Introduction

Overview of road traffic accidents

The global prevalence of road traffic accidents (RTAs) is disturbing and leading to massive loss of properties and precious lives that are indispensable for the economy and nation-building. The occurrence of this problem has driven researchers to become increasingly interested in figuring out what can be done about it. The financial loss attributed to RTAs has been estimated to cost most countries 3% of their gross domestic product (GDP), especially in developing nations [58]. Annually, Road Traffic crashes claim over 1.2 million lives, with over 90% occurring in third-world countries [59]. In Nigeria, RTA happens daily, affecting the country’s socioeconomic well-being [51], thus negatively impacting the quality of living. Over a thousand five hundred people died out of 3345 road traffic accidents in the first quarter of 2022 [34]. Although RTAs have been reported to be due to environmental, mechanical, and human factors, human factors are ranked the highest [22] due to vehicle drivers' risky driving behavior comprising driving violations and errors.

Even though the significant role commercial drivers play in every nation's economic growth cannot be underestimated as the movement of persons, freight, and services thrive on effective and efficient transport operations, the road safety menace attributed to them is enormous. This may be because they primarily drive long distances and long hours, resulting in risky driving behaviors due to stress and fatigue. Specifically, commercial truck drivers contribute to RTAs in no small way [42, 65] due to their size and role in freight transport, which usually demands long-distance travel [20]. According to Dayyabu et al. [9], truck accidents in Nigeria are usually severe. Also, Useche et al. [54] found that speeding elevates the probability of crashes among truck drivers. Despite the facts, it is also interesting to note that in the study by Mehdizadeh et al. [32], taxi drivers exhibited more violations and errors than truck drivers.

Commercial taxi drivers have also been reported to be involved in RTAs due to their long hours driving in scouting for passengers and to make more income, which often results in stress culminating in RTAs [64]. Ba et al. [2] also asserted that taxi drivers often indulge in driving violations due to speeding to save time in the quest for more passengers. Similar results were found in the study by Y. W. Huang et al. [24] among taxi drivers in China. Consequently, Wang et al. [57] reported that road traffic accidents among taxi drivers are a function of socioeconomic pressures. This indicates the adverse effects of the taxi industry’s income per distance system of operation. Furthermore, violations and errors were found in taxi drivers with high chances of RTA involvement based on the study of Rejali et al. [41]. The high frequency of driving and violations also increased the propensity for RTA in taxi drivers [55]. These require critical investigation, especially on risky driving behaviors and the role of positive driving behavior. One of the methods of collecting driving behavior data is through the use of the Driver behavior Questionnaire (DBQ).

Driver behavior questionnaire

The DBQ is an in-depth model and tool for measuring daily driving behavior [10, 25], the rate of the exhibition, and how they trigger traffic collisions [8]. The DBQ has undergone several modifications, revisions, and improvements, resulting in its heterogeneity (concerning versions) from different researchers [8, 37] culminating in published DBQ researched articles with variations in results [19]. DBQ has been used for over 30 years with different versions and still stands as a crucial instrument for risky behavioral analysis [53]. In other words, it stands out with a superimposing advantage of creating the borderline between intentional and unintentional aberrant practices [21, 52]. Significant effort has been made to studying driver behavior using the Driver Behavior Questionnaire with Reason et al. [40] for several years, initiating the DBQ theory. The DBQ classified driver behavior as violation and error, which are differentiated based on intention. Driving violations are considered deliberate disregard for driving rules, while errors are not intentional.

