Running Head: TRAFFIC OFFENCES: PLANNED OR HABITUAL?
1
Running head: TRAFFIC OFFENCES: PLANNED OR HABITUAL?
Traffic offences: Planned or habitual? Using
the Theory of Planned Behaviour and habit
strength to explain frequency and magnitude of
speeding and driving under the influence of
alcohol
Florent LHEUREUX*, Laurent AUZOULT, Colette CHARLOIS,
Sandrine HARDY-MASSARD and Jean-Pierre MINARY
Laboratoire de Psychologie (EA3188), Université de FrancheComté, Besançon, FRANCE
*Requests for reprints should be addressed to Florent LHEUREUX, 30-32 rue Mégevand F25030 Besançon cedex, FRANCE (e-mail: [email protected]).
Running Head: TRAFFIC OFFENCES: PLANNED OR HABITUAL?
2
Traffic Offences: Planned or Habitual?
Using the Theory of Planned Behaviour and habit strength to explain frequency and
magnitude of speeding and driving under the influence of alcohol
Abstract
This study addresses the socio-cognitive determinants of traffic offences, in particular of
speeding and drinking and driving. It has two aims: (1) to test the hypothesis of a direct effect
of habits on offences (i.e. independent of intentions) by employing a specific measure of
habits (i.e. the SRIH) and (2) to analyse the offences by taking account of three distinct
parameters: frequency, usual magnitude (i.e. the most frequent deviation from the law) and
maximal magnitude (i.e. the greatest deviation occasionally adopted) in order to represent
more accurately the variability of the offending behaviours. 642 drivers replied to a
questionnaire. The results corroborate the idea that intention and habit are distinct and direct
determinants of offences. The use of the SRIH dismiss the criticisms made with regard to the
measure of past behaviour. The distinction between the three behavioural parameters proves
to be relevant, as their determinants are not exactly similar. Finally, attitude and subjective
norm had direct effects on the maximal magnitude and/or on the frequency of the offence. The
discussion concerns the contribution of this study to the analysis of offences as well as its
limitations, and addresses the theoretical plausibility of the direct effects of attitude and the
subjective norm.
Keywords: traffic offences; behavioural intention; Self-Reported Index of Habit strength
(SRIH); drinking and driving; speeding; attitude
Running Head: TRAFFIC OFFENCES: PLANNED OR HABITUAL?
3
According to the World Health Organisation (2013), 1.24 million deaths are caused by road
traffic accidents every year. Amongst the reasons given, the adoption of risky and offending
behaviour plays a major role, in particular speeding and driving under the influence of
alcohol. From the different approaches drawn on to understand the adoption of this behaviour,
the Theory of Planned Behaviour (TPB, Ajzen, 1991) has been very widely employed. Within
this framework, the central factor is behavioural intention. This intention is produced under
the influence of three factors: attitude (i.e. the extent to which the individual evaluates the
behaviour positively or negatively), the subjective norm (i.e. the extent to which the
individual thinks that important people in their life would approve or disapprove of them
carrying out the behaviour) and the perceived behavioural control (i.e. the extent to which the
individual considers that it will be easy for them to adopt this behaviour if they wish).
Behavioural control also has a direct effect on the behaviour to the extent it reflects accurate
perceptions of control.
This theory has proved effective for analysing very varied behaviours (Armitage &
Conner, 2001; McEachan, Conner, Taylor, & Lawton, 2011). In the field of driving behaviour,
TPB has been employed to explain speeding (e.g. Conner, Lawton, Parker, Chorlton,
Manstead, & Stradling, 2007; Elliott, Armitage, & Baughan, 2003; Letirand & Delhomme,
2005), drinking and driving (e.g. Castanier, Deroche, & Woodman, 2013; Marcil, Bergeron, &
Audet, 2001; Moan & Rise, 2011), dangerous overtaking, and tailgating (Parker, Manstead,
Stradling, Reason, & Baxter, 1992), the use of mobile phones (Walsh, White, Hyde, &
Watson, 2008; Zhou, Wu, Rau, & Zhang, 2009) and aggressive behaviour at the wheel (e.g.
Efrat & Shoham, 2013; Parker, Lajunen, & Stradling, 1998). This theory is also invoked to
explain the behaviour of pedestrians on the public highway (Evans & Norman, 1998; Holland,
Hill, & Cooke, 2009; Moyano-Diaz, 2002), the wearing of seat belts (Brijs, Daniels, Brijs, &
Wets, 2011; Okamura, Fujita, Kihira, Kosuge, & Mitsui, 2012; Şimşekoğlu & Lajunen, 2008)
and driving without a licence (Tseng, Chang, & Woo, 2013).
Running Head: TRAFFIC OFFENCES: PLANNED OR HABITUAL?
4
With the aim of increasing its explicatory power, the TPB’s constructs have been
subject to significant adaptation and extension (for a general discussion, see Conner &
Armitage, 1998). The concept of past behaviour or habit is one of the recurring extensions.
The idea associated with this addition is that driving behaviours are not exclusively “planned"
and “reasoned” but are also habitual, in the sense that they are adopted partially independently
of intentions. Verplanken and Aarts (1999, p.104) define habits as "learned sequences of acts
that have become automatic responses to specific cues, and are functional in obtaining certain
goals or end-states". The notion of habit thus highlights the automatic character that a given
behaviour assumes when the individual has performed it on numerous occasions. The
encounter with a familiar situation triggers, in this case, the largely automatic implementation
of a series of actions. So, a habit is not only characterised by the frequency of the behaviour
but above all by its “automatic” nature (unconscious, difficult to control and ability to take
place simultaneously with other cognitive or behavioural activities; Gardner, 2012; Orbell &
Verplanken, 2010). This approach considers that “most of the behaviours social psychologists
are interested in are repetitive and habitual." (Verplanken, 2006, p.654). From this viewpoint,
a habit is, originally, a series of behaviours initiated under conscious control which, after
sufficient and satisfactory repetitions, is adopted in a more or less unconscious fashion
(Verplanken & Faes, 1999; Verplanken, 2006). In doing so, a habit may be considered as
giving rise to the formation of scripts (Schank & Abelson, 1977), implemented automatically
when a familiar situation is encountered. Moreover, the development of a habit tends to
diminish the influence of intention on the actual behaviour (Ouellette & Wood, 1998;
Verplanken & Aarts, 1999; Verplanken, Herabadi, Perry, & Silvera, 2005).
Numerous studies have recorded the formation of habits (e.g. Åberg, 1993; Castanier
et al., 2013; Conner et al., 2007; Delhomme, Cristea, & Paran, 2013; Dinh & Kubota, 2013;
Elliott & Thomson, 2010; Horvath, Lewis, & Watson, 2012; Nemme & White, 2010;
Okamura et al., 2012; Palat & Delhomme, 2012; Xu, Li, & Zhang, 2013; Zhou, Wu, Rau, &
Running Head: TRAFFIC OFFENCES: PLANNED OR HABITUAL?
5
Zhang, 2009), whether it be explicitly (i.e. by directly citing this concept) or implicitly (i.e. by
only including one measurement, without mentioning the concept, and/or without having the
aim of demonstrating the importance of habits). The results obtained converge massively in
favour of the inclusion of the concept of habit as a direct predictor of behaviours (i.e. distinct
from behavioural intention). The addition of this factor systematically increases the explained
variance of the analysis model, even in the presence of all the other TPB constructs.
For instance, Elliott and collaborators (2010) conducted a study based on an extended
TPB framework, with the frequency of speeding in the past six months as an indicator of
habit. They measured all TPB constructs and past behaviour on a first occasion, and measured
speeding behaviour (using self-report) six months later (second occasion) in order to estimate
the predictive power of their extended TPB. They observed that past behaviour had a direct
and large effect on subsequent behaviour, independent of the effects of other constructs. In
another study, Castanier and collaborators (2013) hypothesised that perceived behavioural
control moderates the influence of attitude and of subjective norm on intention and behaviour.
They used a design similar to that of Elliott and collaborators (2010) and analysed five road
violations (including drink-driving and speeding). They measured the frequency of these
violations during the last twelve months on a first occasion (Time 1), without any theoretical
reference to habit formation. For each violation, the subsequent behaviour measured on a
second occasion (six months later) was significantly predicted by past behaviour measured at
Time 1, even when the effects of TPB constructs were controlled.
