces comparative self-assessment. What does a change in behaviour imply? It implies changing the image we have about ourselves, reassessing it in light of the new behaviour, coming out of our “comfort zone” (see the case of Iranian students in Section 2.1).
The observation of the continually evolving reality seems to be a better approach, made possible thanks to new technologies (sensors, smartphones, etc.) allowing massive data collection that, combined with context-specific understanding of human behaviour and analysed thanks to appropriate mathematical and statistical tools, could provide “powerful insights about the different ways people interact with one another” [131]. For sure, a paradigmatic change in data collection is a necessary further step towards understanding human behaviour and resolving the attitude-behaviour gap. Indeed, it is difficult to get a general picture of the mobility determinants that should be included in the transport models through the travel surveys—similarly to that related to the socio-economic variables—but thanks to the big data and social networks such information will be more and more available, supporting the data coming from attitudinal surveys. However, psycho-social variables will make the difference in the transport models only when the psycho-social theories will have gone beyond the limits described in this paper.
To this end a true interdisciplinary approach, bringing together transport science, artificial intelligence, behavioural and social sciences by addressing a series of gaps in transport planning and their impact on the development of contemporary cities seems necessary.
We believe that agent-based social simulation could be a key tool to understanding to what an extent both cognitive processes (Section 3.1 and Section 3.2) and interpersonal interactions and social behaviour (Section 3.3) can reproduce empirical results of the complex and dynamic system that urban transport is. Even unrealistic social simulations can give us some interesting clues about the importance of the threshold effect, feedback loops, emergent phenomena, crowd behaviour or dynamic equilibrium.
After more than 25 years of fruitful psychological studies on travel behaviour, it seems that we now have a better understanding of the cognitive processes behind modal choice but we have not yet understood the mobility related dynamics and, unfortunately, we have not yet been effective in leading people towards a more sustainable mobility: nowhere in the world have researchers really succeeded in implementing a sustainable transport system, in terms of infrastructure, activity re-localisation or behavioural shifts. We have gained insights about what drives behaviours but the real interest still remains unclear: what is the way to achieve massive car-use reduction, which policy may have a significant impact [132] or how to use fear-driven communication in order to influence behaviour, as is often done for health-related issues. Definitely, failing to trigger sustainable mobility has and will have harmful effects on our lives. Policy makers should take responsibility for activating risk perception through education, advertising and so forth and with the support of different disciplines.

Author Contributions

This study is a joint work of the two authors. C.P. and J.-B.G. conceived the paper and defined the approach to use and the methodology to follow. J.-B.G. carried out the literature search and wrote down a first scheme. C.P. revised the first draft, made some additional literature search and re-wrote and added parts/sections. C.P. made the final revision. Both authors read and approved the final manuscript.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. International Energy Agency. Key World Energy Statistics. Retrieved from the International Energy Agency Website. 2016. Available online: https://www.iea.org/publications/freepublications/publication/key-world-energy-statistics.html (accessed on 4 June 2018).
