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Exploring Factors Affecting Consumer Behavioral Intentions toward Online Food Ordering in Thailand
(2022-07-01)
Inthong, Chidchanok
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Champahom, Thanapong
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Jomnonkwao, Sajjakaj
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Chatpattananan, Vuttichai
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Ratanavaraha, Vatanavongs
Due to the COVID-19 epidemic, ordering food online has become very popular. This study used a structural equation model to analyze the indicators that influence the decision to order food through a food-delivery platform. The theory of planned behavior and the technology acceptance model were both used, along with a new factor, the task–technology fit (TTF) model, to study platform suitability. Data were collected using a questionnaire given to a group of 1320 consumers. The results showed that attitudes toward on-line delivery most significantly affected the behavioral intentions of the consumers, followed by subjective norms. Among attitudes, perceived ease of use was the most significant, followed by perceived usefulness and trust. The study’s results revealed that TTF had the most significant impact on perceived ease of use, followed by perceived usefulness. This means that, if a food-ordering platform is deemed appropriate, consumers will continue to use it, and business sustainability will be enhanced.
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Applying hierarchical logistic models to compare urban and rural roadway modeling of severity of rear-end vehicular crashes
(2020-06-01)
Champahom, Thanapong
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Jomnonkwao, Sajjakaj
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Watthanaklang, Duangdao
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Karoonsoontawong, Ampol
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Chatpattananan, Vuttichai
A rear-end crash is a widely studied type of road accident. The road area at the crash scene is a factor that significantly affects the crash severity from rear-end collisions. These road areas may be classified as urban or rural and evince obvious differences such as speed limits, number of intersections, vehicle types, etc. However, no study comparing rear-end crashes occurring in urban and rural areas has yet been conducted. Therefore, the present investigation focused on the comparison of diverse factors affecting the likelihood of rear-end crash severities in the two types of roadways. Additionally, hierarchical logistic models grounded in a spatial basis concept were applied by determining varying parameter estimations with regard to road segments. Additionally, the study compared coefficients with multilevel correlation model and those without multilevel correlation. Four models were established as a result. The data used for the study pertained to rear-end crashes occurring on Thai highways between 2011 and 2015. The results of the data analysis revealed that the model parameters for both urban and rural areas are in the same direction with the larger number of significant parameter values present in the rural rear-end crash model. The significant variables in both the urban and rural road segment models are the seat belt use, and the time of the incident. To conclude, the present study is useful because it provides another perspective of rear-end crashes to encourage policy makers to apply decisions that favor rules that assure safety.
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Modeling user perception of bus service quality: A case study in mauritius
(2020-01-01)
Champahom, Thanapong
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Jomnonkwao, Sajjakaj
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Karoonsoontawong, Ampol
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Hantanong, Natthaporn
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Beeharry, Roodheer
This paper evaluates the perceived bus service quality by passengers in Mauritius. Structural equation modeling (SEM) was used to identify the unobserved latent variables relevant to public passenger bus service and reveal the relationships between these latent variables and customer satisfaction. The data were obtained from a customer satisfaction survey collected from 501 participants in Mauritius and three latent variables that described the expected service quality characteristics, namely Vehicle, Management, and Driver. The SEM results revealed that the most significant expectation was the quality of the vehicle that included the cleanliness and vehicle body. The second was driver and crews and the personal attributes of the bus service crew. The third anticipation was the overall management of the service. The findings of this research can provide crucial information for public transport bus operators to enable them not only to retain their current customers but also attract new customers.
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Analysis of Rear-End Crash on Thai Highway: Decision Tree Approach
(2019-01-01)
Champahom, Thanapong
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Jomnonkwao, Sajjakaj
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Chatpattananan, Vuttichai
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Karoonsoontawong, Ampol
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Ratanavaraha, Vatanavongs
Objective. Among crash types on Thai highways, rear-end crashes have been found to cause the largest number of fatalities. This study aims to find ways to decrease rear-end crashes and fatal rear-end crashes. Methods. Classification and regression tree (CART) was used to analyze the complicated relationship of variables of big data. The analysis was conducted by creating two models: (1) a model which indicates the causes of rear-end crashes by applying Quasi-Induced Exposure to at-fault driver characteristics; (2) a determined model which studies fatal crashes. Results. Predictor variables in the model of at-fault and not-at-fault drivers found that driver age is most significant, followed by number of lanes and median opening area. For the mode of fatality, the use of safety equipment was found to be of most importance. Conclusion. The model results can be used to develop guidelines for public awareness programs for motorists and to propose policy changes to the Department of Highway in order to reduce the severity of rear-end crashes. Moreover, this paper discusses the variables that may result in both the perspective of rear-end crash number and the fatality rate of rear-end crashes as strategies in future research.

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