However, these studies present a major limitation: the taking into account of past
behaviour as a single measure of habit (more precisely, the frequency with which the driver
has adopted the behaviour in question in the past). Yet this measure is open to criticism. Ajzen
(2002, p.110) states on this subject, that "residual effects of the past on later behaviour are not
evidence of habit". For him, the fact that a behaviour is repeated several times and continues
to be adopted currently by the individual is not necessarily due to the presence of a habit, but
Running Head: TRAFFIC OFFENCES: PLANNED OR HABITUAL?
6
may be due to other factors. Repetition of the behaviour, although essential for the formation
of a habit, does not however signify that it exists. Moreover, even though a statistical link
remains between past behaviour and current behaviour, despite taking account of the intention
to act, this may simply indicate that determinants of past behaviours influence current
behaviour. It is also possible that the individual has changed intention recently but that they
are not acting entirely in accordance within it. In fact, many factors exist for preventing
intentions from materializing into actions (see Gollwitzer, 1999). This observation tends to
invalidate the studies employing the past frequency of the behaviour as the measure of a habit.
This point of view is also adopted by Verplanken and Orbell (Orbell & Verplanken,
2010; Verplanken & Orbell, 2003) and by Gardner (2012), who argue in favour of the use of a
specific measure of habits, a measure which particularly takes into account their automatic,
uncontrolled and difficult to repress nature. Verplanken and Orbell (2003) have developed
such a measure: the Self-Reported Index of Habit strength (SRIH). This consists of 12 items
and measures the presence of a habit beyond the simple past repetition of the behaviour. This
scale has been used in several earlier studies and has confirmed that habits influence the
choice of modes of transport (e.g. g. De Bruijn, Kremers, Singh, van den Putte, & van
Mechelen, 2009; Gardner, 2009), driving while sending text messages on a mobile phone
(Bayer & Campbell, 2012) and the wearing of personal protective equipment by motorcyclists
(Norris & Myers, 2013), and this independently of intentions. However, the SRIH has not
been employed in relation to behaviours such as driving above the speed limit and driving
under the influence of alcohol, which constitutes a significant limitation in the estimation of
their possible habitual nature (past behaviour alone having been measured until now).
Consequently, the first and principal objective of the empirical study described in this article
is to remedy this lack, by conducting a study employing the measures inspired by the TPB and
the SRIH in relation to these two types of driving behaviours. The hypothesis being tested in
this connection is that habits really do have a direct effect on these behaviours, distinct from
Running Head: TRAFFIC OFFENCES: PLANNED OR HABITUAL?
7
TPB constructs.
This research also pursues a second objective. The great majority of studies analysed
the frequency with which the behaviour is adopted (non-exhaustive list: Castanier et al., 2013;
de Pelsmacker & Janssens, 2007; Dinh & Kubota, 2013; Elliott, Thomson, Robertson,
Stephenson, & Wicks, 2013; Elliott & Armitage, 2009; Elliott & Thomson, 2010; Marcil et
al., 2001; Özkan, Lajunen, Doğruyol, Yıldırım, & Çoymak, 2012; Stead, Tagg, Mackintosh, &
Eadie, 2005; Steg & van Brussel, 2009; Wallén Warner, Özkan, & Lajunen, 2009). More
rarely, these studies measured the magnitude of the offence (i.e. the extent to which the driver
deviates from the law, Conner et al., 2007; Cristea, Paran, & Delhomme, 2013; Dinh &
Kubota, 2013; Lawton, Conner, & Parker, 2007; Leandro, 2012; Letirand & Delhomme,
2005; Lheureux, 2012). For example, two drivers may exceed speed limits with the same
frequency but may differ in regard to the extent to which they exceed it (e.g. one may exceed
the law by 5 km/h [3.11 mph] while the other exceeds it by 20 km/h [12.43 mph]). Moreover,
the extent to which a driver exceeds the speed limit may be more or less stable (homogenous)
or, on the contrary, very variable (heterogeneous). Indeed, a driver may drive generally 10
km/h [6.21 mph] above the limit, and more exceptionally up to 15 km/h [9.24 mph] above,
while another driver may sometimes exceed the limit by 30 km/h [18.64 mph] although he
usually exceeds it by 10 km/h [6.21 mph]. In other words, it’s not just a question of analysing
the speed which is the most representative of the driver (i.e. the usual magnitude of their
speeding) but also the most exceptional speed they adopt from time to time (i.e. the maximal
magnitude). Doing this allows us to take into consideration the intra-individual variability of
offending behaviours. The same reasoning can be adopted for drinking and driving. And so
the aim of the following detailed study is to distinguish three parameters in terms of speeding
and drinking and driving (i.e. the frequency, the usual magnitude and the maximal magnitude
of the offence). The distinction between these three parameters has not, to our knowledge,
been made until now. Their consideration allows us to test the hypotheses originating from the
Running Head: TRAFFIC OFFENCES: PLANNED OR HABITUAL?
8
TPB in a more robust manner than if only one aspect of the behaviour were being measured.
Moreover, the comparison of these parameters within the same study offers the possibility of
identifying any differences with regard to their determinants (e.g. attitude, subjective norm,
behavioural control etc.).
Method
Participants
Six hundred and forty-two French drivers took part in this study. Table 1 below presents their
principal characteristics. For the majority of the variables the sample was diverse, thus
ensuring a good generalizability of results across sub-groups.
Insert Table 1 here
Procedure
These drivers replied to a questionnaire on a voluntary basis. They were approached via the
intermediary of contacts established by the authors during a contract for research within
public administrative bodies, associations, a hospital and a business. In addition, staff and
students from the university to which the research team belonged were approached. They
were invited to respond to an electronic questionnaire created through the Limesurvey®
programme and accessible via a URL sent to the addressees via internal distribution lists.
After a general presentation of the study (i.e. its aim and institutional context, as well as the
contact details of the research team), anonymity and confidentiality of individual responses
were guaranteed. Then, the attitudes, subjective norms, perceived behavioural controls,
intentions and habits relative to driving above the speed limit and drinking under the influence
of alcohol were measured.
Material
Attitudes
Attitudes with regards to speeding and drinking and driving were measured using 3 items for
each. This involved the drivers indicating how the fact of driving above the speed limit (or
Running Head: TRAFFIC OFFENCES: PLANNED OR HABITUAL?
9
driving after drinking alcohol) in the coming months would be for them to a greater or lesser
extent "pleasant/unpleasant", "beneficial/harmful" and "positive/negative". For example, they
had to say whether driving above the speed limit in the coming months would be for them
"very unpleasant", (0), “rather unpleasant” (1), “neutral or mixed” (2), “rather pleasant” (3) or
“very pleasant” (4).
Subjective norms
The drivers were firstly invited to write the initials of the people whose opinion
counted the most for them in their decision making, next those of the people with whom they
went out most often or with whom they celebrated most often, and then the initials of the
people in whose presence they drove most often.
The subjective norm was measured next using three items for each behaviour: “The
majority of these people would approve of me driving above the speed limit [drinking alcohol
before driving] in the coming months” (response options from “completely false” (0) to
“completely true” (4)); “On the whole, I have the feeling that these people expect me to
observe the speed limit [not to drink alcohol before driving] in the coming months” (reverse)
(from “strongly agree” (0) to “strongly disagree” (4)); “If you were to drive above the speed
limit [drink alcohol before driving] in the coming months, to what extent would you feel
uneasy or upset if all these people came to know about it?” (“not at all uneasy or upset” (0) to
“very uneasy and upset" (4)).
Perceived behavioural controls
Eight items were used to measure the perceived behavioural control associated with speeding
(4 items) and drinking and driving (4 items). For each behaviour, the questioning began with
an introductory expression (“to what extent would you find it more or less easy or difficult
to…”) and, then, each item was presented with a response scale. Two items concerned the
perceived control of danger while breaking the law (the extent to which the driver considered
that avoiding danger while speeding [drinking and driving] was under their control), for
Running Head: TRAFFIC OFFENCES: PLANNED OR HABITUAL?
10
example “Not having a road accident while speeding (driving above the speed limit)?”. The
two other items concerned the perceived control of respect for the law, for example “Never
speeding (=to what extent do you think it would be easy for you to always observe the speed
limit?)”. For each question they had to reply on a five point scale, ranging from "very
difficult" (0) to "very easy" (4).
The items used to measure the “perceived control of respect for the law” were similar
to items used by several earlier TPB studies (Castanier et al., 2013; Elliott et al., 2013;
Letirand & Delhomme, 2005). The items assessing the “perceived control of danger while
breaking the law” originated from studies concerning illusory control and optimistic bias in
risk-taking (e.g. Horswill et al., 2004; Klein & Helweg-Larsen, 2002; McKenna, 1993; White
et al., 2011) which observed that drivers tend to overestimate their control in risky situations
(i.e. their possibility of overcoming danger when necessary through their driving skills).