  2. Goodwin, P. Car Dependence. Transp. Policy 1995, 2, 151–152. [Google Scholar] [CrossRef] [Scilit]
  3. Bonnes, M.; Carrus, G.; Passafaro, P. Psicologia Ambientale, Sostenibilità e Comportamenti Ecologici [Environmental Psychology, Sustainability and Ecological Behaviours]; Carocci: Italy, Rome, 2006. [Google Scholar]
  4. Kaiser, F.G.; Fuhrer, U. Ecological behavior’s dependency on different forms of knowledge. Appl. Psychol. 2003, 52, 598–613. [Google Scholar] [CrossRef] [Scilit]
  5. Messick, D.M.; McClintock, C.G. Motivational bases of choice in experimental games. J. Exp. Soc. Psychol. 1968, 4, 1–25. [Google Scholar] [CrossRef] [Scilit]
  6. Schwartz, S.H. Universals in the content and structure of values: Theoretical advances and empirical tests in 20 countries. Adv. Exp. Soc. Psychol. 1992, 25, 1–65. [Google Scholar]
  7. Maslow, A.H. A theory of human motivation. Psychol. Rev. 1943, 50, 370–396. [Google Scholar] [CrossRef] [Scilit]
  8. Inglehart, R. Public support for environmental protection: Objective problems and subjective values in 43 societies. Political Sci. Politics 1995, 28, 57–72. [Google Scholar] [CrossRef] [Scilit]
  9. Douglas, M.; Wildavsky, A. How can we know the risks we face? Why risk selection is a social process. Risk Anal. 1982, 2, 49–58. [Google Scholar] [CrossRef] [Scilit]
  10. Steg, L.; Sievers, I. Cultural theory and individual perceptions of environmental risks. Environ. Behav. 2000, 32, 250–269. [Google Scholar] [CrossRef] [Scilit]
  11. Sherkat, D.E.; Ellison, C.G. Structuring the Religion-Environment Connection: Identifying Religious Influences on Environmental Concern and Activism. J. Sci. Study Relig. 2007, 46, 71–85. [Google Scholar] [CrossRef] [Scilit]
  12. Cialdini, R.B.; Reno, R.R.; Kallgren, C.A. A focus theory of normative conduct: Recycling the concept of norms to reduce littering in public places. J. Personal. Soc. Psychol. 1990, 58, 1015–1026. [Google Scholar] [CrossRef]
  13. Schwartz, S.H. Normative influences on altruism. Adv. Exp. Soc. Psychol. 1977, 10, 221–279. [Google Scholar]
  14. Ajzen, I. From intentions to actions: A theory of planned behavior. In Action Control: From Cognition to Behavior; Kuhl, J., Beckmann, J., Eds.; Springer: Berlin, Germany, 1985; pp. 11–39. [Google Scholar]
  15. Ajzen, I. The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 1991, 50, 179–211. [Google Scholar] [CrossRef] [Scilit]
  16. Engler, B. Personality Theories, 9th ed.; Cengage: Belmont, CA, USA, 2013. [Google Scholar]
  17. Allport, F.H.; Allport, G.W. Personality Traits: Their Classification and Measurement. J. Abnorm. Psychol. Soc. Psychol. 1921, 16, 6–40. [Google Scholar] [CrossRef] [Scilit]
  18. Cattell, H.; Mead, A. The sixteen personality factor questionnaire (16PF). In SAGE Handbook of Personality Theory and Assessment: Volume 2—Personality Measurement and Testing; Boyle, G., Matthews, G., Saklofske, D., Eds.; SAGE: London, UK, 2008; pp. 135–160. [Google Scholar]
  19. Goldberg, L.R. An alternative “description of personality”: The big-five factor structure. J. Personal. Soc. Psychol. 1990, 59, 1216–1229. [Google Scholar] [CrossRef]
  20. Carrus, G.; Passafaro, P.; Bonnes, M. Emotions, habits and rational choices in ecological behaviours: The case of recycling and use of public transportation. J. Environ. Psychol. 2008, 28, 51–62. [Google Scholar] [CrossRef] [Scilit]
  21. Farag, S.; Lyons, G. What affects use of pretrip public transport information? Empirical results of a qualitative study. Transp. Res. Rec. J. Transp. Res. Board 2008, 2069, 85–92. [Google Scholar] [CrossRef] [Scilit]
  22. Bamberg, S.; Fujii, S.; Friman, M.; Gärling, T. Behaviour theory and soft transport policy measures. Transp. Policy 2011, 18, 228–235. [Google Scholar] [CrossRef] [Scilit]
