Assessing Significant Factors Affecting Risky Riding Behaviors of Vietnamese Motorcyclists Using a Contextual Mediated Model

Vuong, Xuan Can; Mou, Rui-Fang; Vu, Trong Thuat; Nguyen, Thi An; Cu, Thi Thuc Anh; Nguyen, Chi Trung

doi:https://doi.org/10.1155/2023/2179828

Journal of Advanced Transportation

On this page

Abstract Introduction Literature Review Materials and Methods Results and Discussion Conclusions Data Availability Conflicts of Interest Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2023 | Article ID 2179828 | https://doi.org/10.1155/2023/2179828

Assessing Significant Factors Affecting Risky Riding Behaviors of Vietnamese Motorcyclists Using a Contextual Mediated Model

Xuan Can Vuong,¹Rui-Fang Mou,²Trong Thuat Vu,¹Thi An Nguyen,³Thi Thuc Anh Cu,¹and Chi Trung Nguyen¹

Academic Editor: Luis Miranda-Moreno

Received26 Jul 2022

Revised02 Apr 2023

Accepted10 Nov 2023

Published21 Nov 2023

Abstract

This study explores the significant factors affecting risky riding behaviors of Vietnamese motorcyclists using a contextual mediated model (CMM) in Hanoi, the capital of Vietnam, where motorcycle crashes are prevalent. The affecting factors include personality traits, riding self-confidence, riding attitude, and risk perception. Personality traits and riding self-confidence are distal factors of CMM that affect risky riding behaviors. On the other hand, riding attitude and risk perception are proximal factors in CMM. A survey was conducted to collect information on motorcyclists’ risky riding behaviors related to the four factors mentioned through a self-reported questionnaire. Statistical Package Social Science (SPSS) and structural equation modeling (SEM) with analysis of moment structures (AMOS) are used to determine the effects of the factors on risky riding behaviors. The results discovered that riding attitude and risk perception were the intermediate variables of personality trait and riding self-confidence affecting the risky riding behaviors, and personality trait and riding self-confidence also affected the risky riding behaviors directly. Findings in the model also show that riding attitude was perceived to play a significant role in increasing risky driving behavior. The recommendation is to increase the safety education programs that reduce risky driving behavior.

1. Introduction

Many countries in Southeast Asia, including Vietnam, heavily rely on motorcycles for transportation. This is because motorcycles have several advantages over cars, such as being cheaper, easier to park, faster to navigate urban roads, and more fuel-efficient [1]. However, crashes and fatality rates on motorcycles are higher than in other transportation modes, primarily passenger cars. In Hanoi (Vietnam), there are over 700 registered motorcycles per 1,000 people, which account for more than 80% of the trips taken [2]. The World Health Organization (WHO) [3] reported that motorcycles are related to 43% of all road crash deaths in Southeast Asia. In Vietnam, road traffic accidents (RTAs) involving motorcycles account for over 66.7%, and human factors account for 71.6% [4]. Even in developed countries, motorcycle-related road crashes are still a recurring problem with no solution.

In contrast to passenger cars, motorcycles are less safe and vulnerable due to the absence of protective devices and the high number of motorcycles on the road in mixed traffic conditions. Motorcyclists are also considered to have more risk-taking and speeding behaviors [5], easily leading to road crashes. The previous studies found that road crashes related to motorcycles have been associated with risky behaviors and affecting factors of the risky behaviors of motorcyclists [1, 6]. It is essential to identify and understand riding behavior that helps predict motorcyclists’ actions on the road, identify crash mechanisms and causes, and then control crashes to improve their health.

Contextual mediated model (CMM) using a self-reported questionnaire survey is an effective tool to explore effecting factors on riding behavior found in many studies related to car drivers. So far, research on the risky riding behaviors of motorcycles is still far from universal compared with other road users such as cars and lacks general consideration of various contributing factors [7]. Most studies on motorcycle behavior using psychological factors are also mainly conducted in developed countries, where a few motorcycles with considerable power are used. There are limited studies on the riding behaviors of motorcyclists in developing countries like Vietnam [1].

Numerous factors can impact the risky behavior of motorcyclists. These may include inadequate motorcycle maintenance, insufficient motorcycle facilities, age group, lack of attention, careless riding, failure to follow traffic rules, riding attitude, self-confidence while riding, perception of risk, personal preferences, and personality traits. Exploring all the factors influencing driving behavior in a model would be more complicated and unnecessary than focusing on the key factors. Therefore, this study emphasizes Vietnamese motorcyclists’ personality traits, riding self-confidence, riding attitude, and risk perception of self-reported performed risky riding behavior using the CMM model. Therefore, a complete understanding of the hazardous behaviors of motorcyclists is necessary to establish a plan for improving traffic safety.

The remainder of this study is structured as follows: Section 2 introduces the literature review, Section 3 presents the materials and methods utilized, Section 4 describes the study’s results along with statistical testing and discussion, and Section 5 provides conclusions and future work.

2. Literature Review

Engaging in risky riding behavior, such as speeding, riding without a helmet, and other violations, is considered unacceptable in public due to the potential negative consequences of negligence [8, 9]. Since risky riding behavior is closely linked to motorcycle accidents, it is crucial to identify and comprehend the factors that contribute to such behavior. Studies have endeavored to define and recognize risky riding behavior by combining various elements and assessing their interactions, such as personality traits, riding self-confidence, riding attitude, and risk perception, among others, using different models, including the CMM model.

The contextual mediated model (CMM) of risky behavior was first proposed by Elander et al. [10] and generalized by Sümer [11] based on the horizontal model of Lajunen [12]. The CMM points out that the combination of various distal and proximal contextual factors in certain conditions can lead to risky behaviors and crashes. The proximal factors mainly emphasize the factors that directly contribute to risky behaviors or accidents. The distal factors mainly emphasize social background, lifestyle, daily habits, and other factors. Elander et al. [10] described a relationship among personality traits, driving behaviors, and crash risk using CMM. Iversen and Rundmo [13] found that risky behaviors (e.g., speeding) partially mediated the impacts of personality on accident involvement in Norway. Sümer [11] proposed an indirect connection between distal factors (e.g., demographic traits and individual differences) and outcomes (e.g., accidents) through the proximal environment (e.g., driver actions). Zhang et al. [14] investigated a relationship among driving anger, aberrant behaviors, and crash risk based on CMM. According to the study, abnormal behaviors were found to be the main factor that caused an increase in the risk of crashes due to driving anger. Just like the risk of crashing or getting into an accident, aggressive driving can also be considered an outcome in the absence of a crash [15]. According to Ullerberg and Rundmo [6], distal factors affect the outcomes of a CMM involving personality, attitude, and behavior. Several studies have used or expanded the model of Ullerberg and Rundmo, and their results indicated that the framework of road traffic safety was helpful from a CMM model [16–20]. The CMM can describe a relation between distal factors, proximal factors, and outcomes. We can easily increase variables and change the roles of variables in different CMM models. Analyzing shows that previous studies often use only a few latent variables in the CMM models in various areas with different study samples under traffic flow conditions predominated by cars. Thus, in the context of Vietnam’s transportation, it is vital to construct a CMM model that suits motorcycles for better analysis of motorcycle accidents, training behavior of motorcyclists, and intervention.