These studies suggest that drivers may intend to break the law because of an exaggerated
perception of their control of danger in such a situation. These items were designed to assess
this possibility.
Behavioural intentions
They had to specify to what extent they intended, to a greater or lesser extent, to drive above
the speed limit or to drive after drinking alcohol in the coming months on a 7 point response
scale ranging from "never" (0) to "always" (6). For each behaviour, two items were employed.
“I intend to drive above the speed limit [drink alcohol before driving] in the coming months”
and “I will probably drive above the speed limit [drink alcohol before driving] in the coming
months”.
Habits
Habits related to speeding and drinking and driving were measured using the SRIH of
Verplanken and Orbell (2003). The SRIH consisted of twelve items which recorded the nature
of the behaviour concerned with respect to its frequency (in the past), automaticity
Running Head: TRAFFIC OFFENCES: PLANNED OR HABITUAL?
11
(unconscious nature), difficulty of control, effortlessness and capacity to contribute to selfconcept. We introduced the behaviour (“Behaviour X is something…”) then invited the
participant to state their position for each item (e.g. “I do it automatically”, “I start doing it
before I realise I’m doing it”, “I have been doing it for a long time”, “I would find it hard not
to do it”). The response scale included seven response options ranging from “completely
disagree" (0) to "completely agree" (6).
Current self-reported behaviour
With regard to speeding, drivers were invited to give three different contexts in which they
drove: a conurbation area, a country road and a motorway. For each context, they had to
indicate the speed they adopted most often and the maximal speed they had already adopted.
Six speeds (3 contexts*2 types of response) were thus obtained. For example, they were asked
"in which town/city (or village) do you drive most often?" After giving their response, the
driver replied to the question “At what speed do you drive most often in this area?” by
choosing a response from eleven response options requiring them to position their driving in
relation to the speed limit. These responses ranged from “more than 40 km/h [24.85 mph]
above the speed limit" (10) to less than 20 km/h [12.43 mph] below the speed limit (0). The
response "exactly on the speed limit" obtained the score of 41. They also replied to the
question “what is the maximal speed at which you have already driven in this area?” with the
same response choices. These questions allowed us to measure the usual magnitude of the
driver’s speeding (i.e. the speed most often adopted) and its maximal level (i.e. the maximal
speed already adopted).
With regard to drinking and driving, they were asked to indicate the number of drinks
1
The eleven response options were the following : More than 40 km/h [24.85 mph] above the speed limit (10),
Between 30 and 40 km/h [18.64 and 24.85 mph] above the speed limit (9), Between 20 and 30 km/h [12.43 and
18.64 mph] above the speed limit (8), Between 10 and 20 km/h [6.21 and 12.43 mph] above the speed limit (7),
Between 5 and 10 km/h [3.11 and 6.21 mph] above the speed limit (6), Less than 5 km/h [3.11 mph] above the
speed limit (5), Exactly on the speed limit (4), Less than 5 km/h [3.11 mph] below the speed limit (3), Between 5
and 10 km/h [3.11 and 6.21 mph] below the speed limit (2), Between 10 and 20 km/h [6.21 and 12.43 mph]
below the speed limit (1), Less than 20 km/h [12.43 mph] below the speed limit (0).
Running Head: TRAFFIC OFFENCES: PLANNED OR HABITUAL?
12
of alcohol which they consumed most often before driving when they found themselves in a
situation conducive to consumption, and the maximal number of drinks of alcohol which they
had already consumed before driving. The number of drinks was not to be reported on the
basis of “home-size measures”, but on the basis of traditional measures served in bars in
France (for example 10cl [3.33 fl.oz] of wine, 2.5cl [0.83 fl.oz] of whisky, 25cl [8.33 fl.oz] of
beer). Here again, the usual magnitude of alcohol consumption and its maximal magnitude
were measured.
Two additional questions allowed us to measure the frequency of speeding and driving
under the influence of alcohol. They invited the drivers to indicate the frequency with which
they adopted these two behaviours on a scale with 7 response options, ranging from "never"
(0) to "extremely frequently" (6).
In an earlier version of our questionnaire all response scales comprised seven response
options, except for the usual and maximal magnitude of offences. Feedback from drivers
questioned during a pilot study suggested reducing to five the number of possible answers for
attitudes, subjective norms and perceived behavioural controls. Similarly, the number of
eleven response options for the usual and maximal magnitude of speeding (with more
possible answers above the limit) was the result of this testing phase of the material. Thus,
these modifications led to the use of differing numbers of response options.
Results
Reliability of measures, descriptive statistics and comparisons of means
Table 2 following summarises the descriptive statistics and Cronbach’s alphas obtained.
Insert table 2 here
With the exception of the measures of perceived behavioural control, the Cronbach’s
alphas obtained exceeded the recommended .70 norm, consequently testifying to the
reliability of these measures. For perceived behavioural control, both for speeding and
alcohol, the alphas were positioned between .60 and .70. However, on account of the low
Running Head: TRAFFIC OFFENCES: PLANNED OR HABITUAL?
13
number of items (2) these values remained acceptable, as the number of items greatly
influence Cronbach’s alpha.
On average, the drivers questioned reported driving above the speed limit with
moderate frequencies (M=2.77) and customary magnitudes (M=5.11). This mean of 5.11 –
when compared with the response options – was close to the response “less than 5 km/h [3.11
mph] above the speed limit” (5). The maximal speed already adopted was situated on average
(M=6.54) between the responses “between 5 and 10 km/h [3.11 and 6.21 mph] above the
speed limit” (6) and "between 10 and 20 km/h [6.21 and 12.43 mph] above the speed limit"
(7). In comparison, the reported frequency of alcohol consumption was significantly less
(M=0.56), t(641) = 34.67, p < .0001. The mean number of drinks usually consumed was
around one (M=1.06), while the maximal number of drinks already consumed was situated on
average between 2 and 3 (M=2.40). This maximal number of drinks was subject, however, to
much greater variation (SD=2.49) than was the case with usual consumption (SD=1.21) and
its frequency (SD=0. 92).
With regard to TPB constructs and habits, the means indicated that the majority of
drivers had little intention of breaking the law while driving (M=1.87 and M=0.39), were
generally unfavourable towards it (M=1.39 and M=0.35), reported that those close to them
were largely disapproving (M=1.60 and M=0.61) and were unaccustomed to them offending
(M=1.44 and M=0.21). These tendencies were stronger for drinking and driving (i.e. weaker
intention, more negative attitude etc.) than for speeding (all values t(641) > 19, p < .0001).
Behavioural control seemed to differentiate between these two offences. Whereas control of
danger and respect for the law were subject to an equivalent assessment synonymous with the
median control for speeding (M=2.04 vs. M=1.96, t(641) = 1.18, ns), control of danger was
judged more difficult (M=1.41) than respect for the law (M=3.33) with regard to drinking and
driving, t(641) = 27.36, p < .0001.
Magnitudes of speeding were analysed using a repeated measures ANOVA, with as
Running Head: TRAFFIC OFFENCES: PLANNED OR HABITUAL?
14
intra-subject IVs the variables “type of road” (conurbation, country road or motorway) and
“type of speed" (usual or maximal). The frequency with which drivers drove above the speed
limit overall was included as a covariate. The analysis of the multivariate effects illustrated
above all the effect of the type of speeding, Wilks’s lambda = .72, F(1, 612), p<.0001, ηp² =
.26, maximal speed being on average significantly higher (M=6.54, SD=1.41) than usual
speed (M=5.11, SD=1.25). The type of road alone, Wilks’s lambda = .98, F(2, 611), p < .01,
ηp² = .02, and its interaction with the type of speeding, Wilks’s lambda = .99, F(2, 611), p <
.03, ηp² = .01, also had significant, although negligible, effects (cf. ηp²). This significant
difference between usual speed and maximal speed confirmed that driving above the speed
limit was not constant for the same driver (intra-individual variability). The distinction
between these two measures was thus confirmed in its relevance.
A similar observation could be made for the distinction between the usual number of
drinks of alcohol consumed before driving and the maximal number of drinks already
consumed (repeated measures ANOVA, with frequency of consumption as covariate). In fact,
the maximal number of drinks was actually greater on average (M=2.40, SD=2.49) than the
usual number of drinks (M=1.06, SD=1.21), Wilk’s lambda = .86, F(1, 639), p<.0001, ηp² =
.15.
