• Journal of Information Technology Applications and Management
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• v.21 no.4
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• pp.127-139
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• 2014

This study presents a logging method to trace the usage patterns of existing smartphone apps. The actual smartphone app itself, not a specially developed similar app with usage logging, would be used best for the experiment of observing the usage patterns. For this purpose, we used a method of injecting logging codes into existing smartphone app. Using this method, we conducted an experiment to trace usage patterns of a commercial IPTV app, and found that the method is very useful for acquiring detail usage log without influencing participants.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.831-840
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• 2001

In this study, We introduce the process of web usage mining, which has lately attracted considerable attention with the fast diffusion of world wide web, and explain the web log data, which Is the main subject of web usage mining. Also, we illustrate some real examples of analysis for web log data and look into practical application of web usage mining for eCRM.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.1129-1136
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• 2020

This study investigates the factors influencing the usage of PSAU Mobile Application by the students at Prince Sattam bin Abdulaziz University (PSAU). This study used Technology Acceptance Model (TAM) as a framework to predict the factors influencing 67 students' usage of PSAU M-Application. The results of this study were consistent with the TAM framework in predicting the actual usage of the PSAU mobile application for academic activities. The results indicated a significantly positive association of the perceived ease-to-use with perceived usefulness, significantly positive associations of the perceived ease-to-use and the perceived usefulness with the attitude toward the usage of the PSAU's mobile application, a significantly positive association of the perceived usefulness with the behavioral intention toward the usage of the PSAU's mobile application, a significantly positive association of the students' attitude toward the usage of the PSAU's mobile application with the behavioral intention of using the application, and a significantly positive association of the behavioral intention with the actual usage of the PSAU's mobile application. The results of this study should be useful to educational policymakers at PSAU University and elsewhere, as there is an opportunity to enhance the existing technology or adopt new technologies for academic activities.

• Journal of Information Technology Applications and Management
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• v.30 no.5
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• pp.41-57
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• 2023

This paper aims to analyse the factors influencing the implementation of smart factories and their performance after implementation, using the grounded theory analysis method based on interview data. The research subjects were 21 companies that were selected by the Smart Manufacturing Innovation Promotion Group under the SME Technology Information Promotion Agency in 2020-2021 as the best case smart factory implementation companies, and introduced the intermediate stage 1 or above. A total of 87 concepts were generated as a result of the analysis. We were able to classify them into 16 detailed categories, and finally derived six broad categories. These six categories are "motivation for adoption", "adoption context", "adoption level", "technology adoption", "usage effect" and "management effect". As a result of the overall structure analysis, it was found that the adoption level of smart factory is determined by the adoption motivation, the IT technology experience affects the adoption level, the adoption level determines the usage and usage satisfaction, internal and external training affects the usage and usage satisfaction, and the performance or results obtained by the usage and usage are reduced defect rate, improved delivery rate and improved productivity. This study was able to derive detailed variables of environmental factors and technical characteristics that affect the adoption of smart factories, and explore the effects on the usage effects and management effects according to the level of adoption. Through this study, it is possible to suggest the direction of adoption according to the characteristics of SMEs that want to adopt smart factories.

• Journal of the Korean Society of School Health
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• v.31 no.3
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• pp.135-145
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• 2018

Purpose: The study aimed to investigate the factors associated with long hours of smartphone usage and to identify the characteristics of the high-risk group among female middle school students in South Korea. Methods: The study analyzed the data of 13,648 female middle school students using their own smartphone extracted from the 13th Youth Health Behavior Online Survey (2017). The factors related to using smartphones for a long time was analyzed by binomial logistic regression. The characteristics of the high-risk group was defined by a decision tree analysis. Results: The average hours spent on smartphone usage was 269.54 minutes per day. The significant factors associated with the long hours of smartphone usage were grade, living with parents, perceived household economic status, perceived academic achievement, stress, sadness and hopelessness, the main purpose of smartphone usage, drinking, body mass index, breakfast, and satisfaction with sleep quality. The subjects showing low academic performance and having breakfast four times a week or less were more likely to use their smartphone for a long time. Conclusion: Based on the results of the research, we need to establish intervention strategies focusing on the factors influencing long-time usage of smartphone. Particularly, the subjects who show poor academic performance and skip breakfast frequently should be considered as the high-risk group for spending long hours on smartphone usage.

• Korean Journal of Computational Design and Engineering
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• v.18 no.6
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• pp.451-460
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• 2013

This paper presents the necessary elements and data flow in developing a monitoring system of energy usage for apartment houses with a Smart TV. Energy consumption data in each home are collected and analyzed in the HUB station by way of measuring instruments. And the amount of energy usage, such as electricity, gas, hot water, heating, water and other utilities are displayed through the Smart TV application. Energy consumption Database in the HUB station are processed and displayed in the browser of a Smart TV through XML, JAVASCRIPT and Flash. Smart TV users can get the energy consumption status through the energy consumption analysis display of the Smart TV application and improve the energy efficiency by comparing the usage patterns with neighboring houses. And the application display energy usage information, consumption ranking, rates to user as well. Furthermore, usage of last month or year can be compared to help to reduce the energy usage. The proposed system can provide the information about the amount of energy use to be reduced and the warning on the waste of energy.

