• Asia pacific journal of information systems
• /
• v.24 no.4
• /
• pp.611-635
• /
• 2014

Information systems (IS) have become ubiquitous and changed every aspect of how people live their lives. While some IS have been successfully adopted and widely used, others have failed to be adopted and crowded out in spite of remarkable progress in technologies. Both the technology acceptance model (TAM) and the IS Success Model (ISSM), among many others, have contributed to explain the reasons of success as well as failure in IS adoption and usage. While the TAM suggests that intention to use and perceived usefulness lead to actual IS usage, the ISSM indicates that information quality, system quality, and service quality affect IS usage and user satisfaction. Upon literature review, however, we found a significant void in theoretical development and its applications that employ either of the two models, and we raise research questions. First of all, in spite of the causal relationship between intention to use and actual usage, in most previous studies, only intention to use was employed as a dependent variable without overt explaining its relationship with actual usage. Moreover, even in a few studies that employed actual IS usage as a dependent variable, the degree of actual usage was measured based on users' perceptual responses to survey questionnaires. However, the measurement of actual usage based on survey responses might not be 'actual' usage in a strict sense that responders' perception may be distorted due to their selective perceptions or stereotypes. By the same token, the degree of system quality that IS users perceive might not be 'real' quality as well. This study seeks to fill this void by measuring the variables of actual usage and system quality using 'fact' data such as system logs and specifications of users' information and communications technology (ICT) devices. More specifically, we propose an integrated research model that bring together the TAM and the ISSM. The integrated model is composed of both the variables that are to be measured using fact as well as survey data. By employing the integrated model, we expect to reveal the difference between real and perceived degree of system quality, and to investigate the relationship between the perception-based measure of intention to use and the fact-based measure of actual usage. Furthermore, we also aim to add empirical findings on the general research question: what factors influence actual IS usage and how? In order to address the research question and to examine the research model, we selected a mobile campus application (MCA). We collected both fact data and survey data. For fact data, we retrieved them from the system logs such information as menu usage counts, user's device performance, display size, and operating system revision version number. At the same time, we conducted a survey among university students who use an MCA, and collected 180 valid responses. A partial least square (PLS) method was employed to validate our research model. Among nine hypotheses developed, we found five were supported while four were not. In detail, the relationships between (1) perceived system quality and perceived usefulness, (2) perceived system quality and perceived intention to use, (3) perceived usefulness and perceived intention to use, (4) quality of device platform and actual IS usage, and (5) perceived intention to use and actual IS usage were found to be significant. In comparison, the relationships between (1) quality of device platform and perceived system quality, (2) quality of device platform and perceived usefulness, (3) quality of device platform and perceived intention to use, and (4) perceived system quality and actual IS usage were not significant. The results of the study reveal notable differences from those of previous studies. First, although perceived intention to use shows a positive effect on actual IS usage, its explanatory power is very weak ($R^2$=0.064). Second, fact-based system quality (quality of user's device platform) shows a direct impact on actual IS usage without the mediating role of intention to use. Lastly, the relationships between perceived system quality (perception-based system quality) and other constructs show completely different results from those between quality of device platform (fact-based system quality) and other constructs. In the post-hoc analysis, IS users' past behavior was additionally included in the research model to further investigate the cause of such a low explanatory power of actual IS usage. The results show that past IS usage has a strong positive effect on current IS usage while intention to use does not have, implying that IS usage has already become a habitual behavior. This study provides the following several implications. First, we verify that fact-based data (i.e., system logs of real usage records) are more likely to reflect IS users' actual usage than perception-based data. In addition, by identifying the direct impact of quality of device platform on actual IS usage (without any mediating roles of attitude or intention), this study triggers further research on other potential factors that may directly influence actual IS usage. Furthermore, the results of the study provide practical strategic implications that organizations equipped with high-quality systems may directly expect high level of system usage.