For speeding, the correlations between frequency and usual magnitude (.62), between
frequency and maximal magnitude (.68) and between usual and maximal magnitudes (.76)
were all strong, positive and significant (p < .0001). For drinking and driving the observation
was the same, they were also strong, positive, and significant (p < .0001): frequency and usual
magnitude (.64), frequency and maximal magnitude (.71), usual and maximal magnitudes
(.73). Although these correlations were strong, the common variance between these measures
ranged from 38% to 58%, which indicated that each time the remaining variance – i.e.
independent of the other parameters – was relatively high.
Hierarchical linear regression analyses
Running Head: TRAFFIC OFFENCES: PLANNED OR HABITUAL?
15
Hierarchical linear regression analyses were carried out, five for speeding and five for
drinking and driving. For each behaviour, the first analysis concerned the prediction of
intention for action (DV) via the other TPB constructs and habit (IV). During the first step,
attitude, subjective norm, perceived control of danger and of respect for the law were included
as predictors. During the second step, habit was added. According to Ajzen’s perspective
(2002), the effect of habit on intention should be mediated by the other TPB constructs,
resulting in a non-significant effect at step 2. Conversely, according to studies which suggest a
direct and distinct effect of habit on behaviour (see Elliott & Thompson, 2010; Horvath et al.,
2012), its effect should be significant. The second analysis took the behaviour as a whole as
dependent variable, in other words estimated on the basis of three measured parameters.
Intention and perceived controls were included firstly (step 1), attitude and subjective norm
were then added (step 2) and, finally, habit completed the prediction of behaviour (step 3).
Here again, the (non-) significance of the effect of habit in the presence of TPB constructs
(including intention) should divide the two theoretical perspectives being compared. For the
three other analyses, the same approach was adopted with as dependent variables the
frequency of offending (3rd analysis), its usual magnitude (4th analysis) and its maximal
magnitude (5th analysis) taken in isolation.
Intentions and current self-reported behaviours
Tables 3 below summarises the results obtained for intentions and overall reported
behaviours.
Insert Table 3 here
With regard to intentions, the constructs measured taken together (step 2) explained
68% and 49% of variance (R² = .68 and .49). The addition of habit as well as TPB constructs
increased this explained variance significantly (ΔR² = .08 and .06, p < .001). The effect of
habit was significant for each behaviour (p < .001). For speeding, habit was the most
important predictor (β = .40), followed by control of respect for the law (β = -.26), the
Running Head: TRAFFIC OFFENCES: PLANNED OR HABITUAL?
16
subjective norm (β = .18), attitude (β = .12) and the perceived control of danger (β = .08)
which was the least important predictor. All these factors had a significant effect (p < .01 or <
.001). For drinking and driving, habit was also the most important predictor (β = .27, p <
.001) of behavioural intention along with the subjective norm (β = .26, p < .001). This was
followed by attitude (β = .22, p < .001) and perceived control of respect for the law (β = -.22,
p < .001). The perceived control of danger had no significant effect (β = .04, ns).
The analyses carried out on overall behaviours revealed that the different constructs
measured (TPB + habit) explained a large proportion of their variances (step 3, R² = .67 and
.59). At each step, the addition of new predictors significantly increased the explained
variance (ΔR² = .03, p < .001). These analyses all led to the same result: the effect of habit on
behaviour was significant for both behaviours (β = .27 and .19). In step 3, the intention for
action was a stronger predictor of behaviour (β = .40) than habit (β = .27 and .19). Despite the
inclusion of intention in step 2 and 3, attitude had a relatively important effect (β =.16, .12
and .13, p < .001). In step 2, the subjective norm had a significant effect on overall behaviour
(β= .08, p < .05 and .12, p < .001) outside of intention.
Frequency, usual magnitude and maximal magnitude of offences
The following table 4 presents the results obtained for the three behavioural parameters taken
in isolation.
Insert Table 4 here
For the two behaviours, each parameter was in a notable proportion predicted by the
TPB + habit theoretical model (step 3, R² ranging from .36 to .72). Whatever the behavioural
variable considered (frequency, usual magnitude, and maximal magnitude), the effect of habit
was significant in the presence of TPB constructs (β ranging from .14 to .28, p < .001).
Attitude had a relatively important direct effect on each parameter (step 2, β ranging from .11,
p < .05 to .17, p < .001). These results also revealed that the subjective norm had a direct
effect on the maximal magnitude of offences (step 2, β = .12 and .11, p < .01), but not on their
Running Head: TRAFFIC OFFENCES: PLANNED OR HABITUAL?
17
usual magnitude (β = .03 and .07, ns), while for their frequency it had solely a direct effect for
speeding (β = .13, p < .001).
Discussion
Traffic offences: intentional AND habitual
The significance of the effect of habit on behaviour despite TPB constructs being taken into
account indicates that habit really has a distinct direct effect. This result was all the stronger
as it concerned two behaviours (speeding and drinking and driving) analysed by means of
three variables (frequency, usual magnitude and maximal magnitude) and was obtained by
means of a specific, theoretically founded and psychometrically valid measure of habits: the
SRIH (Verplanken & Orbell, 2003). However, intention for action remained the most
important direct predictor as it had higher beta weight than all the other constructs. In other
words, traffic offences are intentional and habitual, with the emphasis on intentions.
Additional observations
Frequency, usual magnitude and maximal magnitude of offences: a relevant distinction
Several results argue in favour of taking into account the magnitude of offences (usual and
maximal) as well as their frequency. First of all, although these three parameters were
strongly correlated, between 42% and 62% of their variance was independent of other
parameters. This signifies that studying the origin of the variations of one only partially
allows us to understand the variations of the two others. Therefore, the variance explained by
the TPB + habit model varied greatly from one parameter to another. Frequency was the most
explained parameter (R² = .72 and .62), whereas the usual magnitude of offence was greatly
less (R² = .39 and .36), the explanation of the maximal magnitude being situated at an
intermediate level (R² = .50 and .44). Moreover, the effects of the TPB constructs varied
depending on the parameter under consideration. For example, with regard to speeding, the
usual magnitude of this offence seemed more linked to control of respect for the law than to
attitude, the subjective norm having no effect. Conversely, the maximal magnitude of this
Running Head: TRAFFIC OFFENCES: PLANNED OR HABITUAL?
18
offence depended more on attitude and the subjective norm and less on control of respect for
the law.
The effects of subjective norm and attitude on behaviour outside of intention
The results obtained with regard to the subjective norm were consistent overall with the TPB,
its influence on behaviours being generally mediated by behavioural intention (additional
analyses not shown here). However, if we consider maximal magnitude of offences in
isolation, the subjective norm had a moderate direct effect. Manning's meta-analysis (2009)
revealed that this phenomenon is common and proposed several explanations for it.
According to him, behaviours which by nature are (1) hedonistic (and not only utilitarian), (2)
socially disapproved and (3) which have a function of social affiliation, are directly adopted
under the influence of the subjective norm. As stated by Manning (2009, p.655-656), when
behaviours have a hedonistic nature for the individual (intrinsically pleasant) they “involve
less cognitive processing [and] may bypass behavioural intentions to a greater extent, making
these behaviours more apt to feature direct effects of subjective norm on behaviour”.
Yet, driving above the speed limit and consuming alcohol are behaviours which can be
strongly hedonistic in nature for certain individuals. In addition, the need to belong/be
affiliated to peer and family groups, and the need to strengthen/maintain the links which unite
them to others, often impel the individual to adopt behaviours for this purpose. This is likely
to promote the direct influence of the subjective norm, as once in context the individual is
likely to act partly independently of their initial intention. Finally this influence will be even
greater according to Manning (2009) if the rest of society condemns the behaviour, as this
reinforces in contrast what the peer/family group thinks and does. This would then become an
important reference point when the moment comes to act and adopt the behaviour which
seems appropriate at the time and to display their adhesion to the group (when it is a question
for example of having another drink to celebrate). This would therefore explain why the
subjective norm has less influence on the usual magnitude of the offence (because at this
Running Head: TRAFFIC OFFENCES: PLANNED OR HABITUAL?
19
moment the individual would for the majority of the time comply with the societal norm and
the law which embodies it) and why it would directly influence its maximal magnitude (to
comply with the norm of the group once in a singular context).
We also observed a direct effect of attitudes on offences (i.e. outside of intentions).
