• Journal of the Korean Society of Safety
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• v.38 no.2
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• pp.52-59
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• 2023

Due to the routine nature of social distancing in accordance with the COVID-19 pandemic, the logistics industry is under rapid development, given that offline demand is focused on online platforms. The number of warehouse workplaces and workers are steadily increasing per annum, and the industrial accident rate of transportation, warehouse, and telecommunication industries to which warehouse employees belong is higher than the total industrial accident rate in Korea. In previous studies, warehouse workers reported exposure to health hazards such as musculoskeletal disorders due to the handling of heavy objects and improper working postures. Accordingly, in this study, a survey was conducted to investigate symptoms of musculoskeletal disorders with focus on parcel delivery workers nationwide. The questionnaire included a musculoskeletal disorder symptom survey table to identify information such as worker occupational history, work type, and signs or symptoms of musculoskeletal disorders. Survey response data from 453 people were obtained to determine the influence of delivery business characteristics on occupational musculoskeletal disorders, and the influencing factors were analyzed. Based on the results, in the analysis of pain with respect to body part, the duration, degree, and frequency of pain were highest in the leg part, and as a result, the average value for the leg part exhibited a significant difference from those of other body parts. In addition, 52.32% of workers exhibited symptoms of musculoskeletal disorders, and a high number of patients with musculoskeletal disorders was observed in the work group with less than three years of service and with ages ranging from 30-39. The results of this study can serve as basic data for the derivation of a management plan that meets the characteristics of musculoskeletal disorders that impact logistics workers overburdened with work due to the rapid increase in parcel delivery volume in accordance with an increase in online consumption.

• Journal of the Korean Society of Clothing and Textiles
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• v.46 no.6
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• pp.1142-1160
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• 2022

Text data plays a significant role in understanding and analyzing trends in consumer, business, and social sectors. For text analysis, there must be a corpus that reflects specific domain knowledge. However, in the field of fashion, the professional corpus is insufficient. This study aims to develop a taxonomy and thesaurus that considers the specialty of fashion products. To this end, about 100,000 fashion vocabulary terms were collected by crawling text data from WSGN, Pantone, and online platforms; text subsequently was extracted through preprocessing with Python. The taxonomy was composed of items, silhouettes, details, styles, colors, textiles, and patterns/prints, which are seven attributes of clothes. The corpus was completed through processing synonyms of terms from fashion books such as dictionaries. Finally, 10,294 vocabulary words, including 1,956 standard Korean words, were classified in the taxonomy. All data was then developed into a web dictionary system. Quantitative and qualitative performance tests of the results were conducted through expert reviews. The performance of the thesaurus also was verified by comparing the results of text mining analysis through the previously developed corpus. This study contributes to achieving a text data standard and enables meaningful results of text mining analysis in the fashion field.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.1-11
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• 2023

With the development of information and communication technology, the size of the online market is increasing, and with it, the mobile shopping market utilizing smartphones is also growing day by day. This indicates that the effective use of mobile apps can be a strategic choice for businesses, and this is also true in the tourism and travel industry. In particular, the continuous growth of the OTA (Online Travel Agency) industry based on platforms has accelerated with With Corona, and due to its importance, this study aims to investigate the impact of perceived quality factors of mobile accommodation apps on reuse intention through emotional responses. To test the hypotheses, 260 users of mobile accommodation apps were analyzed, and the results showed that information quality has a positive impact on pleasure and dominance, and service quality has a positive impact on arousal and dominance, and pleasure and dominance have a positive impact on reuse intention. Through these findings, this study clarifies the relationship between mobile accommodation app quality factors and reuse intention, and effective marketing strategies were suggested by providing basic data for improving app quality.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.361-368
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• 2021

Daily life such as society, economy, and culture is fundamentally changing due to COVID-19, and digital transformation based on information technology (IT) such as artificial intelligence, data, and cloud is accelerating. In this study, we focused on the metaverse, which is based on the interaction between the virtual world and the real world, and explored the possibility of using the metaverse-based platform for education. The metaverse-based platform was approached from the perspective of the online education ecosystem, which means that not only online teaching and learning activities but also holistic educational activities such as learning, communication, and empathy are performed within the metaverse. In this metaverse platform, learners can feel the presence of learning, and learning motivation and immersion can be promoted. In addition, it is possible to experience self-directed learning based on the autonomy of spatial movement. Although there are technical and ethical limitations to applying the metaverse platform, it would be preferable to focus more on the interaction between learners in the metaverse world rather than high expectations.

