• Journal of the Daesoon Academy of Sciences
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• v.46
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• pp.69-122
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• 2023

This study is an integral exploration of Feng Shui associated with the area around the birthplace of Kang Jeungsan, a sacred site of Daesoon Jinrihoe which holds that the Supreme God descended in human form at that location (through Kang Jeungsan). Through an on-site Feng Shui survey, the main focus of the research method was to explore the Feng Shui configurations around Kang Jeungsan's birthplace especially as it pertains to the connections among geomagnetic veins which lead to the Mount Shiru area. As a method of investigation, this study explored the Feng Shui of Gaekmang-ri Village and the geomantic veins leading up to Mount Shiru. This involved examining the landforms, topography, water flow, and geomantic veins of the area to reveal the overall Feng Shui configurations. Throughout the course of that on-site survey, this study first examined Mount Duseung and Mount Bangjang, also known as Mount Yeongju (sometimes collectively known as Mount Samshin), Mount Dongjuk, Mount Mangje-bong, Mount Maebong, and Mount Shiru. Then, this study stated some of the underlying issues through a scholarly approach based on various theories such as traditional geographical texts and theories on mountain-growth and water-flow from the perspective of Feng Shui. In particular, attention was paid to theoretical aspects of the uninterrupted and undulating flow of the terrain leading to Shiru Mountain. As a result, from a Feng Shui point of view, the connected network geomantic veins in the area of Kang Jeungsan's birthplace and the feng shui features and conditions were all examined through an on-site survey. The survey results revealed that the area forms a large Feng Shui site due to the vast interconnectivity among all the mountains that extend from the Honam vein and form organic relationships with one another. This even includes Mount Samshin in Honam. Considering the geographical conditions that formed a site that enabled harmony between divine beings and humankind, the surrounding place names also provide allusions to the understanding of the birth of Kang Jeungsan as the descent of Supreme God into the human world through the historical figure, Kang Jeungsan. This area is an ideal spot with a propitious spatial arrangement in terms of its Feng Shui. Feng Shui analysis reveals the site to be a place that holds an earth energy-hub transmitting a great energy of nature that cannot be measured by human power alone.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.2
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• pp.1-20
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• 2023

The purpose of this study is to identify the unique entrepreneurial behavioral attributes of social enterprises that are distinct from for-profit enterprises at the organizational level, derive a social entrepreneurship model that reflects the unique characteristics of social enterprises as strategic decision-making and organizational behavioral tendencies. In order to effectively achieve the purpose of this study, previous studies were reviewed, and qualitative studies were conducted using the grounded theory method based on this. In this study, social entrepreneurship was identified as five sub-factors through a series of analysis processes, and 'Social value orientation; Innovativeness; Pro-activeness; Risk taking; Cooperation orientation' was newly proposed. It also proposed a new social entrepreneurship framework that integrates and explains the multidimensional model of social entrepreneurship by reviewing and connecting the relationships between each sub-factor of the research model. The 'social entrepreneurship framework' classified the social entrepreneurship model into 'pro-social motivation', 'pro-social behavior', and 'entrepreneurial behavior' attributes and explained them by linking them with each sub-factor that constitutes social entrepreneurship. The most remarkable difference between this study and previous studies is that it identified and added 'Cooperation orientation' as a sub-factor constituting social entrepreneurship from the organizational-level behavioral point of view. Through this study, 'Cooperation orientation' was identified as a major behavioral tendency for social enterprises to materialize pro-social motivation, strengthen the economic foundation of business activities, and improve the efficiency of business operations. 'Cooperation orientation' is a major behavioral tendency that strengthens the legitimacy of business activities between pro-social motivation and profit-seeking of social enterprises, improves the performance of social value creation activities, and overcomes the difficulties of resource constraints through cooperation with the outside and improves operational efficiency. In addition, it was confirmed that 'Cooperation orientation' is a major behavioral tendency of social enterprises that is manifested simultaneously in social value-oriented activities and entrepreneurial activities pursuing profit. The 'Cooperation orientation' newly identified in the study supplements the previous research, increases the explanatory power of the theory of social entrepreneurship, and provides the basis for theoretical expansion to subsequent researchers.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.2
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• pp.33-51
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• 2022

