• Journal of the Korean Institute of Landscape Architecture
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• v.38 no.1
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• pp.1-11
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• 2010

The purpose of this paper is to evaluate supply factors of urban parks to answer the research question: What are the causal effects of supply factors of urban parks on visitor satisfaction? After reviewing the literature and the Korean park planning process, we constructed a conceptual framework and have formulated the hypothesis of this research. We had obtained data through a questionnaire, which surveyed 452 visitors at 8 urban parks in Daegu Metropolitan City in 2008, based on a stratified sampling method. After the elimination of 96 unsuitable samples, we have analyzed the data using descriptive statistical methods, Pearson's correlation analysis and a path analysis method. We have found that: 1) While the direct and indirect effect of accessibility(ACC) on visitor satisfaction(VS) turned out to be 0.184 and 0.220, respectively, the indirect effect of information(IFM) and promotion(PRM) on VS turned out to be 0.101 and 0.177, respectively. 2) While the direct and indirect effect of service(SVR) on VS turned out to be 0.130 and 0.236, respectively, the direct effect of ACC turned out to be 0.698. 3) While the direct effect of ACC, SVR and attraction(ATT) on VS turned out to be 0.184, 0.130 and 0.698, respectively, composing 67.96% of causal effect, the indirect effect of ACC, IFM, PRM and SVR on VS turned out to be 0.220, 0.101, 0.177 and 0.236, respectively, composing 42.04% of causal effect. 4) The magnitude of causal effect of supply factors on VS turned out to be ATT(39.98%), ACC(23.14%), SVR(20.96%), PRM(10.14%) and IFM(5.78%) in order, and 5) the causal effect of external supply factors of ACC, IFM and PRM compose 39.06% of the causal effect while that of the internal supply factors of SVR and ATT is 69.94%. The research results suggest that: 1) Planning for park marketing strategy and remedial directions for existing urban parks, in order to increase visitor satisfaction, be focused on IFM and PRM, especially. 2) The research approach and path analysis method adopted by this research be valid and highly useful for planning and evaluation of other recreation areas. It is recommended that: 1) Structural Equation Model on supply factors of urban parks be established in the future. 2) Evaluation of supply factors by type of urban park be performed.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.36 no.3
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• pp.149-159
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• 2018

The study is aimed at establishing basic data to identify the original forms of the garden in Unbo's House. Through interviews and related data comparison analysis the ideas and major events of the person involved in establishing a garden in Unbo's House. Also, derived the direct and indirect effects of these people on gardening. The results are as follows: First, Those directly related to garden in Unbo's House are representative figures of cultural heritage and traditional art in Korea. Based on their expertise and abundant experience in traditional spaces, they contributed greatly to the creation of Unbo's House as a traditional space. Unbo Kim, Gi-Chang who directly influenced Unbo's House gardening, which affected the site selection, location, and the installation of major garden plant plants and traditional landscaping facilities. Hyegok Choi, Soon-Woo recommended Wabon Kim, Dong-Hyun at the request of Kim Ki-Chang and was involved in the overall plan. Housing design, space design and design of major facilities such as pond were confirmed Wabon Kim Dong-Hyun. Second, Kim, Gi-Chang's wife Jeong, Rae-Hyun, who motived to construct a garden. Nosan Lee, Eun-Sang and Korean artist Lee, Seok-Ho were created a signboard and board of the Pillar to encourage simple life in paintings. The themes of the article motived image as creative. In addition, Kim, Hyeong-Sik and Kim, Wan who son of Unbo, has been with Unbo for a long time, watching and influencing garden changes in Unbo's House. Third, The main factors that influenced the garden by character are as follows. Unbo Kim, Ki-Chang had a thorough record-setting spirit and his longing for his mother and wife affected the selection of the site and setting the direction of the garden. His art world with the symbolic emphasis of traditional landscapes, including traditional facilities, and especially plant materials. Choi, Sun-Woo reflected his traditional consciousness and experience in the construction of Unbo's House. Kim, Dong-Hyun applied the basic framework of a traditional building based on Yeonkyongdang hall in Changdeokgung palace. He also reflected on the traditional landscaping design the facilities of ponds acquired through excavation of Donggung Palace and Wolji Pond, Gyeongju. Nosan Lee, Eun-Sang and Lee, Seok-Ho completed their unique place in Unbo's House. Kim, Hyung-Sik was involved in the process of changing, while Kim Wan ran the Unbo's House which he inherited from Kim, Ki-Chang.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.67-101
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• 2014

