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Emotional intelligence and risky driving behaviour in adults
Transportation Research Part F 49 (2017) 124–131
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Transportation Research Part F journal homepage: www.elsevier.com/locate/trf
Emotional intelligence and risky driving behaviour in adults Amie C. Hayley a, Byron de Ridder a, Con Stough a, Talitha C. Ford a, Luke A. Downey a,b,⇑ a b
Centre for Human Psychopharmacology, Swinburne University of Technology, Hawthorn, Australia Institute for Breathing and Sleep, Austin Hospital, Melbourne, Australia
a r t i c l e
i n f o
Article history: Received 26 November 2015 Received in revised form 1 March 2017 Accepted 9 June 2017
Keywords: Emotional intelligence Emotional control Risk Risky driving behaviour Personality
a b s t r a c t Background: Risky driving is considered a key predictive factor in road traffic accidents resulting in morbidity and mortality. Intra-individual emotional and personality factors have been shown to influence risk-taking behaviours among drivers. Despite this, there is limited research investigating the individual dimensions of these constructs and their relationship to risky driving behaviour (RDB). The current study therefore aimed to assess whether the individual dimensions of Emotional Intelligence (EI) were implicated in RDB. Methods: The sample comprised 179 adults (55% male) aged between 18 and 64 years (M = 29.85, SD = 11.46) and who currently held a valid driver’s licence completed an online survey. Emotional Intelligence was assessed via self-report using the Swinburne University Emotional Intelligence Test (SUEIT), and RDB was measured using both the Brief Distracted Driving Scale (BDDS) and the Dula Dangerous Driving Index (DDDI). Results: Regression analyses revealed that ‘Risky Driving’ was related to greater levels of Emotional Recognition and Expression and lesser Age [F(6, 172) = 2.27, p < 0.05: R2 = 7.3%], and the Negative Emotions sub-scale of DDDI, was significantly predicted by Emotional Control and Age [F(6, 172) = 6.41, p < 0.05: R2 = 18.3%]. A mediation model incorporating Age, Emotional Control scores and the Negative Emotions driving behaviour score indicated that a significant indirect effect of Age through Emotional Control (K2 = 0.08, 95% CI [ 0.02, 0.03]). (H [3] = 10.98, p = 0.012). Conclusions: Lower scores on specific indices of EI are associated with increased rates of RDB, suggesting that poor emotional control may impede an individuals’ ability to make safe behavioural decisions when driving. The effect sizes for these models were small, however, and further research is needed to explore the contributory components in this association. Greater awareness of the role of emotional regulation and driving behaviours may be useful in preventing RDB in adults. Ó 2017 Elsevier Ltd. All rights reserved.
1. Introduction Road traffic accidents constitute a significant source of morbidity and mortality among the general population, and both the economic and personal costs of these events are considerable. Engagement in risky driving behaviour (RDB) is a leading cause of preventable road-traffic incidents, and is recognised to increase the likelihood of accidents and crashes leading to both injuries and material damage for both the driver and other road users. The role of a number of external contributory factors in engagement in RDB have been examined in some detail; however, emerging research has highlighted the possible ⇑ Corresponding author at: Centre for Human Psychopharmacology, Swinburne University of Technology, Hawthorn, Australia. E-mail address: [email protected] (L.A. Downey). http://dx.doi.org/10.1016/j.trf.2017.06.009 1369-8478/Ó 2017 Elsevier Ltd. All rights reserved.
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modifiable role of individual differences (e.g., emotional intelligence) as a determinant of these behaviours (Rivers, Brackett, Sickler, & Salovey, 2013; Wozniak, 2013). Characterising the role of individual differences in producing risky driving behaviours may have implications for future assessments of estimated crash risk among these individuals. Risky driving behaviours are defined as any that may increase the probability of a car accident on the road (Dula & Geller, 2003). This includes aggressive driving, speeding, tailgating, failing to wear a seatbelt, driving under the influence of alcohol or other drugs, and driving while fatigued or distracted (Scott-Parker, 2012). Road accidents can sometimes be unavoidable, such as those caused by low driver skill, inexperience, mechanical failure, or environmental factors (Laflamme, Hasselberg, Kullgren, & Vaez, 2006; Scott-Parker, 2012), however, many fatal accidents are caused by both intentional and unintentional risk-taking. A recent analysis conducted by Beanland, Fitzharris, Young, and Lenné (2013) examined the prevalence of high risk choices leading to serious road accidents for 340 randomly selected, serious car crashes in Victoria and New South Wales. All incidents observed in the research involved hospitalisation due to crash related injuries, with many involving fatalities. Using data obtained from forensic examination of fatal crash scenes, anonymous witness accounts, and police reports between the years 2000 and 2011, Beanland and colleagues reported that 57.6% of car crashes involved some form of driver inattention or distraction. Driver intoxication (13.5%), driver falling asleep (11.8%), and driver fatigue (10.9%) were the three most commonly cited causes of car accidents – these are all examples of preventable risky driving behaviours (Beanland et al., 2013; Lam, 2003; Scott-Parker, 2012; Willemsen, Dula, Declercq, & Verhaeghe, 2008). Extant research on how risk-taking is related to driving behaviour suggests that risk-taking is at its highest with young drivers, and decreases as drivers become older (Jessor, Turbin, & Costa, 1997; Vassallo et al., 2013). This coincides with patterns in road fatality rates, with the likelihood of fatal car accidents reducing as driver age increases (Bureau of Infrastructure, 2013). Although driver age and driver experience a tightly linked, the parallels between risk-taking behaviour of young adults both on and off the roads and national fatality rates suggests that risk-taking is a contributing factor in the over-representation of young adult road fatalities (Beanland et al., 2013; Bingham, Shope, Zakrajsek, & Raghunathan, 2008; Vassallo et al., 2013; Jessor et al., 1997; Jonah, 1986). RDBs, such as reckless speeding and using mobile phones while driving, is mostly a deliberate driver choice (Scott-Parker, 2012); and these behavioural choices seem more prevalent in younger drivers. Emerging adulthood is therefore a period of life associated with heightened risk of physical injury (Bingham et al., 2008); gravely demonstrated by the over-representation of 18 to 25 years olds in road fatalities. This risk of physical injury from general risk-taking declines with age due to a variety of factors, such as engagement in adult roles (Schulenberg, Sameroff, & Cicchetti, 2004), and the maturation of brain, cognitive and emotional facilities (Rivers, Brackett, Sickler, & Salovey, 2013). Whilst people’s age is a significant predictive factor in risk-taking behaviour on the road, it must be acknowledged that factors such as driving experience and other individual differences that impact risk-taking propensity can influence the types of actions and behaviours people display on the road. Studies have demonstrated a relationship between age (Jonah, 1986), emotional and/or personality factors (Ulleberg & Rundmo, 2003) and the likelihood of engaging in risky driving behaviours as strong predictors of future accident risk (Norris, Matthews, & Riad, 2000). Such studies typically focus on the role of overt personality traits, such as excitement or adventure-seeking (Iversen & Rundmo, 2002; Machin & Sankey, 2008), temperament (Vassallo et al., 2007), or more transient emotional states, such