There have been limited studies in Vietnam that focus on the behaviors of motorcyclists and the factors that affect them. For instance, Trinh and Vo [21] used a combination of the theory of planned behavior and the health belief model to explain the relationship between drunk driving, illegal changing of direction, and speeding, but they did not focus on motorcyclists. However, Trinh and Le [22] found a significant link between speeding and helmet-wearing among motorcyclists using the theory of planned behavior. Truong et al. [23] examined the relationship between phone use while riding and other risky behaviors among university students on motorcycles, with calling while riding being the most common risky behavior. Nguyen-Phuoc et al. [24] investigated the risky behaviors of app-based motorcycle taxi riders. They found that phone use while riding was the most frequent, followed by failing to use turn signals, encroaching on car lanes, exceeding speed limits, running red lights, and carrying more than one passenger. Meanwhile, Luot et al. [25] found no correlation between risk behaviors and risk perception among Vietnamese motorcyclists. Li et al. [26] conducted a cross-sectional study in Ho Chi Minh City and found an increase in the use of improper and poor-quality helmets, which is one of four main issues (i.e., drink-driving, motorcycle helmets, seat belts, and speed management) implemented by road safety projects in 10 low- and middle-income countries [27]. Bui et al. [28] provided a Motorcycle Rider Behavior Questionnaire (MRBQ) with 36 items adapted from the original MRBQ of Elliott et al. However, they did not consider the factors influencing risk behavior in a given model. Nguyen-Phuoc et al. [29] investigated the factors that affect the frequency of turn signals at intersections, such as environmental characteristics, perceived risk, and beliefs. In addition, Nguyen-Phuoc et al. [30] examined the factors related to the risk-taking attitude and road safety compliance of motorcycle delivery riders, such as job demands and resources. Vuong et al. [4] explored riding attitudes and attempted to explain the relationship between the detected factors and the riding attitudes of motorcyclists. Lastly, Can et al. [31] considered the reliability and validity of a risk perception scale of motorcyclists in Vietnam. Generally, in recent years, some scholars have been interested in and tried to discover the risky behaviors of motorcyclists in Vietnam. However, they mainly focused on given violations and have not provided comprehensive models or a general picture of the risky riding behaviors of motorcyclists in the transport context of Vietnam.

In our study, we utilized a deductive approach by leveraging the results of prior research [15, 32] and engaging with industry experts through qualitative interviews to construct a theoretical framework using the CMM model. This model significantly emphasizes five key elements—personality traits, riding attitudes, risk perception, and riding self-confidence—to comprehensively analyze the effects of risky riding behaviors on road traffic safety in developing nations like Vietnam. Our ultimate objective is to propose effective safety measures and interventions that can meaningfully reduce the incidence of dangerous riding practices among motorcyclists.

3. Materials and Methods

3.1. Formulation of the Theoretical Model

In this study, the proposed theoretical model using CMM considers personality traits, riding self-confidence, riding attitude, and risk perception as factors contributing to risky riding behaviors among motorcyclists. The first two factors (i.e., personality traits and riding self-confidence) are distal factors, and the remaining factors (i.e., riding attitude and risk perception) are proximity factors of the proposed CMM, as shown in Figure 1. We review the considered factors and propose the research hypotheses of the proposed model as follows:

3.1.1. Personality Trait

The most studied personality traits are those that cause individuals to disregard or underestimate the dangers of the road, leading to reckless and unsafe behavior. These traits are characterized by an increased probability of engaging in risky riding practices and being involved in accidents. Several scales of personality traits are used in the context of risky behaviors, such as the Revised NEO Personality Inventory [33], the Big Five Questionnaire [34], the Zuckerman-Kuhlman Personality Questionnaires Cross Cultural [35], and normlessness [36]. For instance, personality traits in the Big Five Questionnaire have comprised extraversion, neuroticism, agreeableness, openness to experience, and conscientiousness; the NEO Personality Inventory measures excitement-seeking, anxiety, and aggression. In addition, many scales of personality traits have combined and developed from the abovementioned scales [6, 16, 37–40].

Previous studies have indicated that certain individual characteristics can significantly encourage unsafe motorcycling behaviors [41, 42]. These traits can exert both a direct and indirect influence on risk perception [43] and riding attitudes. Our current study postulates that personality traits can impact hazardous riding conduct among motorcyclists via both direct and indirect routes, encompassing risk perception and riding attitudes.

3.1.2. Riding Self-Confidence

Wong et al. first proposed the riding self-confidence scale for motorcyclists [37–39, 44]. It expresses the individuals’ judgment of their riding ability. In [45, 46], the scale is expanded and expressed in terms of motorcyclists’ technical capacity and judgment ability. Compared with other scales, this scale is still less applicable when studying the riding behaviors of motorcyclists.

3.1.3. Riding Attitude

A positive safety mindset is essential for riders to prioritize traffic safety while on the road. This mindset encompasses their thoughts, emotions, and cognitive processes [47], significantly impacting their riding behaviors. Riders who engage in risky behaviors often hold negative attitudes towards traffic safety. To assess a rider’s attitude towards riding, various scales such as Ulleberg and Rundmo’s scale (which evaluates traffic flow and rule obedience, speeding, and enjoyment of riding) [6, 16] and Iversen and Rundmo’s scale (focusing on attitude towards rule violations and speeding, careless driving by others, and drinking and driving) are utilized [47].

3.1.4. Risk Perception

Risk perception is “the subjective experience of risk in potential traffic hazards” [48]. Therefore, risk perception is considered a precursor to actual driving behavior and is also commonly examined concerning traffic safety. Risk perception can be assessed using several scales. For example, the scale of Rundmo and Iversen consists of three different dimensions (i.e., worry and insecurity, probability assessments, and concern) [49]. The scale of Machin and Sankey is compacted into three subfactors (i.e., worry and concern, the likelihood of a crash, and aversion to risk-taking) [42, 48]. The scale of Wang et al. includes three different subfactors (i.e., level of danger, worry, and probability assessments) [45, 46].

