Influencing Driver Offending Behavior: Using an Integrated Deterrence-based Model

Introduction

In the last 20 years, the death toll resulting from road traffic crashes remained largely unchanged. The World Health Organization (WHO, 2018) estimates that 1.3 million people die and as many as 50 million people are injured from road traffic crashes each year . These unacceptable figures raise concerns and flag the importance of the problem. The severe consequences of fatal crashes have motivated researchers to investigate the causes of crashes. The majority of these crashes involved a human factor which includes errors, misjudgment, and intentional risky driving behaviors. Risky driving behavior such as alcohol and drug violations, negligence in the use of seatbelts, speeding, running a red light and in-vehicle distraction were identified as key contributors to road injury and death (de Winter & Dodou, 2010; Redelmeier et al., 2003; Siskind et al., 2011; Sümer, 2003). The use of road policing to change driver behavior is a key strategy to reduce road crashes (Bates et al., 2012; Faulks et al., 2012). This paper will investigate an integrated deterrence-based model to influence the driving offending behavior.

Literature Review

Present Study

While there has been a significant amount of research investigating the association between deterrence theory and offending behavior (Pogarsky & Piquero, 2003; Sitren & Applegate, 2006) and driving behaviors more generally (Freeman & Watson, 2006; Poirier et al., 2018; Szogi et al., 2017; Truelove et al., 2017), few studies have investigated classical deterrence, reconceptualized deterrence model and informal deterrence mechanisms simultaneously. Theoretical insights in road policing may be gained by modeling punishment and punishment avoidance process, as well as indirect learning process, as predictors of driving offenses. This study builds upon the existing literature to address the gap of lacking an integrated deterrence-based model.

Therefore, the overarching aim of this study was to develop a more nuanced understanding of the relationship between deterrence factors, including formal and informal sanctions on self-reported driving offending behavior. Additionally, it investigated the influence of avoiding and experiencing punishment on continued offending behavior. The proposed integrated model is outlined in Figure 1. The figure clearly represents the theories incorporated in this study but not the hypotheses of this study.

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Figure 1.

The proposed integrated deterrence-based model.

Based on this proposed model, this study hypotheses that:

Method

Analysis

The survey data were analyzed using the Statistical Package for the Social Sciences (SPSS) V.27. Descriptive statistics in terms of mean and standard deviation for each variable was calculated and presented to describe the data. The significance level was set at .05 unless mentioned otherwise. The sample included 25 partially completed surveys. Specifically, these surveys had 90% of the questions completed and were therefore included in the analysis. Missing values were excluded listwise from the analyses as there was little impact on the sample size. There were an additional 276 surveys that had less than 90% of the questions answered and therefore not included in the sample. These surveys were not qualitatively different than the completed ones after comparing the basic characteristics of cases with missing data and those without missing data. However, it was decided not to include these surveys to prevent nonresponse bias and avoid invalid conclusions (Bennett, 2001; Cook et al., 2000).

The various scales used in this study were assessed for reliability. Scale reliability analyses were conducted to combine the scale items into a total score (Howitt & Cramer, 2008). Cronbach’s alpha was computed to measure the internal consistency of the scale. While Cronbach’s alpha scores greater than .80 are considered strong scales, those between .70 and .80 are considered to have adequate reliability (Bresciani et al., 2009). The summary of Cronbach’s alpha for each scale used in this study is presented in Table 1.

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Table 1.

Descriptive Statistics and Cronbach’s Alpha for the Scales.