  23. Chawla, L. Life paths into effective environmental action. J. Environ. Educ. 1999, 31, 15–26. [Google Scholar] [CrossRef] [Scilit]
  24. Allport, G.W. Attitudes. In Handbook of Social Psychology; Murchison, C., Ed.; Clark Univ. Press: Worcester, MA, USA, 1935; pp. 798–844. [Google Scholar]
  25. Triandis, H.C. Interpersonal Behavior; Brooks-Cole: Monterey, CA, USA, 1977. [Google Scholar]
  26. Aarts, H.; Dijksterhuis, A.P. The automatic activation of goal-directed behaviour: The case of travel habit. J. Environ. Psychol. 2000, 20, 75–82. [Google Scholar] [CrossRef] [Scilit]
  27. Verplanken, B. Beyond frequency: Habit as mental construct. Br. J. Soc. Psychol. 2006, 45, 639–656. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  28. Hines, J.M.; Hungerford, H.R.; Tomera, A.N. Analysis and synthesis of research on responsible environmental behavior: A meta-analysis. J. Environ. Educ. 1987, 18, 1–8. [Google Scholar] [CrossRef] [Scilit]
  29. Kaiser, F.G.; Ranney, M.; Hartig, T.; Bowler, P.A. Ecological behavior, environmental attitude, and feelings of responsibility for the environment. Eur. Psychol. 1999, 4, 59–74. [Google Scholar] [CrossRef] [Scilit]
  30. Kaiser, F.G.; Wölfing, S.; Fuhrer, U. Environmental attitude and ecological behaviour. J. Environ. Psychol. 1999, 19, 1–19. [Google Scholar] [CrossRef] [Scilit]
  31. Dietz, T.; Stern, P.C.; Guagnano, G.A. Social structural and social psychological bases of environmental concern. Environ. Behav. 1998, 30, 450–471. [Google Scholar] [CrossRef] [Scilit]
  32. Aoyagi-Usui, M.; Vinken, H.; Kuribayashi, A. Pro-environmental attitudes and behaviors: An international comparison. Hum. Ecol. Rev. 2003, 10, 23–31. [Google Scholar]
  33. Nordlund, A.M.; Garvill, J. Effects of values, problem awareness, and personal norm on willingness to reduce personal car use. J. Environ. Psychol. 2003, 23, 339–347. [Google Scholar] [CrossRef] [Scilit]
  34. Bamberg, S.; Hunecke, M.; Blöbaum, A. Social context, personal norms and the use of public transportation: Two field studies. J. Environ. Psychol. 2007, 27, 190–203. [Google Scholar] [CrossRef] [Scilit]
  35. Pronello, C.; Camusso, C. Travellers’ profiles definition using statistical multivariate analysis of attitudinal variables. J. Transp. Geogr. 2011, 19, 1294–1308. [Google Scholar] [CrossRef] [Scilit]
  36. Sheller, M. Automotive emotions feeling the car. Theory Cult. Soc. 2004, 21, 221–242. [Google Scholar] [CrossRef] [Scilit]
  37. Steg, L. Car use: Lust and must. Instrumental, symbolic and affective motives for car use. Transp. Res. Part A Policy Pract. 2005, 39, 147–162. [Google Scholar] [CrossRef] [Scilit]
  38. Fishbein, M.; Ajzen, I. Attitudes and opinions. Annu. Rev. Psychol. 1972, 23, 487–544. [Google Scholar] [CrossRef] [Scilit]
  39. Bergman, M.M. A theoretical note on the differences between attitudes, opinions, and values. Swiss Political Sci. Rev. 1998, 4, 81–93. [Google Scholar] [CrossRef] [Scilit]
  40. Wicker, A.W. Attitudes versus actions: The relationship of verbal and overt behavioral responses to attitude objects. J. Soc. Issues 1969, 25, 41–78. [Google Scholar] [CrossRef] [Scilit]
  41. Fishbein, M.; Ajzen, I. Belief, Attitudes, Intention, and Behavior. An Introduction to Theory and Research; Addison-Wesley: Reading, MA, USA, 1975. [Google Scholar]
  42. Webb, T.L.; Sheeran, P. Does changing behavioral intentions engender behavior change? A meta-analysis of the experimental evidence. Psychol. Bull. 2006, 132, 249–268. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  43. Aarts, H.; Verplanken, B.; Van Knippenberg, A. Habit and information use in travel mode choices. Acta Psychol. 1997, 96, 1–14. [Google Scholar] [CrossRef] [Scilit]
