REDUCINGMOBILEPHONEUSEWHILEDRIVINGINSOUTH

AUSTRALIA

Report prepared for:

The Motor Accident Commission (MAC) Level 2, 121 King William Street, �GPO Box 2438, Adelaide SA 5001

Report prepared by:

The Institute for Choice (I4C) University of South Australia Level 13, 140 Arthur Street, North Sydney NSW 2060

Internal contributors:

Akshay Vij Maria Lambides Karen Cong Subodh Dubey Spring Sampson

Version:

November 30, 2016

Table of Contents

1 Introduction ………………………………………………………………………………………….. 4

2 Literature review …………………………………………………………………………………….. 4 2.1 Mobile phone use while driving …………………………………………………………………. 5 2.2 Behavioral constructs ……………………………………………………………………………. 6 2.2.1 Attitudes ………………….……………………………………………………………… 6 2.2.2 Perceived behavioral control …………………………………………………………….. 7 2.2.3 Normative beliefs ………………………………………………………………………... 8 2.2.4 Other constructs ………………………………………………………………………...... 8 2.3 External instrumental factors ……….…………………………………………………………… 9 2.4 Demographic variables …………………………………………………………………………... 9

3 Data ……………………………………………………………………………………………………. 9

4 Descriptive analysis …………………………………………………………………………………... 10

5 Multivariate analysis …………………………………………………………………………………. 15 5.1 Measurement model ……………………………………………………………………………... 15 5.1.1 Attitudes …………………………………………………………………………………. 16 5.1.2 Perceived behavioral control …………………………………………………………….. 18 5.1.3 Normative beliefs ………………………………………………………………………... 18 5.2 Structural model ………………………………………..………………………………………... 21 5.2.1 MPUWD behaviors ……………………………………………………………………… 21 5.2.2 Compensating behaviors ………………………………………………………………… 24 5.2.3 Normative beliefs ………………………………………………………………………... 26

6 Policy analysis ………………………………………………………………………………………… 28

References ………………………………………………………………………………………………… 31

Appendix A: Survey instrument ……………………………………………………………………........ 34

Appendix B: Respondent open text feedback ……………………………………………………………. 43

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1. Introduction

The Motor Accident Commission (MAC) manages South Australia’s “road safety communications program and provides funding for projects that aim to reduce the number and impact of road injuries and deaths.” Road traffic crashes are a global problem. In South Australia, official statistics show that in 2015 alone, road traffic crashes resulted in 759 serious injuries and 102 fatalities. Distracted driving is a significant contributor to motor vehicle accidents and fatalities, and mobile phone use while driving (MPUWD) is a particularly significant form of driver distraction that continues to be on the rise (Rumschlag et al., 2015).

Studies that have evaluated driver performance have repeatedly found that MPUWD impacts driving behavior through its deleterious effects on the driver’s situation awareness and cognitive workload, compromising the safety of the driver, passengers and other road users (Owens et al., 2011; Hosking et al., 2007; Strayer and Drews, 2007; Strayer et al., 2006; Beede and Kass, 2006; Ma and Kaber, 2005). For example, a North American study by Redelmeier and Tibshirani (1997) finds that MPUWD increases the risk of collision by a factor of four. MPUWD is illegal throughout Australia, “yet observational studies reveal that, at any one time, approximately 2% of Australian drivers are engaging in this behaviour” (Walsh et al., 2008; for comparison, refer to Glendon and Sutton, 2005; McEvoy et al., 2005; Taylor et al., 2003).

MAC is interested in understanding ways in which MPUWD can be reduced through the design of public information campaigns. This study will use choice-based methods to achieve the following broad objectives: (1) identify the key factors underlying an individual’s engagement in MPUWD; and (2) develop a set of decision tools and strategies that can be used by MAC to inform the design of interventions seeking to reduce MPUWD.

The remainder of the report is organized as follows: Section 2 reviews past studies that have sought to identify and measure the effect of different determinants of MPUWD; Section 3 describes the data used by this study to address our research objectives; Section 4 undertakes a descriptive analysis of the responses to statements measuring each of the behavioral constructs; Section 5 presents findings from a multivariate analysis of different aspects of MPUWD, such as texting and talking, as a function of external instrumental factors, such as fines and enforcement rates, internal behavioral factors, such as attitudes, perceived behavioral control and normative beliefs, and demographic variables, such as age and gender; and Section 6 concludes the report with a discussion on what our findings imply for practice and policy. There are two appendices to this report: Appendix A contains a copy of the survey instrument that was used to collect the data described in Section 3; and Appendix B comprises a transcription of the open text feedback received from study participants at the end of the survey exercise.

2. Literature review

In this section, we review the literature on the major determinants of MPUWD. Section 2.1 discusses how MPUWD has typically been measured and evaluated in the past; Section 2.2 summarizes findings from previous studies that have examined the relationship between behavioral constructs, such as attitudes and normative beliefs, and MPUWD; Section 2.3 reviews past research on the impact of external instrumental factors, such as fines and enforcement rates; and Section 2.4 concludes this section with a discussion of relevant demographic determinants of MPUWD, such as age and gender.

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2.1 Mobile phone use while driving

Past studies that have sought to understand the determinants of MPUWD have measured the behavior in one of three broad ways: (1) direct observation; (2) analysis of historical crash data; or (3) self-reported behavior. Over subsequent paragraphs, we review the advantages and disadvantages of each method.

MPUWD may be observed directly, through human observers, driving simulators or video camera footage. For example, Horberry et al. (2001) used roadside observers stationed at 19 observation points with hand counters to measure the incidence of MPUWD in Western Australia. Glendon and Sutton (2005) report findings from an observational study of nearly 2,700 driving cases in Queensland, Australia, where data was collected by a human observer in a moving vehicle. Foss and Goodwin (2014) equipped vehicles of 52 high-school age drivers for 6 months with unobtrusive event-triggered data recorders that obtain 20-second clips of video, audio, and vehicle kinematic information when triggered. A low recording trigger threshold was set to obtain a sample of essentially random driving segments along with those indicating rough driving behaviors. Driving simulators have been used extensively to examine the effects of MPUWD on driver performance under different contexts and conditions (see, for example, Rumschlag et al., 2015; Owens et al 2011; Strayer and Drews, 2007; Beede and Kass, 2006; Ma and Kaber, 2005). Direct observation, by virtue of being labor intensive, can often prove to be operationally difficult and financially unviable. However, insights from actual observed data are expected to offer a high degree of validity, and the collection of the same should be worth exploring during future research.

Historical crash data may be gathered through incident reports prepared by traffic authorities or insurance companies. Incident reports often contain basic demographic information relating to the individual(s) involved in the incident, such as gender or age. Unfortunately, information pertaining to more intangible behavioral factors, such as attitudes towards risk-taking behaviors, is rarely recorded, and must be collected through supplementary methods. For example, Redelmeier and Tibshirani (1997) recruited study participants from visitors to the North York Collision Reporting Centre in Toronto, Canada. Consenting subjects provided access to both crash reports and mobile phone records, and other information related to personal characteristics was collected through additional questionnaires. However, as with direct observation, such methods can be operationally difficult and financially unviable as well, and we leave them as a subject for future research.

Due to reasons mentioned above, studies have usually examined self-reported data, where individuals are asked to indicate the frequency with which they have engaged in different dimensions of MPUWD, such as texting or talking, in the past (Bingham et al., 2015; Carter et al., 2014; Bayer and Campbell, 2012; Weller et al., 2012; Atchley et al., 2011; Feldman et al., 2011; Harrison, 2011). Such data is typically collected through phone or web-based survey instruments that may include additional questions about a respondent’s demographics, attitudes, normative beliefs, etc. The method offers an inexpensive and information-rich alternative to both direct observation and analysis of historical data. While we are not aware of any studies that have compared self-reported measures of MPUWD with directly observed measures, similar comparisons across other contexts, such as speeding (see, for example, West et al., 1993 and Abers et al., 1997), have found the two measures to be significantly and positively correlated.

For these reasons, this study will employ self-reported measures of MPUWD. In particular, we want to measure the frequency with which study participants engage in the following four aspects of behavior while driving: (1) initiate phone calls; (2) receive phone calls; (3) read incoming mail or text messages; and (4) send mail or text messages. Given the interest in distinguishing between hand-held and hands-free use, we will also collect information about availability and use of hands free technologies.

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2.2 Behavioral constructs

Public information campaigns seeking to change particular health and safety related behaviors, such as MPUWD, do not often have a sound theoretical basis (Maibach and Parrott, 1995). Research has shown that campaigns that are built around an identifiable theoretical model tend to be more successful than those in which such a framework is absent (Elliott, 1993). “Social cognition models play a key role in health psychology in that they assist in highlighting the behaviours that influence health and determine which individuals will and those who will not perform such behaviours… social cognition models may have a two fold role within the realm of driver behaviour in that not only can … they be used as a framework to understand specific behaviours under review but that they may also provide a framework for describing and developing remedial strategies” (Carcary, 2000). If the beliefs and values that motivate individuals to engage in MPUWD can be identified, then attempts to change that behavior can be more precisely targeted (Parker et al., 1996). For an excellent review of the different models of social cognition used for the design of media campaigns seeking to discourage risky road use behaviors, see Delaney et al. (2004).

In the context of MPUWD, the Theory of Planned Behavior has emerged as the preeminent social cognition model. For example, Walsh et al. (2008) find that the theory accounts for 32% of the variability in people’s intentions to use a mobile phone while driving and Zhou et al. (2009) find that that the theory explains 43% and 48% variance in intention to use a hands-free and hand-held mobile phone, respectively, while driving. More recently, attempts have been made to expand the original theory to include the influence of other determinants, such as past behavior (Nemme and White, 2010), emotion and attention regulation (Feldman et al., 2011), personality traits (Rozario et al., 2010) etc.