	N	Mean	Std. D.	Cronbach’s alpha
DV. Self-reported offending driving behavior scale	8	12.18	4.27	.80
Disregard the speed limit on a freeway or rural highway.		2.34	1.29
Disregard the speed limit on a residential road.		2.00	1.09
Enter an intersection knowing that the traffic lights have already changed against you.		1.57	0.80
Drive even though you suspect you over the legal BAC limit (Alcohol limit).		1.22	0.52
Drive while having a conversation on the mobile phone held in your hands.		1.23	0.62
Drive while texting on the mobile phone or reading a text message.		1.36	0.72
Drive while using social media apps, such as Snapchat, Instagram, etc.		1.13	0.54
Drive while using a mobile phone for tasks that require you to hold the phone.		1.34	0.68
Classical deterrence scale	9	34.28	9.66	.59
When I drive over the speed limit, I am worried that I might get caught. (certainty)		4.06	2.48
When I drive through a yellow light, I am worried that I might get caught. (certainty)		3.78	2.18
If I have to drive after a couple of drinks, I am worried that I might get caught. (certainty)		4.83	2.44
When I am holding my mobile phone while driving, I am worried that I might get caught. (certainty)		4.65	2.49
The chances of me being caught for violating road rules are high. (certainty)		3.05	1.89
In general, the penalties I would receive for traffic offences would be severe. (severity)		3.31	2.32
The penalties I would receive for traffic offences would have a significant impact on my life. (severity)		3.88	2.43
In general, the time between committing a driving offence and receiving the fine notice would be long. (swiftness)		3.16	1.73
In general, the time between a traffic offence and going to court would be long. (swiftness)		3.56	1.83
Indirect punishment experiences scale	12	33.86	11.79	.82
In general, my friends have been caught for breaking road rules.		3.18	1.82
My family members (adults in my family) have been caught for breaking road rules.		3.04	1.86
My friends have been caught for driving over the speed limit.		3.10	1.97
My family members have been caught for driving over the speed limit.		2.95	1.97
My friends have been caught for driving after having a few drinks.		2.39	1.61
My family members have been caught for driving after having a few drinks.		2.06	1.44
My friends have been caught for holding their phones while driving.		2.27	1.46
My family members have been caught for holding their phones while driving.		2.03	1.34
In general, the penalties that my friends received for traffic offences have been severe.		3.34	1.67
In general, the penalties that my family members received for traffic offences have been severe.		3.21	1.66
In general, the penalties that my friends received for traffic offences have had a significant impact on their lives.		3.17	1.70
In general, the penalties that my family members received for traffic offences have had a significant impact on their lives.		3.12	1.70
Direct punishment avoidance scale	7	17.75	7.93	.76
I often break traffic rules without getting caught by police officers.		2.60	1.75
I often drive over the speed limit without getting caught.		2.96	1.90
I often drive after having a few drinks without getting caught.		1.57	1.15
I often park illegally without getting caught.		1.95	1.44
I often hold my mobile phone while driving without getting caught.		1.74	1.35
I prefer driving on roads where no presence of police officers.		3.35	2.16
I prefer driving on roads where no presence of speed cameras.		3.57	2.27
Indirect punishment avoidance scale	10	28.16	10.35	.83
In general, my friends often break road rules without getting caught by police officers.		3.25	1.83
My family members (adults in my family) often break traffic rules without getting caught.		3.00	1.69
My friends often drive over the speed limit without getting caught.		3.25	1.88
My family members often drive over the speed limit without getting caught.		3.10	1.76
My friends often drive after having a few drinks without getting caught.		2.57	1.56
My family members often drive after having a few drinks without getting caught.		2.28	1.43
My friends often park illegally without getting caught.		2.86	1.60
My family members often park illegally without getting caught.		2.65	1.53
My friends often hold their phones while driving without getting caught.		2.76	1.67
My family members often hold their phones while driving without getting caught.		2.44	1.54
Informal deterrence scale	6	23.41	10.27	.78
I would feel embarrassed if the police stopped me for breaking the road rules.		3.70	2.54
I would feel ashamed of myself if the police caught me for breaking the road rules.		3.78	2.49
If I were caught by the police for breaking the road rules, I would feel embarrassed in front of my family.		3.72	2.49
If I was to break road rules, I would be concerned that I might lose my friends’ respect.		3.15	1.84
I would be concerned to lose demerit points if I broke the road rules.		4.15	2.67
I would be concerned to lose my license if I broke the road rules.		4.92	2.69

A hierarchical regression model was conducted to explore the influence of formal and informal deterrence and punishment avoidance compared to punishment experiences on self-reported driving behavior. In terms of model specifications, potential predictors of self-reported offending driving behavior were entered in blocks. Socio-demographic variables were entered at block 1 to control for their contribution towards offending driving behavior. In the second block, the classical deterrence variable was entered to control for the influence of the threat of legal sanctions. Afterwards, the key constructs of the reconceptualized deterrence were entered in block three. Finally, the inclusion of informal deterrence occurred in the final block.

