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Thrill and adventure seeking in risky driving at work: The moderating role of safety climate
Journal of Safety Research 63 (2017) 83–89
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Journal of Safety Research journal homepage: www.elsevier.com/locate/jsr
Thrill and adventure seeking in risky driving at work: The moderating role of safety climate Darren Wishart, Klaire Somoray, ⁎ Amanda Evenhuis Centre for Accident Research and Road Safety Queensland (CARRS-Q), Institute for Health and Biomedical Innovation, School of Psychology, Queensland University of Technology, Brisbane, Australia
a r t i c l e
i n f o
Article history: Received 21 March 2017 Received in revised form 30 June 2017 Accepted 17 August 2017 Available online 6 September 2017 Keywords: Risky driving behaviors Road safety Occupational safety Traffic psychology Organizational behavior
a b s t r a c t Introduction Within many industrialized countries, the leading cause of worker fatalities and serious injuries can be attributed to road trauma. In non-occupational research, high levels of sensation seeking personality, and specifically thrill and adventure seeking, have been associated with risky driving behaviors. In work driving literature, high organizational safety climate has been associated with reduced risky driving in work drivers. However, the extent that factors such as safety climate and thrill seeking interact in regard to work driving safety remains unclear, and the current research examined this interaction. Methods A total of 1,011 work drivers from four organizations participated in the research. Surveys were distributed online and hardcopies were sent via mail. The survey included measures of thrill and adventure seeking, safety climate and work-related driving behaviors, as well as questions relating to participant demographics and information about their work driving. Results The results demonstrated that safety climate significantly moderated the effect of thrill and adventure seeking trait on driving errors, driving violations, and driving while fatigued. Conclusion These results suggest that the development of a strong safety climate has the potential to improve work driving safety outcomes by reducing the impact of particular personality traits such as thrill seeking within an organizational context. Practical application To improve work driving safety, organizations and management need to develop strategies to encourage and foster positive work driving safety climate, particularly within work settings that may attract thrill and adventure seeking employees. © 2017 Published by Elsevier Ltd.
1. Introduction A high proportion of the traffic activity in Australia involves workrelated driving. The Australian Bureau of Statistics (2015) reported that 18% of the total number of kilometers traveled by Australians in 2014 were business-related and 25.4% involved travel to and from work. In addition, previous research also indicates the majority of new vehicles purchased within Australia were for organizational fleet use (Australiasian Fleet Managers Association, 2008). Given the representation of vehicles used for work, it is not surprising that a high proportion of road crashes in Australia are work-related and that crashes have been identified as the leading cause of fatalities at work (Safe Work Australia, 2015; Wishart, Rowland, Freeman, & Davey, 2011b). Safe Work Australia (2015) reported that two-thirds of the total work-related fatalities in Australian organizations involved a vehicle (e.g., vehicle collision, rollover of non-road vehicle) over the 2003 to 2015 period (Safe Work Australia, 2015). On average, around 30% to 35% of work fatalities are attributed to a vehicle collision each year (Safe Work ⁎ Corresponding author at: School of Psychology and Counselling, Queensland University of Technology, 130 Victoria Park Road, Kelvin Grove, QLD 4059, Brisbane, Australia. E-mail address: [email protected] (K. Somoray).
http://dx.doi.org/10.1016/j.jsr.2017.08.007 0022-4375/© 2017 Published by Elsevier Ltd.
Australia, 2015). This finding is similar to other industrialized countries such as France (Fort, Ndagire, Gadegbeku, Hours, & Charbotel, 2016) and the United States (Bureau of Labor Statistics, 2016). In addition to these human costs, work-related road crash injuries and fatalities place a noticeable financial burden on the Australian government and the organizations (Davey & Banks, 2005). Davey and Banks (2005) estimated that a work-related crash costs organizations an average of $28,000 for each insurance claim. These crashes also have indeterminate costs, not necessarily factored into cost calculations to businesses and to the community, such as absence from work, compensation, and loss of productivity due to down time (Murray, Newnam, Watson, Davey, & Schonfeld, 2003). Despite the alarming statistics and the legislative requirement for organizations to manage employees' safety while driving for work, previous research suggests that organizations often fail to adequately manage this risk with the same commitment as other workplace hazards (Rowland, Davey, Freeman, & Wishart, 2008; Wishart, 2015; Wishart et al., 2011b; Wishart, Rowland, Freeman, & Davey, 2011a). One of the challenges associated with managing work driving safety is the complexity of the inter-related issues that influence work driving safety as demonstrated by the Occupational Light Vehicle (OLV) Use Systems Model (Stuckey, Lamontagne, Glass, & Sim, 2010). The OLV-use Systems Model highlights five levels of influence which consist
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of: the driver, immediate physical environment (the vehicle), external environment (road), organizational environment, and external influences (policy and legislation). This model has been used within the work driving safety research to better understand the complex factors outside the direct control of the driver (e.g., organizational environment) as well as the drivers' characteristics (e.g., gender, driving exposure) that can have an influence on their safety while driving for work (Stuckey et al., 2010; Wishart, 2015). In an attempt to better understand these inter-related influences, researchers have investigated the impact of psychosocial and personality factors within the work driving setting (e.g., Newnam & Watson, 2011; Seibokaite & Endriulaitiene, 2012; Wills, Watson, & Biggs, 2006; Wills, Watson, & Biggs, 2009). For instance, when considering the interaction between personality, safety climate, and work motivation and their effect on risky driving practices, inferences made by Seibokaite and Endriulaitiene (2012) suggest that “socially-oriented” (i.e., those high on agreeableness, conscientiousness, extraversion, and openness) drivers are more likely to drive safely if their perception of safety climate was high and they have high work motivation. This argument suggests that safety climate may have the potential to mitigate work driving risk by exerting a positive influence on the relationship between various personality factors (e.g., thrill and adventure seeking) and work driving behaviors. The role of dispositional characteristics on occupational safety incidents has been explored by a number of researchers (e.g., Clarke & Robertson, 2005, 2008). However, recent studies have shown that only particular facets of personality have more influential associations with occupational safety behaviors (Beus, Dhanani, & McCord, 2015). Beus et al. (2015) found that sensation seeking (ρ = .27) is more strongly related to workers' risky behaviors compared to the Big Five personality traits of neuroticism (ρ = .13) and extraversion (ρ = .10). In general road safety literature, sensation seeking has also been found to be a better predictor of risky driving behaviors compared to other higher-order personality traits (e.g., Dahlen, Martin, Ragan, & Kuhlman, 2005; Jonah, 1997; Schwebel, Severson, Ball, & Rizzo, 2006). For instance, Schwebel et al. (2006) found that sensation seeking is a better predictor of self-reported driving violations (measured by the Driving Behaviour Questionnaire and Driving Habits Questionnaire), than conscientiousness and hostile traits. Sensation seeking is a trait defined by an individual's tendency to search for novel, intense, and varied sensations and his or her willingness to take risks to experience such sensations (Zuckerman, 1994). Individuals who have high levels of sensation seeking traits are more likely to seek out pleasure and excitement and tend to underestimate risks or perceive them as a challenge (Zuckerman, 1994). Earlier research synthesis of sensation seeking and risky driving behaviors found a positive and moderate relationship between sensation seeking personality and risky driving behaviors (i.e., drink driving, speeding, and non-seatbelt use) with correlations between 0.30 and 0.40 (Jonah, 1997). Further, Jonah (1997) noted that sensation seeking accounted for 10–15% of variance when measuring risky driving behaviors. Within this synthesis, Jonah (1997) also found that in one of the four dimensions of sensation seeking identified by Zuckerman (1994), thrill and adventure seeking appears to have the strongest relationship with risky driving behavior. Thrill and adventure seeking is defined as one's desire to engage in physical activities that elicit unusual experiences and sensations from the individual (Zuckerman, 1994). When applied to the driving setting, individuals with high levels of thrill and adventure seeking trait may perceive the risks associated with their unsafe driving behaviors, but still accept the risk in order to experience the thrill associated with that behavior (Jonah, 1997). In non-occupational research, researchers found that individuals are more likely to engage in various risky behaviors especially if they value the risk in a positive manner (Hatfield, Fernandes, & Job, 2014). Furthermore, if individuals are engaging in risky behaviors that elicit high levels of thrill and adventure seeking
and no immediate negative consequences were experienced, then those individuals are more likely to engage in similar risky behavior in the future (Jonah, 1997). Thrill and adventure seeking may explain at an individual level why some employees tend to engage in risky driving behaviors even within a work setting. Observational field research using male taxi drivers found that those with high-risk personalities were more likely to exhibit risky driving behaviors, such as speeding and careless maneuvering (Burns & Wilde, 1995). Furthermore, in a study using a large sample of Australian work drivers, it was found that employees high in the thrill and adventure-seeking trait were more likely to engage in risky driving behaviors, such as committing errors and violations and driving while distracted and fatigued (Wishart, Somoray, & Rowland, 2017). These studies suggest an employee's thrill and adventure seeking personality profile is an important factor to consider when examining risky behaviors in occupational settings, such as risky behaviors when driving for work. 1.1. Safety climate and work-related driving behaviors In addition to workers' thrill and adventure seeking personality trait, safety climate has been identified as one of the integral factors that impact on employees' driving behavior (Amponsah-Tawiah & Mensah, 2016; Newnam & Watson, 2011; Wills et al., 2006; Wills et al., 2009). Within occupational safety research, safety climate represents workers' perceptions of their organization's safety culture and practices (Zohar, 1980, 2010), which consequently influence their own occupational behavior such as driving for work. DeJoy (1994) proposed that safety climate could be explained using the attribution theory. Based on this theory, safety climates are inferences that employees make about their organization's safety practices, which consequently shape their behaviors at work (DeJoy, 1994). In other words, employee behavior is shaped or influenced by their perceptions about the importance and relevance placed on aspects of safety within the organizational context. This concept has also been applied within the work driving setting, with safety climate specifically attributed to the importance that organizations place on fleet safety policies and practices, and safe driving behaviors of their workers (Wills et al., 2009). Previous research has suggested that improving safety climate in organizations may have a positive impact on employees' safety while driving for work (Amponsah-Tawiah & Mensah, 2016; Wills et al., 2006; Wills et al., 2009). For instance, Wills et al. (2009) found that employees' perception of safety climate was a significant predictor of safe work driving behavior. Furthermore, safety climate explained the largest variance of work driver's behaviors compared to the employees' safety attitudes, perceived behavioral control, subjective norms, situational factors, and driving experience (Wills et al., 2009). Similarly, other studies found that safety climate significantly predicted fatiguerelated near misses in work drivers (Strahan, Watson, & Lennon, 2008). These researches on safety climate have demonstrated that, while driving behaviors are influenced by dispositional characteristics such as age, gender, and personality traits (Darby, Murray, & Raeside, 2009), management practices could also impact on their workers' driving behaviors (Mooren, Grzebieta, Williamson, Olivier, & Friswell, 2014; Wills et al., 2009). Outside work driving research, several studies found the interacting effect of safety climate on the relationship of personality and safetyrelated behaviors. For example, in the meta-analysis conducted by Beus et al. (2015), the combination of conscientiousness, agreeableness, and neuroticism personality traits and safety climate measures accounted for nearly half (R2 = .43) of the total variance found in safety behaviors. Safety climate, however, accounted for more of the explained variance (66.8%) compared to personality traits (33.2%). This finding demonstrated that, while personality accounted for a substantial variance in safety behaviors, safety climate was the stronger correlate. However, although thrill and adventure seeking has been found to be a better predictor of risky driving behavior compared to other personality
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traits, the relationship between thrill and adventure seeking trait and work driving behaviors, in conjunction with any potential interaction with safety climate has not been identified in the literature. While personality traits demonstrate a strong association with work driving behaviors (e.g., Wishart et al., 2017), it is important to consider the importance of the social context in order to move beyond trait-focused approaches (Beus et al., 2015) and examine social influences that could impact on work driving behaviors. Therefore, this study will examine the interactional effect of safety climate and thrill and adventure seeking and their relationship with risky work driving behaviors. Fig. 1 represents the conceptual model that will be examined in this study. The primary aim of this research is to explore whether there is a moderating relationship between organizational safety climate and thrill and adventure seeking on work driving behavior utilizing a large sample of Australian work drivers. Based on the literature reviewed, it is hypothesized that: H1. Thrill and adventure seeking will have a positive relationship with risky work driving behaviors. H2. Safety climate will have a negative relationship with risky work driving behaviors. H3. Safety climate will have a moderating effect on thrill and adventure seeking and risky work driving behavior. The addition of safety climate in the regression model will weaken the relationship between sensation seeking and risky work driving behaviors.
