• Journal of Korean Institute of Industrial Engineers
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• v.42 no.1
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• pp.50-56
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• 2016

Social media (SM) for expressing thoughts and opinions of people in network communities can support collaboration among stakeholders in business activities. To enhance collaboration for product design, this study integrates SM with a product data management (PDM) system that manages main product development processes and data in manufacturing companies. This study proposes a text messaging SM model that shares core components of PDM systems and uses product structures in PDM systems as networks for SM broadcasting. The SM model is implemented with a research purpose PDM system and applied to product design experiments to show feasibility of the proposed SM model in PDM systems.

• Journal of Digital Convergence
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• v.18 no.10
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• pp.195-206
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• 2020

This study aims to analyze the effect of consumer characteristics such as consumer innovativeness on using popular social media in Korea. Social media usage is estimated by probit and multinomial probit model with user characteristics using Korea media panel data of 2019. According to the analysis, users with hedonoc innovativeness are likely to use social media, while users with cognitive innovativeness are not likely to use it. Regarding individual social media usage, functional innovativeness increases the probability of using Kakaostory, and hedonic innovativeness increases the likelihood of using Instagram. However, cognitive innovativeness decreases the probability of using Kakaosotry and Naver Band. This study gives insights into finding out specific social media for marketing certain products with innovativeness. In future research, it may be worthwhile to analyze under the assumption that a social media user is using several social media simultaneously.

• The Journal of the Korea Contents Association
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• v.11 no.6
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• pp.495-509
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• 2011

According to the rapid technological development such as ubiquitous environments, there has been growing interest in learning with social media as known as social learning. This study was conducted to analyze various participation types of social media contents aiming to explore strategies for facilitating learning. Specifically, the research model was established by two aspects in using social media contents. First was classified by writings and readings in contents, which consists of prosumers, producers, consumers, and non-participants. Second criterion was categorized by instruction-related and instruction-nonrelated, which is learning contents, learning management, emotional expression, and social activities. In order to acquire empirical data, a set of fourteen undergraduate students participated in this research for eight weeks using a microblog. Based on the analyses on the data through learning activities, three learning strategies were suggested to facilitate social media based learning: analysis on learners, role of the instructor, and instructional model design.

• Journal of Information Technology Applications and Management
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• v.27 no.6
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• pp.31-51
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• 2020

During the last decade, social live streaming service like Periscope, Ustream, and YouNow has developed from a niche market into a mainstream activity. In this media environment, social live streaming service has a tremendous impact on the social behaviors of users. Despite the rapid development, there are a lack of studies to make better understand the media environment changes through social live streaming service. This study adopted an affordances approach that leads us to identify six distinctive social affordances (visibility, accessibility, information sharing, social interaction, role-taking, interactive revenue) for user engagement in social live streaming service. Specifically, this study explores the impact of social affordances on perceived flow, followed by user engagement including passive and active engagement. Empirical data analysis with 258 questionnaires suggests that social affordances affected users' flow perception, and flow has an effect on active as well as passive engagement. Contrary to the expectation in a hypothesized research model, only the impact of accessibility on flow was rejected.

• International Journal of Contents
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• v.17 no.1
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• pp.1-10
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• 2021

Visual analytics is an emerging research field that combines the strength of electronic data processing and human intuition-based social background knowledge. This study demonstrates useful visual analytics with Tableau in conjunction with semantic network analysis using examples of sentiment flow and strategic communication strategies via Twitter in a blockchain domain. We comparatively investigated the sentiment flow over time and language usage patterns between companies with a good reputation and firms with a poor reputation. In addition, this study explored the relations between reputation and marketing communication strategies. We found that cryptocurrency firms more actively produced information when there was an increased public demand and increased transactions and when the coins' prices were high. Emotional language strategies on social media did not affect cryptocurrencies' reputations. The pattern in semantic representations of keywords was similar between companies with a good reputation and firms with a poor reputation. However, the reputable firms communicated on a wide range of topics and used more culturally focused strategies, and took more advantages of social media marketing by expanding their outreach to other social media networks. The visual big data analytics provides insights into business intelligence that helps informed policies.

• The Journal of the Korea Contents Association
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• v.13 no.4
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• pp.73-85
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• 2013

This study examines the online social capital of local press using the website and social media. Moreover, the paper respectively visualizes web feature as Web 1.0 and social feature analysis as Web 2.0 by applying correspondence analysis. For data, the study analyzes 10 representative local press in Yeungnam areas. To collect the data, two coders coded web features from the websites and we employed NodeXL, an open-source software tool, for social media data. The results reveal that local websites expend online social capital using social media account. Especially, the social features of local presses attach importance to Twitter as the main press keep the well-balance use among all platforms.

