• Journal of Distribution Science
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• v.10 no.11
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• pp.5-10
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• 2012

Purpose - The purpose of this study is to investigate the impact of awareness on the characteristics of a consumers' social commerce. This study examines whether the characteristics of social commerce influence the purchase intentions in accommodating these types of social commerce. Research design, data, methodology - The data for the study were collected and analyzed from a sample of 126 adult customers, comprising both males and females, using social commerce. The survey was conducted and the results aggregated through distributing a copy to each participant. For statistical analysis of the data collected, SPSS 18.0 statistical package was used. Results - The results can be summarized as follows. First, the perceptions about the characteristics of Social Commerce demonstrated a significant effect for attitudes. Second, the attitudes demonstrated positive effects on purchase intention. Third, the subjective norm affected the purchase intention. Fourth, perceived behavioral control influenced the purchase intentions. Conclusions - As a result, perceptions about the characteristics of Social Commerce may be seen in the positive effects on purchase intention. Using social commerce in the future, retailers would need to increase the scope of the study, through applying more diverse characteristics of Social Commerce.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.3935-3949
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• 2021

With the rapid development of Internet and big data technology, various online social network platforms have been established, producing massive information every day. Hot topic discovery aims to dig out meaningful content that users commonly concern about from the massive information on the Internet. Most of the existing hot topic discovery methods focus on a single network data source, and can hardly grasp hot spots as a whole, nor meet the challenges of text sparsity and topic hotness evaluation in cross-network scenarios. This paper proposes a novel hot topic discovery method across social network based on an im-proved LDA model, which first integrates the text information from multiple social network platforms into a unified data set, then obtains the potential topic distribution in the text through the improved LDA model. Finally, it adopts a heat evaluation method based on the word frequency of topic label words to take the latent topic with the highest heat value as a hot topic. This paper obtains data from the online social networks and constructs a cross-network topic discovery data set. The experimental results demonstrate the superiority of the proposed method compared to baseline methods.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.1
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• pp.75-84
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• 2021

For analyzing big data, the social network is increasingly being utilized through relational data, which means the connection characteristics between entities such as people and objects. When the relational data does not exist directly, a social network can be configured by calculating relational data such as attribute similarity from attribute data of entities and using it as links. In this paper, the composition method of the social network using the attribute similarity between entities as a connection relationship, and the clustering method using subgroups for the configured social network are suggested, and the clustering effectiveness of the clustering results is evaluated. The analysis results can vary depending on the type and characteristics of the data to be analyzed, the type of attribute similarity selected, and the criterion value. In addition, the clustering effectiveness may not be consistent depending on the its evaluation method. Therefore, selections and experiments are necessary for better analysis results. Since the analysis results may be different depending on the type and characteristics of the analysis target, options for clustering, etc., there is a limitation. In addition, for performance evaluation of clustering, a study is needed to compare the method of this paper with the conventional method such as k-means.

• Journal of the Korean Society of Clothing and Textiles
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• v.35 no.5
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• pp.547-560
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• 2011

This study examines the effect of corporate social responsibility on the corporate image and purchase intention. The data were obtained from 320 male and female 'C' university students in Daejeon in October of 2010. The data were analyzed by descriptive statistics, factor analysis, reliability analysis, regression analysis using the SPSS-WIN 15.0 and AMOS 7.0 program. The results were as follows. First, corporate social responsibility consists of five dimensions: community/cultural service, social contribution, environmental protection, consumer protection/legal responsibility, and economic responsibility. Purchase intention consists of comparative purchase and priority purchase. Second, social contribution, consumer protection/legal responsibility, and economic responsibility affect the corporate image. Third, social contribution and consumer protection/legal responsibility also affect purchase intention. Forth, the corporate image affects purchase intention. The findings of this study are expected to be used as basic data for establishing differentiated marketing strategies in fashion company.

• Asia pacific journal of information systems
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• v.30 no.4
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• pp.785-807
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• 2020

The novel Coronavirus disease 2019 (COVID-19) is unprecedentedly changing the world since its outbreak in late 2019. Using the collected the data related to COVID-19 and the social media user data from a mobile application market research agency from January 25 to April 7, this study empirically examines the effect of the number of confirmed COVID-19 cases worldwide, the number news COVID-19, and the enforcement of social distancing measures on the daily active users (DAU) of two social media services - Twitter and Facebook - in South Korea. There are three important findings from the results of econometric analysis. First, the number of confirmed COVID-19 cases worldwide has a negative effect on the DAU of social media. Second, the number of COVID-19 news is negatively associated with the DAU of social media. Finally, the implementation of social distancing measures has no significant effect on the DAU of the social media. Theoretical implications and managerial guidelines are also discussed.

• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.3
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• pp.71-86
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• 2013

This research explores the question how interactions between customer and firm affect firm growth. To test suggested hypotheses, this study collects data on social commerce industry in Korea during the period from the beginning of social commerce industry in Korea, May 2010, to March 2012, and investigates the effect of social media on the growth of social commerce firms. We suggest two hypotheses in this study. First, as web traffic inflow through social media into a focal social commerce increases, the growth rate of the focal social commerce increases. Second, the more diverse social media channel through which web traffic inflows into a focal social commerce, the weaker the positive effect of web traffic inflow on the growth rate of the focal social commerce. Analysis of data shows that inflow through social media is positively related to the growth of social commerce. In addition, our analysis shows that inflow channel diversity weakens the positive relationship between web traffic inflow through social media and growth rate of social commerce firms. These results suggest that firms need to concentrate on few social media in order to attract customers. The study contributes to understanding how interaction between firms and customers influences the growth of the firm.

• Journal of the Korean Society of Clothing and Textiles
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• v.35 no.11
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• pp.1322-1332
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• 2011

This study investigates the influence of social desirability to questionnaire response and data analysis in order to identify the need for social desirability control in clothing consumer research. A questionnaire measuring social desirability, social face sensitivity, clothing shopping behavior, and demographic characteristics was developed. Responses of 234 respondents were analyzed using factor analysis, simple regression analysis, hierarchical regression analysis, descriptive analysis, and Cronbach's alpha analysis. The results were as follow. First, respondents were influenced by social desirability when they responded to items measuring other-conscious social face. Second, the result of regression analysis (that the independent variable was social formality) was less influenced by social desirability control because the influence of social desirability to social formality was insignificant. Conversely, the result of regression analysis (that the independent variable was other-conscious social face) was more influenced by social desirability control because the influence of social desirability to other-conscious social face was significant. This study is an initial study that notices the need for social desirability control in clothing consumer research.

• International Journal of Contents
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• v.13 no.1
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• pp.31-37
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• 2017

Using the network analysis method, this study investigates the communication structure of Open Data on the Twitter sphere. It addresses the communication path by mapping influential activities and comparing the contents of tweets about Open Data. In the years 2015 and 2016, the NodeXL software was applied to collect tweets from the Twitter network, containing the term "opendata". The structural patterns of social media communication were analyzed through several network characteristics. The results indicate that the most common activities on the Twitter network are related to the subjects such as new applications and new technologies in Open Data. The study is the first to focus on the structural and informational pattern of Open Data based on social network analysis and content analysis. It will help researchers, activists, and policy-makers to come up with a major realization of the pattern of Open Data through Twitter.

• Asian Journal of Business Environment
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• v.8 no.3
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• pp.5-12
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• 2018

Purpose - The purpose of current paper is to identify features of advertisements at social media that generate the ad-click and to further identify if these advertisements lead to purchase. If no purchase is made, then reasons for not making purchase are identified. Users' purchase experience after users clicked at advertisements are also studied. Research design, data, and methodology - Research design followed is exploratory research, where various factors leading to ad-clicks and generating purchase at social media platform were explored. Raw data was gathered by means of survey among a sample of 185 respondents in India using online structured questionnaire. GLM model and multinomial regression were used to analyze the data. Results - Several factors including endorsement by friends, advertisement aesthetics, product reviews, and aggressive pricing played major role in generating ad-clicks. Major impediment to purchase on were product misrepresentation in advertisement, false discounts, and site security. Female users clicked more on social media advertisements and made more purchases compared to their male counterpart. Conclusions - Social media advertisements have significant positive effect on buying behavior of online customers. Transactions culminating from social media ad-click generated significant positive experience for social media users. Thus, social media can be effective marketing tool.

• Journal of Information Technology Services
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• v.14 no.2
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• pp.143-158
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• 2015

Companies and governments in an era of big data have been tried to create new values with their data resources. Among many data resources, many companies especially pay attention to data which is obtained from Social Network Service (SNS) because it reveals precise opinion of customers and can be used to estimate profiles of them from their social relationships. However, it is not only hard to collect, store, and analyze the data, but system applications are also insufficient. Therefore, this study proposes a S-POS (Social POS) system which consists of three parts; Twitter Side, POS Side and TPAS (Twitter&POS Analysis System). In this system, SNS data and POS data which are collected from Twitter Side and POS Side are stored in Mongo D/B. And it provides several services with POS terminal based on analysis and matching results which are generated from TPAS. Through S-POS system, we expect to efficient and effective store and sales managements of system users. Moreover, they can provide some differentiated services such as cross-selling and personalized recommendation services.