3.1.5. Risky Riding Behavior

In 2004, Sexton et al. [50] employed a 24-item Motorcycle Rider Behavior Questionnaire (MRBQ) to evaluate the dangerous behaviors exhibited by motorcyclists, which were classified into four distinct categories: traffic errors, speeding, stunts, and control errors. Later, Elliott et al. [51] expanded on the original MRBQ, drawing on Reason et al.’s [52] research to identify the various factors that influence motorcyclist behavior in Great Britain. These factors were categorized into five groups, including traffic errors, control errors, speed violations, performing stunts, and the use of safety equipment. Subsequent studies have also used the MRBQ to explore the risky behaviors of motorcyclists. For example, Motevalian et al. [53] developed the MRBQ for Persian motorcycle riders, which included six subscales (i.e., speed violations, traffic errors, safety violations, traffic violations, stunts, and control errors). In their model, some items related to protective clothing, as in Elliot’s MRBQ, were deleted to match Persian motorcycle riders. Besides, some items were also added, such as helmet use, red light running, etc. Özkan et al. [54] deployed the MRBQ for Turkish people with factors similar to those of Elliott et al. According to researchers, the correlation between rider accidents and offenses and annual mileage was significant, while rider age had a comparably more minor influence. Stunts were identified as the primary cause of accidents and offenses, whereas traffic violations were primarily caused by speeding. In a study of Australian riders, Sakashita et al. [55] used the MBRQ to identify four important factors: control errors, speeding violations, stunts, and protective gear. Similarly, Uttra et al. [56] studied Thai riders and found that traffic errors, control errors, stunts, and safety equipment were critical factors. Stephens et al. [57] also analyzed the correlation between the MBRQ and traffic accidents in Australia. They discovered that while speeding violations and control errors were uncommon, they still increased the likelihood of an accident. Furthermore, stunts raise the chance of an accident. Bui et al. [28] investigated the factors of the MRBQ for Vietnamese motorcyclists. Their scale included 36 items, which were arranged into four dimensions in terms of previous studies on the factors influencing riding behaviors. Some studies considered either four factors [28, 50, 55, 56], five factors [51, 54, 57], or six factors [53]. From there, the consideration of factors is not consistent, depending on the traffic characteristics of each region. Besides, most of the above studies have been conducted in a traffic environment with predominantly cars, which differs from the mixed traffic flow that depends on motorcycles, like in Vietnam. Besides, several items related to the use of safety equipment and safety devices, such as protective clothing, are unsuitable for Vietnamese traffic conditions due to the lack of regulations and traffic culture. Therefore, they are not considered in this study.

3.1.6. Research Hypotheses

Based on the abovementioned discussions, ten hypotheses are proposed to formulate the theoretical model for this study as follows: H1a: Personality traits significantly have a positive direct effect on riding attitude. H1b: Personality traits significantly have a positive direct effect on risk perception. H1c: Personality traits significantly have a positive direct effect on risky riding behavior. H1d: Personality traits significantly have a positive direct effect on riding self-confidence. H2a: Riding self-confidence significantly has a positive direct effect on riding attitude. H2b: Riding self-confidence significantly has a negative direct effect on risk perception. H2c: Riding self-confidence significantly has a negative direct effect on risky riding behavior H3: Riding attitude significantly has a positive direct effect on risky riding behavior. H4a: Risk perception has a positive direct effect on risky riding behavior. H4b: Risk perception significantly has a negative direct effect on riding attitude.

The mediated effect through riding attitude and risk perception of the theoretical model is significant when the product of paths is significantly different from zero [58, 59].

3.2. Methods of Data Collection and Analysis

3.2.1. Data Collection

To match the theoretical model proposed in Sub-section 3.1, we designed a structured three-section questionnaire as a self-reported questionnaire. For the first section, we gathered socio-demographic information from the participants, such as their age, gender, and riding experience. The second section was dedicated to analyzing trip patterns, such as the purpose and frequency of rides. Lastly, in the third section, we sought to collect data on personality traits, riding attitude, self-confidence, risk perception, and any risky riding behavior.

The research analyzed personality traits (PT) through five subfactors that comprised a total of 24 items. These subfactors were anxiety (AN, four items), anger (AG, five items), sensation-seeking (SS, five items), altruism (AL, six items), and normlessness (NL, four items), which were sourced from materials such as [6, 16, 38, 40]. Participants were assigned a rating on a five-point Likert-type scale, with responses ranging from “strongly disagree (1)” to “strongly agree (5).” A higher score indicated a greater level of the personality traits under examination.

Riding self-confidence (RC) was evaluated utilizing seven items adapted from [45, 46] (except “I can judge the speed of a motor vehicle accurately”). All items were evaluated based on a 5-point scale from low to high (strongly disagree (1) to strongly agree (5)). The higher the score, the higher the self-confidence.

To assess one’s riding attitude (RA), we analyzed three key components as a set of subfactors. These subfactors included attitudes towards traffic flow (AT), consisting of three distinct items; attitudes towards rule adherence and speeding (AR), consisting of nine items; and attitudes towards enjoying the ride (AF), consisting of three items. These were sourced from [4, 16, 60]. The quantitative items were evaluated on a 5-level scale, ranging from strongly disagree (1) to strongly agree (5). A higher score indicates a more negative attitude, indicating a greater inclination towards disregarding traffic laws and regulations [4].

Risk perception (RP) was considered using three aspects as three subfactors, including worry and concern (WC, four items), probability assessments (PA, four items), and cognition of danger level (WC, six items), as modified from [45, 46, 49]. Quantitative items were assessed on a 5-level scale from low to high (strongly disagree/no danger (1) to strongly agree/very dangerous (5)). The higher the score, the more worried you are about traffic risks and feelings. The higher the risk of behavior is subjective, the greater the perceived consequences [31].

Risky riding behavior (RB) was measured through four subfactors of 28 items. The first three subfactors, including ordinary violations (OV, eight items), speed and riding violations (SV, six items), and lapses and errors (LE, six items), were modified from [37, 38, 51, 53–55, 61–63]. The last subfactor, aggressive violations (AV, eight items), was adapted from [45, 61, 62, 64]. The items were established based on a Likert 5-point scale (1 = never; 2 = hardly ever; 3 = occasionally; 4 = often; 5 = very often).

The questionnaire used for the official survey was initially written in English and later translated into Vietnamese. Pilot tests were conducted to finalize the questionnaire before data collection began in Hanoi, Vietnam’s largest city by area and second-largest by population, from January to March 2019. Before participation, all participants were informed of the survey’s purpose and gave their consent.

After collecting 750 responses, we carefully screened for incomplete and unreliable responses, resulting in a final sample of 716 participants to test the proposed model. Of those participants, 52.5% were male, and 50% were between the ages of 16 and 25. About 33.2% had a college or intermediate-level education, while 66.8% had a university degree or higher. In terms of income, 42.8% earned 5–10 million VND per month, 51.7% earned below 5 million VND per month, and 5.9% earned over 10 million VND per month.

3.2.2. Analytical Method

The model underwent rigorous testing and validation using structural equation modeling (SEM), a widely accepted analytical technique for discovering patterns and relationships among variables and parameters across various fields, such as traffic behavior modeling [65, 66]. To execute SEM, this study employed two key stages, namely, confirmatory factor analysis (CFA) for measurement models and SEM for a structural model, with the aid of SPSS software and the AMOS tool.