• Journal of Information Technology Applications and Management
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• v.30 no.4
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• pp.65-76
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• 2023

On social media, companies create brand experiences while customers actively seek, consume, and generate brand-related content. Customer engagement plays a vital role in the marketing performance of social media-driven brands. This study explores the positive relationship between brand authenticity, aligning brand identity with image, and self-brand connection, aligning brand identity with consumers' self-concepts, on customer engagement and its subsequent impact on brand loyalty. The study surveyed 243 consumers engaged with brand-related social media content, validating hypotheses using structural equation modeling. Results confirmed that brand authenticity and self-brand connection positively affect customer engagement, which, in turn, boosts brand loyalty. These findings highlight the importance of companies enhancing brand authenticity and self-brand connection to drive customer engagement, with theoretical and practical implications provided.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2571-2586
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• 2022

The rapid popularity of government social media has generated huge amounts of text data, and the analysis of these data has gradually become the focus of digital government research. This study uses Python language to analyze the big data of the Chinese provincial government Weibo. First, this study uses a web crawler approach to collect and statistically describe over 360,000 data from 31 provincial government microblogs in China, covering the period from January 2018 to April 2022. Second, a word separation engine is constructed and these text data are analyzed using word cloud word frequencies as well as semantic relationships. Finally, the text data were analyzed for sentiment using natural language processing methods, and the text topics were studied using LDA algorithm. The results of this study show that, first, the number and scale of posts on the Chinese government Weibo have grown rapidly. Second, government Weibo has certain social attributes, and the epidemics, people's livelihood, and services have become the focus of government Weibo. Third, the contents of government Weibo account for more than 30% of negative sentiments. The classified topics show that the epidemics and epidemic prevention and control overshadowed the other topics, which inhibits the diversification of government Weibo.

• Journal of the Korea Society of Computer and Information
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• v.24 no.12
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• pp.85-92
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• 2019

Companies can identify user groups or consumption trends by collecting and analyzing opinions of many users on special subjects or their products as well as utilize them as various purposes such as predicting some specific trends or marketing strategies. Therefore current analyzing tools of social media have come into use as a means to measure the performances of social media marketing through network's statistical analysis. However these tools require expensive computing and network resources including burden of costs for building up and operating complex software platforms and much operating know-how. Hence, small companies or private business operators have difficulty in utilizing those social media data effectively. This paper proposes a framework applied into developing analysis system of social media. The framework could be set up and operate the system to extract necessary social media's data. Also to design the system, this study suggests a meta-model of proposed framework and to guarantee completeness and consistency, a formal specification of meta-model by using Z language is suggested. Finally, we could verify the clearness of framework's design by performing Z model checking of formal specification's output through Z-EVES tool.

• The Journal of Information Systems
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• v.26 no.2
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• pp.1-23
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• 2017

Purpose Facebook is a social network service that has the highest number of Monthly Active Users around the world. Hence, marketers have selected Facebook as the most important platform to get customer engagement. With respect to the customer engagement enhancement, the most popular and engaging post type in the Facebook brand fan pages related to what was usually classified as 'sweepstakes'. Sweepstakes refer to a form of gambling where the entire prize may be awarded to the winner. Which makes customers more engaged with the brand. This study aims to explore sweepstakes-oriented social media marketing approaches based on the application of big data analytics. Design/methodology/approach we collect sweepstakes data from each company based on the data crawling from the Facebook brand fan pages. The output of this study explains how companies in each category of FCB grid can design and apply sweepstakes for their social media marketing. Findings The results show that they have one thing in common across the four quadrants of FCB grid. Regardless of the quadrants, most frequently observed type is 'Simple/Quiz or Comments/Quatrains [event type of sweepstakes] + Gifticon [type of reward prize] + Image [type of message display] + No URL [Link toother website] +Single-Gift-Offer [type of reward prize payment]'. So, if the position of the brand is hard to be defined by the FCB grid model, then this general rule can be applied to all types of brands. Also some differences between the quadrants of the FCB grid were observed. This study offers several research implications by analyzing Sweepstakes-oriented social media marketing approaches in Facebook brand fan pages. By using the FCB grid model, this study provides guidance on how companies can design their sweepstakes-oriented social media marketing approaches in the context of Facebook brand fan pages by considering their context.