scholarly journals Erratum to “Factors affecting analysis of the severity of accidents in cold and snowy areas using the ordered probit model” [Asian Transp. Stud. 7 (2021) 100035]

2022 ◽  
Vol 8 ◽  
pp. 100047
Author(s):  
Satoshi Hyodo ◽  
Kenta Hasegawa
Keyword(s):  
Probit Model ◽  
Ordered Probit ◽  
Ordered Probit Model ◽  
Factors Affecting

scholarly journals Consumers’ Buying Behavior Towards Online Shopping During The Covid-19 Pandemic: An Empirical Study In Malaysia

2021 ◽  
Vol 7 (2) ◽  
pp. 1-7
Author(s):  
Nurul Sima Mohamad Shariff ◽  
Nur Hayani Izzati Abd Hamid
Keyword(s):  
Online Shopping ◽  
Probit Model ◽  
Product Variety ◽  
Marketing Strategies ◽  
Ordered Probit ◽  
Ordered Probit Model ◽  
Buying Behavior ◽  
Factors Affecting ◽  
The Relationship ◽  
Insight Into

The world has been affected by the COVID-19 outbreak recently, affecting the economy worldwide. Due to the booming of online activities, especially online shopping, this study is interested in finding the relationship between factors affecting consumers’ buying behavior on online shopping in Malaysia during the COVID-19 pandemic. The least studies related to this issue is needed in Malaysia to further understand the behavior of the consumers on online shopping. Based on available literatures, the factors of interest were trust, convenience, price, product variety and promotion on consumers’ buying behavior in Malaysia. The study employed survey procedures to collect the data, whereby online questionnaires were disseminated and recorded from a total of 335 respondents. The data was then analysed using several statistical analyses, namely pilot test, descriptive statistics and an ordered probit model.  The result from an ordered probit model indicated that convenience, product variety, trust and promotion affected the Malaysian consumers’ buying behavior during the pandemic. Only price showed an insignificant impact on online shopping. This gave the sellers insight into understanding the consumers’ buying behavior on the online platform by planning marketing strategies to fascinate more customers.

scholarly journals Investigation on the Injury Severity of Drivers in Rear-End Collisions Between Cars Using a Random Parameters Bivariate Ordered Probit Model

2019 ◽  
Vol 16 (14) ◽  
pp. 2632 ◽  
Author(s):  
Chen ◽  
Song ◽  
Ma
Keyword(s):  
Injury Severity ◽  
Unobserved Heterogeneity ◽  
Seat Belt ◽  
Probit Model ◽  
Ordered Probit ◽  
Ordered Probit Model ◽  
Random Parameters ◽  
Factors Affecting ◽  
Bivariate Ordered Probit ◽  
Bivariate Ordered Probit Model

The existing studies on drivers’ injury severity include numerous statistical models that assess potential factors affecting the level of injury. These models should address specific concerns tailored to different crash characteristics. For rear-end crashes, potential correlation in injury severity may present between the two drivers involved in the same crash. Moreover, there may exist unobserved heterogeneity considering parameter effects, which may vary across both crashes and individuals. To address these concerns, a random parameters bivariate ordered probit model has been developed to examine factors affecting injury sustained by two drivers involved in the same rear-end crash between passenger cars. Taking both the within-crash correlation and unobserved heterogeneity into consideration, the proposed model outperforms the two separate ordered probit models with fixed parameters. The value of the correlation parameter demonstrates that there indeed exists significant correlation between two drivers’ injuries. Driver age, gender, vehicle, airbag or seat belt use, traffic flow, etc., are found to affect injury severity for both the two drivers. Some differences can also be found between the two drivers, such as the effect of light condition, crash season, crash position, etc. The approach utilized provides a possible use for dealing with similar injury severity analysis in future work.

scholarly journals Analyzing Household Vehicle Ownership in the Japanese Local City: Case Study in Toyota City

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Jia Yang ◽  
Hideki Kato ◽  
Ryosuke Ando ◽  
Yasuhide Nishihori
Keyword(s):  
Probit Model ◽  
Substitution Effect ◽  
Ordered Probit ◽  
Motor Vehicles ◽  
Ordered Probit Model ◽  
Annual Income ◽  
Vehicle Ownership ◽  
Unknown Parameters ◽  
Factors Affecting ◽  
Sample Data

This study aims to understand the crucial factors affecting vehicle ownership in the local city, Japan. 14,855 household sample data in Toyota City are used as the research sample. The sample data are extracted from the 5th Person Trip Survey data in the Chukyo region. First, the unknown annual income is complemented by using an ordered probit model. Then, a trivariate ordered probit model is utilized to analyze ownership of light motor vehicles, ordinary motor vehicles, and small trucks simultaneously. To estimate unknown parameters effectively and efficiently, one type of Markov Chain Monte Carlo methods called the Gibbs Sampler algorithm is applied in this study. The significant findings suggest the following: (1) the annual income only affects the ownership of ordinary motor vehicles; (2) a household with a 60-year-old or older householder is more likely to own small trucks, compared to that with a householder below the age of 60; (3) the population density negatively affects the number of light motor vehicles and that of small trucks; (4) there is a substitution effect of vehicle ownership between light motor vehicles and small trucks.

Do the heterogeneous determinants of life satisfaction affect differently across borrowers of diverse credit sources? A propensity score approach

2018 ◽  
Vol 45 (8) ◽  
pp. 1142-1158 ◽  
Author(s):  
Tiken Das ◽  
Manesh Choubey
Keyword(s):  
Life Satisfaction ◽  
Propensity Score ◽  
Probit Model ◽  
Ordered Probit ◽  
Primary Data ◽  
Ordered Probit Model ◽  
Future Research ◽  
Content Type ◽  
Credit Access ◽  
Physical Assets

Purpose The purpose of this paper is to evaluate the non-monetary effect of credit access by providing an econometric framework which controls the problem of selection bias. Design/methodology/approach The study is conducted in Assam, India and uses a quasi-experiment design to gather primary data. The ordered probit model is used to evaluate the non-monetary impact of credit access. The paper uses a propensity score approach to check the robustness of the ordered probit model. Findings The study confirms the positive association of credit access to life satisfaction of borrowers. It is found that, in general, rural borrower’s life satisfaction is influenced by the ability and capacity to work, the value of physical assets of the borrowers as well as some other lenders’ and borrowers’ specific factors. But, the direction of causality of the factors influencing borrowers’ life satisfaction is remarkably different across credit sources. Research limitations/implications The study argues to provide productive investment opportunities to semiformal and informal borrowers while improving their life satisfaction score. Although the results are adjusted for selection and survivorship biases, it is impossible with the available data to assess which non-income factors explain the findings, and therefore this limitation is left to future research. Originality/value The study contributes to the literature of rural credit by assessing the probable differences among formal, semiformal and informal credit sources with respect to non-monetary impacts.

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