Several goodness-of-fit statistics are used to test the hypothesis, as well as to judge the fit and validity of the measurement model and structural model, as follows:(i)Factor loading is not less than 0.5 for CFA [67, 68]. In addition, to ensure reliability and convergence, average variance extracted (AVE) is not less than 0.5 [67], and the composite reliability index (CRI) is not less than 0.6 [69, 70].(ii)The goodness-of-fit index (GFI) and the root mean square error of approximation (RMSEA) are evaluated as absolute fit measures. The comparative fit index (CFI) [71] and incremental fit index (IFI) are taken as incremental fit indexes. Besides, some other indexes are also used to test the hypothesis, such as the value of Chi-square/df and the Tucker-Lewis index (TLI). In general, 3 to 4 relevant indicators are present for each study, and the evidence is sufficient to provide the appropriateness of the model without considering all other indicators [67]. The CFI, TLI, IFI, and GFI values are between 0 and 1, and close to 1 is better [72]. The value of Chi-square/df should be smaller than 5 [73, 74]. RMSEA should be smaller than 0.08 [73]. TLI should be equal to or larger than 0.80 [75].

4. Results and Discussion

4.1. Results of the CFA

We employed the CFA method to evaluate the reliability of measurement items and confirm the suitability of the factorial structure. Through this process, we pinpointed key personality traits such as riding self-confidence, risk perception, riding attitude, and risky riding behavior. We eliminated items that exhibited low loading values, as determined by the multiple goodness-of-fit statistics discussed earlier.

First, we obtained four dimensions for personality traits, including anger (AG, five items), sensation-seeking (SS, four items), normlessness (NL, three items), and altruism (AL, two items). Secondly, riding self-confidence was extracted from one dimension of six items. There are three subscales for riding attitude: attitude towards traffic flow (3 items), attitude towards rule obedience and speeding (6 items), and attitude towards fun-riding (2 items). After that, risk perception consisted of three dimensions: worry and concern (WC, four items), probability assessments (PA, four items), and cognition of danger level (CD, six items). Finally, we achieved the scale of risk-riding behavior with four subscales: ordinary violations (OV, four items), speed and riding violations (SV, four items), lapses and errors (LE, five items), and aggressive violations (AV, seven items). The items of the factors are shown in Table 1.

4.2. Results of SEM

This study tested the theoretical model using SEM to analyze the factors that affect risky riding behaviors among motorcyclists. In the first analysis of the theoretical model, the goodness-of-fit statistics met the requirements. However, we did not see a significant relationship between personality traits (PT) and riding self-confidence (RC) (Sig. = 0.122), as well as between personality traits (PT) and risk perception (RP) (Sig. = 0.931). This means that hypotheses H1b and H1d have been rejected. After removing the insignificant relationships from the theoretical model, we achieved a final model, as shown in Figure 2. The model had the value of Chi-square/df = 2.454 < 5; RMSEA = 0.045 < 0.08. In addition, the GFI, CFI, IFI, and TLI values were all greater than 0.8, close to 1.0, indicating that the model had a reasonable fit. In the model, only the relationship between riding self-confidence (RC) and risky riding behaviors (RB) was accepted with a significance of 0.05 (Sig. = 0.024), and other links were accepted with a significance of 0.001. Since then, we have obtained the results of the research hypotheses, as shown in Table 2.

The direct and indirect effects of various factors, such as riding self-confidence, personality traits, risk perception, and riding attitude, are listed in Table 3.

After considering both direct and indirect effects, it was found that the most influential factors on risky riding behavior were one’s riding attitude (β = 0.60) and one’s personality traits (β = 0.44). Factors that had less impact included risk perception (β = −0.04) and riding self-confidence (β = 0.06).

4.3. Discussion, Findings, and Implications

This study first described the relationship between personality traits, riding self-confidence, riding attitude, and risk perception related to risky riding behaviors in Vietnamese motorcyclists using the CMM. After that, we estimated the effects of these factors on risky riding behaviors using a path analysis of SEM. The factors were considered in many aspects, so they were also appropriate to human behavior.

Our research reveals fascinating insights into the intricate relationship between a rider’s personality, confidence level, attitude, and risk perception and how they impact the likelihood of engaging in risky riding behavior. We observed that a positive riding attitude, strong self-confidence, and certain personality traits were associated with an increased likelihood of engaging in risky behavior, both directly and indirectly. Conversely, a negative perception of risk was linked to a decreased likelihood of such behavior. Moreover, we found that riding attitude and risk perception partially mediated the relationship between behavior, personality traits, and riding self-confidence. Particularly, riding attitude partially mediated the association between risk perception and risky riding behavior. Our study uncovers the intricate interplay between these factors and their impact on risky riding behavior.

For personality traits, there was a direct positive causal relationship () between personality traits and risky riding behaviors. Similarly, a positive causal relationship () was also found between personality traits and riding attitudes. This was also found in [16, 76]; therefore, the results of this study reinforce the statement related to personality traits. Hence, personality traits may be mitigated to address risky riding behaviors.

Multiple studies have indicated a favorable connection between risk perception and a reduction in risky behavior [8, 43, 77]. This implies that an individual who perceives a particular behavior as hazardous is less likely to partake in it. Nonetheless, this association is sometimes not as strong as anticipated. Moreover, some research suggests that risk perception might be an effect rather than a causal factor. In other words, there was no cause of risky behavior by risk perception [6, 25] or only an indirect effect on risky behavior via riding attitude [78] or a negative relationship on reduction of risky behavior [8, 77, 78]. Hence, risk perception was found to influence risky riding behaviors in both positive and negative, depending on the traffic characteristics and culture. This study found a positive direct relationship () between risk perception and risky riding behavior, suitable for the results found in [8, 78, 79]. This result can be described when motorcyclists perceive and behave consistently with those perceptions while riding on the road. It stands to reason that one’s likelihood of a potential crash influences one’s driving habits. Individuals who engage in riskier driving behaviors may see themselves as more susceptible to a collision, which may result in further risky driving behavior [42]. Moreover, a few motorcyclists could accept a certain risk when riding to unaffect their enjoyment and may take risky actions [80]. Many motorcyclists believe that they are immune to accidents [46]. However, our research revealed a negative correlation (estimated at −0.29) between risk perception and risky riding behaviors, primarily influenced by riding attitude. This indirect impact is more significant than the direct impact. Therefore, our findings indicate that risk perception has a negative impact on risky riding behavior. Simply put, the higher an individual’s risk perception score, the lower their score in risky riding behavior. As a result, it is crucial to educate motorcyclists about the dangers of reckless behavior and the severity of traffic accidents through laws and awareness campaigns.

