• Journal of the Korean Regional Science Association
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• v.40 no.2
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• pp.3-19
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• 2024

The purpose of this study is to analyze the impact of attracting a university campus on the local economies of small and medium-sized cities, focusing on changes in local commercial neighborhoods(such as the number of startups, closures, and stores counts). For this study, a Difference-in-Difference(DID) analysis was used to compare the period before and after the attraction of university campuses in four local small and medium-sized cities. These include the Yangsan Campus of Pusan National University, the Jincheon Campus of Woosuk University, the Taean Campus of Hanseo University, and the Dangjin Campus of Hoseo University. The comparison was based on the number of startups, closures, and store counts, using local data provided by the Ministry of the Interior and Safety. The main findings of the study are as follows. First, attracting a university campus has a positive impact on the number of startups, both spatially and temporally. The spatial factors for the number of closures and stores showed a decrease, while the interaction terms representing the period before and after attracting the university campus all indicated an increase. Second, the number of startups in cultural and food-related sectors increase, reflecting the new demand created by attracting the university campus. However, there was also an increase in the number of closures, indicating rapidly changing consumption trends among university students. Third, physical environmental factors such as the number of building floors, land use zoning, and officially assessed land prices have a significant impact on the number of startups, closures, and stores. This supports the assertion that attracting university campus have a positive impact on the revitalization of local commercial neighborhoods.

• Asia-Pacific Journal of Business
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• v.14 no.4
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• pp.67-85
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• 2023

Purpose - This study seeks to investigate the determinants of global sourcing intention in clothing sewing industry, in particular with its focus on women's knit wear production. Design/methodology/approach - This study collected a unique set of qualitative data through 31 in-depth interviews with fashion brands, promotion agencies, and sewing factories between July 2023 and October 2023. In addition, it analyzed the dataset using the MAXQDA to complement the research findings. Findings - We have two findings. First, the interviewees commonly mentioned the following factors as reasons for considering global sourcing: the human factors(aging of skilled technicians and labor shortages), the financial factors(gap in production unit prices at home and abroad), the relational factors(lack of novelty), and the physical factors(loss of production infrastructure and network), while the human factors(skilled workforce), the production factors(delivery date and product quality), and the relational factors(timely communication and mutual trust) as reasons for continuing domestic sourcing. Additional code analysis of interview also supports this finding. On the other hand, there was also a subtle difference between buyers(brands) and suppliers(promotion agencies and processing plants), and buyers consider the exact delivery date critical so that they could see trend-sensitive women's knit wear on time, and suppliers took production costs, labor costs, and labor shortages, which are financial factors, more seriously. Research implications or Originality - This study provides a richer and more balanced view of existing literature, which has generally tended to introduce global sourcing across the clothing industry despite the existence of various diversity within the industry. In addition, through qualitative research, we introduce that the sewing industry is carried out according to complex factors, and by revealing and categorizing the determinants of global sourcing, we supplement the existing research on the clothing sewing industry centered on survey. On a practical note, this study introduces that there is a difference in view of domestic sourcing and global sourcing between buyers(brands) and suppliers(promotion agencies and sewing factories), suggesting practical implications for revitalizing networks and deriving win-win cooperation network models among members in the future.

• Journal of Korean Home Economics Education Association
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• v.35 no.3
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• pp.169-190
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• 2023

Uzbekistan, one of the top five cotton-producing countries in the world, primarily focuses its textile and fashion industry on raw cotton exports and the sewing industry. For Uzbekistan to achieve high added value, it is essential for the textile and fashion industry, which is currently at the CMT(cut, make, and trim) stage, to upgrade to OEM (original equipment manufacturing), ODM (original design manufacturing), and OBM (original brand manufacturing). South Korea recognizes Uzbekistan as a potential manufacturing base and trading partner and has invested Official Development Assistance (ODA) funds for the development of Uzbekistan's textiles and apparel sector. This study aims to evaluate Uzbekistan's fashion higher education in the context of global competitiveness and measure the need and prospects for education ODA from the Korean government in this field. Comprehensive investigations, including surveys of academics, industry experts, and government officials, in-depth interviews, and focus group interviews, were conducted to understand Uzbekistan's current fashion education environment. According to the research results, despite the textile and fashion sectors playing a pivotal role in the Uzbek economy, there is room for improvement in the curricula and teaching and learning methods of the fashion higher education programs. This study holds significance as foundational data for establishing education ODA strategies.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.4
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• pp.109-120
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• 2020

