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Continuous Behavior of Using Food Delivery Mobile Applications in Vietnam after Covid-19 Pandemic
Ha Thu LUONG(Ha Thu LUONG ),Nhi Lan DAO(Nhi Lan DAO ),Trang Thu NGUYEN(Trang Thu NGUYEN ),Uyen Thu Thi LA(Uyen Thu Thi LA ),Na Thi Le TRAN(Na Thi Le TRAN ),Hoa Thi DUONG(Hoa Thi DUONG ) 한국유통과학회 2023 유통과학연구 Vol.21 No.3
Purpose: During and after Covid-19 pandemic, technology has emerged as a key factor in supporting the recovery of the economy and the rise of living standards. This study examines seven factors affecting the intention of food delivery apps usage, which include Performance Expectancy, Effort Expectancy, Social Influence, Hedonic Motivation, Price Value, and Habit, and how much influence they have on the customers' behavioral continuance of food delivery apps after Covid-19 Pandemic. Research methodology: This research is a quantitative descriptive research with 473 qualified respondents from 550 respondents collected. Besides using the UTAUT2 model (Venkatesh et al., 2012), Information Quality was added to give a better explanation for the consumers’ intention towards continuance behavior using food delivery apps. The collected data is then processed using SPSS 22.0. Results: Habit factors and Information Quality factors have significant positive effects on promoting food delivery apps usage intention, which in turn influences continuance behavior. In addition, Habit factors and Information Quality factors together have an effect of 48.57% on Behavioral Intention. Conclusion: The result proves that positive habits and food information quality can increase the usage intention towards the behavioral continuance of consumers. Higher usage frequency can be improved by increasing these two factors.
Thi Thu Trang HA(Thi Thu Trang HA ),Hiroyuki SHIBUSAWA(Hiroyuki SHIBUSAWA ) 한국유통과학회 2023 The Journal of Asian Finance, Economics and Busine Vol.10 No.2
This paper proposes and examines the economic impact of infrastructure improvement on the San-En-Nanshin region in the Chubu area of Japan. We develop a single transportation computable general equilibrium (CGE) model for each subregion within the San-En-Nanshin region. The explicit modeling of the transportation infrastructure is defined based on interregional commuting flows and business trips, considering the spatial structure of the San-En-Nanshin economy. A CGE model is integrated with an interregional transportation network model to enhance the framework’s potential for understanding the infrastructure’s role in regional development. To evaluate the economic impact of transportation improvement, we analyze the interrelationship between travel time savings and regional output and income. The economic impact analysis under the CGE framework reveals how transportation facilities and systems affect firm and household behavior and therefore induce changes in the production and consumption of commodities and transportation services. The proposed theoretical model was tested by using data from the 2005 IO tables of each subregion and the 2006 transport flow dataset issued by the Ministry of Land, Infrastructure, Transport, and Tourism in Japan. As a result, the paper confirms the positive effect of transportation investment on the total output and income of the studied region. Specifically, we found that while economic benefits typically appear in urban areas, rural areas can still potentially benefit from transportation improvement projects.
Detecting user status from smartphone sensor data
Thu-Trang Nguyen,Thi-Hau Nguyen,Ha-Nam Nguyen,Duc-Nhan Nguyen,GyooSeok Choi 국제문화기술진흥원 2016 International Journal of Advanced Culture Technolo Vol.4 No.1
Due to the high increment in usage and built-in advanced technology of smartphones, human activity recognition relying on smartphone sensor data has become a focused research area. In order to reduce noise of collected data, most of previous studies assume that smartphones are fixed at certain positions. This strategy is impractical for real life applications. To overcome this issue, we here investigate a framework that allows detecting the status of a traveller as idle or moving regardless the position and the direction of smartphones. The application of our work is to estimate the total energy consumption of a traveller during a trip. A number of experiments have been carried out to show the effectiveness of our framework when travellers are not only walking but also using primitive vehicles like motorbikes.