In the sections that follow, we will review some of the more salient constructs identified across one or more studies, and how they relate to MPUWD. Sections 2.2.1, 2.2.2 and 2.2.3 summarize findings from studies that have respectively examined the impact of attitudes, perceived behavioral control, and normative beliefs on MPUWD; and Section 2.2.4 concludes the subsection with an overview of other constructs that may also be relevant in the context of MPUWD.

2.2.1 Attitudes

An attitude may be defined as an expression of favor or disfavor towards a behavior. The effect of attitudes on different behaviors has attracted considerable attention across multiple disciplines. The theory of planned behavior (Ajzen, 1991), and its close cousin the theory of reasoned action (Fishbein and Ajzen, 1974), have emerged as powerful paradigms with which to measure and evaluate attitude-behavior relationships. Attitudes are typically understood as multidimensional constructs with cognitive, affective and conative components (Rosenberg & Hovland, 1960). Over subsequent paragraphs, we describe each of these three constituent constructs in greater detail.

Cognitive attitudes denote an individual’s thoughts and beliefs towards a particular behavior. In the context of MPUWD, relevant cognitive attitudes include attitudes towards perceived risk, such as severity (e.g. “It would be very costly if I was involved in a crash because I was talking on the phone”) and susceptibility (e.g. “If I text while driving, it is very unlikely that I will be involved in a traffic crash), and attitudes towards perceived benefits (e.g. “Using a mobile phone while driving increases my productivity”). Cognitive attitudes are an important component of almost all social cognition models, and have been found to be significant determinants of MPUWD (see, for example, Nemme and White, 2010; Zhou et al., 2009; Walsh et al., 2008; White et al 2007). For this study, we adapted the 16-item scale developed by Weller et al. (2010) to measure individual

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perceptions of severity and susceptibility. Similarly, based on findings reported by Lissy et al. (2000) and White et al. (2007), we developed a 5-item scale to measure individual perceptions of perceived benefits.

Affective attitudes denote an individual’s feelings and emotions towards a particular behavior. In the context of MPUWD, relevant affective attitudes may include anxiety (e.g. “I feel anxious if I am without my phone for long periods of time”) and sensation seeking (e.g. “Talking on the phone while driving cheers me up”). The construct has been referred to variously as possession attachment (Weller et al., 2012), emotion regulation (Feldman et al., 2011) and cell phone dependence (Struckman-Johnson et al., 2015). While the influence of affective attitudes has been found to be significant across each of these studies, the ability to change these attitudes through public information campaigns is not clear. Therefore, for the purposes of the present study, we do not include affective attitudes in our analysis.

Conative attitudes denote an individual’s intentions and personal motivations towards a particular behavior. In the context of MPUWD, relevant conative attitudes may include goal-setting (e.g. “I will not text while driving”) and willingness to change behavior (e.g. “I really wish to text less while driving”). Conative attitudes overlap with intentions, as defined by Ajzen (1991) in his exposition of the theory of planned behavior. Empirical evidence indicates that conative attitudes, or intentions, are important determinants of MPUWD (see, for example, Walsh et al., 2008), However, empirical evidence also indicates that conative attitudes themselves are determined by cognitive and affective attitudes and other behavioral constructs, and empirical evidence that shows that behavior intervention strategies, such as public information campaigns, can change conative attitudes directly is limited. Therefore, we exclude conative attitudes from our analysis as well.

2.2.2 Perceived behavioral control

Perceived behavioral control refers to an individual’s perception of the ease or difficulty of performing a particular behavior (Ajzen, 1991). Perceived behavioral control, as defined in the theory of planned behavior, is consistent with Bandura’s (1982) notion of perceived self-efficacy, defined as an individual’s judgment “of how well one can execute courses of action required to deal with prospective situations”. Self-efficacy and perceived behavioral control can, and usually do, vary across situations and actions. Evidence across multiple empirical contexts indicates that an individual’s behavior is strongly influenced by their perceived level of control. In the context of MPUWD, perceived behavioral control may include self-belief (e.g. “I am a more skillful driver than most other drivers on the road”) and ability to withstand external pressures (e.g. “It isn’t always up to me whether or not I use a mobile phone while driving”). Multiple studies have suggested that drivers “experience social pressure to respond as quickly as possible to incoming calls and text messages” (Waddell and Wiener, 2014; for comparison, refer to Atchley et al., 2011; Lindqvist & Hong, 2011; Nemme & White, 2010).

However, empirical evidence on the relationship between MPUWD and perceived behavioral control is not consistent. Results from Walsh et al. (2008) and Bayer and Campbell (2012) indicate that the influence of perceived behavioral control is not statistically significant, results from Rozario et al. (2010), Holland and Rathod (2013) and Waddell and Wiener (2014) indicate that the influence is significant, and results from Nemme and White (2010) and Struckman-Johnson et al. (2015) are ambivalent (the impact of the construct is found to be statistically significant for certain behaviors and statistically insignificant for others). However, given the importance of the construct to multiple social cognition models, and empirical evidence from other behavioral contexts in support of the relationship between perceived behavioral control and behavior, it was deemed important to empirically test the relationship. And therefore, measures of perceived behavioral control were included in our analysis. Measures of self-belief were adapted from Ho and Gee (2008) and measures of external pressures were adapted from Waddell and Wiener (2014) and Nemme and White (2010).

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2.2.3 Normative beliefs

Normative beliefs may be understood in one of three broad ways. Injunctive normative beliefs denote an individual’s perception of the extent to which other individuals who are important to them think they should or should not engage in particular behaviors. Injunctive beliefs have been studied as part of the theory of planned behavior, but have not received as much attention from other social cognition models. In the context of MPUWD, the impact of injunctive beliefs has found to be significant by a number of studies (see, for example, Carter et al., 2014; Bayer and Campbell, 2012; and Rozario et al., 2010).

However, it has been argued that significant others are often not present in traffic situations, and the effect of injunctive beliefs on behavior, while significant, may not be large (Nemme and White, 2010). Descriptive beliefs denote an individual’s perception of how other individuals who are important to them behave in a given situation. Studies that have examined the influence of descriptive beliefs on MPUWD have typically found them to be statistically significant (see, for example, Bingham et al., 2015 and Carter et al., 2014).

Moral beliefs have been defined as values that individuals think of as important to their social self-image. If a particular behavior is inconsistent with their sense of self as viewed from the perspective of others important to them, the anticipated regret from engaging in that behavior may prevent them from engaging in said behavior (De Pelsmacker and Janssens, 2007). Moral norms have not attracted as much attention, though some recent studies have reported statistically significant relationships between moral norms and MPUWD (see, for example, Bayer and Campbell, 2012 and Nemme and White, 2010). This study will examine the impact of injunctive, descriptive and moral beliefs on MPUWD.

2.2.4 Other constructs

Though the names used to describe particular constructs may often vary across different social cognition models, we’ve attempted to include most of the constructs that fall within the theory of planned behavior, the health belief model, and the protection motivation theory. For reasons described in subsequent paragraphs, we will be limiting attention to the constructs identified in Sections 2.2.1, 2.2.2 and 2.2.3, and not include other constructs, namely habits and personality traits, in our analysis of MPUWD behavior.

Habit and past behavior have frequently been included by past studies on the determinants of MPUWD. For example, Bayer and Campbell (2012) tested the potential of the frequency-independent components of habit, or automaticity, to predict the rate of texting while driving, and found it to be statistically significant. Similar results have been reported by other studies, such as Nemme and White (2010). It should be unsurprising that past behavior is a good predictor of current and future behavior. Human beings are creatures of habit, and many particular behaviors are performed almost automatically (Bargh and Chartrand, 1999). However, past behavior is not necessarily a construct that can be influenced by public information campaigns, and understanding its impact on MPUWD would be of limited value to an agency such as MAC.

The influence of personality traits on risky driving behaviors has also been examined by past studies, both in the context of MPUWD and other behaviors, such as speeding. However, evidence in support of including these variables within extant representations of MPUWD is limited. For example, Rozario et al. (2010) tested the influence of drivers’ personality traits of neuroticism and extroversion, measured using the Neuroticism Extroversion Openness-Five Factor Inventory (NEO-FFI). “Generally, neuroticism and extroversion did not significantly predict drivers’ willingness above and beyond the TPB and demographic variables.”

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2.3 External instrumental factors

Fines and enforcement rates are perhaps the most important external instrumental factors that policy-makers can use to influence dangerous driving behaviors, such as MPUWD. And yet, studies that have examined the impact of internal behavioral constructs have typically overlooked the impact of these external instrumental factors (c.f. Carter et al., 2014; Nemme and White, 2010; Zhou et al., 2009). And analogously, studies that have examined the impact of external instrumental factors have typically overlooked the impact of internal behavioral constructs. Moreover, the evidence itself on the effectiveness of such deterrence measures is, at best, limited. For example, Johal et al. (2005) used roadside observation to determine that mobile phone usage rates while driving dropped from 1.85% to 0.97%, following the introduction of legislation in the United Kingdom banning the use of hand-held mobile phones while driving. However, a similar study by Foss et al. (2008) found that the introduction of legislation in North Carolina banning MPUWD among drivers younger than 18 years had “little to no effect on teenage drivers’ use of cell phones.” Given MAC’s interest in understanding how the impact of fines and enforcement rates compares to the impact of softer measures, we will include these variables in our analysis through the use of stated preference experiments (described in greater detail in Section 3).

