EDITORIAL

<p>To make comparisons among the different modes for each strain gauge, each strain was normalized with respect to the average among the fixation modes. The average and standard deviation of the seven specimens are plotted.</p

Results of discriminant validity.

Low-speed driving is an underestimated dangerous behavior that may cause safety issues, such as speed dispersion and traffic flow bottlenecks. To investigate the influence mechanism of low-speed driving behavior, this study constructed the low-speed specific model (LSSM) by extending theory of planned behavior (TPB). The LSSM incorporated two factors, namely, risk perception and behavior habit, into the standard TPB components (attitude, subjective norm, perceived behavioral control, and behavior intention). Web-based questionnaires were used to collect data from a valid sample of 374, of which males accounted for 50%. The participants were aged from 18 to 65 years (M = 35.40, SD = 0.88). The structural equation model was applied to calculate and validate the interrelationships among the components of LSSM. Results showed that the LSSM could explain the variance in low-speed driving behavior and behavior intention by 46% and 76%, respectively. Meanwhile, attitude (β = 0.52, p 0.01) and perceived behavioral control (β = -0.12, p > 0.01) showed few significant in influencing the intention. LSSM also showed that people who were sensitive to driving risk perception would avoid low-speed driving behaviors and attitudes. Our findings may provide theoretical support for interventions on low-speed driving behavior.</div

Modified model results.

Low-speed driving is an underestimated dangerous behavior that may cause safety issues, such as speed dispersion and traffic flow bottlenecks. To investigate the influence mechanism of low-speed driving behavior, this study constructed the low-speed specific model (LSSM) by extending theory of planned behavior (TPB). The LSSM incorporated two factors, namely, risk perception and behavior habit, into the standard TPB components (attitude, subjective norm, perceived behavioral control, and behavior intention). Web-based questionnaires were used to collect data from a valid sample of 374, of which males accounted for 50%. The participants were aged from 18 to 65 years (M = 35.40, SD = 0.88). The structural equation model was applied to calculate and validate the interrelationships among the components of LSSM. Results showed that the LSSM could explain the variance in low-speed driving behavior and behavior intention by 46% and 76%, respectively. Meanwhile, attitude (β = 0.52, p 0.01) and perceived behavioral control (β = -0.12, p > 0.01) showed few significant in influencing the intention. LSSM also showed that people who were sensitive to driving risk perception would avoid low-speed driving behaviors and attitudes. Our findings may provide theoretical support for interventions on low-speed driving behavior.</div

Descriptive statistics of the LSSM measures.

Low-speed driving is an underestimated dangerous behavior that may cause safety issues, such as speed dispersion and traffic flow bottlenecks. To investigate the influence mechanism of low-speed driving behavior, this study constructed the low-speed specific model (LSSM) by extending theory of planned behavior (TPB). The LSSM incorporated two factors, namely, risk perception and behavior habit, into the standard TPB components (attitude, subjective norm, perceived behavioral control, and behavior intention). Web-based questionnaires were used to collect data from a valid sample of 374, of which males accounted for 50%. The participants were aged from 18 to 65 years (M = 35.40, SD = 0.88). The structural equation model was applied to calculate and validate the interrelationships among the components of LSSM. Results showed that the LSSM could explain the variance in low-speed driving behavior and behavior intention by 46% and 76%, respectively. Meanwhile, attitude (β = 0.52, p 0.01) and perceived behavioral control (β = -0.12, p > 0.01) showed few significant in influencing the intention. LSSM also showed that people who were sensitive to driving risk perception would avoid low-speed driving behaviors and attitudes. Our findings may provide theoretical support for interventions on low-speed driving behavior.</div

Results of convergent validity.

Low-speed driving is an underestimated dangerous behavior that may cause safety issues, such as speed dispersion and traffic flow bottlenecks. To investigate the influence mechanism of low-speed driving behavior, this study constructed the low-speed specific model (LSSM) by extending theory of planned behavior (TPB). The LSSM incorporated two factors, namely, risk perception and behavior habit, into the standard TPB components (attitude, subjective norm, perceived behavioral control, and behavior intention). Web-based questionnaires were used to collect data from a valid sample of 374, of which males accounted for 50%. The participants were aged from 18 to 65 years (M = 35.40, SD = 0.88). The structural equation model was applied to calculate and validate the interrelationships among the components of LSSM. Results showed that the LSSM could explain the variance in low-speed driving behavior and behavior intention by 46% and 76%, respectively. Meanwhile, attitude (β = 0.52, p 0.01) and perceived behavioral control (β = -0.12, p > 0.01) showed few significant in influencing the intention. LSSM also showed that people who were sensitive to driving risk perception would avoid low-speed driving behaviors and attitudes. Our findings may provide theoretical support for interventions on low-speed driving behavior.</div

Results of hypothesis testing.

Low-speed driving is an underestimated dangerous behavior that may cause safety issues, such as speed dispersion and traffic flow bottlenecks. To investigate the influence mechanism of low-speed driving behavior, this study constructed the low-speed specific model (LSSM) by extending theory of planned behavior (TPB). The LSSM incorporated two factors, namely, risk perception and behavior habit, into the standard TPB components (attitude, subjective norm, perceived behavioral control, and behavior intention). Web-based questionnaires were used to collect data from a valid sample of 374, of which males accounted for 50%. The participants were aged from 18 to 65 years (M = 35.40, SD = 0.88). The structural equation model was applied to calculate and validate the interrelationships among the components of LSSM. Results showed that the LSSM could explain the variance in low-speed driving behavior and behavior intention by 46% and 76%, respectively. Meanwhile, attitude (β = 0.52, p 0.01) and perceived behavioral control (β = -0.12, p > 0.01) showed few significant in influencing the intention. LSSM also showed that people who were sensitive to driving risk perception would avoid low-speed driving behaviors and attitudes. Our findings may provide theoretical support for interventions on low-speed driving behavior.</div

Certification of the ethical review for the experiment.

https://figshare.com/articles/figure/Certification_of_the_ethical_review_for_the_experiment/21707582. (PDF)</p

Pearson correlation tests of six variables.

Low-speed driving is an underestimated dangerous behavior that may cause safety issues, such as speed dispersion and traffic flow bottlenecks. To investigate the influence mechanism of low-speed driving behavior, this study constructed the low-speed specific model (LSSM) by extending theory of planned behavior (TPB). The LSSM incorporated two factors, namely, risk perception and behavior habit, into the standard TPB components (attitude, subjective norm, perceived behavioral control, and behavior intention). Web-based questionnaires were used to collect data from a valid sample of 374, of which males accounted for 50%. The participants were aged from 18 to 65 years (M = 35.40, SD = 0.88). The structural equation model was applied to calculate and validate the interrelationships among the components of LSSM. Results showed that the LSSM could explain the variance in low-speed driving behavior and behavior intention by 46% and 76%, respectively. Meanwhile, attitude (β = 0.52, p 0.01) and perceived behavioral control (β = -0.12, p > 0.01) showed few significant in influencing the intention. LSSM also showed that people who were sensitive to driving risk perception would avoid low-speed driving behaviors and attitudes. Our findings may provide theoretical support for interventions on low-speed driving behavior.</div