• Management & Information Systems Review
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• v.25
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• pp.371-386
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• 2008

In this study, the hypothesized model of internet user satisfaction in e-commerce environment is developed and the relationships among constructs in the model are examined. A Confirmatory factor analysis is employed to analyze the relationships between the multiple dependent variables and independent variables.

• Journal of Information Processing Systems
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• v.6 no.2
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• pp.185-196
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• 2010

This paper proposes a personal information protection model that allows a user to regulate his or her own personal information and privacy protection policies to receive services provided by a service provider without having to reveal personal information in a way that the user is opposed to. When the user needs to receive a service that requires personal information, the user will only reveal personal information that they find acceptable and for uses that they agree with. Users receive desired services from the service provider only when there is agreement between the user's and the service provider's security policies. Moreover, the proposed model utilizes a mobile agent that is transmitted from the user's personal space, providing the user with complete control over their privacy protection. In addition, the mobile agent is itself a self-destructing program that eliminates the possibility of personal information being leaked. The mobile agent described in this paper allows users to truly control access to their personal information.

• Journal of Information Technology Applications and Management
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• v.19 no.4
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• pp.41-59
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• 2012

This study examines the roles of user resistance and social influences on the acceptance and continuous usage of smartphones at different stages of adoption. The respondents were classified into three groups according to their innovation adoption stage : non-user group, the potential user group and the trial user group. Theories relevant to user resistance, social influences including normative social influences and informational social influences, as well as user adoption and continuance behavior were reviewed and integrated into our research model. In order to verify the proposed structured equation model, we conducted an online survey by targeting mobile phone users and collected data to be analyzed through a partial least squares (PLS) test. This study tested whether there exists differences in the effects of user resistance and different types of social influence on user's adoption or continuance intetion among these three groups. The results showed that user resistance exists in all adopter groups and that it has significant negative influences on intention to use a smartphone. The findings also revealed that user resistance can be enhanced or resolved by two types of social influence; informational social influence resolves user resistance regardless of the adopter category, while normative social influence enhances the user resistance of potential users. Furthermore, the findings show that social influence regardless of the type positively affects user intention. Several theoretic and practical implications pertaining to the results are discussed.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.1
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• pp.75-94
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• 2005

Research in the decision sciences has continued to develop a variety of mathematical models as well as software tools supporting corporate decision-making. Yet. in spite of their potential usefulness, the models are little used in real-world decision making since the model solution processes are too complex for ordinary users to get accustomed. This paper proposes an intelligent and flexible model-solver integration framework that enables the user to solve decision problems using multiple models and solvers without having precise knowledge of the model-solution processes. Specifically, for intuitive model-solution, the framework enables a decision support system to suggest the compatible solvers of a model autonomously without direct user intervention and to solve the model by matching the model and solver parameters intelligently without any serious conflicts. Thus, the framework would improve the productivity of institutional model solving tasks by relieving the user from the burden of leaning model and solver semantics requiring considerable time and efforts.

• Management & Information Systems Review
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• v.3
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• pp.47-76
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• 1999

Management Information System(MIS) User satisfaction, which was introduced at the earlier 1990's, has become a core way of strategic method. But domestic companies have little experience toward this concept and the methodological knowledge was not cumulated yet. So the definition of User satisfaction, as well as the validity and objectivity toward the estimation, is being under discussion In this report we will contain the contents as follows, 1) investigate limitations of exposed theories about Methodology, 2) look into the situations and handicaps of User satisfaction estimation which is being used in practical domains on company, 3) search for the better way in application through comparative analysis between case study of customer satisfaction structure by structural equation model and by ordinary estimation model. We have conclusions through model that Index values like GFI AGFL RMR are accepted as within limited range for estimating validity of used model. so we can expect to use the results for deduce accurate User satisfaction index. Therefore satisfied Users became lesser at complaint behavior instead higher at repurchase likelihood, while unsatisfied Users became lower. Results as above are accordance with the previous studies, and that proves ACSI model to be applied toward local MIS Users' case. Even though the scopes of this research are restricted in domestic market of MIS Users, ACSI model itself has developed with the purpose for compatibility toward inter-company and inter-industry. So this model can be applied beneficially in the future study to analyse the extent that latent variables affect to User satisfaction.