2.4 Demographic variables

Numerous studies have reported significant correlations between MPUWD and demographic characteristics. The correlation between age and gender and risky driving behaviors is well established (see, for example, Struckman-Johnson et al., 2015; Carter et al., 2014; Feldman et al., 2011; Atchley et al., 2011; Ho and Gee, 2008). Other demographic variables that have been found to be correlated with risky driving behaviors include driver experience (Rothengatter and de Bruin, 1988), car ownership (Levelt and Swov, 1998), car type (Evans and Wasielewski, 1983) and insurance (Bolderdijk et al., 2011). In our analysis, we will be testing the influence of these and other variables denoting income, education, household structure and employment status.

3. Data

Data for our analysis came from a sample of South Australian residents. In all, 413 respondents were drawn from a consumer panel of a major national market research company. The survey was administered online, through a web-based interface. Given the higher incidence of MPUWD among younger adults, minimum quotas were set for the following three age groups: 18-21 years, 22-25 years, and 26-30 years. As can be seen from Table 1, we have a good spread of respondents across all age groups for both genders. The sample was additionally stratified geographically, with a quota of 50 respondents corresponding to regional South Australia and the remainder of the sample drawn from the Adelaide metropolitan region. All respondents reside in South Australia, own a mobile phone, usually drive a car and reported driving it at least once in the last two weeks.

Table 1: Sample distribution by age and gender

Age group Male Female Total 18-21 years 30 42 72 22-25 years 24 56 80 26-29 years 20 31 51 30-39 years 19 30 49 40-49 years 16 21 37 50-59 years 37 18 55 60 years or older 39 30 69 Total 185 228 413

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Figure 1: Example screenshot of choice task asking respondents to indicate how frequently they would have used their mobile phone if current fines and enforcements rates differed as indicated

The questionnaire consisted of four major parts (a copy of the full instrument can be found in Appendix A). In the first section, respondents were asked demographic questions needed for quota sampling, such as whether they reside in Adelaide or regional South Australia, and whether they drive a car or a motorcycle. The second section included questions about their MPUWD. Respondents were asked to indicate on a 7-point Likert scale how frequently they have engaged in different aspects of mobile phone use, namely sending and receiving texts and taking and initiating phone calls, under current fine regimes and enforcement rates. They were then asked to indicate how often they would engage in the same behaviors, if fine regimes and enforcement rates differed from what they currently are. Each respondent was presented with four such hypothetical scenarios, where fines and enforcement rates were systematically varied using a fractional factorial design. Figure 1 shows an example screenshot of the task. Additional information was also collected about use of handheld and handsfree technologies, and engagement in compensating behaviours. The third section asked respondents to indicate their level of agreement or disagreement on a 7-point Likert scale with various statements measuring their attitudes, perceived behavioral control and normative beliefs. The final section asked respondents to report their demographic and mobility information, such as age and gender, and the type of vehicle that they usually drive.

4. Descriptive analysis

We begin with a descriptive analysis of different aspects of MPUWD behaviors within our sample. Table 2 reports the frequency with which respondents indicated engaging in different MPUWD behaviors in the last two weeks. Cumulatively, one in three people in the sample population reported never engaging in any of these behaviors, one in two reported rare or occasional engagement, and one in five reported frequent engagement. The most commonly reported behavior is receiving incoming phone calls while driving, with approximately 71% of the sample reported having received at least one call while driving in the last two weeks.

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Table 2: Sample distribution by MPUWD behaviors

Behavior Frequency

Never Very rarely Rarely Occasionally Frequently Very frequently All the time

Cumulative MPUWD 32% 20% 9% 20% 12% 4% 4% Engagement in particular MPUWD behaviors

Read text/email 55% 16% 9% 14% 4% 1% 1% Send text/email 69% 11% 8% 8% 2% 1% <1% Receive phone call 39% 19% 8% 18% 9% 3% 3% Initiate phone call 58% 11% 7% 14% 5% 2% 2%

Table 3: Sample distribution by use of hands-free and voice-controlled mobile phone features while driving “When ___ while driving, I always use the hands-free and voice-controlled features on my phone”

Level of agreement

Strongly disagree Disagree Somewhat

disagree

Neither agree nor

disagree

Somewhat agree Agree Strongly

agree Not

applicable

Reading a text/email 10% 5% 5% 7% 14% 19% 14% 26% Sending a text/email 10% 4% 4% 10% 16% 7% 12% 37% Receiving a phone call 8% 1% 3% 7% 10% 15% 53% 3% Initiating a phone call 7% 4% 4% 5% 11% 16% 41% 11%

Table 4: Sample distribution by frequency of compensating behaviors to control for MPUWD “When using your mobile phone while driving, how frequently do you try to compensate through the following”

Frequency

Never Very rarely Rarely Occasionally Frequently Very frequently

All the time

Lower driving speed 38% 7% 8% 22% 11% 5% 10% Pull over and/or stop car 14% 7% 8% 20% 11% 14% 26% Increase space between the car in front of me 33% 5% 6% 16% 16% 11% 13%

Hold phone below wheel to avoid being caught 57% 6% 4% 11% 9% 5% 8%

With regards to the availability of hands-free and voice-controlled features, only 23% of the sample reported having a mobile phone cradle in their cars, while 43% reported having a hands-free headset. While we did not ask this explicitly in the survey, in the open text feedback at the end of the survey, at least ten respondents mentioned having access to and using Bluetooth to operate their mobile phones through their car’s navigation system. The 73 respondents who reported having both a cradle and hands-free headset were subsequently asked about the frequency with which they use the hands-free and voice-controlled features on their phones while driving, and the results are listed in Table 3. As can be seen, use of hands-free and voice-controlled features is more popular for initiating and receiving calls than it is for reading and sending text messages and emails.

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Table 5: Sample distribution across indicators of risk-related attitudes

Statement

Level of agreement

Stro

ngly

di

sagr

ee

Dis

agre

e

Som

ewha

t di

sagr

ee

Nei

ther

ag

ree

nor

disa

gree

Som

ewha

t ag

ree

Agr

ee

Stro

ngly

ag

ree

Using a mobile phone while driving sometimes becomes too distracting 3% 1% 3% 5% 11% 16% 62%

People who use mobile phones while driving are acting irresponsibly 2% 2% 2% 7% 13% 13% 60%

Using a mobile phone while driving is no different than talking to another passenger 28% 17% 11% 12% 16% 9% 6%

Using a mobile phone while driving takes a person’s eyes off the road for too long 2% 2% 3% 9% 17% 20% 46%

Driving next to someone who is using their mobile phone makes me nervous 7% 4% 6% 15% 15% 17% 36%

An accident caused by using a mobile phone while driving is less likely to cause major damage to one’s �car, compared with other causes of traffic accidents

44% 17% 6% 20% 5% 3% 6%

Using a mobile phone while driving reduces a person’s ability to mentally focus on the road 2% <1% 3% 9% 16% 19% 52%

People who use mobile phones while driving are likely to cause an accident 2% 2% 3% 16% 16% 22% 40%

Using a mobile phone while driving is dangerous 2% 1% 3% 9% 13% 19% 53% Using a mobile phone while driving affects everyone on the road, not just the person using it 2% 1% 1% 10% 14% 19% 53%

When I see someone driving and using a mobile phone, I don’t think anything of it 38% 20% 13% 12% 9% 5% 4%

Using a mobile phone while driving limits a person’s ability to control one’s car 3% 1% 3% 11% 16% 23% 44%

Table 6: Sample distribution across indicators of perceived benefits of MPUWD

Statement

Level of agreement

Stro

ngly

di

sagr

ee

Dis

agre

e

Som

ewha

t di

sagr

ee

Nei

ther

ag

ree

nor

disa

gree

Som

ewha

t ag

ree

Agr

ee

Stro

ngly

ag

ree

Using a mobile phone while driving can often reduce travel, through more effective communication with others 32% 10% 8% 25% 15% 7% 4%

Using a mobile phone while driving increases my productivity 38% 11% 9% 18% 15% 6% 4%

Using a mobile phone while driving allows for more efficient execution of household and/or work responsibilities

38% 11% 9% 18% 14% 6% 4%

In cases where I'm running late, using a mobile phone while driving to notify others at my destination can reduce my tendency to speed

29% 8% 6% 16% 17% 16% 8%

The ability to use a mobile phone while driving reduces worry and stress while contributing to peace of mind 33% 10% 9% 23% 14% 9% 3%

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Table 7: Sample distribution across indicators of perceived behavioral control

Statement

Level of agreement

Stro

ngly

di

sagr

ee

Dis

agre

e

Som

ewha

t di

sagr

ee

Nei

ther

ag

ree

nor

disa

gree

Som

ewha

t ag

ree

Agr

ee

Stro

ngly

ag

ree

I don't always have complete control over whether or not I use a mobile phone while driving 54% 11% 10% 10% 9% 3% 3%

It isn't always up to me whether or not I use a mobile phone while driving 59% 11% 7% 10% 6% 4% 3%

I often find myself in situations where I'm driving and I'm forced to use my mobile phone 51% 10% 8% 11% 12% 6% 2%

Table 8: Sample distribution across indicators of self-assessed driving ability

Statement

Level of agreement

Stro

ngly

di

sagr

ee

Dis

agre

e

Som

ewha

t di

sagr

ee

Nei

ther

ag

ree

nor

disa

gree

Som

ewha

t ag

ree

Agr

ee

Stro

ngly

ag

ree

I react quickly when faced with unexpected traffic hazards <1% 2% 2% 12% 17% 33% 35% My driving skills allow me to negotiate traffic hazards safely 1% <1% 3% 10% 16% 35% 34% I am a more skillful driver than most other drivers on the road 2% 2% 6% 30% 23% 23% 14% I am a skillful driver and am always in control of my driving <1% <1% 3% 17% 21% 33% 24% I am proficient at changing lanes in heavy traffic 2% 2% 7% 15% 19% 28% 29%

Finally, all respondents were asked the extent to which they compensate the increased risks from MPUWD with other behaviors, such as reducing speed or pulling over. As Zhou et al. (2012) write, “it may be important to know whether and how drivers self-regulate their driving to compensate for the impairment caused by phone use. With more research now documenting the effect of mobile phone use on driving safety, some recent attention has been given to the possible compensatory behaviours involved in mobile use while driving, including stopping the vehicle (e.g., Gras et al., 2007), reducing the speed (e.g., Haigney et al., 2000; Rakauskas et al., 2004), and increasing the following distance (e.g., Alm and Nilsson, 1995; Strayer et al., 2003, 2006; Strayer and Drews, 2004). However, evidence from studies exploring whether drivers adopt these behaviours to compensate for driving performance decrements during cell phone is complicated, and the major of the research does not show compensation or its opposite (Haigney et al., 2000; Caird et al., 2008; Rosenblatt and Li, 2010).” Our results are reported in Table 4. The most commonly reported compensating behavior is pulling over and/or stopping the car, with at least 86% of the sample reporting having done it at least once, and the least common is lowering driving speed.