as anger (Dula & Ballard, 2003), anxiety (Ulleberg, 2001), and positive affect towards traffic violations (Lawton, Parker, Manstead, & Stradling, 1997) as indicators of past, present and future accident risk. At present, a limited, albeit increasing, amount of research has suggested the possible mediating role of the expression of emotion with regard to relative accident risk (Deery & Fildes, 1999) and driving performance (Hancock, Hancock, & Janelle, 2012). This indicates the usefulness of characterising emotional regulation and expression as a marker of an individuals’ likelihood of engaging in risk-taking behaviour whilst driving. How drivers react to transient mood states whilst driving (e.g., anger, frustration, anxiety or nervousness) may depend upon the emotional regulation skills encompassing emotional intelligence (EI: Gohm, 2003). EI refers to the skills and attributes that enable someone to monitor personal and other people’s emotions, discriminate between different emotions, label emotions, and use emotions to guide behaviour or thinking (Salovey & Mayer, 1989). Research has demonstrated that higher levels of EI are associated with greater skill in anger control and improved ability to appropriately moderate immediate mood states and subsequent behaviour (Sahin Baltaci & Demir, 2012). Complementary, albeit limited studies have similarly indicated that levels of EI are related to an individual’s propensity to engage in risky behaviours, specifically emotional regulation and/or control (Hancock et al., 2012), with lower levels of EI often associated with greater engagement in risk taking (Rivers et al., 2013; Wozniak, 2013). Furthermore, EI appears to develop with age (Luebbers, Downey, & Stough, 2008), which is a known mediator of risk-taking behaviour (Jonah, 1986). One potentially predictive aspect of EI is the dimension of emotional control; which is defined as one’s ability to control strong emotions, such as anger and frustration. A reduced ability to control strong emotions whilst driving may predispose people to act in an ill-conceived or risky manner; whether by driving more dangerously, or engaging in unnecessarily aggressive driving behaviours. This conceptualisation shares some overlap with previous studies examining the predictive role of impulsivity in relation to driver behaviour and accident involvement (e.g., Pearson, Murphy, & Doane, 2013), where greater levels of impulsiveness have been found to be associated with reduced levels of emotional control whilst driving, and increased levels of dangerous activities (e.g., mobile phone usage) and outcomes (e.g., poorer driving ability, traffic fines and collisions) whilst driving. In sum, given the existing evidence, it is possible that better emotional regulation, thus higher EI, may reduce risky driving behaviours whilst driving. To date, extensive literature suggests a strong relationship between risk-taking behaviour and driver-related road-traffic accidents (Beanland et al., 2013; Lam, 2003; Scott-Parker, 2012; Willemsen et al., 2008), and between risk-taking behaviour
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and EI (Rivers et al., 2013; Wozniak, 2013), and emerging research suggests a relationship between RDBs and emotional and behavioural factors (Pearson et al., 2013). There is limited research investigating explicitly the relationship between EI and risky driving behaviour, however. Therefore, the aim of the current study was to assess the role of the dimensions of EI in the expression of RDBs, and to further elucidate the possible contributory role of age with regard to the expression of these in a sample of Australian drivers. 2. Methods 2.1. Participants We utilised a sample of 179 Australian adults (55% male) aged between 18 and 64 years (M = 29.85, SD = 11.46). Approximately half (51%) of the sample were aged 18–26 years. Inclusion criteria for the current study required participants to be aged 18 years of age, currently hold a valid provisional or full Australian driver’s licence and report English as a first language. Participants involved in this study were primarily recruited from the Swinburne University’s undergraduate student population; however snowballing techniques were also employed to widen the study demographic and to include older drivers. This study was approved by Swinburne University’s Human Research Ethics Committee (SUHREC), and written informed consent was obtained from each participant. 2.2. Procedure Participants completed an anonymous online web survey, which consisted of a demographic questionnaire for age, sex and country of birth, as well as additional questionnaires designed to measure levels of EI, their driving experience and driving behaviour. Data was collected via the web-based Opinio online surveying platform (ObjectPlanet, 1998–2016), and assessment measures were presented in the following order: the Brief Distracted Driving Scale (BDDS: Eastman, 2013), the Dula Dangerous Driving Index (DDDI: Dula, 2003), and the Swinburne University Emotional Intelligence Test (SUEIT: Palmer & Stough, 2001). 2.3. Risky driving behaviour (RDB) RDBs were assessed using two self-report measures which assess aspects of distracted and dangerous driving behaviours: 2.3.1. Distracted driving behaviours Driver distraction was assessed by the BDDS (Eastman, 2013), which is a five-item questionnaire assessing how often the participant performs behaviours that distracts drivers while driving in a car (e.g., configuring a GPS device). Responses for four of the items are presented on a 6-point Likert scale from Never to >75%, and one item was a dichotomous yes/no response. Responses to the four Likert scale items were coded numerically for regression analyses from 0(None) to 5 (>75%) to yield a total score out of 20. Previous psychometric evaluation of the BDDS has demonstrated high internal reliability, with a Cronbach’s a coefficient of 0.88 (Eastman, 2013). 2.3.2. Dangerous driving behaviours Dangerous driving behaviours were assessed by the DDDI (Dula, 2003). The DDDI is a self-report questionnaire that predicts driver’s propensity to engage in dangerous driving behaviour, which has been adapted to measure other risky, aggressive or negative emotional driving behaviours (Dula, 2003). The DDDI consists of 28 items on a 5-point Likert scale from 1 (Almost Never) to 5(Almost Always); scores are summed to produce a DDDI total score from 28–140. Items are categorised into three sub-scales: Risky Driving (12 items, score 12–60), Negative Emotions (while driving, nine items, score 9–45), and Aggressive Driving (seven items, score 7–35). The DDDI and its sub-scales have demonstrated high internal reliability with a Cronbach’s a coefficients from 0.83 to 0.92 (Dula, 2003). 2.4. Emotional Intelligence The SUEIT (Palmer & Stough, 2001) was utilised to assess each participant’s level of EI and its dimensions. The SUEIT is a self-report questionnaire containing 64 items presented on a 5-point Likert scale from 1(Almost Never) to 5(Almost Always). Originally, the SUEIT was designed to measure the participant’s perception of their feelings, thoughts and actions, based on their workplace experience (Palmer & Stough, 2001). Thirty of the 64 items are reverse-scored, before scores are summed across items for a total score from 64–320. Higher scores indicate better performance in the EI and its dimensions. The SUEIT comprises five sub-scales; Emotional Control (nine items, score 9–45), Emotional Management (12 items, score 12–60), Emotions Direct Cognition (12 items, score 12–60), Emotional Recognition and Expression (11 items, score 11–55), and Understanding the Emotions of Others (20 items, score 20–100). Briefly, Emotional Control assesses the ability to effectively control strong emotional states experienced such as anger, stress, anxiety and frustration. Emotional Management assesses the ability to manage positive and negative emotions both within oneself and others. Emotions Direct Cognition
