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Article

Understanding Risky Behavior in Sustainable Driving among Young Adults: Exploring Social Norms, Emotional Regulation, Perceived Behavioral Control, and Mindfulness

by
Andrei-Lucian Marian
1,*,
Laura-Elena Chiriac
2,
Vlad Ciofu
2 and
Manuela Maria Apostol
2
1
Teacher Training Department, Faculty of Psychology and Educational Sciences, “Alexandru Ioan Cuza” University of Iasi, Toma Cozma Street, No. 3, 700554 Iasi, Romania
2
Psychology Department, Faculty of Psychology and Educational Sciences, “Alexandru Ioan Cuza” University of Iasi, Toma Cozma Street, No. 3, 700554 Iasi, Romania
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(15), 6620; https://doi.org/10.3390/su16156620
Submission received: 9 July 2024 / Revised: 29 July 2024 / Accepted: 31 July 2024 / Published: 2 August 2024
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Abstract

:
This study examines the effectiveness of a predictive model for risky driving behavior among young adults, focusing on psychological factors such as self-deceptive enhancement, impression management, emotional regulation difficulties, and perceived behavioral control. Additionally, it explores the mediating effect of mindfulness on the relationship between self-deceptive enhancement and risky driving behavior, with an emphasis on sustainable driving practices. Using a nonexperimental, cross-sectional design, the study investigates risky driving behavior among young Romanian drivers through a quantitative methodology. Data were collected from 436 participants using a pretested and adapted set of questionnaires (CR, PDS, ICI, DERS, MAAS). The analysis was conducted with SPSS (version 20) and Hayes’s PROCESS tool. The findings indicate that impression management strongly predicts risky driving behavior. The model’s efficiency differs by gender: for drivers who are men, impression management and perceived behavioral control are crucial predictors, whereas for drivers who are women, impression management and self-deceptive enhancement are more significant. Moreover, the study identifies a significant indirect effect of self-deceptive enhancement on risky driving behavior through mindfulness. Specifically, lower levels of self-deceptive enhancement indirectly reduce risky driving behaviors by fostering mindfulness, which promotes adaptive and sustainable driving styles and consequently encourages safer driving practices.

1. Introduction

Recent studies have provided substantial evidence on the nature and impact of risky driving behaviors, including actions such as using a mobile phone, texting, applying makeup, and eating while driving. These behaviors have become increasingly prevalent, posing significant risks to road safety [1]. In addition, drivers who engage in risky behaviors such as speeding, aggressive driving, or ignoring traffic laws are less likely to practice sustainable driving [2].
Various psychological factors affecting driving behavior have been extensively studied. For instance, personality traits such as conscientiousness and openness are linked to lower instances of risky driving and greater engagement in sustainable driving practices. In contrast, impulsiveness and sensation-seeking are associated with higher risky driving behavior and lower sustainability efforts [3]. Sensation-seeking, particularly among young drivers, is strongly correlated with risky driving behaviors [4]. Conversely, drivers with a heightened risk perception tend to adopt eco-friendly driving practices, such as maintaining steady speeds and avoiding rapid acceleration and braking, which contribute to reduced emissions [5].
Moreover, workplace stress has been identified as a significant predictor of risky driving behavior among specific groups like taxi drivers. Hăvârneanu, Măierean, and Popușoi (2019) demonstrated that conflicts with clients and colleagues positively correlate with risky driving behavior, with the drivers’ affective state and driving experience acting as a mediator and a moderator, respectively [6].
Despite extensive research, a comprehensive understanding of how these psychological factors interact to affect driving behaviors, particularly across different demographic contexts such as gender, remains underexplored. Additionally, the role of mindfulness as a potential mediator in the relationship between psychological factors and risky driving behavior has not been adequately addressed in previous studies.
This study aims to bridge these gaps by investigating the interplay of various psychological factors—perceived social norms, emotional regulation difficulties, perceived behavioral control, and mindfulness—in predicting risky driving behavior. Unlike previous research that often examines these factors in isolation, this study integrates them into a cohesive predictive model, offering a more nuanced understanding of their interactions and combined effects on driving behavior. A key innovation of this study is its exploration of mindfulness as a mediating factor between self-deceptive enhancement and risky driving behavior. By demonstrating how mindfulness can mitigate the influence of self-deceptive enhancement on risky driving, this research suggests new avenues for interventions aimed at enhancing mindfulness to promote safer and more sustainable driving practices. Furthermore, the study delves into gender differences, providing specific insights into how psychological predictors of risky driving may vary between drivers who are women and those who are men. This gender-specific analysis adds a valuable dimension to the literature, highlighting the need for tailored interventions to effectively address risky driving behaviors.

1.1. Meanings of Risky Driving Behavior

Risky driving behavior refers to actions that increase the likelihood of accidents [7]. It is characterized by a tendency to engage in high-risk behaviors while driving [8]. Bond (2022) describes this conduct as fraught with tension and peril, particularly among drivers who perceive themselves as experienced but fail to refine their driving skills, often exhibiting aggressive behaviors on the road [9]. Singh and Kathuria (2021) emphasize the efficacy of behavioral modeling and training programs in modifying driving behavior to enhance road safety [10].
Impulsive aggression, distinguished from premeditated aggression, occurs without deliberate thought and is characterized by uncontrolled emotion [11]. Barati et al. (2020) found significant associations between impulsivity as a trait and risky driving behaviors, contributing to adverse driving outcomes [12]. Reckless driving and speeding are identified as primary factors increasing accident risks [13].
Driving behavior varies subjectively, encompassing individual driving styles that involve monitoring surroundings, speedometer, mirrors, interpreting signs, adhering to signals, and using pedals, all of which influence accident risks. Multitasking while driving is cited as a perilous action associated with risky driving behavior, necessitating caution to mitigate the associated risks [14].
Drivers’ actions, such as impatience with slower traffic and hasty lane changes, contribute to heightened accident risks [10]. Operational definitions of risky driving behavior include maintaining insufficient following distances, speeding, driving under the influence, fatigue, and neglecting seatbelt use [15]. These behaviors underscore the multifaceted nature of risky driving and the importance of proactive measures to promote safer driving practices.