  44. Verplanken, B.; Aarts, H.; Van Knippenberg, A. Habit, information acquisition, and the process of making travel mode choices. Eur. J. Soc. Psychol. 1997, 27, 539–560. [Google Scholar] [CrossRef]
  45. Oyserman, D. Values: Psychological perspectives. In International Encyclopedia of the Social and Behavioral Sciences; Eisenberg, N., Ed.; Elsevier Science: New York, NY, USA, 2001; Volume 22, pp. 16150–16153. [Google Scholar]
  46. Hunecke, M.; Haustein, S.; Böhler, S.; Grischkat, S. Attitude-Based Target Groups to Reduce the Ecological Impact of Daily Mobility Behavior. Environ. Behav. 2010, 42, 3–43. [Google Scholar] [CrossRef] [Scilit]
  47. Gärling, T. Value priorities, social value orientations and cooperation in social dilemmas. Br. J. Soc. Psychol. 1999, 38, 397–408. [Google Scholar] [CrossRef] [Scilit]
  48. Vugt, M.; Meertens, R.M.; Lange, P.A. Car versus Public Transportation? The Role of Social Value Orientations in a Real-Life Social Dilemma. J. Appl. Soc. Psychol. 1995, 25, 258–278. [Google Scholar] [CrossRef] [Scilit]
  49. Stern, P.C.; Dietz, T.; Abel, T.; Guagnano, G.A.; Kalof, L. A value-belief-norm theory of support for social movements: The case of environmentalism. Hum. Ecol. Rev. 1999, 6, 81–98. [Google Scholar]
  50. Poortinga, W.; Steg, L.; Vlek, C. Environmental risk concern and preferences for energy-saving measures. Environ. Behav. 2002, 34, 455–478. [Google Scholar] [CrossRef] [Scilit]
  51. Kearns, L. Noah’s ark goes to Washington: A profile of evangelical environmentalism. Soc. Compass 1997, 44, 349–366. [Google Scholar] [CrossRef] [Scilit]
  52. White, L. The historical roots of our ecological crisis. Science 1967, 155, 1203–1207. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  53. Hand, C.M.; Van Liere, K.D. Religion, mastery-over-nature, and environmental concern. Soc. Forces 1984, 63, 555–570. [Google Scholar] [CrossRef] [Scilit]
  54. Hayes, B.G.; Marangudakis, M. Religion and attitudes towards nature in Britain. Br. J. Sociol. 2001, 52, 139–155. [Google Scholar] [PubMed]
  55. Dunlap, R.E.; Van Liere, K.D. The “new environmental paradigm”. J. Environ. Educ. 1978, 9, 10–19. [Google Scholar] [CrossRef] [Scilit]
  56. Dunlap, R.E. The new environmental paradigm scale: From marginality to worldwide use. J. Environ. Educ. 2008, 40, 3–18. [Google Scholar] [CrossRef] [Scilit]
  57. Harland, P.; Henk, S.; Henk, A.M.W. Explaining proenvironmental intention and behavior by personal norms and the theory of planned behavior. J. Appl. Soc. Psychol. 1999, 29, 2505–2528. [Google Scholar] [CrossRef] [Scilit]
  58. Pournasir Roudboneh, M. The Behavioural Intention towards the Public Transport. A Comparative Case Study between Iran and Italy; Politecnico di Torino: Turin, Italy, 2015. [Google Scholar]
  59. Hildebrand, E.D. Dimensions in elderly travel behaviour: A simplified activity-based model using lifestyle clusters. Transportation 2003, 30, 285–306. [Google Scholar] [CrossRef] [Scilit]
  60. Mokhtarian, P.L.; Salomon, I.; Redmond, L.S. Understanding the demand for travel: It’s not purely “derived”. Innov. Eur. J. Soc. Sci. Res. 2001, 14, 355–380. [Google Scholar] [CrossRef] [Scilit]
  61. Cao, X.; Mokhtarian, P.L. How do individuals adapt their personal travel? A conceptual exploration of the consideration of travel-related strategies. Transp. Policy 2005, 12, 199–206. [Google Scholar] [CrossRef] [Scilit]
  62. Cao, X.; Mokhtarian, P.L. How do individuals adapt their personal travel? Objective and subjective influences on the consideration of travel-related strategies for San Francisco Bay Area commuters. Transp. Policy 2005, 12, 291–302. [Google Scholar] [CrossRef] [Scilit]
