• Management & Information Systems Review
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• v.36 no.3
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• pp.167-184
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• 2017

In this study, we investigated the effect of offline banking trust on smart banking trust. As influencing factors of smart banking trust, this study compared offline banking trust, smart banking's system quality, and information quality. For the empirical study, 186 questionnaire data were collected from smart banking users and the data were analyzed using Smart-PLS 2.0. As results, it was verified that there is trust transfer in FinTech service, by the significant effect of offline banking trust on smart banking trust. And it was proved that the effect of offline banking trust on smart banking trust is lower than that of smart banking itself. The contribution of this study can be seen in both academic and industrial aspects. First, it is the contribution of the academic aspect. Previous studies on banking were focused on either offline banking or smart banking. But this study, focus on the relationship between offline banking and online banking, proved that offline banking trust affects smart banking trust. Next, it is the industrial contribution. This study showed that offline banking characteristics of traditional commercial banks affect the trust of emerging smart banking service. This means that the emerging FinTech companies are not advantageous in the competition of trust building compared to traditional commercial banks. Unlike traditional commercial banks, the emerging FinTech is innovating the convenience of customers by arming them with new technologies such as mobile Internet, social network, cloud technology, and big data. However, these FinTech strengths alone can not guarantee sufficient trust needed for financial transactions, because banking customers do not change a habit or an inertia that they already have during using traditional banks. Therefore, emerging FinTech companies should strive to create destructive value that reflects the connection with various Internet services and the strength of online interaction such as social services, which have an advantage over customer contacts. And emerging FinTech companies should strive to build service trust, focused on young people with low resistance to new services.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.143-159
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• 2015

Predicting IT trends has been a long and important subject for information systems research. IT trend prediction makes it possible to acknowledge emerging eras of innovation and allocate budgets to prepare against rapidly changing technological trends. Towards the end of each year, various domestic and global organizations predict and announce IT trends for the following year. For example, Gartner Predicts 10 top IT trend during the next year, and these predictions affect IT and industry leaders and organization's basic assumptions about technology and the future of IT, but the accuracy of these reports are difficult to verify. Social media data can be useful tool to verify the accuracy. As social media services have gained in popularity, it is used in a variety of ways, from posting about personal daily life to keeping up to date with news and trends. In the recent years, rates of social media activity in Korea have reached unprecedented levels. Hundreds of millions of users now participate in online social networks and communicate with colleague and friends their opinions and thoughts. In particular, Twitter is currently the major micro blog service, it has an important function named 'tweets' which is to report their current thoughts and actions, comments on news and engage in discussions. For an analysis on IT trends, we chose Tweet data because not only it produces massive unstructured textual data in real time but also it serves as an influential channel for opinion leading on technology. Previous studies found that the tweet data provides useful information and detects the trend of society effectively, these studies also identifies that Twitter can track the issue faster than the other media, newspapers. Therefore, this study investigates how frequently the predicted IT trends for the following year announced by public organizations are mentioned on social network services like Twitter. IT trend predictions for 2013, announced near the end of 2012 from two domestic organizations, the National IT Industry Promotion Agency (NIPA) and the National Information Society Agency (NIA), were used as a basis for this research. The present study analyzes the Twitter data generated from Seoul (Korea) compared with the predictions of the two organizations to analyze the differences. Thus, Twitter data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. To overcome these challenges, we used SAS IRS (Information Retrieval Studio) developed by SAS to capture the trend in real-time processing big stream datasets of Twitter. The system offers a framework for crawling, normalizing, analyzing, indexing and searching tweet data. As a result, we have crawled the entire Twitter sphere in Seoul area and obtained 21,589 tweets in 2013 to review how frequently the IT trend topics announced by the two organizations were mentioned by the people in Seoul. The results shows that most IT trend predicted by NIPA and NIA were all frequently mentioned in Twitter except some topics such as 'new types of security threat', 'green IT', 'next generation semiconductor' since these topics non generalized compound words so they can be mentioned in Twitter with other words. To answer whether the IT trend tweets from Korea is related to the following year's IT trends in real world, we compared Twitter's trending topics with those in Nara Market, Korea's online e-Procurement system which is a nationwide web-based procurement system, dealing with whole procurement process of all public organizations in Korea. The correlation analysis show that Tweet frequencies on IT trending topics predicted by NIPA and NIA are significantly correlated with frequencies on IT topics mentioned in project announcements by Nara market in 2012 and 2013. The main contribution of our research can be found in the following aspects: i) the IT topic predictions announced by NIPA and NIA can provide an effective guideline to IT professionals and researchers in Korea who are looking for verified IT topic trends in the following topic, ii) researchers can use Twitter to get some useful ideas to detect and predict dynamic trends of technological and social issues.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• Korean Journal of Food Science and Technology
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• v.52 no.1
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• pp.1-10
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• 2020