This study analyzes newly listed companies on KOSDAQ from 2011 to 2020 for both firms having experience in attracting venture investment before listing (VI) and those without having experience in attracting venture investment (NVI) by examining differences between two groups (VI and NVI) with respect to both the level of listing performance and that of firm performance (growth) after the listing. This paper conducts descriptive statistics, mean difference, and multiple regression analysis. Independent variables for regression models include VC investment, firm age at the time of listing, firm type, firm location, firm size, the age of VC, the level of expertise of VC, and the level of fitness of VC with investment company. Throughout this paper, results suggest that listing performance and post-listed growth are better for VI than NVI. VC investment shows a negative effect on the listing period and a positive effect on the sales growth rate. Also, the amount of VC investment has negative effects on the listing period and positive effects on the market capitalization at the time of IPO and on sales growth among growth indicators. Our evidence also implies a significantly positive effect on growth after listing for firms which belong to R&D specialized industries. In addition, it is statistically significant for several years that the firm age has a positive effect on the market capitalization growth rate. This shows that market seems to put the utmost importance on a long-term stability of management capability. Finally, among the VC characteristics such as the age of VC, the level of expertise of VC, and the level of fitness of VC with investment company, we point out that a higher market capitalization tends to be observed at the time of IPO when the level of expertise of anchor VC is high. Our paper differs from prior research in that we reexamine the venture ecosystem under the outbreak of coronavirus disease 2019 which stimulates the degradation of the business environment. In addition, we introduce more effective variables such as VC investment amount when examining the effect of firm type. It enables us to indirectly evaluate the validity of technology exception policy. Although our findings suggest that related policies such as the technology special listing system or the injection of funds into the venture ecosystem are still helpful, those related systems should be updated in a more timely fashion in order to support growth power of firms due to the rapid technological development. Furthermore, industry specialization is essential to achieve regional development, and the growth of the recovery market is also urgent.

• Journal of the Daesoon Academy of Sciences
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• v.47
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• pp.241-279
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• 2023

The Scripture of Myriad Laws was first published in 1986 and then reprinted in 1994 and 1995. It gained widespread recognition as a mysterious text or a Buddhist document containing enigmatic content that deemed difficult to comprehend. Through the analysis of the content of The Scripture of Myriad Laws, it was revealed that the book was published by the Dragon Flower Order, a Jeungsanist religion founded by Seo Baek-Il (徐白一). Therefore, the various texts included in The Scripture of Myriad Laws can be classified as 'Songs of Jeungsanism' (Jeungsan-gyo Gasa 甑山敎歌辭). The contents included in The Scripture of Myriad Laws often mention terms unique to the Jeungsanist orders, such as 'the Reordering Works of Heaven and Earth' (天地公事), 'presiding over cures' (醫統), 'Degree Number' (度數), 'the West God' (西神), 'the nobility of heaven' (天尊), 'the nobility of earth' (地尊), 'the nobility of humanity' (人尊), 'ruling the world for 50 years' (治天下五十年), and 'the era of Resolving Grievances (解冤時代).' It also discusses the birthplace and birth date of Kang Jeungsan, his family name, and the duration of his existence. The contents directly quote the words spoken by Jeungsan and incorporate them into songs. They also mention unique Jeungsan terms such as 'Five Immortals Playing Baduk' (五仙圍碁), 'open-weight wresting match,' 'birth, growth, harvest, and storage' (生長斂藏), 'the god who listens to words' (言聽神), 'pillar of foundation' (基礎棟樑),' 'Ocean Seal' (海印), and 'the higher gods' (上計神). It is also notable that some verses of Chinese poetry that Jeungsan taught his disciples are directly quoted in this scripture. Furthermore, the scripture shows traces of Buddhist salvational beliefs; particularly those that emphasize faith in Maitreya Bodhisattva (彌勒信仰). However, The Scripture of Myriad Laws differs from traditional Buddhist beliefs in that it anticipates and emphasizes the birth of a specific individual endowed with the power and abilities of Maitreya Buddha. While emphasizing Maitreya Buddha (彌勒世尊) and the Dragon Flower Gathering (龍華會上), the scripture also specifically mentions Geumsan-sa Temple (金山寺) located on Mount Moak (母岳山) in North Jeolla Province, and these details are sung about in a special manner. This positive portrayal serves to affirm the belief of followers that Jeungsan, centered around Geumsan-sa Temple, was an incarnation of Maitreya Buddha. Moreover, The Scripture of Myriad Laws subtly asserts that Seo Baek-il, the leader of the Dragon Flower Order, who is mentioned in the scripture, is the absolute savior who has come to this world in place of Jeungsan. In support of this teaching, his birth date, birthplace, years of imprisonment, release date, and honorific name (號) are all recorded in precise detail.