  63. Clay, M.J.; Mokhtarian, P.L. Personal travel management: The adoption and consideration of travel-related strategies. Transp. Plan. Technol. 2004, 27, 181–209. [Google Scholar] [CrossRef] [Scilit]
  64. Anable, J. Complacent Car Addicts or Aspiring Environmentalists? Identifying Travel Behaviour Segments Using Attitude Theory. Transp. Policy 2005, 12, 65–78. [Google Scholar] [CrossRef] [Scilit]
  65. Pronello, C.; Rappazzo, V.; Veiga Simao, J.; Duboz, A.; Gaborieau, J.-B. SmartMoov’-Navigateur Multimodal Temps Réel [SmartMoov’-Real-Time Multimodal Navigator]; Final Report WP 5; OPTIMD’LYON: Lyon, France, 2015. [Google Scholar]
  66. Gobster, P.H. Forest Aesthetics, Biodiversity, and the Perceived Appropriateness of Ecosystem Management Practices. In Defining Social Acceptability in Ecosystem Management: A Workshop Proceedings; Brunson, M.W., Kruger, L.E., Tyler, C.B., Schroeder, S.A., Eds.; U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station: Portland, OR, USA, 1996; pp. 77–97. [Google Scholar]
  67. Bamberg, S.; Schmidt, P. Incentives, morality, or habit? Predicting students’ car use for university routes with the models of Ajzen, Schwartz, and Triandis. Environ. Behav. 2003, 35, 264–285. [Google Scholar] [CrossRef] [Scilit]
  68. Davies, D.G.; Halliday, M.E.; Mayes, M.; Pockock, R.L. Attitudes to Cycling: A Qualitative Study and Conceptual Framework (Report Ref. 266); Publication of Transport Research Laboratory: Crowthorne, Berkshire, 1997. [Google Scholar]
  69. Haustein, S.; Hunecke, M. Identifying target groups for environmentally sustainable transport: Assessment of different segmentation approaches. Curr. Opin. Environ. Sustain. 2013, 5, 197–204. [Google Scholar] [CrossRef] [Scilit]
  70. Jensen, M. Passion and heart in transport: A sociological analysis on transport behavior. Transp. Policy 1999, 6, 19–33. [Google Scholar] [CrossRef] [Scilit]
  71. Ory, D.T.; Mokhtarian, P.L.; Redmond, L.S.; Salomon, I.; Collantes, G.O.; Choo, S. When is Commuting Desirable to the Individual? Growth Chang. J. Urban Reg. Policy 2004, 35, 334–359. [Google Scholar] [CrossRef] [Scilit]
  72. Pas, E.; Huber, J.C. Market segmentation analysis of potential inter-city rail travellers. Transportation 1992, 19, 177–196. [Google Scholar] [CrossRef] [Scilit]
  73. Ouellette, J.A.; Wood, W. Habit and intention in everyday life: The multiple processes by which past behavior predicts future behavior. Psychol. Bull. 1998, 124, 54–74. [Google Scholar] [CrossRef]
  74. Aarts, H.; Verplanken, B.; Van Knippenberg, A. Predicting behavior from actions in the past: Repeated decision making or a matter of habit? J. Appl. Soc. Psychol. 1998, 28, 1355–1374. [Google Scholar] [CrossRef] [Scilit]
  75. Hanson, S.; Huff, J.O. Assessing day-to-day variability in complex travel patterns. Transp. Res. Rec. J. Transp. Res. Board 1981, 891, 18–24. [Google Scholar]
  76. Stern, P.C. Towards a Coherent Theory of Environmentally Significant Behavior. J. Soc. Issues 2000, 56, 407–424. [Google Scholar] [CrossRef] [Scilit]
  77. Armitage, C.J.; Conner, M. Efficacy of the theory of planned behaviour: A meta-analytic review. Br. J. Soc. Psychol. 2001, 40, 471–499. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  78. Sniehotta, F.F.; Presseau, J.; Araújo-Soares, V. Time to retire the theory of planned behaviour. Health Psychol. Rev. 2014, 8, 1–7. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  79. Perugini, M.; Bagozzi, R.P. The role of desires and anticipated emotions in goal-directed behaviours: Broadening and deepening the theory of planned behaviour. Br. J. Soc. Psychol. 2001, 40, 79–98. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  80. Haustein, S.; Hunecke, M. Reduced use of environmentally friendly modes of transportation caused by perceived mobility necessities: An extension of the theory of planned behavior. J. Appl. Soc. Psychol. 2007, 37, 1856–1883. [Google Scholar] [CrossRef] [Scilit]