The use of new technology greatly shapes the marketing strategies used by companies to engage their consumers. Among these new technologies, social media is used to reach out to the organization's audience online. One of the most popular social media channels to date is the microblogging platform Twitter. With 500 million tweets sent on average daily, the microblogging platform is definitely a rich source of data for researchers, and a lucrative marketing medium for companies. Nonetheless, one of the challenges for companies in developing an effective Twitter campaign is the limited theoretical and empirical evidence on the proper organizational usage of Twitter despite its potential advantages for a firm's external communications. The current study aims to provide empirical evidence on how firms can utilize Twitter effectively in their marketing communications using the association between brand personality and brand engagement that several branding researchers propose. The study extends Aaker's previous empirical work on brand personality by applying the Brand Personality Scale to explore whether Twitter brand communities convey distinctive brand personalities online and its influence on the communities' level or intensity of consumer engagement and sentiment quality. Moreover, the moderating effect of the product involvement construct in consumer engagement is also measured. By collecting data for a period of eight weeks using the publicly available Twitter application programming interface (API) from 23 accounts of Twitter-verified business-to-consumer (B2C) brands, we analyze the validity of the paper's hypothesis by using computerized content analysis and opinion mining. The study is the first to compare Twitter marketing across organizations using the brand personality concept. It demonstrates a potential basis for Twitter strategies and discusses the benefits of these strategies, thus providing a framework of analysis for Twitter practice and strategic direction for companies developing their use of Twitter to communicate with their followers on this social media platform. This study has four specific research objectives. The first objective is to examine the applicability of brand personality dimensions used in marketing research to online brand communities on Twitter. The second is to establish a connection between the congruence of offline and online brand personalities in building a successful social media brand community. Third, we test the moderating effect of product involvement in the effect of brand personality on brand community engagement. Lastly, we investigate the sentiment quality of consumer messages to the firms that succeed in communicating their brands' personalities on Twitter.

• Journal of Food Hygiene and Safety
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• v.30 no.3
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• pp.217-226
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• 2015

This study was conducted to establish a standardized process for developing food safety health indicators. With this aim, we proposed a standardized process, accessed the validity of the suggested process by performing simulations, and provided a method to utilize the indicators. Developing process for domestic environmental health indicators was benchmarked to propose a standardized process for developing food safety health indicators, and DPSEEA framework was applied to the development of indicators. The suggested standardized process consists of an exploitation stage and a management stage. In the exploitation stage, a total of 6 procedures (initial indicators suggestion, candidate indicators selection, data availability assessment, feasibility assessment, pilot study, and final indicator selection) are conducted, and the indicators are routinely calculated and officially announced in the management stage. The exploitation stage is operated by an interaction between a task force team who manages the overall process, and an advisory committee (minimum of 4 in academia, 2 in research, 4 in specialists of Ministry of Food and Drug Safety) who reviews and performs evaluations on the indicators. The standardized process was simulated with 45 initial indicators, and total of 4 indicators (17 detailed indicators) were selected: 'Proportion of domestic fruit/vegetable receiving 'acceptable' in the evaluation of pesticide/herbicide residues', 'Food-borne disease outbreaks', 'Food-borne legal infectious disease incidence', 'Salmonellosis incidence'. Synthetic food safety health index was derived by calculating percent difference with the data from 2010 to 2012. Results showed that when comparing the year 2010 to 2011, and 2011 to 2012, the overall food safety status improved by 10.37% and 9.87%, respectively. In addition, the contribution of indicators to the overall food safety status can be determined by looking into the individual indicators, and the synthetic index may be illustrated to enhance the ease of interpretation to the public and policy makers. In overall, food health safety indicators can be useful in many ways and therefore, attention should be drawn to conduct further studies and establish related legislations.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.6
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• pp.189-207
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• 2020