• Food Science and Preservation
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• v.31 no.1
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• pp.183-196
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• 2024

This study investigated the protective effect of the aqueous extract of Eucommia ulmoides leaves (AEEL) against high glucose-induced human colon epithelial HT-29 cells. The 2,2'-azino-bis (3-ethyl benzothiazoline-6-sulfonic acid) (ABTS), 1,1-diphenyl-2-picrylhydrazy (DPPH) radical scavenging activities, ferric reducing/antioxidant power (FRAP), and malondialdehyde (MDA) analyses indicated that AEEL had significant antioxidant activities. The 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay showed that AEEL increased cell viability against high glucose-, H₂O₂-, and lipopolysaccharide (LPS)-induced cytotoxicity in HT-29 cells. Also, the 2'-7'-dichlorodihydrofluorescein diacetate (DCF-DA) assay indicated that AEEL decreased intracellular reactive oxygen species (ROS) against high glucose-, H₂O₂-, and lipopolysaccharide (LPS)-induced cytotoxicity in HT-29 cells. AEEL showed inhibitory activities against α-glucosidase and inhibited the formation of advanced glycation end products (AGEs). AEEL showed significant positive effects on the viability and titratable acidity of L. brevis. The high-performance liquid chromatogram (HPLC) analysis identified chlorogenic acid and rutin as the major compounds of AEEL. These results suggested that AEEL has the potential to be used as a functional food source to suppress blood glucose levels and protect the gut from high glucose-induced oxidative stress.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.34 no.4
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• pp.109-116
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• 2024

β-Ga₂O₃ is a representative ultra-wide bandgap (UWBG) semiconductor that has attracted much attention for power device applications due to its wide-bandgap of 4.9 eV and high-breakdown voltage of 8 MV/cm. In addition, because solution growth is possible, it has advantages such as fast growth rate and lower production cost compared to SiC and GaN [1-2]. In this study, we have successfully grown Si-doped 10 mm thick Si-doped β-Ga₂O₃ single crystals by the EFG (Edge-defined Film-fed Growth) method. The growth direction and growth principal plane were set to [010] / (010), respectively, and the growth speed was 7~20 mm/h. The as-grown β-Ga₂O₃ single crystal was cut into various crystal planes (001, 100, ${\bar{2}}01$) and off-angles (1^o, 3^o, 4^o), and then surface processed. After processed, the homoepitaxial layer was grown on the epi-ready substrate using the HVPE (Halide vapor phase epitaxy) method. The processed samples and the epi-layer grown samples were analyzed by XRD, AFM, OM, and Etching to compare the surface properties according to the crystal plane and off-angle.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.205-225
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• 2018

Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.

• Economic and Environmental Geology
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• v.5 no.4
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• pp.187-209
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• 1972

Drilling position is one of the most important factors affecting on the blasting effects. There has been many reports on several blasting factors of burn-cut by Messrs. Brown and Cook, but in this study the author tried to compare drilling positions of burn-cut to pyramid-cut, and also to correlate burn-cut effects of drilling patterns, not being dealt by Prof. Ito in his theory, which emphasized on dynamical stress analysis between explosion and free face. According to former theories, there break out additional tensile stress reflected at the free face supplemented to primary compressive stress on the blasting with one-free-face. But with these experimented new drilling patterns of burn-cut, more free faces and nearer distance of each drilling holes make blasting effects greater than any other methods. To promote the above explosive effect rationary, it has to be considered two important categories under-mentioned. First, unloaded hole in the key holes should be drilled in wider diameter possibly so that it breaks out greater stress relief. Second, key holes possibly should have closer distances each other to result clean blasting. These two important factors derived from experiments with, theories of that the larger the dia of the unloaded hole, it can be allowed wider secondary free faces and closes distances of each holes make more developed stress relief, between loaded and unloaded holes. It was suggested that most ideal distance between holes is about 4 clearance in U. S. A., but the author, according to the experiments, it results that the less distance allow, the more effective blasting with increased broken rock volume and longer drifted length can be accomplished. Developed large hole burn-cut method aimed to increase drifting length technically under the above considerations, and progressive success resulted to achieve maximum 7 blasting cycles per day with 3.1m drifting length per cycle. This achievement originated high-speed-drifting works, and it was also proven that application of Metallic AN-FO on large hole burn-cut method overcomes resistance of one-free-face. AN-FO which was favored with low price and safety handling is the mixture of the fertilizer or industrial Ammonium-Nitrate and fuel oil, and it is also experienced that it shows insensible property before the initiation, but once it is initiated by the booster, it has equal explosive power of Ammonium Nitrate Explosives (ANE). There was many reports about AN-FO. On AN-FO mixing ratio, according to these experiments, prowdered AN-FO, 93.5 : 6.5 and prilled AN-FO 94 : 6, are the best ratios. Detonation, shock, and friction sensities are all more insensitive than any other explosives. Residual gas is not toxic, too. On initation and propagation of the detonation test, prilled AN-FO is more effective than powered AN-FO. AN-FO has the best explosion power at 7 days elapsed after it has mixed. While AN-FO was used at open pit in past years prior to other conditions, the author developed new improved explosives, Metallic AN-FO and Underwater explosive, based on the experiments of these fundmental characteristics by study on its usage utilizing AN-FO. Metallic AN-FO is the mixture of AN-FO and Al, Fe-Si powder, and Underwater explosive is made from usual explosive and AN-FO. The explanations about them are described in the other paper. In this study, it is confirmed that the blasting effects of utilizing AN-FO explosives are very good.