2. Method 2.1. Participants Four Australian companies operating light vehicle fleets agreed to participate in the study. The exact number of workers currently employed was not provided by the organizations, but the estimated response rate ranged from 10% to 30%. From the participants who completed the survey, a total of 1,011 employees indicated that they drive for work as part of their employment. The sample had an age range of 17–69 (M = 43.88; SD = 11.08) and consisted of 629 males (62.2%) and 372 females (36.8%). Ten participants did not provide their gender. Although there were more males than females in the sample, this demographic is representative of the proportion of work drivers within organizations that operate vehicle fleets. On average, participants held a driver's license for 26 years (SD = 11.30). Most participants indicated they drive between 1 and 10 h per week (68.1%) and between 1 and 10,000 km per year (50.4%) for work. See Table 1 for more information about their vehicle usage for work. 2.2. Procedure All of the employees from four organizations were invited to participate in the study via the respective organizations' internal email and communication mechanisms promoting the research. Surveys were distributed online via a secure internet link that was password protected.
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Table 1 Participants' work-related driving exposure.
Hours driven per week
Kilometers driven per year
1 to 10 h 11–20 h 21–30 h ≥31 h 1 to 10,000 km 10,001–20,000 km 20,001–30,000 km 30,001–40,000 km 40,001–50,000 km ≥51,000 km
Thrill and Adventure Seeking
H1
Percentage 68.1% 20.8% 6.3% 4.8% 50.4% 21.8% 15.1% 6.6% 3.9% 2.2%
Note. N = 1,011.
Hardcopies of the survey, letter of introduction, participant consent form and reply paid envelopes were also distributed throughout each organization via their internal mailing system to ensure that field workers and those without convenient computer access were provided with an opportunity to participate. Participants were told that the survey is anonymous and the data collected is confidential. 2.3. Measures 2.3.1. Thrill and Adventure Seeking The Thrill and Adventure Seeking measure used in this study was based on the Thrill and Adventure Seeking subscale of Zuckerman's Sensation Seeking Scale (Zuckerman, 1971). While Zuckerman's Sensation Seeking Scale had been widely used in studies of risk taking, the Thrill and Adventure Seeking subscale was shown to have the most relevance to risky driving behaviors (Jonah, 1997). The Thrill and Adventure Seeking subscale was adapted to suit the road safety context (see Matthews, Desmond, Joyner, Carcary, & Gilliland, 1997 for the original use of this measure). An example item describing thrill and adventure seeking personality included: “I sometimes like to frighten myself a little while driving.” A high score on this questionnaire indicated a greater tendency to engage in thrill and adventure seeking activities while driving. The questionnaire used consisted of 8 items and used a 5-point Likert response scale ranging from 1 (never) to 5 (always). The reliability estimate of the Thrill and Adventure Seeking measure within the current study was acceptable (α = 0.87). 2.3.2. Driving Behaviour Questionnaire A modified version of Reason, Manstead, Stradling, Baxter, and Campbell (1990) Manchester Driving Behaviour Questionnaire (DBQ) was used to measure the work-related driving behaviors. The modifications were carried out to reflect Australian terminology and to better reflect the work driving context. For example, references to specific directions regarding where the car is turning (“left” or “right”) were removed and replaced with general terms like “turning.” In the original Manchester DBQ measure, Reason et al. (1990) identified two broad categories for aberrant driving behaviors: errors and violations. Additional modifications included the inclusion of 7 items relating to driving
Safety Climate
H3
n 688 210 64 49 510 220 153 67 39 22
H2
Risky Work Driving Behaviours
Fig. 1. Conceptual model of the current study.
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while fatigued and 4 items relating to distracting behaviors (e.g., mobile phone use) while driving. The inclusion of these items in the questionnaire was based on past research in the Australian work-related driving context (e.g., Davey, Wishart, Freeman, & Watson, 2007; Rowland et al., 2008) and discussions with the fleet management staff within the participating organizations. Previous research on work-related driving behaviors utilized this version of the DBQ (e.g., Davey, Freeman, & Wishart, 2006; Wishart et al., 2017). Items of the modified DBQ included statements such as: “Have difficulty driving because of tiredness or fatigue.” The modified DBQ used consisted of 31 items and used a 5-point Likert response scale ranging from 1 (never) to 5 (always). The reliability estimates of the DBQ subscales within the current study were acceptable (see Table 2) except for the driving while distracted subscale with a Cronbach alpha of .47. 2.3.3. Safety Climate Questionnaire The modified Safety Climate Questionnaire (SCQ) by Glendon and Litherland (2001) was also used in the study. For the development (including the factor structure and reliability statistics) of this modified questionnaire, please consult Wills, Biggs, and Watson (2005). The minor modifications in the questionnaire ensured that the questions measured the employees' perception of the company's safety climate with regard to work-related driving safety. The measure included items such as: “Fleet safety problems are openly discussed between employees and managers/supervisors”. The questionnaire items were also reduced to minimize administration time of the survey for participants. The modified version of the SCQ consisted of 12 items with a 5 point Likert response scale ranging from 1 (never) to 5 (always). Previous research within the Australian light vehicle fleet context has demonstrated that SCQ is a reliable tool in measuring drivers' perception of safety climate within an organization (Davey et al., 2007; Wills et al., 2006; Wills et al., 2009). The Cronbach alpha of the modified SCQ within the current study was strong (α = .94). 3. Results 3.1. Assumption checking All analyses were performed in IBM SPSS and AMOS version 23. Missing data, outliers, and normality of data were checked before conducting the main analyses. Two participants had substantial missing data and were deleted from subsequent analyses. Overall, there was minimal missing data (maximum of 0.8% for each item) and to preserve power, mean imputation was carried out. However, data within the variables of interest with a z-score of ≥ 3.29 were considered extreme outliers (Tabachnick & Fidell, 2012) and were deleted in the dataset (n = 29). The final number of participants included in the main analyses was N = 980. Inspection of the normality statistics demonstrated non-normal univariate and multivariate data, which could be problematic in the interpretation of results. Therefore, bootstrapping with 1,000 samples was performed to assess the proposed model and research hypotheses of the current study.
Table 2 Means, standard deviations, and correlations between age, driving behaviors, thrill and adventure seeking, and safety climate.
1. 2. 3. 4. 5. 6.
Driving errors Driving violations Driving distractions Driving fatigued Thrill and adventure seeking Safety climate
N = 980. ⁎⁎ p b .001.