1.2. Perception of Social Norms and Risky Driving Behavior

Understanding the impact of social norms on driving behavior is crucial for developing effective interventions to reduce risky driving. Bicchieri et al. (2023) highlight that the perception of social approval plays a critical role in predicting risky driving behavior, significantly influencing adolescents through parental norms and driving models, with the vehicle often serving as a primary social setting for young drivers [16].
Expanding on the influence of social norms, Geber et al. (2021) distinguish between individual perceptions of social norms and collective group norms, asserting that collective norms within close-knit groups strongly shape driving behaviors. These norms act as a behavioral code that influences individuals toward risky driving behaviors [17].
While the role of social norms is evident, Tscharaktschiew (2020) emphasizes that changing risky driving behaviors is more effective through behavioral modifications rather than increasing sanctions [18]. McBride et al. (2020) note that perceptions of social norms influencing phone use while driving vary, with legal norms proving effective under robust law enforcement, while moral norms deter texting while driving [19].
Furthermore, Fruhen et al. (2021) explore attitudes and social norms predicting aggressive behaviors towards motorcyclists, revealing that socially acceptable attitudes often conflict with aggressive driving behaviors in traffic [20]. Adding to this complexity, Bosnjak et al. (2020) extend the theory of planned behavior to include attitudes, normative beliefs, and perceived susceptibility as determinants of risky traffic behaviors [21].
Lajunen et al. (1997) and subsequent studies have provided insights into the role of impression management and self-deceptive enhancement in driving behavior, offering a nuanced understanding of these psychological factors. Impression management correlates negatively with accidents, overtaking, speeding, and aggressive driving, while positively linked to adherence to traffic rules. In contrast, self-deceptive enhancement correlates positively with perceived traffic control [22]. Similarly, Yılmaz et al. (2022) in Turkey found that impression management is negatively associated with violations and positively with safety skills, while self-deception is positively correlated with violations and driver behaviors [23].
Lastly, Long (2021) discusses the broader implications of impression management in social sciences, linking it to prosocial behaviors and moral judgments, which are crucial in understanding driving behaviors [24]. However, research indicates that impression management biases may minimally impact self-reported risky driving behaviors [25].

1.3. Perceived Behavioral Control and Risky Driving Behavior

Perceived behavioral control is an essential factor in analyzing drivers’ behaviors on the road, as it reflects their confidence in their ability to perform specific driving actions and make safe decisions. It encompasses the belief that one can successfully execute necessary behaviors to avoid risky situations, thus playing a vital role in determining whether a driver partakes in high-risk or safe driving practices. Research indicates that drivers are more influenced by internal moral impulses than by external factors such as sanctions for traffic violations. Society often attempts to increase penalties for high-risk driving behaviors to make ignoring traffic safety too costly. The belief that one might be caught has a stronger impact on driving behavior than the actual frequency of penalties. Increased traffic control tends to reduce drivers’ autonomy over their behavior, and changes in traffic regulations usually correspond with changes in perceived risk. High-risk driving behavior decreases when the perceived risk of being caught increases. Promoting prudent driving behavior supports the effectiveness of the overall traffic regulatory framework [26].
Building on these findings, Atombo et al. (2016) found that perceived control significantly impacts the intention to engage in risky driving behaviors. This study measured the locus of internal control as an indicator of perceived behavioral control, drawing on Ajzen’s (2020) work, which highlighted that perceived control affects the intention to perform a behavior. Ajzen noted that the locus of control is not the same as perceived behavioral control; only the self-efficacy aspect of perceived behavioral control aligns with the locus of internal control, whereas the locus of external control does not contribute to perceived controllability [27,28].

1.4. Difficulties in Emotional Regulation and Risky Driving Behavior

The role of emotional regulation in predicting risky behavior has been pointed out by Weiss et al. (2014) [29]. Individuals prone to risky behaviors often struggle with managing their emotions, using these behaviors as a means to distract themselves from unpleasant feelings. This pattern not only increases stress levels over time but also diminishes experiences of adaptive emotional responses, leading to a driving style characterized by heightened risk and frequent negative emotions such as shame or guilt. Consequently, these individuals perceive limited access to effective emotional regulation strategies due to sustained high stress [29].
This concept is further illustrated by the fact that difficulties in emotional regulation can manifest in traffic as “road rage”, a common phenomenon linked to aggressive driving behaviors that can result in accidents [30].
A study of 137 drivers provides additional insight, showing that difficulties in emotional regulation across various emotional states were correlated with increased anxiety, anger, and engagement in risky driving behaviors. Conversely, fewer difficulties were associated with a more cautious driving style. Gender differences were observed, with women exhibiting a range of driving styles, including dissociative or anxious behaviors along with cautiousness, while men tended towards riskier and more aggressive driving styles. Furthermore, less adaptive driving styles tend to decrease with age, while more cautious behaviors increase positively with age [31].
Highlighting another dimension of this issue, Pizzo et al. (2024) discuss the importance of emotional regulation in fostering a cautious driving style, which is crucial for maintaining focus on driving tasks and enhancing performance [32]. Pruessner et al. (2020) emphasize the connection between mood-related issues, emotional regulation, and risky driving behaviors [33].
Additionally, Herrero-Fernández et al. (2021) stress the importance of addressing emotional regulation difficulties in interventions aimed at reducing risky driving behaviors. Research has shown that emotional regulation difficulties, aggressive behaviors (manifested as stubbornness and revenge), attentional biases towards emotional stimuli, and cognitive inhibition collectively predict driving errors and traffic rule violations [34].
Several studies reinforce the significance of emotional self-regulation in driving behaviors. Strategies focusing on goals or emotions are associated with a cautious driving style. Moreover, studies indicate that effective emotional regulation strategies are connected to safer driving behaviors, while challenges in emotional regulation are related to risky driving patterns and violations, such as speeding and using a mobile phone while driving [35].

1.5. Mindfulness and Risky Driving Behavior

In terms of cautious driving, mindfulness significantly enhances driving behavior by preventing aberrant behaviors such as aggressive or distracted driving [36]. Aberrant driving behaviors, including deliberate violations like speeding or displays of aggression, as well as unintentional errors such as sudden braking on slippery roads, pose substantial risks to road safety. Mindfulness practices involve self-regulating attention, with higher levels of mindfulness associated with careful driving and reduced errors or lapses (Hasan et al., 2022) [37].
Further supporting this point, research by Koppel et al. (2019) reveals a negative correlation between mindfulness and self-reported aberrant driving behaviors, particularly lapses and errors [38]. Distracted driving behavior represents a significant safety concern, stemming from various sources such as conversations with passengers, interaction with technology, and cognitive distractions. Mindfulness has been shown to mitigate distracted driving by increasing awareness and focus, thereby promoting sustained attention to driving tasks [39]. Furthermore, the link between mindfulness and driving performance is indirect, with personality traits such as conscientiousness and neuroticism possibly mediating this relationship [40].
Along with all these, mindfulness techniques are effective in reducing rumination, enhancing emotional regulation, increasing metacognitive awareness, and fostering adaptive driving styles [41].

1.6. Gender Differences and Risky Driving Behavior

The link between gender differences and risky driving behavior has been extensively studied, consistently showing that men generally exhibit more risky driving behaviors compared to women. Recent research by Cordellieri et al. (2016) supports this finding, revealing that young men generally exhibit more negative attitudes toward traffic rules and a higher tolerance for speeding compared to women. Additionally, men were found to be less concerned about the risks of road accidents, despite having the same level of risk perception as women. This difference in concern levels likely contributes to the higher incidence of risky driving behaviors among men [42]. Byrnes, Miller, and Schafer (1999) conducted a meta-analysis and found that men are more prone to becoming involved in risky behaviors across various domains, including driving, with biological and social factors contributing to these gender differences [43].
Hatfield and Fernandes (2009) investigated attitudes towards risky driving and found that men are more likely to underestimate the risks associated with dangerous driving behaviors, contributing to higher incidences of such behaviors [44]. Romer and Hennessy (2007) found that sensation seeking, a personality trait more commonly found in men, is strongly associated with risky driving behaviors. Men score higher on measures of sensation seeking, correlating with more frequent engagement in risky driving [45].
In 2008, Ginsburg et al. examined the influence of parental supervision on teen driving behaviors and found that teens who are men were more likely to engage in risky driving when unsupervised compared to teens who are women, indicating that societal expectations and gender norms shape these behaviors [46]. Rhodes and Pivik (2011) identified gender disparities among drivers, observing that men are considerably more likely than women to take part in risky behaviors such as driving under the influence of alcohol or breaking other driving-related rules [47].
Finally, Aluja et al. (2023) founded that women reported more lapses while driving, whereas men reported more ordinary and aggressive violations. These studies collectively highlight the multifaceted nature of gender differences in risky driving behavior, with men generally exhibiting higher levels of such behaviors due to a combination of biological, psychological, and social factors [48].

