Improving Older Drivers’ Behaviors Using Theory of Planned Behavior

Ni, Dingan; Guo, Fengxiang; Zhang, Hui; Li, Mingyuan; Zhou, Yanning

doi:10.3390/su14084769

Cited by 3 publications

(3 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The theory of planned behavior (TPB) is one of the most commonly used and influential models for studying individual behavior (Yuriev et al, 2020). To date, the TPB has been broadly used in various fields of human life, including health (Broers et al, 2020;Rajeh, 2022), environmental protection (Arya and Chaturvedi, 2020;Sun et al, 2022), tourist behavior (Japutra et al, 2019;Ulker-Demirel and Ciftci, 2020), organizational citizenship behavior Tsai et al, 2022), electronic shopping (Tang et al, 2021;Theodorou et al, 2023), and transportation (Chen, 2022;Ni et al, 2022). In terms of education, it is mainly used in innovation and entrepreneurship intention research.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Factors that influence the intent to pursue a master’s degree: evidence from Shandong Province, China

Zhou,

Liu,

Xue

et al. 2024

Front. Psychol.

View full text Add to dashboard Cite

IntroductionIn recent years, the pursuit of a master’s degree has become a social phenomenon of wide concern. It is essential to understand why large number of students choose to pursue master’s degree. This study aims to empirically analyze the factors that influence the intent to pursue a master’s degree.MethodBased on the extended theory of planned behavior, this study conducts a questionnaire survey of university students in Shandong Province, which has had the highest number of people taking the postgraduate entrance examination in China for several years. A total of 440 questionnaires were finally collected, including 417 valid questionnaires. And then ordinary least squares (OLS) regression was used to analyze the factors that influence the intent to pursue a master’s degree.ResultsIn general, the intent to pursue a master’s degree is positively influenced by attitude (β = 0.161, p < 0.01) and subjective norms (β = 0.208, p < 0.01), and negatively influenced by risk perception (β = −0.084, p < 0.05). Compared with male students, female students’ intent is more likely to be influenced by risk perception (β = −0.144, p < 0.05) and social factors (β = 0.140, p < 0.05). The intent of upperclass students tends to be positively influenced by perceived behavioral control (β = 0.125, p < 0.05), whereas the negative impact of risk perception (β = −0.219, p < 0.05) on the intent is significant for underclass students. The intent of students in rural areas are more sensitive to risk perception (β = −0.194, p < 0.01) than those of students in cities. In private universities, social factors (β = 0.445, p < 0.05) significantly affect the intent to pursue a master’s degree. In ordinary public universities, the intent of students is more likely to be influenced by risk perception (β = −0.082, p < 0.05).ConclusionThe study is helpful to strengthen the understanding of the influencing factors of the intent to pursue a master’s degree. In general, the intent to pursue a master’s degree is mainly influenced by attitude, subjective norms and risk perception. Moreover, the influencing factors vary among different groups (e.g., female vs. male, rural areas vs. cities). Furthermore, attitude, subjective norms, perceived behavioral control, risk perception, and social factors have greater impacts on the intent of students from low-income households than those from high-income households. This study can provide policy implications for universities to take targeted educational measures to encourage students to make a choice that suits their own development after graduation.

show abstract

Section: Theoretical Backgroundmentioning

confidence: 99%

Factors that influence the intent to pursue a master’s degree: evidence from Shandong Province, China

Zhou,

Liu,

Xue

et al. 2024

Front. Psychol.