This result is inconsistent with the TPB, which assumes that the effect of attitude on
behaviour is fully mediated by intention. One possible explanation for this finding relies on
the distinction that has been made between “instrumental attitudes” and “affective attitudes
(e.g. Efrat & Shoham, 2013; Elliott et al., 2013; Lawton et al., 2007; Lawton, Parker,
Manstead, & Stradling, 1997). Instrumental attitudes correspond to attitudes as defined by
Ajzen (1991). They result from the evaluation which the individuals have of the most
probable consequences of the behaviour. This evaluation leads the individuals to judge the
behaviour as being more or less beneficial or harmful. The concept of affective attitudes
concerns all emotions associated with the behaviour. In this regard, Loewenstein, Weber, Hsee
and Welch (2001) made a distinction between anticipated emotions, (i.e. the emotions
associated with the expected consequences of the behaviour) and anticipatory emotions (i.e.
the degree to which the idea of performing the behaviour intrinsically elicits pleasant or
unpleasant emotions). According to them, anticipatory emotions have a direct effect on
behaviour, non-mediated by cognitive activity (for a meta-analysis and empirical study of the
overlap between these two types of emotions in the context of the TPB, see Conner,
McEachan, Taylor, O'Hara, & Lawton, 2014). In the same vein, the meta-analysis of Sandberg
and Conner (2008) showed that anticipated regret has a direct effect on behaviour. Likewise,
recent studies by Lawton, Conner, McEachan (2009) and by Connor, Godin, Sheeran and
Germain (2013) confirmed that emotions (affective attitudes, anticipated affective reactions)
frequently have a strong direct effect on behaviour. Thus, the remaining effect of attitudes on
behaviours observed in our study, after the inclusion of intentions in regression analyses, may
be explained by an unexpected effect of the emotions elicited by the target behaviours.
Running Head: TRAFFIC OFFENCES: PLANNED OR HABITUAL?
20
The measure of attitudes used in this study was based on three items: one item with a
general evaluative formulation (positive/negative), one item with a more instrumental
connotation (expressing the overall evaluation of consequences: beneficial/harmful) and one
item with an affective connotation (expressing the overall evaluation of the affectivity
associated with the performance of the behaviour: pleasant/unpleasant). Additional
hierarchical linear regression analyses which distinguished the two instrumental and affective
items in place of the overall measure of attitude corroborated this interpretation, as affective
attitude had no effect on intention, but had a direct effect on behaviour (the effects of other
variables were controlled). Not only did affective attitude have an effect on behaviour distinct
from instrumental attitude, but this effect remained significant and was not diminished after
intention was added to the predictive model. Because of the lack of reliability of one-item
measures, these results were not shown here and must be viewed cautiously (they are
available from the authors on request). Nevertheless, they were consistent with the “risk as
feeling” hypothesis of Loewenstein and collaborators (2001) and highlight the necessity for
further research on that topic with multiple-item measures.
Limitations of the study and advances for further study
The method that has been used in this study presents some limitations. Firstly, the use of selfreported measures of behaviours tends to overestimate the variance explained by the TPB
(Armitage & Conner, 2001; McEachan et al., 2011), because of a possible bias towards
consistency in responses amongst individuals (Budd & Spencer, 1986) and of a bias towards
social desirability (Paulhus, 2002), which will be all the greater if all the measures are carried
out at the same time (Armitage & Conner, 1999b). However, as this type of measure has a
relatively high correlation with real behaviour (Elliott, Armitage, & Baughan, 2007) and these
two biases are limited when studying driving behaviours or measuring TPB constructs
(Armitage & Conner, 1999a, Lajunen & Summala, 2003), the correlations obtained in this
study gave a sufficiently reliable estimation of the existing links between TPB, habit and
Running Head: TRAFFIC OFFENCES: PLANNED OR HABITUAL?
21
behaviour, for the test of the principal hypothesis of this study to be valid.
The second limitation that has to be discussed concerns the use of a cross-sectional
design. Although quite common, this type of design does not permit us to make strong
inferences concerning causal relationships between variables. Nevertheless, as the results
were consistent with our hypothesis, our research legitimates the implementation of a study
with a more powerful although more demanding design (e.g. a longitudinal design similar to
Elliott and collaborators, 2007).
Speeding and drinking and driving are repeated behaviours that may be adopted in a
wide variety of situations and at different times. Our measures of behaviours satisfied the
“repeated-observation criterion” described by Fishbein and Ajzen (1975, p.353), as they
aimed to estimate the general tendency of drivers to break the law across situations and times.
The cognitions that we measured followed their principle of correspondence, as they focus on
the specific behaviour independently from a specific situation and/or a precise time.
Unfortunately, differentiating the three behavioural parameters lowered the correspondence
with the measured cognitions (third limitation). Consequently, the results obtained for each
distinct parameter should be viewed cautiously because of a lesser respect for the principle of
correspondence. Nonetheless, the results obtained towards what we called the “overall
behaviour” were exempt of such a potential bias in measurement/estimation.
Possible future studies concern primarily the overcoming of the above-mentioned
limitations (measuring behaviours directly, using a longitudinal / prospective design, etc.).
Investigating the factors that moderate the relationships between TPB constructs, habits and
driver behaviours would be also of great relevance. For instance, cultural differences along
the individualism-collectivism dimension (e.g. Triandis, 1993) may moderate the attitudeintention and the subjective norm-intention relationships. In individualistic cultures (personal)
attitudes towards traffic offences may be stronger predictors of behavioural intentions than
subjective norms and vice versa for collectivistic cultures. Such a moderating role of cultural
Running Head: TRAFFIC OFFENCES: PLANNED OR HABITUAL?
22
differences was theoretically hypothesised and empirically observed in other domains such as
physical exercise (e.g. Nigg, Lippke, & Maddock, 2009), technology acceptance (e.g. Sun &
Zhang, 2006) and job seeking (e.g. Van Hooft & De Jong, 2009).
Conclusion
This study confirms that habits have a direct impact on offending behaviours (speeding and
drinking and driving), distinct from TPB constructs. In this connection, the use of the SRIH
allowed us to overcome the limitations of the measure of past behaviour. Moreover, the
simultaneous consideration of the three parameters characterising the offences (frequency,
usual magnitude and maximal magnitude) allowed us to test the hypotheses resulting from the
TPB more strongly and demonstrated the relevance of their distinction. The results also
suggest that attitudes and subjective norms have direct effects on the maximal magnitude
and/or the frequency of the offence. Consequently, this research contributes to the scientific
literature concerning traffic offences and, more generally, to the theoretical conceptualisation
of human behaviour, by illustrating empirically its simultaneously intentional and
unintentional (i.e. habitual) nature. Thus, our results highlight the necessity of implementing
policies directly oriented towards the modification of habits in addition to intentions. To that
end, the "downstream-plus" and "upstream" interventions described by Verplanken and Wood
(2006) appear to be particularly suitable as they allow the identification of cues which elicit
habitual behaviours and favour the formation of new habits, all the more so as implementation
intentions (Gollwitzer, 1999) do not change behaviours successfully in the case of individuals
with strong habits (Webb, Sheeran, & Luszczynska, 2009).
References
Åberg, L. (1993). Drinking and driving: Intentions, attitudes, and social norms of Swedish
male drivers. Accident Analysis & Prevention, 25(3), 289–296. doi:10.1016/00014575(93)90023-P
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human
Decision Processes, 50(2), 179–211. doi:10.1016/0749-5978(91)90020-T
Running Head: TRAFFIC OFFENCES: PLANNED OR HABITUAL?
23
Ajzen, I. (2002). Residual Effects of Past on Later Behavior: Habituation and Reasoned
Action Perspectives. Personality and Social Psychology Review, 6(2), 107–122.
doi:10.1207/S15327957PSPR0602_02
Armitage, C. J., & Conner, M. (1999a). Predictive validity of the theory of planned behaviour:
the role of questionnaire format and social desirability. Journal of Community & Applied
Social Psychology, 9(4), 261–272. doi:10.1002/(SICI)10991298(199907/08)9:4<261::AID-CASP503>3.0.CO;2-5
Armitage, C. J., & Conner, M. (1999b). The theory of planned behaviour: Assessment of
predictive validity and ’perceived control. British Journal of Social Psychology, 38(1),
35–54. doi:10.1348/014466699164022
Armitage, C. J., & Conner, M. (2001). Efficacy of the Theory of Planned Behaviour: A metaanalytic review. British Journal of Social Psychology, 40, 471–499.
doi:10.1348/014466601164939
Bayer, J. B., & Campbell, S. W. (2012). Texting while driving on automatic: Considering the
frequency-independent side of habit. Computers in Human Behavior, 28(6), 2083–2090.
doi:10.1016/j.chb.2012.06.012
Brijs, K., Daniels, S., Brijs, T., & Wets, G. (2011). An experimental approach towards the
evaluation of a seat belt campaign with an inside view on the psychology behind seat
belt use. Transportation Research Part F: Traffic Psychology and Behaviour, 14(6),
600–613. doi:10.1016/j.trf.2011.07.003
Budd, R. J., & Spencer, C. P. (1986). Lay theories of behavioural intention: A source of
response bias in the theory of reasoned action? British Journal of Social Psychology,
25(2), 109–117. doi:10.1111/j.2044-8309.1986.tb00709.x
Castanier, C., Deroche, T., & Woodman, T. (2013). Theory of planned behaviour and road
violations: The moderating influence of perceived behavioural control. Transportation
Research Part F: Traffic Psychology and Behaviour, 18, 148–158.
doi:10.1016/j.trf.2012.12.014
Conner, M., & Armitage, C. J. (1998). Extending the Theory of Planned Behavior: A Review
and Avenues for Further Research. Journal of Applied Social Psychology, 28(15), 1429–
1464. doi:10.1111/j.1559-1816.1998.tb01685.x
Conner, M., Godin, G., Sheeran, P., & Germain, M. (2013). Some feelings are more important:
cognitive attitudes, affective attitudes, anticipated affect, and blood donation. Health
Psychology, 32(3), 264–272. doi:10.1037/a0028500
Conner, M., Lawton, R., Parker, D., Chorlton, K., Manstead, A. S. R., & Stradling, S. (2007).
