• Asia Marketing Journal
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• v.11 no.1
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• pp.73-92
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• 2009

Consumers influence other consumers' brand choice behavior by delivering a variety of objective or subjective information on a particular product, which is called WOM (Word-Of-Mouth) activities. For WOM activities, WOM senders should choose messages to deliver to other consumers. We classify the contents of the messages a consumer chooses for WOM delivery into two categories: Subjective (positive or negative) evaluation and objective information on products. In our study, we regard WOM senders' activities as a choice behavior and introduce a choice model to study the relationship between the choice of different WOM information (WOM with positive or negative subjective evaluation and WOM with objective information) and its influencing factors (information sources and consumer characteristics) by developing two bivariate Probit models. In order to consider the mediating effects of WOM senders' product involvement, product attitude, and their characteristics (gender and age), we develop three second-level models for the propagation of positive evaluations, of negative evaluations, and of objective information on products in an hierarchical Bayesian modeling framework. Our empirical results show that WOM senders' information choice behavior differs according to the types of information sources. The effects of information sources on WOM activities differ according to the types of WOM messages (subjective evaluation (positive or negative) and objective information). Therefore, our study concludes that WOM activities can be partially managed with effective communication plans influencing on consumers' WOM message choice behavior. The empirical results provide some guidelines for consumers' propagation of information on products companies want.

• Knowledge Management Research
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• v.25 no.2
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• pp.115-134
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• 2024

In an age where consumers can easily search and pass on their opinions of products and purchasing decisions through the internet, Electronic-word-of-mouth(Ewom) plays an important role in decision making of other potential customers. In this study, we empirically analyze the impact EWOM on consumer purchase decisions, when contradictory Ewom is presented from varying sources of information, such as experts and general consumers. First, we find that when there is a consensus among different information sources there exists a positive relationship between Ewom sentiment and purchase intent, confirming the results of previous literature. However, when expert opinion and consumer opinion do not match we find that consumer opinion is more impactful on purchasing decisions compared to the expert opinion, regardless of product types. The findings of this study add insight to the current literature by examining the effect of contradictory Ewom on purchase decisions, and also to industry marketers by presenting a more efficient strategy in promoting positive Ewom for different product types.

• The Korean Journal of Applied Statistics
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• v.37 no.3
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• pp.339-353
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• 2024

In cases where the response variable in proportional data is confined to a limited interval, a regression model based on the assumption of normality can yield inaccurate results due to issues such as asymmetry and heteroscedasticity. In such cases, the beta regression model can be considered as an alternative. This model reparametrizes the beta distribution in terms of mean and precision parameters, assuming that the response variable follows a beta distribution. This allows for easy consideration of heteroscedasticity in the data. In this paper, we therefore aim to analyze proportional data using the beta regression model in two empirical analyses. Specifically, we investigate the relationship between smoking rates and coffee consumption using data from the 6th National Health Survey, and examine the association between regional characteristics in the U.S. and cumulative mortality rates based on COVID-19 data. In each analysis, we apply the ordinary least squares regression model, the beta regression model, and the extended beta regression model to analyze the data and interpret the results with the selected optimal model. The results demonstrate the appropriateness of applying the beta regression model and its extended version in proportional data.

• Journal of Ecology and Environment
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• v.48 no.2
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• pp.196-206
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• 2024