View full text Add to dashboard Cite

show abstract

“…It is also used to verify an assumption through linear regression [47] with factor analysis to study the observed variables through exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) [28]. By considering the variables with the impacts on driver characteristics and safety in road traffic, the researchers found complex relations among the variables and [19] analyzed them with SEM by using the SPSS program to conduct EFA and AMOS program, to conduct CFA and SEM.…”

Section: Structural Equation Modelmentioning

confidence: 99%

“…The model can be used in strategic planning and accident management by responsible authorities to make further improvement in reducing the incidents in each area of the country. The study conducts literature reviews on the four dimensions by gathering and analyzing the documents as listed in Table 1 [11][12][13][14][15][16][17][18][19][20]. The literature reviews have found that a driver is influenced by the behaviors of other drivers under complex traffic situations, time pressure or at-risk events as they are causes of frustration in driving [21].…”

Section: Introductionmentioning

confidence: 99%

Development of a Safety Heavy-Duty Vehicle Model Considering Unsafe Acts, Unsafe Conditions and Near-Miss Events Using Structural Equation Model

Pumpugsri,

Rattanawong,

Vongmanee

2023

Sustainability

View full text Add to dashboard Cite

The World Health Organization has revealed that Thailand ranks first in Asia with regard to the region’s road traffic death rate. Due to the growth in the domestic economy and demands in logistics, traffic congestion regularly occurs and brings higher risks to transportation, resulting in a constant increase in the accident rate involving heavy-duty vehicles (HDVs), with a tendency to escalate in the future. To prevent its occurrence and solve the problem, this research aims to present a “Safety HDV Model” based on four dimensions, namely, driver behaviors, unsafe roadway environment, types of vehicles and near-miss events, which are all considered as causes of accidents. In this study, the researchers use the Delphi method to obtain a consensus from experts in logistics and safety from both public and private organizations, and then they define indicators and assess the complex dimensions. Based on the consensus, the researchers find 4 dimensions, 15 factors and 55 indicators with a high level of consensus at the Kendall’s coefficient of concordance (W) of 0.402 and P less than 0.001 to be relevant to safety in logistics. To estimate the influences among dimensions, the researchers apply a structural equation model and find that both absolute fit indices and incremental fit indices demonstrate good fit, with a CMIN/DF of 1.90, RMSEA of 0.048, GFI of 0.95, AGFI of 0.92 and RMR of 0.032 for the absolute fit indices and NFI of 0.97, CFI of 0.98, TLI of 0.98 and IFI of 0.98 for the incremental fit indices. As the model is consistent with data and variables, it is considered to be valid to be adopted by responsible authorities to improve unsafe roadway environments and behaviors of HDV drivers. As the data in the model can be altered by location, the model can be utilized as a tool in strategic planning and management to prevent accidents in each area of the country in the future.

show abstract

A Transfer Learning Methodology for Recognizing Unsafe Behavior during Lifting Operations in a Chemical Plant

Xi-cheng

Wang³

et al. 2023

Processes

View full text Add to dashboard Cite

Large lifting equipment is used regularly in the maintenance operations of chemical plant installations, where safety controls must be carried out to ensure the safety of lifting operations. This paper presents a convolutional neural network (CNN) methodology, based on the PyTorch framework, to identify unsafe behavior among lifting operation drivers, specifically, by collecting 22,352 images of equipment lifting operations over a certain time period in a chemical plant. The lifting drivers’ behavior was divided into eight categories, and a ResNet50 network model was selected to identify the drivers’ behavior in the pictures. The results show that the proposed ResNet50 network model based on transfer learning achieves a 99.6% accuracy rate, a 99% recall rate and a 99% F1 value for the expected behaviors of eight lifting operation drivers. This knowledge regarding unsafe behavior in the chemical industry provides a new perspective for preventing safety accidents caused by the dangerous behaviors of lifting operation drivers.

show abstract

Improving Older Drivers’ Behaviors Using Theory of Planned Behavior

Cited by 3 publications

References 41 publications

Factors that influence the intent to pursue a master’s degree: evidence from Shandong Province, China

Factors that influence the intent to pursue a master’s degree: evidence from Shandong Province, China

Development of a Safety Heavy-Duty Vehicle Model Considering Unsafe Acts, Unsafe Conditions and Near-Miss Events Using Structural Equation Model

A Transfer Learning Methodology for Recognizing Unsafe Behavior during Lifting Operations in a Chemical Plant

Contact Info

Product

Resources

About