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The COVID-19 Epidemiological Investigation Support System (EISS) is a digital epidemiological tool, which utilizes location data from cellular base stations, credit card transactions records, and QR codes. It is a mass surveillance system that uses big data to track the entire infected population, featuring an extensive, automated, and speedy processing of data on personal location and the linkage of multiple databases from various governmental agencies. Based on interviews with people who have developed Korean digital epidemiology systems, this paper explores the technical, infrastructural, social, and institutional factors that have shaped Korean digital epidemiology since the 2014 avian flu crisis and examines the essential conditions of big data for digital epidemiology. The main findings are as follows: The feasibility of EISS goes beyond the matter of privacy; it is closely connected to technological infrastructures such as a high density of cellular base stations and private cloud systems; people's behavior such as a high rate of smartphone and credit card usage; and new forms of governance and institutions for speedy data processing. Multiple database linkage would develop EISS into a big data surveillance system that enables the prediction of risk-prone groups in a more preemptive manner. [ABSTRACT FROM AUTHOR]

Skippable ads are known to provide better advertising experiences by giving viewers a sense of control with the ability to skip an ad after watching it for a few seconds. Despite the growing interest, few studies have investigated factors that influence skipping or viewership of pre-roll skippable ads. This study examines the determinants of pre-roll ad acceptance by using clickstream data of 2,078,090 users' ad and content viewing behaviors on a popular online video content platform in South Korea. We found that skippable ad acceptance is impacted by personal, situational, and contextual factors that influence the level of goal impediment in online video consumption contexts. We conclude with theoretical and practical implications of the findings. [ABSTRACT FROM AUTHOR]

This study characterizes a destination network based on travel flow. Although the tourism literature has mostly discussed the static configurations of network structures, this study presents a dynamic destination network considering four seasons and daily periods. Given the advancement of connected technology, mobile sensor big data collected from international travelers visiting Korea were analyzed to explore movement behaviors across 250 cities in the country. Results demonstrate the dynamics of complex network systems in tourism destinations over time, such as seasons and diurnal variations. Its findings are crucial for developing vibrant destination management and contextual marketing. [ABSTRACT FROM AUTHOR]

COVID-19 is substantially reshaping the tourism and hospitality industries but studies on the changes in travel behaviour in response to the pandemic are limited. Using tourism big data, this research applies network science analytics to determine behavioural changes in travel mobility of domestic travellers who visited Jeju Island, Korea, from June 2019 to December 2020. The findings reveal significant reductions in the number of trips to a destination but also limited spatial connectivity and diversity in travel flow during the pandemic. A higher intensity of travel mobility to outdoor and coastal areas and shorter travel distances are evident during COVID-19. [ABSTRACT FROM AUTHOR]