• Journal of Distribution Science
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• v.14 no.10
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• pp.165-178
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• 2016

Purpose - This study aims to empirically verify the impact of media richness, social presence, and prior life satisfaction on various continual usage or withdrawal behaviors of SNS users via both a positive path of satisfaction and a negative path of relative deprivation. By identifying these causal paths, we observe dynamic interactions of SNS user psychology in a balanced view, and provide some implications about design principles for SNS providers. Research design, data, and methodology - We developed 16 hypothesis based on media richness theory, social presence theory, social comparison theory, the literature about relative deprivation, and the literature about the various reactions of IS users. The rich SNS media, social presence recognition among peer SNS users, and prior life satisfaction could generate positive experience, attitude, and virtuous behavioral intentions among SNS users. At the same time, rich media, low social presence, and low prior life satisfaction could generate relative deprivation and could increase withdrawal behavioral intentions such as refusal to provide information, misrepresentation of information, and removal of uploaded information in SNS. Scenario surveys were conducted to collect data from potential SNS users. Data from 357 surveys were collected and analyzed through a PLS algorithm to test the hypotheses. Results - Media richness, social presence, and prior life satisfaction could significantly increase perceived enjoyment, satisfaction, and behavioral intention of continual usage and knowledge sharing. They also could significantly decrease refusal and misrepresentation intention. Relative deprivation is significantly decreased only by prior life satisfaction. Relative deprivation could not significantly decrease satisfaction, but it could significantly increase misrepresentation and removal intention, which could be regarded as information distortion intention. Conclusions - SNS providers should focus on developing rich media and social presence support because these two variables could impact the positive experiences of SNS users. Moreover, the positive experiences could heavily influence SNS user behavior. Some management is needed to prevent relative deprivation and its consequences of misrepresentation and removal intention. SNS providers should prevent SNS users from excessive image misrepresentation and removal as this information distortion could be the source of relative deprivation.

• Journal of the Korean Data and Information Science Society
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• v.22 no.4
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• pp.819-826
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• 2011

In this paper, we develop the reference priors for the scale and shape parameters in the nonregular Pareto distribution. We derive the reference priors as noninformative priors and prove the propriety of joint posterior distribution under the general priors including reference priors in the order of inferential importance. Through the simulation study, we compare the reference priors with respect to coverage probabilities of parameter of interest in a frequentist sense.

• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.689-699
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• 2013

This paper studies the partial credibility application method by assuming the empirical prior or noninformative prior informations in auto insurnace business where intensive rating segmentation is expanded because of premium competition. Expanding of rating factor segmetation brings the increase of pricing cells, as a result, the number of cells for partial credibility application will increase correspondingly. This study is trying to suggest more accurate estimation method by considering the Bayesian framework. By using empirically well-known or noninformative information, inducing the proper posterior distribution and applying the Bayes estimate which is minimizing the error loss into the credibility method, we will show the advantage of Bayesian inference by comparison with current approaches. The comparison is implemented with square root rule which is a widely accepted method in insurance business. The convergence level towarding to the true risk will be compared among various approaches. This study introduces the alternative way of redcuing the error to the auto insurance business fields in need of various methods because of more segmentations.

• Journal of Distribution Science
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• v.20 no.7
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• pp.119-127
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• 2022

Purpose: The underdog effect refers to a positive attitude towards the underdog who lacks resources and opportunities but has passion and determination. Extensive research has been investigated how the underdog effect affects distributing consumer preference and purchase intention. The purpose of this study is to identify the theoretical basis on which the underdog effect is grounded by analysing the prior research in the underdog literature. Research design, data and methodology: This study organizes the literature on the underdog effect by analyzing a total of 33 journals published in the past 16 years. Constructs and variables were extracted from the selected articles through the content analysis on a systematic literature review. Results: This literature review reveals that identity theories served as an overarching theoretical lens when examining the underdog effect. Additionally, the underdog effect was rooted in theories of justice and equity to explain the relationship between underdog brand positioning and consumer preference. Conclusions: This paper paves the way for future research based on prior research and trend analysis of underdog brand positioning. This article also provides an insightful interpretation and synthesis of the theoretical underpinnings of the impact of underdog effects on consumers' overall brand evaluations.

• Journal of Digital Convergence
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• v.13 no.8
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• pp.295-300
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• 2015

In vocabulary recognition using an HMM model which models the prior distribution for the observation of a discrete probability distribution indicates the advantages of low computational complexity, but relatively low recognition rate. The Bayesian techniques to improve vocabulary recognition model, it is proposed using a convergence of two methods to improve recognition noise-canceling recognition. In this paper, using a convergence of the prior probability method and techniques of Bayesian posterior probability based on HMM remove noise and improves the recognition rate. The result of applying the proposed method, the recognition rate of 97.9% in vocabulary recognition, respectively.

• Journal of Soil and Groundwater Environment
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• v.25 no.4
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• pp.87-97
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• 2020

In this study, the applicability of the geostatistical evolution strategy as an inverse analysis method of estimating hydraulic properties of small-scale basin was tested. The geostatistical evolution strategy is a type of data assimilation method that can effectively estimate aquifer hydraulic conductivity by combining a global optimization model of the evolution strategy and a local optimization model of the ensemble Kalman filtering. In the applicability test, the geometry, hydraulic boundary conditions, and the distribution of groundwater monitoring wells of Hanlim-Eup were employed. On the other hand, a synthetic hydraulic conductivity distribution was generated and used as the reference property for ease of estimation quality assessment. In the estimations, two different cases were tested where, in Case I, both groundwater levels and hydraulic conductivity measurements were assumed to be available, and only the groundwater levels were available, in Case II. In both cases, the reference and estimated hydraulic conductivity fields were found to show reasonable similarity, even though the prior information for estimation was not accurate. The ability to estimate hydraulic conductivity without accurate prior information suggests that this method can be used effectively to estimate mathematical properties in real-world cases, many of which little prior information is available for the aquifer conditions.

• Proceedings of the Safety Management and Science Conference
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• 2010.11a
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• pp.27-33
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• 2010

The paper proposes the guidelines of application and interpretation for quality and reliability methodologies using deductive or inductive reasoning. The research also reviews Bayesian quality and reliability tools by deductive prior function and inductive posterior function.

• Journal of the Korean Data and Information Science Society
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• v.4
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• pp.109-120
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• 1993

Using a noninformative prior and a gamma prior, the Bayesian predictive density and the prediction intervals for a future observation or the p-th order statistic of n' future observations from the Burr distribution have been obtained. In additions, we examine the sensitivities of the results to the choice of model.

• 한국데이터정보과학회:학술대회논문집
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• 2003.10a
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• pp.49-56
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• 2003

When X and Y have independent normal distributions, we develop a Bayesian testing procedure for the equality of two coefficients of variation. Under the reference prior of the coefficient of variation, we propose a Bayesian test procedure for the equality of two coefficients of variation using fractional Bayes factor. A real data example is provided.

• Communications for Statistical Applications and Methods
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• v.14 no.1
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• pp.111-119
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• 2007

In this paper, we develop a nonparametric Bayesian methodology of estimating an unknown distribution function F at the given survival time with current status data under the assumption of Dirichlet process prior on F. We compare our algorithm with the nonparametric maximum likelihood estimator through application to simulated data and real data.