• Journal of the Korean BIBLIA Society for library and Information Science
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• v.35 no.2
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• pp.65-84
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• 2024

As the number of Instagram users continues to grow, an increasing number of public libraries are establishing and maintaining accounts on the Instagram platform. The objective of this study is to classify and analyze the content of Instagram posts from 14 public libraries that are actively engaged on the platform. A classification of post types, divided into seven large, 16 medium, and 76 small categories, was employed to analyze the content of posts on each library's Instagram account from the account's inception to the end of December 2023. The analysis revealed that library posts focused on a few items, including book recommendations, library introductions and news, and event announcements of literary and arts programs. Program event announcements and reviews, book recommendations and reading programs were found to be highly correlated with user engagement and teen reading programs. Based on these findings, it is recommended that future Instagram posts should be more user-centered and interactive, and that libraries should actively promote their events on Instagram and other social media platforms.

• Journal of Korea Society of Industrial Information Systems
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• v.13 no.4
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• pp.35-55
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• 2008

The primary objective of his study is to investigate the factors influencing the implementation performance of K public corporation's ERP system from end user perspective. For this purpose, a research model and hypotheses are developed based on the literature reviews of IS Success Models. Data has been collected from 276 users who have used ERP system in K public corporation and the research hypotheses were tested using covariance structure model analysis. The hypotheses test results of this study are summarized as follows. Firstly, system quality has a positive influence on system usage, but information quality and service quality do not have significant influence on system usage. Secondly, all of three quality factors which are information quality, system quality, and service quality, have positive influence on user satisfaction. Lastly, both of system usage and user satisfaction have positive influence on user performance. From the analyses, this research ends with implications, as well as limitations and future research directions.

• Culinary science and hospitality research
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• v.16 no.3
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• pp.1-13
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• 2010

This study aims to clear the relation between perceived risk and user's attitude and behavior toward a food service event. To accomplish this, theoretical reviews and empirical analysis were jointly carried out. For the empirical analysis, a survey was conducted from April 3 to April 6, and total 291 copies of the questionnaire were used for the statistical analysis, SPSS 15.0 and LISREL 8.30. The results of the test of the hypotheses can be summarized as follows: First, the analysis shows that there is significant difference between the perceived risk and attitude of a food service event user. The perceived risk of food service event users causes effect that is contradictory in attitude. As users' perceived risk is less, attitude improved. Second, the analysis of the relationship between user's attitude and behavior intention showed that user's attitude affected behavior intention. Therefore, under these circumstances, there should be more concern in solving perceived risk among food service event users and a special program for promoting satisfaction with an event. And food service event director should make more efforts in nutritive value, organic food, time saving, etc.

• Asia pacific journal of information systems
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• v.23 no.3
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• pp.77-104
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• 2013

Mobile technology is accelerating innovative changes across all fields of our society as well as business environments. Especially, demands on mobile government (M-government) services have been increased gradually since e-government has improved national administration services dramatically. However, high-quality services which are acceptable to may users are not actually supplied because technical issues such as security on mobile e-government services have not solved and governance policy was not established yet. Previous studies show that most researches are devoted to technical ones or limited to theoretical exploratory study. As a result, developing useful guidelines which are practically and theoretically proved is one of the very important research issues. This study reviews the previous research works such as concept of mobile, e-government, M-government, technical trends of mobile, market situations, present status, and various case studies. And then we develop a research model with five factors, twenty four variables and seventy six measurement for measuring the influencing factors to adoption of M-government services. The model is composed of total 16 hypotheses, 22 variables, and 76 measurements. The model is analyzed by using statistical package SPSS (18.0) and AMOS (18.0) together with structural equation method based on 294 samples. The results show that the model is valid and there are statistically significant influence between ease of use and usefulness, ease of use and user's satisfactions, usefulness and intent of re-use, and user's satisfactions and intent of re-use, excepting usefulness and user's satisfaction, ease of use and intent of re-use did not affect significant influences. Especially, service quality, system quality, and relationship quality are identified as influencing factors to adaption of M-government service. The results are expected to provide a theoretical research framework which generate new research issues in M-government service area. It also can provide an useful guidelines to practical experts in successfully implementing M-government services. Further research directions are as follows. User's intents have to be studied in details by classifying users by individual, enterprise, and government as well as developing a new hypothetical model. Since M-government service is at the initial stage, longitudinal studies have to be conducted to trace the peoples' need in order to develop new high-quality mobile services.