There was a positive correlation (0.60) between risky behavior and unsafe riding attitudes found in this study and sources [37, 76, 81, 82]. It found that high scores of riding attitudes indicated negative attitudes towards traffic safety, leading to a positive relation to risky behaviors. In other words, participants who reported unsafe attitudes towards riding also reported more risky behaviors. Besides, there was an association between riding attitudes and risk perception, as well as the affecting riding behaviors [6, 81]. We found a negative relationship () between risk perception and riding attitudes. It revealed that a high-risk perception score indicated a negative attitude towards risk-taking behaviors, i.e., a positive attitude towards traffic safety. This was consistent with the results in [37]. These results reinforced the direct effect of risk perception on riding attitudes. Therefore, strategies intended to change riding behaviors will be effective only by changing riding attitudes towards unsafe behaviors. In other words, traffic laws and education programs are necessary to improve safety-riding attitudes.

Regarding riding self-confidence, there was a positive effect of riding self-confidence on riding attitude towards risk-taking behavior found in [37, 39]. At the same time, there was a negative relationship between it and risk perception [45, 46]. This study also found these results, i.e., riding self-confidence increased attitudes toward unsafe behaviors and decreased perceived risk. However, we found a negative direct relationship () between riding self-confidence and risky riding behavior, the same as in [76]. Experienced motorcyclists often exhibit greater self-assurance while on the road, which can reduce the likelihood of accidents for themselves and others [76]. Moreover, there are indirect correlations between self-confidence and risky behavior, as evidenced by risk perception (with an estimated value of −0.12), riding attitudes (with an estimated value of 0.15), and the combination of both (with an estimated value of 0.14). In general, the advantages of riding self-confidence outweigh the disadvantages. To sum up, riding self-confidence positively impacts risky riding behavior. Nonetheless, it is worth noting that excessive confidence can result in increased risk-taking and a greater chance of accidents. Improving the traffic infrastructure for motorcycles is necessary to increase new riders’ confidence on the road. In addition, stronger law enforcement is needed to address motorcyclists who exhibit excessive confidence.

For risky riding behavior, as presented in Table 1, 20 items of the riding behavior scale were found to result from complex traffic conditions and the limitations of safety legislation in Vietnam. Several behaviors (e.g., SV04 “Illegal overtaking,” OV04 “Get into the wrong lane,” and SV03 “Exceed the speed limit on a road,” OV06 “Use a cell phone while riding,” etc.) need to be prioritized for handling. Furthermore, aggressive violations (AV) were also identified as important risky behaviors that impact the crash risk of motorcyclists. Aggressive violations have been associated with personality traits of motorcyclists, such as anger. They also reflect the low culture of a part of motorcyclists.

Based on the abovementioned discussions, the controlling measures and changes in risky behaviors of Vietnamese motorcyclists are proposed as follows:(i)Motorcycles will still be the primary means of transportation people choose in the long term, but facilities (e.g., motorcycle lanes, signal lights, etc.) for motorcycles are still limited. The facility is a key part of riding behaviors, and it impacts the behaviors of the motorcyclist. It is necessary to increase facilities for motorcycles.(ii)Strengthening and consistency in enforcing road traffic rules and laws can increase awareness and result in a change in riding behaviors.(iii)Installing surveillance cameras at intersections and road sections will make motorcyclists pay attention to their surveillance, thereby helping to control risky behaviors related to violations.(iv)To enhance road safety in crucial zones, it is imperative to adopt effective measures that cover various aspects such as speed control, enforcement, and communication. Strategies such as imposing higher penalties, introducing a license point system, and defining speed limits can be employed [27]. It is also vital to regularly conduct awareness campaigns to educate motorcyclists on safe riding practices and risk perception. Furthermore, imparting safety education can positively influence the personality traits of motorcyclists, particularly in managing anger and avoiding sensation-seeking behavior.(v)Analyzing the causes of motorcycle crashes to increase public awareness is necessary.

This study has the following implications:(i)This study can help policymakers introduce a riding behavior test in training.(ii)We can predict future behavior tendencies of motorcyclists, as well as determine how to relate to motorcycle crashes through the affecting factors, including personality traits, riding self-confidence, riding attitude, risk perception, and so on.(iii)The study’s findings can inform road safety measures and programs for motorcyclists.(iv)This study also provides future scholars with new insights into exploring the riding behaviors of motorcyclists in developing countries like Vietnam.

5. Conclusions

A CMM model study explored how personality traits, riding self-confidence, riding attitude, risk perception, and risky riding behavior were interrelated among motorcyclists in Hanoi, Vietnam. The findings indicated that these factors played a significant role in shaping the risky riding behavior of the participants. Both personality traits and riding self-confidence directly and indirectly affect risky riding behavior, mediated by riding attitude and risk perception. The study revealed that riding attitude and personality traits were the most influential factors, whereas risk perception and riding self-confidence had a comparatively lower impact. These results align with earlier research and establish the validity of the measurement scales for varying traffic cultures. The study provides crucial groundwork for developing strategies to mitigate risky riding behavior among motorcyclists.

The study’s data collection scope was confined to urban areas of Hanoi, Vietnam, which could potentially limit its ability to represent riding behaviors in other areas accurately. Moreover, the study did not incorporate controlling factors for risky behaviors. These constraints will be thoroughly scrutinized in forthcoming studies.

Data Availability

The data used to support the findings of this study are included within the article.

Conflicts of Interest

The authors declare that there are no conflicts of interest.

Acknowledgments

The survey was implemented by a group of students from the University of Transport and Communications (UTC). The authors sincerely thank them for permission to access their collected data. The authors gratefully thank all motorcyclists who participated in this study.