Background: Most of the biomass equations were developed using sample trees collected mainly from pan-tropical and tropical regions that may over- or underestimate biomass. Site-specific models would improve the accuracy of the biomass estimates and enhance the country's measurement, reporting, and verification activities. The aim of the study is to develop site-specific biomass estimation models and validate and evaluate the existing generic models developed for pan-tropical forest and newly developed allometric models. Total of 140 trees was harvested from each diameter class biomass model development. Data was analyzed using SAS procedures. All relevant statistical tests (normality, multicollinearity, and heteroscedasticity) were performed. Data was transformed to logarithmic functions and multiple linear regression techniques were used to develop model to estimate aboveground biomass (AGB). The root mean square error (RMSE) was used for measuring model bias, precision, and accuracy. The coefficient of determination (R² and adjusted [adj]-R²), the Akaike Information Criterion (AIC) and the Schwarz Bayesian information Criterion was employed to select most appropriate models. Results: For the general total AGB models, adj-R² ranged from 0.71 to 0.85, and model 9 with diameter at stump height at 10 cm (DSH₁₀), ρ and crown width (CW) as predictor variables, performed best according to RMSE and AIC. For the merchantable stem models, adj-R² varied from 0.73 to 0.82, and model 8) with combination of ρ, diameter at breast height and height (H), CW and DSH₁₀ as predictor variables, was best in terms of RMSE and AIC. The results showed that a best-fit model for above-ground biomass of tree components was developed. AGB_Stem = exp {-1.8296 + 0.4814 natural logarithm (Ln) (ρD²H) + 0.1751 Ln (CW) + 0.4059 Ln (DSH₃₀)} AGB_Branch = exp {-131.6 + 15.0013 Ln (ρD²H) + 13.176 Ln (CW) + 21.8506 Ln (DSH₃₀)} AGB_Foliage = exp {-0.9496 + 0.5282 Ln (DSH₃₀) + 2.3492 Ln (ρ) + 0.4286 Ln (CW)} AGB_Total = exp {-1.8245 + 1.4358 Ln (DSH₃₀) + 1.9921 Ln (ρ) + 0.6154 Ln (CW)} Conclusions: The results demonstrated that the development of local models derived from an appropriate sample of representative species can greatly improve the estimation of total AGB.

• KDI Journal of Economic Policy
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• v.14 no.1
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• pp.147-165
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• 1992

This paper analyzes the macroeconomic effects of elections on the Korean economy and their future ramifications. It measures the shocks to the Korean economy caused by elections by taking the average of sample forecast errors from four major elections held in the 1980s. The seven variables' Bayesian Vector Autoregression Model which includes the Monetary Base, Industrial Production, Consumption, Consumer Price, Exports, and Investment is based on the quarterly time series data starting from 1970 and is updated every quarter before forecasts are made for the next quarter. Because of this updating of coefficients, which reflects in part the rapid structural changes of the Korean economy, this study can capture the shock effect of elections, which is not possible when using election dummies with a fixed coefficient model. In past elections, especially the elections held in the 1980s, $M_2$ did not show any particular movement, but the currency and base money increased during the quarter of the election was held and the increment was partly recalled in the next quarter. The liquidity of interest rates as measured by corporate bond yields fell during the quarter the election and then rose in the following quarter, which is somewhat contrary to the general concern that interest rates will increase during election periods. Manufacturing employment fell in the quarter of the election because workers turned into campaigners. This decline in employment combined with voting holiday produce a sizeable decline in industrial production during the quarter in which elections are held, but production catches up in the next quarter and sometimes more than offsets the disruption caused during the election quarter. The major shocks to price occur in the previous quarter, reflecting the expectational effect and the relaxation of government price control before the election when we simulate the impulse responses of the VAR model, imposing the same shocks that was measured in the past elections for each election to be held in 1992 and assuming that the elections in 1992 will affect the economy in the same manner as in the 1980s elections, 1992 is expected to see a sizeable increase in monetary base due to election and prices increase pressure will be amplified substantially. On the other hand, the consumption increase due to election is expected to be relatively small and the production will not decrease. Despite increased liquidity, a large portion of liquidity in circulation being used as election funds will distort the flow of funds and aggravate the fund shortage causing investments in plant and equipment and construction activities to stagnate. These effects will be greatly amplified if elections for the head of local government are going to be held this year. If mayoral and gubernatorial elections are held after National Assembly elections, their effect on prices and investment will be approximately double what they normally will have been have only congressional and presidential elections been held. Even when mayoral and gubernatorial elections are held at the same time as congressional elections, the elections of local government heads are shown to add substantial effects to the economy for the year. The above results are based on the assumption that this year's elections will shock the economy in the same manner as in past elections. However, elections in consecutive quarters do not give the economy a chance to pause and recuperate from past elections. This year's elections may have greater effects on prices and production than shown in the model's simulations because campaigners' return to industry may be delayed. Therefore, we may not see a rapid recall of money after elections. In view of the surge in the monetary base and price escalation in the periods before and after elections, economic management in 1992 should place its first priority on controlling the monetary aggregate, in particular, stabilizing the growth of the monetary base.