This study examines how the government's fairness in entrepreneurship support affects satisfaction and management performance, and examines whether these influences are different for each growth stage of start-up companies. For this study, data were collected for start-up companies that received government support for start-up within the past 5 years. Total 611 copies of the data were used in this study. The collected data were analyzed using SPSS and AMOS. The fairness used in this study was divided into three types, procedural fairness, interactive fairness, and distributed fairness. The effect of the three fairness on the satisfaction of start-up support project was analyzed. In addition, the effect of business support satisfaction on business performance was analyzed, where the business performance was evaluated by the questioner's satisfaction with their business. The start-up phase was divided into the start phase, early growth phase, stagnant phase, and high-level growth phase, and the moderating effect between the fairness and satisfaction of the government-supported projects by start-up phase was analyzed. As a result, it was found that every concept of fairness had a positive (+) effect on the satisfaction of the entrepreneurship support project, and the satisfaction of the entrepreneurship support project had a positive (+) effect on the management performance. The concept of procedural fairness in the start phase, procedural fairness in the early growth phase, interactive fairness, and the concept of all fairness in the stagnant phase influenced the satisfaction of the start-up support project. In this study, the fairness and effect of government-supported projects affecting the management performance of start-ups were identified by growth phase of start-ups. The results of these studies will help build a systematic system for entrepreneurship support and for start-ups, it will also greatly contribute to finding differentiated growth plans by growth stages of start-up companies.

• Asia pacific journal of information systems
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• v.20 no.3
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• pp.115-137
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• 2010

Information technology (IT) outsourcing, the use of a third-party vendor to provide IT services, started in the late 1980s and early 1990s in Korea, and has increased rapidly since 2000. Recently, firms have increased their efforts to capture greater value from IT outsourcing. To date, there have been a large number of studies on IT outsourcing. Most prior studies on IT outsourcing have focused on outsourcing practices and decisions, and little attention has been paid to objectively measuring the value of IT outsourcing. In addition, studies that examined the performance of IT outsourcing have mainly relied on anecdotal evidence or practitioners' perceptions. Our study examines the contribution of IT outsourcing to economic growth in Korean industries over the 1990 to 2007 period, using a production function framework and a panel data set for 54 industries constructed from input-output tables, fixed-capital formation tables, and employment tables. Based on the framework and estimation procedures that Han, Kauffman and Nault (2010) used to examine the economic impact of IT outsourcing in U.S. industries, we evaluate the impact of IT outsourcing on output and productivity in Korean industries. Because IT outsourcing started to grow at a significantly more rapid pace in 2000, we compare the impact of IT outsourcing in pre- and post-2000 periods. Our industry-level panel data cover a large proportion of Korean economy-54 out of 58 Korean industries. This allows us greater opportunity to assess the impacts of IT outsourcing on objective performance measures, such as output and productivity. Using IT outsourcing and IT capital as our primary independent variables, we employ an extended Cobb-Douglas production function in which both variables are treated as factor inputs. We also derive and estimate a labor productivity equation to assess the impact of our IT variables on labor productivity. We use data from seven years (1990, 1993, 2000, 2003, 2005, 2006, and 2007) for which both input-output tables and fixed-capital formation tables are available. Combining the input-output tables and fixed-capital formation tables resulted in 54 industries. IT outsourcing is measured as the value of computer-related services purchased by each industry in a given year. All the variables have been converted to 2000 Korean Won using GDP deflators. To calculate labor hours, we use the average work hours for each sector provided by the OECD. To effectively control for heteroskedasticity and autocorrelation present in our dataset, we use the feasible generalized least squares (FGLS) procedures. Because the AR1 process may be industry-specific (i.e., panel-specific), we consider both common AR1 and panel-specific AR1 (PSAR1) processes in our estimations. We also include year dummies to control for year-specific effects common across industries, and sector dummies (as defined in the GDP deflator) to control for time-invariant sector-specific effects. Based on the full sample of 378 observations, we find that a 1% increase in IT outsourcing is associated with a 0.012~0.014% increase in gross output and a 1% increase in IT capital is associated with a 0.024~0.027% increase in gross output. To compare the contribution of IT outsourcing relative to that of IT capital, we examined gross marginal product (GMP). The average GMP of IT outsourcing was 6.423, which is substantially greater than that of IT capital at 2.093. This indicates that on average if an industry invests KRW 1 millon, it can increase its output by KRW 6.4 million. In terms of the contribution to labor productivity, we find that a 1% increase in IT outsourcing is associated with a 0.009~0.01% increase in labor productivity while a 1% increase in IT capital is associated with a 0.024~0.025% increase in labor productivity. Overall, our results indicate that IT outsourcing has made positive and economically meaningful contributions to output and productivity in Korean industries over the 1990 to 2007 period. The average GMP of IT outsourcing we report about Korean industries is 1.44 times greater than that in U.S. industries reported in Han et al. (2010). Further, we find that the contribution of IT outsourcing has been significantly greater in the 2000~2007 period during which the growth of IT outsourcing accelerated. Our study provides implication for policymakers and managers. First, our results suggest that Korean industries can capture further benefits by increasing investments in IT outsourcing. Second, our analyses and results provide a basis for managers to assess the impact of investments in IT outsourcing and IT capital in an objective and quantitative manner. Building on our study, future research should examine the impact of IT outsourcing at a more detailed industry level and the firm level.