References

A. Chakraborty, S. Mitra, and B. Maitra, “A comprehensive review of motorcyclists’ riding behaviour and safety,” Proceedings of the Fifth International Conference of Transportation Research Group of India: 5th CTRG Volume, vol. 3, pp. 285–301, 2022.
View at: Google Scholar
R. F. Mou, X. C. Vuong, and T. T. Vu, “Analysis of road accident in Hanoi, Vietnam,” in Proceedings fo the in 13th International Conference of Eastern Asia Society for Transportation Studies (EASTS), Colombo, Sri Lanka, September 2019.
View at: Google Scholar
Who, Global Status Report on Road Safety 2018, World Health Organization, Geneva, Switzerland, 2018.
C. Vuong Xuan, F. Mou Rui, T. Vu Trong, and A. Cu Thi Thuc, “Exploring factors associated with riding attitudes of motorcyclists in Vietnam,” Transport and Communications Science Journal, vol. 74, no. 1, pp. 81–96, 2023.
View at: Publisher Site | Google Scholar
C. B. Musselwhite, E. Avineri, Y. O. Susilo, and D. Bhattachary, “Public attitudes towards motorcyclists’ safety: a qualitative study from the United Kingdom,” Accident Analysis & Prevention, vol. 49, pp. 105–113, 2012.
View at: Publisher Site | Google Scholar
P. Ulleberg and T. Rundmo, “Personality, attitudes and risk perception as predictors of risky driving behaviour among young drivers,” Safety Science, vol. 41, no. 5, pp. 427–443, 2003.
View at: Publisher Site | Google Scholar
A. Theofilatos and G. Yannis, “A review of powered-two-wheeler behaviour and safety,” International Journal of Injury Control and Safety Promotion, vol. 22, no. 4, pp. 284–307, 2015.
View at: Publisher Site | Google Scholar
D. M. P. Wedagama, “Analysing self-reported risky behaviours of motorcyclists in Bali using Structural Equation Modelling,” Journal of the Eastern Asia Society for Transportation Studies, vol. 11, 2015.
View at: Google Scholar
J. M. R. Hernández, F. A. C. Tovar, and L. K. A. Ruiz, “Factores asociados al uso del casco de protección en dos ciudades de Colombia,” Ciência & Saúde Coletiva, vol. 21, no. 12, pp. 3793–3801, 2016.
View at: Publisher Site | Google Scholar
J. Elander, R. West, and D. French, “Behavioral correlates of individual differences in road-traffic crash risk: an examination of methods and findings,” Psychological Bulletin, vol. 113, no. 2, pp. 279–294, 1993.
View at: Publisher Site | Google Scholar
N. Sümer, “Personality and behavioral predictors of traffic accidents: testing a contextual mediated model,” Accident Analysis & Prevention, vol. 35, no. 6, pp. 949–964, 2003.
View at: Publisher Site | Google Scholar
T. Lajunen, Personality Factors, Driving Style and Traffic Safety, University of Helsinki, Helsinki, 1997.
H. Iversen and T. Rundmo, “Personality, risky driving and accident involvement among Norwegian drivers,” Personality and Individual Differences, vol. 33, no. 8, pp. 1251–1263, 2002.
View at: Publisher Site | Google Scholar
T. Zhang, A. H. Chan, H. Xue, X. Zhang, and D. Tao, “Driving anger, aberrant driving behaviors, and road crash risk: testing of a mediated model,” International Journal of Environmental Research and Public Health, vol. 16, no. 3, p. 297, 2019.
View at: Publisher Site | Google Scholar
B. Demir, S. Demir, and T. Özkan, “A contextual model of driving anger: a meta-analysis,” Transportation Research Part F: Traffic Psychology and Behaviour, vol. 42, pp. 332–349, 2016.
View at: Publisher Site | Google Scholar
C. F. Chen, “Personality, safety attitudes and risky driving behaviors—evidence from young Taiwanese motorcyclists,” Accident Analysis & Prevention, vol. 41, no. 5, pp. 963–968, 2009.
View at: Publisher Site | Google Scholar
L. Mallia, L. Lazuras, C. Violani, and F. Lucidi, “Crash risk and aberrant driving behaviors among bus drivers: the role of personality and attitudes towards traffic safety,” Accident Analysis & Prevention, vol. 79, pp. 145–151, 2015.
View at: Publisher Site | Google Scholar
F. Lucidi, L. Girelli, A. Chirico et al., “Personality traits and attitudes toward traffic safety predict risky behavior across young, adult, and older drivers,” Frontiers in Psychology, vol. 10, p. 536, 2019.
View at: Publisher Site | Google Scholar
R. Sadia, “Predictors of aberrant driving behaviours and accident involvement: quantitative objective and subjective evidence from Chinese and Pakistani drivers,” Lingnan University, Hong Kong, China, 2020, PhD Thesis.
View at: Google Scholar
R. P. Monteiro, G. L. D. H. Coelho, P. H. Hanel, C. E. Pimentel, and V. V. Gouveia, “Personality, dangerous driving, and involvement in accidents: testing a contextual mediated model,” Transportation Research Part F: Traffic Psychology and Behaviour, vol. 58, pp. 106–114, 2018.
View at: Publisher Site | Google Scholar
T. A. Trinh and T. T. A. Vo, “Evaluating the powerful prediction of integrated behavioral model for risky road behaviors,” Procedia Engineering, vol. 142, pp. 71–78, 2016.
View at: Publisher Site | Google Scholar
T. A. Trinh and T. P. Linh Le, “The association between risk-taking behavior and helmet use among motorcyclist,” IOP Conference Series: Earth and Environmental Science, vol. 143, no. 1, Article ID 012069, 2018.
View at: Publisher Site | Google Scholar
L. T. Truong, H. T. Nguyen, and C. De Gruyter, “Correlations between mobile phone use and other risky behaviours while riding a motorcycle,” Accident Analysis & Prevention, vol. 118, pp. 125–130, 2018.
View at: Publisher Site | Google Scholar
D. Q. Nguyen-Phuoc, C. De Gruyter, H. A. Nguyen, T. Nguyen, and D. Ngoc Su, “Risky behaviours associated with traffic crashes among app-based motorcycle taxi drivers in Vietnam,” Transportation Research Part F: Traffic Psychology and Behaviour, vol. 70, pp. 249–259, 2020.
View at: Publisher Site | Google Scholar
N. V Luot, D. T. Linh, N. D. Phong, N. V Long, B. M. Duc, and P. V Hue, “Traffic risk perception, risky road use behaviors among Vietnamese people,” The Open Psychology Journal, vol. 13, no. 1, pp. 49–57, 2020.
View at: Publisher Site | Google Scholar
Q. Li, O. Adetunji, C. V. Pham, N. T. Tran, E. Chan, and A. M. Bachani, “Helmet use among motorcycle riders in Ho Chi Minh City, Vietnam: results of a five-year repeated cross-sectional study,” Accident Analysis & Prevention, vol. 144, Article ID 105642, 2020.
View at: Publisher Site | Google Scholar
A. Esperato, D. Bishai, and A. A. Hyder, “Projecting the health and economic impact of road safety initiatives: a case study of a multi-country project,” Traffic Injury Prevention, vol. 13, pp. 82–89, 2012.