• Journal of Korea Entertainment Industry Association
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• v.5 no.1
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• pp.65-72
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• 2011

The major purpose of this study is to investigate the influencing factors toward continuance intention in e-learning services. For this purpose, we introduced Post Acceptance Model(PAM) proposed by Bhattacherjee(2001) as basic analysis framework. Based on the relevant literature reviews, this study posits seven characteristics, that is, contents quality, interactivity, expectation confirmation, perceived ease of use, perceived usefulness, user satisfaction, and continuance intention as key variables to describe the post acceptance behavior in e-learning services. Data have been collected from users who have used e-learning services and the research model and hypotheses were tested through covariance structural model analysis. The results of this study are summarized as follows. First, contents quality, interactivity, and expectation confirmation have positive influence upon perceived usefulness. Second, contents quality, interactivity, expectation confirmation, and perceived ease of use have positive influence upon user satisfaction. Lastly, perceived usefulness have positive effect on the user satisfaction, and perceived usefulness and user satisfaction positively related to continuance intention in e-learning services. The findings have significant implications for e-learning service providers and academic researchers.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.273-285
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• 2024

With the active utilization of Online Judge (OJ) systems in the field of education, various studies utilizing learner data have emerged. This research proposes a problem recommendation based on a user-based collaborative filtering approach with learner data to support learners in their problem selection. Assistance in learners' problem selection within the OJ system is crucial for enhancing the effectiveness of education as it impacts the learning path. To achieve this, this system identifies learners with similar problem-solving tendencies and utilizes their problem-solving history. The proposed technique has been implemented on an OJ site in the fields of algorithms and programming, operated by the Chungbuk Education Research and Information Institute. The technique's service utility and usability were assessed through expert reviews using the Delphi technique. Additionally, it was piloted with site users, and an analysis of the ratio of correctness revealed approximately a 16% higher submission rate for recommended problems compared to the overall submissions. A survey targeting users who used the recommended problems yielded a 78% response rate, with the majority indicating that the feature was helpful. However, low selection rates of recommended problems and low response rates within the subset of users who used recommended problems highlight the need for future research focusing on improving accessibility, enhancing user feedback collection, and diversifying learner data analysis.

• Information Systems Review
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• v.23 no.3
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• pp.51-75
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• 2021

Global e-commerce websites offer personalized recommendation services to gain sustainable competitiveness. Existing studies have offered personalized recommendation services using quantitative preferences such as ratings. However, offering personalized recommendation services using only quantitative data has raised the problem of decreasing recommendation performance. For example, a user gave a five-star rating but wrote a review that the user was unsatisfied with hotel service and cleanliness. In such cases, has problems where quantitative and qualitative preferences are inconsistent. Recently, a growing number of studies have considered review data simultaneously to improve the limitations of existing personalized recommendation service studies. Therefore, in this study, we identify review and rating mismatches and build a new user profile to offer personalized recommendation services. To this end, we use deep learning algorithms such as CNN, LSTM, CNN + LSTM, which have been widely used in sentiment analysis studies. And extract sentiment features from reviews and compare with quantitative preferences. To evaluate the performance of the proposed methodology in this study, we collect user preference information using real-world hotel data from the world's largest travel platform TripAdvisor. Experiments show that the proposed methodology in this study outperforms the existing other methodologies, using only existing quantitative preferences.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.157-173
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• 2021