Results

Participants

The sample size for this study was 623 participants. All participants were from Queensland, Australia and held a car driver’s license. Of these, 84.1% (n = 524) were recruited through social media platforms, and 15.9% (n = 90) were recruited via the university-wide invitation email.

As shown in Table 2, 43.8% of the sample were females (n = 273), and 55.1% were males (n = 343). Participants were categorized according to their age into three groups, younger drivers (16–25 years old), middle-aged drivers (26–55 years old) and older drivers (>56 years old). The categorization approach was consistent with previous research on driving behavior (Vardaki & Yannis, 2013; Zhang et al., 2013). Participants were aged between 16 and 80 years old, with an average age of 42.06 (SD = 18.91). Further, most of the sample (n = 490, 78.7%) held a full/ open license class (O), 42 (6.7%) of participants held a provisional (P2) car licence, 52 (8.3%) held a provisional (P1) car license, and 39 (6.3%) held a learner class license (L). Half of the respondents reported being frequent drivers, driving at least a few times a day (n = 343, 55.1%). The majority of the respondents identified themselves as Caucasian/ European (n = 561, 90%). A relatively small proportion identified as being from an Aboriginal/ Torres Strait Islander background (n = 16, 2.6%) and the remainder identified as being from another ethnic background (n = 46, 7.4%). Most of the respondents had completed some form of formal education (n = 596, 95.6%) and were employed at the time of completing the survey (n = 434, 69.66%).

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Table 2.

Descriptive Statistics Summary for Socio-Demographic Characteristics.

Variable	N	Percent
Age
Young drivers (16–25 years old)	174	28
Middle-aged drivers (26–55 years old)	270	43.3
Older drivers (>56 years old)	179	28.7
Gender
Female	273	43.8
Male	343	55.1
Other	7	1.1
Ethnicity
Aboriginal/Torres Strait Islander	16	2.6
Caucasian/European	561	90.0
Other	46	7.4
Education
No qualification	27	4.3
High school	222	35.6
TAFE or Apprenticeship	151	24.2
Undergraduate degree	139	22.3
Postgraduate degree	84	13.5
Employment status
Unemployed	189	30.3
Casual	143	23.0
Part-time	83	13.3
Full-time	208	33.4
Car license class
Learner (L)	39	6.3
Provisional (P1)	52	8.3
Provisional (P2)	42	6.7
Full license/ Open license (O)	490	78.7
Driving frequency
Once a month	9	1.4
A few times a month	9	1.4
Once a week	16	2.6
A few times a week	111	17.8
Once a day	135	21.7
A few times a day	343	55.1

Prediction of Offending Behavior Using Three Types of Deterrence

Table 3 presents the results from the hierarchical regression conducted to explore the influence of the three types of deterrence in predicting offending driving behavior and the influence of punishment avoidance compared to punishment experiences in predicting self-reported offending driving behavior. The results of the first block show that the socio-demographic variables contributed significantly to the model, F (8,589) = 13.08, p < .001 with an adjusted R² of 0.14, explaining 14% of the variation in self-reported offending driving behavior. Age was a significant predictor β = −.23, p < .001, indicating that young drivers were more likely to report offending driving behavior. Further, driving frequency was a significant predictor of self-reporting offending driving behavior β = .07, p < .05, indicating that those who drove more frequently were more likely to report offending driving behavior.

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Table 3.

Classical, Reconceptualized and Informal Deterrence Predicting Offending Driving Behavior.