View at: Google Scholar
H. Trung Bui, I. Saadi, and M. Cools, “Investigating on-road crash risk and traffic offences in Vietnam using the motorcycle rider behaviour questionnaire (MRBQ),” Safety Science, vol. 130, Article ID 104868, 2020.
View at: Publisher Site | Google Scholar
D. Q. Nguyen-Phuoc, C. De Gruyter, O. Oviedo-Trespalacios, S. Diep Ngoc, and A. T. P. Tran, “Turn signal use among motorcyclists and car drivers: the role of environmental characteristics, perceived risk, beliefs and lifestyle behaviours,” Accident Analysis & Prevention, vol. 144, Article ID 105611, 2020.
View at: Publisher Site | Google Scholar
D. Quy Nguyen-Phuoc, N. An Ngoc Nguyen, M. H. Nguyen, L. Ngoc Thi Nguyen, and O. Oviedo-Trespalacios, “Factors influencing road safety compliance among food delivery riders: an extension of the job demands-resources (JD-R) model,” Transportation Research Part A: Policy and Practice, vol. 166, pp. 541–556, 2022.
View at: Publisher Site | Google Scholar
V. X. Can, M. Rui-Fang, V. T. Thuat, and N. T. An, “Reliability and validity of scale measuring motorcyclists’ risk perception: the case study of Hanoi, Vietnam,” Tap Chí Khoa Hoc Va Cong Nghe-Dại Hoc Da Nang, vol. 52, pp. 29–33, 2023.
View at: Google Scholar
E. R. Dahlen, B. D. Edwards, T. Tubré, M. J. Zyphur, and C. R. Warren, “Taking a look behind the wheel: an investigation into the personality predictors of aggressive driving,” Accident Analysis & Prevention, vol. 45, pp. 1–9, 2012.
View at: Publisher Site | Google Scholar
P. T. Costa and R. R. McCrae, Revised NEO Personality Inventory (NEO-PI-R) and NEO Five Factor Inventory (NEO-FF-I), Professional Manual, Psychological Assessment Resources, Odessa, Ukraine, 1992.
G. V. Caprara, C. Barbaranelli, L. Borgogni, and M. Perugini, “The “Big Five Questionnaire”: a new questionnaire to assess the five factor model,” Personality and Individual Differences, vol. 15, no. 3, pp. 281–288, 1993.
View at: Publisher Site | Google Scholar
M. Zuckerman, D. M. Kuhlman, J. Joireman, P. Teta, and M. Kraft, “A comparison of three structural models for personality: the big three, the big five, and the alternative five,” Journal of Personality and Social Psychology, vol. 65, no. 4, pp. 757–768, 1993.
View at: Publisher Site | Google Scholar
M. Kohn and C. Schooler, Work and Personality: An Inquiry into the Impact of Social Stratification, Norwood Ablex, New York, NY, USA, 1983.
J. T. Wong, Y. S. Chung, and L. W. Hsiao, “Structure discrepancy of riding behavior of heterogeneous young motorcyclists in Taiwan,” Transportation Research Record, vol. 2194, no. 1, pp. 107–114, 2010.
View at: Publisher Site | Google Scholar
Y. S. Chung and J. T. Wong, “Beyond general behavioral theories: structural discrepancy in young motorcyclist's risky driving behavior and its policy implications,” Accident Analysis & Prevention, vol. 49, pp. 165–176, 2012.
View at: Publisher Site | Google Scholar
J. T. Wong, Y. S. Chung, and S. H. Huang, “Determinants behind young motorcyclists’ risky riding behavior,” Accident Analysis & Prevention, vol. 42, no. 1, pp. 275–281, 2010.
View at: Publisher Site | Google Scholar
T. Nordfjærn and T. Rundmo, “Personality, risk cognitions and motivation related to demand of risk mitigation in transport among Norwegians,” Safety Science, vol. 73, pp. 15–22, 2015.
View at: Publisher Site | Google Scholar
E. R. Dahlen, R. C. Martin, K. Ragan, and M. M. Kuhlman, “Driving anger, sensation seeking, impulsiveness, and boredom proneness in the prediction of unsafe driving,” Accident Analysis & Prevention, vol. 37, no. 2, pp. 341–348, 2005.
View at: Publisher Site | Google Scholar
M. A. Machin and K. S. Sankey, “Relationships between young drivers’ personality characteristics, risk perceptions, and driving behaviour,” Accident Analysis & Prevention, vol. 40, no. 2, pp. 541–547, 2008.
View at: Publisher Site | Google Scholar
A. Falco, A. Piccirelli, D. Girardi, L. Dal Corso, and N. A. De Carlo, “Risky riding behavior on two wheels: the role of cognitive, social, and personality variables among young adolescents,” Journal of Safety Research, vol. 46, pp. 47–57, 2013.
View at: Publisher Site | Google Scholar
J. T. Wong, Y. H. Chang, and S. H. Huang, “Some Insights of young motorcyclists' risky behavior,” in Proceedings of the Transportation Research Board 88th Annual Meeting, Washington, DC, USA, January 2009.
View at: Google Scholar
T. Wang, Electric Bike Rider the Mechnism Study on the Risk Driving Behavior and Accident Doctoral, Southeast University, Dhaka, Bangladesh, 2017.
T. Wang, S. Xie, X. Ye, X. Yan, J. Chen, and W. Li, “Analyzing E-bikers’ risky riding behaviors, safety attitudes, risk perception, and riding confidence with the structural equation model,” International Journal of Environmental Research and Public Health, vol. 17, no. 13, p. 4763, 2020.
View at: Publisher Site | Google Scholar
H. Iversen and T. Rundmo, “Attitudes towards traffic safety, driving behaviour and accident involvement among the Norwegian public,” Ergonomics, vol. 47, no. 5, pp. 555–572, 2004.
View at: Publisher Site | Google Scholar
M. A. Machin and K. S. Sankey, “Factors influencing young drivers' risk perceptions and speeding behaviour,” in Proceedings of the 2006 Australasian Road Safety Research, Policing and Education Conference, Able Video & Multimedia Pty Ltd, Southport, Australia, June 2006.
View at: Google Scholar
T. Rundmo and H. Iversen, “Risk perception and driving behaviour among adolescents in two Norwegian counties before and after a traffic safety campaign,” Safety Science, vol. 42, no. 1, pp. 1–21, 2004.
View at: Publisher Site | Google Scholar
B. Sexton, C. Baughan, M. Elliott, and G. Maycock, “The accident risk of motorcyclists,” 2004, https://strathprints.strath.ac.uk/20274/1/strathprints020274.pdf.
View at: Google Scholar
M. A. Elliott, C. J. Baughan, and B. F. Sexton, “Errors and violations in relation to motorcyclists’ crash risk,” Accident Analysis & Prevention, vol. 39, no. 3, pp. 491–499, 2007.
View at: Publisher Site | Google Scholar
J. Reason, A. Manstead, S. Stradling, J. Baxter, and K. Campbell, “Errors and violations on the roads: a real distinction?” Ergonomics, vol. 33, no. 10-11, pp. 1315–1332, 1990.
View at: Publisher Site | Google Scholar
S. A. Motevalian, M. Asadi-Lari, H. Rahimi, and M. Eftekhar, “Validation of a Persian version of motorcycle rider behavior questionnaire,” Annals of Advances in Automotive Medicine/Annual Scientific Conference, vol. 55, p. 91, 2011.