Application of the theory of planned behaviour to the prediction of objectively assessed
breaking of posted speed limits. British Journal of Psychology, 98(3), 429–453.
doi:10.1348/000712606X133597
Conner, M., McEachan, R., Taylor, N., O'Hara, J., & Lawton, J. (2014). Role of affective
attitudes and anticipated affective reactions in predicting health behaviors. Health
Psychology. epub ahead of print.
Running Head: TRAFFIC OFFENCES: PLANNED OR HABITUAL?
24
Cristea, M., Paran, F., & Delhomme, P. (2013). Extending the theory of planned behavior: The
role of behavioral options and additional factors in predicting speed behavior.
Transportation Research Part F: Traffic Psychology and Behaviour, 21, 122–132.
doi:10.1016/j.trf.2013.09.009
De Bruijn, G.-J., Kremers, S. P. J., Singh, A., van den Putte, B., & van Mechelen, W. (2009).
Adult Active Transportation: Adding Habit Strength to the Theory of Planned Behavior.
American Journal of Preventive Medicine, 36(3), 189–194.
doi:10.1016/j.amepre.2008.10.019
De Pelsmacker, P. De, & Janssens, W. (2007). The effect of norms, attitudes and habits on
speeding behavior: Scale development and model building and estimation. Accident
Analysis & Prevention, 39(1), 6–15. doi:10.1016/j.aap.2006.05.011
Delhomme, P., Cristea, M., & Paran, F. (2013). Self-reported frequency and perceived
difficulty of adopting eco-friendly driving behavior according to gender, age, and
environmental concern. Transportation Research Part D: Transport and Environment,
20, 55–58. doi:10.1016/j.trd.2013.02.002
Dinh, D. D., & Kubota, H. (2013). Speeding behavior on urban residential streets with a 30
km/h speed limit under the framework of the theory of planned behavior. Transport
Policy, 29, 199–208. doi:10.1016/j.tranpol.2013.06.003
Efrat, K., & Shoham, A. (2013). The theory of planned behavior, materialism, and aggressive
driving. Accident Analysis & Prevention Prevention, 59, 459–465.
doi:10.1016/j.aap.2013.06.023
Elliott, M. A., & Armitage, C. J. (2009). Promoting drivers’ compliance with speed limits:
Testing an intervention based on the theory of planned behaviour. British Journal of
Psychology, 100, 111–132. doi:10.1348/000712608X318626
Elliott, M. A., Armitage, C. J., & Baughan, C. J. (2003). Drivers’ Compliance With Speed
Limits: An Application of the Theory of Planned Behavior. Journal of Applied
Psychology, 88(5), 964–972. doi:10.1037/0021-9010.88.5.964
Elliott, M. A., Armitage, C. J., & Baughan, C. J. (2007). Using the theory of planned
behaviour to predict observed driving behaviour. British Journal of Social Psychology,
46(1), 69–90. doi:10.1348/014466605X90801
Elliott, M. A., & Thomson, J. A. (2010). The social cognitive determinants of offending
drivers’ speeding behaviour. Accident Analysis & Prevention, 42(6), 1595–1605.
doi:10.1016/j.aap.2010.03.018
Elliott, M. A., Thomson, J. A., Robertson, K., Stephenson, C., & Wicks, J. (2013). Evidence
that changes in social cognitions predict changes in self-reported driver behavior: Causal
analyses of two-wave panel data. Accident Analysis & Prevention, 50, 905–916.
doi:10.1016/j.aap.2012.07.017
Evans, D., & Norman, P. (1998). Understanding pedestrians’ road crossing decisions: an
application of the theory of planned behaviour. Health Education Research, 13(4), 481–
489. Retrieved from http://her.oxfordjournals.org/content/13/4/481.1.full.pdf
Running Head: TRAFFIC OFFENCES: PLANNED OR HABITUAL?
25
Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to
Theory and Research (p. 578). Reading, MA: Addison-Wesley Publishing Company.
Gardner, B. (2009). Modelling motivation and habit in stable travel mode contexts.
Transportation Research Part F: Traffic Psychology and Behaviour, 12(1), 68–76.
doi:10.1016/j.trf.2008.08.001
Gardner, B. (2012). Habit as automaticity, not frequency. The European Health Psychologist,
14(2), 32–36. Retrieved from
http://www.ehps.net/ehp/issues/2012/v14iss2_June2012/14_2_Gardner.pdf
Gollwitzer, P. M. (1999). Implementation intentions: Strong effects of simple plans. American
Psychologist, 54(7), 493–503. doi:10.1037/0003-066X.54.7.493
Holland, C. A., Hill, R., & Cooke, R. (2009). Understanding the role of self-identity in
habitual risky behaviours: pedestrian road-crossing decisions across the lifespan. Health
Education Research, 24(4), 674–685. doi:10.1093/her/cyp003
Horswill, M. S., Waylen, A. E., & Tofield, M. I. (2004). Drivers’ Ratings of Different
Components of Their Own Driving Skill: A Greater Illusion of Superiority for Skills That
Relate to Accident Involvement. Journal of Applied Social Psychology, 34(1), 177–195.
doi:10.1111/j.1559-1816.2004.tb02543.x
Horvath, C., Lewis, I., & Watson, B. (2012). The beliefs which motivate young male and
female drivers to speed: A comparison of low and high intenders. Accident Analysis &
Prevention, 45, 334–341. doi:10.1016/j.aap.2011.07.023
Klein, C. T. F., & Helweg-Larsen, M. (2002). Perceived Control and the Optimistic Bias: A
Meta-Analytic Review. Psychology & Health, 17(4), 437–446.
doi:10.1080/0887044022000004920
Lajunen, T., & Summala, H. (2003). Can we trust self-reports of driving? Effects of
impression management on driver behaviour questionnaire responses. Transportation
Research Part F: Traffic Psychology and Behaviour, 6(2), 97–107. doi:10.1016/S13698478(03)00008-1
Lawton, R., Conner, M., & McEachan, R. (2009). Desire or reason: predicting health
behaviors from affective and cognitive attitudes. Health Psychology, 28(1), 56–65.
doi:10.1037/a0013424
Lawton, R., Conner, M., & Parker, D. (2007). Beyond Cognition: Predicting Health Risk
Behaviors From Instrumental and Affective Beliefs. Health Psychology, 26(3), 259–267.
doi:10.1037/0278-6133.26.3.259
Lawton, R., Parker, D., Manstead, A. S. R., & Stradling, S. G. (1997). The Role of Affect in
Predicting Social Behaviors: The Case of Road Traffic Violations. Journal of Applied
Social Psychology, 27(14), 1258–1276. doi:10.1111/j.1559-1816.1997.tb01805.x
Leandro, M. (2012). Young drivers and speed selection: A model guided by the Theory of
Planned Behavior. Transportation Research Part F: Traffic Psychology and Behaviour,
15(3), 219–232. doi:10.1016/j.trf.2011.12.011
Running Head: TRAFFIC OFFENCES: PLANNED OR HABITUAL?