According to Han and Zhao [19], the different results from researched DBQ studies stem from the population/cultural differences and variations in DBQ versions. Sullman et al. [49], also found that most times, due to different versions of the DBQ, results from studies appear to diverge. While the DBQ has been used in different languages after translation [6], many DBQ studies have been done in different countries, especially high-income countries (developed). Some of the developed countries with DBQ studies include India [15], North America [3, 8], Turkey [37], Denmark [28], Malaysia [1], New Zealand [49], East Europe [48], Serbia [30], Australia [47], Czech [48], China [19, 60], Finland [31] while documented peer-reviewed studies from developing countries in African include Nigeria [35], Ghana [12], South Africa [44]. Based on the authors’ search, DBQ studies from Nigeria are sparse, indicating a need for more research on risky driving behaviors due to its advantage in collecting data from different and large categories of drivers. Also, most DBQ studies have used the factor structures involving violations and errors for driver assessment, with few including positive driving behavior.

Literature review

Positive driving behaviors are driving practices exhibited by drivers that can aid in ensuring a safe and smooth driving environment for other road users [29, 37]. They include, not driving too closely to a vehicle ahead, being careful and not splashing water on other road users, ensuring smooth traffic by not blocking other vehicles, and avoiding indiscriminate use of horns while driving. On the other hand, risky driving behaviors are behaviors (both deliberate and unintentional disregard of traffic rules) that have the likelihood of causing RTAs, for instance, I keep driving ahead even when the traffic light has turned red, I become angry at another driver and chase them with the intention of showing them how angry I am, I do force my way into the traffic, and I take alcohol immediately before or during driving. Not until the introduction of positive driving behavior in studies relating to driver behavior, did most studies focus on risky driving behavior and road traffic accidents, broadly examining driving violations and errors. Notably, there are sparse studies on positive driving behavior [14, 19]. However, among the existing studies reviewed, Özkan et al. [37] and Guého et al. [14] have found a correlation between positive and risky driving behaviors. Also, studies on positive driving behaviors have adopted correlation tests for the relationship between positive driving behaviors and other driving factors like aggressive violation, errors, and inattention [6, 29, 30, 36]. Specifically, RTAs among professional drivers decrease as positive driving behavior increases in the study by Maslać et al. [29]. Similarly, bus drivers were found to have an appreciable decrease in RTAs as their positive driving behavior increased [19, 56]. Beyond correlation, the current study will contribute to the scanty literature on positive driving behaviors [19] and examine the moderating role of positive driving behavior on the relationship between risky behavior and accident involvement for commercial drivers in Nigeria.

Many studies have assessed the behavioral factors causing RTA among the general driving population, but detailed studies on specific drivers like commercial trucks and taxi drivers are scanty [11, 61]. Additionally, there are sparse studies on positive driving behavior [14, 45], while research on the moderating effect of positive driving behavior is scarce based on a literature search (Table 1). Furthermore, available studies (Table 1) on positive driving behavior have adopted correlation tests for the relationship between positive driving behavior and other driving factors [29, 30, 37]. The justification for using positive driving behavior as a moderator is hinged on the fact that potential moderating variables can be antecedent or independent variables from previous studies [33]. As shown in Table 1, positive driving behavior has been used as an independent variable in past studies. Moreover, based on a literature search, no study was found on moderating roles of positive driving behaviors specifically for truck and commercial taxi drivers in Africa, where Nigeria is situated. Hence, studying the moderating effect of positive driving behavior is expedient in Nigeria with larger sample sizes. This will aid in a detailed understanding of the variations between taxi and truck drivers in Nigeria based on their positive and risky driving behaviors.

Table 1 Previous studies on positive driving behaviors
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Therefore, this study aims to assess the moderating effect of positive driving behavior on risky behavior and traffic crash involvement among Nigerian truck and taxi drivers. It is imperative to examine their moderating role as this will reveal the complexity of commercial driver behavior patterns, give an understanding of their significance in curtailing RTAs among commercial drivers, and the need to encourage them to exhibit more positive driving behaviors. Therefore, the following hypotheses are postulated for this study:

  • H1: A significant relationship exists between taxi drivers’ risky driving behaviors and accident involvement.

  • H2: There is a significant relationship between truck drivers' risky driving behaviors and accident involvement.

  • H3: The relationship between risky driving behaviors and their involvement in RTA will increase when positive driving behavior decreases among taxi drivers.