1.7. Predictive Models and Indirect Effects on Risky Driving Behavior Proposed by the Current Study

This research makes a significant contribution to understanding risky driving behavior among young adult drivers by identifying potential predictive factors. To explain the variability in risky driving, the study assesses the predictive power of social norms perception, operationalized through two constructs: self-deceptive enhancement and impression management, emotional regulation difficulties, and perceived behavioral control. Additionally, the study examines sustainable driving practices and conducts a comparative examination of predictive models for risky driving, focusing on psychological factors and their explanatory power differences according to gender. Finally, the paper analyses the mediating effect of mindfulness on the relationship between self-deceptive enhancement and risky driving behavior.
In light of previous studies, we put forward the following hypotheses:
H1. 
Self-deceptive enhancement, impression management, difficulties in emotional regulation, and perceived behavioral control constitute a significant predictive model of risky driving behavior in a regression model. In addition, it is anticipated that there will be, on one hand, a positive relationship between self-deceptive enhancement, difficulties in emotional regulation, and risky driving behavior among drivers, and, on the other hand, a negative relationship between impression management, perceived behavioral control, and risky driving behavior.
H2. 
The predictive power of self-deceptive enhancement, impression management, difficulties in emotional regulation, and perceived behavioral control on risky driving behavior significantly varies depending on gender.
H3. 
There is a mediation effect of mindfulness on the relationship between self-deceptive enhancement and risky driving behavior, whereby self-deceptive enhancement impacts risky driving behavior both directly and indirectly through mindfulness. Thus, a high level of self-deceptive enhancement is associated with engaging in risky driving behaviors, both directly and indirectly, by reducing mindfulness, which, in turn, is associated with engaging in risky driving behaviors.

2. Materials and Methods

2.1. Research Design

This study utilized a nonexperimental, cross-sectional design to investigate various factors that might explain risky driving behaviors. A quantitative approach was taken to collect and analyze data from all participants. The research team translated, adapted, and pretested the questionnaires to ensure they were suitable for the Romanian cultural context (Risky Driving Behavior—Măierean et al., 2018; The Paulhus Deception Scales—PDS, Paulhus, 1998; Perceived Behavioral Control—ICI, Duttweiler, 1984; Emotional Regulation Difficulties—DERS, Gratz and Roemer, 2004; the Mindful Attention Awareness Scale—MAAS, Brown and Ryan, 2003) before administering them to 436 participants. SPSS (Version 20) and Hayes’s PROCESS tool were utilized for data analysis [49,50,51,52,53].

2.2. Participants

A total of 436 drivers took part in the study by filling out the questionnaires, which were distributed online via Google Forms. The convenience sampling method was used, selecting drivers who were readily accessible and willing to participate. Of the participants, 23.90% were men and 76.1% were women. The mean age of the participants was M = 29.32, with a standard deviation of SD = 9.25. In terms of educational attainment, 28% had completed secondary education, whereas 72% had tertiary education qualifications. Furthermore, 75.7% of participants reported driving less than 100 km per week, while 24.3% drove more than 100 km per week.

2.3. Measurements

The concepts discussed in the present study were measured using specific instruments that were pretested on Romanian participants. This process involved several steps, including translation and back-translation by bilingual experts, cultural adaptation, and pretesting with a small sample. The translation was carefully reviewed to maintain the semantic and conceptual equivalence of the original items.
A pilot study was conducted to assess the reliability of the instruments using a sample of 33 drivers. This initial phase aimed to identify any issues with the translation, cultural relevance, and overall comprehension of the questionnaire items. Participants in the pilot study provided feedback on the clarity and relevance of the questions, which was then used to make necessary adjustments. The results from the pilot study showed satisfactory reliability indices, justifying the use of the instruments in the main study. Detailed analysis of the pilot study data confirmed that the translated questionnaires retained their psychometric properties, ensuring that the instruments were appropriate for the Romanian context.
The internal consistency of the translated questionnaire was assessed using Cronbach’s Alpha. The Cronbach’s Alpha coefficients for the various scales were as follows: for the Risky Driving Behavior Scale, α = 0.84; for the Paulhus Deception Scales (Self-Deceptive Enhancement and Impression Management), α = 0.70 for both constructs; for the Internal Control Index, α = 0.79; for the Difficulties in Emotion Regulation Scale, α = 0.95; and for the Mindful Attention Awareness Scale, α = 0.88. These coefficients indicate that the translated instruments have good to excellent reliability.
Risky driving behavior was measured using the Risky Driving Behavior Scale (Măierean et al., 2018), adapted for the Romanian population. The instrument includes items targeting self-reported risky behaviors in various traffic situations. The scale consists of 21 statements, evaluated on a 6-point Likert scale, where 0 means never, and 5 means very often [49].
To measure the perception of social norms, we used the Paulhus Deception Scales (PDS, 1998), a dimensional scale comprising two concepts: self-deceptive enhancement and impression management. The questionnaire contains 40 items, with the first 20 items belonging to self-deceptive enhancement and the remaining 20 to impression management. Participants respond on a 5-point Likert scale, where 1 means never true and 4 means always true [50].
Perceived behavioral control was assessed using the Internal Control Index developed by Duttweiler (1984). The author indicated that the ICI focuses on elements of personal choice, self-confidence, and self-determination. Additionally, he described internal control as the perception of controlling one’s actions to achieve necessary security. The scale contains 28 statements, using a five-point scale where 1 means rarely (less than 10% of the time) and 5 means usually (more than 90% of the time) [51].
To measure the concept of difficulties in emotion regulation, the Difficulties in Emotion Regulation Scale (DERS) by Gratz and Roemer (2004) was used. This tool evaluates several aspects of emotion regulation: recognizing and comprehending emotions, accepting emotions, maintaining goal-directed behavior, avoiding impulsive actions during negative emotional states, and having access to effective strategies for managing emotions. The instrument contains 16 items to which participants responded using a 5-point scale, from 0 (almost never) to 5 (almost always) [52].
Mindfulness was measured using the Mindful Attention Awareness Scale (MAAS), developed by Brown and Ryan (2003). The instrument is unidimensional, consisting of 15 items with self-reported mindfulness statements, with responses ranging on a Likert scale from 1 to 6, where 1 means almost never and 6 almost always. Higher scores indicate a higher level of mindfulness as a disposition [53].