With the development of information technology, the amount of available information increases daily. However, having access to so much information makes it difficult for users to easily find the information they seek. Users want a visualized system that reduces information retrieval and learning time, saving them from personally reading and judging all available information. As a result, recommendation systems are an increasingly important technologies that are essential to the business. Collaborative filtering is used in various fields with excellent performance because recommendations are made based on similar user interests and preferences. However, limitations do exist. Sparsity occurs when user-item preference information is insufficient, and is the main limitation of collaborative filtering. The evaluation value of the user item matrix may be distorted by the data depending on the popularity of the product, or there may be new users who have not yet evaluated the value. The lack of historical data to identify consumer preferences is referred to as data sparsity, and various methods have been studied to address these problems. However, most attempts to solve the sparsity problem are not optimal because they can only be applied when additional data such as users' personal information, social networks, or characteristics of items are included. Another problem is that real-world score data are mostly biased to high scores, resulting in severe imbalances. One cause of this imbalance distribution is the purchasing bias, in which only users with high product ratings purchase products, so those with low ratings are less likely to purchase products and thus do not leave negative product reviews. Due to these characteristics, unlike most users' actual preferences, reviews by users who purchase products are more likely to be positive. Therefore, the actual rating data is over-learned in many classes with high incidence due to its biased characteristics, distorting the market. Applying collaborative filtering to these imbalanced data leads to poor recommendation performance due to excessive learning of biased classes. Traditional oversampling techniques to address this problem are likely to cause overfitting because they repeat the same data, which acts as noise in learning, reducing recommendation performance. In addition, pre-processing methods for most existing data imbalance problems are designed and used for binary classes. Binary class imbalance techniques are difficult to apply to multi-class problems because they cannot model multi-class problems, such as objects at cross-class boundaries or objects overlapping multiple classes. To solve this problem, research has been conducted to convert and apply multi-class problems to binary class problems. However, simplification of multi-class problems can cause potential classification errors when combined with the results of classifiers learned from other sub-problems, resulting in loss of important information about relationships beyond the selected items. Therefore, it is necessary to develop more effective methods to address multi-class imbalance problems. We propose a collaborative filtering model using CGAN to generate realistic virtual data to populate the empty user-item matrix. Conditional vector y identify distributions for minority classes and generate data reflecting their characteristics. Collaborative filtering then maximizes the performance of the recommendation system via hyperparameter tuning. This process should improve the accuracy of the model by addressing the sparsity problem of collaborative filtering implementations while mitigating data imbalances arising from real data. Our model has superior recommendation performance over existing oversampling techniques and existing real-world data with data sparsity. SMOTE, Borderline SMOTE, SVM-SMOTE, ADASYN, and GAN were used as comparative models and we demonstrate the highest prediction accuracy on the RMSE and MAE evaluation scales. Through this study, oversampling based on deep learning will be able to further refine the performance of recommendation systems using actual data and be used to build business recommendation systems.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.311-328
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• 2022

Because of the spread of smartphones due to the development of information and communication technology, online shopping mall services can be used on computers and mobile devices. As a result, the number of users using the online shopping mall service increases rapidly, and the types of products traded are also growing. Therefore, to maximize profits, companies need to provide information that may interest users. To this end, the recommendation system presents necessary information or products to the user based on the user's past behavioral data or behavioral purchase records. Representative overseas companies that currently provide recommendation services include Netflix, Amazon, and YouTube. These companies support users' purchase decisions by recommending products to users using ratings, purchase records, and clickstream data that users give to the items. In addition, users refer to the ratings left by other users about the product before buying a product. Most users tend to provide ratings only to products they are satisfied with, and the higher the rating, the higher the purchase intention. And recently, e-commerce sites have provided users with the ability to vote on whether product reviews are helpful. Through this, the user makes a purchase decision by referring to reviews and ratings of products judged to be beneficial. Therefore, in this study, the correlation between the product rating and the helpful information of the review is identified. The valuable data of the evaluation is reflected in the recommendation system to check the recommendation performance. In addition, we want to compare the results of skipping all the ratings in the traditional collaborative filtering technique with the recommended performance results that reflect only the 4 and 5 ratings. For this purpose, electronic product data collected from Amazon was used in this study, and the experimental results confirmed a correlation between ratings and review usefulness information. In addition, as a result of comparing the recommendation performance by reflecting all the ratings and only the 4 and 5 points in the recommendation system, the recommendation performance of remembering only the 4 and 5 points in the recommendation system was higher. In addition, as a result of reflecting review usefulness information in the recommendation system, it was confirmed that the more valuable the review, the higher the recommendation performance. Therefore, these experimental results are expected to improve the performance of personalized recommendation services in the future and provide implications for e-commerce sites.

