• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.3
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• pp.73-86
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• 2021

The primary purpose of this paper is to explore the advanced models that predict entrepreneurial innovativeness most accurately. For the first time in the field of entrepreneurship research, it presents a model that predicts entrepreneurial innovativeness based on machine learning corresponding to data scientific approaches. It uses 22,099 the Global Entrepreneurship Monitor (GEM) data from 62 countries to build predictive models. Based on the data set consisting of 27 explanatory variables, it builds predictive models that are traditional statistical methods such as multiple regression analysis and machine learning models such as regression tree, random forest, XG boost, and artificial neural networks. Then, it compares the performance of each model. It uses indicators such as root mean square error (RMSE), mean analysis error (MAE) and correlation to evaluate the performance of the model. The analysis of result is that all five machine learning models perform better than traditional methods, while the best predictive performance model was XG boost. In predicting it through XG boost, the variables with high contribution are entrepreneurial opportunities and cross-term variables of market expansion, which indicates that the type of entrepreneur who wants to acquire opportunities in new markets exhibits high innovativeness.

• Journal of Practical Agriculture & Fisheries Research
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• v.12 no.1
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• pp.137-150
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• 2010

Traditionally, like many people in mountain region of the Himalaya, the Lwang communities depend on mix of subsistence agriculture, animal husbandry, and seasonal migrant labor for their livelihoods. These traditional systems are characterized by low productivity, diverse use of available natural resources (largely for home consumption), limited markets, and some aversion for innovation. The potential to generate wealth through commerce has largely been untapped by these mountain residents and thus is undervalued in local and national economies. Introduction of organic tea farming is a part of Lwang community's several initiatives to break the vicious poverty cycle Annapurna Conservation Area Project (ACAP) played facilitating roles in all their efforts since beginning. In five years, the tea plantation emerged as a new means for secured a livelihood. This study aims to analyze the current practices in tea farming both in terms of farm management and soil nutrient status(technical) and the prosperity of the tea farmers (social). The technical aspect covers the soil and tea leaf analysis of various nutrients contents in the soil and tea leaf. Originally, the technical aspect of the study was not planned but later during the consultation with the advisor it was taken into consideration which added value to the research study. The sample were collected from different locations and analyzed on the field itself. The other part of the study i.e. the social aspect was done through questionnaire survey and focus group discussion. the tea farming provided them not only a new opportunity but also earned an identity in the region. This initiative was undertaken as a piloting measure. Now that the tea is in production with processing unit established locally, more serious consideration has to be given for better yield and economic prosperity. This research finding will help the community to analyze their efforts and make correction measures in tea garden management and application of fertilizer. It is also expected to fill up the gaps of knowledge and information required to reduce economic stresses and enhance capacity of farmers to make the tea farming a sustainable and beneficial business. The findings are expected to Sustainability of organic tea farming has direct impacts on biodiversity conservation compared to the other traditional farming practices that are more resource intensive. The study will also contribute to identify key action points required for reducing poverty while conserving environment and enhancing livelihoods

• The Korean Journal of Franchise Management
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• v.3 no.1
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• pp.73-91
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• 2012

This study is in the purpose for the revitalization of traditional market as comparing to the relevancy between the central characteristics of a floating population going around for buying something or eating food and lots of small-sized businesses comprising of the commercial districts. The several traditional markets such as Cheonan station, Dujeong-dong, Sinbu-dong in Cheon-An city has been investigated repeatedly almost every two or three years by the Small Enterprise development Agency(SEDA) since 2001. By analyzing the raw data of those commercial districts made by SEDA, we can calculate the number of firms andthe ratio of business type of each commercial districts. In this research, the type of each business is classified into four groups such as restaurant, service, retail and the rest. Moreover, the central character of the floating population is derived from the raw data, which means the customer information about sex, age structure or the most populous time zones. From these characteristics, one commercial districts has his own specific features distinguishing from the others. The most important differences of past researches are firstly the dynamic viewpoint rather than a static one. Secondly it suggests that the relation between the central characteristics of districts and the floating population would exist. Lastly, it suggests that the interaction between both of them have a significant effect on the growth or decline of the districts and the rates of business type, other adjacent commercial districts as well. Eventually, this study provides several meaningful points for the revitalization of commercial districts to government or stakeholder such as management organization, business owners and new starter etc.

