• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.167-181
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• 2023

This study utilized TEXTOM, a social network analysis program to analyze changes in current tourism behavior after travel restrictions were eased after the outbreak of COVID-19. Data on the keywords 'domestic travel' and 'overseas travel' were collected from blogs, cafes, and news provided by Naver, Google, and Daum. The collection period was set from April to December 2022 when social distancing was lifted, and 2019 and 2020 were each set as one year and compared and analyzed with 2022. A total of 80 key words were extracted through text mining and centrality analysis was performed using NetDraw. Finally, through the CONCOR, the correlated keywords were clustered into 4. As a result of the study, tourism behavior in 2022 shows tourism recovery before the outbreak of COVID-19, segmentation of travel based on each person's preferred theme, prioritization of each country's corona mitigation policy, and then selecting a tourist destination. It is expected to provide basic data for the development of tourism marketing strategies and tourism products for the newly emerging tourism ecosystem after COVID-19.

• The Journal of the Korea Contents Association
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• v.22 no.7
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• pp.384-394
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• 2022

This study aimed to examine how COVID-19 affected life and health of welfare recipients by comparing groups associated with sex, living with children, and household type and suggest directions for welfare policy and services. Researchers collected a random sample of 500 recipients from 𐩒𐩒gu, Seoul who are registered in Haengbok E-eum, the social security information system and conducted a survey on overall life domains and physical and mental health. For data analysis, t-test, Anova and chi-square test were used. The results showed that factors of sex, living with children, and household type made significant differences in difficulties of employment status, child care, leisure and cultural activities and overall social activities. In terms of health status, mental health status and life satisfaction have been decreased after COVID-19. There were statistical differences in COVID blue and thinking about death among different groups. Based on these findings, this study suggested directions for welfare services for the future New Normal society.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.4
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• pp.163-172
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• 2022

The purpose of the study is to determine the impact of several factors on employees' job commitment in ten FDI manufacturing businesses in Vietnam's Nghi Son Economic Zone during the COVID-19 Pandemic. The researchers used reliability tests, exploratory factor analysis, confirmatory factor analysis, and structural equation modeling to confirm the relationships between factors when analyzing the effect of variables on job commitment in FDI enterprises. Based on a poll of 481 respondents, the test findings showed that 07 parameters have positive effects on job satisfaction. Professional assurance, compensation, fringe benefits, supervisor, promotion, coworkers, and working conditions all have varying levels of impact. Job satisfaction, in particular, has a significant and positive impact on employees' commitment to their jobs. According to research findings, the COVID-19 pandemic has been and continues to be a unique burden for Vietnamese firms in general and FDI manufacturing enterprises in Nghi Son EZ in particular. As a result, businesses must have appropriate HRM strategies to incentivize employees to recover, stabilize, and develop in the new normal, ensuring employee loyalty to the company.

• Journal of Distribution Science
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• v.22 no.3
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• pp.33-47
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• 2024

Purpose: Most emerging-market countries are concerned about the technology boom, which is accompanied by an increase in revenue from online sales and services. This finding has been demonstrated during the COVID-19 pandemic; however, is this tendency continuing in the new normal, and what factors are driving the increase in consumer decisions? The purpose of this research is to investigate how the decision to utilize online services will be affected in the new normal as well as propose a new research approach in this field. Research Design, Methodology and Approach: By following a deductive research method associated with positivist philosophy, a survey in South Vietnam with 426 respondents using a convenience sampling method was conducted. The reliability of the measurement scales was examined by using the SPSS program. The SmartPLS programme was utilised to assess the measurement and structural models as well as test hypotheses by using partial least squares structural equation modelling. Results: According to the research findings, decision-making has been impacted by social influences, perceived usefulness, perceived ease of use, perceived trust, perceived price, and perceived convenience. Conclusions: The research results also bring significant contributions not only in practice in providing management implications but also in theory. The research model has also demonstrated the feasibility of employing the stimuli-organism-response framework and combining the theory of perceived risk with the technology acceptance model via the explanation of decision-making.

• Imaging Science in Dentistry
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• v.50 no.3
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• pp.265-267
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• 2020

• Journal of Digital Convergence
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• v.19 no.2
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• pp.509-514
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• 2021

The pandemic of COVID-19 is driving the demand for non-face-to-face diagnosis, observation, and treatment in the healthcare environment, which has led to increased interest in helathcare robots. The authors intend to predict the direction in which the quarantine healthcare robots should be utilized in the post-corona era through analysis of national agency reports, on-offline press reports, and domestic and foreign robot company press releases. The COVID-19 pandemic has raised interest in medical robots. And there is a need to apply healthcare robots that can perform tasks such as disinfection, logistics transfer, screening tests, monitoring of patients, remote medical treatment support for isolated patients, and video calls with family members. Therefore, it is considered that future correct development and application of healthcare robots and empirical research to verify them should be continued based on sufficient consideration for various problems associated with the practical application of robots.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.155-174
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• 2022

From January 2020 to October 2021, more than 500,000 academic studies related to COVID-19 (Coronavirus-2, a fatal respiratory syndrome) have been published. The rapid increase in the number of papers related to COVID-19 is putting time and technical constraints on healthcare professionals and policy makers to quickly find important research. Therefore, in this study, we propose a method of extracting useful information from text data of extensive literature using LDA and Word2vec algorithm. Papers related to keywords to be searched were extracted from papers related to COVID-19, and detailed topics were identified. The data used the CORD-19 data set on Kaggle, a free academic resource prepared by major research groups and the White House to respond to the COVID-19 pandemic, updated weekly. The research methods are divided into two main categories. First, 41,062 articles were collected through data filtering and pre-processing of the abstracts of 47,110 academic papers including full text. For this purpose, the number of publications related to COVID-19 by year was analyzed through exploratory data analysis using a Python program, and the top 10 journals under active research were identified. LDA and Word2vec algorithm were used to derive research topics related to COVID-19, and after analyzing related words, similarity was measured. Second, papers containing 'vaccine' and 'treatment' were extracted from among the topics derived from all papers, and a total of 4,555 papers related to 'vaccine' and 5,971 papers related to 'treatment' were extracted. did For each collected paper, detailed topics were analyzed using LDA and Word2vec algorithms, and a clustering method through PCA dimension reduction was applied to visualize groups of papers with similar themes using the t-SNE algorithm. A noteworthy point from the results of this study is that the topics that were not derived from the topics derived for all papers being researched in relation to COVID-19 (