26
Letirand, F., & Delhomme, P. (2005). Speed behaviour as a choice between observing and
exceeding the speed limit. Transportation Research Part F: Traffic Psychology and
Behaviour, 8(6), 481–492. doi:10.1016/j.trf.2005.06.002
Lheureux, F. (2012). Speeding or not speeding? When subjective assessment of safe,
pleasurable and risky speeds determines speeding behaviour. European Journal of
Psychology Applied to Legal Context, 4(1), 79–98. Retrieved from
http://www.webs.uvigo.es/sepjf/index.php?option=com_docman&task=doc_download&
gid=45&Itemid=110&lang=en
Loewenstein, G. F., Weber, E. U., Hsee, C. K., & Welch, N. (2001). Risk as feelings.
Psychological Bulletin, 127(2), 267–286. doi:10.1037/0033-2909.127.2.267
McEachan, R. R. C., Conner, M., Taylor, N. J., & Lawton, R. J. (2011). Prospective prediction
of health-related behaviours with the Theory of Planned Behaviour: a meta-analysis.
Health Psychology Review, 5(2), 97–144. doi:10.1080/17437199.2010.521684
Manning, M. (2009). The effects of subjective norms on behaviour in the theory of planned
behaviour: a meta-analysis. British Journal of Social Psychology, 48(4), 649–705.
doi:10.1348/014466608X393136
Marcil, I., Bergeron, J., & Audet, T. (2001). Motivational factors underlying the intention to
drink and drive in young male drivers. Journal of Safety Research, 32(4), 363–376.
doi:10.1016/S0022-4375(01)00062-7
McKenna, F. P. (1993). It won’t happen to me: Unrealistic optimism or illusion of control?
British Journal of Psychology, 84(1), 39–50. doi:10.1111/j.2044-8295.1993.tb02461.x
Moan, I. S., & Rise, J. (2011). Predicting intentions not to “drink and drive” using an
extended version of the theory of planned behaviour. Accident Analysis & Prevention,
43(4), 1378–1384. doi:10.1016/j.aap.2011.02.012
Moyano-Diaz, E. (2002). Theory of planned behavior and pedestrians’ intentions to violate
traffic regulations. Transportation Research Part F: Traffic Psychology and Behaviour,
5(3), 169–175. doi:10.1016/S1369-8478(02)00015-3
Nemme, H. E., & White, K. M. (2010). Texting while driving: Psychosocial influences on
young people’s texting intentions and behaviour. Accident Analysis & Prevention, 42(4),
1257–1265. doi:10.1016/j.aap.2010.01.019
Nigg, C. R., Lippke, S., & Maddock, J. E. (2009). Factorial invariance of the theory of
planned behavior applied to physical activity across gender, age, and ethnic groups.
Psychology of Sport and Exercise, 10(2), 219–225.
doi:10.1016/j.psychsport.2008.09.005
Norris, E., & Myers, L. (2013). Determinants of Personal Protective Equipment (PPE) use in
UK motorcyclists: Exploratory research applying an extended theory of planned
behaviour. Accident Analysis & Prevention, 60, 219–230. doi:10.1016/j.aap.2013.09.002
Okamura, K., Fujita, G., Kihira, M., Kosuge, R., & Mitsui, T. (2012). Predicting motivational
determinants of seatbelt non-use in the front seat: A field study. Transportation Research
Running Head: TRAFFIC OFFENCES: PLANNED OR HABITUAL?
27
Part F: Traffic Psychology and Behaviour, 15(5), 502–513.
doi:10.1016/j.trf.2012.05.001
Orbell, S., & Verplanken, B. (2010). The Automatic Component of Habit in Health Behavior:
Habit as Cue-Contingent Automaticity. Health Psychology, 29(4), 374–383.
doi:10.1037/a0019596
Ouellette, J. A., & Wood, W. (1998). Habit and intention in everyday life: The multiple
processes by which past behavior predicts future behavior. Psychological Bulletin,
124(1), 54–74. doi:10.1037/0033-2909.124.1.54
Özkan, T., Lajunen, T., Doğruyol, B., Yıldırım, Z., & Çoymak, A. (2012). Motorcycle
accidents, rider behaviour, and psychological models. Accident Analysis & Prevention,
49, 124–132. doi:10.1016/j.aap.2011.03.009
Palat, B., & Delhomme, P. (2012). What factors can predict why drivers go through yellow
traffic lights? An approach based on an extended Theory of Planned Behavior. Safety
Science, 50(3), 408–417. doi:10.1016/j.ssci.2011.09.020
Parker, D., Lajunen, T., & Stradling, S. (1998). Attitudinal predictors of interpersonally
aggressive violations on the road. Transportation Research Part F: Traffic Psychology
and Behaviour, 1(1), 11–24. doi:10.1016/S1369-8478(98)00002-3
Parker, D., Manstead, A. S. R., Stradling, S. G., Reason, J. T., & Baxter, J. S. (1992). Intention
to Commit Driving Violations: An Application of the Theory of Planned Behavior.
Journal of Applied Psychology, 77(1), 94–101. doi:10.1037/0021-9010.77.1.94
Paulhus, D. L. (2002). Socially Desirable Responding: The Evolution of a Construct. In H. I.
Braun, D. N. Jackson, & D. E. Wiley (Eds.), The role of constructs in psychological and
educational measurement (pp. 49–69). Mahwah, NJ: Lawrence Erlbaum Associates.
Sandberg, T., & Conner, M. (2008). Anticipated regret as an additional predictor in the theory
of planned behaviour: a meta-analysis. British Journal of Social Psychology, 47(4), 589–
606. doi:10.1348/014466607X258704
Schank, R., & Abelson, R. (1977). Scripts, plans, goals, and understanding: An inquiry into
human knowledge structure. Hillsdale, NJ: Lawrence Erlbaum Associates.
Şimşekoğlu, Ö., & Lajunen, T. (2008). Social psychology of seat belt use : A comparison of
theory of planned behavior and health belief model. Transportation Research Part F:
Traffic Psychology and Behaviour, 11, 181–191. doi:10.1016/j.trf.2007.10.001
Stead, M., Tagg, S., Mackintosh, A. M., & Eadie, D. (2005). Development and evaluation of a
mass media Theory of Planned Behaviour intervention to reduce speeding. Health
Education Research, 20(1), 36–50. doi:10.1093/her/cyg093
Steg, L., & van Brussel, A. (2009). Accidents, aberrant behaviours, and speeding of young
moped riders. Transportation Research Part F: Traffic Psychology and Behaviour, 12(6),
503–511. doi:10.1016/j.trf.2009.09.001
Sun, H., & Zhang, P. (2006). The role of moderating factors in user technology acceptance.
International Journal of Human-Computer Studies, 64(2), 53–78.
Running Head: TRAFFIC OFFENCES: PLANNED OR HABITUAL?
28
doi:10.1016/j.ijhcs.2005.04.013
Triandis, H. C. (1993). Collectivism and Individualism as Cultural Syndromes. CrossCultural Research, 27(3-4), 155–180. doi:10.1177/106939719302700301
Tseng, C., Chang, H., & Woo, T. H. (2013). Modeling motivation and habit in driving
behavior under lifetime driver’s license revocation. Accident Analysis & Prevention, 51,
260–267. doi:10.1016/j.aap.2012.11.017
Van Hooft, E. A. J., & De Jong, M. (2009). Predicting job seeking for temporary employment
using the theory of planned behaviour: The moderating role of individualism and
collectivism. Journal of Occupational and Organizational Psychology, 82(2), 295–316.
doi:10.1348/096317908X325322
Verplanken, B. (2006). Beyond frequency: Habit as mental construct. British Journal of
Social Psychology, 45(3), 639–656. doi:10.1348/014466605X49122
Verplanken, B., & Aarts, H. (1999). Habit, Attitude, and Planned Behaviour: Is Habit an
Empty Construct or an Interesting Case of Goal-directed Automaticity? European
Review of Social Psychology, 10, 101–134. doi:10.1080/14792779943000035
Verplanken, B., & Faes, S. (1999). Good intentions, bad habits, and effects of forming
implementation intentions on healthy eating. European Journal of Social Psychology,
29(5-6), 591–604. doi:10.1002/(SICI)1099-0992(199908/09)29:5/6<591::AIDEJSP948>3.0.CO;2-H
Verplanken, B., Herabadi, A. G., Perry, J. A., & Silvera, D. H. (2005). Consumer style and
health: The role of impulsive buying in unhealthy eating. Psychology & Health, 20(4),
429–441. doi:10.1080/08870440412331337084
Verplanken, B., & Orbell, S. (2003). Reflections on Past Behavior: A Self-Report Index of
Habit Strength. Journal of Applied Social Psychology, 33(6), 1313–1330.
doi:10.1111/j.1559-1816.2003.tb01951.x
Verplanken, B., & Wood, W. (2006). Interventions to Break and Create Consumer Habits.