  • H4: The relationship between risky driving behaviors and their involvement in RTA will increase when positive driving behavior decreases among truck drivers.

In sum, the driving behaviors of taxi and truck drivers in Nigeria might be affected by cultural aspects unique to the country. These cultural factors can include norms, attitudes toward traffic regulations, and customary driving practices, which can differ significantly across various regions. By re-examining these hypotheses within Nigeria, it ensures that cultural intricacies are taken into account when comprehending the correlation between driving behavior and road traffic accidents.

Methodology

Participants and data collection

In this study, 1823 registered commercial drivers (943 taxi and 880 truck drivers) from the 5 economic states (Abuja, Kano, Kogi, Lagos, and Rivers) in Nigeria filled out the questionnaires in person. Ethical clearance was sought and approved by the Nigerian Institute of Transport Technology Zaria before data collection. At the same time, participation in the study was voluntary and based on the willful verbal consent from the commercial drivers. The participants completed a questionnaire consisting of demographic data, accident history, and the Driver Behavior Questionnaire (DBQ). G* Power software was used to ascertain the sample effectiveness level to ensure that the sample size is sufficient based on the requirement of SEM sample size  [18, 53]. According to Hair et al. [18], the most recommended settings for G power are 0.15 (medium effect) for effect size, a minimum of 0.80 for power level, and 0.05 for confidence level. Therefore, considering the recommended G power settings, the minimum sample size was 77, which was at least doubled for each state for more statistical power. The data were screened for missing data and questionnaire misconduct, while the linear interpolation method handled 9 and 7 missing truck and taxi samples.

Instruments

The Driver Behavior Questionnaire (DBQ) [40] is a self-report questionnaire that measures the different risky behaviors of drivers while driving. In this study, the DBQ used was a modified version of the study of Han and Zhao [19], Mehdizadeh et al. [32], and Useche et al. [53], which were validated on Nigerian drivers through a pilot test. The questionnaire comprised three sections namely, driver sociodemographic factors, accident involvement, and driving behaviors (i.e., driving violation, driving error, inattention errors, and positive driving behavior). The driving behavior consisted of 30 items with a 5-point Likert type (1 = never, 2 = rarely, 3 = occasionally, 4 = very frequently, and 5 = always). The formulation of the DBQ is centered on Reason's taxonomy [40], which explains the difference in the drivers' behavior based on intention. A driving violation was defined as deliberate disregard for stipulated road traffic rules, while errors are misbehaviors that are not intentional but due to a mistake of intention. Inattention errors or lapses are a type of error that occurs as a result of memory loss or deficient attention. Özkan et al. [37] introduced positive driving behavior as a new inclusion in the DBQ. They are behaviors that aid a smooth driving environment for both drivers and other road users.

Data analysis

The data were coded, and the descriptive characteristics of the drivers, experience, and gender were analyzed using SPSS27. SmartPLS4 was used for the moderation analysis in which the moderators’ significant level and effect sizes were examined. First, the variance inflation factor (VIF) values were examined to ensure no multicollinearity issues due to common method variance (CMV), as the same self-reported questionnaire was used to collect responses for the predictor, dependent, and moderator variables from the same respondents. Testing the threats for multicollinearity is essential to ensure the constructs (driving violation, driving errors, inattention errors, positive driving behavior, and accident involvement) are valid and have no bias in the study [50]. Thereafter, the factor loadings (> 0.6 is considered acceptable) and the reliability of the scales were assessed using composite reliability (> 0.7), while convergent (> 0.5) and discriminant validity (< 0.9) were used to determine the validity. The convergent validity measured through the average variance extracted (AVE) shows how the driving behavior items converge to explain each construct distinctively. Also, the discriminant validity indicates the constructs in the DBQ are not related but distinct.