Detecting user status from smartphone sensor data
Nguyen, Thu-Trang,Nguyen, Thi-Hau,Nguyen, Ha-Nam,Nguyen, Duc-Nhan,Choi, GyooSeok The International Promotion Agency of Culture Tech 2016 International Journal of Advanced Culture Technolo Vol.4 No.1
Due to the high increment in usage and built-in advanced technology of smartphones, human activity recognition relying on smartphone sensor data has become a focused research area. In order to reduce noise of collected data, most of previous studies assume that smartphones are fixed at certain positions. This strategy is impractical for real life applications. To overcome this issue, we here investigate a framework that allows detecting the status of a traveller as idle or moving regardless the position and the direction of smartphones. The application of our work is to estimate the total energy consumption of a traveller during a trip. A number of experiments have been carried out to show the effectiveness of our framework when travellers are not only walking but also using primitive vehicles like motorbikes.
A Multiple Variable Regression-based Approaches to Long-term Electricity Demand Forecasting
Lan Dong Thi Ngoc,Khai Phan Van,Ngo-Thi-Thu-Trang,Gyoo Seok Choi,Ha-Nam Nguyen 한국인터넷방송통신학회 2021 Journal of Advanced Smart Convergence Vol.10 No.4
Electricity contributes to the development of the economy. Therefore, forecasting electricity demand plays an important role in the development of the electricity industry in particular and the economy in general. This study aims to provide a precise model for long-term electricity demand forecast in the residential sector by using three independent variables include: Population, Electricity price, Average annual income per capita; and the dependent variable is yearly electricity consumption. Based on the support of Multiple variable regression, the proposed method established a model with variables that relate to the forecast by ignoring variables that do not affect lead to forecasting errors. The proposed forecasting model was validated using historical data from Vietnam in the period 2013 and 2020. To illustrate the application of the proposed methodology, we presents a five-year demand forecast for the residential sector in Vietnam. When demand forecasts are performed using the predicted variables, the R square value measures model fit is up to 99.6% and overall accuracy (MAPE) of around 0.92% is obtained over the period 2018-2020. The proposed model indicates the population's impact on total national electricity demand.
Detecting smartphone user habits using sequential pattern analysis
Lu Dang Nhac,Nguyen Thu Trang,Nauyen Thi Hau,Nguyen Ha Nam,Gyoo Seok Choi 한국인터넷방송통신학회 2015 International Journal of Internet, Broadcasting an Vol.7 No.1
Recently, the study of smart phone user habits has become a highly focused topic due to the rapid growth of the smart phone market. Indeed, sequential pattern analysis methods were efficiently used for web-based user habit mining long time ago. However, by means of simulations, it has been observed that these methods might fail for smart phone-based user habit mining. In this paper, we propose a novel approach that leads to a considerably increased performance of the traditional sequential pattern analysis methods by reasonably cutting off each chronological sequence of user logs on a device into shorter ones, which represent the sequential user activities in various periods of a day.
A Multiple Variable Regression-based Approaches to Long-term Electricity Demand Forecasting
Ngoc, Lan Dong Thi,Van, Khai Phan,Trang, Ngo-Thi-Thu,Choi, Gyoo Seok,Nguyen, Ha-Nam The Institute of Internet 2021 International journal of advanced smart convergenc Vol.10 No.4
Electricity contributes to the development of the economy. Therefore, forecasting electricity demand plays an important role in the development of the electricity industry in particular and the economy in general. This study aims to provide a precise model for long-term electricity demand forecast in the residential sector by using three independent variables include: Population, Electricity price, Average annual income per capita; and the dependent variable is yearly electricity consumption. Based on the support of Multiple variable regression, the proposed method established a model with variables that relate to the forecast by ignoring variables that do not affect lead to forecasting errors. The proposed forecasting model was validated using historical data from Vietnam in the period 2013 and 2020. To illustrate the application of the proposed methodology, we presents a five-year demand forecast for the residential sector in Vietnam. When demand forecasts are performed using the predicted variables, the R square value measures model fit is up to 99.6% and overall accuracy (MAPE) of around 0.92% is obtained over the period 2018-2020. The proposed model indicates the population's impact on total national electricity demand.