• Knowledge Management Research
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• v.22 no.4
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• pp.103-118
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• 2021

Gross Domestic Product(GDP) is affected by the economic power of each industry. Therefore, using statistical data related to the food and drug industry, we tried to determine the proportion of GDP and analyzed the impact of the food, medical & drug industry on the domestic economy through comparison with other industries. The food, medical & drug industry has a wide range of industries among domestic industries and is closely related to the lives of the people. In addition, human lifespan is increasing, and recently, due to the spread of an infectious disease called COVID-19, the bio sector belonging to the food, medical & drug industry is in the spotlight. Attention is needed to the industry as the competitiveness of the food, medical & drug industry is expected to increase. The Ministry of Food and Drug Safety provides statistics on the food, medical & drug industry, but does not provide a systematic share of GDP. Since it is difficult to determine how influential the industry is compared to other industries, this study attempts to obtain the share of GDP in the food, medical & drug industry and compare it with other industries. In the process of obtaining GDP in the food, medical & drug industry sector, there was a difficulty in that the figures in statistical data were not unified by time point. In order to overcome the limitations, statistical data as a standard are determined. The GDP of the Food, Medical & Drug Industry was estimated using total added value, production, sales, and added value by industry. Compared to other industries, the Food, Medical & Drug Industry's GDP ranked second after the GDP of the manufacturing industry. As a result, it suggests that the food, medical & drug industry has a great influence on the national economic power among domestic industries.

• Journal of Navigation and Port Research
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• v.46 no.1
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• pp.1-10
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• 2022

This study aims to investigate changes in the demand for ship officers in response to changes in the shipping industry environment in which Maritime Autonomous Surface Ships (MASS) emerge according to the application of the fourth industrial revolution technology to ships, and it looks into changes in the skill of ship officer. It also analyzes and proposes a plan for nurturing ship officers accordingly. As a result of the degree of recognition and AHP analysis, this study suggests that a new training system is required because the current training and education system may cover the job competencies of emergency response, caution and danger navigation, general sailing, cargo handling, seaworthiness maintenance, emergency response, and ship maintenance and management, but tasks such as remote control, monitoring diagnosis, device management capability, and big data analysis require competency for unmanned and shore-based control. By evaluating the importance of change factors in the duties of ship officers in Maritime Autonomous Surface Ships, this study provides information on ship officer educational institutions' response strategies for nurturing ship officers and prioritization of resource allocation, etc. The importance of these factors was compared and evaluated to suggest changes in the duties of ship officers and methods of nurturing ship officers according to the introduction of Maritime Autonomous Surface Ships. It is expected that the findings of this study will be meaningful as it systematically derives the duties and competency factors of ship officers of Maritime Autonomous Surface Ships from a practical point of view and analyzed the perception level of each relevant expert to diagnose expert-level responses to the introduction of Maritime Autonomous Surface Ships.