Furthermore, the path and moderation analyses were used to examine the influence of positive driving behavior on the drivers' risky driving behaviors and their involvement in traffic crashes. The standard level of p < 0.05 was chosen as the statistical significance criterion, and the effect sizes of the moderators were identified to determine the range of their effects. Guidelines used to assess f2 values were 0.02, 0.15, and 0.35 as small, medium, and large effects, respectively [7]. The moderating effect aids in examining the relationship between an independent and dependent variable is influenced by another variable known as the moderator [33].

Results

Participants characteristics

The sample characteristics comprising driving experience, gender, and accident history of the taxi and truck drivers are presented in Fig. 1. As shown, there are more commercial male drivers than female drivers. Also, commercial truck drivers were more involved in road accidents than commercial taxi drivers, while driving experience was reportedly higher for truck drivers than taxi drivers.

Fig. 1
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Commercial drivers’ characteristics

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Common Method Variance (CMV)

This is associated with measurement methods due to the variance attributed to them. The variance inflation factor (VIF) was examined to avoid potential bias due to the multiple variables measured using the same measurement method. Hence, the full collinearity test was performed to assess if any driving behavior construct indicates the VIF values equal to or greater than 3.3 [27]. As shown in Table 2, the results indicate that VIF for the constructs range from 1.095 to 2.230, confirming that the multicollinearity (CMV) issue was not a threat to this study.

Table 2 VIF
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Factor loadings

The factor loadings are coefficients representing the strength and direction of a relationship between indicators (items) and their respective constructs. They are loadings that quantify the degree of variability in the indicators accounted for by the constructs. Tables 3 and 4 summarize the factor loadings for the DBQ items for commercial drivers. Each item is associated with a factor loading for their respective constructs. The factor loadings greater than 0.6, which indicate variance explained by the items on a latent variable (factors) were retained in the model [16].

Table 3 Factor loadings for commercial taxi drivers
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Table 4 Factor loadings for the DBQ items for commercial truck drivers’ sample
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Reliability and validity

The composite reliability for the driving violation, driving errors, inattention, positive driving behaviors, and accident involvement of the drivers were greater than 0.6, while the convergent validity was greater than 0.5 (Table 5) [17] to satisfy that the scale is reliable. Composite reliability indicates the consistency and reliability of the measurement model, while Convergent validity shows driving behavior items reflect their assigned constructs.

Table 5 Composite reliability and convergent validity for taxi and truck driving behaviors
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The extent to which constructs are distinct from each other and not overlapping with other constructs in a model is discriminant validity. It ensures items measuring constructs are not highly correlated, which could affect the interpretation of results. The discriminant validity measured using the Heterotriat Monotrait (HTMT) values for the taxi and truck driving behavior constructs were less than 0.9 (Tables 6 and 7) [17]. This indicates that the constructs in the scale are valid and distinct in measuring their respective items.

Table 6 Discriminant validity (HTMT values) for taxi driving behaviors
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Table 7 Discriminant validity (HTMT values) for truck driving behaviors
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Path analysis

Table 8 shows the results of the path coefficient (direct effect) of the taxi and truck risky behaviors and their involvement in road traffic crashes, indicating a significant (p < 0.05) influence of their risky behavior. Also, the relationship between their positive driving behavior and involvement in road traffic accidents indicates a significant relationship (p < 0.05).

Table 8 Path relationships of taxi and truck drivers’ behavior and accident involvement
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Moderating effect

Moderation analysis was performed (after examining the direct effect) to assess positive driving behavior moderating effects on the relationship between risky driving behavior and RTA involvement among commercial truck and taxi drivers in Nigeria (Figs. 2 and 3). The results (Table 9) show a significant moderating role of positive driving behaviors for commercial taxi drivers (β = 0.209, t = 5.895, p = 0.000) and commercial truck drivers (β =  − 0.082, t = 2.040, p = 0.041). The results indicate that positive driving behaviors moderate the risky behavior of drivers and their involvement with RTAs. However, the direction of the moderating effect varies among the drivers (taxi and truck) considering the β values, as shown in Figs. 4 and 5.