M
SD
α
1
2
1.24 1.47 1.68 1.93 1.65
0.26 0.46 0.59 0.58 0.69
.70 .78 .47 .85 .87
– .47⁎⁎ .31⁎⁎ .53⁎⁎ .24⁎⁎
– .35⁎⁎ – .59⁎⁎ .44⁎⁎ – .43⁎⁎ .28⁎⁎ .36⁎⁎ –
4.04 0.93 .94 0.00
0.04
3
4
5
6
.11⁎⁎ .09⁎⁎ 0.06 –
3.2. Descriptives Age was negatively and significantly correlated with driving violations (r = −.13, p b .001), driving while fatigued (r = −.08, p = .014), and thrill and adventure seeking (r = −.16, p b .001). These findings suggest that younger work drivers are more likely to experience traffic violations while driving and driving while fatigued and have higher levels of thrill and adventure seeking. Age was also negatively correlated with driving errors (r = −.04, p = .221) and driving while distracted (r = −.04, p = .246) and positively correlated with safety climate (r = .06, p = .077) although these relationships were not significant. The influence of gender was also explored and a t-test showed that significant differences were only observed in distracted driving behaviors, with males more likely to drive while distracted (M = 1.77; SD = 0.60) compared to females, (M = 1.55; SD = 0.55), t(970) = 5.71, p b .001. Male work drivers also showed higher levels of thrill and adventure seeking (M = 1.77; SD = 0.76) compared to female work drivers, (M = 1.46; SD = 0.52), t(956.38) = 7.73, p b .001. There were no significant gender differences observed in any of the other variables. Table 2 presents the means, standard deviations, and correlations for driving behaviors, thrill and adventure seeking, and safety climate. As seen in Table 2, thrill and adventure seeking had moderate positive correlations (.24 to .43) with driving behaviors and the strongest relationship with driving violations. Safety climate, on the other hand, had weak positive correlations with driving behaviors (0.00 to 0.11), only showing significant relationships with driving while distracted and driving while fatigued. The correlation between thrill and adventure seeking and safety climate was positive but not significant. 3.3. Main analyses The hypothesized moderation model (Fig. 2) was investigated using path analysis in IBM AMOS 23. Due to the poor reliability score of the driving distraction subscale (.47), this subscale was excluded from the main analysis. The independent variables (thrill and adventure seeking, safety climate, and the interaction effect) were mean-centered prior to the analysis. Initially, the model fit was unsatisfactory, χ2(3) = 596.28, Bollen– Stine bootstrap p b .001, GFI = 0.82, CFI = 0.54, TLI = − 1.31, RMSEA = 0.45. The regression weights indicated that the main effects of safety climate to driving violations (β = − 0.04, p = .260) and driving while fatigued (β = −0.01, p = .851) were not significant as well as the covariance between safety climate and thrill and adventure seeking (β = 0.06, p = .066). Additionally, modification indices indicated a need to correlate the error terms for the driving behavior variables to provide a better fit. Since driving error, driving violations, and driving while fatigued are logically correlated, it is theoretically consistent to relate their error terms. Therefore, an alternative model was carried out (see Fig. 3). The hypothesized relationships between safety climate and driving violations as well as safety climate and driving while fatigued were taken out. The covariance between thrill and adventure seeking and safety climate was also removed from the model and the error terms were correlated. The second model provided a better fit χ2(3) = 4.87, Bollen–Stine bootstrap p = .214. The goodness-of-fit indices showed good adjustment of the model to the data (GFI = 0.99, CFI = 0.99, TLI = 0.99, RMSEA = 0.03). Table 3 shows the unstandardized and standardized path estimates and standard errors of the model. As shown in Table 3, thrill and adventure seeking, safety climate, and the interaction effect explained 8% of the variance for driving errors (p = .003), 19% of the variance for driving violations (p = .002), and 15% of the variance for driving while fatigued (p = .002). Thrill and adventure seeking significantly predicted driving errors, driving violations, and driving while fatigued, showing the strongest relationship with driving while fatigued. Safety climate, on the other hand, only significantly predicted driving errors.
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Fig. 2. Hypothesized model.
The interaction term between thrill and adventure seeking and safety climate was a significant predictor of driving errors, driving violations, and driving while fatigued in the model. These results indicated that safety climate negatively moderated the relationship between thrill and adventure seeking and risky driving behaviors that were measured in the current study. Fig. 4 demonstrates that when safety climate is low, the positive relationship between thrill and adventure seeking and risky driving behaviors is fairly strong. However, when safety climate is high, the positive relationship between risky driving behaviors and thrill and adventure seeking weakens. 4. Discussion The current study investigated the relationship between safety climate and thrill and adventure seeking, and their effect on risky work driving behavior. In regard to H1, the results demonstrated that thrill and adventure seeking has a significant positive relationship with risky work driving behaviors suggesting that employees that drive for work and possess a high need for thrill and adventure seeking are more likely to engage in risky work driving behaviors. This finding is consistent with previous research that examined the role of thrill and adventure seeking on risky driving behaviors within non-occupational (e.g., Jonah, 1997) and occupational settings (e.g., Wishart et al., 2017). The hypothesis that safety climate will have a negative relationship with risky work driving behaviors was only partially supported. The results of the current study only showed a significant negative relationship between safety climate and driving errors, which is a similar finding to previous studies that examined the impact of safety climate on work driving behaviors (e.g., Amponsah-Tawiah & Mensah, 2016; Wills et al., 2006). This result suggests that within an organizational work driving context, high levels of safety climate may contribute to a
reduction in drivers' work driving errors. Through a strong safety climate, drivers become more conscious of their driving and consequently awareness of safety, which is associated with less driving errors overall. However, safety climate appears to have little effect on work driver violations and driving while fatigued. This finding is in contrast with previous studies that found a significant relationship between safety climate, work-related driving violations, and driving while fatigued (Amponsah-Tawiah & Mensah, 2016). These results may indicate that other factors of influence were at play beyond the direct control of the driver (e.g., occupational stress and time pressure; Strahan et al., 2008). Another explanation could be that, the measure used in the current study did not include all the safety climate factors used by previous studies. For instance, Wills et al. (2006) only found a significant relationship between the safety rules dimension of safety climate and driving violations. On the other hand, the safety climate dimensions of management commitment, safety rules, and communication were significantly related to driving error, with management commitment acting as the strongest predictor (Wills et al., 2006). While not directly studied, the current results may imply that particular aspects of safety climate are more strongly related to certain aspects of work driving behaviors. The results show support for H3 indicating a moderating effect of safety climate on the relationship between thrill and adventure seeking and risky work driving behaviors. Specifically, the current study demonstrated that a high organizational safety climate can reduce the propensity to drive in a risky manner among workers with high levels of thrill and adventure seeking. 4.1. Practical applications This result has some implications for organizations seeking to improve the safety of employees within their work driving context. For
Fig. 3. Model 2 path analysis showing the significant standardized estimates between the variables of interest.
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such as in-vehicle monitoring systems, de-identified crash and traffic data or observations to measure the workers' risky driving behaviors. Second, the causal nature of the model is not possible to determine in cross-sectional research, therefore, a longitudinal research design is warranted to further study the depth of the relationship between the variables studied. The cross-sectional nature of the study also meant that the researchers have to rely on employees' self-report to assess the organization's safety climate. Objective assessment of the organization's safety climate or an employers' report may provide more support or complimentary assessment of safety climate. Lastly, not all factors of influence or behaviors were measured and therefore, results need to be interpreted accordingly. Potential future research could examine other factors such as work pressure, job demands, and occupational stress that may impact on work driving behaviors and its interplay with safety climate and sensation seeking personalities. Future research could also evaluate the impact of such programs and further seek to explore the interaction of safety climate on a range of other psychosocial and driving behavior factors believed to impact on work driving safety.
Table 3 Unstandardized and standardized path estimates and their corresponding standard errors. R2
Model paths
B
S.E.
β
Driving errors ← Thrill and adventure seeking 0.08 0.09⁎⁎ 0.01 0.24⁎⁎ Driving errors ← Safety climate −.02⁎ 0.01 −.07⁎ Driving errors ← TAS × SC −.06⁎⁎ 0.02 −.15⁎⁎ Driving violations ← Thrill and adventure seeking 0.19 .28⁎⁎ .02 .42⁎⁎ −.02 0.02 −.04 Driving violations ← Safety climatea Driving violations ← TAS×SC −.09⁎⁎ 0.02 −.10⁎⁎ Driving while fatigued ← Thrill and adventure seeking 0.15 0.29⁎⁎ 0.03 0.34⁎⁎ −0.00 0.02 −0.01 Driving while fatigued ← Safety climatea Driving while fatigued ← TAS × SC −.16⁎⁎ 0.03 −.17⁎⁎ Notes: TAS = thrill and adventure seeking; SC = safety climate. ⁎ p b .05. ⁎⁎ p b .001. a These paths were not included in the analysis of the second model.
instance, various industry settings due to the nature of the work required, may have the propensity to attract a higher proportion of employees with a predisposition for thrill and adventure seeking (e.g., Beus et al., 2015; Clarke & Robertson, 2008). However, although this type of employee may provide a favorable employee–organization or job fit, this match may also result in challenges in managing the safety of work driving and the enthusiasm and propensity for thrill and adventure seeking. For example, although the primary work or job role may require people with this particular trait, propensity to seek out activities that satisfy the thrill and adventure seeking desire while working may have adverse effects on other aspects of various job roles which may be considered not as engaging or dangerous (e.g., engaging in risky driving behaviors while working in order to experience thrill). However, the results of the current study indicated that a positive safety climate towards work driving safety, which consists of aspects associated with management commitment to safety and positive safety message communication, can temper the effects of thrill and adventure seeking tendencies. Therefore, organizations need to ensure that generating a positive work driving safety climate becomes a high priority. Consequently, to improve work driving safety, organizations and management may need to develop strategies to encourage and foster positive work driving safety climate, particularly within settings that may attract employees high in the thrill and adventure seeking personality trait.
References Amponsah-Tawiah, K., & Mensah, J. (2016). The impact of safety climate on safety related driving behaviors. Transportation Research Part F: Traffic Psychology and Behaviour, 40, 48–55. http://dx.doi.org/10.1016/j.trf.2016.04.002. Australian Bureau of Statistics (2015). Motor Vehicle Census. Canberra. Retrieved from http://www.abs.gov.au/ausstats/[email protected]/PrimaryMainFeatures/9208.0? OpenDocument. Australiasian Fleet Managers Association (2008). Review of the automotive industry 2008. Retrieved from http://www.afma.net.au/. Beus, J. M., Dhanani, L. Y., & McCord, M. A. (2015). A meta-analysis of personality and workplace safety: Addressing unanswered questions. Journal of Applied Psychology, 100(2), 481. http://dx.doi.org/10.1037/a0037916. Bureau of Labor Statistics (2016). Census of Fatal Occupational Injuries summary, 2015. Retrieved from https://www.bls.gov/news.release/cfoi.nr0.htm. Burns, P. C., & Wilde, G. J. (1995). Risk taking in male taxi drivers: Relationships among personality, observational data and driver records. Personality and Individual Differences, 18 (2), 267–278. http://dx.doi.org/10.1016/0191-8869(94)00150-q. Clarke, S., & Robertson, I. (2005). A meta-analytic review of the Big Five personality factors and accident involvement in occupational and non-occupational settings. Journal of Occupational and Organizational Psychology, 78(3), 355–376. http://dx.doi. org/10.1348/096317905x26183. Clarke, S., & Robertson, I. (2008). An examination of the role of personality in work accidents using meta-analysis. Applied Psychology, 57(1), 94–108. http://dx.doi.org/10. 1111/j.1464-0597.2007.00267.x. Dahlen, E. R., Martin, R. C., Ragan, K., & Kuhlman, M. M. (2005). Driving anger, sensation seeking, impulsiveness, and boredom proneness in the prediction of unsafe driving. Accident Analysis & Prevention, 37(2), 341–348. http://dx.doi.org/10.1016/j.aap. 2004.10.006. Darby, P., Murray, W., & Raeside, R. (2009). Applying online fleet driver assessment to help identify, target and reduce occupational road safety risks. Safety Science, 47(3), 436–442. http://dx.doi.org/10.1016/j.ssci.2008.05.004. Davey, J., & Banks, T. (2005). Estimating the cost of work motor vehicle incidents in Australia. Paper presented at the Australasian Road Safety Research Policing Education Conference, Wellington, New Zealand. Davey, J., Freeman, J., & Wishart, D. (2006). A study predicting crashes among a sample of fleet drivers. Paper presented at the Proceedings Road Safety Research, Policing and Education Conference, Gold Coast, Queensland (http://eprints.qut.edu.au/5959/1/ 5959_1.pdf).