• Information Systems Review
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• v.23 no.4
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• pp.165-182
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• 2021

In nowadays, Covid-19 has transformed patterns of consumers' behavior into a non-face-to-face mode. As the patterns of consumption have been digitalized, it has become a daily routine for consumers who perform so-called shadow work, which involves unpaid jobs that they have to do by themselves. In mobile grocery service context, consumers' shadow work could lead to shopping avoidance as well as switching toward other shopping channels. Thus, this study is to examine how consumers' perception of shadow work affect mobile shopping avoidance and switching intention toward other shopping channels. This study collected 283 survey data from online respondents who have experience on subscription services for ordering groceries in online. We also tested our research model by using partial least squares. Based on our results, this study has found that the perception of shadow work had a positive effect on mobile shopping avoidance as well as switching intention. We expect that our findings could contribute to relevant research on shadow work and suggest practical implications for digital platforms dealing with subscription business models

• Journal of the Korean Society of Clothing and Textiles
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• v.48 no.1
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• pp.20-36
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• 2024

Over the past three years, even amidst viral threats, a notable shift towards online interactions has been observed. This trend persists the presence of significant viral concerns. Our study centered on female office workers in their twenties and thirties in Korea, seeking to comprehend how they enhance and present their external image in the digital era. We explored the use of digital devices and fashion choices that enable them to amplify their self-expression in video conferences. Using a mix of surveys and in-depth interviews, we employed snowball sampling to recruit twelve participants. These women were given the opportunity to shape their digital persona either to uphold their current image or to adapt it for interactions where they weren't face-to-face. Their desired images fell into three distinct categories: an authoritative professional image, a clean modern image, and a natural image. Depending on the context, the participants aimed to convey these images independently or in various combinations. Our findings suggest the need to develop strategies for acknowledging and projecting individual fashion identities in non-face-to-face interactions. Such strategies would empower individuals to better align their online personas with their desired self-image, whether it's professional, modern, clean, natural, or a combination thereof.

• KIPS Transactions on Computer and Communication Systems
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• v.13 no.1
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• pp.10-20
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• 2024

As the online commerce market continues to expand with an increase of diverse products and content, users find it challenging in navigating and in the selection process. Thereafter both platforms and shopping malls are actively working in conducting continuous research on recommendations system to select and present products that align with user preferences. Most existing recommendation studies have relied on user data which is relatively easy to obtain. However, these studies only use a single type of event and their reliance on time dependent data results in issues with reliability and complexity. To address these challenges, this paper proposes a recommendation system that analysis user preferences in consideration of the relationship between various types of event data. The proposed recommendation system analyzes the correlation of multiple events, extracts weights, learns the recommendation model, and provides recommendation services through it. Through extensive experiments the performance of our system was compared with the previously studied algorithms. The results confirmed an improvement in both complexity and performance.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.21-41
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• 2019