• Journal of Digital Convergence
• /
• v.7 no.4
• /
• pp.43-56
• /
• 2009

The concept causing all the fuss is "the utility computing or the usage-based IT service", which now represents the future for IT asset in all aspects of the way they work in business, the commercial and public sector. The core of "utility computing or usage-based IT service" is changing the IT assert from "ownership" to "borrowing", which enables managers to get greater utilization of data-centre resources at lower operating costs. This trend is spreaded in public sector centering the Governmental Internet data Center of Korea(NCIA). So, it has need to make an usage-based IT service model that is suitable for public sector. In this paper, we propose the usage-based IT service model that is composed of IT service framework, service pricing model and IT service architecture.

• The Research Journal of the Costume Culture
• /
• v.31 no.4
• /
• pp.489-503
• /
• 2023

This study utilized the unified theory of acceptance and use of technology 2 (UTAUT2) to examine usage intentions associated with virtual fitting services. Six independent variables were examined: performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, and habit. The study collected responses from 445 participants who had utilized virtual fitting services. Regarding factors related to usage intentions associated with these services, performance expectancy and social influence were found to significantly influence the usage intentions associated with photo-based virtual fitting services. Furthermore, performance expectancy, social influence, and habit significantly influenced the usage intention of avatar-based virtual fitting services. This suggests that higher levels of performance expectancy and social influence positively impact the usage intentions associated with both types of virtual fitting services, while habit influences only avatar-based virtual fitting services. Moreover, the findings confirm that effort expectancy, facilitating conditions, and hedonic motivation from UTAUT2 do not significantly influence usage intentions associated with virtual fitting services. By analyzing factors influencing potential customers' virtual fitting service usage intentions, this study can suggest effective strategies to increase usage intentions for companies providing virtual fitting services. Additionally, these findings can be utilized in the formulation of virtual fitting service marketing strategies.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.65 no.1
• /
• pp.16-22
• /
• 2016

This paper addresses the load usage scheduling in the HEMS for residential power consumers. The HEMS would lead the residential users to change their power usage, so as to minimize the cost in response to external information such as a time-varying electricity price, the outside temperature. However, there may be a consumer's inconvenience in the change of the power usage. In order to improve this, it is required to understand the pattern of load usage according to the external information. Therefore, this paper suggests a methodology to model the load usage pattern, which classifies home appliances according to external information affecting the load usage and models the usage pattern for each appliance based on a copula function representing the correlation between variables. The modeled pattern would be reflected as a constraint condition for an optimal load usage scheduling problem in HEMS. To explain an application of the methodology, a case study is performed on an electrical water heater (EWH) and an optimal load usage scheduling for EHW is performed based on the branch-and-bound method. From the case study, it is shown that the load usage pattern can contribute to an efficient power consumption.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.18 no.9
• /
• pp.2272-2279
• /
• 2014

Mobile applications are software systems running on handheld devices, such as smartphones, PDAs, tablets and so on. The market of mobile application has rapidly expanded in the past few years. In this paper, we present a novel approach to track smartphone application usage from a event logs on the mobile device and analyzed both on client system and usage analysis server. We implemented our client system on Android device based usage analytics platform. Based on the analysis server system, we obtained over 47,000 user base, and we get the user's app usage informations on realtime. In this paper, we describe a large scale deployment-based research for a smartphone usage patterns and usage information visualization techniques.

• Journal of Internet Computing and Services
• /
• v.24 no.2
• /
• pp.57-66
• /
• 2023

Currently, a new energy system is emerging that implements consumption reduction by improving energy efficiency. Accordingly, as smart grids spread, the rate system by timing is expanding. The rate system by timing is a rate system that applies different rates by season/hour to pay according to usage. In this study, external factors such as temperature/day/time/season are considered and the time series prediction model, LSTM, is used to predict energy power usage data. Based on this energy usage prediction model, energy usage charges are reduced by analyzing usage patterns for each device and transferring power energy from the maximum load time to the light load time. In order to analyze the usage pattern for each device, a clustering technique is used to learn and classify the usage pattern of the device by time. In summary, this study predicts usage and usage fees based on the user's power data usage, analyzes usage patterns by device, and provides customized demand transfer services based on analysis, resulting in cost reduction for users.