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assesses the extent to which emotions and emotional knowledge is incorporated in decision-making and/or problem solving. Emotional Recognition and Expression assesses the ability to identify one’s own feelings and emotional states, and the ability to express those inner feelings to others. Finally, Understanding the Emotions of Others assesses the ability to identify and understand the emotions of others and those manifest in external stimuli (i.e., workplace environments, staff meetings, literature, artwork etc.). Psychometric assessment of the SUEIT has yielded good overall internal reliability (Cronbach’s a = 0.91), with sub-scale reliabilities ranging from 0.78 to 0.86 (Downey et al., 2008). Test–retest reliabilities have similarly been found to range between 0.82 and 0.95 for a one month retest period (Palmer & Stough, 2001). 2.5. Statistical analyses Multiple linear regressions were conducted to evaluate how well the EI measures predicted RDB as measured by both the DDDI and BDDS. All SUEIT sub-score were entered simultaneously into the model, along with the participants’ age. To assess the mediating effect of the EI sub-scale Emotional Control on the relationship between age and Negative Emotions, the Preacher and Hayes (2004), Preacher and Hayes (2008) method of assessing mediation was utilised. They advocate utilising the indirect effect, i.e., the combined effects of the ‘a’ and ‘b’ path, to establish whether mediation has occurred. In these analyses, mediation is significant if the 95% confidence intervals for the indirect effect do not include 0 (Preacher & Hayes, 2008). Effect sizes are measured with kappa-squared (K2), which expressed the indirect effect as a ratio to the maximum possible indirect effect possible in a study (Preacher & Kelley, 2011). A K2 of 0 indicates no indirect effect relative to the maximum possible indirect effect, while K2 of 1 indicates a that the indirect effect is equivalent to the maximum possible indirect effect (Preacher & Kelley, 2011). All statistical analyses were completed using IBM SPSS version 23 (IBM Corp., 2015), and all tests were two-tailed with conventional p < 0.05 as significance threshold. 3. Results 3.1. Characteristics Of the 179 participants, 157 reported to being born in Australia, with the remaining 22 being born in 17 different countries. The majority (97%) of the sample indicated that English was their primary language. For the whole sample, total years of independent driving experience ranged from less than 1 year to 46 years (M = 11.32 years, SD = 11.63). 3.2. Emotional Intelligence and driving behaviours The mean scores for DDDI, BDDS, and SUEIT scales were consistent with previous work (Dula, 2003; Eastman, 2013; Wan, Downey, & Stough, 2014). The means, standard deviations and internal reliability for the DDDI scale, and BDDS and SUEIT sub-scales appear in Table 1. Initial correlational assessments of the RDB scales indicated that the DDDI sub-scales and BDDS were significantly, positively related (see Table 2). Examination of the relationship between EI sub-scales and the RDBs indicated that the Emotional Control dimension of the SUEIT was significantly, negatively related to the Aggressive Driving and Negative Emotions driving behaviours (see Table 2). As expected. Participants’ age was weak to moderately, significantly, inversely related to DDDI sub-scales and BDDS total (see Table 2). Standard multiple regression analyses were undertaken to determine the total, shared, and unique amounts of variance in the four driving behaviours attributable to levels of the EI dimensions and the age of participants. All sub-scales were entered simultaneously, and a summary of the respective regression analyses appears in Table 3. With regard to the regression concerning the level of Risky Driving, lesser Emotional Recognition and Expression and lesser age emerged as significant predictors of Risky Driving, and the model predicted 7.3% of the variation in Risky Driving (F(6, 172) = 2.27, p < 0.05). For Aggressive Driving, the regression model was not significant, and only predicted 6.4% of the variation in Aggressive Driving (F(6, 172)
Table 1 Means (M) and Standard Deviations (SD) of the questionnaire indices. Variable
M
SD
Emotional recognition and expression Understanding the emotions of others Emotions direct cognition Emotional management Emotional control Negative emotions Aggressive driving Risky driving Distracted driving
37.28 72.69 34.01 37.27 29.93 21.42 10.55 16.55 6.92
45.85 9.43 5.28 5.68 4.86 5.62 4.01 5.10 2.27
Note: N = 179.
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Table 2 Bivariate correlations between study variables.
(1) Negative emotions (2) Aggressive driving (3) Risky driving (4) Brief distracted driving scale (5) Emotional recognition and expression (6) Understanding others emotion (7) Emotions direct cognition (8) Emotional management (9) Emotional control (10) Age
1
2
3
– .62* .59** .44** .07 .09 .18* .19* .33** .30**
– .56** .42** .08 .02 .11 .06 .17* .19*
– .30** .11 .04 .07 .07 .04 .18*
4
5
6
7
8
9
10
– .52** .27** .39** .22** .05
– .29** .44** .21** .15*
– .04 .26** .05
– .56** .16*
– .29**
–
– .02 .11 .15* .04 .12 .28**
Note: N = 179. * p < .05. ** p < .01.
Table 3 Regression analyses of driving behaviours and distracted driving with emotional intelligence. Beta
t
p-value
Partial correlations
Risky driving Emotional recognition & expression Understanding emotions Emotions direct cognition Emotional management Emotional control Age
.195 .013 .098 .177 .019 .183
3.868 2.179 .134 1.171 1.797 .191 2.291
.000 .031 .893 .243 .074 .849 .023
.160 .010 .086 .132 .014 .168
Aggressive driving Emotional recognition & expression Understanding emotions Emotions direct cognition Emotional management Emotional control Age
.102 .023 .093 .048 .112 .154
3.963 1.128 .237 1.108 .481 1.140 1.916
.000 .261 .813 .269 .631 .256 .057
.083 .018 .082 .036 .084 .141
Negative emotions Emotional recognition & expression Understanding emotions Emotions direct cognition Emotional management Emotional control Age
.093 .162 .091 .061 .237 .184
6.336 1.099 1.801 1.161 .654 -2.580 2.459
.000 .000 .273 .073 .247 .514 .011
.076 .124 .080 .045 .178 .170
Distracted driving Emotional recognition & expression Understanding emotions Emotions direct cognition Emotional management Emotional control Age
.098 .037 .129 .146 .076 .260
3.386 1.113 .398 1.579 1.515 .791 3.334
.001 .267 .691 .116 .132 .430 .001
.080 .029 .113 .109 .057 .240
= 1.96, p > 0.05). For Negative Emotions, Emotional Recognition and Expression and age were found to be significant predictors, and the model predicted 18.3% of the variation in Negative Emotions scores (F(6, 172) = 6.41, p < 0.05). For distracted driving, only the participants age emerged as a significant predictor, with none of the EI dimensions reaching significance in the model. The model was found to predict 11.2% of the variation in distracted driving (F(6, 172) = 3.62, p < 0.05). To further examine the role of Emotional Control on the DDDI sub-scale Negative Emotions, a mediation model incorporating age, Emotional Control score and Negative Emotions score was conducted (see Fig. 1). As can be seen in Fig. 1, there was a significant indirect effect of age through Emotional Control upon Negative Emotions while driving. There was also a small to medium effect for Emotional Recognition and Expression (K2 = 0.08, 95% CI [ 0.02, 0.03]). 4. Discussion The present study provides the first assessment of the relationship between driver age, level of EI, and dangerous and distracted driving behaviour. Younger age was a significant predictor of a higher degree of distracted driving, risky driving and negative emotions associated with driving, and was associated with a lesser degree of EI dimensions in relation to under-
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Emotional Control
-.31**
.12***
Age
Negative Emotions -.11* (-.04*) 2 Indirect effect, K = .08, 95% CI [-.07, -.02]
Fig. 1. Mediational model of the effect of emotional control on age and negative emotions.