We also asked respondents if they engage in other behaviors not intended to offset risk but evade detection, such as lowering the mobile phone below the steering wheel. While 57% of the sample indicated never having done it, 8% of the sample reported doing it all the time. In the subsequent section, we use multivariate analysis to explore in greater detail potential determinants of the behavior.

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Table 9: Sample distribution across indicators of injunctive normative beliefs towards MPUWD

Statement

Level of agreement

Stro

ngly

di

sagr

ee

Dis

agre

e

Som

ewha

t di

sagr

ee

Nei

ther

ag

ree

nor

disa

gree

Som

ewha

t ag

ree

Agr

ee

Stro

ngly

ag

ree

Not

ap

plic

able

My best friend (not partner) would not want me to use a mobile phone while driving 4% 3% 5% 17% 14% 15% 36% 7%

My parents would not want me to use a mobile phone while driving <1% <1% 3% 12% 11% 11% 51% 10%

My partner would not want me to use a mobile phone while driving 2% 2% 4% 12% 10% 13% 44% 14%

My children would not want me to use a mobile phone while driving <1% 2% 3% 11% 8% 9% 36% 30%

My passengers would not want me to use a mobile phone while driving <1% 2% 3% 15% 12% 15% 51% 3%

Table 10: Sample distribution across indicators of descriptive normative beliefs towards MPUWD

Statement

Level of agreement

Stro

ngly

di

sagr

ee

Dis

agre

e

Som

ewha

t di

sagr

ee

Nei

ther

ag

ree

nor

disa

gree

Som

ewha

t ag

ree

Agr

ee

Stro

ngly

ag

ree

Not

ap

plic

able

My best friend (not partner) would never use a mobile phone while driving 9% 8% 15% 20% 9% 12% 20% 7%

My parents would never use a mobile phone while driving 4% 3% 9% 14% 9% 12% 36% 12% My partner would never use a mobile phone while driving 8% 8% 9% 14% 9% 13% 25% 15% My children would never use a mobile phone while driving 3% 2% 4% 21% 4% 9% 20% 36% Most adults in South Australia would never use a mobile phone while driving 21% 18% 16% 19% 12% 5% 7% 2%

Table 11: Sample distribution across indicators of moral normative beliefs towards MPUWD

Statement

Level of agreement

Stro

ngly

di

sagr

ee

Dis

agre

e

Som

ewha

t di

sagr

ee

Nei

ther

ag

ree

nor

disa

gree

Som

ewha

t ag

ree

Agr

ee

Stro

ngly

ag

ree

I would feel guilty if I used a mobile phone while driving 5% 3% 7% 10% 18% 17% 40% I personally think that using a mobile phone while driving is wrong 3% 2% 5% 13% 13% 17% 47%

Using a mobile phone while driving goes against my principles 4% 3% 9% 20% 12% 14% 39%

Table 5 reports response distributions to each of the 12 measures of risk-related attitudes. In general, a majority of respondents agree that MPUWD increases the risk to all road users. However, roughly 5-8% of the sample contends that MPUWD does not compromise driving ability. It would be interesting to see in the subsequent section if we can uncover any systematic patterns to these responses that can be linked to observable differences

15

in demographic variables, such as age and gender. Table 6 reports the analogous distributions to the 5 measures of perceived benefits to MPUWD. Most people are either neutral or disagree that MPUWD offers benefits. Interestingly, the greatest agreement is with regards to cases where one is running late, and using a mobile phone while driving can not only help notify people at the destination, but also reduce one’s own tendency to speed: 24% of the sample agrees or strongly agrees with the corresponding statement. If MPUWD is indeed safer than speeding, then there might be some unexpected safety related benefits to the behavior.

Table 7 reports distributions to the 3 measures of perceived behavioral control. While a majority of respondents indicate having a high degree of control over whether or not they use their mobile phones while driving, 15% of the sample agrees to some extent that they do not always have complete control and 20% of the sample report being in situations where they are driving and they are forced to use their mobile phones. These findings are consistent with those from past studies cited in Section 2.2.2 that report the salient influence of both social and situational pressures.

Table 8 reports distributions to the 5 measures of self-assessed driving ability. As is typically the case with these questions, 60% of the individuals in our sample view themselves as better than the average driver, and only 10% of the individuals view themselves as worse (the remaining 30% are neutral)! The skews are similar across other measures, indicating that most drivers have a high degree of confidence in their driving abilities.

Tables 9 and 10 report distributions to the 5 measures of injunctive and descriptive normative beliefs. Most people agree to varying degrees that their friends and family would not want them to use their mobile phones while driving (disagreement is generally between 5% and 12%). However, a significantly smaller proportion agree that their friends and family would never use their mobile phones themselves while driving (disagreement is generally between 9% and 32%). Most interestingly, 55% of the sample disagrees with the assertion that most adults in South Australia would never use a mobile phone while driving.

Table 11 reports distributions to the 3 measures of moral normative beliefs. 80% of the sample believes that MPUWD is wrong, 75% of the sample states feelings of guilt associated with engaging in the behavior, and 65% of the sample assert that MPUWD would go against their principles. These results suggest that moral beliefs might have a strong bearing on MPUWD, and we test this hypothesis in the subsequent section.

5. Multivariate analysis

In this section, we undertake a formal multivariate analysis. Figure 2 shows a simplified version of the model framework that we will be developing. The likelihood of mobile phone use is formulated as a function of external instrumental factors, such as the enforcement of lawful behaviours and the consequences of being caught using a mobile phone unlawfully while driving, internal behavioral constructs, such as attitudes and normative beliefs, and other compensating behaviours, such as reducing speed or pulling over to use the mobile phone. Since the internal behavioral constructs are latent constructs, they will be measured using responses to the statements described in Section 4, and we discuss the process in greater detail in Section 5.1. Once we have scores for each of the behavioral constructs of interest, we can quantify the other relationships shown in Figure 12, between MPUWD, external instrumental factors, internal behavioral constructs, compensating behaviors and demographic variables, and we discuss the process in greater detail in Section 5.2.

16

Figure 2: Model framework

5.1 Measurement models

In Section 2, we identified a number of behavioral constructs as potential determinants of MPUWD. In Sections 3 and 4, we described the items that we used to measure each of these constructs. As the reader may recall, multiple questions were used to measure each independent construct. In this section, we employ a confirmatory factor analytic approach to: (1) test whether these measures are consistent with our understanding of the nature of these constructs; and (2) extract factor scores for each of these constructs that we can subsequently treat as observable variables in subsequent analysis. Sections 5.1.1, 5.1.2 and 5.1.3 will report results from the confirmatory factor analysis for measures corresponding to attitudes, perceived behavioral control and normative beliefs, respectively.

5.1.1 Attitudes

From each respondent, we collected responses to twelve Likert-scale statements corresponding to the risk-related attitudinal constructs and five Likert-scale statements corresponding to the perceived benefits of MPUWD, as identified in Section 2.2.1.

An initial exploratory factor analysis on the twelve measures of risk-related attitudes identified two distinct latent factors related to perceived absolute and relative risks associated with MPUWD. Tables 12 and 13 report the estimation results for the measurement model for these two latent factors. Factor loadings are statistically significant, indicating convergent validity. The variances are statistically significant, indicating there is measurement error. For the latent factor denoting absolute risks, standardized regression weights for all indicators except one are greater than 0.7, indicating that these statements are valid indicators of the latent factor1. For the latent factor denoting relative risks, standardized regression weights for the three indicators are0.535, 0.646 and 0.811, respectively, indicating that the first two items might not be reliable measures of the factor. For this reason, we do not include this second factor in any of the subsequent analysis.

1The standardized regression coefficients are coefficients that would be estimated if regression were performed between the same set of dependent and explanatory variables, but after each variable has been standardized (or normalized). Standardization of any variable requires subtracting the sample mean and dividing by the sample standard error, such that the resulting transformation has mean zero and standard deviation one. The square of the standardized regression coefficient denotes the R! for the measurement indicator, or the percentage of variance in responses to the measurement indicator explained by the latent factor. Therefore, when standardized regression weights are less than 0.7, the R! for the corresponding indicator is less than 0.5. In other words, the latent factor explains less than half of the variance in the indicator, and the indicator itself may be construed as an unreliable measure of the factor.