Fig. 2
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Moderating model of taxi drivers

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Fig. 3
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Moderating model of truck drivers

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Table 9 Moderating effects of positive driving behaviors
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Fig. 4
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Graph of moderating effect of positive driving behavior on commercial taxi drivers’ risky behavior-accident involvement

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Fig. 5
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Graph of moderating effect of positive driving behavior on commercial truck drivers risky behavior-accident involvement

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Effect size

The effect size of the moderators indicates the strength of their moderating effects on the relationship between the independent and dependent variables. The results of the effect sizes show small effects (< 0.02) of the positive driving behaviors for the taxi and truck drivers. However, the effect is stronger for the taxi drivers (Table 10).

Table 10 Effect sizes of the moderators
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The slope analysis of the moderating effect of positive driving behavior on the relationship between commercial taxi drivers’ risky behavior and RTA involvement (Fig. 4) reveals that the slope of high positive driving behavior is steeper than that of low positive behavior. This implies an increase in the relationship between the taxi driver’s risky behavior and RTA despite their highly positive driving behavior. In contrast, as their positive driving behavior increases, commercial truck drivers' risky driving behavior and RTA involvement decrease (Fig. 5). In other words, as their positive driving behavior decreases, it appears as though risky driving behavior increases with increasing RTA. The results suggest that risky driving behavior increases with increasing RTA at low positive driving behavior for truck drivers.

Discussion

The moderating role of positive driving behavior on the risky behavior and RTA involvement of commercial (taxi and truck) drivers in Nigeria was examined and found to moderate the risky driving behavior and their involvement in RTA. Also, a significant relationship was found between risky driving behavior and accident involvement among the drivers.

Taxi drivers’ risky behavior significantly influenced RTA, supporting hypothesis 1. The influence of their risky behavior on RTA involvement may be connected to long hours of driving and scouting for passengers. The potential connection between their risky behavior and their involvement in road traffic accidents (RTAs) may be attributed to the extensive time spent driving and scouting for passengers, thereby establishing a potential reciprocal relationship. On one hand, prolonged periods of driving which could be due to the continuous pursuit of passengers can result in fatigue [39], consequently augmenting the probability of engaging in risky driving practices. Conversely, engaging in risky driving practices, particularly while fatigued, may heighten the risk of being involved in accidents [43]. Consequently, this connection implies a multifaceted interplay in which the extensive hours of driving and the active search for passengers may contribute to the manifestation of risky driving practices and the likelihood of being involved in RTAs. Furthermore, taxi drivers make their daily income through the number of passengers they can transport, which could influence them to walk behind the clock to make more money by getting more passengers. Consequently, this may raise the likelihood of their involvement in RTA as fatigue and tiredness could set in. Overspeeding may also be a factor that makes taxi drivers involved in RTA. In an attempt to complete a trip and make more, the chances of overspeeding may not be ruled out, which can cause RTA Thus, the involvement of taxi drivers in RTA due to overspeeding as a fall-out of the quest for more trips could be a factor to note. This aligns with the findings of Zahid et al. [63], who suggested that speeding violations among taxi drivers are prevalent. This could increase the chances of RTA involvement [62]. Also, Vahedi et al. [55] and Peng et al. [38] found a high number of trips made by taxi drivers and financial burdens as RTA influence. Although most of the previous studies were done in developed countries, there is an alignment in the results on the taxi drivers in Nigeria which is a developing country. The consistency in our findings with previous studies could be because taxi drivers were specifically examined in the studies, which suggests taxi drivers’ behaviors are similar irrespective of location.

