Self-reported engagement in driver distraction: An application of the Theory of Planned Behaviour

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Highlights

  • We surveyed 578 drivers ages 18 and up on self-reported distraction engagement.

  • Attitudes, perceived control, descriptive norms are predictors of engagement.

  • Injunctive norms is not a significant predictor of engagement.

  • Engagement is correlated with other unsafe driving behaviours.

  • It is also correlated with impulsiveness, venturesomeness, and sensation seeking.

Abstract

Driver distraction is a significant concern for roadway safety. As drivers often engage willingly in secondary tasks, it is crucial to understand the social-psychological factors underlying these behaviours. A useful framework for understanding these factors is the Theory of Planned Behaviour (TPB). This paper investigates the efficacy of TPB in predicting self-reported engagement behaviour in a number of distraction tasks through the assessment of attitudes, perceived behavioural control, descriptive norms, and injunctive norms. This work also investigates the relation of self-reported distraction engagement with personality traits and other unsafe driving behaviours. Data collection utilised the Susceptibility to Driver Distraction Questionnaire (SDDQ), which was built with TPB as the framework, as well as the Manchester Driver Behaviour Questionnaire (DBQ) and various personality questionnaires. A total of 578 drivers, both genders, ages 18+, were surveyed. Self-reported distraction engagement was associated with impulsive, venturesome, and sensation seeking personalities, and with reportedly higher level of unsafe driving behaviours. Further, attitudes, perceived behavioural control, and descriptive norms were found to be significant predictors of self-reported engagement after controlling for age group and gender. Injunctive norms, which describe the perceived expectations of what the driver ought to do, were not significant. Gender was not significant in predicting engagement, but older drivers (60+) reported a marginally lower level of engagement than drivers between the ages of 26 and 39. Our findings demonstrate the usefulness of TPB for analysing self-reported distraction engagement, and suggest that drivers may be more heavily influenced by what other drivers do on the road, rather than what they perceive they ought to do, when it comes to engaging in distractions.

Introduction

The National Highway Traffic Safety Administration (NHTSA) reported that approximately 3154 crash-related fatalities (10% of all fatal crashes) and 424,000 injuries (18% of injury crashes) in 2013 were distraction affected (National Highway Traffic Safety Administration, 2015). Furthermore, an Australian national crash study identified over 70% of distractions to be voluntary, including the use of mobile phones, adjusting in-vehicle systems, and interacting with passengers (Beanland, Fitzharris, Young, & Lenné, 2013). It is therefore important to understand the social and psychological aspects that guide drivers’ willingness to engage in these voluntary distractions. However, these aspects remain relatively little studied despite the mounting evidence of detrimental effects of distractions on driver performance (e.g., Beanland et al., 2013, Horberry et al., 2006, Ranney, 2008, Wierwille, 1993, Young et al., 2007).

Horrey and colleagues led the efforts in understanding social-psychological factors associated with voluntary distractions by conducting surveys as part of a laboratory study, which evaluated training as a means to mitigate driver distraction (Horrey and Lesch, 2008, Horrey et al., 2009). In this study, 40 participants were surveyed on their willingness to engage in driver distractions before and after receiving a video-based training intervention. The researchers collected data on demographics, personality (sensation seeking, impulsivity, and anger), cognitive failures, and opinions regarding various in-vehicle distractions. The study found that self-reported willingness to engage in driver distraction was associated with past behaviour, confidence in dealing with distractions, perceived risk of distractions, and tendencies towards sensation seeking (Horrey & Lesch, 2008). Drivers in the training group, but not the control group, showed a decline in their self-reported willingness to engage in distracting activities along with a corresponding increase in perceived risk (Horrey et al., 2009). These findings provide important insights towards the psychology of drivers around distractions.

In this paper, we aim to further investigate the social and psychological factors that underlie voluntary driver distractions. To this end, we report a survey study with a sample size of 578 respondents and data collected on demographics, personality, engagement in risky driving behaviours, and perceptions and behaviours about distractions. In this survey, we adopted the Theory of Planned Behaviour (Ajzen, 1991), a widely used framework from the social sciences, for understanding perceptions and behaviours about driver distractions.

The Theory of Planned Behaviour (TPB) stipulates that behaviour extends from intent, which in turn is a product of social-psychological constructs of attitudes, subjective norms, and perceived behavioural control (Ajzen, 1991). More specifically, attitudes refer to the (positive or negative) evaluation of the expected outcomes following the behaviour in question. Subjective norms describe the perceived pressure or expectation from others to commit the particular behaviour. Perceived behavioural control is the belief of how well one is able to carry out the particular behaviour. Ajzen’s theorized relationships between behaviour, intention, and these three social-psychological constructs are shown in Fig. 1a (Ajzen, 1991).

