Analysis of the influencing factors of the unsafe driving behaviors of online car-hailing drivers in china

Xiao, Yitian

doi:10.1371/journal.pone.0231175

Cited by 17 publications

(14 citation statements)

References 23 publications

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“…In comparison to men, we found that this factor received almost double the loading. The perceived behavior control factor finding also agrees with Razmara et al [15] and Xiao [14]. Attitude (severity) also influenced riding as reported by Bazargan-Hejazi et al [16].…”

Section: Discussion Of the Tpbsupporting

confidence: 87%

“…This finding is supported by Bazargan-Hejazi et al [16] and Nguyen et al [31], who found that attitude had the greatest effect on riding behavior. The next most influential factor was perceived behavior control, in agreement with Xiao [14], Razmara et al [15] and Bazargan-Hejazi et al [16], who both reported the direct and indirect effects of this factor on the theory of planned behavior (TPB). Although we only found a direct influence, attitude (severity) was also found to influence riding as was also reported by Bazargan-Hejazi et al [16].…”

Section: Discussion Of the Tpbsupporting

confidence: 79%

“…Xiao [14] used TPB and SEM to study vehicle driving behavior and found that perceived behavioral control can directly and indirectly predict self-reported unsafe driving behavior. Razmara, et al [15] used multiple regression analysis to determine that subjective norms, perceived behavioral control, and habits were the main predictors of one's intention to drive safely.…”

Section: Theory Of Planned Behavior (Tpb)mentioning

confidence: 99%

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Explaining Sex Differences in Motorcyclist Riding Behavior: An Application of Multi-Group Structural Equation Modeling

Uttra

Laddawan

Ratanavaraha

et al. 2020

IJERPH

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Road accidents are caused by humans, vehicles, and road environments. Human attitudes affect behavioral changes and can lead to unsafe riding behavior. The sex of an individual is a key factor that affects their riding behavior. We aimed to use structural equation modeling (SEM) by analyzing the multi-group SEM between men and women and applying the theory of planned behavior (TPB) and the locus of control (LC) theory. The data used in the research were collected from all over Thailand, consisting of 1516 motorcycle riders (903 men and 613 women) aged over 20 years. A self-administered questionnaire was designed for data collection of the riding behavior using the Motorcycle Rider Behavior Questionnaire (MRBQ), including traffic errors, control errors, stunt frequency, and safety equipment. We found that riding behaviors between men and women were significantly different in both theories. For men, TPB showed that the main factors that highly influenced motorcycle riding behavior (MRB) were the attitudes based on health motivation (AHM) and perceived behavior control (PC); for women, AHM produced a stronger effect than in men. However, for the subjective norms (SN) factor, we found no direct effect on MRB, but did find an indirect effect through the attitudes based on severity (ASE) in both sexes. Particularly for women, the indirect influence value of the SN factor was higher. For women, the LC showed that internal factors had more influence than external factors. The same was found for men, but the effect in women was significantly stronger. We found that sex significantly affected the MRB. Therefore, policies must be implemented that address each group specifically as their attitudes and behaviors are different.

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Section: Discussion Of the Tpbsupporting

confidence: 87%

Section: Discussion Of the Tpbsupporting

confidence: 79%

Section: Theory Of Planned Behavior (Tpb)mentioning

confidence: 99%

See 1 more Smart Citation

Explaining Sex Differences in Motorcyclist Riding Behavior: An Application of Multi-Group Structural Equation Modeling

Uttra

Laddawan

Ratanavaraha

et al. 2020

IJERPH

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“…Online ride-hailing refers to the business activities of providing non-cruise booking taxi service based on a service platform that integrates information of supply and demand relying on Internet technology [ 1 ]. Compared with the traditional taxi, it has the advantages of high efficiency, good experience, convenience, and low price [ 2 , 3 ].…”

Section: Introductionmentioning

confidence: 99%

Factors influencing smoking behaviour of online ride-hailing drivers in China: a cross-sectional analysis

