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

• Korean Management Science Review
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• v.13 no.3
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• pp.115-128
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• 1996

System usage, the utilization of information technology(IT) by individuals, groups or organizations, is a core variable in IS research. Indeed, there is widespread agreement among researchers that system usage is the primary variable through which IT affects white collar performance because it is a requisite, albeit insufficient, for deriving the benefits of IT. Furthermore, system usage has a notable practical value for managers interested in evaluating the impact of IT. Despite the number of studies of targeted at explaining system usage, there are more areas which required for the verification of system usage. The purpose of this paper is to address, using the Technological Acceptance Model (TAM), the usage pattern of AHP toward the various task in group decision (GD). The result showed that, for the all of the task types, perceived usage (PU) has more effect than perceived ease-of-use(PEU) on the AHP usage. However, more researches are required to generalize the result of this study.

• Journal of Information Technology Services
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• v.9 no.2
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• pp.129-149
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• 2010

Although many organizations have implemented and used ERP systems for years, industry reports point out that the usage level does not reach their expectation. As a result, they do not enjoy the benefits from the investment into ERP systems. This study attempts to develop a research model and test the model empirically for identifying factors that affect the usage of an ERP system at a public organization. This study has classified potential factors into three groups of technical system characteristics (usability, usefulness), organizational support characteristics (operational support, education and training, evaluation and measurement), and user characteristics (organizational citizenship, self-efficacy). Then, a structural equation model has been established on the basis of previous literature and tested with empirical data. In summary, this study has found that self-efficacy, usefulness, and operational support have an effect on the ERP usage. Self-efficacy is the strongest factor; Usefulness is the second; and the third is operational support. On the contrary to the previous literature, this study has not found a significant effect of organizational citizenship on the usage. The result confirms that an organization can increase the ERP usage by improving the usefulness of an ERP system to some extent. However, to boost the usage further, the result implies that organizations need to strengthen the self-efficacy of their members by reinforcing operational support, providing education and training steadily, and establishing an evaluation mechanism in relation with the ERP usage.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.2
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• pp.181-188
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• 2012

The fee system on spectrum usage is a usage fee that is charged for using spectrum provided by a wireless tower, and is used for management and promotion of the waves. The current fee system for spectrum usage in South Korea has faced many problems, such as complex calculation for fees, unjustified charges, unfairness in cost sharing among providers, and general inefficiency of operation. This study focuses on comparison of fee systems for spectrum usage of South Korea and other foreign countries, extraction of the root causes and problems by case analyses, and recommendation for better solutions to make a reasonable fee system for spectrum usage. The result of this study can be used as a solution to render spectrum usage more effective.

• Journal of the military operations research society of Korea
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• v.17 no.2
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• pp.100-112
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• 1991

Software system is a hierarchical structure with collection of program units. Software system can import external packages globally or locally depending on the usage within a system. If the imported package is used globally, the soft-ware system can be influenced globally by any change of package and programmer's debugging time for the program maintenance will be greater. To solve these problems, it is desirable to use the imported package locally right on the usage point within the system. The model presented in this paper analyzed entity usage of package in structure of program, identified the usage level to obtain localization and provided information for restructure of the program to localize package usage. To obtain localization, it identified declared entities inside the imported package and analyzed the specification and body part of program unit to identify entities referenced from the imported package. The proposed model can be used to improve the maintainability of software system and contributed to reduction of programmer's debugging time in program maintenance.

• Journal of the Korea Society of Computer and Information
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• v.23 no.7
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• pp.39-47
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• 2018

This paper confirms the risk of using smartphone through the analysis of collected usage pattern and proposes the smartphone intervention system in risk situations. In order to check the risk of smartphone usage, we made information collecting application and collected smartphone usage pattern from 11 experiment participants for two months. By analyzing smartphone usage pattern, we confirmed that about 12% of smartphone usage is being used in driving, walking, and on the street. In addition, we analyzed the response rate of smartphone notification in risk situations and confirmed that user responds the smartphone notifications in real-time even in risk situations. Therefore, it is required to present a system that intervenes the use of smartphone in order to protect smartphone users in risk situations. In this paper, we classify risk situations of using smartphone. Also, the proposed smartphone intervention system is designed to periodically detect risk situations. In risk situations, smartphone function can be restricted according to user setting of smartphone. And smartphone can be used normally when safe situation is restored.

• Journal of Information Technology Applications and Management
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• v.20 no.3_spc
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• pp.259-278
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• 2013

At the center of the repeating evolution of IT industry, there is mobile computing in the ubiquitous environment, and the increasing usage of smart phones contribute more to its growth. P corporation co-developed with Google and implemented smart work system called SWP for the purpose of 'communication and collaboration with co-workers' beyond the general goal of increasing productivity and comfort of employees. The primary objective of this study is to objectively analyze the influence of the effect factor from end user's perspective and user satisfaction on SWP usage, intention for continuous usage and individual performance. To accomplish this research purpose, this study established research models and hypothesis by collective review about information system success models, and tested the research hypotheses using the structural equation modeling technique by data collected from 320 SWP system users of P corporation. Variations for system satisfaction evaluation standard are 1)SW system usage 2) intention for continuous usage 3) individual performance, effect variations are 1) social factors 2) technical factors 3) combination of social and technical factors. Therefore, this study is about influence factors on success of SWP system and how the factors impact the outcome. The test results of this research model is summarized as follows. Firstly, system user's satisfaction had positive impact on all three; SWP system usage, intention for continuous usage and individual performance. Secondly, social factor 'SWP control system' and technical factor 'information quality' and 'system quality' had positive impact on SWP system user satisfaction, while social factor 'shared value' and technical factor 'service quality' did not have significant effect on user satisfaction. Shared value however, had interactive effect with 'information quality' and 'service quality'. This study is expected to contribute to spread of academic research on smart work system by suggesting a model that can show important factors for corporation while explaining the successful implementation of SWP and its continuous usage.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.9
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• pp.2272-2279
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• 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 Information Technology Applications and Management
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• v.25 no.1
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• pp.105-124
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• 2018

The usage level of SMEs' win-win payment system is still lower than originally expected. In order to find the answer, we studied SMEs' usage performance of win-win payment system and analyzed the influencing factors of SMEs' usage level of win-win payment system. This study found that the more SMEs utilize win-win payment system, the higher they achieve the desired performance in finance, customer, process, learning and growth perspective. And this study found that usage level of win-win payment system is high in order of large corporations' pressure, government policy, and SMEs' readiness. This study is expected to improve win-win growth by increasing usage level of win-win payment system between large corporations and SMEs, while establishing desirable ecosystems.

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