• Korean Journal of Organic Agriculture
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• v.17 no.2
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• pp.135-150
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• 2009

This paper was to use choice experiments in the analysis of consumer choice behavior and preferences for five different attributes(the origin of rice, a quality certificate, a quality control, a traceability system, the price of rice) in Cheongju City. Completed surveys yielded 712 responses which were analyzed using the conditional logit model to analyze the marginal willingness to pay of the four attributes(the origin of rice, a quality certificate, a quality control and a traceability system) per household and estimated the marginal willingness to pay of the set of feasible options. The result of this study can be used as a guide for the rice industry in the design of possible labeling schemes.

• Proceeding of Spring/Autumn Annual Conference of KHA
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• 1992.11a
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• pp.45-51
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• 1992

The purpose of this study was to predict the spatial housing demand of households in Seoul with the use of residential choice probabilitics. An multinomial legit model is developed using socio-demographic and housing characteristics. SAS package was utilized to estimate this model. This study used the data obtained by the Korea Rosearch Institute for Human Settlemente in 1989. The sample size was 3941 households in Seoul.The residential choice probability varicd depending upon the residential area, head age, head age, tenure and work place. The households with students were more likely to choose kangnam are. The households without young children had higher probability to choose new town near Seoul. Prime reason of this two results were considered the chi Id education and their better housing, Kangnam area was known to be the first consideration for residential choice regardless of work place. Low level of choice probability of kangman area for future residences however, was evidenced. Prime reason of such seemingly contradicting phenomenon is suspected for higher housing prices and limited affordability of people surveyed.

• Journal of Korean Institute of Industrial Engineers
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• v.36 no.2
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• pp.87-93
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• 2010

The partial least squares (PLS) method is popularly used for estimating the structural equation model, but the existing algorithm may not be directly implemented when probabilities are involved in some constructs or manifest variables. We propose a structural equation model including the brand choice as one construct having brand choice probabilities as its manifest variables. Then, we develop a PLS-based algorithm for the structural equation model by utilizing the multinomial logit model. A case is introduced as an application and simulation studies are performed to validate the proposed algorithm.

• Asia Marketing Journal
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• v.22 no.2
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• pp.1-18
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• 2020

We estimate a choice-based aggregate demand model accounting for consumers' consideration sets, and study its implications on market structure. In contrast to past research, we model and estimate consumer demand using aggregate-level consumer browsing data in addition to aggregate-level choice data. The use of consumer browsing data allows us to study consumer demand in a realistic setting in which consumers choose from a subset of products. We calibrate the proposed model on both data sets, avoid biases in parameter estimates, and compute the price elasticity measures. As an empirical application, we estimate consumer demand in the camcorder category and study its implications on market structure. The proposed model predicts a limited consumer price response and offers a more discriminating competitive landscape from the one assuming universal consideration set.

• Journal of the Korean Institute of Landscape Architecture
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• v.19 no.3
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• pp.128-143
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• 1991

To understand and predict destination choice behavior for urban parks, two hypotheses, which are based on extended Fishbein model, were proposed and tested in Kwangju. Data was analyzed by the citizens of Kwangju and by target markets segmented by demographic variables. Analysis by the citizens of Kwangju showed that postulated hypotheses are true. However, they were partially thru in the other cases : it was a general tendency that suggested model applied well in the high-involvement product. Among the demographic variables, sex was the most useful to understand park choice behavior. Park management directions were suggested, and several future research implications were discussed.

• Journal of the Korean Operations Research and Management Science Society
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• v.26 no.4
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• pp.55-70
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• 2001

Artificial neural network(ANN) models have been widely used for the classification problems in business such as bankruptcy prediction, credit evaluation, etc. Although the application of ANN to classification of consumers＇ choice behavior is a promising research area, there have been only a few researches. In general, most of the researches have reported that the classification performance of the ANN models were better than conventional statistical model Because the survey data on consumer behavior may include much noise and missing data, ANN model will be more robust than conventional statistical models welch need various assumptions. The purpose of this paper is to study the potential of the ANN model for forecasting consumers＇ choice behavior based on survey data. The data was collected by questionnaires to the shoppers of department stores and discount stores. Then the correct classification rates of the ANN models for the training and test sample with that of multiple discriminant analysis(MDA) and logistic regression(Logit) model. The performance of the ANN models were betted than the performance of the MDA and Logit model with respect to correct classification rate. By using input variables identified as significant in the stepwise MDA, the performance of the ANN models were improved.