standing others emotions, and management and control of own emotions. Greater levels of Emotional Recognition and Expression also emerged as a significant predictor of Risky Driving and Negative Emotions while driving. A mediation model indicated that there was a significant indirect effect of age through the EI sub-scale Emotional Control upon the DDDI subscale Negative Emotions, suggesting as we age our negative driving emotions decrease as our levels of emotional control improve. Together, these results suggest there are links between an individual’s age, emotional processing abilities and the behaviours they demonstrate on the road. As highlighted in the current study, age is a significant predictor of behavioural and psychological outcomes associated with driving. Specifically, driver age has been shown to be closely related to the relative risk of having road-traffic accidents, as well as the likelihood of engaging in risky or distracted driving behaviours. This is evident among both young (16– 19 years) and older (75+ years) cohorts being overly represented in the number of fatalities and accidents on the road (Massie, Campbell, & Williams, 1995; McGwin & Brown, 1999). We report an inverse relationship between age and selfreported RDBs, with increased age predicting lower scores on both the BDDS and DDDI dimensions, except Aggressive Driving. This is comparable to similar research which has suggested that the relative risk of reporting a road-traffic accident due to subjective or human factors, as well as the likelihood off engaging in RDB, decreases incrementally as a function of increasing age (Jessor et al., 1997; Jonah, 1986). This finding suggests that drivers seem to become more law abiding and take less risks on the road as they get older, ostensibly because they grow to recognise the risk (emotionally and physically), which is consistent with findings from other international studies (e.g., Karlaftis & Golias, 2002; Willemsen et al., 2008). Although there was a relationship between the degree of aggressive driving and age, with aggressive driving decreasing slightly as age increased, age was only a trend predictor of aggressive driving in the regression model. The direct association between aspects of EI and measures of dangerous and distracted driving behaviours have insofar been unstudied. Nonetheless, research assessing the role of EI in other risk taking behaviour has noted a significant association between emotional regulation, specifically the ability to regulate negative emotions, and engagement in risky behaviours (such as gambling: Wang, Hsu, Lin, Cheng, & Lee, 2010; West & Hall, 1997). Contrary to our expectations, the dimensions of EI did not predict participants’ degree of distracted driving, and the EI dimension assessing the degree to which emotions direct an individual’s cognitions was shown to slightly increase with more distracted driving. These data suggest therefore, that one’s degree EI is more likely to contribute to the emotional dimensions of dangerous driving behaviour (i.e., negative emotions while driving and aggressive driving), than taking greater risks per se. It is feasible to speculate that emotional regulation may influence RDBs by attenuating or inhibiting the expression of strong emotions such as anxiety, anger or hostility, of which has been previously associated with increased driving risk (Oltedal & Rundmo, 2006; Ulleberg, 2001; Ulleberg & Rundmo, 2003). As the reported associations were seen in this study to occur alongside the increasing age of participants; it is probable that maturational factors related to emotional control interact to moderate this relationship. We demonstrated that when assessed in isolation, there was a significant association between age and three EI dimensions, with emotional control and management shown to improve with age, while the ability to understand the emotions of others was shown to decrease slightly with age. Despite the association between EI and RDBs above, when assessed as singular constructs, Emotional Recognition and Expression was the only EI dimension to predict RBDs, specifically the DDDI sub-scales Risky Driving and Negative Emotions. The finding that peoples’ inability to accurately identify what emotions they may be experiencing and expressing them accurately produces risky on-road behaviours such as speeding, weaving in and out of traffic, or drink driving parallels findings from the impulsivity and driving behaviour literature (e.g., Pearson et al., 2013). Lower levels of self-reported emotional awareness and expression has been associated with engaging in a behaviour without giving the situation enough consideration (e.g., passing too close a car or truck), and this can result in engaging in behaviours that are dangerous, ill-conceived and result in erratic car driving. The mediation model confirmed the inter-relationship with increasing age and the development of Emotional Control reducing the experience of Negative Emotions when driving. Other aspects of EI were observed to be less predictive of the assessed driving behaviours such as distractibility; suggesting that for driving; only the ability to control strong emotions translates to actual on-road behaviour. Given the items that assess the Negative Emotions dimension refer to losing one’s
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temper, getting angry or irritated; reduced levels of Emotional Control would likely account for people experiencing these emotions, and then engage in more dangerous or risky driving behaviours. The findings of the current study must be considered in light of some methodological limitations. Firstly, the use of selfreport measures to assess aspects of both EI and RDB may lead to some degree of self-report bias. Despite this, assessments of these factors among similar cohorts have typically utilised comparable self-report measures when assessing emotional and/or personality factors (e.g. Schwebel, Severson, Ball, & Rizzo, 2006; Vassallo et al., 2007). Moreover, self-report measures of RDB have been shown to be reflective of driving risk and ability, as these measures have been shown to correlate with objective assessment of dangerous and/or risky driving, such as accidents, weaving between traffic and speed deviations as assessed by driving simulator tasks (Reimer, D’ambrosio, Coughlin, Kafrissen, & Biederman, 2006). We acknowledge the homogeneity of the study sample, and thus concede that the reported findings may not be applicable to other driving populations, such as professional drivers. Nonetheless, we included a relatively large age-range, and the number of younger and older drivers was comparably distributed, thereby improving the generalisability of these results. Lastly, the effect sizes of the reported associations were relatively small, thereby suggesting that a significant amount of variability of these findings is due to other, uncharacterised factors. As additional dimensions of personality and/or emotional factors (e.g., impulsivity) were not assessed as part of this study, it is possible that these aspects may, in part, account for these results. Assessment of the human factors related to driving, including the complex interrelationship of driving behaviour, attitudes and emotion with accident involvement represents a growing area of research. The findings of this study provide evidence for the need to incorporate emotional intelligence, particularly drivers’ ability to recognise, express and control emotions, when investigating drivers’ RDB, particularly for young adults. Furthermore, characterising the relative impact of emotional factors as a predictive factor in RDB may have implications for the development of risk-profiles for individuals. Ultimately, driver programs that assess and develop EI might be helpful to reduce the degree of risky behaviours carried out by drivers, especially those in early adulthood. 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Transportation Research Part F journal homepage: www.elsevier.com/locate/trf