2.4. Data Analysis

The data were analysed using SPSS software, Version 20.0. Prior to this, a pilot study involving 33 drivers was carried out to evaluate the reliability of the instruments. Subsequently, the questionnaires were distributed to 450 participants. From the 450 questionnaires submitted, only 436 responses that met the SPSS screening and cleaning criteria were included in the analysis. Hypotheses were developed to investigate statistically significant relationships between risky driving behaviors and selected variables derived from a literature review. Pearson’s correlation, stepwise multiple regression, and mediation analysis using Hayes’s PROCESS tool (Version 4.1) were identified as the most suitable methods for testing the hypotheses in this study.

3. Results

Before testing the hypotheses, we evaluated the normality of the data collected. According to Chan et al. (2022), for samples ranging from 50 to 300, the z-scores for normal distribution should fall between −3.29 and +3.29. As indicated in Table 1, the skewness and kurtosis values suggest that the data are normally distributed [54].
An a priori power analysis was performed using G*Power version 3.1.9.7 to establish the minimum sample size necessary for testing the study hypothesis [55]. The analysis revealed that a sample size of N = 85 is needed to achieve 80% power for detecting a medium effect size at a significance level of α = 0.05 in a linear multiple regression model. Hence, the actual sample size of N = 436 is adequate for testing the study hypothesis.
The statistical analysis showed a positive and significant relationship between self-deceptive enhancement and difficulties in emotional regulation with risky driving behaviors. This suggests that drivers with higher levels of self-deceptive enhancement and difficulties in emotional regulation are more likely to engage in risky driving behaviors, supporting the formulated hypothesis. Furthermore, a positive and significant correlation was identified between impression management, mindfulness, and risky driving behaviors, consistent with previous research findings (Table 2).
To evaluate the effectiveness of explanatory models for risky driving behaviors involving self-deceptive enhancement, impression management, difficulties in emotional regulation, and perceived behavioral control, a stepwise multiple regression analysis was conducted. Significant standardized coefficients and changes in R2 were examined to assess the contribution of interaction terms in explaining additional variance beyond that explained by the main effects in the equation.
The results of the regression analysis revealed that impression management was the only factor significantly associated with risky driving behaviors, F (1434) = 51.25, p < 0.01. The coefficient of determination, R2, was 0.11, meaning that 11% of the variance in risky driving behaviors is explained by impression management (Table 3).
To determine the extent to which the explanatory power of predictors of risky driving behavior varies among drivers who are men and those who are women, we conducted a comparative regression analysis based on gender differences.
As illustrated in Table 4, the statistical results reveal that regression model 2, which includes impression management and perceived behavioral control, is the most precise explanatory model, with a coefficient of determination of R2 = 0.12. This indicates that 12% of the variance in risky driving behavior among drivers who are men can be explained by the variables included in the regression equation.
Among drivers who are women, the findings demonstrated that certain predictors had a specific effect in explaining risky driving behavior. In addition to impression management, F (1330) = 49.06, p < 0.001, the only factor that was significantly associated with risky driving behavior in a regression model was self-deceptive enhancement, F (2329) = 27.35, p < 0.001. Hence, regression model 2, which comprises impression management and self-deceptive enhancement, is the most accurate explanatory model, exhibiting a coefficient of determination R2 = 0.14. Put differently, impression management and self-deceptive enhancement explain 14% of the variance in risky driving behavior among drivers who are women (Table 5).
The explanatory models of risky driving behavior tested so far reveal a direct relationship between impression management, self-deceptive enhancement, and perceived behavioral control (as predictors) and risky driving behavior (as the outcome). However, due to the intricate nature of this mechanism explaining risky driving behavior and informed by prior research, it is evident that the relationships among these factors are complex. Consequently, we hypothesized that the association between self-deceptive enhancement and risky driving behavior is mediated by mindfulness. This conceptual framework posits that the link between self-deceptive enhancement and risky driving behavior is not direct but rather operates through an increasing in mindfulness. To examine this hypothesis, a mediation analysis was conducted using Hayes’s PROCESS tool (Figure 1).
Statistical analysis revealed a notable indirect effect of self-deceptive enhancement on risky driving behavior, β = 0.172, 95% CI [0.07, 0.27], and similarly, K2 = 0.087, 95% BCa CI [0.036, 0.136]. With the K2 index value falling between 1 and 2, this indirect effect can be interpreted as representing 8.7% of the maximum possible value, indicating that the effect is relatively minor. Therefore, a low level of self-deceptive enhancement indirectly contributes to reduced engagement in risky driving behaviors by increasing mindfulness, which in turn leads to a reduction in risky driving behaviors. Additionally, the results of the Sobel test confirmed full mediation within the proposed conceptual model (z = 3.27, p = 0.01). This implies that mindfulness completely mediates the relationship between self-deceptive enhancement and risky driving behavior.

