• Journal of Service Research and Studies
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• v.14 no.3
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• pp.60-78
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• 2024

Social Network Service(SNS) has fundamentally changed the scope, boundaries, and dynamics of social interactions, becoming an integral part of everyday social communication for individuals and significantly altering the decision-making processes of individuals and organizations. Although prior studies have agreed that individual motivations significantly affect travel experience sharing behavior on SNS, different motivations need to be further examined. Also, there is little empirical study that examines the relationships between social capital and motivations. To address these gaps, this study developed a research model to investigate how two types of social capital (i.e., bridging and bonding) influence individual motivations (i.e., self-enhancement and altruism motivations), which in turn contributes to travel experience sharing behavior on SNS. The online survey was conducted from March 3 to March 17, 2021, and 516 responses were included in the data analysis. Structural Equation Modeling was applied to test the hypotheses in a research model. This research provided a comprehensive exploration of the relationship between motivations and social capital, contributing to a better understanding of why tourists share their travel experiences on SNS.

• The Journal of the Korea Contents Association
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• v.16 no.5
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• pp.623-637
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• 2016

This study examined the importance of social capital in facilitating older adults' learning and adaptation of information technology as well as alleviating depressive symptoms. At two senior community centers in South Korea, 144 adults aged 60 and older were recruited to participate in 12 week-long technology classes to learn computers, smart phone, and internet skills. At the baseline interviews were conducted to assess their health status, depression, and online social relationships. Online and offline social capital (bonding vs. bridging) was assessed (Williams, 2006). Four-step Hierarchical Linear Regression analysis was conducted to examine the effects of online social relationship on depression. Findings suggested that depressive symptoms were associated with being widowed, being unemployed, and perceiving poor health status. Adding social capital variables in the final step, older adults who perceived less stressors, greater level of subjective health and high online bonding capitals had less depressive symptoms. Only online social bonding was significant in alleviating depression. This final model explained 48% of the variance. Computer/Internet training for older adults need to consider the significant role bonding social capital can play. The findings of this pilot study provided a preliminary base of knowledge about acceptable community-based interventions for older adults.

• (The) Korean Journal of Advertising
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• v.28 no.3
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• pp.53-81
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• 2017

As online video advertising market grows, viral advertising is drawing attention. This study investigated factors influencing consumer's sharing intent of viral advertising each from four dimensions which are content, sender, consumer, and network. As a result, factors of persuasive intent, brand-ad image fit, perceived self-presentation, and bridging social capital were found to affect consumer's sharing intent of viral advertising. And persuasive intent of content was found to be negatively affect consumer's sharing intent. Social value and bonding social capital were not found to have significant influence on consumer's sharing intent of viral advertising. From the analysis of this result, this study suggested future research topic and academic/practical implications.

• Journal of Digital Convergence
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• v.12 no.11
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• pp.541-548
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• 2014

This study aims to examine how mobile instant messenger (MIM) usage pattern and MIM intensity influence MIM users' social capital. Total 253 MIM users in their 20's and 30's participated an online survey. Results showed that MIM frequency and network size affected bonding social capital. Meanwhile, MIM intensity was the only variable that influenced users' bridging social capital. Overall, the strength of strong ties in mobile media use was confirmed again. It was also confirmed that measuring the qualitative level of emotional attachment to the MIM (MIM intensity) was more important than gauging mere usage pattern when evaluating social capital enhancement through mobile media use.

• Journal of Digital Convergence
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• v.15 no.11
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• pp.547-555
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• 2017

The purpose of this research is to examine the relationships between online and offline social capital and psychological well-being of university students. Data came from 236 university students who attended 4-year universities in Seoul and had used Social Network Service. The results of this study indicated that only offline social capital was statistically significant to predict psychological well-being including depression and happiness when both online and offline social capital variables were entered. In detail, the university students' depression was associated with their gender, satisfaction with their economic status, and offline bridging social capital. In addition the students' happiness was associated with their satisfaction with their economic status, offline bridging, and bonding social capital. These results indicate that offline social capital is more important for improving psychological well-being of university students compared to online social capital. Policy implications for improving psychological well-being of university students are discussed.