• Journal of the Korean Geographical Society
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• v.30 no.1
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• pp.68-92
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• 1995

Recentry the structure of the retail trade have been chanaed with its environmantal changes. Some studies may be necessary on the changing process of environment and fundamental structure analyses of the retail trade. This study analyzes the location of retail trades, inhabitants' behavior in retail tredes and their desirable utilization scheme of them in Kwangju-si. Some study methods, contents and coming-out results are as follows: 1. Retail trades can be classified into independent stores, chain-stores (supermarket, voluntary chain and frenchiise system and convenience store), department stores, cooperative associations, traditional, markets mail-order marketing, automatic vending and others by service levels, selling-items, prices, managements, methods of retailing and store or nonstore type. 2. In Kwangju, the environment of retail trades is related to the consumers of population structure: chanes in consumers pattern, trends toward agings and nuclear family, increase of leisur: time and female advances to society. Rapid structural shift in retail trade has also been occurred due to these social changes. Traditionl and premodern markets until 1970s altere to supermarkets or department stores in 1980s, and various types, large enterprises and foreign capitals came into being in 1990s. 3. The locational characteristics of retail trades are resulted from the spatial analysis of the total population distribution, and from the calculation of segregation index in the light of potential demand. The densely-populated areas occurs in newly-built apartment housing complex which is distributed with a ring-shaped pattern around the old urban core. The numbers and rates of the aged over sixty in Kwangsan-gu and the circumference area of Mt.Moodeung, are larger and higher where rural elements are remarkable. A relation between population distribution and retail trade are analysed by the index of population per shop. The index of the population number per shop is lower in urban center, as a whole, being more convenient for consumers. In newly-formed apartment complex areas, on the other, the index more than 1,000 per shop, meeting not the demands for consumers. Because both the younger and the aged are numerous in these areas, the retail trade pattern pertinent to both are needed. Urban fringes including Kwangsan-gu and the vicinity of Mt.Moodeung have some problems owing to the most of population number per shop (more than 1, 500) and the most extensive as well. 4. The regional characteristic of retail trade is analyzed through the location quotient of shops by locational patterns and centerality index. Chungkum-dong is the highest-order central place in CBD. It is the core of retail trades, which has higher-ordered specialty store including three big department stores, supermarkets and large stores. Taegum-dong, Chungsu-dong, Taeui-dong, and Numun-dong that are neiahbored to Chungkum-dong fall on the second group. They have a central commercial section where large chain stores, specialty shopping streets, narrow-line retailing shops (furniture, amusement service, and gallary), supermarkets and daily markets are located. The third group is formed on the axis of state roads linking to Naju-kun, Changseong-kun, Tamyang-kun, Hwasun-kun and forme-Songjeong-eup. It is related to newly, rising apartment housing complex along a trunk road, and characterized by markets and specialty stores. The fourth group has neibourhood-shopping centers including older residential area and Songjeong-eup area with independent stores and supermarkets as main retailing functions. The last group contains inner residential area and outer part of a city including Songjeong-eup. Outer part of miscellaneous shops being occasionally found is rural rather than urban (Fig. 7). 5. The residents' behaviors using retail trade are analyzed by factors of goods and facilities. Department stores are very high level in preference for higher-order shopping-goods such as clothes for full dress in view of both diversity and quality of goods(28.9%). But they have severe traffic congestions, and high competitions for market ranges caused by their sma . 64.0% of respondents make combined purpose trips together with banking and shopping. 6. For more efficiency of retail-trading, it is necessary to induce spatial distribution policy with regard to opportunity frequency of goods selection by central place, frontier regions and age groups. Also we must consider to analyze competition among different types of retail trade and analyze the consumption behaviors of working females and younger-aged groups, in aspects of time and space. Service improvement and the rationalization of management should be accomplished in such as cooperative location (situation) must be under consideration in relations to other functions such as finance, leisure & sports, and culture centers. Various service systems such as installment, credit card and peremium ticket, new used by enterprises, must also be carried service improvement. The rationalization and professionalization in for the commercial goods are bsically requested.

• Korean Journal of Heritage: History & Science
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• v.44 no.4
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• pp.42-55
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• 2011