4.2. Limitations and future research While the current study provided meaningful results, there are several limitations that should be mentioned and would provide directions for future research. First, it is important to acknowledge that the study is based on self-report measures and although considerable effort is implemented to decrease self-report bias (e.g., ensuring anonymity and confidentiality), it is possible that workers are unwilling to provide the most accurate response due to the fear of negative response from their managers. Future research could use other forms of data collection
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Fig. 4. Interaction effect of safety climate and thrill and adventure seeking on driving errors, driving violations, and driving while fatigued.
D. Wishart et al. / Journal of Safety Research 63 (2017) 83–89 Davey, J., Wishart, D., Freeman, J., & Watson, B. (2007). An application of the driver behaviour questionnaire in an Australian organisational fleet setting. Transportation Research Part F: Traffic Psychology and Behaviour, 10(1), 11–21. http://dx.doi.org/10. 1016/j.trf.2006.03.001. DeJoy, D. M. (1994). Managing safety in the workplace: An attribution theory analysis and model. Journal of Safety Research, 25(1), 3–17. http://dx.doi.org/10.1016/0022-4375 (94)90003-5. Fort, E., Ndagire, S., Gadegbeku, B., Hours, M., & Charbotel, B. (2016). Working conditions and occupational risk exposure in employees driving for work. Accident Analysis & Prevention, 89, 118–127. http://dx.doi.org/10.1016/j.aap.2016.01.015. Glendon, A. I., & Litherland, D. K. (2001). Safety climate factors, group differences and safety behaviour in road construction. Safety Science, 39(3), 157–188. http://dx.doi. org/10.1016/s0925-7535(01)00006-6. Hatfield, J., Fernandes, R., & Job, R. S. (2014). Thrill and adventure seeking as a modifier of the relationship of perceived risk with risky driving among young drivers. Accident Analysis & Prevention, 62, 223–229. http://dx.doi.org/10.1016/j.aap.2013.09.028. Jonah, B. A. (1997). Sensation seeking and risky driving: A review and synthesis of the literature. Accident Analysis & Prevention, 29(5), 651–665. http://dx.doi.org/10.1016/ s0001-4575(97)00017-1. Matthews, G., Desmond, P. A., Joyner, L., Carcary, B., & Gilliland, K. (1997). A comprehensive questionnaire measure of driver stress and affect. Traffic and transport psychology. Theory and Application, 317–324. Mooren, L., Grzebieta, R., Williamson, A., Olivier, J., & Friswell, R. (2014). Safety management for heavy vehicle transport: A review of the literature. Safety Science, 62, 79–89. http://dx.doi.org/10.1016/j.ssci.2013.08.001. Murray, W., Newnam, S., Watson, B. C., Davey, J., & Schonfeld, C. C. (2003). Evaluating and improving fleet safety in Australia. Canberra: Australian Transport Bureau (Retrieved from https://infrastructure.gov.au/roads/safety/publications/2003/pdf/eval_fleetsafe.pdf). Newnam, S., & Watson, B. (2011). Work-related driving safety in light vehicle fleets: A review of past research and the development of an intervention framework. Safety Science, 49(3), 369–381. http://dx.doi.org/10.1016/j.ssci.2010.09.018. Reason, J., Manstead, A., Stradling, S., Baxter, J., & Campbell, K. (1990). Errors and violations on the roads: A real distinction? Ergonomics, 33(10–11), 1315–1332. http://dx.doi.org/10.1080/00140139008925335. Rowland, B. D., Davey, J., Freeman, J. E., & Wishart, D. (2008). Work-related road safety risk assessment: Utilisation of self-report surveys to predict organisational risk. Paper presented at the Australasian Road Safety Research, Policing and Education Conference, Adelaide, SA. Safe Work Australia (2015). Work-related traumatic injury fatalities, Australia 2015. Canberra: Safe Work Australia (Retrieved from http://www.safeworkaustralia.gov. au/sites/SWA/about/Publications/Documents/979/work-related-traumatic-injuryfatalities.pdf). Schwebel, D. C., Severson, J., Ball, K. K., & Rizzo, M. (2006). Individual difference factors in risky driving: The roles of anger/hostility, conscientiousness, and sensation-seeking. Accident Analysis & Prevention, 38(4), 801–810. http://dx.doi.org/10.1016/j.aap. 2006.02.004. Seibokaite, L., & Endriulaitiene, A. (2012). The role of personality traits, work motivation and organizational safety climate in risky occupational performance of professional drivers. Baltic Journal of Management, 7(1), 103–118. http://dx.doi.org/10.1108/ 17465261211195892. Strahan, C., Watson, B., & Lennon, A. (2008). Can organisational safety climate and occupational stress predict work-related driver fatigue? Transportation Research Part F: Traffic Psychology and Behaviour, 11(6), 418–426. http://dx.doi.org/10.1016/j.trf. 2008.04.002. Stuckey, R., Lamontagne, A. D., Glass, D. C., & Sim, M. R. (2010). Occupational light vehicle use: Characterising the at-risk population. Journal of Health and Safety Research and Practice, 2(1). http://dx.doi.org/10.1179/oeh.2005.11.2.167 (online-online). Tabachnick, B. G., & Fidell, L. S. (2012). Using multivariate statistics (6th ed.). Boston, NY: Pearson.
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Wills, A. R., Biggs, H. C., & Watson, B. (2005). Analysis of a safety climate measure for occupational vehicle drivers and implications for safer workplaces. The Australian Journal of Rehabilitation Counselling, 11(01), 8–21. http://dx.doi.org/10.1017/ s1323892200000132. Wills, A. R., Watson, B., & Biggs, H. C. (2006). Comparing safety climate factors as predictors of work-related driving behavior. Journal of Safety Research, 37(4), 375–383. http://dx.doi.org/10.1016/j.jsr.2006.05.008. Wills, A. R., Watson, B., & Biggs, H. (2009). An exploratory investigation into safety climate and work-related driving. Work, 32(1), 81–94. http://dx.doi.org/10.3233/WOR-20090818. Wishart, D. (2015). The challenge of developing a fleet driving risk assessment tool: What can be learned from the process? Brisbane: Queensland University of Technology. Wishart, D., Rowland, B. D., Freeman, J. E., & Davey, J. (2011a). Driving for work: A series of organisational audit results. Paper presented at the Australasian College of Road Safety National Conference. Melbourne Convention and Exhibition Centre. Wishart, D., Rowland, B. D., Freeman, J. E., & Davey, J. (2011b). Fleet safety: Risk reduction for work related vehicle use. Paper presented at the Proceedings of 9th Industrial and Organisational Psychology Conference. Brisbane, Qld: Brisbane Convention and Exhibition Centre. Wishart, D., Somoray, K., & Rowland, B. (2017). Role of thrill and adventure seeking in risky work-related driving behaviours. Personality and Individual Differences, 104, 362–367. http://dx.doi.org/10.1016/j.paid.2016.08.033. Zohar, D. (1980). Safety climate in industrial organizations: Theoretical and applied implications. Journal of Applied Psychology, 65(1), 96. http://dx.doi.org/10.1037// 0021-9010.65.1.96. Zohar, D. (2010). Thirty years of safety climate research: Reflections and future directions. Accident Analysis & Prevention, 42(5), 1517–1522. http://dx.doi.org/10.1016/j.aap. 2009.12.019. Zuckerman, M. (1971). Dimensions of sensation seeking. Journal of Consulting and Clinical Psychology, 36(1), 45. http://dx.doi.org/10.1037/h0030478. Zuckerman, M. (1994). Behavioral expressions and biosocial bases of sensation seeking. New York, NY: Cambridge University Press. Dr. Darren Wishart is a registered psychologist and is currently employed in the Centre for Accident Research and Road Safety Queensland (CARRS-Q) at Queensland University of Technology. Dr. Wishart is involved in a variety of research and consultancies in areas such as work related road safety, motorcycle safety, and international road safety and community capacity building. In the organizational psychology domain Dr. Wishart's research and consultancy work has primarily focused on projects involving industry and safety from a systemic approach along with creating behavior change. Ms. Klaire Somoray is a Research Officer and Ph.D. Student in the Centre for Accident Research and Road Safety Queensland (CARRS-Q) at Queensland University Technology. Ms. Somoray is involved in various research projects within the work driving safety and traffic psychology domains. She is currently undertaking her Ph.D. in safety behaviors within the work driving context. Ms. Somoray has a particular interest in data analytics, role of technology in human behavior and organizational psychology. Ms. Amanda Evenhuis is a Research Officer in the Centre for Accident Research and Road Safety Queensland (CARRS-Q) at Queensland University of Technology. Ms. Evenhuis is involved in a variety of research projects including cycling and young driver safety and has a particular interest in training and capacity building. She is currently finishing her undergraduate in Psychology with a focus on Organisational Psychology. Ms. Evenhuis also has a Bachelor of Built Environment (Urban and Regional Planning) and a Graduate Diploma in Urban Design.