• Journal of Animal Science and Technology
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• v.50 no.1
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• pp.33-44
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• 2008

The purpose of this study was to estimate the genetic parameters and trend of Landrace and Yorkshire pigs, which were raised on private farms from 1999 to 2005 and tested for their reproductive performance by the Korea Animal Improvement Association. Prior to analysis, records without pedigree or having value with larger than±3×standard deviation for the Total number of born were excluded. The effects of breed and environmental factors were estimated with least square method(Harvey, 1979), and estimation of breeding values and genetic parameters were performed on the data of 1’st litter only with GIBBSF90(Misztal, 2001) which was programmed according to Gibbs Sampling method based on Bayesian Inference by Gianola and Fernando(1986), Jensen(1994) and others. Gibbs sampling was performed 50,000 times for each parameter, and the first 5000 samples were regarded as those in burn-in period and thus, excluded for post hoc analysis. Total number of born and total number of accident were statistically significant(p<0.01) for the breed, farrowing year, farrowing season and parity effects, and the number born alive at birth was statistically significantp<(0.01) for the breed, farrowing year, farrowing season and parity effects. No particular trend was observed in the genetic and phenotypic improvement of the total number of born and number born alive at birth before 2001, when the piglet registration system started, but the tendencies of increasing for the total number of born and number born alive and decreasing for the total number of accident were observed since 2001. Somewhat higher heritability estimates of our study seems to be attributed to the situations that first parity records with poor farrowing performances were used in the analyses and it was impossible to obtain accurate reproductive performance due to the absence of criteria for record keeping at the level of individual farms.

• Journal of Korea Water Resources Association
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• v.53 no.2
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• pp.107-119
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• 2020

This study analyzed past drought characteristics based on the observed rainfall data and performed a long-term outlook for future extreme droughts using Representative Concentration Pathways 8.5 (RCP 8.5) climate change scenarios. Standardized Precipitation Index (SPI) used duration of 1, 3, 6, 9 and 12 months, a meteorological drought index, was applied for quantitative drought analysis. A single long-term time series was constructed by combining daily rainfall observation data and RCP scenario. The constructed data was used as SPI input factors for each different duration. For the analysis of meteorological drought observed relatively long-term since 1954 in Korea, 12 rainfall stations were selected and applied 10 general circulation models (GCM) at the same point. In order to analyze drought characteristics according to climate change, trend analysis and clustering were performed. For non-stationary frequency analysis using sampling technique, we adopted the technique DEMC that combines Bayesian-based differential evolution ("DE") and Markov chain Monte Carlo ("MCMC"). A non-stationary drought frequency analysis was used to derive Severity-Duration-Frequency (SDF) curves for the 12 locations. A quantitative outlook for future droughts was carried out by deriving SDF curves with long-term hydrologic data assuming non-stationarity, and by quantitatively identifying potential drought risks. As a result of performing cluster analysis to identify the spatial characteristics, it was analyzed that there is a high risk of drought in the future in Jeonju, Gwangju, Yeosun, Mokpo, and Chupyeongryeong except Jeju corresponding to Zone 1-2, 2, and 3-2. They could be efficiently utilized in future drought management policies.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.243-264
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• 2018