The government has achieved quantitative growth in university start-up education while supporting start-up education. However, it failed to systematize start-up education from an academic, policy, and practical perspective and to reveal the relationship between education and achievements in supporting start-ups. Therefore, there is a lack of interest and effort to promote effective education. In Europe, in-depth research has already been done over many years to establish an EntreComp system. Competences create values for others and attempt to apply them to education, viewing them as the people's lifelong competitiveness. On the other hand, it is urgent to improve the education system as domestic university start-up education is mainly focused on cultural level start-up skills and easy-to-access education from a business administration perspective. Based on this, the entrepreneurship competence-based start-up education system was designed. Next, eight EntreComp frames were drawn for university students through the Focus Group Interview (FGI) and Delphi survey methods, as well as domestic and international prior studies on EntreComp. In 2018, 919 start-up education programs of 42 start-up leading universities were conducted to derive the status of education by EntreComp. Prior studies of 25 entrepreneurship competences, including data from Bacigalupo et al.(2016), which studied EntreComp in the EU, were investigated and reflected the frequency of research and the importance of education and start-up perspectives. Based on the purpose of the university start-up education presented in this study, the entrepreneurship competence frame consisting of a total of eight, including spotting opportunities, value creation, self improvement, mobilising resources, technology application, strategic management, relationship, and learning through experience, was derived through expert verification. It also investigated the current status of education by competence, the degree of reflection of competence education, and the relationship with the results of support for start-ups that reflect the number of students enrolled in each university. Through this, it was suggested that future start-up education at universities could be improved from the EntreComp perspective. It has a differentiation in research in that it conducted a thorough survey using the data on start-up courses operated by leading startup universities for a certain period. However, it is difficult to generalize because the number of samples of leading startup universities is limited. Nevertheless, this study proposes the educational goal of advancing university start-up education from the perspective of entrepreneurial competence, cultivating future required competences, and fostering entrepreneurial talents that create value for others. In addition, it is meaningful in that it presents a clear direction for subsequent research by preparing a framework for research from a more essential perspective on the entrepreneurship competence frame.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.69-88
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• 2022

One of the characteristics of South Korea's economic structure is that it is highly dependent on exports. Thus, many businesses are closely related to the global economy and diplomatic situation. In addition, small and medium-sized enterprises(SMEs) specialized in exporting are struggling due to the spread of COVID-19. Therefore, this study aimed to develop a model to forecast exports for next year to support SMEs' export strategy and decision making. Also, this study proposed a strategy to recommend promising export countries of each item based on the forecasting model. We analyzed important variables used in previous studies such as country-specific, item-specific, and macro-economic variables and collected those variables to train our prediction model. Next, through the exploratory data analysis(EDA) it was found that exports, which is a target variable, have a highly skewed distribution. To deal with this issue and improve predictive performance, we suggest a separated learning method. In a separated learning method, the whole dataset is divided into homogeneous subgroups and a prediction algorithm is applied to each group. Thus, characteristics of each group can be more precisely trained using different input variables and algorithms. In this study, we divided the dataset into five subgroups based on the exports to decrease skewness of the target variable. After the separation, we found that each group has different characteristics in countries and goods. For example, In Group 1, most of the exporting countries are developing countries and the majority of exporting goods are low value products such as glass and prints. On the other hand, major exporting countries of South Korea such as China, USA, and Vietnam are included in Group 4 and Group 5 and most exporting goods in these groups are high value products. Then we used LightGBM(LGBM) and Exponential Moving Average(EMA) for prediction. Considering the characteristics of each group, models were built using LGBM for Group 1 to 4 and EMA for Group 5. To evaluate the performance of the model, we compare different model structures and algorithms. As a result, it was found that the separated learning model had best performance compared to other models. After the model was built, we also provided variable importance of each group using SHAP-value to add explainability of our model. Based on the prediction model, we proposed a second-stage recommendation strategy for potential export countries. In the first phase, BCG matrix was used to find Star and Question Mark markets that are expected to grow rapidly. In the second phase, we calculated scores for each country and recommendations were made according to ranking. Using this recommendation framework, potential export countries were selected and information about those countries for each item was presented. There are several implications of this study. First of all, most of the preceding studies have conducted research on the specific situation or country. However, this study use various variables and develops a machine learning model for a wide range of countries and items. Second, as to our knowledge, it is the first attempt to adopt a separated learning method for exports prediction. By separating the dataset into 5 homogeneous subgroups, we could enhance the predictive performance of the model. Also, more detailed explanation of models by group is provided using SHAP values. Lastly, this study has several practical implications. There are some platforms which serve trade information including KOTRA, but most of them are based on past data. Therefore, it is not easy for companies to predict future trends. By utilizing the model and recommendation strategy in this research, trade related services in each platform can be improved so that companies including SMEs can fully utilize the service when making strategies and decisions for exports.