17

Table 12: Measurement model for the latent variable denoting perceived absolute risks of MPUWD Variable Estimate t-stat

Using a mobile phone while driving limits a person’s ability to control one’s car Factor loading 1.158 19.25 *** Variance 0.773 13.05 ***

Using a mobile phone while driving takes a person’s eyes off the road for too long Factor loading 1.113 18.28 *** Variance 0.856 13.22 ***

Using a mobile phone while driving is dangerous Factor loading 1.245 23.78 *** Variance 0.327 10.73 ***

Using a mobile phone while driving affects everyone on the road, not just the person using it Factor loading 1.155 22.74 *** Variance 0.369 11.58 ***

Driving next to someone who is using their mobile phone makes me nervous Factor loading 0.984 11.35 *** Variance 2.475 14.06 ***

Using a mobile phone while driving sometimes becomes too distracting Factor loading 1.059 17.48 *** Variance 0.889 13.25 ***

Using a mobile phone while driving reduces a person’s ability to mentally focus on the road Factor loading 1.084 19.33 *** Variance 0.666 13.00 ***

People who use mobile phones while driving are likely to cause an accident Factor loading 1.236 21.56 *** Variance 0.552 12.24 ***

People who use mobile phones while driving are acting irresponsibly Factor loading 1.198 19.51 *** Variance 0.780 12.78 ***

Table 13: Measurement model for the latent variable denoting perceived relative risks of MPUWD Variable Estimate t-stat

Using a mobile phone while driving is no different than talking to another passenger Factor loading 1.046 9.72 *** Variance 2.723 11.94 ***

An accident caused by using a mobile phone while driving is less likely to cause major damage to one’s car, compared with other causes of traffic accidents Factor loading 1.176 11.23 *** Variance 1.928 9.24 ***

When I see someone driving and using a mobile phone, I don’t think anything of it Factor loading 1.433 13.13 *** Variance 1.066 4.29 ***

18

Table 14: Measurement model for the latent variable denoting perceived benefits of MPUWD Variable Estimate t-stat

Using a mobile phone while driving can often reduce travel, through more effective communication with others Factor loading 1.369 17.38 *** Variance 1.498 13.09 ***

Using a mobile phone while driving increases my productivity Factor loading 1.745 24.36 *** Variance 0.509 8.78 ***

Using a mobile phone while driving allows for more efficient execution of household and/or work responsibilities Factor loading 1.727 24.34 *** Variance 0.516 8.74 ***

In cases where I'm running late, using a mobile phone while driving to notify others at my destination can reduce my tendency to speed Factor loading 1.534 16.76 *** Variance 2.076 13.02 ***

The ability to use a mobile phone while driving reduces worry and stress while contributing to peace of mind Factor loading 1.486 19.11 *** Variance 1.258 12.32 ***

Table 14 reports estimation results for the measurement model for the latent factor denoting perceived benefits of MPUWD. In this case, for each of the five measurement indicators, factor loadings are statistically significant, indicating convergent validity; variances are statistically significant, indicating measurement error; and standardized regression weights are greater than 0.7, indicating that these statements are valid indicators of the latent factor.

5.1.2 Perceived behavioral control

From each respondent, we collected responses to multiple Likert-scale statements corresponding to two separate constructs related to the notion of perceived behavioral control, as identified in Section 2.2.2: (1) self-assessed driving ability; and (2) external pressure. Tables 15 and 16 report estimation results. For both factors and each of the measurement indicators, factor loadings are statistically significant, indicating convergent validity; variances are statistically significant, indicating measurement error; and standardized regression weights are greater than 0.7, indicating that these statements are valid indicators of their respective latent factors.

5.1.3 Normative beliefs

From each respondent, we collected responses to multiple Likert-scale statements corresponding to three separate constructs related to normative beliefs towards MPUWD, as identified in Section 2.2.3: (1) injunctive beliefs; (2) descriptive beliefs; and (3) moral beliefs. Tables 17-19 report estimation results.

For the model of injunctive beliefs, the factor loadings and variances are both statistically significant. Standardized regression weights for the indicators corresponding to best friend, parents, partner and passengers are greater than 0.7, confirming that they are reliable measures. However, the standardized regression weight

19

Table 15: Measurement model for the latent variable denoting self-assessed driving ability Variable Estimate t-stat

I react quickly when faced with unexpected traffic hazards Factor loading 1.132 23.85 *** Variance 0.261 10.11 ***

My driving skills allow me to negotiate traffic hazards safely Factor loading 1.167 25.26 *** Variance 0.170 7.53 ***

I am a more skillful driver than most other drivers on the road Factor loading 1.000 17.10 *** Variance 0.834 13.04 ***

I am a skillful driver and am always in control of my driving Factor loading 1.073 21.45 *** Variance 0.412 11.43 ***

I am proficient at changing lanes in heavy traffic Factor loading 1.132 18.55 *** Variance 0.824 12.77 ***

Table 16: Measurement model for the latent variable denoting external pressure Variable Estimate t-stat

I don't always have complete control over whether or not I use a mobile phone while driving Factor loading 1.408 18.52 *** Variance 1.045 9.56 ***

It isn't always up to me whether or not I use a mobile phone while driving Factor loading 1.387 18.10 *** Variance 1.121 10.08 ***

I often find myself in situations where I'm driving and I'm forced to use my mobile phone Factor loading 1.552 20.05 *** Variance 0.844 7.33 ***

for the indicator corresponding to one’s children is 0. 633, and the variance is high as well. This indicates that the item is likely not a reliable measure of injunctive beliefs. The finding is not particularly surprising, given that young adults are more likely to engage in these behaviors, and therefore less likely to disapprove of others engaging in the same behaviors. For the sake of completeness, we have retained the indicator in our analysis, but note that the indicator has a very small contribution to the factor scores constructed for injunctive normative beliefs.

For the model of descriptive beliefs, the factor loadings and variances are both statistically significant. However, only the standardized regression weight for the indicator corresponding to best friend is greater than 0.7, with standardized regression weights for indicators corresponding to the behavior of parents, partner, children, and other adults in South Australia being 0.501, 0.591, 0.227 and 0.408, respectively. There is likely great heterogeneity in responses across the sample population, with behaviors likely differing across different peer

20

Table 17: Measurement model for the latent variable denoting injunctive beliefs Variable Estimate t-stat

My best friend (not partner) would not want me to use a mobile phone while driving Factor loading 1.244 15.71 *** Variance 1.516 12.15 ***

My parents would not want me to use a mobile phone while driving Factor loading 1.108 17.63 *** Variance 0.828 11.13 ***

My partner would not want me to use a mobile phone while driving Factor loading 1.207 16.27 *** Variance 1.249 11.57 ***

My children would not want me to use a mobile phone while driving Factor loading 1.026 13.26 *** Variance 1.578 12.56 ***

My passengers would not want me to use a mobile phone while driving Factor loading 1.196 20.24 *** Variance 0.534 8.44 ***

Table 18: Measurement model for the latent variable denoting descriptive beliefs Variable Estimate t-stat

My best friend (not partner) would never use a mobile phone while driving Factor loading 1.763 14.66 *** Variance 1.153 3.56 ***

My parents would never use a mobile phone while driving Factor loading 0.954 9.49 *** Variance 2.720 12.87 ***

My partner would never use a mobile phone while driving Factor loading 1.297 10.63 *** Variance 3.126 10.86 ***

My children would never use a mobile phone while driving Factor loading 0.451 3.86 *** Variance 3.745 13.99 ***

Most adults in South Australia would never use a mobile phone while driving Factor loading 0.775 7.64 *** Variance 3.013 13.54 ***

21

Table 19: Measurement model for the latent variable denoting moral beliefs Variable Estimate t-stat

I would feel guilty if I used a mobile phone while driving Factor loading 1.546 23.28 *** Variance 0.551 9.86 ***

I personally think that using a mobile phone while driving is wrong Factor loading 1.490 24.71 *** Variance 0.322 7.34 ***

Using a mobile phone while driving goes against my principles Factor loading 1.568 23.33 *** Variance 0.558 9.78 ***

groups. Again, for the sake of completeness, we have retained all indicators in our analysis, but in interpreting the effect of descriptive normative beliefs, we ask the reader to keep in mind that these beliefs are most strongly associated with the behaviors of one’s friends (and not family members, or the general population).

For the model of moral beliefs, all factor loadings are statistically significant, all variances are statistically significant, and all standardized regression weights are greater than 0.7 (0.902, 0.934 and 0.903, respectively), indicating that the questions are valid and reliable measures of the same.

5.2 Structural models

This section reports results for multiple regression models. Section 5.2.1 discusses the estimated impact of external instrumental factors, internal behavioral constructs and compensating behaviors on different dimensions of MPUWD behaviors; Section 5.2.2 examines the impact of internal behavioral constructs on particular compensating behaviors; and Section 5.2.3 discusses the estimated correlation between different demographic variables on one hand, and the constructs corresponding to attitudes, perceived behavioral control, and normative beliefs on the other. At the outset, we’d like to mention that the structural models for the MPUWD behaviors are linear regressions, the structural models for the compensating behaviors are ordinal regressions, and the structural models for the internal behavioral constructs are log-linear regressions. We discuss how to interpret parameter estimates from the latter two models in Sections 5.2.2 and 5.2.3, but we wish to warn the reader not to compare parameter estimates across different structural models.

5.2.1 MPUWD behaviors

We have four dependent variables: frequency of reading text messages or email, sending text messages or email, answering incoming phone calls, and initiating phone calls, all while driving. Table 20 presents estimation results for the regression models corresponding to each of these four dependent variables. In general, external instrumental factors, internal behavioral constructs and compensating behaviors together explain between 29 and 32 percent of the variance in MPUWD behaviors across our sample (as indicated by the R-squares). These numbers are consistent with findings elsewhere in the literature on MPUWD behaviors.