Thanks to the rapid development of information technologies, the data available on Internet have grown rapidly. In this era of big data, many studies have attempted to offer insights and express the effects of data analysis. In the tourism and hospitality industry, many firms and studies in the era of big data have paid attention to online reviews on social media because of their large influence over customers. As tourism is an information-intensive industry, the effect of these information networks on social media platforms is more remarkable compared to any other types of media. However, there are some limitations to the improvements in service quality that can be made based on opinions on social media platforms. Users on social media platforms represent their opinions as text, images, and so on. Raw data sets from these reviews are unstructured. Moreover, these data sets are too big to extract new information and hidden knowledge by human competences. To use them for business intelligence and analytics applications, proper big data techniques like Natural Language Processing and data mining techniques are needed. This study suggests an analytical approach to directly yield insights from these reviews to improve the service quality of hotels. Our proposed approach consists of topic mining to extract topics contained in the reviews and the decision tree modeling to explain the relationship between topics and ratings. Topic mining refers to a method for finding a group of words from a collection of documents that represents a document. Among several topic mining methods, we adopted the Latent Dirichlet Allocation algorithm, which is considered as the most universal algorithm. However, LDA is not enough to find insights that can improve service quality because it cannot find the relationship between topics and ratings. To overcome this limitation, we also use the Classification and Regression Tree method, which is a kind of decision tree technique. Through the CART method, we can find what topics are related to positive or negative ratings of a hotel and visualize the results. Therefore, this study aims to investigate the representation of an analytical approach for the improvement of hotel service quality from unstructured review data sets. Through experiments for four hotels in Hong Kong, we can find the strengths and weaknesses of services for each hotel and suggest improvements to aid in customer satisfaction. Especially from positive reviews, we find what these hotels should maintain for service quality. For example, compared with the other hotels, a hotel has a good location and room condition which are extracted from positive reviews for it. In contrast, we also find what they should modify in their services from negative reviews. For example, a hotel should improve room condition related to soundproof. These results mean that our approach is useful in finding some insights for the service quality of hotels. That is, from the enormous size of review data, our approach can provide practical suggestions for hotel managers to improve their service quality. In the past, studies for improving service quality relied on surveys or interviews of customers. However, these methods are often costly and time consuming and the results may be biased by biased sampling or untrustworthy answers. The proposed approach directly obtains honest feedback from customers' online reviews and draws some insights through a type of big data analysis. So it will be a more useful tool to overcome the limitations of surveys or interviews. Moreover, our approach easily obtains the service quality information of other hotels or services in the tourism industry because it needs only open online reviews and ratings as input data. Furthermore, the performance of our approach will be better if other structured and unstructured data sources are added.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.2977-2997
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• 2018

In big data era, fresh data grows rapidly every day. More than 30,000 gigabytes of data are created every second and the rate is accelerating. Many organizations rely heavily on real time streaming, while big data stream computing helps them spot opportunities and risks from real time big data. Storm, one of the most common online stream computing platforms, has been used for big data stream computing, with response time ranging from milliseconds to sub-seconds. The performance of Storm plays a crucial role in different application scenarios, however, few studies were conducted to evaluate the performance of Storm. In this paper, we investigate the performance of Storm under different application scenarios. Our experimental results show that throughput and latency of Storm are greatly affected by the number of instances of each vertex in task topology, and the number of available resources in data center. The fault-tolerant mechanism of Storm works well in most big data stream computing environments. As a result, it is suggested that a dynamic topology, an elastic scheduling framework, and a memory based fault-tolerant mechanism are necessary for providing high throughput and low latency services on Storm platform.

• Asian Journal for Public Opinion Research
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• v.5 no.3
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• pp.192-203
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• 2018

Alcohol consumption among soldiers impairs health status, performance, and increases the risks of injuries and violence. This study examined drinking behaviors, health problems, and violence among enlisted soldiers at Adisorn military unit in Saraburi, Thailand. Data collection using self-reported questionnaires were distributed to 256 enlisted male soldiers in May 2017. Participants were age 20-22 (93%), Buddhists (98%), high school education or lower (93%). They purchased alcohol at their own expense (46.5%). For alcohol consumption, all were lifetime drinkers (100%). The current drinking patterns were different 28.5% were current drinkers, 65.5% are currently abstaining from drinking (64.5%), and 6.6% stopped drinking permanently. The top three alcohol beverages were beer (52.3%), brandy (25.0%), and hard liquor (19.5%). Problems related to alcohol were from lost balance/falls (6.7%), illness (10.2%), driving under the influence (19.5%), and accidents (24.2%). Violence from drinking in the past month was from fighting (28.1%). This study is the first to provide information about alcohol-related problems in enlisted male soldiers. There is the need to offer straightforward advice, brief counseling, and refer soldiers to receive treatment to prevent alcohol-related problems. Online social media and web-based programs were recommended as platforms to provide preventive alcohol message to the enlisted.