• Journal of Korea Society of Industrial Information Systems
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• v.14 no.2
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• pp.1-7
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• 2009

This paper proposes a context gathering system that obtains user's environment information from sensor and generates user's context profile. To design the system, we classify context and design context model based on traditional context-aware computing. The proposed system supports contexts adaptation service by gathering user's environment characteristics and biological characteristics and generating user profile.

• Asia pacific journal of information systems
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• v.30 no.3
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• pp.547-567
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• 2020

As the complexity of managing online personal information is increasing and data breach incidents frequently occur, online users feel a loss of control over their privacy. Such a situation leads to their cynical attitudes towards privacy protection, called privacy cynicism. This study aims to examine the role of privacy cynicism in online users' privacy behaviors. Data were gathered from a survey that 281 people participated in and were analyzed with covariance-based structural equation modeling. The findings of this study reveal that privacy cynicism has not only a direct influence on disclosure intention but also moderates an effect of privacy concerns on the intention. The analytical results also indicate that there is a nonlinear effect of privacy cynicism on the outcome variable. This study developed the concept of privacy cynicism—a phenomenon that significantly affects online privacy behavior but has been rarely examined. The study is an initial research into the nature and implications of privacy cynicism and furthermore clarified its role by the nonlinear relationship between privacy cynicism and the willingness to disclose personal information.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.30-45
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• 2024

This study introduces an effective method for predicting individual local tax delinquencies using prevalent machine learning and deep learning algorithms. The evaluation of credit risk holds great significance in the financial realm, impacting both companies and individuals. While credit risk prediction has been explored using statistical and machine learning techniques, their application to tax arrears prediction remains underexplored. We forecast individual local tax defaults in Republic of Korea using machine and deep learning algorithms, including convolutional neural networks (CNN), long short-term memory (LSTM), and sequence-to-sequence (seq2seq). Our model incorporates diverse credit and public information like loan history, delinquency records, credit card usage, and public taxation data, offering richer insights than prior studies. The results highlight the superior predictive accuracy of the CNN model. Anticipating local tax arrears more effectively could lead to efficient allocation of administrative resources. By leveraging advanced machine learning, this research offers a promising avenue for refining tax collection strategies and resource management.

• Asia pacific journal of information systems
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• v.29 no.2
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• pp.181-202
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• 2019

The purpose of this study is to explore the effect of different types of simultaneous presentation (i.e., reviewer information, textual and visual content, and similarity between textual-visual contents) on review usefulness and review enjoyment in online restaurant reviews (ORRs), as they are interrelated yet have rarely been examined together in previous research. By using Latent Dirichlet Allocation (LDA) topic modeling and state-of-the-art machine learning (ML) methodologies, we found that review readability in textual content and salient objects in images in visual content have a significant impact on both review usefulness and review enjoyment. Moreover, similarity between textual-visual contents was found to be a major factor in determining review usefulness but not review enjoyment. As for reviewer information, reputation, expertise, and location of residence, these were found to be significantly related to review enjoyment. This study contributes to the body of knowledge on ORRs and provides valuable implications for general users and managers in the hospitality and tourism industries.

• Asia pacific journal of information systems
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• v.27 no.3
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• pp.176-190
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• 2017

This study aimed to investigate the roles of three health-oriented personal factors-health technology innovativeness (HTI), health consciousness (HC), and health information orientation (HIO)-in determining Koreans' perceptions about telemedicine. Based on an extended version of the technology acceptance model (TAM), two perceptual components-perceived usefulness (PU) and perceived ease of use (PEOU)-of telemedicine were considered for this investigation. Data from 699 usable surveys were analyzed using path analysis. The results from the path analysis indicated that while HTI and HC had no or limited effects on the PU and PEOU of telemedicine, the effects of HIO on those two perceptual components of telemedicine were statistically significant. Moreover, the results from the path analysis showed that there were significant regional differences in the effects of HTI and HC on the PU and PEOU of telemedicine. In general, these effects were greater among the metropolitan residents than they were among the rural residents.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.2
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• pp.109-114
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• 2002

E-commerce customers can reduce purchasing cost by the help of comparison shopping agents that collect price information of products in the shopping malls. However, user expects a software agent that can recommend product information satisfying various purchase conditions besides price. In this paper, we present a MAS (multi-agent system) which retrieves and recommends book information suitable for various user needs to realize an agent-based E-Commerce. We implemented and tested our MAS to help on-line bookstore customers. From the results, we could provide E-commerce customers various book purchase conditions for several online bookstores in real-time.