Risky driving behavior of truck drivers influences accident involvement, confirming hypothesis 2. Truck drivers’ behavior had a stronger influence than taxi drivers due to its higher path coefficient. The potential for truck drivers’ behavior to influence more of the RTA could be due to their vehicle’s size, weight, complexity in maneuvering, braking effect time, and fatigue. Considering the size and weight of trucks, which outweighs other categories of vehicles, more devasting effects from injuries, death, and property damage are expected when RTA involves trucks. Also, the technicalities in maneuvering a truck are complex and require expertise, which, if not properly done, can result in RTA. This is related to the braking effect time in which truck vehicle brakes rarely take effect immediately after application. It may require some distance for the truck to completely halt, increasing the chances of RTA compared to taxi drivers. Although all categories of drivers may experience driving fatigue, the chances could be high among truck drivers, especially long-haul drivers who usually travel longer distances and may not comply with the daily driving limit, resulting in fatigue that may cause RTA. This is in agreement with the findings of Song and Choi [46] that commercial truck drivers' behavior contributes significantly to high RTAs. Similarly, truck drivers were also reported to have more propensity to RTA due to negligence and lapses [20], substance abuse, poor working conditions, and driving styles [4], fatigue, long hours driving, and insufficient sleep [13] which are risky driving behaviors. Their contribution to accidents could be a function of long-distance driving and inconsistent schedules peculiar to professional truck drivers. Despite the different methods of analysis like the logistic regression and fuzzy logic adopted in the previous studies in contrast to structural equation modelling used in our studies, which offers less measurement error, our findings still align in terms of truck drivers behavior influencing RTA.

The relationship between the taxi drivers’ risky behavior and RTA involvement increases when positive driving behavior increases, even though the significant moderating effect did not support hypothesis 3. The negative moderating role of positive driving behavior on commercial taxi drivers’ risky behavior and accident involvement suggests positive driving behavior had a negative effect on risky driving behaviors and accident involvement for commercial taxi drivers. Positive driving behavior should curtail some careless driving behavior, but the reverse is true for taxi drivers based on our findings. The risky driving behavior increases with accident involvement, even with highly positive driving behavior. The potential justification for this could be that commercial taxi drivers may be perceptive that road traffic crashes involving them are not as fatal as commercial truck drivers, as confirmed by the study by Chen et al. [5]. Also, positive driving behavior may not consistently serve as a reliable moderator because of disparities in driving circumstances. Variances in road conditions, traffic scenarios, and geographic locations in Nigeria may impact the efficacy of positive driving habits in attenuating the consequences of hazardous behaviors. Additionally, individual differences among taxi drivers may have an impact. For instance, some taxi drivers may possess a greater capacity for accepting risk, thereby reducing the effectiveness of positive driving behavior as a mitigating factor. Moreso, personal attributes such as experience, personality traits, and coping mechanisms may differ, affecting the capability of positive behavior to moderate hazardous actions. If the frequency and severity of risky behaviors among taxi drivers are consistently high, the moderating effect of positive driving behavior may be limited. Specifically, the quest to make more trips for more money could result in driving violations and limit their positive driving behaviors. This is consistent with the findings of Ba et al. [2] and Y. Huang et al. [23] that taxi drivers indulge in driving violations to save time in the quest for more passengers. Also, the results align with the findings of Zhao et al. [64] that commercial taxi drivers spend long hours scouting for passengers to make more income, which often results in stress culminating in RTAs. In such cases, positive actions might struggle to offset the cumulative impact of frequent or severe risky behaviors. Additionally, the potential impact of positive driving behavior as a moderator may be subject to the influence of taxi drivers’ skill level and training. In the event that drivers possess insufficient skills or have not undergone appropriate training in positive driving techniques, the mitigating impact may be diminished.