  81. Conner, M.; Armitage, C.J. Extending the theory of planned behavior: A review and avenues for further research. J. Appl. Soc. Psychol. 1998, 28, 1429–1464. [Google Scholar] [CrossRef] [Scilit]
  82. Schwartz, S.H. Moral decision making and behavior. In Altruism and Helping Behavior: Social Psychological Studies of Some Antecedents and Consequences; Macaulay, J., Berjowitz, L., Eds.; Academic Press: Cambridge, MA, USA, 1970; pp. 127–141. [Google Scholar]
  83. Schwartz, S. The justice of need and the activation of humanitarian norms. J. Soc. Issues 1975, 31, 111–136. [Google Scholar] [CrossRef] [Scilit]
  84. Schwartz, S.H.; Howard, J.A. Internalized values as motivators of altruism. In Development and Maintenance of Prosocial Behavior: International Perspectives on Positive Morality; Staub, E., Bar-Tal, D., Karylowski, J., Reykowski, J., Eds.; Springer: New York, NY, USA, 1984; pp. 229–255. [Google Scholar]
  85. Prochaska, J.O.; DiClemente, C.C. Transtheoretical therapy: Toward a more integrative model of change. Psychother. Theory Res. Pract. 1982, 19, 276–288. [Google Scholar] [CrossRef] [Scilit]
  86. Prochaska, J.O.; DiClemente, C.C.; Norcross, J.C. In search of how people change: Applications to addictive behaviors. Am. Psychol. 1992, 47, 1102–1114. [Google Scholar] [CrossRef] [PubMed]
  87. Rogers, R.W. Cognitive and physiological processes in fear appeals and attitude change: A revised theory of protection motivation. In Social Psychophysiology: A Sourcebook; Cacioppo, J.T., Petty, R.E., Eds.; Guilford Press: New York, NY, USA, 1983; pp. 153–176. [Google Scholar]
  88. Bandura, A. Social cognitive theory of personality. In Handbook of Personality: Theory and Research, 2nd ed.; Pervin, L.A., John, O.P., Eds.; Guiford Press: New York, NY, USA, 1999; pp. 154–196. [Google Scholar]
  89. Festinger, L. A theory of social comparison processes. Hum. Relat. 1954, 7, 117–140. [Google Scholar] [CrossRef] [Scilit]
  90. Schultz, P.W.; Zelezny, L.C. Values and proenvironmental behavior a five-country survey. J. Cross-Cult. Psychol. 1998, 29, 540–558. [Google Scholar] [CrossRef] [Scilit]
  91. Vining, J.; Ebreo, A. Predicting recycling behavior from global and specific environmental attitudes and changes in recycling opportunities. J. Appl. Soc. Psychol. 1992, 22, 1580–1607. [Google Scholar] [CrossRef] [Scilit]
  92. Gärling, T.; Fujii, S.; Gärling, A.; Jakobsson, C. Moderating effects of social value orientation on determinants of proenvironmental behavior intention. J. Environ. Psychol. 2003, 23, 1–9. [Google Scholar] [CrossRef] [Scilit]
  93. Steg, L.; Dreijerink, L.; Abrahamse, W. Factors influencing the acceptability of energy policies: A test of VBN theory. J. Environ. Psychol. 2005, 25, 415–425. [Google Scholar] [CrossRef] [Scilit]
  94. Harland, P.; Staats, H.; Wilke, H.A. Situational and personality factors as direct or personal norm mediated predictors of pro-environmental behavior: Questions derived from norm-activation theory. Basic Appl. Soc. Psychol. 2007, 29, 323–334. [Google Scholar] [CrossRef] [Scilit]
  95. De Groot, J.I.; Steg, L. Morality and prosocial behavior: The role of awareness, responsibility, and norms in the norm activation model. J. Soc. Psychol. 2009, 149, 425–449. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  96. Steg, L.; De Groot, J. Explaining prosocial intentions: Testing causal relationships in the norm activation model. Br. J. Soc. Psychol. 2010, 49, 725–743. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  97. West, R. Time for a change: Putting the Transtheoretical (Stages of Change) Model to rest. Addiction 2005, 100, 1036–1039. [Google Scholar] [PubMed]
