• Journal of the Korea Society of Computer and Information
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• v.23 no.9
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• pp.141-156
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• 2018

The purpose of this study is how personal information violation risks affect the intention to use domestic cryptocurrency services. VAM(Value based Adoption Model) model is validated as a theoretical background, selecting perceived ease of use, perceived usefulness and perceived security as a benefit factors, and considers perceived cost, technical complexity, and risk of personal information violation risks as sacrifice factors. The method of this study used questionnaire survey to collect 150 data on user's perception on cryptocurrency services, and also performed a structural equation modeling method using by AMOS 23. The result of this paper shows that all hypotheses are accepted statistically significant except 2 hypothesis. This research is concluded that perceived value is affected on statistically positive impact on perceived ease of use, perceived usefulness and perceived security, and negative impact on risk of personal information violation risk, not statistically perceived fee and technical complexity.

• Educational Technology International
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• v.14 no.1
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• pp.109-136
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• 2013

We conducted an empirical study with 8 middle school students in Korea to investigate the usability and usefulness of our self-designed Digital Textbook. The Stimulated Recall (SR) Method using Morae software was utilized to analyze the learners' task behaviors and mental operations while using the Digital Textbook to learn English. Collected qualitative data indicated several problems in terms of the usability and usefulness of the Digital Textbook. The findings are summarized and some implications are discussed for further revision of the Digital Textbook and validation of the SR method as a usability-usefulness test tool.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.173-181
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• 2024

The healthy products dedicated for young people are qualified as a solution to protect the future generation, especially that most commercial deals do not consider the consumer's health and environment. Therefore, it is crucial to define the antecedent of healthy purchases and to examine their impact on teenagers. This research aims to explore the antecedents and the consequences of the consumption of Saudis teenagers. Therefore, we develop a research model in the conceptual framework and the hypotheses to test. The empirical analysis required two samples from Saudis youth consumers. The first sample was utilized in the exploratory study with SPSS software. Then, the second was employed to the confirmatory part with the Amos software, as well as the validation of the hypotheses, and model with Fuzzy Set approach. The findings of this study have significant insights into the Saudi consumption and implications for both practitioners and researchers. Then, we have particularly strenuous on intention purchase antecedents of organic foods, and their consume habit moderation.

• Asia pacific journal of information systems
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• v.28 no.3
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• pp.183-203
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• 2018

In the era of Web 2.0, social media advertising can simultaneously stimulate consumers' brand purchase intention and brand information sharing intention. Product sales and brand information diffusion are equally important for a company that conducts advertising. This study investigates how features of brand content influence social media advertising effectiveness by integrating the stimulus-organism-response model and classic advertising effectiveness models. An analysis of 267 survey questionnaires shows that brand content-related cues, including perceived uniqueness, perceived vividness, and perceived interactivity have significant effects on consumers' affective and cognitive involvement, which then affect their attitude toward brand content. As a result, the consumers' attitude toward the brand and their brand purchase intention, as well as their brand content sharing intention, are positively affected by attitude toward brand content. This study contributes to a better understanding of how social advertising works, which suggests that managers should effectively use social media to conduct advertising.

• Communications for Statistical Applications and Methods
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• v.17 no.2
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• pp.293-308
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• 2010

In this paper, we examine the forecasting KOSPI 200 realized volatility by volatility measurements. The empirical investigation for KOSPI 200 daily returns is done during the period from 3 January 2003 to 29 June 2007. Since Korea Exchange(KRX) will launch VKOSPI futures contract in 2010, forecasting VKOSPI can be an important issue. So we analyze which volatility measurements forecast VKOSPI better. To test this hypothesis, we use 5-minute interval returns to measure realized volatilities. Also, we propose a new methodology that reflects the synchronized bidding and simultaneously takes it account the difference between overnight volatility and intra-daily volatility. The t-test and F-test show that our new realized volatility is not only different from the realized volatility by a conventional method at less than 0.01% significance level, also more stable in summary statistics. We use the correlation analysis, regression analysis, cross validation test to investigate the forecast performance. The empirical result shows that the realized volatility we propose is better than other volatilities, including historical volatility, implied volatility, and convention realized volatility, for forecasting VKOSPI. Also, the regression analysis on the predictive abilities for realized volatility, which is measured by our new methodology and conventional one, shows that VKOSPI is an efficient estimator compared to historical volatility and CRR implied volatility.