4. Discussion

Nowadays, a key concern in sustainable driving is to find and implement solutions to avoid aggressive and risky driving behaviors. To achieve this effectively, it is essential to first decipher the psychological explanatory mechanisms underlying these behaviors, followed by increasing awareness among drivers about the environmental impact of their driving habits and providing education on sustainable driving practices. As the global goal is to cultivate “green” driving behaviors [56] that are adaptive and have minimal environmental impact [57], the need to understand environmentally unfriendly behaviors becomes even more pertinent. In this respect, the current study sought to elucidate the explanatory power of several psychological factors, such as self-deceptive enhancement, impression management, difficulties in emotional regulation, perceived behavioral control, and mindfulness, on risky driving behavior.
Therefore, firstly, we found that 11% of the Romanian drivers participating in this research who are high impression managers appear to engage significantly less in risky driving behavior. This result aligns with the theoretical paradigm and previous evidence, which have demonstrated a negative association between impression management and various correlates of risky driving behavior [22,23,24,25]. Moreover, substantial evidence suggests that individuals who score high on measures of impression management report lower levels of alcohol and drug use, fewer sexual partners, less risky sexual behavior, and less frequent gambling [58,59]. These findings are typically interpreted to indicate that impression managers tend to downplay or underreport their engagement in these activities to present themselves in a more favorable light.
Secondly, given the hypothesis that drivers who are men are more prone to engage in risky driving behaviors compared to drivers who are women, which is a finding supported by numerous previous studies [43,44,47,48], this work aimed to understand the explanations behind these gender differences. It was observed that for the Romanian drivers who are men participating in the study, impression management and perceived behavioral control form a predictive model that explains 12% of the variance in risky driving behavior. As we have seen, the data obtained are consistent with previous studies, with our results adding a specific element in explaining the risky driving behavior of drivers who are men. This involves the significant role of perceived behavioral control. In 2019, Intini et al. reported that the perception of being caught has a greater effect on driving behavior than the actual number of sanctions. Increased traffic control tends to reduce drivers’ autonomy over their behavior, and changes in traffic regulations usually correspond with changes in perceived risk. High-risk driving behavior decreases when the perceived risk of being caught increases [26]. We can expand on this finding by suggesting that this effect is particularly specific to drivers who are men.
Among drivers who are women, the results showed that some predictors had a significant impact on explaining risky driving behavior. Impression management and self-deceptive enhancement together account for 14% of the variability in risky driving behavior among drivers who are women. While previous studies have shown the connection between these psychological factors and risky driving behavior [22,23,24,25], the novelty and specificity of our findings lie in self-deceptive enhancement positively predicting risky driving behavior exclusively among drivers who are women.
Finally, in our exploration of the conceptual and relational framework surrounding risky driving behavior, we recognized that our models focusing solely on direct predictors provide only a partial understanding. Based on prior research [36,38,39], we proposed a mediation model where mindfulness serves as a mediator in the relationship between self-deceptive enhancement and risky driving behavior. Our findings reveal that a low level of self-deceptive enhancement indirectly reduces engagement in risky driving behaviors by promoting mindfulness, which subsequently leads to a further decrease in such behaviors. In summary, mindfulness fully mediates the relationship between self-deceptive enhancement and risky driving behavior. This result is even more important as it supports the hypothesis of Baltruschat et al. (2021) that mindfulness techniques are effective in reducing rumination, enhancing emotional regulation, metacognitive awareness, and fostering adaptive driving styles [41]. By diminishing negative thought patterns and improving emotional control, drivers can make more conscious and deliberate decisions on the road. In addition, increased mindfulness allows drivers to better understand and regulate their own thought processes, leading to safer driving habits. Together, these benefits support sustainable driving practices by promoting safer, more considerate, and environmentally conscious behavior behind the wheel. Our findings are consistent with the recent study by Qu et al. (2024), which found that mindfulness reduces driving anger expression through the mediating effects of driving anger and anger rumination. This supports the notion that mindfulness not only helps in regulating immediate emotional responses but also reduces the tendency to ruminate on anger-provoking incidents, thereby promoting safer driving practices. By incorporating mindfulness training into driver education programs, we can potentially reduce the incidence of road rage and other forms of risky driving behavior [60].
The study’s findings can be applied in real-world contexts in the following ways: developing mindfulness training programs for drivers to enhance emotional regulation and safer driving practices; designing educational campaigns that promote safe driving as socially desirable behavior to leverage impression management; creating gender-specific interventions addressing unique psychological factors influencing drivers who are men and those who are women; incorporating psychological assessments into the driver licensing process to identify and support at-risk drivers; enhancing training programs for professional drivers by emphasizing mindfulness and impression management; developing advanced driver monitoring technologies that provide real-time feedback and interventions to maintain safe driving habits.

5. Conclusions

The current study strongly argues for the accurate, thorough, and clear conceptual capture of risky driving behavior, thereby enhancing understanding of the definitions and goals involved. The methodological aspect of our work reflects practical applications of the epistemological principles discussed and theorized within this study.
In contemporary discourse on sustainable driving, addressing aggressive and risky driving behaviors is paramount. Effective solutions necessitate understanding the underlying psychological mechanisms driving such behaviors, alongside promoting awareness of their environmental repercussions and fostering education on sustainable driving practices. The global aspiration for “green” driving behaviors underscores the urgency of comprehending behaviors that oppose environmental sustainability. This study elucidated the roles of self-deceptive enhancement, impression management, emotional regulation difficulties, perceived behavioral control, and mindfulness in shaping risky driving behavior.
Our findings underscored that high impression management among Romanian drivers correlated significantly with lower engagement in risky behaviors, aligning with existing literature on impression management’s role in behavior regulation. Moreover, our study corroborated previous assertions regarding gender differences in risky driving, revealing that, for Romanian drivers who are men, perceived behavioral control and impression management jointly predicted a substantial portion of risky behaviors. Conversely, among drivers who are women, self-deceptive enhancement emerged as a predictor uniquely associated with increased risky driving behaviors, offering novel insights into gender-specific risk factors.
Furthermore, our exploration advanced understanding by proposing a mediation model where mindfulness mitigates the impact of self-deceptive enhancement on risky driving behaviors. This mediation highlights the crucial role of cognitive regulation in promoting safer driving practices. As we move forward, interventions aimed at enhancing mindfulness and addressing psychological predictors are pivotal for cultivating sustainable driving behaviors globally.
To sum up, our study elucidates the intricate interplay between psychological factors and risky driving behaviors. Variables such as self-deceptive enhancement, impression management, emotional regulation difficulties, and perceived behavioral control are crucial in shaping drivers’ inclination towards risky behaviors on the road. Drivers grappling with self-deceptive enhancement, impression management challenges, or perceiving lower control over their actions are more likely to engage in behaviors such as speeding and aggression, which compromise both road safety and environmental sustainability. Conversely, mindfulness practices that enhance emotional regulation and awareness contribute to a more cautious and sustainable driving style. These insights underscore the imperative of addressing psychological factors in road safety initiatives to foster safer and more environmentally conscious driving behaviors among all road users.
The main limitation of this research was that the sample consisted exclusively of Romanian participants. While an a priori power analysis using G*Power version 3.1.9.7 indicated that a sample size of N = 85 would be sufficient to achieve 80% power for detecting a medium effect with a significance level of α = 0.05, the actual number of participants did not adequately capture the full range of variability in survey responses, limiting the ability to generalize the findings to a broader population in terms of age, gender, education, and driving experience. Additionally, although the questionnaires were adapted to the cultural context and showed good psychometric properties, this adaptation might also introduce some limitations. Despite these issues, the study provides important insights and broad conclusions about potential explanations for risky driving behavior.
Our study sets the stage for future research and analysis on risky driving behavior. It highlights the need for a more detailed examination of the impact of mindfulness on risky driving, potentially utilizing a scale that captures various dimensions of mindfulness. Additionally, future research could delve into other predictor or mediator variables such as attentional bias, optimism bias, temporal perspective, dysfunctional impulsivity, texting while driving, and contextual awareness. This last factor represents a crucial aspect that influences a driver’s ability to adapt their emotional responses to changing contexts, such as traffic conditions, unexpected maneuvers by other drivers, and road hazards. A higher level of situational awareness can help prevent incidents of road rage and other emotional responses that may lead to risky driving behavior. Therefore, incorporating situational awareness as a study variable can provide deeper insights into the psychological factors that contribute to safe driving practices. In addition, future research could explore the model developed in the current study as a tool for detecting the conditions under which socio-technical variables, such as strict schedules, tightly coupled subtasks, and time constraints, trigger critical risky behaviors in drivers. By integrating modern risk analysis methodologies, researchers can examine how these work-related factors interact with individual psychological traits to affect driving behavior, providing a comprehensive framework for developing targeted interventions. Organizations can use this model to identify high-risk scenarios and implement preventive measures, such as adjusting schedules to reduce time pressure or providing training to improve situational awareness and emotional regulation under stress. This approach can enhance the relevance of the model in real-world applications, particularly in professional driving contexts where socio-technical factors play a significant role. By linking the psychological predictors identified in our study with socio-technical variables, we can develop a more holistic understanding of the determinants of risky driving behavior and improve road safety through evidence-based interventions.