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Thrill and adventure seeking in risky driving at work: The moderating role of safety climate Darren Wishart, Klaire Somoray, ⁎ Amanda Evenhuis Centre for Accident Research and Road Safety Queensland (CARRS-Q), Institute for Health and Biomedical Innovation, School of Psychology, Queensland University of Technology, Brisbane, Australia
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Article history: Received 21 March 2017 Received in revised form 30 June 2017 Accepted 17 August 2017 Available online 6 September 2017 Keywords: Risky driving behaviors Road safety Occupational safety Traffic psychology Organizational behavior
a b s t r a c t Introduction Within many industrialized countries, the leading cause of worker fatalities and serious injuries can be attributed to road trauma. In non-occupational research, high levels of sensation seeking personality, and specifically thrill and adventure seeking, have been associated with risky driving behaviors. In work driving literature, high organizational safety climate has been associated with reduced risky driving in work drivers. However, the extent that factors such as safety climate and thrill seeking interact in regard to work driving safety remains unclear, and the current research examined this interaction. Methods A total of 1,011 work drivers from four organizations participated in the research. Surveys were distributed online and hardcopies were sent via mail. The survey included measures of thrill and adventure seeking, safety climate and work-related driving behaviors, as well as questions relating to participant demographics and information about their work driving. Results The results demonstrated that safety climate significantly moderated the effect of thrill and adventure seeking trait on driving errors, driving violations, and driving while fatigued. Conclusion These results suggest that the development of a strong safety climate has the potential to improve work driving safety outcomes by reducing the impact of particular personality traits such as thrill seeking within an organizational context. Practical application To improve work driving safety, organizations and management need to develop strategies to encourage and foster positive work driving safety climate, particularly within work settings that may attract thrill and adventure seeking employees. © 2017 Published by Elsevier Ltd.
1. Introduction A high proportion of the traffic activity in Australia involves workrelated driving. The Australian Bureau of Statistics (2015) reported that 18% of the total number of kilometers traveled by Australians in 2014 were business-related and 25.4% involved travel to and from work. In addition, previous research also indicates the majority of new vehicles purchased within Australia were for organizational fleet use (Australiasian Fleet Managers Association, 2008). Given the representation of vehicles used for work, it is not surprising that a high proportion of road crashes in Australia are work-related and that crashes have been identified as the leading cause of fatalities at work (Safe Work Australia, 2015; Wishart, Rowland, Freeman, & Davey, 2011b). Safe Work Australia (2015) reported that two-thirds of the total work-related fatalities in Australian organizations involved a vehicle (e.g., vehicle collision, rollover of non-road vehicle) over the 2003 to 2015 period (Safe Work Australia, 2015). On average, around 30% to 35% of work fatalities are attributed to a vehicle collision each year (Safe Work ⁎ Corresponding author at: School of Psychology and Counselling, Queensland University of Technology, 130 Victoria Park Road, Kelvin Grove, QLD 4059, Brisbane, Australia. E-mail address: [email protected] (K. Somoray).
http://dx.doi.org/10.1016/j.jsr.2017.08.007 0022-4375/© 2017 Published by Elsevier Ltd.
Australia, 2015). This finding is similar to other industrialized countries such as France (Fort, Ndagire, Gadegbeku, Hours, & Charbotel, 2016) and the United States (Bureau of Labor Statistics, 2016). In addition to these human costs, work-related road crash injuries and fatalities place a noticeable financial burden on the Australian government and the organizations (Davey & Banks, 2005). Davey and Banks (2005) estimated that a work-related crash costs organizations an average of $28,000 for each insurance claim. These crashes also have indeterminate costs, not necessarily factored into cost calculations to businesses and to the community, such as absence from work, compensation, and loss of productivity due to down time (Murray, Newnam, Watson, Davey, & Schonfeld, 2003). Despite the alarming statistics and the legislative requirement for organizations to manage employees' safety while driving for work, previous research suggests that organizations often fail to adequately manage this risk with the same commitment as other workplace hazards (Rowland, Davey, Freeman, & Wishart, 2008; Wishart, 2015; Wishart et al., 2011b; Wishart, Rowland, Freeman, & Davey, 2011a). One of the challenges associated with managing work driving safety is the complexity of the inter-related issues that influence work driving safety as demonstrated by the Occupational Light Vehicle (OLV) Use Systems Model (Stuckey, Lamontagne, Glass, & Sim, 2010). The OLV-use Systems Model highlights five levels of influence which consist
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of: the driver, immediate physical environment (the vehicle), external environment (road), organizational environment, and external influences (policy and legislation). This model has been used within the work driving safety research to better understand the complex factors outside the direct control of the driver (e.g., organizational environment) as well as the drivers' characteristics (e.g., gender, driving exposure) that can have an influence on their safety while driving for work (Stuckey et al., 2010; Wishart, 2015). In an attempt to better understand these inter-related influences, researchers have investigated the impact of psychosocial and personality factors within the work driving setting (e.g., Newnam & Watson, 2011; Seibokaite & Endriulaitiene, 2012; Wills, Watson, & Biggs, 2006; Wills, Watson, & Biggs, 2009). For instance, when considering the interaction between personality, safety climate, and work motivation and their effect on risky driving practices, inferences made by Seibokaite and Endriulaitiene (2012) suggest that “socially-oriented” (i.e., those high on agreeableness, conscientiousness, extraversion, and openness) drivers are more likely to drive safely if their perception of safety climate was high and they have high work motivation. This argument suggests that safety climate may have the potential to mitigate work driving risk by exerting a positive influence on the relationship between various personality factors (e.g., thrill and adventure seeking) and work driving behaviors. The role of dispositional characteristics on occupational safety incidents has been explored by a number of researchers (e.g., Clarke & Robertson, 2005, 2008). However, recent studies have shown that only particular facets of personality have more influential associations with occupational safety behaviors (Beus, Dhanani, & McCord, 2015). Beus et al. (2015) found that sensation seeking (ρ = .27) is more strongly related to workers' risky behaviors compared to the Big Five personality traits of neuroticism (ρ = .13) and extraversion (ρ = .10). In general road safety literature, sensation seeking has also been found to be a better predictor of risky driving behaviors compared to other higher-order personality traits (e.g., Dahlen, Martin, Ragan, & Kuhlman, 2005; Jonah, 1997; Schwebel, Severson, Ball, & Rizzo, 2006). For instance, Schwebel et al. (2006) found that sensation seeking is a better predictor of self-reported driving violations (measured by the Driving Behaviour Questionnaire and Driving Habits Questionnaire), than conscientiousness and hostile traits. Sensation seeking is a trait defined by an individual's tendency to search for novel, intense, and varied sensations and his or her willingness to take risks to experience such sensations (Zuckerman, 1994). Individuals who have high levels of sensation seeking traits are more likely to seek out pleasure and excitement and tend to underestimate risks or perceive them as a challenge (Zuckerman, 1994). Earlier research synthesis of sensation seeking and risky driving behaviors found a positive and moderate relationship between sensation seeking personality and risky driving behaviors (i.e., drink driving, speeding, and non-seatbelt use) with correlations between 0.30 and 0.40 (Jonah, 1997). Further, Jonah (1997) noted that sensation seeking accounted for 10–15% of variance when measuring risky driving behaviors. Within this synthesis, Jonah (1997) also found that in one of the four dimensions of sensation seeking identified by Zuckerman (1994), thrill and adventure seeking appears to have the strongest relationship with risky driving behavior. Thrill and adventure seeking is defined as one's desire to engage in physical activities that elicit unusual experiences and sensations from the individual (Zuckerman, 1994). When applied to the driving setting, individuals with high levels of thrill and adventure seeking trait may perceive the risks associated with their unsafe driving behaviors, but still accept the risk in order to experience the thrill associated with that behavior (Jonah, 1997). In non-occupational research, researchers found that individuals are more likely to engage in various risky behaviors especially if they value the risk in a positive manner (Hatfield, Fernandes, & Job, 2014). Furthermore, if individuals are engaging in risky behaviors that elicit high levels of thrill and adventure seeking
and no immediate negative consequences were experienced, then those individuals are more likely to engage in similar risky behavior in the future (Jonah, 1997). Thrill and adventure seeking may explain at an individual level why some employees tend to engage in risky driving behaviors even within a work setting. Observational field research using male taxi drivers found that those with high-risk personalities were more likely to exhibit risky driving behaviors, such as speeding and careless maneuvering (Burns & Wilde, 1995). Furthermore, in a study using a large sample of Australian work drivers, it was found that employees high in the thrill and adventure-seeking trait were more likely to engage in risky driving behaviors, such as committing errors and violations and driving while distracted and fatigued (Wishart, Somoray, & Rowland, 2017). These studies suggest an employee's thrill and adventure seeking personality profile is an important factor to consider when examining risky behaviors in occupational settings, such as risky behaviors when driving for work. 1.1. Safety climate and work-related driving behaviors In addition to workers' thrill and adventure seeking personality trait, safety climate has been identified as one of the integral factors that impact on employees' driving behavior (Amponsah-Tawiah & Mensah, 2016; Newnam & Watson, 2011; Wills et al., 2006; Wills et al., 2009). Within occupational safety research, safety climate represents workers' perceptions of their organization's safety culture and practices (Zohar, 1980, 2010), which consequently influence their own occupational behavior such as driving for work. DeJoy (1994) proposed that safety climate could be explained using the attribution theory. Based on this theory, safety climates are inferences that employees make about their organization's safety practices, which consequently shape their behaviors at work (DeJoy, 1994). In other words, employee behavior is shaped or influenced by their perceptions about the importance and relevance placed on aspects of safety within the organizational context. This concept has also been applied within the work driving setting, with safety climate specifically attributed to the importance that organizations place on fleet safety policies and practices, and safe driving behaviors of their workers (Wills et al., 2009). Previous research has suggested that improving safety climate in organizations may have a positive impact on employees' safety while driving for work (Amponsah-Tawiah & Mensah, 2016; Wills et al., 2006; Wills et al., 2009). For instance, Wills et al. (2009) found that employees' perception of safety climate was a significant predictor of safe work driving behavior. Furthermore, safety climate explained the largest variance of work driver's behaviors compared to the employees' safety attitudes, perceived behavioral control, subjective norms, situational factors, and driving experience (Wills et al., 2009). Similarly, other studies found that safety climate significantly predicted fatiguerelated near misses in work drivers (Strahan, Watson, & Lennon, 2008). These researches on safety climate have demonstrated that, while driving behaviors are influenced by dispositional characteristics such as age, gender, and personality traits (Darby, Murray, & Raeside, 2009), management practices could also impact on their workers' driving behaviors (Mooren, Grzebieta, Williamson, Olivier, & Friswell, 2014; Wills et al., 2009). Outside work driving research, several studies found the interacting effect of safety climate on the relationship of personality and safetyrelated behaviors. For example, in the meta-analysis conducted by Beus et al. (2015), the combination of conscientiousness, agreeableness, and neuroticism personality traits and safety climate measures accounted for nearly half (R2 = .43) of the total variance found in safety behaviors. Safety climate, however, accounted for more of the explained variance (66.8%) compared to personality traits (33.2%). This finding demonstrated that, while personality accounted for a substantial variance in safety behaviors, safety climate was the stronger correlate. However, although thrill and adventure seeking has been found to be a better predictor of risky driving behavior compared to other personality
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traits, the relationship between thrill and adventure seeking trait and work driving behaviors, in conjunction with any potential interaction with safety climate has not been identified in the literature. While personality traits demonstrate a strong association with work driving behaviors (e.g., Wishart et al., 2017), it is important to consider the importance of the social context in order to move beyond trait-focused approaches (Beus et al., 2015) and examine social influences that could impact on work driving behaviors. Therefore, this study will examine the interactional effect of safety climate and thrill and adventure seeking and their relationship with risky work driving behaviors. Fig. 1 represents the conceptual model that will be examined in this study. The primary aim of this research is to explore whether there is a moderating relationship between organizational safety climate and thrill and adventure seeking on work driving behavior utilizing a large sample of Australian work drivers. Based on the literature reviewed, it is hypothesized that: H1. Thrill and adventure seeking will have a positive relationship with risky work driving behaviors. H2. Safety climate will have a negative relationship with risky work driving behaviors. H3. Safety climate will have a moderating effect on thrill and adventure seeking and risky work driving behavior. The addition of safety climate in the regression model will weaken the relationship between sensation seeking and risky work driving behaviors.
