In the post-COVID-19, the food industry is rapidly reshaping its market structure toward online distribution. Rapid delivery system driven by large distribution platforms has ushered in an era of online distribution of fresh seafood that was previously limited. This study surveyed 1,000 consumers nationwide to determine their online seafood purchasing behaviors. The research methodology used factor analysis of consumer lifestyle and Heckman’s ordered probit sample-selection model. The main results of the analysis are as follows. First, quality, freshness, selling price, product reviews from other buyers, and convenience are particularly important considerations when consumers purchase seafood from online shopping. Second, online retailers and the government must prepare measures to expand seafood consumption by considering household characteristics and consumer lifestyles. Third, it was analyzed that consumers trust the quality and safety of seafood distributed online platforms. It is not possible to provide purchase incentives to consumers who consider value consumption important, so improvement measures are needed. The results of this study are expected to provide implications on consumer preferences to online platforms, seafood companies, and producers, and can be used to establish future marketing strategies.

본 연구는 한국노동패널 자료를 사용하여 선천적 직업적성이 직업만족도에 미치는 영향을 실증분석하였다. 분석을 위해서는 선천적 직업적성을 파악하는 것이 관건인데, 서양의 적성검사 기법은 한계가 있어서 동양사회에서 오랫동 안 실생활에 활용하고 있는 사주분석 기법을 적용하여 선천적 직업적성을 도 출하였다. 실증분석 결과, 첫째, 선천적 직업적성과 실제로 종사하고 있는 직업 유형이 일치한 사람의 직업만족도는 그렇지 않은 사람의 직업만족도보다 높았 다. 둘째, 선천적 직업적성과 종사하고 있는 직업유형과 일치한 사람은 그렇지 않은 사람보다 더 오랫동안 근무하며, 근속기간이 길수록 직업만족도는 높았 다. 셋째, 선천적 직업적성이 직장형이면 임금근로자가 될 가능성이 높았고, 선 천적 직업적성이 사업형이면 비임금근로자가 될 가능성이 높았다. 넷째, 경쟁심리가 강한 사람의 직업만족도는 경쟁 심리가 강하지 않은 사람의 직업만족 도보다 낮았다. 이러한 실증분석 결과는 선천적 직업적성이 직업만족도에 지 대한 영향을 미치고 있음을 입증한다. 직무만족은 삶의 만족과 긍정적인 관계 에 있다. 직무만족이 높을수록 삶의 만족 또한 높아지기 때문에 개인의 삶의 만족을 높이기 위해서는 자신의 선천적 직업적성을 정확히 이해하고, 선천적 직업적성에 맞는 직업유형을 선택하는 것이 무엇보다 중요하다.

The purpose of this study is to investigate the determinants that affect individual’s decision on whether or not to visit a winery in their state, and estimate the economic value that winery provide in terms of tourism. Survey data obtained from the residents in North Carolina, New York, and Virginia are used. First, a probit model is adopted to examine the factors that affect an individual’s decision whether or not to visit a winery in their state. Second, the economic value that wineries provide their visitors is derived from the winery trip demand that is estimated by a truncated negative binomial model. The consumer surplus estimates of visiting wineries are $28.9 per visit when the opportunity cost of time is not included, and $149.6 per visit when one-third of the wage rate is assumed as the opportunity cost of time.

PURPOSES : For vehicle-alone accidents with a high mortality rate, it is necessary to analyze the factors influencing the severity of roadside fixed-object traffic accidents. METHODS : A total of 313 roadside fixed obstacle traffic accidents, variables related to fixed obstacles, and variables related to road geometry were collected. The estimation model was constructed with data collected using an ordinal probit regression model. RESULTS : Piers, vertical slopes, and distances between roads and objects were the primary causes of increased accident severity. CONCLUSIONS : Countermeasures, such as object removal, relocation, clear zones, frangibles, breakaway poles, etc., are required for accident-prone or dangerous points.