Chen

et al. 2021

BMC Public Health

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Background Online ride-hailing is a fast-developing new travel mode. However, tobacco control policies on its drivers remain underdeveloped. This study aims to reveal the status and determine the influencing factors of ride-hailing drivers’ smoking behaviour to provide a basis for the formulation of tobacco control policies. Methods We derived our cross-sectional data from an online survey of full-time ride-hailing drivers in China. We used a survey questionnaire to collect variables, including sociodemographic and work-related characteristics, health status, health behaviour, health literacy and smoking status. Finally, we analysed the influencing factors of current smoking by conducting chi-square test and multivariate logistic regression. Results A total of 8990 ride-hailing drivers have participated in the survey, in which 5024 were current smokers, accounting to 55.9%. Nearly one-third of smokers smoked in their cars (32.2%). The logistic regression analysis results were as follows: male drivers (OR = 0.519, 95% CI [0.416, 0.647]), central regions (OR = 1.172, 95% CI [1.049, 1.309]) and eastern regions (OR = 1.330, 95% CI [1.194, 1.480]), working at both daytime and night (OR = 1.287, 95% CI [1.164, 1.424]) and non-fixed time (OR = 0.847, 95% CI [0.718, 0.999]), ages of 35–54 years (OR = 0.585, 95% CI [0.408, 0.829]), current drinker (OR = 1.663, 95% CI [1.526, 1.813]), irregular eating habits (OR = 1.370, 95% CI [1.233, 1.523]), the number of days in a week of engaging in at least 10 min of moderate or vigorous exercise ≥3 (OR = 0.752, 95% CI [0.646, 0.875]), taking the initiative to acquire health knowledge occasionally (OR = 0.882, 95% CI [0.783, 0.992]) or frequently (OR = 0.675, 95% CI [0.591, 0.770]) and underweight (OR = 1.249, 95% CI [1.001, 1.559]) and overweight (OR = 0.846, 95% CI [0.775, 0.924]) have association with the prevalence of current smoking amongst online ride-hailing drivers. Conclusion The smoking rate of ride-hailing drivers was high. Sociodemographic and work-related characteristics and health-related factors affected their smoking behaviour. Psychological and behavioural interventions can promote smoking control management and encourage drivers to quit or limit smoking. Online car-hailing companies can also establish a complaint mechanism combined with personal credit.

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Section: Introductionmentioning

confidence: 99%

The influencing factors of the smoking behavior of online ride-hailing drivers in China: A cross-sectional analysis

Chen

et al. 2021

Preprint

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Background：Online ride-hailing is a fast-developing new travel mode, and tobacco control policies on it have not yet been improved. This study aims to reveal the smoking status and influencing factors of ride-hailing drivers, so as to provide a basis for the formulation of tobacco control policies.Methods：The cross-sectional data used in this study were derived from an online survey of full-time ride-hailing drivers in China. Questionnaires were employed to collect variables including sociodemographic and work-related characteristics, health status, health behavior, health literacy, and smoking status. Chi-Square test and multivariate logistic regression were used to analyze the influencing factors of current smoking.Results：A total of 8990 ride-hailing drivers were investigated, in which 5024 were current smokers, accounted to 55.9%. Current smokers (53.7% (2696/5024) v 44.2% (1752/3966); P<0.001) and drivers who smoked on the car (85.8% (1389/1618) v 38.4 (1307/3406); P<0.001) were more likely to allow passengers to smoke. Logistic regression analysis showed that men (OR=0.519, 95%CI (0.416, 0.647)), central regions (OR=1.172, 95%CI (1.049, 1.309)), eastern regions (OR=1.330, 95%CI (1.194, 1.480)), working at both daytime and night (OR=1.287, 95%CI (1.164, 1.424)), and working at non-fixed time (OR=0.847, 95%CI (0.718, 0.999)), 35-54 years old (OR=0.585, 95%CI (0.408, 0.829)), current drinker (OR=1.663, 95%CI (1.526, 1.813)), eating very irregularly (OR=1.370, 95%CI (1.233, 1.523)), the number of days a week of engaging in at least 10 minutes of moderate or vigorous exercise ≥ 3 (OR=0.752, 95%CI (0.646, 0.875)), taking the initiative to acquire health knowledge occasionally (OR=0.882, 95%CI (0.783, 0.992)) or frequently (OR=0.675, 95%CI (0.591, 0.770)) , underweight (OR=1.249, 95%CI (1.001, 1.559)) and overweight (OR=0.846, 95%CI (0.775, 0.924)) were associated with the prevalence of current smoking among online ride-hailing drivers (P<0.05). Conclusions：The smoking rate of ride-hailing drivers was high, and the social demographic and work-related characteristics, and health-related factors all affected their smoking behavior. Tobacco control measures targeted at online-hailing drivers should correct their cultural beliefs about smoking, increase their health literacy, guide them to exercise more and keep a regular schedule, as well as combine with drinking intervention and weight intervention.

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Analysis of the influencing factors of the unsafe driving behaviors of online car-hailing drivers in china

Cited by 17 publications

References 23 publications

Explaining Sex Differences in Motorcyclist Riding Behavior: An Application of Multi-Group Structural Equation Modeling

Explaining Sex Differences in Motorcyclist Riding Behavior: An Application of Multi-Group Structural Equation Modeling

Factors influencing smoking behaviour of online ride-hailing drivers in China: a cross-sectional analysis

The influencing factors of the smoking behavior of online ride-hailing drivers in China: A cross-sectional analysis

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