2. Method 2.1. Participants Four Australian companies operating light vehicle fleets agreed to participate in the study. The exact number of workers currently employed was not provided by the organizations, but the estimated response rate ranged from 10% to 30%. From the participants who completed the survey, a total of 1,011 employees indicated that they drive for work as part of their employment. The sample had an age range of 17–69 (M = 43.88; SD = 11.08) and consisted of 629 males (62.2%) and 372 females (36.8%). Ten participants did not provide their gender. Although there were more males than females in the sample, this demographic is representative of the proportion of work drivers within organizations that operate vehicle fleets. On average, participants held a driver's license for 26 years (SD = 11.30). Most participants indicated they drive between 1 and 10 h per week (68.1%) and between 1 and 10,000 km per year (50.4%) for work. See Table 1 for more information about their vehicle usage for work. 2.2. Procedure All of the employees from four organizations were invited to participate in the study via the respective organizations' internal email and communication mechanisms promoting the research. Surveys were distributed online via a secure internet link that was password protected.
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Table 1 Participants' work-related driving exposure.
Hours driven per week
Kilometers driven per year
1 to 10 h 11–20 h 21–30 h ≥31 h 1 to 10,000 km 10,001–20,000 km 20,001–30,000 km 30,001–40,000 km 40,001–50,000 km ≥51,000 km
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Percentage 68.1% 20.8% 6.3% 4.8% 50.4% 21.8% 15.1% 6.6% 3.9% 2.2%
Note. N = 1,011.
Hardcopies of the survey, letter of introduction, participant consent form and reply paid envelopes were also distributed throughout each organization via their internal mailing system to ensure that field workers and those without convenient computer access were provided with an opportunity to participate. Participants were told that the survey is anonymous and the data collected is confidential. 2.3. Measures 2.3.1. Thrill and Adventure Seeking The Thrill and Adventure Seeking measure used in this study was based on the Thrill and Adventure Seeking subscale of Zuckerman's Sensation Seeking Scale (Zuckerman, 1971). While Zuckerman's Sensation Seeking Scale had been widely used in studies of risk taking, the Thrill and Adventure Seeking subscale was shown to have the most relevance to risky driving behaviors (Jonah, 1997). The Thrill and Adventure Seeking subscale was adapted to suit the road safety context (see Matthews, Desmond, Joyner, Carcary, & Gilliland, 1997 for the original use of this measure). An example item describing thrill and adventure seeking personality included: “I sometimes like to frighten myself a little while driving.” A high score on this questionnaire indicated a greater tendency to engage in thrill and adventure seeking activities while driving. The questionnaire used consisted of 8 items and used a 5-point Likert response scale ranging from 1 (never) to 5 (always). The reliability estimate of the Thrill and Adventure Seeking measure within the current study was acceptable (α = 0.87). 2.3.2. Driving Behaviour Questionnaire A modified version of Reason, Manstead, Stradling, Baxter, and Campbell (1990) Manchester Driving Behaviour Questionnaire (DBQ) was used to measure the work-related driving behaviors. The modifications were carried out to reflect Australian terminology and to better reflect the work driving context. For example, references to specific directions regarding where the car is turning (“left” or “right”) were removed and replaced with general terms like “turning.” In the original Manchester DBQ measure, Reason et al. (1990) identified two broad categories for aberrant driving behaviors: errors and violations. Additional modifications included the inclusion of 7 items relating to driving
Safety Climate
H3
n 688 210 64 49 510 220 153 67 39 22
H2
Risky Work Driving Behaviours
Fig. 1. Conceptual model of the current study.
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while fatigued and 4 items relating to distracting behaviors (e.g., mobile phone use) while driving. The inclusion of these items in the questionnaire was based on past research in the Australian work-related driving context (e.g., Davey, Wishart, Freeman, & Watson, 2007; Rowland et al., 2008) and discussions with the fleet management staff within the participating organizations. Previous research on work-related driving behaviors utilized this version of the DBQ (e.g., Davey, Freeman, & Wishart, 2006; Wishart et al., 2017). Items of the modified DBQ included statements such as: “Have difficulty driving because of tiredness or fatigue.” The modified DBQ used consisted of 31 items and used a 5-point Likert response scale ranging from 1 (never) to 5 (always). The reliability estimates of the DBQ subscales within the current study were acceptable (see Table 2) except for the driving while distracted subscale with a Cronbach alpha of .47. 2.3.3. Safety Climate Questionnaire The modified Safety Climate Questionnaire (SCQ) by Glendon and Litherland (2001) was also used in the study. For the development (including the factor structure and reliability statistics) of this modified questionnaire, please consult Wills, Biggs, and Watson (2005). The minor modifications in the questionnaire ensured that the questions measured the employees' perception of the company's safety climate with regard to work-related driving safety. The measure included items such as: “Fleet safety problems are openly discussed between employees and managers/supervisors”. The questionnaire items were also reduced to minimize administration time of the survey for participants. The modified version of the SCQ consisted of 12 items with a 5 point Likert response scale ranging from 1 (never) to 5 (always). Previous research within the Australian light vehicle fleet context has demonstrated that SCQ is a reliable tool in measuring drivers' perception of safety climate within an organization (Davey et al., 2007; Wills et al., 2006; Wills et al., 2009). The Cronbach alpha of the modified SCQ within the current study was strong (α = .94). 3. Results 3.1. Assumption checking All analyses were performed in IBM SPSS and AMOS version 23. Missing data, outliers, and normality of data were checked before conducting the main analyses. Two participants had substantial missing data and were deleted from subsequent analyses. Overall, there was minimal missing data (maximum of 0.8% for each item) and to preserve power, mean imputation was carried out. However, data within the variables of interest with a z-score of ≥ 3.29 were considered extreme outliers (Tabachnick & Fidell, 2012) and were deleted in the dataset (n = 29). The final number of participants included in the main analyses was N = 980. Inspection of the normality statistics demonstrated non-normal univariate and multivariate data, which could be problematic in the interpretation of results. Therefore, bootstrapping with 1,000 samples was performed to assess the proposed model and research hypotheses of the current study.
Table 2 Means, standard deviations, and correlations between age, driving behaviors, thrill and adventure seeking, and safety climate.
1. 2. 3. 4. 5. 6.
Driving errors Driving violations Driving distractions Driving fatigued Thrill and adventure seeking Safety climate
N = 980. ⁎⁎ p b .001.