The basic approach of this study was to take the theory of cultural fluctuations to investigate the early modern and modern patterns of the use of ox carts and@ the social and economic appropriateness and cultural significance of ox carts. The study chose a village that was the only place that used ox carts in Bugye-myeon. The findings will help to understand how traditional cultural elements would continue or change according to the natural, geographical, economical, and cultural characteristics of a village. Located in Gaho-2-ri, Bugye-myeon, Gunwi-gun, Gyeongbuk Province, Dongrim Village started to use ox carts during the Japanese rule and replaced the traditional version with an improved one in 1972 when a reservoir was built. Until the 1970s, they used ox carts to carry agricultural products and luggage and to visit the markets in distant Bugye-myeon or Gunwi-eup. In the early 1980s when a cultivator was first introduced into the village, ox carts gradually disappeared in the village and eventually remained as a mere means of transportation. As the younger generations were active in introducing modern means of transportation, a cultivator became the main means of transportation in the village in the 1980s and a truck since the latter half of the 1990s. Despite those changes, however, the elderly in their seventies or older continued to use ox carts. With aged labor and inability to use modern means of transportation, they grew cows and oxen to cultivate the inclined fields and gain easy access to fields distributed in distant locations and continued to ox carts through reform. In Dongrim Village, the heritage of using reformed ox carts is the practice of appropriate technology by the old farmers and a cultural representation of an aged agricultural society. That is, the elderly recognized the appropriateness and practicality of traditional culture and renewed a traditional means of transportation called an ox cart. The phenomenon of the old men and women frequently using ox carts in an agricultural village in the mountain with geographical limitations has settled down as a cultural representation of the elderly in Dongrim Village. The continuing usage of ox carts in Dongrim Village is attributed to the fact that ox carts well suit the natural, geographical, and economic aspects of the village and the cultural inertia of the elderly with the aging of the farmers. Thus it is once again shown that human beings transmit and alter culture according to their overall situations and conditions.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.23-38
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• 2012

There is a large difference between purchasing patterns in an online shopping mall and in an offline market. This difference may be caused mainly by the difference in accessibility of online and offline markets. It means that an interval between the initial purchasing decision and its realization appears to be relatively short in an online shopping mall, because a customer can make an order immediately. Because of the short interval between a purchasing decision and its realization, an online shopping mall transaction usually contains fewer items than that of an offline market. In an offline market, customers usually keep some items in mind and buy them all at once a few days after deciding to buy them, instead of buying each item individually and immediately. On the contrary, more than 70% of online shopping mall transactions contain only one item. This statistic implies that traditional data mining techniques cannot be directly applied to online market analysis, because hardly any association rules can survive with an acceptable level of Support because of too many Null Transactions. Most market basket analyses on online shopping mall transactions, therefore, have been performed by expanding the co-occurrence criteria of traditional association rule mining. While the traditional co-occurrence criteria defines items purchased in one transaction as concurrently purchased items, the expanded co-occurrence criteria regards items purchased by a customer during some predefined period (e.g., a day) as concurrently purchased items. In studies using expanded co-occurrence criteria, however, the criteria has been defined arbitrarily by researchers without any theoretical grounds or agreement. The lack of clear grounds of adopting a certain co-occurrence criteria degrades the reliability of the analytical results. Moreover, it is hard to derive new meaningful findings by combining the outcomes of previous individual studies. In this paper, we attempt to compare expanded co-occurrence criteria and propose a guideline for selecting an appropriate one. First of all, we compare the accuracy of association rules discovered according to various co-occurrence criteria. By doing this experiment we expect that we can provide a guideline for selecting appropriate co-occurrence criteria that corresponds to the purpose of the analysis. Additionally, we will perform similar experiments with several groups of customers that are segmented by each customer's average duration between orders. By this experiment, we attempt to discover the relationship between the optimal co-occurrence criteria and the customer's average duration between orders. Finally, by a series of experiments, we expect that we can provide basic guidelines for developing customized recommendation systems. Our experiments use a real dataset acquired from one of the largest internet shopping malls in Korea. We use 66,278 transactions of 3,847 customers conducted during the last two years. Overall results show that the accuracy of association rules of frequent shoppers (whose average duration between orders is relatively short) is higher than that of causal shoppers. In addition we discover that with frequent shoppers, the accuracy of association rules appears very high when the co-occurrence criteria of the training set corresponds to the validation set (i.e., target set). It implies that the co-occurrence criteria of frequent shoppers should be set according to the application purpose period. For example, an analyzer should use a day as a co-occurrence criterion if he/she wants to offer a coupon valid only for a day to potential customers who will use the coupon. On the contrary, an analyzer should use a month as a co-occurrence criterion if he/she wants to publish a coupon book that can be used for a month. In the case of causal shoppers, the accuracy of association rules appears to not be affected by the period of the application purposes. The accuracy of the causal shoppers' association rules becomes higher when the longer co-occurrence criterion has been adopted. It implies that an analyzer has to set the co-occurrence criterion for as long as possible, regardless of the application purpose period.