• The Journal of Bigdata
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• v.1 no.2
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• pp.1-8
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• 2016

User activity log includes lots of hidden information, however it is not structured and too massive to process data, so there are lots of parts uncovered yet. Especially, it includes time series data. We can reveal lots of parts using it. But we cannot use log data directly to analyze users' behaviors. In order to analyze user activity model, it needs transformation process through extra framework. Due to these things, we need to figure out user activity model analysis framework first and access to data. In this paper, we suggest a novel framework model in order to analyze user activity model effectively. This model includes MapReduce process for analyzing massive data quickly in the distributed environment and data architecture design for analyzing user activity model. Also we explained data model in detail based on real online service log design. Through this process, we describe which analysis model is fit for specific data model. It raises understanding of processing massive log and designing analysis model.

• Journal of the Ergonomics Society of Korea
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• v.31 no.2
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• pp.299-308
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• 2012

Objective: This study presents the conceptual framework and the conceptual model to account for user experience by expanding the existing concepts of usability, in particular considering the user-interface environment in digital convergence. Background: To better understand a variety of users interacting with a converged product based on digital technologies, there seems to be a limit to consider the existing concepts of usability. All possible aspects of user's interaction with a product in a context of use need to be taken into consideration. Method: This study identifies the concept of user experience through a comprehensive literature review. Results: First, this study reviews the existing concepts of usability and user experience. And then this study describes four main components in the conceptual framework of user experience: user's internal states, user's external states, a product, and various outcomes, each of which encompasses distinct sub-components. The conceptual model of user experience accounts for how user's internal states change over time and for how different sub-components affect actual behavior of use. Conclusion: It is expected that these user experience concepts can be used in basic resources to better understand different behavioral characteristics of users and to better design interactive products in converged digital environments.

• Journal of the Korea Society of Computer and Information
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• v.14 no.1
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• pp.1-8
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• 2009

Learning user's preference is a key issue in intelligent system such as personalized service. The study on user preference model has adapted simple user preference model, which determines a set of preferred keywords or topic, and weights to each target. In this paper, we recommend multi-perspective user preference model that factors sentiment information in the model. Based on the topicality and sentimental information processed using natural language processing techniques, it learns a user's preference. To handle timc-variant nature of user preference, user preference is calculated by session, short-term and long term. User evaluation is used to validate the effect of user preference teaming and it shows 86.52%, 86.28%, 87.22% of accuracy for topic interest, keyword interest, and keyword favorableness.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1963-1978
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• 2015

With the advancement of mobile web environments, identification and analysis of the user behavior play a significant role and remains a challenging task to implement with variations observed in the model. This paper presents an efficient method for mining optimized user behavior prediction model using genetic algorithm on mobile web structure. The framework of optimized user behavior prediction model integrates the temporary and permanent register information and is stored immediately in the form of integrated logs which have higher precision and minimize the time for determining user behavior. Then by applying the temporal characteristics, suitable time interval table is obtained by segmenting the logs. The suitable time interval table that split the huge data logs is obtained using genetic algorithm. Existing cluster based temporal mobile sequential arrangement provide efficiency without bringing down the accuracy but compromise precision during the prediction of user behavior. To efficiently discover the mobile users' behavior, prediction model is associated with region and requested services, a method called optimized user behavior Prediction Model using Genetic Algorithm (PM-GA) on mobile web structure is introduced. This paper also provides a technique called MAA during the increase in the number of models related to the region and requested services are observed. Based on our analysis, we content that PM-GA provides improved performance in terms of precision, number of mobile models generated, execution time and increasing the prediction accuracy. Experiments are conducted with different parameter on real dataset in mobile web environment. Analytical and empirical result offers an efficient and effective mining and prediction of user behavior prediction model on mobile web structure.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3972-3985
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• 2016

Organizations have been implementing mobile applications that actually connect to their backend enterprise applications (e.g. ERP, SCM, etc.) in order to increase the enterprise mobility. However, most of the organizations are still struggling to fully satisfy their mobile application users with the enterprise mobility. Even though it has been regarded as the right direction that the traditional enterprise system should move on, the studies on the success model for mobile enterprise applications in user's acceptance perspective can hardly be found. Thus, this study focused not only to redefine the success of the mobile enterprise application in user's acceptance persepective, but also to find the impacts of the factors on user's usage behavior of the mobile enterprise applications. In order to achieve this, we adopted the Technology Acceptance Model 2 (TAM2) as a model to figure out the user's behavior on mobile applications. Among various mobile enterprise applications, this study chose mobile ERP since it is the most representing enterprise applications that many organizations have implemented in their backend. This study found that not all the constructs defined by Davis in TAM2 have a significant influence on user's behavior of the mobile-ERP applications. However, it is also found that most social influence processes of TAM2 influence user's perception of the degree of interaction by mobile-ERP applications.