• The Journal of Information Systems
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• v.29 no.3
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• pp.119-144
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• 2020

Purpose Real estate usually takes charge of the highest proportion of physical properties which individual, organizations, and government hold and instability of real estate market affects the economic condition seriously for each economic subject. Consequently, practices for predicting the real estate market have attention for various reasons, such as financial investment, administrative convenience, and wealth management. Additionally, development of machine learning algorithms and computing hardware enhances the expectation for more precise and useful prediction models in real estate market. Design/methodology/approach In response to the demand, this paper aims to provide a framework for forecasting the real estate market with machine learning algorithms. The framework consists of demonstrating the prediction efficiency of each machine learning algorithm, interpreting the interior feature effects of prediction model with a state-of-art algorithm, LIME(Local Interpretable Model-agnostic Explanation), and comparing the results in different cities. Findings This research could not only enhance the academic base for information system and real estate fields, but also resolve information asymmetry on real estate market among economic subjects. This research revealed that macroeconomic indicators, real estate-related indicators, and Google Trends search indexes can predict real-estate prices quite well.

• The Journal of Information Systems
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• v.18 no.3
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• pp.27-46
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• 2009

Korean society frenzy about ubiquitous computing is in a rapid change into so called 'Nomadic' information environment. In many previous studies, a number of intrinsic and extrinsic motivation factors were empirically found to affect users' continuance to use mobile hand-held devices. However, the role of device attachment, a new intrinsic motivation factor with which users are known to care about their own mobile devices personally, in determining users' post-adoption behavior was not explored yet to the full scale. To fill the research void like this, this study proposes a new research model in which device attachment as well as perceived value are positively linked to satisfaction and continuance to use. The statistical results obtained by applying PLS to the valid 137 questionnaires showed that the device attachment has stronger positive influence on satisfaction and continuance to use than the perceived value. Therefore, a practical implication is suggested thai the mobile devices need 10 be designed in a way of arousing users' device attachment more strongly.

• Asia pacific journal of information systems
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• v.15 no.2
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• pp.1-21
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• 2005

This study is an empirical study about the effects of perceived risks and perceived benefits on purchase intention in mobile commerce. Perceived risks and perceived benefits are proposed as mediators that make a significant impact on purchase intention in mobile commerce. Also, this study compared the direct effect of perceived risks, perceived benefits and consumer subjective knowledge to purchase intention. As antecedents of perceived risks and perceived benefits, subjective knowledge, mobile Internet familiarity, credibility for the purchase and provided information level are proposed and verified. Results from this study are as follows: First, the effect of perceived risks to purchase intention is not significant whereas that of perceived benefits is significant. Second, this study revealed that mobile Internet familiarity, credibility for the purchase and information level are significantly related to mobile purchase intention through perceived benefit. Third, subjective knowledge makes a significant impact on purchase intention directly not mediated by perceived risk or perceived benefit. Based on these results, managerial implications for mobile commerce vitalization and marketing strategy are discussed. Finally, limitation for this research and further research issues are suggested.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.2
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• pp.133-137
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• 2003

Various bioinformatics tools for biological data processing have been developed and most of them are available in public. Most bioinformatics works are carried out by a composite application of those tools. Several integration approaches have been proposed for easy use of the tools. This paper proposes a new multi agent system to integrate bioinformatics tools in the perspective of workflow since the composite applications of tools can be regarded as workflows. For the easy integration, the proposed system employs wrapper agents for existing tools, uses XML-based messages in the inter-agent communication, and agents are supposed to extract necessary information from the received messages. This allows new tools to be easily added on the integration framework. The proposed method allows various control structures in workflow definition and provides the progress monitoring capability of the on-going workflows. In particular, agents in this system have the rule-based architecture which allows the defined rule set to be a special role agent. This feature provides fast and flexible agent development to aid in managing the complexity of bioinformatics application. This system has been partially implemented and has been proven to be a viable implementation for workflow-based bioinformatics tool integration.