• Journal of Global Scholars of Marketing Science
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• v.20 no.2
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• pp.110-122
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• 2010

The emergence of the field of organizational buying behavior in the mid-1960’s with the publication of Industrial Buying and Creative Marketing (1967) set the stage for a new paradigm of thinking about how business was conducted in markets other than those serving ultimate consumers. Whether it is "industrial marketing" or "business-to-business marketing" (B-to-B), organizational buying behavior remains the core differentiating characteristic of this domain of marketing. This paper explores the impact of several dynamic factors that have influenced how organizations relate to one another in a rapidly increasing interdependence, which in turn can impact organizational buying behavior. The paper also raises the question of whether or not the major conceptual models of organizational buying behavior in an interdependent world are still relevant to guide research and managerial thinking, in this dynamic business environment. The paper is structured to explore three questions related to organizational interdependencies: 1. What are the factors and trends driving the emergence of organizational interdependencies? 2. Will the major conceptual models of organizational buying behavior that have developed over the past half century be applicable in a world of interdependent organizations? 3. What are the implications of organizational interdependencies on the research and practice of organizational buying behavior? Consideration of the factors and trends driving organizational interdependencies revealed five critical drivers in the relationships among organizations that can impact their purchasing behavior: Accelerating Globalization, Flattening Networks of Organizations, Disrupting Value Chains, Intensifying Government Involvement, and Continuously Fragmenting Customer Needs. These five interlinked drivers of interdependency and their underlying technological advances can alter the relationships within and among organizations that buy products and services to remain competitive in their markets. Viewed in the context of a customer driven marketing strategy, these forces affect three levels of strategy development: (1) evolving customer needs, (2) the resulting product/service/solution offerings to meet these needs, and (3) the organization competencies and processes required to develop and implement the offerings to meet needs. The five drivers of interdependency among organizations do not necessarily operate independently in their impact on how organizations buy. They can interact with each other and become even more potent in their impact on organizational buying behavior. For example, accelerating globalization may influence the emergence of additional networks that further disrupt traditional value chain relationships, thereby changing how organizations purchase products and services. Increased government involvement in business operations in one country may increase costs of doing business and therefore drive firms to seek low cost sources in emerging markets in other countries. This can reduce employment opportunitiesn one country and increase them in another, further accelerating the pace of globalization. The second major question in the paper is what impact these drivers of interdependencies have had on the core conceptual models of organizational buying behavior. Consider the three enduring conceptual models developed in the Industrial Buying and Creative Marketing and Organizational Buying Behavior books: the organizational buying process, the buying center, and the buying situation. A review of these core models of organizational buying behavior, as originally conceptualized, shows they are still valid and not likely to change with the increasingly intense drivers of interdependency among organizations. What will change however is the way in which buyers and sellers interact under conditions of interdependency. For example, increased interdependencies can lead to increased opportunities for collaboration as well as conflict between buying and selling organizations, thereby changing aspects of the buying process. In addition, the importance of communication processes between and among organizations will increase as the role of trust becomes an important criterion for a successful buying relationship. The third question in the paper explored consequences and implications of these interdependencies on organizational buying behavior for practice and research. The following are considered in the paper: the need to increase understanding of network influences on organizational buying behavior, the need to increase understanding of the role of trust and value among organizational participants, the need to improve understanding of how to manage organizational buying in networked environments, the need to increase understanding of customer needs in the value network, and the need to increase understanding of the impact of emerging new business models on organizational buying behavior. In many ways, these needs deriving from increased organizational interdependencies are an extension of the conceptual tradition in organizational buying behavior. In 1977, Nicosia and Wind suggested a focus on inter-organizational over intra-organizational perspectives, a trend that has received considerable momentum since the 1990's. Likewise for managers to survive in an increasingly interdependent world, they will need to better understand the complexities of how organizations relate to one another. The transition from an inter-organizational to an interdependent perspective has begun, and must continue so as to develop an improved understanding of these important relationships. A shift to such an interdependent network perspective may require many academicians and practitioners to fundamentally challenge and change the mental models underlying their business and organizational buying behavior models. The focus can no longer be only on the dyadic relations of the buying organization and the selling organization but should involve all the related members of the network, including the network of customers, developers, and other suppliers and intermediaries. Consider for example the numerous partner networks initiated by SAP which involves over 9000 companies and over a million participants. This evolving, complex, and uncertain reality of interdependencies and dynamic networks requires reconsideration of how purchase decisions are made; as a result they should be the focus of the next phase of research and theory building among academics and the focus of practical models and experiments undertaken by practitioners. The hope is that such research will take place, not in the isolation of the ivory tower, nor in the confines of the business world, but rather, by increased collaboration of academics and practitioners. In conclusion, the consideration of increased interdependence among organizations revealed the continued relevance of the fundamental models of organizational buying behavior. However to increase the value of these models in an interdependent world, academics and practitioners should improve their understanding of (1) network influences, (2) how to better manage these influences, (3) the role of trust and value among organizational participants, (4) the evolution of customer needs in the value network, and (5) the impact of emerging new business models on organizational buying behavior. To accomplish this, greater collaboration between industry and academia is needed to advance our understanding of organizational buying behavior in an interdependent world.