M
SD
α
1
2
1.24 1.47 1.68 1.93 1.65
0.26 0.46 0.59 0.58 0.69
.70 .78 .47 .85 .87
– .47⁎⁎ .31⁎⁎ .53⁎⁎ .24⁎⁎
– .35⁎⁎ – .59⁎⁎ .44⁎⁎ – .43⁎⁎ .28⁎⁎ .36⁎⁎ –
4.04 0.93 .94 0.00
0.04
3
4
5
6
.11⁎⁎ .09⁎⁎ 0.06 –
3.2. Descriptives Age was negatively and significantly correlated with driving violations (r = −.13, p b .001), driving while fatigued (r = −.08, p = .014), and thrill and adventure seeking (r = −.16, p b .001). These findings suggest that younger work drivers are more likely to experience traffic violations while driving and driving while fatigued and have higher levels of thrill and adventure seeking. Age was also negatively correlated with driving errors (r = −.04, p = .221) and driving while distracted (r = −.04, p = .246) and positively correlated with safety climate (r = .06, p = .077) although these relationships were not significant. The influence of gender was also explored and a t-test showed that significant differences were only observed in distracted driving behaviors, with males more likely to drive while distracted (M = 1.77; SD = 0.60) compared to females, (M = 1.55; SD = 0.55), t(970) = 5.71, p b .001. Male work drivers also showed higher levels of thrill and adventure seeking (M = 1.77; SD = 0.76) compared to female work drivers, (M = 1.46; SD = 0.52), t(956.38) = 7.73, p b .001. There were no significant gender differences observed in any of the other variables. Table 2 presents the means, standard deviations, and correlations for driving behaviors, thrill and adventure seeking, and safety climate. As seen in Table 2, thrill and adventure seeking had moderate positive correlations (.24 to .43) with driving behaviors and the strongest relationship with driving violations. Safety climate, on the other hand, had weak positive correlations with driving behaviors (0.00 to 0.11), only showing significant relationships with driving while distracted and driving while fatigued. The correlation between thrill and adventure seeking and safety climate was positive but not significant. 3.3. Main analyses The hypothesized moderation model (Fig. 2) was investigated using path analysis in IBM AMOS 23. Due to the poor reliability score of the driving distraction subscale (.47), this subscale was excluded from the main analysis. The independent variables (thrill and adventure seeking, safety climate, and the interaction effect) were mean-centered prior to the analysis. Initially, the model fit was unsatisfactory, χ2(3) = 596.28, Bollen– Stine bootstrap p b .001, GFI = 0.82, CFI = 0.54, TLI = − 1.31, RMSEA = 0.45. The regression weights indicated that the main effects of safety climate to driving violations (β = − 0.04, p = .260) and driving while fatigued (β = −0.01, p = .851) were not significant as well as the covariance between safety climate and thrill and adventure seeking (β = 0.06, p = .066). Additionally, modification indices indicated a need to correlate the error terms for the driving behavior variables to provide a better fit. Since driving error, driving violations, and driving while fatigued are logically correlated, it is theoretically consistent to relate their error terms. Therefore, an alternative model was carried out (see Fig. 3). The hypothesized relationships between safety climate and driving violations as well as safety climate and driving while fatigued were taken out. The covariance between thrill and adventure seeking and safety climate was also removed from the model and the error terms were correlated. The second model provided a better fit χ2(3) = 4.87, Bollen–Stine bootstrap p = .214. The goodness-of-fit indices showed good adjustment of the model to the data (GFI = 0.99, CFI = 0.99, TLI = 0.99, RMSEA = 0.03). Table 3 shows the unstandardized and standardized path estimates and standard errors of the model. As shown in Table 3, thrill and adventure seeking, safety climate, and the interaction effect explained 8% of the variance for driving errors (p = .003), 19% of the variance for driving violations (p = .002), and 15% of the variance for driving while fatigued (p = .002). Thrill and adventure seeking significantly predicted driving errors, driving violations, and driving while fatigued, showing the strongest relationship with driving while fatigued. Safety climate, on the other hand, only significantly predicted driving errors.
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Fig. 2. Hypothesized model.
The interaction term between thrill and adventure seeking and safety climate was a significant predictor of driving errors, driving violations, and driving while fatigued in the model. These results indicated that safety climate negatively moderated the relationship between thrill and adventure seeking and risky driving behaviors that were measured in the current study. Fig. 4 demonstrates that when safety climate is low, the positive relationship between thrill and adventure seeking and risky driving behaviors is fairly strong. However, when safety climate is high, the positive relationship between risky driving behaviors and thrill and adventure seeking weakens. 4. Discussion The current study investigated the relationship between safety climate and thrill and adventure seeking, and their effect on risky work driving behavior. In regard to H1, the results demonstrated that thrill and adventure seeking has a significant positive relationship with risky work driving behaviors suggesting that employees that drive for work and possess a high need for thrill and adventure seeking are more likely to engage in risky work driving behaviors. This finding is consistent with previous research that examined the role of thrill and adventure seeking on risky driving behaviors within non-occupational (e.g., Jonah, 1997) and occupational settings (e.g., Wishart et al., 2017). The hypothesis that safety climate will have a negative relationship with risky work driving behaviors was only partially supported. The results of the current study only showed a significant negative relationship between safety climate and driving errors, which is a similar finding to previous studies that examined the impact of safety climate on work driving behaviors (e.g., Amponsah-Tawiah & Mensah, 2016; Wills et al., 2006). This result suggests that within an organizational work driving context, high levels of safety climate may contribute to a
reduction in drivers' work driving errors. Through a strong safety climate, drivers become more conscious of their driving and consequently awareness of safety, which is associated with less driving errors overall. However, safety climate appears to have little effect on work driver violations and driving while fatigued. This finding is in contrast with previous studies that found a significant relationship between safety climate, work-related driving violations, and driving while fatigued (Amponsah-Tawiah & Mensah, 2016). These results may indicate that other factors of influence were at play beyond the direct control of the driver (e.g., occupational stress and time pressure; Strahan et al., 2008). Another explanation could be that, the measure used in the current study did not include all the safety climate factors used by previous studies. For instance, Wills et al. (2006) only found a significant relationship between the safety rules dimension of safety climate and driving violations. On the other hand, the safety climate dimensions of management commitment, safety rules, and communication were significantly related to driving error, with management commitment acting as the strongest predictor (Wills et al., 2006). While not directly studied, the current results may imply that particular aspects of safety climate are more strongly related to certain aspects of work driving behaviors. The results show support for H3 indicating a moderating effect of safety climate on the relationship between thrill and adventure seeking and risky work driving behaviors. Specifically, the current study demonstrated that a high organizational safety climate can reduce the propensity to drive in a risky manner among workers with high levels of thrill and adventure seeking. 4.1. Practical applications This result has some implications for organizations seeking to improve the safety of employees within their work driving context. For
Fig. 3. Model 2 path analysis showing the significant standardized estimates between the variables of interest.
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such as in-vehicle monitoring systems, de-identified crash and traffic data or observations to measure the workers' risky driving behaviors. Second, the causal nature of the model is not possible to determine in cross-sectional research, therefore, a longitudinal research design is warranted to further study the depth of the relationship between the variables studied. The cross-sectional nature of the study also meant that the researchers have to rely on employees' self-report to assess the organization's safety climate. Objective assessment of the organization's safety climate or an employers' report may provide more support or complimentary assessment of safety climate. Lastly, not all factors of influence or behaviors were measured and therefore, results need to be interpreted accordingly. Potential future research could examine other factors such as work pressure, job demands, and occupational stress that may impact on work driving behaviors and its interplay with safety climate and sensation seeking personalities. Future research could also evaluate the impact of such programs and further seek to explore the interaction of safety climate on a range of other psychosocial and driving behavior factors believed to impact on work driving safety.
Table 3 Unstandardized and standardized path estimates and their corresponding standard errors. R2
Model paths
B
S.E.
β
Driving errors ← Thrill and adventure seeking 0.08 0.09⁎⁎ 0.01 0.24⁎⁎ Driving errors ← Safety climate −.02⁎ 0.01 −.07⁎ Driving errors ← TAS × SC −.06⁎⁎ 0.02 −.15⁎⁎ Driving violations ← Thrill and adventure seeking 0.19 .28⁎⁎ .02 .42⁎⁎ −.02 0.02 −.04 Driving violations ← Safety climatea Driving violations ← TAS×SC −.09⁎⁎ 0.02 −.10⁎⁎ Driving while fatigued ← Thrill and adventure seeking 0.15 0.29⁎⁎ 0.03 0.34⁎⁎ −0.00 0.02 −0.01 Driving while fatigued ← Safety climatea Driving while fatigued ← TAS × SC −.16⁎⁎ 0.03 −.17⁎⁎ Notes: TAS = thrill and adventure seeking; SC = safety climate. ⁎ p b .05. ⁎⁎ p b .001. a These paths were not included in the analysis of the second model.
instance, various industry settings due to the nature of the work required, may have the propensity to attract a higher proportion of employees with a predisposition for thrill and adventure seeking (e.g., Beus et al., 2015; Clarke & Robertson, 2008). However, although this type of employee may provide a favorable employee–organization or job fit, this match may also result in challenges in managing the safety of work driving and the enthusiasm and propensity for thrill and adventure seeking. For example, although the primary work or job role may require people with this particular trait, propensity to seek out activities that satisfy the thrill and adventure seeking desire while working may have adverse effects on other aspects of various job roles which may be considered not as engaging or dangerous (e.g., engaging in risky driving behaviors while working in order to experience thrill). However, the results of the current study indicated that a positive safety climate towards work driving safety, which consists of aspects associated with management commitment to safety and positive safety message communication, can temper the effects of thrill and adventure seeking tendencies. Therefore, organizations need to ensure that generating a positive work driving safety climate becomes a high priority. Consequently, to improve work driving safety, organizations and management may need to develop strategies to encourage and foster positive work driving safety climate, particularly within settings that may attract employees high in the thrill and adventure seeking personality trait.
References Amponsah-Tawiah, K., & Mensah, J. (2016). The impact of safety climate on safety related driving behaviors. Transportation Research Part F: Traffic Psychology and Behaviour, 40, 48–55. http://dx.doi.org/10.1016/j.trf.2016.04.002. Australian Bureau of Statistics (2015). Motor Vehicle Census. Canberra. Retrieved from http://www.abs.gov.au/ausstats/[email protected]/PrimaryMainFeatures/9208.0? OpenDocument. Australiasian Fleet Managers Association (2008). Review of the automotive industry 2008. Retrieved from http://www.afma.net.au/. Beus, J. M., Dhanani, L. Y., & McCord, M. A. (2015). A meta-analysis of personality and workplace safety: Addressing unanswered questions. Journal of Applied Psychology, 100(2), 481. http://dx.doi.org/10.1037/a0037916. Bureau of Labor Statistics (2016). Census of Fatal Occupational Injuries summary, 2015. Retrieved from https://www.bls.gov/news.release/cfoi.nr0.htm. Burns, P. C., & Wilde, G. J. (1995). Risk taking in male taxi drivers: Relationships among personality, observational data and driver records. Personality and Individual Differences, 18 (2), 267–278. http://dx.doi.org/10.1016/0191-8869(94)00150-q. Clarke, S., & Robertson, I. (2005). A meta-analytic review of the Big Five personality factors and accident involvement in occupational and non-occupational settings. Journal of Occupational and Organizational Psychology, 78(3), 355–376. http://dx.doi. org/10.1348/096317905x26183. Clarke, S., & Robertson, I. (2008). An examination of the role of personality in work accidents using meta-analysis. Applied Psychology, 57(1), 94–108. http://dx.doi.org/10. 1111/j.1464-0597.2007.00267.x. Dahlen, E. R., Martin, R. C., Ragan, K., & Kuhlman, M. M. (2005). Driving anger, sensation seeking, impulsiveness, and boredom proneness in the prediction of unsafe driving. Accident Analysis & Prevention, 37(2), 341–348. http://dx.doi.org/10.1016/j.aap. 2004.10.006. Darby, P., Murray, W., & Raeside, R. (2009). Applying online fleet driver assessment to help identify, target and reduce occupational road safety risks. Safety Science, 47(3), 436–442. http://dx.doi.org/10.1016/j.ssci.2008.05.004. Davey, J., & Banks, T. (2005). Estimating the cost of work motor vehicle incidents in Australia. Paper presented at the Australasian Road Safety Research Policing Education Conference, Wellington, New Zealand. Davey, J., Freeman, J., & Wishart, D. (2006). A study predicting crashes among a sample of fleet drivers. Paper presented at the Proceedings Road Safety Research, Policing and Education Conference, Gold Coast, Queensland (http://eprints.qut.edu.au/5959/1/ 5959_1.pdf).