• Industrial Engineering and Management Systems
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• v.15 no.4
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• pp.324-334
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• 2016

Manufacturers and retailers are interested in how prices, promotions, discounts and other marketing variables can influence the sales and shares of the products that they produce or sell. Therefore, many models have been developed to predict the brand share. Since the customer choice models are usually used to predict the market share, here we use hybrid model of Probabilistic Neural Network and Artificial Bee colony Algorithm (PNN-ABC) that we have introduced to model consumer choice to predict brand share. The evaluation process is carried out using the same data set that we have used for modeling individual consumer choices in a retail coffee market. Then, to show good performance of this model we compare it with Artificial Neural Network with one hidden layer, Artificial Neural Network with two hidden layer, Artificial Neural Network trained with genetic algorithms (ANN-GA), and Probabilistic Neural Network. The evaluated results show that the offered model is outperforms better than other previous models, so it can be use as an effective tool for modeling consumer choice and predicting market share.

• Journal of Distribution Science
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• v.13 no.10
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• pp.135-142
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• 2015

Purpose - Following the speedy development of the smart phone industry, tourism companies started to increase their brand recognition and sales volume by adopting mobile applications. However, applications for tourism industries are still insignificant. This study tries to analyze empirical evidence from Korean and Chinese consumers who have used mobile tour applications. By using an expansion of the technology acceptance model (TAM), this study will find what factors have effects on user intention for mobile tour applications. The findings will be helpful for the development of mobile tour applications and the tourism industries. Research design, data, and methodology - This study uses the TAM, which was presented by Davis in 1989. This study uses consumer acceptance level, consumer choice attitude, and use intention as the basic variables to fit to the TAM, and adopts choice content quality, brand value, and usage motivation as additional variables to analyze. This study has developed several hypotheses and collected data from 620 users who used mobile applications for tourism during April 1 to April 30, 2015. A total of 612 valid questionnaires were collected and used in the data analysis. The data was analyzed with structural equation modeling using SPSS Win/pc and Amos 22.0. Results - The findings can be summarized as follows: First, the content quality affects the consumer acceptance degree and choice attitude. Second, the brand value has a directly positive effect on the consumer acceptance degree and choice attitude. It is clear that the content quality and brand value play important roles in raising consumer acceptance and choice attitude. Third, usage motivation has no effect on the consumer acceptance degree and choice attitude. Fourth, the acceptance degree does not have any effect on the consumer choice attitude. Fifth, the acceptance degree affects the use intention. Last, the consumer choice attitude affects the use intentions. This indicates that consumer acceptance and choice attitude must both be achieved to induce use intention among consumers. Finally, the effects of the mobile tour application content quality and brand value on consumer acceptance degree and choice attitude were confirmed. Additionally, the effects of the consumer acceptance degree and choice attitude on use intentions were analyzed. Conclusion - It is not meaningful for tourism marketing to launch tour applications in the mobile market without understanding tourism consumer characteristics. When developing mobile tour applications, companies should focus on the characters of consumer choice attitudes as high quality, high brand value, usefulness, and ease of mobile tour applications. This study has limitations in that it did not consider negative factors such as perceived risks or analyze whether there are differences between Korean and Chinese consumers. In the future, we will consider equipping the same mobile tour applications commonly used by both Korean and Chinese consumers, and then examine negative factors as well as the differences in mobile tour applications between Korean and Chinese consumers.

• Journal of Technology Innovation
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• v.13 no.1
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• pp.1-24
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• 2005

This paper applies a prelaunch forecasting model to the Home-Networking (HN) market of South Korea. The HN market of Korea is categorized into two distinctive markets. One HN market consists of new apartments in which builders install HN and the other HN market consists of existing houses in which residents purchase HN Among these markets, this paper focuses on existing houses as capturing consumers' choice. To forecast sales of HN for existing houses, we use a conjoint model based on our survey data of consumer preferences. By incorporating various indicators of HN technologies into our conjoint model, we also forecast diffusion of HN system embodied in PLC or Wireless Lan. We call this model Choice-Based Diffusion Model. In addition, based on the simulation experiments, we also identify important factors that affect the demands of HN system.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.279-293
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• 2023

This study investigates network effects to measure how strongly the early adopters affect the brand choice of the potential consumer. By using the Gumbel-Hougaard (GH) copula, this study checks the magnitude of network effects varied from country to country. To consider consumer heterogeneity and network effects in the growth market, this study proposes the multi-brand Gamma/Shifted-Gompertz (m-G/SG) model based on the GH copula. Out of eighteen Western European cellular phone market data and South Korea smartphone data sets, the m-G/SG model provides an improvement in the estimation accuracy over the Libai, Muller, and Peres model. The results show that network effects enhance (i) the polarization of brand choice probabilities as time elapses; (ii) the dominance of the more preferred and the earlier entered brand; and (iii) the deceleration of category-level diffusion. Potential followers can analyze their relationship with earlier entrants through the m-G/SG model and also establish an optimal market entry strategy.