• Knowledge Management Research
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• v.20 no.4
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• pp.169-183
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• 2019

The growth of mobile commerce (m-commerce) has been accelerated around the world. Why do e-retailers have to put a great deal of effort for the distribution of their mobile apps? The literature has paid little attention to the influence of the introduction of an e-commerce app on shopping behaviors of consumers. By analyzing the dataset of 2,342 users in Korea, this study aims to broaden our understanding of mobile shopping app usage across competing e-retailers and different channels. We found that a user's prior usage of a specific e-commerce mobile app increases her subsequent usage of its website through a mobile web browser. Thus, mobile apps do not cannibalize the mobile web channel, and there could be a complementary relationship. We also found that a user's usage of competitors' apps is positively associated with her subsequent usage of a specific e-commerce app. Because many consumers search products and compare prices across multiple e-retailers, having a mobile app helps an e-retailer be exposed to more potential consumers. This study is among the first to study the role of mobile apps in e-commerce by showing the dynamics of cross-channel and cross-vendor usage by a user.

• Journal of Soil and Groundwater Environment
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• v.22 no.5
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• pp.13-22
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• 2017

Accurate assessment of agricultural groundwater usage is an essential task to cope with drought that occurs irregularly in time and location. In this study, the agricultural groundwater usage was calculated in nationwide public wells (1,386 bedrock wells) during 5-year period (2010-2014) by using electric power consumption and well specification data. National average of agricultural groundwater usage per each well was estimated as $66.2m^3/day$, corresponding to 21.6% of total permitted volume of groundwater in each well. Chungcheong Nam-do had the highest usage with 38-55.6%. The value increased to 58.1% when the total permitted volume was based upon the supply standard against drought, and the value reached 100% in Chungcheong Nam-do. In Ganghwa distirct that suffered from severe drought in recent years, the average groundwater usage was 61.4%. In 2014, when the drought was the most severe with 45% precipitation of the average annual rainfall, the nationwide usage was turned out to be 25.6%, indicating about 4% higher than average agricultural groundwater usage 21.6%. Therefore, the quantitative assessment of groundwater usage in this study signifies that adequate use of groundwater is crucial to cope with agricultural drought.

• The Journal of Bigdata
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• v.1 no.1
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• pp.33-39
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• 2016

Recently, predicting personality with the help of smartphone usage becomes very interesting and attention grabbing topic in the field of research. At present there are some approaches towards detecting a user's personality which uses the smartphones usage data, such as call detail records (CDRs), the usage of short message services (SMSs) and the usage of social networking services application. In this paper, we focus on the assessing the correlation between MBTI based user personality and the smartphone usage data. We used $Na{\ddot{i}}ve$ Bayes and SVM classifier for classifying user personalities by extracting some features from smartphone usage data. From analysis it is observed that, among all extracted features facebook usage log working as the best feature for classification of introverts and extraverts; and SVM classifier works well as compared to $Na{\ddot{i}}ve$ Bayes.

• Journal of the Korean Society of Clothing and Textiles
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• v.39 no.3
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• pp.433-445
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• 2015

This study examines factors that influence consumer behavior regarding the usage intentions and repurchasing behavior of counterfeits. Based on the Theory of Planned Behavior by Ajzen (1991), this study looks at the impact of attitude, subjective norm, perceived behavioral control, as well as the moral norm of consumers about intentions to use and repurchase counterfeits. An online survey was conducted on males and females over the age of 20 with previous experiences of purchasing counterfeits; subsequently, 209 responses were collected and analyzed. The covariance structure model was used to analyze the data in order to measure the factors that influence the usage of counterfeits and repurchasing behavior. The results show that attitude, subjective norm, and perceived behavioral control have significantly positive effects on usage intentions, while moral norm has a significantly negative effect on usage intentions. Usage intentions also have a significantly positive effect on repurchasing behavior and confirmed that usage intentions functions as the mediator. The variables that influence usage intentions are attitude, perceived behavioral control, moral norm, and subjective norm, in descending order. Consumer attitudes towards counterfeits is a major factor that influences usage intentions. Therefore, ethical problems should be emphasized to encourage a negative attitude towards counterfeits. This study identifies an important aspect to show that usage intentions toward counterfeits, and not purchasing intentions, influences repurchasing behavior. Findings are significant in that they reveal the role of the moral norm variable added to the TPB model.