4.2. Limitations and future research While the current study provided meaningful results, there are several limitations that should be mentioned and would provide directions for future research. First, it is important to acknowledge that the study is based on self-report measures and although considerable effort is implemented to decrease self-report bias (e.g., ensuring anonymity and confidentiality), it is possible that workers are unwilling to provide the most accurate response due to the fear of negative response from their managers. Future research could use other forms of data collection
3
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Fig. 4. Interaction effect of safety climate and thrill and adventure seeking on driving errors, driving violations, and driving while fatigued.
D. Wishart et al. / Journal of Safety Research 63 (2017) 83–89 Davey, J., Wishart, D., Freeman, J., & Watson, B. (2007). An application of the driver behaviour questionnaire in an Australian organisational fleet setting. Transportation Research Part F: Traffic Psychology and Behaviour, 10(1), 11–21. http://dx.doi.org/10. 1016/j.trf.2006.03.001. DeJoy, D. M. (1994). Managing safety in the workplace: An attribution theory analysis and model. Journal of Safety Research, 25(1), 3–17. http://dx.doi.org/10.1016/0022-4375 (94)90003-5. Fort, E., Ndagire, S., Gadegbeku, B., Hours, M., & Charbotel, B. (2016). Working conditions and occupational risk exposure in employees driving for work. Accident Analysis & Prevention, 89, 118–127. http://dx.doi.org/10.1016/j.aap.2016.01.015. Glendon, A. I., & Litherland, D. K. (2001). Safety climate factors, group differences and safety behaviour in road construction. Safety Science, 39(3), 157–188. http://dx.doi. org/10.1016/s0925-7535(01)00006-6. Hatfield, J., Fernandes, R., & Job, R. S. (2014). Thrill and adventure seeking as a modifier of the relationship of perceived risk with risky driving among young drivers. Accident Analysis & Prevention, 62, 223–229. http://dx.doi.org/10.1016/j.aap.2013.09.028. Jonah, B. A. (1997). Sensation seeking and risky driving: A review and synthesis of the literature. Accident Analysis & Prevention, 29(5), 651–665. http://dx.doi.org/10.1016/ s0001-4575(97)00017-1. Matthews, G., Desmond, P. A., Joyner, L., Carcary, B., & Gilliland, K. (1997). A comprehensive questionnaire measure of driver stress and affect. Traffic and transport psychology. Theory and Application, 317–324. Mooren, L., Grzebieta, R., Williamson, A., Olivier, J., & Friswell, R. (2014). Safety management for heavy vehicle transport: A review of the literature. Safety Science, 62, 79–89. http://dx.doi.org/10.1016/j.ssci.2013.08.001. Murray, W., Newnam, S., Watson, B. C., Davey, J., & Schonfeld, C. C. (2003). Evaluating and improving fleet safety in Australia. Canberra: Australian Transport Bureau (Retrieved from https://infrastructure.gov.au/roads/safety/publications/2003/pdf/eval_fleetsafe.pdf). Newnam, S., & Watson, B. (2011). Work-related driving safety in light vehicle fleets: A review of past research and the development of an intervention framework. Safety Science, 49(3), 369–381. http://dx.doi.org/10.1016/j.ssci.2010.09.018. Reason, J., Manstead, A., Stradling, S., Baxter, J., & Campbell, K. (1990). Errors and violations on the roads: A real distinction? Ergonomics, 33(10–11), 1315–1332. http://dx.doi.org/10.1080/00140139008925335. Rowland, B. D., Davey, J., Freeman, J. E., & Wishart, D. (2008). Work-related road safety risk assessment: Utilisation of self-report surveys to predict organisational risk. Paper presented at the Australasian Road Safety Research, Policing and Education Conference, Adelaide, SA. Safe Work Australia (2015). Work-related traumatic injury fatalities, Australia 2015. Canberra: Safe Work Australia (Retrieved from http://www.safeworkaustralia.gov. au/sites/SWA/about/Publications/Documents/979/work-related-traumatic-injuryfatalities.pdf). Schwebel, D. C., Severson, J., Ball, K. K., & Rizzo, M. (2006). Individual difference factors in risky driving: The roles of anger/hostility, conscientiousness, and sensation-seeking. Accident Analysis & Prevention, 38(4), 801–810. http://dx.doi.org/10.1016/j.aap. 2006.02.004. Seibokaite, L., & Endriulaitiene, A. (2012). The role of personality traits, work motivation and organizational safety climate in risky occupational performance of professional drivers. Baltic Journal of Management, 7(1), 103–118. http://dx.doi.org/10.1108/ 17465261211195892. Strahan, C., Watson, B., & Lennon, A. (2008). Can organisational safety climate and occupational stress predict work-related driver fatigue? Transportation Research Part F: Traffic Psychology and Behaviour, 11(6), 418–426. http://dx.doi.org/10.1016/j.trf. 2008.04.002. Stuckey, R., Lamontagne, A. D., Glass, D. C., & Sim, M. R. (2010). Occupational light vehicle use: Characterising the at-risk population. Journal of Health and Safety Research and Practice, 2(1). http://dx.doi.org/10.1179/oeh.2005.11.2.167 (online-online). Tabachnick, B. G., & Fidell, L. S. (2012). Using multivariate statistics (6th ed.). Boston, NY: Pearson.
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Wills, A. R., Biggs, H. C., & Watson, B. (2005). Analysis of a safety climate measure for occupational vehicle drivers and implications for safer workplaces. The Australian Journal of Rehabilitation Counselling, 11(01), 8–21. http://dx.doi.org/10.1017/ s1323892200000132. Wills, A. R., Watson, B., & Biggs, H. C. (2006). Comparing safety climate factors as predictors of work-related driving behavior. Journal of Safety Research, 37(4), 375–383. http://dx.doi.org/10.1016/j.jsr.2006.05.008. Wills, A. R., Watson, B., & Biggs, H. (2009). An exploratory investigation into safety climate and work-related driving. Work, 32(1), 81–94. http://dx.doi.org/10.3233/WOR-20090818. Wishart, D. (2015). The challenge of developing a fleet driving risk assessment tool: What can be learned from the process? Brisbane: Queensland University of Technology. Wishart, D., Rowland, B. D., Freeman, J. E., & Davey, J. (2011a). Driving for work: A series of organisational audit results. Paper presented at the Australasian College of Road Safety National Conference. Melbourne Convention and Exhibition Centre. Wishart, D., Rowland, B. D., Freeman, J. E., & Davey, J. (2011b). Fleet safety: Risk reduction for work related vehicle use. Paper presented at the Proceedings of 9th Industrial and Organisational Psychology Conference. Brisbane, Qld: Brisbane Convention and Exhibition Centre. Wishart, D., Somoray, K., & Rowland, B. (2017). Role of thrill and adventure seeking in risky work-related driving behaviours. Personality and Individual Differences, 104, 362–367. http://dx.doi.org/10.1016/j.paid.2016.08.033. Zohar, D. (1980). Safety climate in industrial organizations: Theoretical and applied implications. Journal of Applied Psychology, 65(1), 96. http://dx.doi.org/10.1037// 0021-9010.65.1.96. Zohar, D. (2010). Thirty years of safety climate research: Reflections and future directions. Accident Analysis & Prevention, 42(5), 1517–1522. http://dx.doi.org/10.1016/j.aap. 2009.12.019. Zuckerman, M. (1971). Dimensions of sensation seeking. Journal of Consulting and Clinical Psychology, 36(1), 45. http://dx.doi.org/10.1037/h0030478. Zuckerman, M. (1994). Behavioral expressions and biosocial bases of sensation seeking. New York, NY: Cambridge University Press. Dr. Darren Wishart is a registered psychologist and is currently employed in the Centre for Accident Research and Road Safety Queensland (CARRS-Q) at Queensland University of Technology. Dr. Wishart is involved in a variety of research and consultancies in areas such as work related road safety, motorcycle safety, and international road safety and community capacity building. In the organizational psychology domain Dr. Wishart's research and consultancy work has primarily focused on projects involving industry and safety from a systemic approach along with creating behavior change. Ms. Klaire Somoray is a Research Officer and Ph.D. Student in the Centre for Accident Research and Road Safety Queensland (CARRS-Q) at Queensland University Technology. Ms. Somoray is involved in various research projects within the work driving safety and traffic psychology domains. She is currently undertaking her Ph.D. in safety behaviors within the work driving context. Ms. Somoray has a particular interest in data analytics, role of technology in human behavior and organizational psychology. Ms. Amanda Evenhuis is a Research Officer in the Centre for Accident Research and Road Safety Queensland (CARRS-Q) at Queensland University of Technology. Ms. Evenhuis is involved in a variety of research projects including cycling and young driver safety and has a particular interest in training and capacity building. She is currently finishing her undergraduate in Psychology with a focus on Organisational Psychology. Ms. Evenhuis also has a Bachelor of Built Environment (Urban and Regional Planning) and a Graduate Diploma in Urban Design.