The relationship between risky driving behaviors and involvement in RTA increases when positive driving behavior decreases among truck drivers; thus, hypothesis 4 is supported. At low positive driving behavior exhibited by truck drivers, risky driving behavior strongly impacts the accident. In other words, risky driving behavior increases with accident involvement when commercial truck drivers exhibit less positive driving behavior. This could result from the truck drivers’ consciousness that they are driving large vehicles and are aware that any form of risky driving behavior can result in fatal crashes. Truck accidents are usually detrimental and colossal compared to other vehicle categories. Thus, the more positive driving behavior among the truck drivers, the fewer RTAs due to risky driving behavior as a good driving safety environment is created. This notion is confirmed by the findings of Han and Zhao [19] that positive driving reduces risky driving behavior among professional drivers. It is also in tandem with the findings in the study of Han et al. [20] and Maslać et al. [29], in which there was a decline in risky driving behaviors due to an increase in positive driving behaviors. Positive driving behavior creates and ensures a good and safe driving environment. In cases where less of it is practiced by drivers, the chances of more risky driving practices and RTA are expected. The consistency of the findings with previous studies may be due to the peculiarity of professional drivers' styles, which is common among them, notwithstanding their countries.

The findings that accidents increase for taxi drivers despite the moderating effect of positive driving behavior suggest that the moderating effect may not be as effective in mitigating the impact of risky behaviors for taxi drivers. This finding opens avenues for further research to explore why positive behavior might have varying effects across different driver populations. In addition, interventions and training initiatives targeted towards taxi drivers may require focused attention towards addressing particular obstacles associated with risky driving behavior, while taking into account the restricted effectiveness of positive driving behavior as a mitigating variable. Furthermore, for truck drivers, finding that accidents reduce truck drivers with the moderating effect of positive driving behavior is a positive outcome. Future research can explore the specific positive behaviors that are effective in reducing accidents among truck drivers. Practically, identifying and promoting positive driving behaviors among truck drivers may prove to be an effective strategy for enhancing road safety. This insight can inform targeted training programs and interventions.

Generally, these results imply that positive driving behaviors among commercial taxi and truck drivers in Nigeria are a vital factor that can enhance road safety. However, suppose positive driving behaviors are not exhibited by drivers, possibly due to cultural influence, pressure to compete with other drivers, ignorance, or personality influence driving. In that case, it may not create a conducive driving environment that should promote road traffic safety through drivers. This further strengthens the importance of positive driving behavior as it produces an atmosphere that curtails risky driving among drivers, especially for commercial drivers who drive long hours with irregular work schedules. Therefore, promoting positive driving behaviors and deliberately exhibiting them should be prioritized for safety.

This study has some limitations. The drivers' participation was based on registered commercial taxis and truck drivers. Therefore, the results cannot be generalized for all categories of commercial drivers. The data were collected through self-reports, and measuring the variables was limited to the participants’ perceptions. The possibility of self-report bias may be a concern, even though anonymity was ensured during data collection and VIF results were within acceptable limits. Also, the moderating effect of drivers’ sociodemographics was not within the scope of this study, which can be an avenue for future studies.

Conclusion

The study investigated the moderating role of positive driving behavior on risky driving behavior and accident involvement among commercial truck and taxi drivers in Nigeria to promote and encourage positive driving behaviors for commercial drivers. Positive driving behavior positively moderated truck drivers’ risky behavior, while taxi drivers’ behavior was negatively moderated. This study significantly advances the knowledge of the moderating effect of positive driving behavior on the relationship between risky driving behavior and accident involvement among commercial taxi and truck drivers in Nigeria. The findings contribute to the existing literature on driver behavior and give practical insights for transport programs among stakeholders like driver trainers, driver employees, transport policymakers, and government agencies. Also, the duality inherent in the moderating effects highlights the necessity for customized methods when tackling road safety concerns among distinct driver demographics.

Therefore, there is a need to ensure positive driving behavior is included in the driver's training and policy formulation to create a safe driving environment for road users. This study contributes to the existing literature by including positive driving behavior as a moderating variable and understanding that only positive driving behavior among drivers may not reduce road traffic accidents but through enforcement, monitoring, and evaluation. Future studies may consider the moderating effect on the relationship between driving anger, fatigue, and accident involvement. This will further give insights into the moderating effect of positive driving behavior.