• Journal of The Korean Society of Grassland and Forage Science
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• v.43 no.4
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• pp.257-267
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• 2023

The grassland section of the greenhouse gas inventory has limitations due to a lack of review and verification of biomass compared to organic carbon in soil while grassland is considered one of the carbon storages in terrestrial ecosystems. Considering the situation at internal and external where the calculation of greenhouse gas inventory is being upgraded to a method with higher scientific accuracy, research on standards and methods for calculating carbon accumulation of grassland biomass is required. The purpose of this study was to identify international trends in the calculation method of the grassland biomass sector that meets the Tier 2 method and to conduct a review of variables applicable to the Republic of Korea. Identify the estimation methods and access levels for grassland biomass through the National Inventory Report in the United Nations Framework Convention on Climate Change and type the main implications derived from overseas cases. And, a field survey was conducted on 28 grasslands in the Republic of Korea to analyse the applicability of major issues. Four major international issues regarding grassland biomass were identified. 1) country-specific coefficients by land use; 2) calculations on woody plants; 3) loss and recovery due to wildfire; 4) amount of change by human activities. As a result of field surveys and analysis of activity data available domestically, it was found that there was a significant difference in the amount of carbon in biomass according to use type classification and climate zone-soil type classification. Therefore, in order to create an inventory of grassland biomass at the Tier 2 level, a policy and institutional system for making activity data should develop country-specific coefficients for climate zones and soil types.

• Journal of Venture Innovation
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• v.5 no.3
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• pp.81-98
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• 2022

In the recent COVID-19 pandemic, urban agriculture is attracting attention as a healing concept. In 2020, 1,848,000 people participated in urban agriculture activities in Korea. Therefore, this study was conducted to empirically analyze the factors affecting the intention to participate in urban agriculture, which is rapidly increasing. The theoretical basis of this study is the experiential economy theory of Pine II and Gilmore and the experiential theory of Schmitt. As independent variables, a total of five variables were set as the four elements of Pine II and Gilmore's experiential economy theory, namely, educational, entertainment, escapist, and aesthetic experiences, and relational experience reclassified using Schmitt's theory. Interest was set as a mediating variable between these independent variables and the dependent variable, intention to participate in urban agriculture. For empirical analysis, data were collected through a survey. Based on the significant 314 samples of the collected data, the hypothesis was tested through statistical analysis. First, as a result of testing the influence relationship between the independent and dependent variables, educational, entertainment, and escapist experiences had a significant positive (+) effect on the intention to participate in urban agriculture. The impact of the influence was in the order of entertainment experience, escapist experience, and educational experience. There was no significant influence relationship between aesthetic experience, relational experience and intention to participate in urban agriculture. On the other hand, as a result of this study, interest introduced as a mediating variable was found to play a mediating role between entertainment, escapist, aesthetic experiences and intention to participate in urban agriculture. The mediating effect of interest was not tested between educational, relational experiences and intention to participate in urban agriculture. This study approached urban agriculture participation from the concept of healing and analyzes the factors affecting participation in urban agriculture activities empirically based on a theoretical framework by combining and analyzing the representative Pine II and Gilmore theories and Schmitt theories. It had academic significance. In addition, it was meaningful to suggest that the healing concept approach is directional in relation to urban agriculture by revealing that entertainment and escapist experiences are important influencing variables in decision-making to participate in urban agriculture in practice.

• Information Systems Review
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• v.14 no.3
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• pp.75-97
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• 2012

