• East Asian Journal of Business Economics (EAJBE)
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• v.9 no.2
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• pp.55-73
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• 2021

Purpose -This study aims to determine how the characteristics of the airline's SNS marketing affects brand image, brand attitude according to perceived values, and to analyze the impact of SNS marketing factors. It was intended to provide theoretical and practical implications for airlines to refer to SNS marketing activities. Research design, data, and methodology -A questionnaire was formed based on previous studies, and then an online questionnaire was created to conduct a survey. Explained the purpose and asked to respond. From February 1 to 14, 2020, 333 responses with a valid number of samples were confirmed for the final analysis of the data. The questionnaire was composed of five areas: demographic characteristics, SNS factor, brand image, brand attitude, and perceived value. Result -Airline's SNS marketing, brand image, and brand attitude are affected by the gender, age, and SNS usage time of the user, and the perceived value of the user is shown to be controlled by the airline's SNS marketing's influence on brand image and brand attitude. Conclusion -When SNS is to be effectively used for airline marketing, it is necessary to pay attention to the demographic characteristics and the control effect of perceived value, and use it for airline management. The perceived value has been shown to affect SNS marketing's brand image and brand attitude.

• The Research Journal of the Costume Culture
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• v.29 no.6
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• pp.865-880
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• 2021

The purpose of this study was to examine job competencies for sales training program development to maximize profits in fashion retailing. An empirical online survey was conducted from September to December 2019, and data was collected from 200 salespeople and store managers working in fashion stores. Results were analyzed using frequency analysis, factor analysis, variance analysis, and regression analysis with SPSS 25.0. The major findings of this study were as follows. First, the most important job competencies identified by fashion store managers were: sales sense know-how, customer service skills, and sales person's fashion style sense, product knowledge, fashion marketing and customer management. The job competency factors for sales training programs included empathy with the customer, product knowledge, communications and networking, basic job requirement, and sales skills. These five factors positively influenced the employment intentions and expectations of work performance of graduates. These factors also had a positive influence on the need of sales training program and intention to participate in retraining. Store managers in fashion retail thought the most appropriate period for on-the-job training was either 2-4 days or more than 1 week. The results of this study can be used as a base to develop training programs for job efficiency for salespeople in fashion retailing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3868-3888
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• 2022

A widely used social networking service like Twitter has the ability to disseminate information to large groups of people even during a pandemic. At the same time, it is a convenient medium to share irrelevant and unverified information online and poses a potential threat to society. In this research, conventional machine learning algorithms are analyzed to classify the data as either non-rumor data or rumor data. Machine learning techniques have limited tuning capability and make decisions based on their learning. To tackle this problem the authors propose a deep learning-based Rumor Detection Neural Network model to predict the rumor tweet in real-world events. This model comprises three layers, AttCNN layer is used to extract local and position invariant features from the data, AttBi-LSTM layer to extract important semantic or contextual information and HPOOL to combine the down sampling patches of the input feature maps from the average and maximum pooling layers. A dataset from Kaggle and ground dataset #gaja are used to train the proposed Rumor Detection Neural Network to determine the veracity of the rumor. The experimental results of the RDNN Classifier demonstrate an accuracy of 93.24% and 95.41% in identifying rumor tweets in real-time events.

• Human Ecology Research
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• v.62 no.3
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• pp.473-486
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• 2024

The purpose of this study was to conduct Latent Profile Analysis to identify the types of marriage values within each generation and explore the influence of gender, family health, and self-determination on each type. This study was conducted as an online survey through social networking sites (SNS) for the Second Generation of Baby Boomers (1965~1974), Generation X (1975~1984), Generation Y (1985~1996), and Generation Z (1997~2003). A total of 1,114 copies were used for the final analysis. Latent Profile Analysis was conducted using Mplus ver. 8.8 software to identify the types of marriage values within each generation and explore the influence of gender, family health, and self-determination on each type. The significance of this study lies in the identification of a group in each generation that holds ambivalent values about marriage. Additionally, we identified differences between gender and self-determination as variables that affect marriage values, excluding family variables. Therefore, it is significant to understand marriage values by considering generational characteristics. Based on this, it is believed that it can provide a basis for education and counseling programs related to marriage, reflecting the important variables unique to each generation.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• The Journal of the Korea Contents Association
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• v.18 no.1
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• pp.19-30
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• 2018