Author Contributions

Conceptualization, L.-E.C., A.-L.M., V.C., and M.M.A.; methodology, A.-L.M. and L.-E.C.; software, A.-L.M. and L.-E.C.; validation, L.-E.C., A.-L.M., V.C., and M.M.A.; formal analysis, A.-L.M.; investigation, L.-E.C.; resources, L.-E.C., A.-L.M., V.C., and M.M.A.; data curation, A.-L.M.; writing—original draft preparation, A.-L.M., L.-E.C., V.C., and M.M.A.; writing—review and editing, A.-L.M., L.-E.C., V.C., and M.M.A.; visualization, A.-L.M.; supervision, A.-L.M.; project administration, A.-L.M.; funding acquisition, L.-E.C., A.-L.M., V.C., and M.M.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Ethics Committee of Alexandru Ioan Cuza University, Faculty of Psychology and Educational Sciences, No. 698/10.06.2024.

Informed Consent Statement

Before participating in the study, the participants were offered the chance to fill out the informed consent in writing.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Papatheocharous, E.; Kaiser, C.; Moser, J.; Stocker, A. Monitoring Distracted Driving Behaviours with Smartphones: An Extended Systematic Literature Review. Sensors 2023, 23, 7505. [Google Scholar] [CrossRef]
  2. Ulleberg, P.; Rundmo, T. Risk-taking attitudes among young drivers: The psychometric qualities and predictive validity of an instrument to measure young drivers’ risk-taking attitudes. Saf. Sci. 2003, 41, 427–443. [Google Scholar] [CrossRef]
  3. Steg, L.; Vlek, C. Encouraging pro-environmental behavior: An integrative review and research agenda. J. Environ. Psychol. 2009, 29, 309–317. [Google Scholar] [CrossRef]
  4. Du, X.; Ma, J.; Chang, R. The interactive effect of vehicle signals and sensation-seeking on driver hazard perception. Transp. Res. Part F Traffic Psychol. Behav. 2020, 73, 174–187. [Google Scholar] [CrossRef]
  5. Gärling, T.; Schuitema, G. Travel Demand Management Targeting Reduced Private Car Use: Effectiveness, Public Acceptability and Political Feasibility. J. Soc. Issues 2007, 63, 139–153. [Google Scholar] [CrossRef]
  6. Havârneanu, C.E.; Măirean, C.; Popuşoi, S.A. Workplace stress as predictor of risky driving behavior among taxi drivers. The role of job-related affective state and taxi driving experience. Saf. Sci. 2019, 111, 264–270. [Google Scholar] [CrossRef]
  7. Măirean, C.; Havârneanu, G.M.; Barić, D.; Havârneanu, C. Cognitive Biases, Risk Perception, and Risky Driving Behaviour. Sustainability 2022, 14, 77. [Google Scholar] [CrossRef]
  8. Taubman-Ben-Ari, O.; Skvirsky, V. The multidimensional driving style inventory a decade later: Review of the literature and re-evaluation of the scale. Accid. Anal. Prev. 2016, 93, 179–188. [Google Scholar] [CrossRef]
  9. Bond, M. Danger, Driving, and Double-Binds: Violent Automobilized Male Bodies in the Hitch-Hiker (1953), Rebel without a Cause (1955), and Duel (1971). Australas. J. Am. Stud. 2022, 41, 39–73. Available online: https://www.jstor.org/stable/48717729 (accessed on 10 April 2024).
  10. Singh, H.; Kathuria, A. Analysing driver behavior under naturalistic driving conditions: A review. Accid. Anal. Prev. 2021, 150, 105908. [Google Scholar] [CrossRef]
  11. Miles, S.R.; Kent, T.A.; Stanley, M.; Thompson, K.E.; Sharp, C.; Niles, B.L.; Peterson, A.L. Manage emotions to reduce aggression: A pilot study of a brief treatment to help veterans reduce impulsive aggression. J. Nerv. Ment. Dis. 2020, 208, 897–903. [Google Scholar] [CrossRef] [PubMed]
  12. Barati, F.; Pourshahbaz, A.; Nosratabadi, M.; Mohammadi, Z. The role of impulsivity, attentional bias and decision-making styles in risky driving behaviors. Int. J. High-Risk Behav. Addict. 2020, 9, e98001. [Google Scholar] [CrossRef]
  13. Guggenheim, N.; Taubman–Ben-Ari, O.; Ben-Artzi, E. The contribution of driving with friends to young drivers’ intention to take risks: An expansion of the theory of planned behavior. Accid. Anal. Prev. 2020, 139, 105489. [Google Scholar] [CrossRef]
  14. Nijboer, M.; Borst, J.P.; van Rijn, H.; Taatgen, N.A. Driving and Multitasking: The Good, the Bad, and the Dangerous. Front. Psychol. 2016, 7, 1718. [Google Scholar] [CrossRef]
  15. Elsagheer Mohamed, S.A.; Alshalfan, K.A.; Al-Hagery, M.A.; Ben Othman, M.T. Safe Driving Distance and Speed for Collision Avoidance in Connected Vehicles. Sensors 2022, 22, 7051. [Google Scholar] [CrossRef]
  16. Bicchieri, C.; Dimant, E.; Gelfand, M.; Sonderegger, S. Social norms and behavior change: The interdisciplinary research frontier. J. Econ. Behav. Organ. 2023, 205, A4–A7. [Google Scholar] [CrossRef]
  17. Geber, S.; Baumann, E.; Czerwinski, F.; Klimmt, C. The Effects of Social Norms Among Peer Groups on Risk Behavior: A Multilevel Approach to Differentiate Perceived and Collective Norms. Commun. Res. 2021, 48, 319–345. [Google Scholar] [CrossRef]
  18. Tscharaktschiew, S. Why are highway speed limits really justified? An equilibrium speed choice analysis. Transp. Res. Part B Methodol. 2020, 138, 317–351. [Google Scholar] [CrossRef]
  19. McBride, M.; Carter, L.; Phillips, B. Integrating the theory of planned behavior and behavioral attitudes to explore texting among young drivers in the US. Int. J. Inf. Manag. 2020, 50, 365–374. [Google Scholar] [CrossRef]
  20. Fruhen, L.S.; Rossen, I.; Kanse, L. Changes in car drivers’ attitudes and behaviours, and cyclist numbers following the introduction of a cyclist minimum passing distance law. Accid. Anal. Prev. 2021, 156, 106108. [Google Scholar] [CrossRef]
  21. Bosnjak, M.; Ajzen, I.; Schmidt, P. The theory of planned behavior: Selected recent advances and applications. Eur. J. Psychol. 2020, 16, 352. [Google Scholar] [CrossRef] [PubMed]
  22. Lajunen, T.; Corry, A.; Summala, H.; Hartley, L. Impression management and Self-Deception in traffic behaviour inventories. Personal. Individ. Differ. 1997, 22, 341–353. [Google Scholar] [CrossRef]
  23. Yılmaz, Ş.; Arslan, B.; Öztürk, İ.; Özkan, Ö.; Özkan, T.; Lajunen, T. Driver social desirability scale: A Turkish adaptation and examination in the driving context. Transp. Res. Part F Traffic Psychol. Behav. 2022, 84, 53–64. [Google Scholar] [CrossRef]