Emotional intelligence and risky driving behaviour in adults Amie C. Hayley a, Byron de Ridder a, Con Stough a, Talitha C. Ford a, Luke A. Downey a,b,⇑ a b
Centre for Human Psychopharmacology, Swinburne University of Technology, Hawthorn, Australia Institute for Breathing and Sleep, Austin Hospital, Melbourne, Australia
a r t i c l e
i n f o
Article history: Received 26 November 2015 Received in revised form 1 March 2017 Accepted 9 June 2017
Keywords: Emotional intelligence Emotional control Risk Risky driving behaviour Personality
a b s t r a c t Background: Risky driving is considered a key predictive factor in road traffic accidents resulting in morbidity and mortality. Intra-individual emotional and personality factors have been shown to influence risk-taking behaviours among drivers. Despite this, there is limited research investigating the individual dimensions of these constructs and their relationship to risky driving behaviour (RDB). The current study therefore aimed to assess whether the individual dimensions of Emotional Intelligence (EI) were implicated in RDB. Methods: The sample comprised 179 adults (55% male) aged between 18 and 64 years (M = 29.85, SD = 11.46) and who currently held a valid driver’s licence completed an online survey. Emotional Intelligence was assessed via self-report using the Swinburne University Emotional Intelligence Test (SUEIT), and RDB was measured using both the Brief Distracted Driving Scale (BDDS) and the Dula Dangerous Driving Index (DDDI). Results: Regression analyses revealed that ‘Risky Driving’ was related to greater levels of Emotional Recognition and Expression and lesser Age [F(6, 172) = 2.27, p < 0.05: R2 = 7.3%], and the Negative Emotions sub-scale of DDDI, was significantly predicted by Emotional Control and Age [F(6, 172) = 6.41, p < 0.05: R2 = 18.3%]. A mediation model incorporating Age, Emotional Control scores and the Negative Emotions driving behaviour score indicated that a significant indirect effect of Age through Emotional Control (K2 = 0.08, 95% CI [ 0.02, 0.03]). (H [3] = 10.98, p = 0.012). Conclusions: Lower scores on specific indices of EI are associated with increased rates of RDB, suggesting that poor emotional control may impede an individuals’ ability to make safe behavioural decisions when driving. The effect sizes for these models were small, however, and further research is needed to explore the contributory components in this association. Greater awareness of the role of emotional regulation and driving behaviours may be useful in preventing RDB in adults. Ó 2017 Elsevier Ltd. All rights reserved.
1. Introduction Road traffic accidents constitute a significant source of morbidity and mortality among the general population, and both the economic and personal costs of these events are considerable. Engagement in risky driving behaviour (RDB) is a leading cause of preventable road-traffic incidents, and is recognised to increase the likelihood of accidents and crashes leading to both injuries and material damage for both the driver and other road users. The role of a number of external contributory factors in engagement in RDB have been examined in some detail; however, emerging research has highlighted the possible ⇑ Corresponding author at: Centre for Human Psychopharmacology, Swinburne University of Technology, Hawthorn, Australia. E-mail address: [email protected] (L.A. Downey). http://dx.doi.org/10.1016/j.trf.2017.06.009 1369-8478/Ó 2017 Elsevier Ltd. All rights reserved.
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modifiable role of individual differences (e.g., emotional intelligence) as a determinant of these behaviours (Rivers, Brackett, Sickler, & Salovey, 2013; Wozniak, 2013). Characterising the role of individual differences in producing risky driving behaviours may have implications for future assessments of estimated crash risk among these individuals. Risky driving behaviours are defined as any that may increase the probability of a car accident on the road (Dula & Geller, 2003). This includes aggressive driving, speeding, tailgating, failing to wear a seatbelt, driving under the influence of alcohol or other drugs, and driving while fatigued or distracted (Scott-Parker, 2012). Road accidents can sometimes be unavoidable, such as those caused by low driver skill, inexperience, mechanical failure, or environmental factors (Laflamme, Hasselberg, Kullgren, & Vaez, 2006; Scott-Parker, 2012), however, many fatal accidents are caused by both intentional and unintentional risk-taking. A recent analysis conducted by Beanland, Fitzharris, Young, and Lenné (2013) examined the prevalence of high risk choices leading to serious road accidents for 340 randomly selected, serious car crashes in Victoria and New South Wales. All incidents observed in the research involved hospitalisation due to crash related injuries, with many involving fatalities. Using data obtained from forensic examination of fatal crash scenes, anonymous witness accounts, and police reports between the years 2000 and 2011, Beanland and colleagues reported that 57.6% of car crashes involved some form of driver inattention or distraction. Driver intoxication (13.5%), driver falling asleep (11.8%), and driver fatigue (10.9%) were the three most commonly cited causes of car accidents – these are all examples of preventable risky driving behaviours (Beanland et al., 2013; Lam, 2003; Scott-Parker, 2012; Willemsen, Dula, Declercq, & Verhaeghe, 2008). Extant research on how risk-taking is related to driving behaviour suggests that risk-taking is at its highest with young drivers, and decreases as drivers become older (Jessor, Turbin, & Costa, 1997; Vassallo et al., 2013). This coincides with patterns in road fatality rates, with the likelihood of fatal car accidents reducing as driver age increases (Bureau of Infrastructure, 2013). Although driver age and driver experience a tightly linked, the parallels between risk-taking behaviour of young adults both on and off the roads and national fatality rates suggests that risk-taking is a contributing factor in the over-representation of young adult road fatalities (Beanland et al., 2013; Bingham, Shope, Zakrajsek, & Raghunathan, 2008; Vassallo et al., 2013; Jessor et al., 1997; Jonah, 1986). RDBs, such as reckless speeding and using mobile phones while driving, is mostly a deliberate driver choice (Scott-Parker, 2012); and these behavioural choices seem more prevalent in younger drivers. Emerging adulthood is therefore a period of life associated with heightened risk of physical injury (Bingham et al., 2008); gravely demonstrated by the over-representation of 18 to 25 years olds in road fatalities. This risk of physical injury from general risk-taking declines with age due to a variety of factors, such as engagement in adult roles (Schulenberg, Sameroff, & Cicchetti, 2004), and the maturation of brain, cognitive and emotional facilities (Rivers, Brackett, Sickler, & Salovey, 2013). Whilst people’s age is a significant predictive factor in risk-taking behaviour on the road, it must be acknowledged that factors such as driving experience and other individual differences that impact risk-taking propensity can influence the types of actions and behaviours people display on the road. Studies have demonstrated a relationship between age (Jonah, 1986), emotional and/or personality factors (Ulleberg & Rundmo, 2003) and the likelihood of engaging in risky driving behaviours as strong predictors of future accident risk (Norris, Matthews, & Riad, 2000). Such studies typically focus on the role of overt personality traits, such as excitement or adventure-seeking (Iversen & Rundmo, 2002; Machin & Sankey, 2008), temperament (Vassallo et al., 2007), or more transient emotional states, such as anger (Dula & Ballard, 2003), anxiety (Ulleberg, 2001), and positive affect towards traffic violations (Lawton, Parker, Manstead, & Stradling, 