The present study identified the restrictions on the use of sous-vide products in the Korean HMR market for small and medium-sized manufacturing companies. A detailed literature review revealed that the HMR market in Korea is close to saturation. Notably, the technologically advanced products produced using sous-vide seem to display significant potential to overcome market saturation. The sous-vide method differs from conventional cooking techniques and is characterized by maintenance of food texture along with flavor enhancement. However, due to the unfamiliarity of the manufacturers with this method and the unclear food safety regulations, mass food manufacturing companies do not agree on using this method; hence, sous-vide production is usually undertaken by small/medium sized companies catering primarily through online marketing portals. This study highlights the various restrictions to the implementation of sous-vide production, and discusses several practical implications of sous-vide production that would help users of this technique enter the HMR market.

• Management & Information Systems Review
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• v.36 no.3
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• pp.131-150
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• 2017

The purpose of this study is to observe the spread pattern of open source software (OSS) while establishing relations with surrounding actors during its operation period. In order to investigate the change pattern of participants in the OSS, we use a netnography on the basis of online data, which can trace the change patterns of the OSS depending on the passage of time. For this, the cases of three OSSs (e.g. jQuery, MooTools, and YUI), which are JavaScript frameworks, were compared, and the corresponding data were collected from the open application programming interface (API) of GitHub as well as blog and web searches. This research utilizes the translation process of the actor-network theory to categorize the stages of the change patterns on the OSS translation process. In the project commencement stage, we identified the type of three different OSS-related actors and defined associated relationships among them. The period, when a master commences a project at first, is refined through the course for the maintenance of source codes with persons concerned (i.e. project growth stage). Thereafter, the period when the users have gone through the observation and learning period by being exposed to promotion activities and codes usage respectively, and becoming to active participants, is regarded as the 'leap of participants' stage. Our results emphasize the importance of promotion processes in participants' selection of the OSS for participation and confirm the crowding-out effect that the rapid speed of OSS development retarded the emergence of participants.

• Information Systems Review
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• v.20 no.1
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• pp.17-39
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• 2018

SNS enables people to easily connect and communicate with each other. People share information, including personal information, through SNS. Users are concerned about their privacies, but they unconsciously or consciously disclose their personal information on SNS to interact with others. The privacy of a self-disclosed person can be intruded by others. A person can write, fabricate, or distribute a story using the disclosed information of another even without obtaining consent from the information owner. Many studies focused on privacy intrusion, especially from the perspective of a victim. However, only a few studies examined privacy intrusion from the perspective of an intruder on SNS. This study focuses on the intention of privacy intrusion from the perspective of an intruder on SNS and the factors that affect intention. Privacy intrusion intentions are categorized into two types. The first type is intrusion of privacy by writing one's personal information without obtaining consent from the information owner;, whereas the other type pertains to intrusion of privacy by distributing one's personal information without obtaining consent from the information owner. A research model is developed based on motivation theory to identify how these factors affect these two types of privacy intrusion intentions on SNS. From the perspective of motivation theory, we draw one extrinsic motivational factor (response cost) and four intrinsic motivational factors, namely, perceived enjoyment, experience of being intruded on privacy, experience of invading someone's privacy, and punishment behavior. After analyzing 202survey data, we conclude that different factors affect these two types of privacy intrusion intention. However, no relationship was found between the two types of privacy intrusion intentions. One of the most interesting findings is that the experience of privacy intrusion is the most significant factor related to the two types of privacy intrusion intentions. The findings contribute to the literature on privacy by suggesting two types of privacy intrusion intentions on SNS and identifying their antecedents from the perspective of an intruder. Practitioners can also use the findings to develop SNS applications that can improve protection of user privacies and legitimize proper regulations relevant to online privacy.