Fostering trusting belief in financial transactions is a challenging task in Internet banking services. Authenticated Certificate had been regarded as an effective method to guarantee the trusting belief for online transactions. However, previous research claimed that this method has some loopholes for such abusers as hackers, who intend to attack the financial accounts of innocent transactors in Internet. Two types of methods have been suggested as alternatives for securing user identification and activity in online financial services. Control transparency uses information over the transaction process to verify and to control the transactions. Outcome feedback, which refers to the specific information about exchange outcomes, provides information over final transaction results. By using these two methods, financial service providers can send signals to involved parties about the robustness of their security mechanisms. These two methods-control transparency and outcome feedback-have been widely used in the IS field to enhance the quality of IS services. In this research, we intend to verify that these two methods can also be used to reduce risks and to increase the security protections in online banking services. The purpose of this paper is to empirically test the effects of the control transparency and the outcome feedback on the risk perceptions in Internet banking services. Our assumption is that these two methods-control transparency and outcome feedback-can reduce perceived risks involved with online financial transactions, while increasing perceived trust over financial service providers. These changes in user attitudes can increase the level of user satisfactions, which may lead to the increased user loyalty as well as users' willingness to pay for the financial transactions. Previous research in IS suggested that the increased level of transparency on the process and the result of transactions can enhance the information quality and decision quality of IS users. Transparency helps IS users to acquire the information needed to control the transaction counterpart and thus to complete transaction successfully. It is also argued that transparency can reduce the perceived transaction risks in IS usage. Many IS researchers also argued that the trust can be generated by the institutional mechanisms. Trusting belief refers to the truster's belief for the trustee to have attributes for being beneficial to the truster. Institution-based trust plays an important role to enhance the probability of achieving a successful outcome. When a transactor regards the conditions crucial for the transaction success, he or she considers the condition providers as trustful, and thus eventually trust the others involved with such condition providers. In this process, transparency helps the transactor complete the transaction successfully. Through the investigation of these studies, we expect that the control transparency and outcome feedback can reduce the risk perception on transaction and enhance the trust with the service provider. Based on a theoretical framework of transparency and institution-based trust, we propose and test a research model by evaluating research hypotheses. We have conducted a laboratory experiment in order to validate our research model. Since the transparency artifact(control transparency and outcome feedback) is not yet adopted in online banking services, the general survey method could not be employed to verify our research model. We collected data from 138 experiment subjects who had experiences with online banking services. PLS is used to analyze the experiment data. The measurement model confirms that our data set has appropriate convergent and discriminant validity. The results of testing the structural model indicate that control transparency significantly enhances the trust and significantly reduces the risk perception of online banking users. The result also suggested that the outcome feedback significantly enhances the trust of users. We have found that the reduced risk and the increased trust level significantly improve the level of service satisfaction. The increased satisfaction finally leads to the increased loyalty and willingness to pay for the financial services.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.1-8
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• 2013

Data mining techniques are used to find important and meaningful information from huge databases, and pattern mining is one of the significant data mining techniques. Pattern mining is a method of discovering useful patterns from the huge databases. Frequent pattern mining which is one of the pattern mining extracts patterns having higher frequencies than a minimum support threshold from databases, and the patterns are called frequent patterns. Traditional frequent pattern mining is based on a single minimum support threshold for the whole database to perform mining frequent patterns. This single support model implicitly supposes that all of the items in the database have the same nature. In real world applications, however, each item in databases can have relative characteristics, and thus an appropriate pattern mining technique which reflects the characteristics is required. In the framework of frequent pattern mining, where the natures of items are not considered, it needs to set the single minimum support threshold to a too low value for mining patterns containing rare items. It leads to too many patterns including meaningless items though. In contrast, we cannot mine any pattern if a too high threshold is used. This dilemma is called the rare item problem. To solve this problem, the initial researches proposed approximate approaches which split data into several groups according to item frequencies or group related rare items. However, these methods cannot find all of the frequent patterns including rare frequent patterns due to being based on approximate techniques. Hence, pattern mining model with multiple minimum supports is proposed in order to solve the rare item problem. In the model, each item has a corresponding minimum support threshold, called MIS (Minimum Item Support), and it is calculated based on item frequencies in databases. The multiple minimum supports model finds all of the rare frequent patterns without generating meaningless patterns and losing significant patterns by applying the MIS. Meanwhile, candidate patterns are extracted during a process of mining frequent patterns, and the only single minimum support is compared with frequencies of the candidate patterns in the single minimum support model. Therefore, the characteristics of items consist of the candidate patterns are not reflected. In addition, the rare item problem occurs in the model. In order to address this issue in the multiple minimum supports model, the minimum MIS value among all of the values of items in a candidate pattern is used as a minimum support threshold with respect to the candidate pattern for considering its characteristics. For efficiently mining frequent patterns including rare frequent patterns by adopting the above concept, tree based algorithms of the multiple minimum supports model sort items in a tree according to MIS descending order in contrast to those of the single minimum support model, where the items are ordered in frequency descending order. In this paper, we study the characteristics of the frequent pattern mining based on multiple minimum supports and conduct performance evaluation with a general frequent pattern mining algorithm in terms of runtime, memory usage, and scalability. Experimental results show that the multiple minimum supports based algorithm outperforms the single minimum support based one and demands more memory usage for MIS information. Moreover, the compared algorithms have a good scalability in the results.