We begin with a discussion of the impact of instrumental factors. The impact of fines is statistically significant for calling behaviors but statistically insignificant for texting behaviors, whereas the impact of demerit points is statistically significant on all behaviors except initiating a phone call. Despite differences in statistical significance, the magnitude of effect of both fines and demerit points is uniformly low across all behaviors

22

when compared to other behaviors. Even worse, the impact of enforcement rates is low and statistically insignificant across all behaviors as well, indicating that most individuals are relatively impervious to fines and enforcement rates when deciding whether or not to engage in these behaviors.

These findings are consistent with anecdotal evidence from news reports and informal media where traffic fines are often viewed as revenue raising mechanisms for the state. For example, in the open-text feedback at the end of the survey, one of the respondents commented, “increasing the fines will not change people's behaviour. It is just another revenue raiser for the Government.” Another respondent questioned the credibility of punitive measures, “Why aren't the courts consistent with the penalties not just for use of mobile phones while driving but all other traffic offences? AFL players or other high profile sports people always appear to be treated more leniently by the courts”.

In contrast, the effect of many of the behavioral factors was found to be statistically significant, and the sentiment was echoed by another respondent, “Personally, I think that the biggest obstacle in tackling this major problem is people's attitudes, and that no single demerit and monetary penalty is going to cut it long term.” Attitudes towards perceived risks have a large negative and statistically significant impact on all forms of MPUWD behaviors. The impact of attitudes towards perceived benefits, on the other hand, is limited largely to the frequency with which respondents initiate phone calls. However, with regards to that particular behavior, the construct has the second highest impact, after moral beliefs. Therefore, when targeting call initiation behaviors, MAC should address perceived benefits from the behavior.

With regards to the constructs related to perceived behavioral control, self-assessed driving ability has a large positive and statistically significant impact on call-related behaviors. In particular, the more confident drivers are in their ability, the more likely they are to receive incoming calls. Therefore, when targeting this behavior, MAC could emphasize how MPUWD can compromise even the best of drivers. External pressures have a large positive and statistically significant impact on all behaviors. However, it may be argued that external pressures are not so much a determinant of MPUWD as they are an excuse that individuals employ to abdicate responsibility. An effective response would be to portray external pressures to use a mobile phone while driving, as captured by responsibilities related to work, family or friends, as an unacceptable excuse.

With regards to normative beliefs, findings are more contradictory. Injunctive beliefs, as measured by social disapproval of the behavior, has a statistically insignificant effect on calling behaviors, but a statistically significant but surprisingly positive effect on texting behaviors, indicating that the greater the disapproval, the more likely the individual is to engage in the behavior. This finding should serve as an important cautionary warning that public information campaigns that rely too heavily on social disapproval to change behaviors can often produce socially undesirable reactionary behaviors. Our suggestion to MAC would be to not rely on the construct in any of their road safety campaigns, particularly those related to MPUWD.

Descriptive and moral beliefs have a strong negative and statistically significant effect on all behaviors. In fact, the magnitude of the effect of descriptive beliefs on text-related behaviors is the highest among all behavioral constructs. Therefore, when designing public information campaigns targeting reading and sending texts while driving, we recommend focusing on relevant measures of descriptive beliefs, such as the behavior of friends. Similarly, the magnitude of the effect of moral beliefs on call-related behaviors is the highest among all behavioral constructs. Therefore, when designing public information campaigns targeting receiving and initiating calls while driving, we recommend focusing on relevant measures of moral beliefs, such as the ethicality of the behavior.

23

Table 20: Parameter estimates (and t-statistics in parenthesis) for the linear structural model for different MPUWD behaviors

Explanatory Variable

MPUWD behavior

Rea

d an

inco

min

g te

xt m

essa

ge o

r em

ail w

hile

dri

ving

Sent

a te

xt m

essa

ge

or e

mai

l whi

le

driv

ing

Ans

wer

ed a

n in

com

ing

phon

e ca

ll w

hile

dri

ving

Initi

ated

a p

hone

ca

ll w

hile

dri

ving

Mean (intercept) 2.749 (11.45)

2.246 (10.69)

4.149 (12.20)

3.73 (11.65)

Instrumental variables

Fine ($, in hundreds) for illegal MPUWD -0.015 (-0.75)

-0.008 (-1.40)

-0.064 (-2.60)

-0.034 (-2.13)

Demerit points for illegal MPUWD -0.046 (-2.30)

-0.025 (-2.50)

-0.041 (-2.05)

-0.029 (-1.45)

Average number of individuals daily ticketed for illegal MPUWD in South Australia (in hundreds) - -0.035

(-0.70) -0.017 (-0.24)

-0.035 (-0.50)

Attitudes

Perceived risks from MPUWD -0.197 (-6.56)

-0.220 (-7.33)

-0.233 (-4.66)

-0.164 (-3.28)

Perceived benefits from MPUWD 0.027 (1.90)

0.036 (1.20)

0.210 (4.20)

0.205 (5.12)

Perceived behavioral control

Self-assessed driving ability 0.026 (1.30)

0.022 (1.10)

0.223 (5.57)

0.123 (4.10)

External pressure 0.203 (6.76)

0.171 (5.70)

0.148 (3.70)

0.149 (3.72)

Normative beliefs

Injunctive beliefs 0.216 (5.40)

0.201 (6.70)

-0.046 (-1.92)

-0.020 (-1.40)

Descriptive beliefs -0.253 (-8.43)

-0.179 (-5.96)

-0.131 (-2.62)

-0.169 (-3.38)

Moral beliefs -0.208 (-5.20)

-0.130 (-4.33)

-0.284 (-5.68)

-0.216 (-4.32)

Compensating behaviors

Lower driving speed -0.016 (-1.80)

-0.021 (-1.05)

0.015 (1.75)

-0.039 (-1.95)

Pull over and/or stop car -0.051 (-5.10)

-0.038 (-3.80)

-0.166 (-8.30)

-0.153 (-7.65)

Leave more space to the car in front 0.003 (0.15)

0.004 (0.40)

0.068 (3.40)

0.058 (2.90)

Hold phone below steering wheel to evade detection 0.087 (8.70)

0.092 (9.20)

-0.092 (-4.60)

-0.048 (-2.40)

Summary statistics

R-squared 0.32 0.29 0.32 0.29

24

Findings related to the effect of compensatory behaviors on MPUWD behaviors re somewhat inconsistent as well. Lowering driving speed has a small and statistically insignificant effect on all behaviors. Pulling over and/or stopping the car has a negative and statistically significant effect on all behaviors, indicating that individuals who are more likely to pull over and/or stop the car are likely not to engage in these behaviors, possibly due to the inconvenience of having to pull over and/or stop the car every time they wish to use their phones. Increasing space to the car in front has a positive effect on all behaviors, but the effect is statistically significant only in the case of call-related behaviors. Therefore, when targeting call-related behaviors, MAC should emphasize that increasing the space to the car in front may not be as effective as people perceive it to be, and it does not justify mobile phone use. Interestingly, individuals who lower their phones below the steering wheel report higher engagement with text-related behaviors and lower engagement with call-related behaviors. Therefore, when targeting text-related behaviors, MAC could design a narrative around how lowering one’s phone below the steering wheel might alleviate the risk of being caught, but it does not reduce any of the other risks associated with the behavior.

5.2.2 Compensating behaviors

We have four dependent variables: frequency of lowering driving speed, pulling over and/or stopping the car, increasing space to the car in front, and holding the phone below the steering wheel, all while engaging in some type of MPUWD behavior. Table 21 presents estimation results for the regression models corresponding to each of these four dependent variables. When comparing parameter estimates across the four behaviors, the reader should keep in mind that the first three behaviors decrease risks associated with MPUWD, but the last behavior increases risks.

All four dependent variables were treated as ordinal variables (i.e. “a variable whose value exists on an arbitrary scale where only the relative ordering between different values is significant”). In our case, ordinality is with regards to self-reported frequency of particular behaviors, measured on a seven-point scale ranging from “never” to “all the time”. In addition to the usual parameters associated with the effects of the explanatory variables on the dependent variable, an ordinal regression also estimates threshold parameters, which help map the ordinal variable to a linear and continuous scale. For example, for the variable denoting frequency of lowering driving speed, the interval −∞, 0.000 corresponds to outcomes that are “never”, the interval 0.000,0.180 corresponds to outcomes that are “very rarely”, the interval 0.180,0.384 corresponds to outcomes that are “rarely”, and so on.

In linear regression, R! summarizes the proportion of variance in the dependent variable that is explained by the explanatory variables. For ordinal regression, it isn’t possible to compute the same R! statistic. Instead, goodness of fit is usually summarized by approximations to the statistics, referred to as pseudo R! statistics. We will be using McFadden’s pseudo R! statistic to summarize goodness of fit, which might be interpreted as the level of improvement over a naive model that ascribes equal probabilities to all possible outcomes for all individuals in the sample population. A pseudo R! of zero denotes a model that offers no improvement, and a pseudo R! of one denotes a model that predicts the correct outcome for all individuals in the sample population. In our case, each of the models offer some improvement over the naïve model.

Individuals who are most likely to lower their driving speed and/or increase the space between themselves and the car in front to compensate for MPUWD typically feel that they do not have full control over whether or not they use their mobile phones while driving, and associate high absolute risks with the behavior. Individuals most likely to pull over and/or stop the car do not associate great benefits with MPUWD.