As a model for predicting social behaviour, TPB has been widely applied in fields as diverse as preventive healthcare, such as smoking (e.g., Conner, Sandberg, McMillan, & Higgins, 2006) and alcohol consumption (e.g., Huchting, Lac, & LaBrie, 2008), as well as education (e.g., cheating, Mayhew, Hubbard, Finelli, Harding, & Carpenter, 2009) and marketing research (e.g., customer satisfaction, Liao, Chen, & Yen, 2007). TPB has also received increasing attention in road safety, where driving violations are often intentional. For instance, TPB has been found useful for understanding speeding (e.g., Paris and den Broucke, 2008, Warner and Åberg, 2006), drinking and driving (e.g., Chan et al., 2010, Moan and Rise, 2011), and aggressive driving behaviours (e.g., Forward, 2009, Iversen, 2004, Parker et al., 1992).

Forward (2009) sampled 275 drivers to study their intentions to commit two driving violations: speeding in an urban area and dangerous overtaking. In addition to the TPB constructs, the author included another norm-related variable in her study: descriptive norms. In contrast with injunctive norms (“What I think others expect me to do”) prescribed in TPB, descriptive norms describe an individual’s beliefs about other people’s behaviour (what is done) rather than what ought to be done. The study found descriptive norms to make a unique contribution towards the prediction of the two driving violation intentions. Both forms of norms have also been studied in the context of driving violations, outside the TPB framework (e.g., for injunctive norms, see Elliott, Armitage, & Baughan, 2003; for descriptive norms, see Åberg et al., 1997, Haglund and Åberg, 2000; for discussions of both, see Cestac et al., 2011, Paris and den Broucke, 2008). However, it should be noted that literature often does not differentiate between the two types of norms and the term social norms may refer to either, depending on the study. In our survey, we explicitly defined and included both injunctive and descriptive norms in order to differentiate their potential influence on driver distraction engagement.

Compared to driving violations such as speeding, the use of TPB is relatively new for driver distraction research, and majority of the work in this area focuses on cell phone usage while driving, such as the work by Walsh and colleagues (Walsh et al., 2008, White et al., 2010). Among other findings, these authors found that attitudes and pressure from significant others (injunctive norms) regarding the use of cell phones (i.e., calling and texting) while driving were significant predictors of a driver’s intention to do so. In this paper, we apply TPB to a broader range of distractions using an online survey.

Lee, Young, and Regan (2008) define driver distraction as the diversion of attention away from activities critical for safe driving toward a competing activity. This definition suggests that competing activities become distractions only when they interfere with safe driving. In this paper, we adopt a broader definition of driver distraction given by the U.S. Department of Transportation, which states that driver distraction is “any activity that could divert a person’s attention away from the primary task of driving” (National Highway Traffic Safety Administrations, n.d.). It is important to note that while distraction is a concern for driver safety in general, within our adopted definition, not all distractions necessarily lead to an increased crash risk (e.g., listening to the radio). In certain circumstances, distractions may even have protective effects. For example, Fitch, Grove, Hanowski, and Perez (2014) found that commercial vehicle drivers engaging in phone conversations to be at decreased risk for safety-critical events due to compensation behaviours, such as a decrease in their frequency of lane changes.

Given our interest in the role of social-psychological factors in facilitating driver distraction engagement, the present work concerns the subset of driver distractions that can be voluntarily engaged in by drivers, regardless of the actual risk level associated with them. We instead capture perceived risk, arguably a better predictor of distraction engagement than actual risk, through survey items on perceived control of the driving task while engaging in distractions (see Section 2.1 for a description of the instrument). Overall, we surveyed six distractions that drivers may voluntarily engage in: (1) conversations on the phone, (2) manual interaction with a phone, (3) manual interaction with in-vehicle technology, (4) reading of roadside advertisements, (5) visual dwelling on roadside accident scenes, and (6) conversation with passengers. This list of distractions was selected in part based on prevalence: conversations with passengers were reported by NHTSA as the secondary activity drivers most frequently engaged in (Schroeder, Meyers, & Kostyniuk, 2013); items regarding cell phone use and in-vehicle technology are also existing distraction attributes identified in NHTSA’s crash databases: Fatality Analysis Reporting System (FARS) and General Estimates System (GES) (National Highway Traffic Safety Administrations, 2015). Furthermore, we followed Regan, Young, Lee, and Gordon’s taxonomy (2008) to include distractions stemming from different kinds of sources: inside (e.g., in-vehicle technology) and outside (roadside advertisements and accident scenes) of the vehicle; technology-based (e.g., cell phones) and non-technology based (passengers).