• Journal of Computing Science and Engineering
• /
• v.5 no.4
• /
• pp.338-345
• /
• 2011

Nowadays mobile devices are used for various applications such as making voice/video calls, browsing the Internet, listening to music etc. The average battery consumption of each of these activities and the length of time a user spends on each one determines the battery lifetime of a mobile device. Previous methods have provided predictions of battery lifetime using a static battery consumption rate that does not consider user characteristics. This paper proposes an approach to predict a mobile device's available battery lifetime based on usage patterns. Because every user has a different pattern of voice calls, data communication, and video call usage, we can use such usage patterns for personalized prediction of battery lifetime. Firstly, we define one or more states that affect battery consumption. Then, we record time-series log data related to battery consumption and the use time of each state. We calculate the average battery consumption rate for each state and determine the usage pattern based on the time-series data. Finally, we predict the available battery time based on the average battery consumption rate for each state and the usage pattern. We also present the experimental trials used to validate our approach in the real world.

• Journal of the Korea Society of Computer and Information
• /
• v.20 no.7
• /
• pp.77-84
• /
• 2015

The study is to investigate which features are perceived by Social Network Service(SNS) users as the most required one to further boost the usage of service, and to examine the perception of these features of SNS sites varies according to their demographic and service usage characteristics. The study also is to suggest a few of research propositions on the relationships between required features of SNS sites and characteristics of SNS users, based on statistical analyses. To accomplish these research purposes, the study defined characteristics of SNS users including demographic(gender, age) and service usage one(start time of service usage, service usage place), and required features of SNS sites(system, service, information, emotion) based on the literature review of SNS. The results show, based on the statistical analyses using survey questionnaire on Korean and Chinese SNS users, that there are differences in perception of required features of SNS sites among the respondents grouped by age, start time of service usage, service usage place. Finally, the study proposed three research propositions, based on the analysis result, that could be used in SNS related researches in the future.

• Journal of Korean Society of Occupational and Environmental Hygiene
• /
• v.26 no.3
• /
• pp.317-323
• /
• 2016

Objectives: This study was performed to estimate domestic usage amount of insecticide for fruit cultivation to suggest job-exposure matrix(JEM) regarding farmers treating agricultural insecticide. Materials and Methods: The domestic usage amount of insecticide for fruit cultivation was investigated according to two research methods. The former method is to use agricultural pesticides published annually from Korea Crop Protection Association(KCPA) and the latter method is to apply cultivation area of fruit provided officially from Statistics Korea(SK). Results: It was found that its domestic usage amount has decreased gradually from the first sale to presence(2012). However, there is a significant difference of annual usage trend of insecticide for fruit cultivation between shipments and estimation. The annual usage trends of insecticide for fruit cultivation based on regional classification were different from those based on total aspect. Conclusions: The region which used insecticide for fruit cultivation the most in Korea was as follows: Gyeonsang-do, followed by Jeolla-do, Chungcheong-do, Jeju-do, Seoul/Gyeonggi-do and Gangwon-do. An averaged ratio of usage amounts of insecticide based on shipments and those based on estimation by cultivation area was $980{\pm}186%$, which means that usage amounts of insecticide estimated by shipments are ten times higher than those based on cultivation area.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2014.11a
• /
• pp.1136-1138
• /
• 2014

Recently, predicting personality with the help of smartphone usage is become 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 predicting user attitude based on MBTI theory by using their smartphone usage data. We used Naïve Bayes and SVM classifier for classifying user personalities by extracting some features from smartphone usage data. From analysis it is observed that, SVM classifier works well as compared to Naïve Bayes.