1997) as indicators of past, present and future accident risk. At present, a limited, albeit increasing, amount of research has suggested the possible mediating role of the expression of emotion with regard to relative accident risk (Deery & Fildes, 1999) and driving performance (Hancock, Hancock, & Janelle, 2012). This indicates the usefulness of characterising emotional regulation and expression as a marker of an individuals’ likelihood of engaging in risk-taking behaviour whilst driving. How drivers react to transient mood states whilst driving (e.g., anger, frustration, anxiety or nervousness) may depend upon the emotional regulation skills encompassing emotional intelligence (EI: Gohm, 2003). EI refers to the skills and attributes that enable someone to monitor personal and other people’s emotions, discriminate between different emotions, label emotions, and use emotions to guide behaviour or thinking (Salovey & Mayer, 1989). Research has demonstrated that higher levels of EI are associated with greater skill in anger control and improved ability to appropriately moderate immediate mood states and subsequent behaviour (Sahin Baltaci & Demir, 2012). Complementary, albeit limited studies have similarly indicated that levels of EI are related to an individual’s propensity to engage in risky behaviours, specifically emotional regulation and/or control (Hancock et al., 2012), with lower levels of EI often associated with greater engagement in risk taking (Rivers et al., 2013; Wozniak, 2013). Furthermore, EI appears to develop with age (Luebbers, Downey, & Stough, 2008), which is a known mediator of risk-taking behaviour (Jonah, 1986). One potentially predictive aspect of EI is the dimension of emotional control; which is defined as one’s ability to control strong emotions, such as anger and frustration. A reduced ability to control strong emotions whilst driving may predispose people to act in an ill-conceived or risky manner; whether by driving more dangerously, or engaging in unnecessarily aggressive driving behaviours. This conceptualisation shares some overlap with previous studies examining the predictive role of impulsivity in relation to driver behaviour and accident involvement (e.g., Pearson, Murphy, & Doane, 2013), where greater levels of impulsiveness have been found to be associated with reduced levels of emotional control whilst driving, and increased levels of dangerous activities (e.g., mobile phone usage) and outcomes (e.g., poorer driving ability, traffic fines and collisions) whilst driving. In sum, given the existing evidence, it is possible that better emotional regulation, thus higher EI, may reduce risky driving behaviours whilst driving. To date, extensive literature suggests a strong relationship between risk-taking behaviour and driver-related road-traffic accidents (Beanland et al., 2013; Lam, 2003; Scott-Parker, 2012; Willemsen et al., 2008), and between risk-taking behaviour
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and EI (Rivers et al., 2013; Wozniak, 2013), and emerging research suggests a relationship between RDBs and emotional and behavioural factors (Pearson et al., 2013). There is limited research investigating explicitly the relationship between EI and risky driving behaviour, however. Therefore, the aim of the current study was to assess the role of the dimensions of EI in the expression of RDBs, and to further elucidate the possible contributory role of age with regard to the expression of these in a sample of Australian drivers. 2. Methods 2.1. Participants We utilised a sample of 179 Australian adults (55% male) aged between 18 and 64 years (M = 29.85, SD = 11.46). Approximately half (51%) of the sample were aged 18–26 years. Inclusion criteria for the current study required participants to be aged 18 years of age, currently hold a valid provisional or full Australian driver’s licence and report English as a first language. Participants involved in this study were primarily recruited from the Swinburne University’s undergraduate student population; however snowballing techniques were also employed to widen the study demographic and to include older drivers. This study was approved by Swinburne University’s Human Research Ethics Committee (SUHREC), and written informed consent was obtained from each participant. 2.2. Procedure Participants completed an anonymous online web survey, which consisted of a demographic questionnaire for age, sex and country of birth, as well as additional questionnaires designed to measure levels of EI, their driving experience and driving behaviour. Data was collected via the web-based Opinio online surveying platform (ObjectPlanet, 1998–2016), and assessment measures were presented in the following order: the Brief Distracted Driving Scale (BDDS: Eastman, 2013), the Dula Dangerous Driving Index (DDDI: Dula, 2003), and the Swinburne University Emotional Intelligence Test (SUEIT: Palmer & Stough, 2001). 2.3. Risky driving behaviour (RDB) RDBs were assessed using two self-report measures which assess aspects of distracted and dangerous driving behaviours: 2.3.1. Distracted driving behaviours Driver distraction was assessed by the BDDS (Eastman, 2013), which is a five-item questionnaire assessing how often the participant performs behaviours that distracts drivers while driving in a car (e.g., configuring a GPS device). Responses for four of the items are presented on a 6-point Likert scale from Never to >75%, and one item was a dichotomous yes/no response. Responses to the four Likert scale items were coded numerically for regression analyses from 0(None) to 5 (>75%) to yield a total score out of 20. Previous psychometric evaluation of the BDDS has demonstrated high internal reliability, with a Cronbach’s a coefficient of 0.88 (Eastman, 2013). 2.3.2. Dangerous driving behaviours Dangerous driving behaviours were assessed by the DDDI (Dula, 2003). The DDDI is a self-report questionnaire that predicts driver’s propensity to engage in dangerous driving behaviour, which has been adapted to measure other risky, aggressive or negative emotional driving behaviours (Dula, 2003). The DDDI consists of 28 items on a 5-point Likert scale from 1 (Almost Never) to 5(Almost Always); scores are summed to produce a DDDI total score from 28–140. Items are categorised into three sub-scales: Risky Driving (12 items, score 12–60), Negative Emotions (while driving, nine items, score 9–45), and Aggressive Driving (seven items, score 7–35). The DDDI and its sub-scales have demonstrated high internal reliability with a Cronbach’s a coefficients from 0.83 to 0.92 (Dula, 2003). 2.4. Emotional Intelligence The SUEIT (Palmer & Stough, 2001) was utilised to assess each participant’s level of EI and its dimensions. The SUEIT is a self-report questionnaire containing 64 items presented on a 5-point Likert scale from 1(Almost Never) to 5(Almost Always). Originally, the SUEIT was designed to measure the participant’s perception of their feelings, thoughts and actions, based on their workplace experience (Palmer & Stough, 2001). Thirty of the 64 items are reverse-scored, before scores are summed across items for a total score from 64–320. Higher scores indicate better performance in the EI and its dimensions. The SUEIT comprises five sub-scales; Emotional Control (nine items, score 9–45), Emotional Management (12 items, score 12–60), Emotions Direct Cognition (12 items, score 12–60), Emotional Recognition and Expression (11 items, score 11–55), and Understanding the Emotions of Others (20 items, score 20–100). Briefly, Emotional Control assesses the ability to effectively control strong emotional states experienced such as anger, stress, anxiety and frustration. Emotional Management assesses the ability to manage positive and negative emotions both within oneself and others. Emotions Direct Cognition