Respondents were surveyed on their frequency of engagement in each of the abovementioned activities. As with any subjective measure, self-reported engagement may not be completely accurate and may in part reflect intentions rather than actual behaviour. Given that TPB distinguishes between intention and behaviour, direct measures of behaviour would be useful to have. However, it is challenging and costly to log certain behaviours in the context of driver distraction; we therefore relied on simple surveys to serve as an initial assessment of voluntary engagement in driver distraction. Tying our findings to actual behaviour is a point of future research.

Survey respondents were also probed on their attitudes, perceived control of the primary driving task, and perceived norms with respect to each of the six distractions. Similar to Forward (2009), we included descriptive norms (what others do) in addition to the TPB construct of injunctive norms (expectations from people important to the respondent). This additional measure allowed us to investigate how drivers perceive, and are influenced by, other drivers’ distraction behaviours. Fig. 1b shows a modified TPB structure that was tested in the present study.

Demographic information of age and gender was also collected, as studies have shown that distraction engagement and distracted driving performance vary with gender and age. NHTSA reported that young drivers 18–20 contribute to 6% of the licensed driver population but disproportionally account for 13% of the drivers involved in a distracted driving crash in the United States (Singh, 2010). In Australia, men, younger drivers, and metropolitan residents were reported to be more likely to use a phone while driving and to have a higher frequency of use (McEvoy, Stevenson, & Woodward, 2006). In terms of performance, a simulator study, which had participants drive through a scenario where they interacted with a cellular phone, found that older participants performed worse than younger participants as assessed by changes in their brake response time, stopping time, stopping distance, and stop light compliance (Hancock et al., 2003, Lesch and Hancock, 2004). Further, another simulator study found older drivers to drive slower than younger drivers when asked to interact with hands-free cell phones and in-vehicle systems (Horberry et al., 2006). This result can be explained by cognitive saturation or by self-regulation as older drivers have been reported to self-regulate their driving as a way to cope with their declining health and cognitive abilities (Donorfio, D’Ambrosio, Coughlin, & Mohyde, 2009).

In terms of gender differences, Lesch and Hancock (2004) found that under distraction conditions, female drivers experienced a larger decline in performance with respect to longer brake response delays and greater red light noncompliance than their male counterparts. This study also found that male drivers’ confidence was more reflective of how they actually performed with a cellphone task than females; and older male drivers who stated that they had the ability to drive well using a cell phone did indeed perform well. In general, young drivers, particularly males, tend to overestimate their driving ability, especially with respect to their peers’ driving ability; they are also prone to underestimating their risk of a crash and are more willing to engage in a behaviour that they know is risky (Deery, 2000, Lee, 2007).

Given the well documented age and gender differences found in the driving literature, our analysis also accounted for these two factors in addition to TPB constructs. Finally, we were also interested in how distraction engagement is associated with personality and other unsafe driving behaviours. As was done in Horrey and Lesch (2008), we administered questionnaires about personality and unsafe driving behaviours.

Section snippets

Instrument

Susceptibility to Driver Distraction Questionnaire (SDDQ, Feng, Marulanda, & Donmez, 2014) was administered online to collect data used in this study. Self-reported frequency of distraction engagement was assessed by pairing the questionnaire item “When driving, you…” with six driver distractions: (1) have phone conversations, (2) manually interact with a phone (e.g., sending text messages), (3) adjust the settings of in-vehicle technology (e.g., radio channel or GPS), (4) read roadside

Internal consistency

Cronbach alpha statistics were computed using standardized coefficients to evaluate the consistency of responses to the six distraction items within each construct. Results found acceptable internal consistency of the constructs: engagement: .715; attitudes: .697; perceived control: .808; descriptive norms: .778; and injunctive norms: .811. It is noted that Cronbach’s alpha for attitudes was borderline in meeting the .70 cut-off value (Yu, 2001). There were no missing data in the sample given

Discussion

We surveyed 578 licensed drivers, ages 18 and up, to learn about the social and psychological factors underlying voluntary engagement in driver distractions, namely attitudes, perceived control, and descriptive and injunctive norms. Categorized into four age groups: 18–24 (young adult), 25–39 (adult), 40–59 (middle age), and 60+ (senior), our respondents were probed on six distractions as described earlier. Not surprisingly, drivers reported different levels of engagement with respect to

Acknowledgements

The funding for this work was provided by the Toyota Collaborative Safety Research Center (CSRC) and Auto 21 Network of Centres of Excellence. Many thanks to James Foley, Kazu Ebe, and Chuck Gulash from Toyota CSRC for their feedback on this paper as well as providing directions for our research in general. Furthermore, a subset of this data (254 out of the current 578 sample size) was previously analysed via simple correlations, and was reported in Feng et al. (2014). Efforts were made to

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