PURPOSES: The purpose of this study is to investigate factors that affect the severity of children’s traffic accidents using the ordered probit model, and to contribute to a safer road environment for children. METHODS: This study used children’s traffic accident data during the last four years in the Incheon Metropolitan area. At this point, to analyze only the direct damage caused to children, the analysis was made of accidents where the victim was under 13 years old. Data from a total of 1,110 accidents was collected. When the model was constructed, as it was judged that there could be a difference in factors affecting accident occurrence depending on the zone characteristics, the model was divided into school and non-school zones. RESULTS: The accident content (severity) is divided into four stages (fatal injury, serious injury, minor injury and injury report) to construct the order-typed probit model. For the analysis, 65 variables of 17 categories were included in the model. The statistical package STATA 13.1 was used to analyze the variables affecting the accident severity with a confidence level of 90% (α·=0.1). Consequently, a total of 15 variables were found to have a statistically significant effect on accident severity in a school zone. In contrast, a total of 22 variables were found to have a statistically significant effect on accident severity in non-school zones. Four variables (daytime, weekday, victim age, intersection) were significant in both models. CONCLUSIONS: Among the significant variables found in school zones, signal violation and type of vehicle (line bus, rent car, bus, business other vehicles) had a relatively greater effect on the accident severity than the other variables. In non-school zones, eight variables comprising daytime, head-on collision, crossing, over-speed, gender of victim (male), victim age, type of vehicle (construction machinery), driver age (50-59) were found to be significant variables. In conclusion, as well as eliminating factors that can lead to accident reductions, it is necessary to consider zone characteristics to reduce the severity of children’s accidents and promote children’s traffic safety.

PURPOSES: This study aims to contribute to a better road environment, which can result in accident reduction from two-wheeled vehicles, by analyzing factors affecting the two-wheeled vehicles’ accident severities in Incheon Metropolitan City. METHODS: In this study, the two-wheeled vehicles’ accident severity was classified into four categories (fatal injury, serious injury, minor injury, and injury report) as a dependent variable, and 97 independent variables out of 14 categories were considered to construct an ordered probit model. To determine the factors affecting accident severity, the statistical package LIMDEP was used. RESULTS: Among the variables used in the analysis, variables related to accident occurrence date (first quarter), region (8-district), accident type (passing the edge of the road of the vehicle for a pedestrian accident, fixed object collision, and overturn of vehicle-only accident), violation type (unobtained safety distance, failure to perform safe driving, violation of intersection driving, and violation of others), the type of road (at the intersection, near the intersection, at the crosswalk, near the crosswalk, etc.), gender of assailant (male), vehicle of victim (pedestrian and motorcycle), and age of victim (under 20) were found to have a statistically significant effect on the severity of the accident. CONCLUSIONS: The variables related to accident type (fixed object collision and overturn of vehicle-only accident), gender of assailant (male), and vehicle of victim (pedestrian and motorcycle) have turned out increasing the accident severity. In addition, accident occurrence for two-wheeled vehicles is more diverse and vulnerable to damage than automobile accidents. Therefore, it is time to recognize the seriousness of two-wheeled vehicle accidents and to improve the environment and systems for safe driving.

OBJECTIVES : Fixed roadside objects are a threat to drivers when their vehicles deviate from the road. Therefore, such roadside objects need to be suitably dealt with to decrease accidents. This study determines the factors affecting the severity of accidents because of fixed roadside objects. METHODS : This study analyzed the crash severity impact of fixed roadside objects by using ordered probit regression as the analysis methodology. In this research, data from 896 traffic accidents reported in the last three years were used. These accidents consisted of sole-car accidents, fixed roadside object accidents, and lane-departure accidents on the national highway of Korea. The accident severity was classified as light injury, severe injury, and death. The factors relating to the road and the driver were collected as independent variables. RESULTS: The result of the analysis showed that the variables of the crash severity impact are the collision location (left side), gender of the driver (female), alcohol use, collision facility (roadside trees, traffic signals, telephone poles), and type of road (rural segments). Additionally, the collision location (left side), gender of the driver (female), alcohol use, collision facility (street trees, traffic signals, telephone poles), and type of road (rural segments), in order of influence, were found to be the factors affecting the crash severity in accidents due to fixed roadside objects. CONCLUSIONS: An alternative solution is urgently required to reduce the crash severity in accidents due to fixed roadside objects. Such a solution can consider the appropriate places to install breakaway devices and energy-absorbing systems.