• Journal of Korea Trade
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• v.24 no.7
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• pp.1-28
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• 2020

Purpose - This paper empirically investigates the predictors and main determinants of consumers' ratings of mobile applications in the Google Play Store. Using a linear and nonlinear model comparison to identify the function of users' review, in determining application rating across countries, this study estimates the direct effects of users' reviews on the application rating. In addition, extending our modelling into a sentimental analysis, this paper also aims to explore the effects of review polarity and subjectivity on the application rating, followed by an examination of the moderating effect of user reviews on the polarity-rating and subjectivity-rating relationships. Design/methodology - Our empirical model considers nonlinear association as well as linear causality between features and targets. This study employs competing theoretical frameworks - multiple regression, decision-tree and neural network models - to identify the predictors and main determinants of app ratings, using data from the Google Play Store. Using a cross-validation method, our analysis investigates the direct and moderating effects of predictors and main determinants of application ratings in a global app market. Findings - The main findings of this study can be summarized as follows: the number of user's review is positively associated with the ratings of a given app and it positively moderates the polarity-rating relationship. Applying the review polarity measured by a sentimental analysis to the modelling, it was found that the polarity is not significantly associated with the rating. This result best applies to the function of both positive and negative reviews in playing a word-of-mouth role, as well as serving as a channel for communication, leading to product innovation. Originality/value - Applying a proxy measured by binomial figures, previous studies have predominantly focused on positive and negative sentiment in examining the determinants of app ratings, assuming that they are significantly associated. Given the constraints to measurement of sentiment in current research, this paper employs sentimental analysis to measure the real integer for users' polarity and subjectivity. This paper also seeks to compare the suitability of three distinct models - linear regression, decision-tree and neural network models. Although a comparison between methodologies has long been considered important to the empirical approach, it has hitherto been underexplored in studies on the app market.

• Korean Journal of Social Welfare
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• v.63 no.3
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• pp.157-185
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• 2011

Although social work values are stressed among social work educators as well as practitioners, empirical studies on values have been very scarce. The objective of this study is to validate a Korean Version of the Professional Opinion Scale(POS). The Korean version was validated with a sample of 325 social worker working in various types of social work agencies. A series of confirmatory factor analysis suggested that 8 items be removed, resulting in 32 items with 4 factors. The Korean version has the same factor structure as the original version of the POS reported by its developer. The Korean version of the POS are found to be a reliable and valid instrument for measuring social work values. However, validation with more representative samples is needed to improve the quality of the Korean version of the POS.

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.4
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• pp.275-281
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• 2016

Subcooled boiling under low pressure was numerically investigated using computational fluid dynamics(CFD). The wall boiling model was used for simulating the subcooled boiling; this model requires sub-models consisting of bubble departure diameter, nucleation site density and bubble departure frequency. The CFD code CFX provides the default models based on experimental data. Because these models are mostly developed under high pressure conditions, it would not be predicted well in low pressure conditions. Thus in this study, CFD validation for subcooled boiling under low pressure was analyzed. The numerical results were compared with experimental data from published paper. Simulations were performed with mass flux ranging from 250 to $750kg/m^2s$, heat flux ranging from 0.37 to $0.77MW/m^2$ and constant outlet pressure of 0.11 MPa. Employing the empirical correlation developed under low pressures could increase the accuracy of numerical analysis.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.24
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• pp.10683-10690
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• 2015

The big gap between efficacy of population level prevention and expectations due to heterogeneity and complexity of cancer etiologic factors calls for selective yet personalized interventions based on effective risk assessment. This paper documents our research protocol aimed at refining and validating a two-stage and web-based cancer risk assessment tool, from a tentative one in use by an ongoing project, capable of identifying individuals at elevated risk for one or more types of the 80% leading cancers in rural China with adequate sensitivity and specificity and featuring low cost, easy application and cultural and technical sensitivity for farmers and village doctors. The protocol adopted a modified population-based case control design using 72, 000 non-patients as controls, 2, 200 cancer patients as cases, and another 600 patients as cases for external validation. Factors taken into account comprised 8 domains including diet and nutrition, risk behaviors, family history, precancerous diseases, related medical procedures, exposure to environment hazards, mood and feelings, physical activities and anthropologic and biologic factors. Modeling stresses explored various methodologies like empirical analysis, logistic regression, neuro-network analysis, decision theory and both internal and external validation using concordance statistics, predictive values, etc..

• Journal of the Ergonomics Society of Korea
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• v.32 no.2
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• pp.159-165
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• 2013

Objective: The aim of this study was to predict human performance time in finding objects on multi-display monitors using ACT-R cognitive architecture. Background: Display monitors are one of the representative interfaces for interaction between people and the system. Nowadays, the use of multi-display monitors is increasing so that it is necessary to research about the interaction between users and the system on multi-display monitors. Method: A cognitive model using ACT-R cognitive architecture was developed for the model-based evaluation on multi-display monitors. To develop the cognitive model, first, an experiment was performed to extract the latency about the where system of ACT-R. Then, a menu selection experiment was performed to develop a human performance model to find objects on multi-display monitors. The validation of the cognitive model was also carried out between the developed ACT-R model and empirical data. Results: As a result, no significant difference on performance time was found between the model and empirical data. Conclusion: The ACT-R cognitive architecture could be extended to model human behavior in the search of objects on multi-display monitors.. Application: This model can help predicting performance time for the model-based usability evaluation in the area of multi-display work environments.