• Journal of Navigation and Port Research
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• v.48 no.3
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• pp.181-191
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• 2024

Ports play a crucial role in the complex global supply chain. While many researchers have used social network analysis (SNA) to study active networks, there is a lack of SNA cohesion analysis specifically related to logistics and trade. Therefore, this study aims to identify time-series structural changes in all domestic import and export logistics networks, including regions, ports, and airports, by utilizing techniques such as k-core and community analysis. To carry out this analysis, we rely on data from the Korea Customs Service's Import and Export Logistics Statistical Yearbook spanning from 2004 to 2022. The findings from the k-core and community analysis indicate that the cohesion of the domestic import and export logistics network has continuously strengthened over time. Moreover, it reveals that regions, ports, and airports are becoming more cohesive and homogeneous, with Busan Port emerging as the central hub of a large community. These insights are expected to enhance our understanding of global logistics dynamics and contribute to the development of policies and sustainable import and export logistics processes.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.8
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• pp.69-76
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• 2016

As the number of SNS users increases, so does their self-disclosure. This study examined the factors affecting self-disclosure based on Social Capital Theory and Regulatory Focus Theory. The (extent of self-disclosure by users/number of users disclosing themselves) in SNSs is expected to differ depending on their social capital (bonding capital vs. bridging capital) and regulatory focus (promotional vs. defensive). As a result of this study, it is found that bridging capital is positively related to self-disclosure in profile and in conversation, while bonding capital is positively related to self-disclosure only in conversation. With regard to regulatory focus, promotional orientation has a significant effect on self-disclosure in profile and in conversation, while defensive orientation is negatively related to self-disclosure in profile, but not related to self-disclosure in conversation. Promotional orientation is found to moderate the effect of bridging capital on self-disclosure.

• Journal of the Korean Geographical Society
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• v.49 no.2
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• pp.139-159
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• 2014

This study problematizes the dominance of developmental state theory and its negative influences in the field of Korean studies, in particular, dealing with the industrialization during the developmental era, 1960s~70s. As is generally known, the theory has been in a position of unchallenged authority on the industrialization experience of East Asian countries, including South Korea. However, at the same time, it has also misled us into overlooking strategic relations that had articulated the state forms at multiple scales. This study aims to reconstruct the historical contexts by the theorizing prompted by recent work on state space. I shed light on the multiscalar strategic relations that had shaped the Ulsan refinery plant as a representative state space of the South Korean industrialization during two decades after liberation. Specifically, the study illustrates the features and roles of Cold War networks and multiscalar agnets such as Nam Goong-Yeon. By identifying the plant as a result of sequential articulations between Ulsan and other scales, this study concludes by suggesting to reframing the strategic relational spaces, beyond the view of methodological nationalism, in the perspective of multiscalar approach.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.3
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• pp.1-4
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• 2010

RS-485, communication bases from small network system must prepare in collision. The collision is that mean the data transfer breaks. For a stabilized communication chooses 1:N polling methods. But polling is low speed in addition to maybe overload Master device. So, usual N:N Prefers a communication. In this case, must be preparing to avoid collision or some solutions. Generally, to after collision retransmits after short time. It's called delay time for short time. When making a delay time, uses address of each systems. (Address of each node) If the many nodes collided, the each node has different delay time. When making a delay time, uses a usual random number. Making a random number is hard job. So uses a usual pseudorandom number. It is more difficult from small size MCU. The Chaos random number provides stabled value. Finally, when uses the Chaos random number, the stability and reliability of system get better.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.23-31
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• 2020

This study was carried out to generate various images of railroad surfaces with random defects as training data to be better at the detection of defects. Defects on the surface of railroads are caused by various factors such as friction between track binding devices and adjacent tracks and can cause accidents such as broken rails, so railroad maintenance for defects is necessary. Therefore, various researches on defect detection and inspection using image processing or machine learning on railway surface images have been conducted to automate railroad inspection and to reduce railroad maintenance costs. In general, the performance of the image processing analysis method and machine learning technology is affected by the quantity and quality of data. For this reason, some researches require specific devices or vehicles to acquire images of the track surface at regular intervals to obtain a database of various railway surface images. On the contrary, in this study, in order to reduce and improve the operating cost of image acquisition, we constructed the 'Defective Railroad Surface Regeneration Model' by applying the methods presented in the related studies of the Generative Adversarial Network (GAN). Thus, we aimed to detect defects on railroad surface even without a dedicated database. This constructed model is designed to learn to generate the railroad surface combining the different railroad surface textures and the original surface, considering the ground truth of the railroad defects. The generated images of the railroad surface were used as training data in defect detection network, which is based on Fully Convolutional Network (FCN). To validate its performance, we clustered and divided the railroad data into three subsets, one subset as original railroad texture images and the remaining two subsets as another railroad surface texture images. In the first experiment, we used only original texture images for training sets in the defect detection model. And in the second experiment, we trained the generated images that were generated by combining the original images with a few railroad textures of the other images. Each defect detection model was evaluated in terms of 'intersection of union(IoU)' and F1-score measures with ground truths. As a result, the scores increased by about 10~15% when the generated images were used, compared to the case that only the original images were used. This proves that it is possible to detect defects by using the existing data and a few different texture images, even for the railroad surface images in which dedicated training database is not constructed.