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assesses the extent to which emotions and emotional knowledge is incorporated in decision-making and/or problem solving. Emotional Recognition and Expression assesses the ability to identify one’s own feelings and emotional states, and the ability to express those inner feelings to others. Finally, Understanding the Emotions of Others assesses the ability to identify and understand the emotions of others and those manifest in external stimuli (i.e., workplace environments, staff meetings, literature, artwork etc.). Psychometric assessment of the SUEIT has yielded good overall internal reliability (Cronbach’s a = 0.91), with sub-scale reliabilities ranging from 0.78 to 0.86 (Downey et al., 2008). Test–retest reliabilities have similarly been found to range between 0.82 and 0.95 for a one month retest period (Palmer & Stough, 2001). 2.5. Statistical analyses Multiple linear regressions were conducted to evaluate how well the EI measures predicted RDB as measured by both the DDDI and BDDS. All SUEIT sub-score were entered simultaneously into the model, along with the participants’ age. To assess the mediating effect of the EI sub-scale Emotional Control on the relationship between age and Negative Emotions, the Preacher and Hayes (2004), Preacher and Hayes (2008) method of assessing mediation was utilised. They advocate utilising the indirect effect, i.e., the combined effects of the ‘a’ and ‘b’ path, to establish whether mediation has occurred. In these analyses, mediation is significant if the 95% confidence intervals for the indirect effect do not include 0 (Preacher & Hayes, 2008). Effect sizes are measured with kappa-squared (K2), which expressed the indirect effect as a ratio to the maximum possible indirect effect possible in a study (Preacher & Kelley, 2011). A K2 of 0 indicates no indirect effect relative to the maximum possible indirect effect, while K2 of 1 indicates a that the indirect effect is equivalent to the maximum possible indirect effect (Preacher & Kelley, 2011). All statistical analyses were completed using IBM SPSS version 23 (IBM Corp., 2015), and all tests were two-tailed with conventional p < 0.05 as significance threshold. 3. Results 3.1. Characteristics Of the 179 participants, 157 reported to being born in Australia, with the remaining 22 being born in 17 different countries. The majority (97%) of the sample indicated that English was their primary language. For the whole sample, total years of independent driving experience ranged from less than 1 year to 46 years (M = 11.32 years, SD = 11.63). 3.2. Emotional Intelligence and driving behaviours The mean scores for DDDI, BDDS, and SUEIT scales were consistent with previous work (Dula, 2003; Eastman, 2013; Wan, Downey, & Stough, 2014). The means, standard deviations and internal reliability for the DDDI scale, and BDDS and SUEIT sub-scales appear in Table 1. Initial correlational assessments of the RDB scales indicated that the DDDI sub-scales and BDDS were significantly, positively related (see Table 2). Examination of the relationship between EI sub-scales and the RDBs indicated that the Emotional Control dimension of the SUEIT was significantly, negatively related to the Aggressive Driving and Negative Emotions driving behaviours (see Table 2). As expected. Participants’ age was weak to moderately, significantly, inversely related to DDDI sub-scales and BDDS total (see Table 2). Standard multiple regression analyses were undertaken to determine the total, shared, and unique amounts of variance in the four driving behaviours attributable to levels of the EI dimensions and the age of participants. All sub-scales were entered simultaneously, and a summary of the respective regression analyses appears in Table 3. With regard to the regression concerning the level of Risky Driving, lesser Emotional Recognition and Expression and lesser age emerged as significant predictors of Risky Driving, and the model predicted 7.3% of the variation in Risky Driving (F(6, 172) = 2.27, p < 0.05). For Aggressive Driving, the regression model was not significant, and only predicted 6.4% of the variation in Aggressive Driving (F(6, 172)
Table 1 Means (M) and Standard Deviations (SD) of the questionnaire indices. Variable
M
SD
Emotional recognition and expression Understanding the emotions of others Emotions direct cognition Emotional management Emotional control Negative emotions Aggressive driving Risky driving Distracted driving
37.28 72.69 34.01 37.27 29.93 21.42 10.55 16.55 6.92
45.85 9.43 5.28 5.68 4.86 5.62 4.01 5.10 2.27
Note: N = 179.
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Table 2 Bivariate correlations between study variables.
(1) Negative emotions (2) Aggressive driving (3) Risky driving (4) Brief distracted driving scale (5) Emotional recognition and expression (6) Understanding others emotion (7) Emotions direct cognition (8) Emotional management (9) Emotional control (10) Age
1
2
3
– .62* .59** .44** .07 .09 .18* .19* .33** .30**
– .56** .42** .08 .02 .11 .06 .17* .19*
– .30** .11 .04 .07 .07 .04 .18*
4
5
6
7
8
9
10
– .52** .27** .39** .22** .05
– .29** .44** .21** .15*
– .04 .26** .05
– .56** .16*
– .29**
–
– .02 .11 .15* .04 .12 .28**
Note: N = 179. * p < .05. ** p < .01.
Table 3 Regression analyses of driving behaviours and distracted driving with emotional intelligence. Beta
t
p-value
Partial correlations
Risky driving Emotional recognition & expression Understanding emotions Emotions direct cognition Emotional management Emotional control Age
.195 .013 .098 .177 .019 .183
3.868 2.179 .134 1.171 1.797 .191 2.291
.000 .031 .893 .243 .074 .849 .023
.160 .010 .086 .132 .014 .168
Aggressive driving Emotional recognition & expression Understanding emotions Emotions direct cognition Emotional management Emotional control Age
.102 .023 .093 .048 .112 .154
3.963 1.128 .237 1.108 .481 1.140 1.916
.000 .261 .813 .269 .631 .256 .057
.083 .018 .082 .036 .084 .141
Negative emotions Emotional recognition & expression Understanding emotions Emotions direct cognition Emotional management Emotional control Age
.093 .162 .091 .061 .237 .184
6.336 1.099 1.801 1.161 .654 -2.580 2.459
.000 .000 .273 .073 .247 .514 .011
.076 .124 .080 .045 .178 .170
Distracted driving Emotional recognition & expression Understanding emotions Emotions direct cognition Emotional management Emotional control Age
.098 .037 .129 .146 .076 .260
3.386 1.113 .398 1.579 1.515 .791 3.334
.001 .267 .691 .116 .132 .430 .001
.080 .029 .113 .109 .057 .240
= 1.96, p > 0.05). For Negative Emotions, Emotional Recognition and Expression and age were found to be significant predictors, and the model predicted 18.3% of the variation in Negative Emotions scores (F(6, 172) = 6.41, p < 0.05). For distracted driving, only the participants age emerged as a significant predictor, with none of the EI dimensions reaching significance in the model. The model was found to predict 11.2% of the variation in distracted driving (F(6, 172) = 3.62, p < 0.05). To further examine the role of Emotional Control on the DDDI sub-scale Negative Emotions, a mediation model incorporating age, Emotional Control score and Negative Emotions score was conducted (see Fig. 1). As can be seen in Fig. 1, there was a significant indirect effect of age through Emotional Control upon Negative Emotions while driving. There was also a small to medium effect for Emotional Recognition and Expression (K2 = 0.08, 95% CI [ 0.02, 0.03]). 4. Discussion The present study provides the first assessment of the relationship between driver age, level of EI, and dangerous and distracted driving behaviour. Younger age was a significant predictor of a higher degree of distracted driving, risky driving and negative emotions associated with driving, and was associated with a lesser degree of EI dimensions in relation to under-
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Emotional Control
-.31**
.12***
Age
Negative Emotions -.11* (-.04*) 2 Indirect effect, K = .08, 95% CI [-.07, -.02]
Fig. 1. Mediational model of the effect of emotional control on age and negative emotions.