25

Table 21: Parameter estimates (and t-statistics in parentheses) for the ordinal structural model for different compensating behaviors

Explanatory Variable

Compensating behavior

Low

er d

rivi

ng

spee

d

Pull

over

and

/or

stop

the

car

Incr

ease

spac

e to

ca

r in

fron

t

Low

er p

hone

bel

ow

stee

ring

whe

el

Mean (intercept) -0.007 (-0.04)

0.822 (4.16)

-0.518 (-2.93)

-0.265 (-1.50)

Attitudes

Perceived risks from MPUWD 0.120 (4.12)

0.081 (2.91)

0.153 (5.77)

0.105 (3.22)

Perceived benefits from MPUWD 0.068 (2.88)

-0.231 (-9.37)

0.064 (2.52)

0.296 (12.21)

Perceived behavioral control

Self-assessed driving ability -0.059 (-2.90)

0.049 (2.62)

0.088 (4.39)

-0.116 (-5.25)

External pressure 0.181 (7.05)

-0.049 (-1.84)

0.160 (6.22)

0.214 (8.17)

Normative beliefs

Injunctive beliefs 0.048 (1.71)

-0.078 (-2.67)

-0.049 (-1.63)

0.220 (6.87)

Descriptive beliefs -0.126 (-4.71) -0.033

(-1.36) -0.509

(-17.18) Moral beliefs -0.070

(-2.51) 0.147 (4.89)

-0.064 (-2.40)

-0.118 (-3.65)

Threshold parameters

Between “never” and “very rarely” 0.000 (-)

0.000 (-)

0.000 (-)

0.000 (-)

Between “very rarely” and “rarely” 0.180 (13.75)

0.272 (14.97)

0.148 (12.05)

0.175 (13.85)

Between “rarely” and “occasionally” 0.384 (20.78)

0.513 (21.20)

0.299 (16.69)

0.307 (18.87)

Between “occasionally” and “frequently” 0.963 (13.19)

1.071 (14.12)

0.726 (16.80)

0.682 (17.28)

Between “frequently” and “very frequently” 1.351 (16.09)

1.390 (19.60)

1.175 (12.93)

1.070 (13.22)

Between “very frequently” and “all the time” 1.596 (15.33)

1.805 (16.00)

1.574 (15.94)

1.380 (13.57)

Summary statistics

McFadden’s pseudo R-squared 0.08 0.15 0.06 0.21

26

Perhaps most interestingly, individuals who are most likely to lower their mobile phones below the steering wheel to avoid detection are also most likely to believe that their friends and family members would never engage in MPUWD themselves. Conversely, the more likely their friends and family members are to disapprove of MPUWD behaviors, the more likely the individual is to engage in the compensating behavior. These individuals also believe that benefits from MPUWD are high, and that they do not always have complete control over whether or not they use their mobile phones while driving.

Therefore, in persuading individuals not to engage in these behaviors to avoid detection, MAC should focus on three aspects: (1) downplay the benefits from MPUWD; (2) emphasize that not having control is not good enough as an excuse; and (3) argue that if one’s friends and family members do not engage in the same behavior, and disapprove of anyone who does, hiding these behaviors is not the appropriate solution.

5.2.3 Internal behavioral constructs

We have seven dependent variables: two latent variables denoting attitudes towards perceived risks and benefits, two latent variables denoting different aspects of perceived behavioral control, and three latent variables denoting injunctive, descriptive and moral normative beliefs. We performed log-linear regressions for each of these variables against a broad range of demographic variables that included age, gender, income, education, employment, driving experience, type of vehicle usually driven, insurance type, etc. The estimation results are listed in Table 22. For the sake of brevity, we only show estimates for variables whose effect was found to be statistically significant. The R-squares for these regressions vary between 0.28 and 0.47. While these might appear to be quite low, given that we are working with individual-level data, this is not wholly unsurprising. However, it does indicate that differences in observable demographic variables explain less than half of the variance in behavioral constructs.

For a log-linear regression, the natural logarithm of the dependent variable is regressed linearly on the set of explanatory variables. As a result, the exponent of any model coefficient represents the percentage change in the dependent variable for a unit change in the corresponding explanatory variable. For example, consider the case where the dependent variable is attitudes towards perceived risks. The coefficient associated with being male is −0.021. If we have two individuals who are identical in every which way except gender, the ratio of attitudes towards perceived risks for the man and the woman may be given by exp −0.021 = 0.98, i.e. the man’s attitudes towards perceived risks are 2% lower than the woman’s.

Using the above discussion as a basis for interpreting model estimates, we identify demographic segments that are of particular interest with respect to each of the seven constructs. We begin with the two attitudinal constructs. Men perceive the risks posed by MPUWD to be less than women; the older an individual, the greater the perceived risks; and having a graduate level degree or higher is negatively correlated with perceived risks. Each of these findings makes behavioral sense, and offers guidelines on which segments of the population to target when targeting an individual’s attitudes towards perceived risks of MPUWD behaviors. For example, young educated men would be the prime demographic for just such a targeted messaging campaign. Similarly, and perhaps unsurprisingly, young educated men also perceive the benefits of MPUWD to be greater than other segments, and therefore any campaign around that message would be directed at the same segment as well.

With regards to self-assessed driving ability, age seemingly has no effect, and education has a negative effect (ceteris paribus, more educated individuals are more likely to rate themselves lower), as does the presence of

27

Table 22: Parameter estimates (and t-statistics in parentheses) for the log-linear structural model for different internal behavioral constructs

Explanatory Variable

Internal behavioral construct

Perc

eive

d ri

sks

Perc

eive

d be

nefit

s

Self-

asse

ssed

dr

ivin

g ab

ility

Ext

erna

l pr

essu

re

Inju

nctiv

e be

liefs

Des

crip

tive

belie

fs

Mor

al

belie

fs

Mean (intercept) 1.611 (77.45)

0.228 (3.47)

1.557 (74.78)

-0.090 (-0.96)

1.537 (54.39)

0.968 (22.59)

1.280 (38.11)

Gender

Female 0.000 (-)

0.000 (-)

0.000 (-)

0.000 (-)

0.000 (-)

0.000 (-)

0.000 (-)

Male -0.021 (-1.41)

0.028 (1.69)

0.028 (1.69)

0.028 (1.69)

-0.021 (-1.41)

0.028 (1.69)

-0.021 (-1.41)

Education

Secondary education or less 0.000 (-)

0.000 (-)

0.000 (-)

0.000 (-)

0.000 (-)

0.000 (-)

0.000 (-)

Certificate level 0.000 (-)

0.000 (-)

0.000 (-)

0.145 (1.76)

-0.035 (-1.44)

-0.113 (-2.37)

-0.037 (-1.43)

Advance diploma 0.000 (-)

0.000 (-)

-0.050 (-2.31)

0.145 (1.76)

-0.035 (-1.44)

-0.113 (-2.37)

-0.037 (-1.43)

Bachelor’s degree 0.000 (-)

0.166 (2.21)

-0.050 (-2.31)

0.262 (2.96)

-0.048 (-2.07)

-0.105 (-2.41)

-0.037 (-1.43)

Ph.D. or graduate level -0.065 (-3.85)

0.300 (3.62)

-0.050 (-2.31)

0.262 (2.96)

-0.048 (-2.07)

-0.105 (-2.41)

-0.037 (-1.43)

Age

18-21 years 0.000 (-)

0.117 (1.73)

0.000 (-)

0.145 (1.96)

0.000 (-)

0.000 (-)

0.000 (-)

22-25 years -0.029 (-1.72)

0.117 (1.73)

0.000 (-)

0.145 (1.96)

0.000 (-)

0.000 (-)

-0.093 (-4.02)

26-29 years -0.029 (-1.72)

0.117 (1.73)

0.000 (-)

0.145 (1.96)

0.000 (-)

0.000 (-)

0.000 (-)

30 years or older 0.000 (-)

0.000 (-)

0.000 (-)

0.000 (-)

0.000 (-)

0.000 (-)

0.000 (-)

Marital status

Unmarried 0.000 (-)

0.000 (-)

0.000 (-)

0.000 (-)

0.000 (-)

0.000 (-)

-0.025 (-1.27)

In a relationship 0.000 (-)

0.000 (-)

0.088 (2.20)

0.000 (-)

0.000 (-)

-0.059 (-1.46)

0.000 (-)

Widowed, divorced or separated

0.000 (-)

0.000 (-)

0.000 (-)

0.000 (-)

0.000 (-)

0.000 (-)

0.000 (-)

Parenthood status

Number of children under the age of 16 years

0.000 (-)

0.000 (-)

-0.021 (-2.72)

0.000 (-)

-0.021 (-2.72)

0.000 (-)

0.000 (-)

Number of children over the age of 16 years

0.000 (-)

-0.045 (-2.03)

0.000 (-)

-0.045 (-2.03)

0.011 (1.39)

0.052 (2.73)

0.011 (1.39)

Summary statistics

McFadden’s pseudo R-squared 0.28 0.46 0.31 0.47 0.39 0.41 0.30

28

young children. However, being male and in a relationship is positively associated with the construct. Therefore, public information campaigns seeking to change the behavior of individuals with high self-assessments of their own driving abilities should target partnered men of all ages.

With regards to external pressures, young educated men are again most likely to feel that they are not in control of if and when they use their mobile phones while driving. Given that this segment is most likely to underestimate the risks posed by these behaviors, overestimate the benefits offered by the same, and consequently engage in these behaviors the most, we argue that some cognitive dissonance is likely at play, where individuals wish to abdicate responsibility for the their actions.

With regards to injunctive beliefs, having a college degree, being male and having young children are all negatively correlated with the construct. However, as we mentioned in Section 5.2.1, we do not recommend using injunctive beliefs as a mechanism for changing MPUWD behaviors.

Interestingly, and somewhat contrary to other results, men believe their friends and family members do not engage in MPUWD more so than women, the less educated an individual, the greater the belief that others follow the law with regards to mobile phone use, and being in a relationship is negatively correlated with descriptive beliefs. Therefore, when targeting an individual’s descriptive beliefs about MPUWD behaviors of others, MAC should tailor the message for partnered highly educated women.