	Model 1						Model 2						Model 3						Model 4
	B	Std. error	β	Sig.	95% CI		B	Std. error	β	Sig.	95% CI		B	Std. error	β	Sig.	95% CI		B	Std. error	β	Sig.	95% CI
					Lower bound	Upper bound					Lower bound	Upper bound					Lower bound	Upper bound					Lower bound	Upper bound
Block 1
Constant	0.36	0.34					0.50	0.34					0.04	0.30					0.24	0.30
Age	−0.01	0.00	−0.40	0.00	−0.01	−0.01	−0.01	0.00	−0.40	0.00	−0.01	−0.01	−0.01	0.00	−0.22	0.00	−0.01	0.00	−0.01	0.00	−0.23	0.00	−0.01	0.00
Gender	0.13	0.04	0.13	0.00	0.05	0.21	0.13	0.04	0.13	0.00	0.05	0.21	0.04	0.04	0.04	0.27	−0.03	0.11	0.02	0.04	0.02	0.48	−0.05	0.09
Ethnicity	0.02	0.07	0.01	0.81	−0.12	0.15	0.03	0.07	0.01	0.70	−0.11	0.16	0.02	0.06	0.01	0.72	−0.09	0.14	0.02	0.06	0.01	0.69	−0.09	0.14
Education	0.02	0.02	0.05	0.25	−0.02	0.06	0.02	0.02	0.05	0.23	−0.02	0.06	0.02	0.02	0.05	0.16	−0.01	0.06	0.03	0.02	0.06	0.13	−0.01	0.06
Employment	0.02	0.02	0.05	0.24	−0.01	0.06	0.02	0.02	0.04	0.31	−0.02	0.05	0.01	0.02	0.03	0.41	−0.02	0.04	0.01	0.02	0.02	0.52	−0.02	0.04
Licence status	0.52	0.29	0.07	0.07	−0.05	1.09	0.52	0.29	0.07	0.07	−0.05	1.08	0.42	0.25	0.05	0.09	−0.07	0.90	0.40	0.25	0.05	0.10	−0.08	0.88
Car licence class	0.14	0.03	0.23	0.00	0.08	0.21	0.14	0.03	0.22	0.00	0.07	0.20	0.04	0.03	0.07	0.14	−0.01	0.10	0.04	0.03	0.07	0.16	−0.02	0.10
Driving frequency	0.05	0.02	0.10	0.01	0.01	0.09	0.05	0.02	0.10	0.01	0.01	0.09	0.04	0.02	0.08	0.03	0.00	0.07	0.04	0.02	0.07	0.03	0.00	0.07
Block 2
Classical deterrence							−0.04	0.02	−0.07	0.05	−0.08	0.00	−0.03	0.02	−0.05	0.12	−0.06	0.01	−0.01	0.02	−0.02	0.46	−0.05	0.02
Block 3 Reconceptualized model variables
DP													0.11	0.05	0.09	0.02	0.02	0.20	0.08	0.05	0.07	0.07	0.01	0.18
IP													0.01	0.02	0.01	0.72	−0.03	0.05	0.01	0.02	0.01	0.70	0.03	0.05
DPA													0.21	0.02	0.46	0.00	0.18	0.25	0.21	0.02	0.44	0.00	0.17	0.24
IPA													0.04	0.02	0.08	0.03	0.01	0.08	0.04	0.02	0.08	0.03	0.00	0.08
Block 4
Informal deterrence																			−0.04	0.01	−0.12	0.00	−0.06	−0.01
R2	0.15***						0.16***						0.39***						0.40***
Adjusted R2	0.14						0.14						0.38						0.39
R2 Change	0.15						0.01						0.23						0.01
F Change	13.08						3.74						56.32						10.75
df	8 (589)						1 (588)						4 (584)						1 (583)

Note. DP = direct punishment; IP = indirect punishment; DPA = direct punishment avoidance; IPA = indirect punishment avoidance.

***

p < .001.