PURPOSES : This study drew factors affecting motorcycle accidents in Seoul by severity using an ordered probit model and aimed to analyze and verify the drawn influence factors. METHODS: As the severity of the accidents could be classified into three types (fatal injury, serious injury and minor injury), this study drew the factors affecting accidents by a comparative analysis employing an ordered probit model, removed the variables that would not secure significance sequentially to construct a model with high explanatory power regarding the factors affecting the severity of motorcycle accidents, and calculated the marginal effect of each factor to understand the degree of each factor’s impact on the severity. First, Model 1 put in all variables; Model 2 was constructed by removing the variables of the road surface conditions that could not meet the level of significance (p=0.608); Model 3 was constructed by removing gender variable (p=0.423); and Model 4 was constructed finally by removing age variable (p=0.320). RESULTS : As a result of an analysis, statistically significant variables were time of occurrence, type of accident, road alignment and motorcycle displacement, and it turned out that the impacts on the severity were in the following order: a road alignment of left downhill, the type of motorcycle-to-vehicle accidents and a road alignment of a flatland on the left. The significance of the models was tested using the likelihood ratio, the level of significance and suitability statistics about them, and as a result of the test, the significance level and suitability of the constructed models were all excellent. In addition, the model accuracy indicating the accuracy of a predicted value compared to that of the value actually observed was 70.3% for minor injury; 70.1% for serious injury; and 68.6% for fatal injury, and the overall accuracy was 70.2%, which was very high. CONCLUSIONS: As a result of an analysis of motorcycle accidents in Seoul through the ordered probit model and the marginal effect, it turned out that their severity increased in nighttime accidents as compared to daytime ones and gradually increased in the order of motorcycleto- vehicle accidents, motorcycle-to-person ones and the ones involving motorcycle only. As a result of an analysis, the severity of accidents in road alignments of left downhill, left flatland and straight downhill increased as compared to those in a road alignment of straight flatland and that the severity of accidents of motorcycles with a displacement larger than 50cc was higher than that of those with a displacement smaller than 50cc.

운전환경에 영향을 주어 사고를 유발하는 원인에는 교통 흐름, 기상상황, 도로 기하구조 등의 환경적 요인이 있으며, 환경변화에 대한 적절치 못한 대응은 심각한 사고를 유발하여 교통혼잡, 시설물피해, 인명피해 등 사회적 비용손실로 이어지게 된다. 이에 현재 국내·외에서는 여러 요인에 따른 사고 심각도 모형의 연구가 활발히 진행되고 있으나 사고정보, 교통흐름, 기상상황, 도로 기하구조 등 다양한 환경적 요인을 복합적으로 고려한 분석은 전무하기 때문에 본 연구에서는 환경적 요인을 복합적으로 고려하여 교통사고 심각도를 모형화 했다. 사고 심각도 분석을 위해 다양한 환경적 요인에 대한 정보를 수집하고 ArcGIS를 활용하여 사고 발생지점에 수집정보를 융합하였으며, 사고로 인한 교통패턴의 변화를 피하기 위해 교통정보와 기상정보는 사고발생 5분 전의 정보를 융합 하였다. 또한 본 연구에서는 다양한 기상상황 중 강설상황의 정보만을 추출하여 사고 심각도의 관계를 분석하여 모형화 하였는데, 강설시의 환경변화는 운전자의 부주의를 유도하고 사고의 심각도를 가중시키기 때문에 그 심각도가 다른 때 보다 더 큰 경향이 있다. 사고 심각도는 순서화된 이산변수(레벨 1~4)로 표현되기 때문에 순서형프로빗(Ordered Probit)모형을 분석에 활용하였으며, 모형 분석결과 사고 심각도에 영향을 주는 변수로는 강설량, 일누적강설량, 속도, 편경사로 나타났다. 개발된 모형을 활용하여 도로이용자에게 교통안전 취약성 정보를 제공할 수 있다면 안전한 도로서비스 수준확보와 취약성 예측 및 도로 운영관리를 통한 사회적 비용 감소에 기여할 수 있을 것으로 기대한다.