• Proceedings of the Korea Association of Information Systems Conference
• /
• 1996.11a
• /
• pp.29-48
• /
• 1996

Previous studies on user training have largely focused on assessing models which describe the determinants of information technology usage or examined the effects of training on user satisfaction, productivity, performance, and so on. Scant research efforts have been made, however, to examine those effects of training by using theoretical models. This study presented a conceptual model to predict intention to use information technology and conducted an experiment to understand how training for computer skill acquisition affects primary variables of the model. The data were obtained from 32 student subjects of an experimental group and 31 students of a control group, and the information technology employed for this study was a university's electronic mail system. The study results revealed that attitude toward usage and perceived behavioral control helped to predict user intentions; outcome expectations were positively related to attitude toward usage; and self - efficacy and perceived behavioral control. The changes in those variables suggest more causal effects of user training than other survey studies.

• The Journal of Information Systems
• /
• v.5
• /
• pp.345-371
• /
• 1996

Previous studies on user training have largely focused on assessing models which describe the determinants of information technology usage or examined the effects of training on user satisfaction, productivity, performance, and so on. Scant research efforts have been made, however, to examine those effects of training by using theoretical models. This study presented a conceptual models to predict intention to use information technology and conducted an experiment to understand how training for computer skill acquisition affects primary variables of the model. The data were obtained from 32 student subjects of an experimental group and 31 students of a control group, and the information technology employed for this study was a university electronic mail system. The study results revealed that attitude toward usage and perceived behavioral control helped to predict user intentions ;; outcome expectations were positively related to attitude toward usage ; and self-efficacy was positively related to perceived behavioral control. The hands-on training for the experimental group led to increases in perceived ease of use, self-efficacy and perceived behavioral control. The changes in those variables suggest more causal effects of user training than other survey studies.

• Proceedings of the Korean Information Science Society Conference
• /
• 2003.04c
• /
• pp.386-388
• /
• 2003

We are developing a web-based movie recommender system that catches and reasons with user profiles and ratings to recommend movies. In the paper, we outline the current status of our implementation with particular emphasis on the mechanisms used to provide effective recommendations. Social recommender systems collect ratings of items from many individuals and use nearest-neighbor techniques to make recommendations to a user. However, these methods only depend on the ratings and ignore other useful information. Our primary concern is to provide an approach that can recommend the movies based on not only the user ratings but also the significant amount of other information that is available about the nature of each items - such as cast list or movie genre. We experimentally evaluate our approach and compare them to conventional social filtering, which suggests merits to our approach.

• Proceedings of the Korea Society for Industrial Systems Conference
• /
• 1996.10a
• /
• pp.29-48
• /
• 1996

Previous studies on user training have largely focused on assessing models which describe the determinants of information technology usage or examined theeffects of training, on user satisfaction, productivity, performance and so on. Scant research efforts have been made, however, to examine those effects of training by using theoretical models. This study presented a conceptural model to predict intention to use information technology and conducted an experimentto understand how training for computer skill acquisition affects primary variables of the model. The data were obtained from 32 student subjects of an experimental group and 31 students of a control group, and the information technology employed for this study was a university's electronic mail system. The study results revealed that attitude toward usage and perceived behavioral control helped to predict user intentions ; outcome expectations were positively related to attitude toward usage ; and self-efficacy was positively related to perceived behavioral control. Thd hands-on training for the experimental group led to increases in perceived ease of use, self-efficacy and perceived behaviroal control. The changes in those variables suggest more causal effects of user training than other survey studies.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.9 no.1
• /
• pp.19-26
• /
• 2010

In this paper, we propose an efficient decision rule in order to get better chance to detect the unused spectrum assigned to a licensed user and improve reliability of spectrum sensing performance. Each secondary user receives the signals from the licensed user. And the resulting signals input to an energy detector. Then, each sensing result is combined and used to make a decision whether the primary user is present at the licensed spectrum band or not. In order to make the reliable decision, we apply an efficient decision rule that is called as a majority rule in this paper. The simulation results show that spectrum sensing performance with the proposed decision rule is more reasonable and efficient than that with conventional decision rules.