In block 2, the classical deterrence variable was entered. This model was statistically significant, F (9, 588) = 12.09, p< .001 with an adjusted R² of 0.14, explaining 14% of the variation in self-reported offending driving behavior when controlling for socio-demographic variables. The classical deterrence mechanism was not able to predict self-reported offending driving behavior. It explained a minimal additional 1% of the variance, R² change = 0.01.

Model 3, including the reconceptualized deterrence constructs, was also statistically significant F (13, 584) = 28.85, p < .001 with an adjusted R² of 0.38, accounting for 38% of the variance in self-reported offending driving behavior. Direct punishment avoidance was a significant predictor of self-reporting offending driving behavior, β = .44, p < .001. Similarly, indirect punishment avoidance was a significant predictor of self-reporting offending driving behavior β = .08, p < .05. These results show that the more participants experience punishment avoidance, the more likely they were to self-report offending driving behavior. Additionally, those who have a friend or a family member who breaks the road rules while driving without being punished were more likely to report offending driving behaviors. The reconceptualized constructs were able to explain an additional 23% of the variance, R² change = 0.23.

The informal deterrence variable was entered in block 4. Model 4 was statistically significant, F (14,583) = 28.01, p < .001 with an adjusted R² of .39. The model results show that informal deterrence significantly predicted self-reported offending driving behavior, β = −.12, p < .001. The results suggest that those who are threatened by internal, or material loss are more likely to report lower levels of offending driving behavior. Informal deterrence was able to explain an additional 1% of the variance.

Discussion

The current study extended the focus beyond classical deterrence constructs in predicting offending driving behavior by considering alternative forms of deterrence. More precisely, this study identified that informal deterrence was a significant predictor of self-reported offending driving behavior. In addition, the hierarchical regression model showed that informal deterrence had a negative influence on offending driving behavior. These results suggest that social sanctions, the feeling of shame and embarrassment, and the fear of losing demerit points or losing a driving license were predictive of reduced offending behavior. These results are consistent with previous research on drink driving (Baum, 1999; Grasmick et al., 1993). In contrast to these findings, Truelove et al. (2017) found that social sanctions and internal loss were not predictors of speeding. The reason for this could be that drivers do not perceive different aberrant behaviors at equal significance.

In the current study, classical deterrence was not a significant predictor of self-reported offending driving behavior. To demonstrate, perceptions of certainty of punishment did not reach significance. This is because the current study suggested a relatively poor perception of certainty for offending driving behavior. Consistent with previous research, drivers do not perceive the likelihood of apprehension for breaking the road rules to be high (Fleiter et al., 2009; Szogi et al., 2017).

Besides increasing road policing activities and presence, there was an early suggestion that mass media campaigns play an important role in increasing public perceptions of the certainty of punishment (Homel, 1988). In their review Davey and Freeman (2011) concluded that well-run media campaigns can enhance the deterrent threat of apprehension and legal sanctions. However, Guttman (2015) suggests that a key challenge associated with the use of media campaigns based on deterrence principles such as the threat of being caught and the severity of penalties is how to depict policing of these activities in a positive way. He suggests that further research is required.

Similarly, perception of the severity of punishment did not reach significance. This could be because the severity of punishment does not influence individuals who do not perceive the punishment to be certain (Grasmick & Bryjak, 1980). However, the positive relationship between perceptions of certainty and severity of punishment supported the notion that high perceptions of severity are likely to follow if there are high perceptions of certainty (Homel, 1988).

Perception of the swiftness of punishment also did not reach significance. Consistent with previous research, perceived swiftness of punishment failed to deter drivers in the case of drug driving (Davey et al., 2008), drink driving (Szogi et al., 2017), and speeding (Truelove et al., 2017). In addition, the extended timeframe between getting caught and appearing in court for drink driving and drug driving could delay the consequences of illegal behavior and thus, weaken the swiftness effect (Nagin & Pogarsky, 2001). Similarly, speeding and being detected by a speed camera takes time until the offender receives the fine or feels its financial effects. In this study, perceptions of the swiftness of punishment were measured using two items to address both delayed receiving a fine notice or going to court. However, participants’ responses for both items show that they somewhat disagree that the time between committing a driving offence and receiving the fine notice or going to court would be long.