• Asia Marketing Journal
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• v.14 no.1
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• pp.83-98
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• 2012

In a market where new and used cars are competing with each other, we would run the risk of obtaining biased estimates of cross elasticity between them if we focus on only new cars or on only used cars. Unfortunately, most of previous studies on the automobile industry have focused on only new car models without taking into account the effect of used cars' pricing policy on new cars' market shares and vice versa, resulting in inadequate prediction of reactive pricing in response to competitors' rebate or price discount. However, there are some exceptions. Purohit (1992) and Sullivan (1990) looked into both new and used car markets at the same time to examine the effect of new car model launching on the used car prices. But their studies have some limitations in that they employed the average used car prices reported in NADA Used Car Guide instead of actual transaction prices. Some of the conflicting results may be due to this problem in the data. Park (1998) recognized this problem and used the actual prices in his study. His work is notable in that he investigated the qualitative effect of new car model launching on the pricing policy of the used car in terms of reinforcement of brand equity. The current work also used the actual price like Park (1998) but the quantitative aspect of competitive price promotion between new and used cars of the same model was explored. In this study, I develop a model that assumes that the cross elasticity between new and used cars of the same model is higher than those amongst new cars and used cars of the different model. Specifically, I apply the nested logit model that assumes the car model choice at the first stage and the choice between new and used cars at the second stage. This proposed model is compared to the IIA (Independence of Irrelevant Alternatives) model that assumes that there is no decision hierarchy but that new and used cars of the different model are all substitutable at the first stage. The data for this study are drawn from Power Information Network (PIN), an affiliate of J.D. Power and Associates. PIN collects sales transaction data from a sample of dealerships in the major metropolitan areas in the U.S. These are retail transactions, i.e., sales or leases to final consumers, excluding fleet sales and including both new car and used car sales. Each observation in the PIN database contains the transaction date, the manufacturer, model year, make, model, trim and other car information, the transaction price, consumer rebates, the interest rate, term, amount financed (when the vehicle is financed or leased), etc. I used data for the compact cars sold during the period January 2009- June 2009. The new and used cars of the top nine selling models are included in the study: Mazda 3, Honda Civic, Chevrolet Cobalt, Toyota Corolla, Hyundai Elantra, Ford Focus, Volkswagen Jetta, Nissan Sentra, and Kia Spectra. These models in the study accounted for 87% of category unit sales. Empirical application of the nested logit model showed that the proposed model outperformed the IIA (Independence of Irrelevant Alternatives) model in both calibration and holdout samples. The other comparison model that assumes choice between new and used cars at the first stage and car model choice at the second stage turned out to be mis-specfied since the dissimilarity parameter (i.e., inclusive or categroy value parameter) was estimated to be greater than 1. Post hoc analysis based on estimated parameters was conducted employing the modified Lanczo's iterative method. This method is intuitively appealing. For example, suppose a new car offers a certain amount of rebate and gains market share at first. In response to this rebate, a used car of the same model keeps decreasing price until it regains the lost market share to maintain the status quo. The new car settle down to a lowered market share due to the used car's reaction. The method enables us to find the amount of price discount to main the status quo and equilibrium market shares of the new and used cars. In the first simulation, I used Jetta as a focal brand to see how its new and used cars set prices, rebates or APR interactively assuming that reactive cars respond to price promotion to maintain the status quo. The simulation results showed that the IIA model underestimates cross elasticities, resulting in suggesting less aggressive used car price discount in response to new cars' rebate than the proposed nested logit model. In the second simulation, I used Elantra to reconfirm the result for Jetta and came to the same conclusion. In the third simulation, I had Corolla offer $1,000 rebate to see what could be the best response for Elantra's new and used cars. Interestingly, Elantra's used car could maintain the status quo by offering lower price discount ($160) than the new car ($205). In the future research, we might want to explore the plausibility of the alternative nested logit model. For example, the NUB model that assumes choice between new and used cars at the first stage and brand choice at the second stage could be a possibility even though it was rejected in the current study because of mis-specification (A dissimilarity parameter turned out to be higher than 1). The NUB model may have been rejected due to true mis-specification or data structure transmitted from a typical car dealership. In a typical car dealership, both new and used cars of the same model are displayed. Because of this fact, the BNU model that assumes brand choice at the first stage and choice between new and used cars at the second stage may have been favored in the current study since customers first choose a dealership (brand) then choose between new and used cars given this market environment. However, suppose there are dealerships that carry both new and used cars of various models, then the NUB model might fit the data as well as the BNU model. Which model is a better description of the data is an empirical question. In addition, it would be interesting to test a probabilistic mixture model of the BNU and NUB on a new data set.