• Korean Management Science Review
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• v.28 no.3
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• pp.83-94
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• 2011

Microblogging is a Web2.0 technology that is used to manage online interpersonal relationship in SNS. Microblogging service allows the users to publish online brief text updates, usually less than 140~200 characters, sometimes images too. Recently, it becomes more and more popular. There are many reasons, the major one of which is that it can be perfectly combined with mobile. Based on technology acceptance model (TAM) and according to mobile service and microblogging service, this research adds four attributes : Perceived enjoyment, Habit, Mobility, Social Influence. It studies the factors that will impact on the way in which people use rapid developing Mobile Microblogging Service. This research will compare the influence factors and intension to use with the results from other studies with SNS and Mobile Microblogging Service, and then conclude the differences that can be generated in Microblogging.

• 한국벤처창업학회:학술대회논문집
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• 2018.04a
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• pp.183-185
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• 2018

Online search has recently become a popular business research field not only because the search volume is used to predict demand, but also consumer search history is effective to predict product prices and investment returns. This study analyzes the relationship between the Internet search volume of IPO stocks and their post-IPO stock returns in Korean Exchange. We find that the lower the amount of Internet search for stocks before IPO, the higher the stock returns after IPO both in short and long-term. Similar results are shown for excess returns over benchmark stocks. This finding suggests that IPO stocks with low investors' attention based on the Internet search volume may be undervalued.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.10a
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• pp.116-136
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• 2004

The objective of this study is to analyze the relationship between offline trust and use of online service focusing on Internet banking. For this purpose, we suggest an extended version of Technology Acceptance Model (TAM) that would explain use or acceptance of infer]nation technology especially in Internet based service areas. In order to connect offline trust with Internet banking use, TAM is extended by adding two variables - technical competence for security services that is one facet of trust on the bank's security technology and security risk that would influence use, perceived usefulness and perceived ease of use. Empirically our model is well proved with low significance level.

• Asia pacific journal of information systems
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• v.28 no.1
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• pp.36-60
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• 2018

As an increasing number of people gained access to social network services (SNS), organizations started to use SNS as a channel for marketing and promotional purposes. The online advertising market has significant growth potential. Brand engagement is a key motive for online advertising, but how SNS users engage with brands, particularly in terms of the promotion of organizations, is poorly understood. This study uses customer value theory to examine brand engagement of users in terms of promoting companies in the context of Korean SNS marketing. This study identifies the antecedents of brand engagement based on customer value theory. Our findings show the significance of three factors of SNS marketing, namely, price discount, relationship support, and convenience, on brand engagement. We further show the consequences of brand engagement, namely, purchase decisions and word-of-mouth activities. These findings help advance customer value theory and offer practical insights into the use of information systems and marketing in the context of SNS.

• Information Systems Review
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• v.26 no.1
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• pp.173-192
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• 2024

The phenomenon of consumers referring to the opinions of other consumers when choosing a hotel based on review platforms is rapidly increasing. Previous research has mainly focused on how online reviews affect travelers' information acceptance and decision making. However, there are only a few studies on how hotels' CSR activities are reflected in online reviews and how this information affects potential consumers. As the importance of CSR in the hotel industry continues to grow, it is meaningful to analyze whether a hotel's CSR activities are communicated to other potential consumers through online reviews. In this study, we explored the relationship by considering the reviewer's culture as a leading factor of CSR information included in online reviews and review usefulness as a subsequent factor. For empirical analysis, 6,632 online reviews of hotels located in Seoul were collected and analyzed using multilevel regression analysis. The LIWC(Linguistic Inquiry and Word Count) methodology was used to measure CSR information, and national culture was measured separately into East and West. As a result of the analysis, consumers in Asian countries tend to write relatively more CSR information in online reviews, and CSR information included in reviews had a positive effect on review usefulness.

• Journal of Power Electronics
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• v.16 no.1
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• pp.217-226
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• 2016

A new method is proposed based on a hidden Markov model (HMM) to estimate and analyze battery states of health. Battery system health states are defined according to the relationship between internal resistance and lifetime of cells. The source data (terminal voltages and currents) can be obtained from vehicular battery models. A characteristic value extraction method is proposed for HMM. A recognition framework and testing datasets are built to test the estimation rates of different states. Test results show that the estimation rates achieved based on this method are above 90% under single conditions. The method achieves the same results under hybrid conditions. We can also use the HMMs that correspond to hybrid conditions to estimate the states under a single condition. Therefore, this method can achieve the purpose of the study in estimating battery life states. Only voltage and current are used in this method, thereby establishing its simplicity compared with other methods. The batteries can also be tested online, and the method can be used for online prediction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.7
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• pp.1868-1887
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• 2024

Recommendation systems research is a subfield of information retrieval, as these systems recommend appropriate items to users during their visits. Appropriate recommendation results will help users save time searching while increasing productivity at work, travel, or shopping. The problem becomes more difficult when the items are geographical locations on the ground, as they are associated with a wealth of contextual information, such as geographical location, opening time, and sequence of related locations. Furthermore, on social networking platforms that allow users to check in or express interest when visiting a specific location, their friends receive this signal by spreading the word on that online social network. Consideration should be given to relationship data extracted from online social networking platforms, as well as their impact on the geolocation recommendation process. In this study, we compare the similarity of geographic locations based on their distance on the ground and their correlation with users who have checked in at those locations. When calculating feature embeddings for users and locations, social relationships are also considered as attention signals. The similarity value between location and correlation between users will be exploited in the overall architecture of the recommendation model, which will employ graph convolution networks to generate recommendations with high precision and recall. The proposed model is implemented and executed on popular datasets, then compared to baseline models to assess its overall effectiveness.

• Journal of the Korean Home Economics Association
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• v.45 no.6
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• pp.113-122
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• 2007

The purpose of this study was to examine the relationship between online apparel impulse buying behavior and apparel website characteristics. The data were collected using an online survey with a structured questionnaire. To recruit participants, online surveys were collected and a total of 236 college students were used in the study. The results of factor analysis showed that website characteristics consisted of two factors (i.e., promotion, product/policy information). The results of MANOVA and multiple regression showed that the impulse purchase group evaluated the website where they bought the last apparel item significantly better in promotion and product/policy information than the non-impulse purchase group. Based on the results, H1, H2, and H3 were supported. The evaluations of the characteristics of websites where impulse purchases and non-impulse purchases of apparel products were made were significantly different.

• Journal of Internet Computing and Services
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• v.18 no.2
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• pp.75-85
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• 2017

Open innovation, a new paradigm which utilizes customer ideas for organizational innovation directly, is evaluated as a useful method to innovate the organization itself. In this research, we analyze the case of Starbucks' online community, MyStarbucksIdea.com to examine how collective intelligence is formed out of mass customers in the community and how open innovation is to be implemented successfully. We review various challenges in implementing open innovation and then suggest practical approaches to the challenges, including customer relationship management, utilization of opinion leaders, application of engineering techniques, etc.

• Journal of Internet Computing and Services
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• v.8 no.4
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• pp.137-147
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• 2007

As Web-based online communities are rapidly growing, the agents in social groups need to know their measurable belief of trust for safe andsuccessful interactions. In this paper, we propose a computational model of trust resulting from available feedbacks in online communities. The notion of trust can be defined as an aggregation of consensus given a set of past interactions. The averagetrust of an agent further represents the center of gravity of the distribution of its trustworthiness and untrustworthiness. And then, we precisely describe the relationship between reputation, trust, and averagetrust through a concrete example of their computations. We apply our trust model to online internet settings in order to show how trust mechanisms are involved in a rational decision-making of the agents.