While punishment experiences did not predict self-reported offending driving behavior, punishment avoidance experiences predicted self-reported offending driving behavior. More precisely, direct punishment avoidance was the strongest predictor of offending driving behavior. This is consistent with previous research, which found that direct punishment avoidance was the strongest predictor of various driving offending behavior, including drink driving (Freeman & Watson, 2006), drug driving (Armstrong et al., 2018) and using Snapchat while driving (Truelove et al., 2019). This result was consistent with a previous study suggesting that experiencing direct punishment avoidance might reduce levels of perceived certainty of punishment (Piquero & Pogarsky, 2002). Furthermore, indirect punishment avoidance was also a significant predictor. However, it was less influential than direct punishment avoidance. The results indicate that individuals are influenced by knowing a family member or friend who has not been caught speeding, running a red light, drink driving, or using their mobile phones while driving.

Direct punishment experiences and indirect punishment experiences were not significant predictors of self-reported offending driving behavior. Although these findings contradict Stafford and Warr (1993) notion of the deterrent effect of punishment experiences, they are consistent with previous research (Armstrong et al., 2018; Freeman & Watson, 2006). There was a significant positive relationship between direct punishment experiences and self-reported offending driving behavior. This relationship can be seen as an “emboldening effect” in which direct punishment experiences might encourage offending rather than deterring it (Allen et al., 2017). Piquero and Pogarsky (2002) offer an explanation for this phenomenon. They argued that defiance accounts for the emboldening effect and previous punishment experiences might be seen as unjust and as result, it triggers a form of social protest. Self-serving bias could be another explanation for the emboldening effect. Potential offenders might perceive that they became immune after receiving the punishment and they can avoid detection in the next offending episode (Bates et al., 2017). Moreover, it might be the case that the item used to measure direct punishment experience was inadequate in capturing the levels of punishment certainty and severity.

Age emerged as a significant predictor of offending driving behavior. The finding that self-reported offending driving behavior was more robust among young drivers is consistent with previous studies indicating that younger drivers are more problematic drivers and less compliant with road rules than middle-aged drivers and older drivers (Armstrong et al., 2018; Bates et al., 2017). Moreover, younger drivers reported lower direct punishment experiences suggesting that deterrence-based approaches may be less effective or, alternatively, they have not had ample time to offend and get caught yet. While Chapman et al. (2014) found that the peak traffic violation rates for young drivers happened after they turn 18 (i.e., having a full license), the former suggestion might provide some insights.

Further, younger drivers reported higher direct punishment avoidance experiences than middle-aged or older drivers. Bates and Anderson (2019) had three explanations for this phenomenon. Firstly, they suggested that parents might take responsibility for the driving offences and facilitate punishment avoidance, limiting the deterrent effect. This can explain why, in this study, they did not perceive the punishment to be severe. Secondly, younger drivers actively engage in punishment avoidance behaviors by avoiding roads with police presence or removing their P-plates that indicate their early driving status. Thirdly, many younger drivers avoided punishment by persuading the police officer not to issue an infringement notice.

Similar to previous research, driving frequency was a significant predictor of engaging in driving offending behavior, indicating that high exposure to roads is associated with driving offending behavior (Truelove et al., 2017). In this study, gender was not a significant predictor of self-reported driving offending behavior. This contrasts with the findings from previous studies that showed that males are more likely to engage in driving offending behavior because they discount negative future consequences (Allen et al., 2017; Freeman et al., 2017).

Implications for Road Safety

This research provided important implications for deterrence theory. Firstly, the combined deterrence model including classical deterrence constructs, the reconceptualized deterrence model and informal deterrence explained 40% of the variance in self-reported offending behavior.