• Asia pacific journal of information systems
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• v.18 no.1
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• pp.25-51
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• 2008

Researchers have been continuously interested in the adoption of information technology (IT) since it is of great importance to the information systems success and it is also an important stage to the success. Adoption alone, however, does not ensure information systems success because it does not necessarily lead to achieving organizational or individual objectives. When an organization or an individual decide to adopt certain information technologies, they have objectives to accomplish by using those technologies. Adoption itself is not the ultimate goal. The period after adoption is when users continue to use IT and intended objectives can be accomplished. Therefore, continued IT use in the post-adoption period accounts more for the accomplishment of the objectives and thus information systems success. Previous studies also suggest that continued IT use in the post-adoption period is one of the important factors to improve long-term productivity. Despite the importance there are few empirical studies focusing on the user behavior of continued IT use in the post-adoption period. User behavior in the post-adoption period is different from that in the pre-adoption period. According to the technology acceptance model, which explains well about the IT adoption, users decide to adopt IT assessing the usefulness and the ease of use. After adoption, users are exposed to new experiences and they shape new beliefs different from the thoughts they had before. Users come to make decisions based on their experiences of IT use whether they will continue to use it or not. Most theories about the user behaviors in the pre-adoption period are limited in describing them after adoption since they do not consider user's experiences of using the adopted IT and the beliefs formed by those experiences. Therefore, in this study, we explore user's experiences and beliefs in the post-adoption period and examine how they affect user's intention to continue to use IT. Through deep literature reviews on the construction of subjective beliefs by experiences, we draw three meaningful constructs which theoretically have great impacts on the continued use of IT: perceived habit, confirmation, and computer self-efficacy. Then, we examine the role of the subjective beliefs on the cognitive/affective attitudes and intention to continue to use that IT. We set up a research model and conducted survey research. Since IT use implies interactions among a user, IT, and a task, we carefully selected the sample of users using same/similar IT to perform same/similar tasks, to exclude unwanted influences of other factors than subjective beliefs on the IT use. We also considered that the sample of users were able to make decisions to continue to use IT volitionally or at least quasi-volitionally. For each construct, we used measurement items recognized for reliability and widely used in the previous research. We slightly modified some items proper to the research context and a pilot test was carried out for forty users of a portal service in a university. We performed a full-scale survey after verifying the reliability of the measurement. The results show that the intention to continue to use IT is strongly influenced by cognitive/affective attitudes, perceived habits, and computer self-efficacy. Confirmation affects the intention to continue indirectly through cognitive/affective attitudes. All the constructs representing the subjective beliefs built by the experiences of IT use have direct and/or indirect impacts on the intention of users. The results also show that the attitudes in the post-adoption period are formed, at least partly, by the experiences of IT use and newly shaped beliefs after adoption. The findings suggest that subjective beliefs built by the experiences have deep impacts on the continued use. The results of the study signify that while experiencing IT in the post-adoption period users form new beliefs, attitudes, and intentions which may be different from those of the pre-adoption period. The results of this study partly demonstrate that the beliefs shaped by the behaviors, those are the experiences of IT use, influence users' attitudes and intention. The results also suggest that behaviors (experiences) also change attitudes while attitudes shape behaviors. If we combine the findings of this study with the results of the previous research on IT adoption, we can propose a cycle of IT adoption and use where behavior shapes attitude, the attitude forms new behavior, and that behavior shapes new attitude. Different from the previous research, the study focused on the user experience after IT adoption and empirically demonstrated the strong influence of the subjective beliefs formed in the post-adoption period on the continued use. This partly confirms the differences between attitudes in the pre-adoption and in the post-adoption period. Users continuously change their attitudes and intentions while experiencing (using) IT. Therefore, to make users adopt IT and to make them use IT after adoption is a different problem. To encourage users to use IT after adoption, experiential variables such as perceived habit, confirmation, and computer self-efficacy should be managed properly.