  24. Long, D. Impression Management. Oxf. Res. Encycl. Psychol. 2021. [Google Scholar] [CrossRef]
  25. Lajunen, T.; Summala, H. Can we trust self-reports of driving? Effects of impression management on driver behaviour questionnaire responses. Transp. Res. Part F Traffic Psychol. Behav. 2003, 6, 97–107. [Google Scholar] [CrossRef]
  26. Intini, P.; Colonna, P.; Ryeng, E. Route familiarity in road safety: A literature review and an identification proposal. Transp. Res. Part F Traffic Psychol. Behav. 2019, 62, 651–671. [Google Scholar] [CrossRef]
  27. Atombo, C.; Wu, C.; Zhong, M.; Zhang, H. Investigating the motivational factors influencing driver’s intentions to unsafe driving behaviours: Speeding and overtaking violations. Transp. Res. Part F Traffic Psychol. Behav. 2016, 43, 104–121. [Google Scholar] [CrossRef]
  28. Ajzen, I. The theory of planned behavior: Frequently asked questions. Hum. Behav. Emerg. Technol. 2020, 2, 314–324. [Google Scholar] [CrossRef]
  29. Weiss, N.H.; Tull, M.T.; Gratz, K.L. A preliminary experimental examination of the effect of emotion dysregulation and impulsivity on risky behaviors among women with sexual assault-related posttraumatic stress disorder. Behav. Modif. 2014, 38, 914–939. [Google Scholar] [CrossRef]
  30. Yang, L.; Li, X.; Guan, W.; Jiang, S. Assessing the relationship between driving skill, driving behavior and driving aggressiveness. J. Transp. Saf. Secur. 2022, 14, 737–753. [Google Scholar] [CrossRef]
  31. Trógolo MA Melchior, F.; Medrano, L.A. The role of difficulties in emotion regulation on driving behavior. J. Behav. Health Soc. Issues 2014, 6, 107–117. [Google Scholar] [CrossRef]
  32. Pizzo, A.; Lausi, G.; Burrai, J.; Quaglieri, A.; Mari, E.; D’Alessio, I.; Cricenti, C. Emotion behind the Wheel: Unravelling the Impact of Emotional (dys) Regulation on Young Driving Behaviour—A Systematic Review. Sustainability 2024, 16, 3384. [Google Scholar] [CrossRef]
  33. Pruessner, L.; Barnow, S.; Holt, D.V.; Joormann, J.; Schulze, K. A cognitive control framework for understanding emotion regulation flexibility. Emotion 2020, 20, 21. [Google Scholar] [CrossRef]
  34. Herrero-Fernández, D.; Parada-Fernández, P.; Jorge, R. The mediation effect of emotion dysregulation in the relationship between anger and aggression on the road in a sample of Spanish drivers. Transp. Res. Part F Traffic Psychol. Behav. 2021, 79, 63–71. [Google Scholar] [CrossRef]
  35. Parlangeli, O.; Bracci, M.; Guidi, S.; Marchigiani, E.; Duguid, A. Risk perception and emotions regulation strategies in driving behaviour: An analysis of the self-reported data of adolescents and young adults. Int. J. Hum. Factors Ergon. 2018, 5, 166. [Google Scholar] [CrossRef]
  36. Hussain, M.; Shi, J.; Batool, Z. An investigation of the effects of motorcycle-riding experience on aberrant driving behaviors and road traffic accidents-A case study of Pakistan. Int. J. Crashworthiness 2022, 27, 70–79. [Google Scholar] [CrossRef]
  37. Hasan, A.S.; Patel, D.; Alfaris, R.; Jalayer, M. Identifying distracted-driving events from on-road observations using a moving vehicle: A case study in New Jersey. Accid. Anal. Prev. 2022, 177, 106827. [Google Scholar] [CrossRef]
  38. Koppel, S.; Bugeja, L.; Hua, P.; Osborne, R.; Stephens, A.N.; Young, K.L.; Chambers, R.; Hassed, C. Do mindfulness interventions improve road safety? A systematic review. Accid. Anal. Prev. 2019, 123, 88–98. [Google Scholar] [CrossRef] [PubMed]
  39. Kass, S.; VanWormer, L.; Mikulas, W.; Legan, S.; Bumgarner, D. Effects of Mindfulness Training on Simulated Driving: Preliminary Results. Mindfulness 2011, 2, 236–241. [Google Scholar] [CrossRef]
  40. Valero-Mora, P.M.; Martí-Belda-Bertolín, A.; Sánchez-García, M. Keep calm, pay attention, and carry on: Anxiety and consciousness mediate the effect of, mindfulness on driving performance in young drivers. Transp. Res. Part F Traffic Psychol. Behav. 2021, 83, 22–32. [Google Scholar] [CrossRef]
  41. Baltruschat, S.; Mas-Cuesta, L.; Cándido, A.; Maldonado, A.; Verdejo-Lucas, C.; Catena-Verdejo, E.; Catena, A. Repeat traffic offenders improve their performance in risky driving situations and have fewer accidents following a mindfulness-based intervention. Front. Psychol. 2021, 11, 567278. [Google Scholar] [CrossRef] [PubMed]
  42. Cordellieri, P.; Baralla, F.; Ferlazzo, F.; Sgalla, R.; Piccardi, L.; Giannini, A.M. Gender effects in young road users on road safety attitudes, behaviors and risk perception. Front. Psychol. 2016, 7, 1412. [Google Scholar] [CrossRef] [PubMed]
  43. Byrnes, J.P.; Miller, D.C.; Schafer, W.D. Gender differences in risk-taking: A meta-analysis. Psychol. Bull. 1999, 125, 367–383. [Google Scholar] [CrossRef]
  44. Hatfield, J.; Fernandes, R. The role of risk-propensity in the risky driving of younger drivers. Accid. Anal. Prev. 2009, 41, 25–35. [Google Scholar] [CrossRef] [PubMed]
  45. Romer, D.; Hennessy, M. A biosocial-affect model of adolescent sensation seeking: The role of affect evaluation and peer-group influence in adolescent drug use. Prev. Sci. 2007, 8, 89–101. [Google Scholar] [CrossRef] [PubMed]
  46. Ginsburg, K.R.; Winston, F.K.; Senserrick, T.M. National young-driver survey: Teen perspective and experience with factors that affect driving safety. Pediatrics 2008, 121, e1391–e1403. [Google Scholar] [CrossRef] [PubMed]
  47. Rhodes, N.; Pivik, K. Age and gender differences in risky driving: The roles of positive affect and risk perception. Accid. Anal. Prev. 2011, 43, 923–931. [Google Scholar] [CrossRef] [PubMed]
  48. Aluja, A.; Balada, F.; García, O.; García, L.F. Psychological predictors of risky driving: The role of age, gender, personality traits (Zuckerman’s and Gray’s models), and decision-making styles. Front. Psychol. 2023, 14, 1058927. [Google Scholar] [CrossRef]