Secondly, the failure of legal sanctions to predict compliance suggests that high levels of police resources and commitment could boost the effect of both specific and general deterrence. Highly visible enforcement operations at random locations and continuous efforts may increase levels of perceived certainty of detection. While deterrence requires high levels of police resources and commitment in order to maintain its effect and reduce punishment avoidance experiences, the implementation of non-legal countermeasures that affect the moral factor and trigger the internal feelings of guilt, shame and embarrassment over engaging in offending behavior could influence the road offending behavior. One possible method of implementing this could be, when police officers are issuing a traffic ticket in person, engaging in a discussion with the driver regarding how a family member, such as a parent or spouse, would feel about them being punished for breaking a road rule. This discussion may explicitly trigger internal feelings of guilt, shame and embarrassment that could lead to a change in behavior.

Moreover, law enforcement agencies and policy makers need to look beyond punishment to identify other methods to increase awareness and encourage compliance. One method that has been effective in reducing drink driving crashes is mass media campaigns (Davey & Freeman, 2011). Well executed and well implemented mass media campaigns that focus on personal and social costs associated with road offending could have the potential to reduce offending driving behavior (Elder et al., 2004).

Strengths and Limitations

Consistent with previous research within the deterrence and road safety field, this study is based on self-reported data, which may be vulnerable to self-report bias. It should be borne in mind that participants might have difficulties in remembering details of punishment experiences and punishment avoidance experiences. However, self-reported data related to offending driving behaviors are considered to have an acceptable level of validity when collected anonymously, and there are no consequences or prosecutions associated with responding (Lajunen & Summala, 2003). Objective measures using in-vehicle monitoring systems would be beneficial for future research instead of relying on self-reported data.

The cross-sectional data used here is not without limitations and the results should be interpreted within this context. The lack of longitudinal data deprived us form addressing the temporal ordering of variables in terms of the offending driving behaviors and the outcome of sanctioning experiences and how the formulation of perceptions changes over time. However, previous research investigated deterrence in the road safety domain measured offending behavior on a scale ranging from “never” to “all the time” (Ochenasek et al., 2022; Szogi et al., 2017; Truelove et al., 2017). Despite this, we acknowledge that the cross-sectional data used in this study neglected the number of prior experiences, the nature, the timing and the recency of past behaviors. To address this limitation in the future, it might be beneficial to replicate the most recent work done in the road safety space by Truelove et al. (2020). In their study, they examined the stability of perceptions of classical and informal deterrence variables over time among young drivers aged between 17 and 25 years old. They concluded that deterrence is a dynamic process and perceptions fluctuate with time for various reasons such as less driving experience, the lack of exposure to countermeasures, punishment avoidance and prior offending experiences.

There were low mean scores for some of the items, making it difficult to accurately picture drivers’ behavior and their perceptions of legal and non-legal sanctions. For example, the mean score for driving over the legal BAC limit was M = 1.22, which was relatively low. To obtain precise and accurate knowledge, future researchers should capture how many times drivers engage in such behaviors over a specific period of time. Moreover, the Cronbach alpha reliability score for classical deterrence scale was low (α = .59) which raises a question concerning the validity of the classical deterrence items and scale. It might be beneficial in the future to use more items to develop a reliable classical deterrence scale or use independent scales for each of the three classical deterrence constructs. Future deterrence-based research could be extended to include additional variables such as personality traits, perceptions of risk and perceptions of safety to encourage compliance with road rules.

This study used a convenience sample of Queensland’s drivers. However, the demographic breakdown of the sample differed slightly from the population of Queensland. According to Queensland census data in 2021, the mean age of the population is 38 years old compared to 42.06 in this study. Further, females in Queensland constitute 50.7% of the total population compared to 43.8% in this study. In addition, the generalizability of the findings is limited by the sampling strategy. The sample included only those who had access to the internet and a device to access social media. Furthermore, the sample was self-selected and may therefore be missing subsets of the population that may differ substantially from those who chose to participate.

The current study focused on the general population of Queensland’s drivers and did not necessarily represent the offences history of Queensland’s drivers. Therefore, it might be beneficial for future research to determine whether these results are similar in a population with experience of offending who have experienced severe and swift punishment for their driving offending behavior.