View at: Google Scholar
T. Özkan, T. Lajunen, B. Doğruyol, Z. Yıldırım, and A. Çoymak, “Motorcycle accidents, rider behaviour, and psychological models,” Accident Analysis & Prevention, vol. 49, pp. 124–132, 2012.
View at: Publisher Site | Google Scholar
C. Sakashita, T. Senserrick, S. Lo, S. Boufous, L. D Rome, and R. Ivers, “The Motorcycle Rider Behavior Questionnaire: psychometric properties and application amongst novice riders in Australia,” Transportation Research Part F: Traffic Psychology and Behaviour, vol. 22, pp. 126–139, 2014.
View at: Publisher Site | Google Scholar
S. Uttra, S. Jomnonkwao, D. Watthanaklang, and V. Ratanavaraha, “Development of self-assessment indicators for motorcycle riders in Thailand: application of the motorcycle rider behavior questionnaire (MRBQ),” Sustainability, vol. 12, no. 7, p. 2785, 2020.
View at: Publisher Site | Google Scholar
A. Stephens, J. Brown, L. de Rome, M. Baldock, R. Fernandes, and M. Fitzharris, “The relationship between Motorcycle Rider Behaviour Questionnaire scores and crashes for riders in Australia,” Accident Analysis & Prevention, vol. 102, pp. 202–212, 2017.
View at: Publisher Site | Google Scholar
D. P. MacKinnon, A. J. Fairchild, and M. S. Fritz, “Mediation analysis,” Annual Review of Psychology, vol. 58, no. 1, pp. 593–614, 2007.
View at: Publisher Site | Google Scholar
A. F. Hayes, Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach, Guilford publications, New York, NY, USA, 2017.
Y. J. Zhang, R. S. Chang, and L. Sun, “Reliability and validity of driving safety attitude scale in Chinese drivers,” Chinese Journal of Ergonomics, vol. 23, no. 5, pp. 25–28, 2017.
View at: Google Scholar
D. Parker, J. T. Reason, A. S. Manstead, and S. G. Stradling, “Driving errors, driving violations and accident involvement,” Ergonomics, vol. 38, no. 5, pp. 1036–1048, 1995.
View at: Publisher Site | Google Scholar
D. Parker, T. Lajunen, and S. Stradling, “Attitudinal predictors of interpersonally aggressive violations on the road,” Transportation Research Part F: Traffic Psychology and Behaviour, vol. 1, no. 1, pp. 11–24, 1998.
View at: Publisher Site | Google Scholar
J. Shi, Y. Bai, X. Ying, and P. Atchley, “Aberrant driving behaviors: a study of drivers in Beijing,” Accident Analysis & Prevention, vol. 42, no. 4, pp. 1031–1040, 2010.
View at: Publisher Site | Google Scholar
R. Lawton, D. Parker, A. S. Manstead, and S. G. Stradling, “The role of affect in predicting social behaviors: the case of road traffic violations,” Journal of Applied Social Psychology, vol. 27, no. 14, pp. 1258–1276, 1997.
View at: Publisher Site | Google Scholar
T. F. Golob, “Structural equation modeling for travel behavior research,” Transportation Research Part B: Methodological, vol. 37, no. 1, pp. 1–25, 2003.
View at: Publisher Site | Google Scholar
E. Nelson, P. Atchley, and T. D. Little, “The effects of perception of risk and importance of answering and initiating a cellular phone call while driving,” Accident Analysis & Prevention, vol. 41, no. 3, pp. 438–444, 2009.
View at: Publisher Site | Google Scholar
J. Hair, W. Black, B. Babin, and R. Anderson, Multivariate Data Analysis, Pearson Prentice Hall, New York, NY, USA, 7th edition, 2009.
J. C. Nunnally, Psychometric Theory, McGraw-Hill, New York, NY, USA, 1978.
C. Fornell and D. F. Larcker, Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics, Sage Publications Sage CA, Los Angeles, CA, USA, 1981.
J. F. Hair, G. T. M. Hult, C. M. Ringle, and M. Sarstedt, A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), Sage publications, Thousand Oaks, CA, USA, 2016.
P. M. Bentler, “Comparative fit indexes in structural models,” Psychological Bulletin, vol. 107, no. 2, pp. 238–246, 1990.
View at: Publisher Site | Google Scholar
S.-F. Lai, “The accident risk measuring model for urban arterials,” in Proceedings of the 3rd International Conference on Road Safety and Simulation, pp. 14–16, Citeseer, Princeton, NY, USA, September, 2011.
View at: Google Scholar
L. T. Hu and P. M. Bentler, “Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives,” Structural Equation Modeling: A Multidisciplinary Journal, vol. 6, no. 1, pp. 1–55, 1999.
View at: Publisher Site | Google Scholar
X. C. Vuong, R.-F. Mou, T. T. Vu, and T. A. Nguyen, “A study of intended unsafe pedestrian crossing behaviors at signalized intersections in Vietnam,” in AUC 2019, pp. 185–193, Springer, Berlin, Germany, 2021.
View at: Google Scholar
D. Hooper, J. Coughlan, and M. R. Mullen, “Structural equation modelling: guidelines for determining model fit,” Electronic Journal of Business Research Methods, vol. 6, no. 1, 2008.
View at: Google Scholar
M. S. Abdullah, M. A. M. Kassim, and M. Mansor, “Risky riding behaviour among Malaysian young motorcyclists,” MATEC Web of Conferences, vol. 150, 2018.
View at: Google Scholar
A. S. Cheng and T. C. Ng, “Risky driving and the perception of motorcycle accident causes among Chinese motorcyclists in Hong Kong,” Traffic Injury Prevention, vol. 13, no. 5, pp. 485–492, 2012.
View at: Publisher Site | Google Scholar
M. Ma, X. Yan, H. Huang, and M. Abdel-Aty, “Occupational driver safety of public transportation: risk perception, attitudes, and driving behavior,” in Proceedings of the Transportation Research Board 89th Annual Meeting, pp. 82–91, Princeton, NY, USA, June 2010.
View at: Google Scholar
W. C. Goh, L. V. Leong, and R. J. X. Cheah, “Assessing significant factors affecting risky riding behaviors of motorcyclists,” Applied Sciences, vol. 10, no. 18, p. 6608, 2020.
View at: Publisher Site | Google Scholar
P. S. Broughton, Risk and Enjoyment in Powered Two wheeler Use, Edinburgh Napier University, Edinburgh, Scotland, 2007.
L. Yao and C. Wu, “Traffic safety for electric bike riders in China: attitudes, risk perception, and aberrant riding behaviors,” Transportation Research Record, vol. 2314, no. 1, pp. 49–56, 2012.
View at: Publisher Site | Google Scholar
Y. Li, Z. Wang, Z. Yin, and P. Zhu, “The relationship of road accidents with motorcyclists' riding behaviors, personality and attitudes towards safety,” Psychological Science, vol. 31, no. 2, pp. 487–489, 2008.
View at: Google Scholar

Copyright

Copyright © 2023 Xuan Can Vuong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PDF Download Citation

Download other formats

Order printed copies

Views

318

Downloads

279

Citations