Journal of Public Policy & Marketing, 25(1), 90–103. doi:10.1509/jppm.25.1.90
Wallén Warner, H., Özkan, T., & Lajunen, T. (2009). Cross-cultural differences in drivers’
speed choice. Accident Analysis & Prevention, 41(4), 816–819.
doi:10.1016/j.aap.2009.04.004
Walsh, S. P., White, K. M., Hyde, M. K., & Watson, B. (2008). Dialling and driving: Factors
influencing intentions to use a mobile phone while driving. Accident Analysis &
Prevention, 40(6), 1893–1900. doi:10.1016/j.aap.2008.07.005
Webb, T. L., Sheeran, P., & Luszczynska, A. (2009). Planning to break unwanted habits: habit
strength moderates implementation intention effects on behaviour change. British
Journal of Social Psychology, 48(3), 507–523. doi:10.1348/014466608X370591
White, M. J., Cunningham, L. C., & Titchener, K. (2011). Young drivers’ optimism bias for
accident risk and driving skill: Accountability and insight experience manipulations.
Accident Analysis & Prevention, 43(4), 1309–1315. doi:10.1016/j.aap.2011.01.013
Running Head: TRAFFIC OFFENCES: PLANNED OR HABITUAL?
29
World Health Organization. (2013). Global status report on road safety 2013. World Health
Organization. Retrieved from
http://www.who.int/violence_injury_prevention/road_safety_status/2013/en/index.html
Xu, Y., Li, Y., & Zhang, F. (2013). Pedestrians’ intention to jaywalk: Automatic or planned? A
study based on a dual-process model in China. Accident Analysis & Prevention, 50, 811–
819. doi:10.1016/j.aap.2012.07.007
Zhou, R., Wu, C., Rau, P. P., & Zhang, W. (2009). Young driving learners’ intention to use a
handheld or hands-free mobile phone when driving. Transportation Research Part F:
Traffic Psychology and Behaviour, 12(3), 208–217. doi:10.1016/j.trf.2008.11.003
Running Head: TRAFFIC OFFENCES: PLANNED OR HABITUAL?
30
Table 1. Characteristics of the participants
Sex (% Women)
53
Age (Mean/Standard Deviation)
34.3 / 14.2
Frequency of use of the vehicle (%)
3 days/week or less
24.7
Living as a couple (%)
61
4-5 days/week
41.1
Is a parent (%)
42
6-7 days/week
34.2
Employment situation (%)
Type of users (%)
In employment
56
Car drivers
100
Job seekers
1.1
Motorcyclists (>50cc)
10
Retired
4.7
Motorcyclists (<50cc)
4
Students
38
HGV drivers
2
Professional status (%) a
Recency of licence (%)
Management, senior executive
17.5
Middle management, team leader
33.4
White collar worker
41.1
Between 10 and 19 years
16.2
Labourer
6.2
Between 20 and 29 years
17.6
Self-employed
1.8
30 years or more
19.7
Educational level (%)
Less than 10 years
- including less than 2 years
46.9
13.9
Number of Km/year (%)
Below A level
6.3
Less than 10,000
29.6
A level
25.2
Between 10,000 and 15,000
23.9
Post A level
68.5
Between 15,000 and 20,000
19.3
Between 20,000 and 25,000
11.9
Between 25,000 and 30,000
6.5
More than 30,000
8.8
Legal situation (%)
Had lost licence points
b
22.8
Had completed a points recovery
course
3.3
Licence already withdrawn on
one occasion
2.9
a
If in employment or last employment status if retired
b
In France, every driver with a driving licence starts with a maximum of 12 points. Each
offence causes them to lose a number of points. When all the points are lost the licence is
withdrawn. The driver has the option of attending a road safety information and awareness
course to recover points before having their licence withdrawn.
Running Head: TRAFFIC OFFENCES: PLANNED OR HABITUAL?
31
Table 2. Means, standard deviations and internal homogeneity of measures (Cronbach’s alpha)
2.77
5.11
5.03
5.39
4.88
6.54
6.43
6.76
6.39
1.87
1.39
1.60
2.04
Standard
deviation
1.60
1.25
1.30
1.45
1.71
1.41
1.44
1.72
1.77
1.45
0.79
0.96
1.00
/
.78
/
/
/
.82
/
/
/
.93
.80
.75
.62
1.96
1.39
.68
1.44
0.56
1.06
2.40
0.39
0.35
0.61
1.61
0.92
1.21
2.49
0.70
0.58
0.74
.91
/
/
/
.90
.80
.71
1.41
1.30
.67
Perceived control of respect for
zero alcohol
3.33
1.12
.63
Habit a
0.21
0.71
.92
Mean
Frequency a
Usual deviation (total) b
Conurbation b
Country road b
Motorway b
Maximal deviation (total) b
Conurbation b
Country road b
Speeding
Motorway b
Intention a
Attitude c
Subjective norm c
Perceived control of danger c
Perceived control of total
respect for the law c
Habit a
Frequency a
Usual number of drinks
Maximal number of drinks
Intention a
c
Drinking and Attitude
Subjective norm c
driving
Perceived control of danger c
a
Alpha
7 point scale (0 to 6)
11 point scale (0 to 10). Note: 4 = “exactly on the limit”, for more details on coding see
method
c
5 point scale (0 to 4)
d
alphas obtained with the three measures (frequency, usual magnitude and maximal
magnitude) after standardisation
b
.87 d
.87 d
Running Head: TRAFFIC OFFENCES: PLANNED OR HABITUAL?
32
Table 3. Results of hierarchical multiple regression analyses (β, R², F): intention and overall
behaviour as dependent variables
Intention
Overall behaviour
Step 1
Step 2
Step 1
Step 2
Step 3
/
/
.65***
.53***
.40***
Perceived control of danger
.12***
.08**
.03
.003
-.01
Perceived control of respect
-.39***
-.26***
-.18***
-.14***
-.10**
Attitude
.22***
.12***
/
.16***
.12***
Subjective norm
.27***
.18***
/
.08*
.05
/
.40***
/
/
.27***
.60
.68
.61
.64
.67
/
.08***
.03***
.03***
/
/
.59***
.49***
.40***
Perceived control of danger
.05
.04
-.01
-.02
-.02
Perceived control of respect
-.27***
-.22***
-.24***
-.21***
-.19***
Attitude
.23***
.22***
/
.13***
.13***
Subjective norm
.34***
.26***
/
.12***
.09**
/
.27***
/
/
.19***
.43
.49
.53
.56
.59
/
.06***
.03***
.03***
Speeding
Intention
Habit
R²
ΔR²
Drinking and driving
Intention
Habit
R²
ΔR²
* p < .05 ; ** p < .01 ; *** p < .00
Running Head: TRAFFIC OFFENCES: PLANNED OR HABITUAL?
33
Table 4. Results of hierarchical multiple regression analyses (β, R², F): frequency, usual and maximal magnitudes as dependent variables
Frequency
Usual Magnitude
Maximal Magnitude
Step 1
Step 2
Step 3
Step 1
Step 2
Step 3
Step 1
Step 2
Step 3
.70***
.59***
.45***
.49***
.42***
.31***
.54***
.41***
.29***
Perceived control of danger
.04
.01
.001
-.01
-.02
-.03
.05
.02
.01
Perceived control of respect
-.15***
-.11***
-.08**
-.16***
-.14***
-.11*
-.15***
-.11**
-.07
Attitude
/
.16***
.12***
/
.11*
.08
/
.17***
.14***
Subjective norm
/
.06
.04
/
.03
.01
/
.12**
.09*
Habit
/
/
.28***
/
/
.22***
/
/
.23***
.67
.69
.72
.36
.37
.39
.45
.48
.50
.02***
.03***
.01*
.02***
.03***
.02***
.62***
.51***
.43***
.45***
.37***
.31***
.51***
.41***
.34***
Perceived control of danger
.02
.01
.01
-.03
-.03
-.03
-.02
-.03
-.03
Perceived control of respect
-.23***
-.20***
-.18***
-.20***
-.17***
-.16***
-.21***
-.18***
-.17***
Attitude
/
.11***
.11***
/
.12**
.12**
/
.12**
.12***
Subjective norm
/
.13***
.11***
/
.07
.05
/
.11**
.09*
Habit
/
/
.20***
/
/
.14***
/
/
.16***
.56
.59
.62
.32
.34
.36
.40
.42
.44
.03***
.03***
.02***
.02***
.02***
.02***
Speeding
Intention
R²
ΔR²
Drinking and driving
Intention
R²
ΔR²
* p < .05 ; ** p < .01 ; *** p < .001