  98. Sutton, S. Back to the drawing board? A review of applications of the transtheoretical model to substance use. Addiction 2001, 96, 175–186. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  99. Weinstein, N.D.; Rothman, A.J.; Sutton, S.R. Stage theories of health behavior: Conceptual and methodological issues. Health Psychol. 1998, 17, 290–299. [Google Scholar] [CrossRef] [PubMed]
  100. Bridle, C.; Riemsma, R.P.; Pattenden, J.; Sowden, A.J.; Mather, L.; Watt, I.S.; Walker, A. Systematic review of the effectiveness of health behavior interventions based on the transtheoretical model. Psychol. Health 2005, 20, 283–301. [Google Scholar] [CrossRef] [Scilit]
  101. Armitage, C.J. Is there utility in the transtheoretical model? Br. J. Health Psychol. 2009, 14, 195–210. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  102. He, H.A.; Greenberg, S.; Huang, E.M. One size does not fit all: Applying the transtheoretical model to energy feedback technology design. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Atlanta, GA, USA, 10–15 April 2010; Volume 10, pp. 927–936. [Google Scholar]
  103. Bamberg, S. Is a stage model a useful approach to explain car drivers’ willingness to use public transportation? J. Appl. Soc. Psychol. 2007, 37, 1757–1783. [Google Scholar] [CrossRef] [Scilit]
  104. Carreno, M.; Bamberg, S.; Welsch, J.; Rye, T.; Hyllenius, P. How best to evaluate mobility management projects: Can psychological theory help? In Proceedings of the 12th World Conference on Transport Research, Lisboa, Portugal, 11–15 July 2010. [Google Scholar]
  105. Carreno, M.; Gaučė, K.; Welsch, J. Enhancing the Effectiveness of Travel Awareness Campaigns via the Inclusion of Theoretical-Based Content. In Proceedings of the 8th International Conference Environmental Engineering, Vilnius, Lithuania, 19–20 May 2011; pp. 879–885. [Google Scholar]
  106. Armitage, C.J.; Arden, M.A. Exploring discontinuity patterns in the transtheoretical model: An application of the theory of planned behaviour. Br. J. Health Psychol. 2002, 7, 89–103. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  107. Rogers, R.W. A protection motivation theory of fear appeals and attitude change. J. Psychol. 1975, 91, 93–114. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  108. Kim, S.; Jeong, S.H.; Hwang, Y. Predictors of pro-environmental behaviors of American and Korean students: The application of the theory of reasoned action and protection motivation theory. Sci. Commun. 2012, 35, 168–188. [Google Scholar] [CrossRef] [Scilit]
  109. Pearl, J. Causality: Models, Reasoning and Inference; Cambridge University Press: New York, NY, USA, 2000. [Google Scholar]
  110. Hidalgo, F.D.; Sekhon, J.S. Causality. In International Encyclopedia of Political Science; Badie, B., Berg-Schlosser, D., Morlino, L., Eds.; SAGE: Thousand Oaks, CA, USA, 2011; pp. 204–211. [Google Scholar]
  111. Bandura, A. Social Learning Theory: Motivational Trends in Society; General Learning Press: Morristown, NY, USA, 1971. [Google Scholar]
  112. Trafimow, D. Multiplicative invalidity and its application to complex correlational models. Genet. Soc. Gen. Psychol. Monogr. 2006, 132, 215–239. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  113. Chorus, C.G.; Molin, E.J.; Van Wee, B. Travel information as an instrument to change car drivers’ travel choices: A literature review. Eur. J. Transp. Infrastruct. Res. 2006, 6, 335–364. [Google Scholar]