Although the worldwide battery market is recently spurring the development of lithium secondary battery, lead acid batteries (rechargeable batteries) which have good-performance and can be reused are consumed in a wide range of industry fields. However, lead-acid batteries have a serious problem in that deterioration of a battery makes progress quickly in the presence of that degradation of only one cell among several cells which is packed in a battery begins. To overcome this problem, previous researches have attempted to identify the mechanism of deterioration of a battery in many ways. However, most of previous researches have used data obtained in a laboratory to analyze the mechanism of deterioration of a battery but not used data obtained in a real world. The usage of real data can increase the feasibility and the applicability of the findings of a research. Therefore, this study aims to develop a model which predicts the battery deterioration using data obtained in real world. To this end, we collected data which presents change of battery state by attaching sensors enabling to monitor the battery condition in real time to dozens of golf carts operated in the real golf field. As a result, total 16,883 samples were obtained. And then, we developed a model which predicts a precursor phenomenon representing deterioration of a battery by analyzing the data collected from the sensors using machine learning techniques. As initial independent variables, we used 1) inbound time of a cart, 2) outbound time of a cart, 3) duration(from outbound time to charge time), 4) charge amount, 5) used amount, 6) charge efficiency, 7) lowest temperature of battery cell 1 to 6, 8) lowest voltage of battery cell 1 to 6, 9) highest voltage of battery cell 1 to 6, 10) voltage of battery cell 1 to 6 at the beginning of operation, 11) voltage of battery cell 1 to 6 at the end of charge, 12) used amount of battery cell 1 to 6 during operation, 13) used amount of battery during operation(Max-Min), 14) duration of battery use, and 15) highest current during operation. Since the values of the independent variables, lowest temperature of battery cell 1 to 6, lowest voltage of battery cell 1 to 6, highest voltage of battery cell 1 to 6, voltage of battery cell 1 to 6 at the beginning of operation, voltage of battery cell 1 to 6 at the end of charge, and used amount of battery cell 1 to 6 during operation are similar to that of each battery cell, we conducted principal component analysis using verimax orthogonal rotation in order to mitigate the multiple collinearity problem. According to the results, we made new variables by averaging the values of independent variables clustered together, and used them as final independent variables instead of origin variables, thereby reducing the dimension. We used decision tree, logistic regression, Bayesian network as algorithms for building prediction models. And also, we built prediction models using the bagging of each of them, the boosting of each of them, and RandomForest. Experimental results show that the prediction model using the bagging of decision tree yields the best accuracy of 89.3923%. This study has some limitations in that the additional variables which affect the deterioration of battery such as weather (temperature, humidity) and driving habits, did not considered, therefore, we would like to consider the them in the future research. However, the battery deterioration prediction model proposed in the present study is expected to enable effective and efficient management of battery used in the real filed by dramatically and to reduce the cost caused by not detecting battery deterioration accordingly.

• Journal of the Korean Recycled Construction Resources Institute
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• v.1 no.1
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• pp.8-16
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• 2013

Currently, global warming has advanced by the usage of fossil fuels such as coal and petroleum. and the atmosphere temperature in the world of 100 years(1906~2005) has been risen $0.74^{\circ}C{\pm}0.18^{\circ}C$, IPCC announced that the global warming effect of last decade was nearly doubled compared to the changes($0.07^{\circ}C{\pm}0.02^{\circ}C$/10year) in the past 100 years. Moreover, due to the global warming, heat wave, heavy snow, heavy rain, super typhoon, were caused and are increasing to happen in the world continuously causing damages and destruction of social infrastructures, where concrete structures are suffering deterioration by long-term extreme climate changes. to solve these problems, the new construction technology and codes are necessary. In this study, to solve these problems, experiments on a variety of cases considering the temperature and humidity, the main factors of climate factors, were performed, and the cases are decided by temperature and humidity. The specimens were tested in compressive strength test and split tensile test by the curing age(3,7,28 days) morever, performance based design(PBD) method was applied by using the satisfaction curve developed from the experiment date. PBD is the design method that gathers the current experimental analysis and past experimental analysis and develops the material properties required for the structure, and carries out the design of concrete mix, and it is recently studied actively worldwide. Also, it is the ultimate goal of PBD to design and perform on structures have sufficient performance during usage and to provide the problem solving for various situations, Also, it can achieve maximum effect in terms of functionality and economy.

• Korean journal of applied entomology
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• v.51 no.4
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• pp.431-438
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• 2012

The developmental time of the nymphs of Myzus persicae was studied in the laboratory (six constant temperatures from 15 to $30^{\circ}C$ with 50~60% RH, and a photoperiod of 14L:10D) and in a green-pepper plastic house. Mortality of M. persicae in laboratory was high in the first(6.7~13.3%) and second instar nymphs(6.7%) at low temperatures and high in the third (17.8%) and fourth instar nymphs(17.8%) at high temperatures. Mortality was 66.7% at $33^{\circ}C$ in laboratory and $26.7^{\circ}C$ in plastic house. The total developmental time was the longest at $14.6^{\circ}C$ (14.4 days) and shortest at $26.7^{\circ}C$ (6.0 days) in plastic house. The lower threshold temperature of the total nymphal stage was $3.0^{\circ}C$ in laboratory. The thermal constant required for nymphal stage was 111.1DD. The relationship between developmental rate and temperature was fitted nonlinear model by Logan-6 which has the lowest value on Akaike information criterion (AIC) and Bayesian information criterion (BIC). The distribution of completion of each developmental stage was well described by the 3-parameter Weibull function ($r^2=0.95{\sim}0.97$). This model accurately described the predicted and observed occurrences. Thus the model is considered to be good for use in predicting the optimal spray time for Myzus persicae.