  49. Măirean, C.; Havârneanu, C.-E. The relationship between drivers’ illusion of superiority, aggressive driving, and self-reported risky driving behaviors. Transp. Res. Part F Traffic Psychol. Behav. 2018, 55, 167–174. [Google Scholar] [CrossRef]
  50. Paulhus, D. Paulhus Deception Scales (PDS): The Balanced Inventory of Desirable Responding—7: User’s Manual; Multi-Health Systems: North Tanawanda, NY, USA, 1998. [Google Scholar]
  51. Duttweiler, P.C. The Internal Control Index: A newly developed measure of locus of control. Educ. Psychol. Meas. 1984, 44, 209–221. [Google Scholar] [CrossRef]
  52. Gratz, K.L.; Roemer, L. Multidimensional assessment of emotion regulation and dysregulation: Development, factor structure, and initial validation of the Difficulties in Emotion Regulation Scale. J. Psychopathol. Behav. Assess. 2004, 26, 41–54. [Google Scholar] [CrossRef]
  53. Brown, K.W.; Ryan, R.M. The benefits of being present: Mindfulness and its role in psychological well-being. J. Personal. Soc. Psychol. 2003, 84, 822–848. [Google Scholar] [CrossRef] [PubMed]
  54. Chan, M.C.Y.; Aryadoust, V.; Nie, Y. CORE 2018: A Quantitative Study of Teaching and Learning in Singapore Classrooms; National Institute of Education: Singapore, 2022; Available online: https://repository.nie.edu.sg/handle/10497/24456 (accessed on 2 February 2024).
  55. Faul, F.; Erdfelder, E.; Lang, A.-G.; Buchner, A. G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav. Res. Methods 2007, 39, 175–191. [Google Scholar] [CrossRef] [PubMed]
  56. Muslim, N.H.B.; Shafaghat, A.; Keyvanfar, A.; Ismail, M. Green Driver: Driving Behaviors Revisited on Safety. Arch. Transp. 2018, 47, 49–78. [Google Scholar] [CrossRef]
  57. Keyvanfar, A.; Shafaghat, A.; Muhammad, N.; Ferwati, M.S. Driving Behaviour and Sustainable Mobility—Policies and Approaches Revisited. Sustainability 2018, 10, 1152. [Google Scholar] [CrossRef]
  58. Meston, C.M.; Heiman, J.R.; Trapnell, P.D.; Paulhus, D.L. Socially desirable responding and sexuality self-reports. J. Sex Res. 1998, 35, 148–157. [Google Scholar] [CrossRef]
  59. Davis, C.G.; Thake, J.; Vilhena, N. Social desirability biases in self-reported alcohol consumption and harms. Addict. Behav. 2010, 35, 302–311. [Google Scholar] [CrossRef]
  60. Qu, W.; Liu, M.; Ge, Y. Mindfulness decreases driving anger expression: The mediating effect of driving anger and anger rumination. Accid. Anal. Prev. 2024, 203, 107642. [Google Scholar] [CrossRef]
Sustainability 16 06620 g001
Figure 1. Model of self-deceptive enhancement as predictor of risky driving behavior, mediated by mindfulness.
Figure 1. Model of self-deceptive enhancement as predictor of risky driving behavior, mediated by mindfulness.
Sustainability 16 06620 g001
Table 1. Descriptives of the measures involved in the study.
Table 1. Descriptives of the measures involved in the study.
Risky Driving BehaviorsSelf-Deceptive EnhancementImpression ManagementDifficulties in Emotional RegulationPerceived Behavioral ControlMindfulness
N436436436436436436
Mean24.4334.9666.3843.1995.263.86
Std. Deviation10.476.2310.7015.8712.010.92
Skewness−0.02−0.01−0.240.030.09−0.28
Std. Error of Skewness0.110.110.110.110.110.11
Kurtosis−0.33−0.220.47−0.27−0.02−0.17
Std. Error of Kurtosis0.230.230.230.230.230.23
Table 2. Correlations between risky driving behaviors and study variables.
Table 2. Correlations between risky driving behaviors and study variables.
Variables123456
1. Risky driving behaviors10.193 **−0.325 **0.136 **0.081−0.237 **
2. Self-deceptive enhancement 1−0.366 **0.606 **0.388 **−0.545 **
3. Impression management 1−0.375 **−0.148 **0.455 **
4. Difficulties in emotional regulation 10.426 **−0.715 **
5. Perceived behavioral control 1−0.428 **
6. Mindfulness 1
N = 436, ** p < 0.01.
Table 3. Summary of stepwise regression analysis for variables predicting risky driving behaviors.
Table 3. Summary of stepwise regression analysis for variables predicting risky driving behaviors.
Variableβtsr2RR2∆R2
Step 1 0.36 **0.11 **0.11 **
Impression management−0.33 **−7.16 **0.11
N = 436, ** p < 0.01.
Table 4. Summary of stepwise regression analysis for variables predicting risky driving behaviors in drivers who are men.
Table 4. Summary of stepwise regression analysis for variables predicting risky driving behaviors in drivers who are men.
VariableΒtsr2RR2∆R2
Step 1 0.28 **0.08 **0.08 **
Impression management−0.28 **−2.94 **0.08
Step 2 0.34 *0.12 *0.04 *
Impression management−0.21 *−2.14 *0.04
Perceived behavioral control0.21 *2.13 *0.04
N = 104, * p < 0.05, ** p < 0.01.
Table 5. Summary of stepwise regression analysis for variables predicting risky driving behaviors in drivers who are women.
Table 5. Summary of stepwise regression analysis for variables predicting risky driving behaviors in drivers who are women.
Variableβtsr2RR2∆R2
Step 1 0.36 **0.13 **0.13 **
Impression management−0.36 **−7.00 **0.13
Step 2 0.38 *0.14 *0.01 *
Impression management−0.32 **−5.99 **0.09
Perceived behavioral control0.12 *2.24 *0.01
N = 332, * p < 0.05, ** p < 0.01.
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Marian, A.-L.; Chiriac, L.-E.; Ciofu, V.; Apostol, M.M. Understanding Risky Behavior in Sustainable Driving among Young Adults: Exploring Social Norms, Emotional Regulation, Perceived Behavioral Control, and Mindfulness. Sustainability 2024, 16, 6620. https://doi.org/10.3390/su16156620

AMA Style

Marian A-L, Chiriac L-E, Ciofu V, Apostol MM. Understanding Risky Behavior in Sustainable Driving among Young Adults: Exploring Social Norms, Emotional Regulation, Perceived Behavioral Control, and Mindfulness. Sustainability. 2024; 16(15):6620. https://doi.org/10.3390/su16156620

Chicago/Turabian Style

Marian, Andrei-Lucian, Laura-Elena Chiriac, Vlad Ciofu, and Manuela Maria Apostol. 2024. "Understanding Risky Behavior in Sustainable Driving among Young Adults: Exploring Social Norms, Emotional Regulation, Perceived Behavioral Control, and Mindfulness" Sustainability 16, no. 15: 6620. https://doi.org/10.3390/su16156620

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