  114. Hunecke, M.; Haustein, S.; Grischkat, S.; Böhler, S. Psychological, sociodemographic, and infrastructural factors as determinants of ecological impact caused by mobility behavior. J. Environ. Psychol. 2007, 27, 277–292. [Google Scholar] [CrossRef] [Scilit]
  115. Godin, G.; Conner, M.; Sheeran, P. Bridging the intention-behaviour gap: The role of moral norm. Br. J. Soc. Psychol. 2005, 44, 497–512. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  116. Moraes, C.; Carrigan, M.; Szmigin, I. The coherence of inconsistencies: Attitude-behaviour gaps and new consumption communities. J. Mark. Manag. 2012, 28, 103–128. [Google Scholar] [CrossRef] [Scilit]
  117. Sheeran, P. Intention—Behavior relations: A conceptual and empirical review. Eur. Rev. Soc. Psychol. 2002, 12, 1–36. [Google Scholar] [CrossRef] [Scilit]
  118. Sniehotta, F.F.; Scholz, U.; Schwarzer, R. Bridging the intention-behaviour gap: Planning, self-efficacy, and action control in the adoption and maintenance of physical exercise. Psychol. Health 2005, 20, 143–160. [Google Scholar] [CrossRef] [Scilit]
  119. Festinger, L. A Theory of Cognitive Dissonance; Stanford University Press: Stanford, CA, USA, 1962. [Google Scholar]
  120. Juvan, E.; Dolnicar, S. The attitude-behaviour gap in sustainable tourism. Ann. Tour. Res. 2014, 48, 76–95. [Google Scholar] [CrossRef] [Scilit]
  121. Tertoolen, G.; Van Kreveld, D.; Verstraten, B. Psychological resistance against attempts to reduce private car use. Transp. Res. Part A Policy Pract. 1998, 32, 171–181. [Google Scholar] [CrossRef] [Scilit]
  122. De Ruyter, K.; Wetzels, M. With a little help from my fans: Extending models of pro-social behaviour to explain supporters’ intentions to buy soccer club shares. J. Econ. Psychol. 2000, 21, 387–409. [Google Scholar] [CrossRef] [Scilit]
  123. Kaiser, F.G.; Byrka, K.; Hartig, T. Reviving Campbell’s paradigm for attitude research. Personal. Soc. Psychol. Rev. 2010, 14, 351–367. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  124. Campbell, D.T. Social attitudes and other acquired behavioral dispositions. In Psychology: A Study of a Science; Koch, S., Ed.; McGraw-Hill: New York, NY, USA, 1963; Volume 6, pp. 94–172. [Google Scholar]
  125. Kaiser, F.G.; Byrka, K. The Campbell paradigm as a conceptual alternative to the expectation of hypocrisy in contemporary attitude research. J. Soc. Psychol. 2015, 155, 12–29. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  126. Smith, E.R.; Conrey, F.R. Agent-based modeling: A new approach for theory building in social psychology. Personal. Soc. Psychol. Rev. 2007, 11, 87–104. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  127. Schwanen, T.; Banister, D.; Anable, J. Rethinking habits and their role in behaviour change: The case of low-carbon mobility. J. Transp. Geogr. 2012, 24, 522–532. [Google Scholar] [CrossRef] [Scilit]
  128. Bamberg, S.; Ajzen, I.; Schmidt, P. Choice of travel mode in the theory of planned behavior: The roles of past behavior, habit, and reasoned action. Basic Appl. Soc. Psychol. 2003, 25, 175–187. [Google Scholar] [CrossRef] [Scilit]
  129. Gärling, T.; Axhausen, K.W. Introduction: Habitual travel choice. Transportation 2003, 30, 1–11. [Google Scholar] [CrossRef] [Scilit]
  130. Taleb, N.N. The Black Swan: The Impact of Highly Improbable; Penguin Random House: New York, NY, USA, 2010. [Google Scholar]
  131. Onnela, J.P. Social Networks and Collective Human Behavior [Blog Entry]. 2011. Available online: http://www.unglobalpulse.org/node/14539 (accessed on 4 June 2018).
  132. Gardner, B.; Abraham, C. Psychological correlates of car use: A meta-analysis. Transp. Res. Part F Traffic Psychol. Behav. 2008, 11, 300–311. [Google Scholar] [CrossRef] [Scilit]

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