• Journal of Food Hygiene and Safety
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• v.27 no.3
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• pp.277-284
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• 2012

The objective of this study was to investigate and evaluate microbial contamination levels of leafy greens (perilla leaf and lettuce) and its distributing conditions at different seasons (Feb, May, Aug, and Nov of the year 2011) in order to provide insight into any potential health hazards associated with consumption of these commodities. Leafy greens were collected from a farm located in Geumsan, Chungnam and wholesale markets (WM) and traditional markets (TM) located in Suwon. At the same time, temperature and relative humidity fluctuations experienced by the leafy greens during distribution from the farm to the distribution center were measured by a data logger. The contamination levels of perilla leaf and lettuce were determined by analyzing total plate count. Coliform groups, Bacillus cereus, Escherichia coli, Escherichia coli O157:H7, Salmonella spp., Listeria monocytogenes and Staphylococcus aureus were determined. The contamination levels of total aerobic bacteria, coliform groups and B. cereus in both vegetables sampled during May and August found to be higher than those sampled during February and November. E. coli O157:H7, Salmonella spp., L. monocytogenes were not detected in the vegetables analyzed in this study. There were no significant trends between samples at WM and TM in the contamination levels. Relative humidity of vegetables distributed from the farm to the distribution center showed over 90% during distribution regardless of measured seasons. In the case of background microflora on leafy greens, the density was significantly increased at 20, 30 and $37^{\circ}C$ during storage of 24h. E. coli O157:H7 and B. cereus inoculated on the leaves also showed similar increases in the storage tests. The microbial contamination levels determined in this study may be used as the fundamental data for microbial risk assessment.

• Korean small business review
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• v.43 no.2
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• pp.59-86
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• 2021

The platform concept has been used for business operations in various forms: product platforms, transaction platforms and industry platforms. All these platforms have common characteristics of having 'core' that is reused frequently and 'peripherals' that are less reusable and changed often. Companies use platforms to enable efficient development and creation of product family, transactions and innovation. These platforms provide new opportunities for many small and medium-sized companies (SMEs) by bringing changes to traditional industrial structures focused on the products rather than platforms. The service robotics industry in South Korea is mainly composed of technology-intensive SMEs due to its small market size. Although these SMEs succeed in developing technologies, they have difficulties creating and expanding markets to sell products. Thus, this study addresses the characteristics and problems of the South Korean service robotics industry and analyses how ROBOPRINT, one of the SMEs in the service robotics industry, successfully creates and continuously expands the service robot market by adopting platform concept. The results indicate that ROBOPRINT has been applying two types of platforms: product and transaction platforms. First, ROBOPRINT created art robots that were apartment mural service robots. Rather than selling art robots, the company developed various robots such as painting robots, building exterior wall-cleaning robots by reusing the core technology of the robots. The company also developed various robots according to the buyers request. In addition, the company used the robots to directly provide apartment mural services for customers. This mural service has been extended into various areas, not only in apartments but also in soundproof walls, underground passages, and retaining walls. Besides, ROBOPRINT added new services continuously by developing technologies such as virtual reality. Second, ROBOPRINT mediated mural service buyers and mural designers. This platform reduced buyers' workload, which necessitates requesting mural services to ROBOPRINT and searching for mural designers. For designers, this opened up new opportunities to participate in the mural business. The platform attracted both mural buyers and designers who were scattered before. Finally, ROBOPRINT seeks to expand the platform's scope to outside company. To share internally reused ROBOPRINT's technology with other companies, the company participated in Daegu city's 'New Technology Platform Industry'. Furthermore, ROBOPRINT is trying to share the service platform by leasing robots to other companies. This allows external agents to develop technologies and provide services by reusing resources from ROBOPRINT. This study contributes to existing theories by showing that SMEs continuously create and expand markets by building various platforms. Moreover, it provides useful implications for practitioners by describing the firm's specific platform-building strategy.