The main purposes of this study are to analyze the contents and text network of online tourism information. For this purpose, Jeju Island, one of the representative tourist destinations in South Korea is selected as a study site. And this study collects the contents of both JeJu official tourism website and Sina Weibo's personal blogs which is one of the most popular Social Network Systems in China. In addition, this study analyzes this online text information using ROST Content Mining System, one of the Chinese big data mining systems. The results of the content analysis show that the formal website of Jeju includes the nouns related to natural, geographical and physical resources, verbs related to existence of resources, and adjectives related to the beauty, cleanness and convenience of resources mainly. Meanwhile, personal blogs include the nouns of Korean-wave, food, local products, other destinations and shopping, verbs related to activity and feeling in Jeju, and adjectives related to their experiences and feeling mainly. Finally, the results of text network show that there are some strong centrality and network of online tourism information at formal website, but there are weak relationships in personal blogs. The results of this study may be able to contribute to the development of demand-based marketing strategies of tourists destination.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.57-69
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• 2020

As more people share their opinions in online communities, such as Internet portals and social networking services, more opinions are manipulated for the benefit of particular individuals and groups. In particular, when manipulations occur for political purposes, they influence election results as well as government policies and the quality of life. This type of manipulation has targeted the general public, and their analysis and detection has also focused on such manipulation. However, to more efficiently spread propaganda, recent manipulations have targeted common interest groups(e.g., a group of those interested in real estate) and propagated information whose content and style are customized to those groups. This work characterizes such manipulations on common interest groups and proposes method to detect manipulations. To this end, we collected and analyzed opinions posted on 10 common interest groups before and after an election. As a result, we found that manipulations on common interest groups indeed occurred and were gradually increasing toward the election date. We also proposed a detection system that examines individual opinions, their authors, and their collaborators. Using the collected opinions, we demonstrated that the proposed system can accurately classify more than 90% of manipulated opinions and that many of these opinions were posted by multiple collaborators. We believe that regular audits of opinions using the proposed system can quickly isolate manipulations and decrease their impact. Moreover, the proposed features can be used to identify manipulations in domains other than politics.

• Science of Emotion and Sensibility
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• v.15 no.2
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• pp.283-296
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• 2012

The goal of this study is to determine the social emotion model as an emotion sharing relationship and information sharing relationship based on the user's relations at social networking services. 26 social emotions were extracted by verification of compliance among 92 different emotions collected from the literature survey. The survey on the 26 emotion words was verified to the similarity of social relation types to the Likert 7-points scale. The principal component analysis of the survey data determined 12 representative social emotions in the emotion sharing relation and 13 representative social emotions in the information sharing relation. Multidimensional scaling developed the two-dimensional social emotion model of emotion sharing relation and of information sharing relation based on online communication environment. Meanwhile, insignificant factors in the suggest social emotion models were removed by the structural equation modeling analysis, statistically. The test result of validity analysis demonstrated the fitness of social emotion models at emotion sharing relationships (CFI: .887, TLI: .885, RMSEA: .094), social emotion model of information sharing relationships (CFI: .917, TLI: .900, RMSEA : 0.050). In conclusion, this study presents two different social emotion models based on two different relation types. The findings of this study will provide not only a reference of evaluating social emotions in designing social networking services but also a direction of improving social emotions.

• Journal of the Society of Disaster Information
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• v.8 no.1
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• pp.1-9
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• 2012

Informatization of society through the computer and the Internet, because large amounts of information production and exchange and new way of communicating is born. Passive way past the one-sided information flows actively interact to evolve in a manner of information producers and information consumers distinction and personal relationships that enhance the online Social Networking Service (SNS) has developed into the social structure of. Thus, the spread of information work closely with the social network structure spark social conflict may act as a factor, and systems and the environment, personal and cultural adaptation of speed to keep up with the rapid development of science and technology as the inability conflict and confusion should lead to even. This paper the characteristics of the information society, with a look at the evolution of social risk factors as the wavelength of information about this look at the role of private security sought to evaluate. Information Society in time and space by shrinking the area of human life that has brought the convenience and simplicity, whereas the non-performance due to the nature of anonymous raises many social side-effects are. This made the preparation of national regulatory measures, but for the protection of personal protection devices in the private sector has not yet been discussed. Way of life and property of the purchaser to protect an individual's private security will have to charge it.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.3
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• pp.29-46
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• 2022

COVID-19 has radically changed the behavior of members of society for exchange. In particular, the strong contagiousness of the virus is increasing networking on online platforms while reducing people's networking in the real world. Recently, the metaverse, which strengthened the presence based on 3D technology, is attracting attention from members of society such as individuals and companies. We present a method to improve metaverse utilization from the perspective of organizations and employees who have introduced metaverse for work. In other words, we check the effect of metaverse social function and telepresence on the employee's intention to offer support by improving the trust of the metaverse participants. We obtained samples through questionnaires targeting employees of organizations that introduced metaverse to their work, and verified the research hypothesis by applying the structural equation model. As a result, social interactivity, reciprocal favor, and telepresence of metaverse partially affected metaverse trust (platform, peer, organization), and metaverse trust increased the intention to offer support. Our study suggests a strategic direction to improve the metaverse utilization and exchange level of employees of organizations who want to use the metaverse for business.