And finally, with regards to moral beliefs, young single men are most likely not to feel guilty about MPUWD. The finding is consistent with other results that consistently show that the demographic is most likely to engage in the behavior. Therefore, cognitive dissonance is likely at play here as well, where any dissonance between one’s beliefs and one’s behaviors may be addressed either by changing one’s behaviors, or by changing one’s beliefs, as appears to be the case with this demographic segment.

Note that while men are in general more likely to score higher on constructs that increase likelihood of engaging in MUPWD, the magnitude of effect is consistently small across all constructs. Therefore, in designing public information campaigns, our general recommendation would be not to focus on any one gender.

6. Policy analysis

The multivariate analysis conducted in the previous section provides guidance on the design of public information campaigns seeking to reduce different forms of MPUWD behaviors. Figures 3 and 4 summarize the major findings from the analysis in the form of a decision tree.

For example, Figure 3 shows that if MAC is interested in reducing the incidence of reading texts while driving, then the constructs to focus on, in order of decreasing importance, are as follows: descriptive normative beliefs, moral beliefs, external pressures and perceived risks. The content of each of these constructs, as distilled through the measurement models presented in Section 5.1, is summarized in the text box corresponding to each construct. For example, external pressures refer mostly to how often individuals find themselves in situations where they are driving and are forced to used their mobile phones. And finally, the figure also illustrates which segments of the sample population to target when directing attention towards a particular behavioral construct. For example, an advertisement that attempts to exhort individuals, who feel that they are often not in control of their mobile phone use, to not engage in MPUWD, should specifically young educated adults. Similarly, an

29

Figure 3: A decision tree showing how to design public information campaigns seeking to reduce texting-related behaviors; the constructs in the middle are ordered in terms of their impact on the behavior, and the demographic variables on the right are ordered in terms of the strength of their correlation with a particular construct

30

Figure 4: A decision tree showing how to design public information campaigns seeking to reduce call-related behaviors; the constructs in the middle are ordered in terms of their impact on the behavior, and the demographic variables on the right are ordered in terms of the strength of their correlation with a particular construct

31

advertisement that seeks to condemn MPUWD for the risks that it poses to the driver, potential passengers and other road users should specifically target all young adults. The decision trees shown in the other figures can similarly be used to develop guidelines for public information campaigns targeting other aspects of MPUWD.

In addition to the decision trees, we have provided MAC with a decision support system (DSS), calibrated using results from the multivariate analysis reported in the previous section. The DSS will allow MAC to visualize how changes in one or more external instrumental factors and internal behavioral constructs are going to affect observable MPUWD behaviors, across either a subset of the sample population or the full set. The DSS should offer a quantitative framework with which to evaluate the likelihood of success of different public information campaign strategies.

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Appendix B: Respondent open text feedback

“good survey”

“Increasing the fines will not change people's behaviour. It is just another revenue raiser for the Government.”

“Too intrusive and don't trust your left wing ideas or this idiotic govt.”

“Interesting survey. Good luck with your research.”

“Why aren't the courts consistent with the penalties not just for use of mobile phones while driving but all other traffic offences ? AFL players or other high profile sports people always appear to be treatd more leniently by the courts than t”

“it was good servey”

“This survey was interesting, and I wish Motor Vehicle Registration would be more serious about fines for mobile phone users, make it over $1,000 and loss of license immediately, these fools are dangerous on the road, same as a drink drivers, dr”

“Myself...I am a retired primary school teacher with 30 years service whilst my husband served as a police officer for 25 years followed by employment as an owner driver of a semi-trailer on interstate work for 30 years. We place strict values”

“No choice given for standard factory fitted blue tooth phone connection which would give a different view of the answers”

“Whilst driving a short distance, I can see people using the phone every day. Why can't the police use better surveillance to cut down on people using mobiles whilst driving?”

“One of your questions said about distance between cars. I always keep a big distance between myself and the car in front and I make sure the person following see's my brake lights otherwise I put my hazzard lights on to say "Back up"”

“I feel very strongly about safety behind the wheel. I wish it was easier to notify police of incidents that everyone sees on a daily basis whilst driving. I have phoned the police a couple of times about driving incidents that are unsafe but th”

“It would be of benefit to include a separate answer section for Not Applicable on some questions. This would tend to produce clearer, more accurate answers - instead of generating 'generic' style answers in questions where such an answer would”

“I see people using mobile phones whilst driving most times I am driving. I believe it is unnecessary, dangerous, inconsiderate & disgraceful. I support making the penalty for doing so the equivalent of drunk driving. Throw the book at the clowns”

“I use a mobile phone when driving using Bluetooth. This was not asked.”

“One of your questions asks about behaviour when using the mobile phone while driving, but allows no response for not using it while driving. My mobile phone is always in a bag, behind the back seat, so I can't ever be tempted to use it, while d”

“Treat every other driver as an idiot and you will remain safe.”

“I think I answered some questions incorrectly because I have blue tooth in my car so I never have to use my hands except to turn the phone button on & NEVER NEVER send or answer texts - they have to wait till I park somewhere. Most are close th”

“Asking sexual habits really has nothing to do with driving habits”

“You didn't ask me about blue-tooth connection to my car's audio. We do not have a cradle, but that is the wording of the Law - which is, as usual, an Ass. When driving wirth my wife, which is 96% of the time, in-coming calls are a joint item.”

“allowing a comment of when I used my mobile while driving would be helpful. I answered out of worry,(had been unable

to contact my partner on a terribly wet stormy night, he was in his car with our son. I told him I was driving and would pull o”

“When asking at the beginning of this survey you asked if I had a handsfree hand set set in the car or head phones set. I answered no to both. What I do have is a blue tooth hands free integrated into the car itself which I can use automatical”

“At the beginning of the survey, I answered that I do not use a mobile phone when driving but your questions disregarded this and continued to ask as if I did act stupidly and use a phone when driving.”

“I think the penalties for using a mobile phone while driving are not strong enough and should be increased, every time I go out in my car I see people on their phones all the time and it doesn't seem to be policed down here in the south east. T”

“You did not include the option for the phone to be answered thru the vehicles entertainment /sat nav system ..the phone does not have a cradle and its nor hands free.”

“Difficult to answer questions on mobile phone use as you assume my behaviour of using a mobile phone while driving is not through a bluetooth device, which allows me to answer and speak on my phone without touching the phone. Has skewed my answ”

“all of the questions that relate to "using" a mobile phone are poor discriminators. There is a big difference between talking on a hand-free phone with voice controls and texting or otherwise using a handheld phone in terms of degradation in s”

“people are always going to think they won't be the one's to get caught/have an accident so larger penalties don't do shit but raise revenue. I am one of those people.”

“Having indicated I never use my mobile phone while driving, it was difficult to respond to one question where it implied I do - there was no N/A selection. It would also have been good to be able to indicate if I felt the phone call may have b”

“I hope my answers were not ambiguous in that I have never used a mobile phone while driving to make a call or text”

“My references to using a mobile phone were via a totally handsfree bluetooth kit, in built to my car. This was not offered as an option. My kit is voice activated. A headset (which was an option) still requires a physical interaction with th”

“The questions on using a mobile phone while driving were answered as if I have a bluetooth in my car.”

“Great survey, very interesting, thank you.”

“When a law is manifestly unjust, it is the duty of every honourable citizen to ignore it, regardless of the degree or extent of enforcement. I believe in personal responsibility for the *consequences* of ones actions, not the actions themselves”

“I only EVER use my mobile while waiting at the lights.”

“Laws need to be enforced to prevent people from texting etc even at traffic lights as it is a danger to other road users”

“the 4 scenarios were repetitive and difficult to follow; other than that, the survey was fine”

“Answered as using a handsfree bluetooth. Never touching phone.”

“Better survey than most!”

“Fines will not impact my likelihood to use mobile phones whilst driving - consider it dangerous regardless of penalty.”

“Nice try to justify a FINE INCREASE your still a bunch of thieves.”

“It would have been good to differentiate the difference between, say texting and talking handsfree through the car's bluetooth... I don't text whilst driving but I do use my handsfree. This is going to skew your results.”

“I believe that while you asked about the illicit behaviours, maybe it would also rattle some people more if directly after that you quickly asked if anyone operated a motor vehicle under the influence. I avoid all driving activities if I believ”

“Great And Relevant Survey”

“My car has full handsfree bluetooth technology with steering controls. I always utilize it to its best abilities while driving.”

“I have a Bluetooth fixture in my vehicle which is what I use to make and receive calls. Other hands free options should be offered. I never touch my phone whilst driving, the Bluetooth built in to my car stereo is the only way I will EVER use m”

“Good survey!”

“my phone is linked to my cars infotainmany system which allowes me to call and answear without even touching the phone”

“Please provide more survey for us.”

“good survey, definitely made me feel guilty about the times I have used my phone while driving”

“I own a very new car with a very good hands free mobile phone system. I use that to take and initiate calls, anyone who uses a mobile phone not hands free should be shot!”

“If you want an accurate reflection of employment, considering the current job market, having a 'casually employed' option would be a good idea.”

“I feel as though the younger portion of the population are somewhat better at leaving their phones alone while driving in comparison to middle aged people.”

“Personally, I think that the biggest obstacle in tackling this major problem is people's attitudes, and that no single demerit and monetary penalty is going to cut it long term. Mobile phones are fantastic pieces of technology, they can be li”

“You have asked about hours driving, but not experience, in my case I currently drive around 4 hours a week, but I worked in the transport industry driving various sizes of trucks for over thirty years, what I learned about using communications”

“I put down that I check emails and texts while driving. This is because I have a smartwatch and can see then displayed there on my wrist. The phone never gets picked up while driving. Also, I have used the phone as a GPS using Google Maps. Agai”