standing others emotions, and management and control of own emotions. Greater levels of Emotional Recognition and Expression also emerged as a significant predictor of Risky Driving and Negative Emotions while driving. A mediation model indicated that there was a significant indirect effect of age through the EI sub-scale Emotional Control upon the DDDI subscale Negative Emotions, suggesting as we age our negative driving emotions decrease as our levels of emotional control improve. Together, these results suggest there are links between an individual’s age, emotional processing abilities and the behaviours they demonstrate on the road. As highlighted in the current study, age is a significant predictor of behavioural and psychological outcomes associated with driving. Specifically, driver age has been shown to be closely related to the relative risk of having road-traffic accidents, as well as the likelihood of engaging in risky or distracted driving behaviours. This is evident among both young (16– 19 years) and older (75+ years) cohorts being overly represented in the number of fatalities and accidents on the road (Massie, Campbell, & Williams, 1995; McGwin & Brown, 1999). We report an inverse relationship between age and selfreported RDBs, with increased age predicting lower scores on both the BDDS and DDDI dimensions, except Aggressive Driving. This is comparable to similar research which has suggested that the relative risk of reporting a road-traffic accident due to subjective or human factors, as well as the likelihood off engaging in RDB, decreases incrementally as a function of increasing age (Jessor et al., 1997; Jonah, 1986). This finding suggests that drivers seem to become more law abiding and take less risks on the road as they get older, ostensibly because they grow to recognise the risk (emotionally and physically), which is consistent with findings from other international studies (e.g., Karlaftis & Golias, 2002; Willemsen et al., 2008). Although there was a relationship between the degree of aggressive driving and age, with aggressive driving decreasing slightly as age increased, age was only a trend predictor of aggressive driving in the regression model. The direct association between aspects of EI and measures of dangerous and distracted driving behaviours have insofar been unstudied. Nonetheless, research assessing the role of EI in other risk taking behaviour has noted a significant association between emotional regulation, specifically the ability to regulate negative emotions, and engagement in risky behaviours (such as gambling: Wang, Hsu, Lin, Cheng, & Lee, 2010; West & Hall, 1997). Contrary to our expectations, the dimensions of EI did not predict participants’ degree of distracted driving, and the EI dimension assessing the degree to which emotions direct an individual’s cognitions was shown to slightly increase with more distracted driving. These data suggest therefore, that one’s degree EI is more likely to contribute to the emotional dimensions of dangerous driving behaviour (i.e., negative emotions while driving and aggressive driving), than taking greater risks per se. It is feasible to speculate that emotional regulation may influence RDBs by attenuating or inhibiting the expression of strong emotions such as anxiety, anger or hostility, of which has been previously associated with increased driving risk (Oltedal & Rundmo, 2006; Ulleberg, 2001; Ulleberg & Rundmo, 2003). As the reported associations were seen in this study to occur alongside the increasing age of participants; it is probable that maturational factors related to emotional control interact to moderate this relationship. We demonstrated that when assessed in isolation, there was a significant association between age and three EI dimensions, with emotional control and management shown to improve with age, while the ability to understand the emotions of others was shown to decrease slightly with age. Despite the association between EI and RDBs above, when assessed as singular constructs, Emotional Recognition and Expression was the only EI dimension to predict RBDs, specifically the DDDI sub-scales Risky Driving and Negative Emotions. The finding that peoples’ inability to accurately identify what emotions they may be experiencing and expressing them accurately produces risky on-road behaviours such as speeding, weaving in and out of traffic, or drink driving parallels findings from the impulsivity and driving behaviour literature (e.g., Pearson et al., 2013). Lower levels of self-reported emotional awareness and expression has been associated with engaging in a behaviour without giving the situation enough consideration (e.g., passing too close a car or truck), and this can result in engaging in behaviours that are dangerous, ill-conceived and result in erratic car driving. The mediation model confirmed the inter-relationship with increasing age and the development of Emotional Control reducing the experience of Negative Emotions when driving. Other aspects of EI were observed to be less predictive of the assessed driving behaviours such as distractibility; suggesting that for driving; only the ability to control strong emotions translates to actual on-road behaviour. Given the items that assess the Negative Emotions dimension refer to losing one’s
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temper, getting angry or irritated; reduced levels of Emotional Control would likely account for people experiencing these emotions, and then engage in more dangerous or risky driving behaviours. The findings of the current study must be considered in light of some methodological limitations. Firstly, the use of selfreport measures to assess aspects of both EI and RDB may lead to some degree of self-report bias. Despite this, assessments of these factors among similar cohorts have typically utilised comparable self-report measures when assessing emotional and/or personality factors (e.g. Schwebel, Severson, Ball, & Rizzo, 2006; Vassallo et al., 2007). Moreover, self-report measures of RDB have been shown to be reflective of driving risk and ability, as these measures have been shown to correlate with objective assessment of dangerous and/or risky driving, such as accidents, weaving between traffic and speed deviations as assessed by driving simulator tasks (Reimer, D’ambrosio, Coughlin, Kafrissen, & Biederman, 2006). We acknowledge the homogeneity of the study sample, and thus concede that the reported findings may not be applicable to other driving populations, such as professional drivers. Nonetheless, we included a relatively large age-range, and the number of younger and older drivers was comparably distributed, thereby improving the generalisability of these results. Lastly, the effect sizes of the reported associations were relatively small, thereby suggesting that a significant amount of variability of these findings is due to other, uncharacterised factors. As additional dimensions of personality and/or emotional factors (e.g., impulsivity) were not assessed as part of this study, it is possible that these aspects may, in part, account for these results. Assessment of the human factors related to driving, including the complex interrelationship of driving behaviour, attitudes and emotion with accident involvement represents a growing area of research. The findings of this study provide evidence for the need to incorporate emotional intelligence, particularly drivers’ ability to recognise, express and control emotions, when investigating drivers’ RDB, particularly for young adults. Furthermore, characterising the relative impact of emotional factors as a predictive factor in RDB may have implications for the development of risk-profiles for individuals. Ultimately, driver programs that assess and develop EI might be helpful to reduce the degree of risky behaviours carried out by drivers, especially those in early adulthood. 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