• Journal of the Korea Society of Computer and Information
• /
• v.22 no.11
• /
• pp.97-104
• /
• 2017

Location prediction has been successfully utilized to provide high quality of location-based services to customers in many applications. In its usual form, the conventional type of location prediction is to predict future locations based on user's past movement history. However, as location prediction needs are expanded into much complicated cases, it becomes necessary quite frequently to make inference on the locations that target user visited in the past. Typical cases include the identification of locations that infectious disease carriers may have visited before, and crime suspects may have dropped by on a certain day at a specific time-band. Therefore, primary goal of this study is to predict locations that users visited in the past. Information used for this purpose include user's demographic information and movement histories. Data mining classifiers such as Bayesian network, neural network, support vector machine, decision tree were adopted to analyze 6868 contextual dataset and compare classifiers' performance. Results show that general Bayesian network is the most robust classifier.

• Current Optics and Photonics
• /
• v.1 no.5
• /
• pp.440-455
• /
• 2017

In this paper, we carry out a performance analysis of a two-user free-space optical (FSO) communication system with photodetector multiplexing, in which the two users are defined as the primary user (PU) and secondary user (SU). Unlike common single-user FSO systems, our photodetector multiplexing FSO system deploys a shared detector at the receiver and enables PU and SU to send their own data synchronously. We conduct the performance analysis of this FSO system for two cases: (1) in the absence of background radiation, and (2) in the presence of background radiation. Decision strategies for PU and SU are presented according to the two scenarios above. Exact and approximate conditional symbol-error probability (SEP) expressions for both PU and SU are derived, in an ideal channel with no fading. Average SEP expressions are also presented in the no-background-radiation scenario. Additionally, in some particular cases where the power ratio tends to 0.5 or 1, approximate SEP expressions are also obtained. Finally, numerical simulations are presented under different conditions, to support the performance analysis.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.15 no.1
• /
• pp.105-110
• /
• 2015

In cognitive radio networks, when the signal-to-noise ratio (SNR) of the link between a primary transmitter and receiver is sufficiently high, the primary transmission does not frequently demand cooperation because direct transmission of the primary communication is usually successful. Therefore, there are few opportunities for a secondary transmitter to cooperate with the primary user system in exchange for an opportunity to share the spectrum. This article proposes a scheme in which the secondary system can deliberately degrade the SNR of the primary transmission, making it so that the primary system needs cooperation from the secondary nodes, and thereby increases opportunities for spectrum sharing.

• Journal of Information Technology Applications and Management
• /
• v.31 no.1
• /
• pp.113-122
• /
• 2024

This comprehensive study delves into the analysis of user review data across various metaverse platforms, employing advanced text mining techniques such as TF-IDF and Word2Vec to gain insights into user perceptions. The primary objective is to uncover the factors that contribute to user satisfaction and dissatisfaction, thereby providing a nuanced understanding of user experiences in the metaverse. Through TF-IDF analysis, the research identifies key words and phrases frequently mentioned in user reviews, highlighting aspects that resonate positively with users, such as the ability to engage in creative activities and social interactions within these virtual environments. Word2Vec analysis further enriches this understanding by revealing the contextual relationships between words, offering a deeper insight into user sentiments and the specific features that enhance their engagement with the platforms. A significant finding of this study is the identification of common grievances among users, particularly related to the processes of refunds and login, which point to broader issues within payment systems and user interface designs across platforms. These insights are critical for developers and operators of metaverse platforms, suggesting a focused approach towards enhancing user experiences by amplifying positive aspects. The research underscores the importance of continuous improvement in user interface design and the transparency of payment systems to foster a loyal user base. By providing a comprehensive analysis of user reviews, this study offers valuable guidance for the strategic development and optimization of metaverse platforms, ensuring they remain responsive to user needs and continue to evolve as vibrant, engaging virtual environments.

• Journal of the Korean Society for Library and Information Science
• /
• v.58 no.2
• /
• pp.289-316
• /
• 2024

This study aimed to develop and validate a research model to investigate how the service quality of public libraries, where a 3-dimensional service quality model was applied, impacts user satisfaction and loyalty. We collected 353 valid responses through a survey targeting public library users across 21 locations, considering various regional and library-specific characteristics. Our analysis revealed that physical environment quality (PQ), interaction quality (IQ), and outcome quality (OQ) of service quality influence user satisfaction (US). Additionally, outcome quality (OQ) of service quality impacts user loyalty (UL), and user satisfaction (US) affects user loyalty (UL). We also found that quality improvement is a leading factor for user satisfaction, user satisfaction is the primary factor of user loyalty, and the perception of service quality directly and indirectly affects user satisfaction and loyalty. We concluded that continuous research should be actively conducted to respond to the evolving needs of public library users.