• Journal of Global Scholars of Marketing Science
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• v.18 no.3
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• pp.115-132
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• 2008

Metal industry is one of the most representative heavy industries and the median sales volume of steel and nonferrous metal companies is over one billion dollars in the case America [Forbes 2006]. As seen in the recent business market situation, an increasing number of industrial manufacturers and suppliers are moving from adversarial to cooperative exchange attitudes that support the long-term relationships with their customers. This article presents the results of an empirical study of the antecedent factors of business relationships in metal industry of the United States. Commitment has been reviewed as a significant and critical variable in research on inter-organizational relationships (Hong et al. 2007, Kim et al. 2007). The future stability of any buyer-seller relationship depends upon the commitment made by the interactants to their relationship. Commitment, according to Dwyer et al. [1987], refers to "an implicit or explicit pledge of relational continuity between exchange partners" and they consider commitment to be the most advanced phase of buyer-seller exchange relationship. Bonds are made because the members need their partners in order to do something and this integration on a task basis can be either symbiotic or cooperative (Svensson 2008). To the extent that members seek the same or mutually supporting ends, there will be strong bonds among them. In other words, the principle that affects the strength of bonds is 'economy of decision making' [Turner 1970]. These bonds provide an important idea to study the causes of business long-term relationships in a sense that organizations can be mutually bonded by a common interest in the economic matters. Recently, the framework of structural bonding has been used to study the buyer-seller relationships in industrial marketing [Han and Sung 2008, Williams et al. 1998, Wilson 1995] in that this structural bonding is a crucial part of the theoretical justification for distinguishing discrete transactions from ongoing long-term relationships. The major antecedent factors of buyer commitment such as technology, CLalt, transaction-specific assets, and importance were identified and explored from the perspective of structural bonding. Research hypotheses were developed and tested by using survey data from the middle managers in the metal industry. H1: Level of technology of the relationship partner is positively related to the level of structural bonding between the buyer and the seller. H2: Comparison level of alternatives is negatively related to the level of structural bonding between the buyer and the seller. H3: Amount of the transaction-specific assets is positively related to the level of structural bonding between the buyer and the seller. H4: Importance of the relationship partner is positively related to the level of structural bonding between the buyer and the seller. H5: Level of structural bonding is positively related to the level of commitment to the relationship. To examine the major antecedent factors of industrial buyer's structural bonding and long-term relationship, questionnaire was prepared, mailed out to the sample of 400 purchasing managers of the US metal industry (SIC codes 33 and 34). After a follow-up request, 139 informants returnedthe questionnaires, resulting in a response rate of 35 percent. 134 responses were used in the final analysis after dropping 5 incomplete questionnaires. All measures were analyzed for reliability and validity following the guidelines offered by Churchill [1979] and Anderson and Gerbing [1988]., the results of fitting the model to the data indicated that the hypothesized model provides a good fit to the data. Goodness-of-fit index (GFI = 0.94) and other indices ( chi-square = 78.02 with p-value = 0.13, Adjusted GFI = 0.90, Normed Fit Index = 0.92) indicated that a major proportion of variances and covariances in the data was accounted for by the model as a whole, and all the parameter estimates showed statistical significance as evidenced by large t-values. All the factor loadings were significantly different from zero. On these grounds we judged the hypothesized model to be a reasonable representation of the data. The results from the present study suggest several implications for buyer-seller relationships. Theoretically, we attempted to conceptualize the antecedent factors of buyer-seller long-term relationships from the perspective of structural bondingin metal industry. The four underlying determinants (i.e. technology, CLalt, transaction-specific assets, and importance) of structural bonding are very critical variables of buyer-seller long-term business relationships. Our model of structural bonding makes an attempt to systematically examine the relationship between the antecedent factors of structural bonding and long-term commitment. Managerially, this research provides industrial purchasing managers with a good framework to assess the interaction processes with their partners and, ability to position their business relationships from the perspective of structural bonding. In other words, based on those underlying variables, industrial purchasing managers can determine the strength of the company's relationships with the key suppliers and its state of preparation to be a successful partner with those suppliers. Both the supplying and customer companies can also benefit by using the concept of 'structural bonding' and evaluating their relationships with key business partners from the structural point of view. In general, the results indicate that structural bonding gives a critical impact on the level of relationship commitment. Managerial implications and limitations of the study are also discussed.

• KDI Journal of Economic Policy
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• v.32 no.2
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• pp.93-143
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• 2010

This paper estimates the impact of Japan's economic sanction on DPRK trade in the 2000s. It conceptualizes the effects of sanction on DPRK trade, econometrically tests whether such effects exist in case of Japan's sanction using currently available DPRK trade statistics, and measures the size of the effects by correcting and reconfiguring the deficiencies of the currently available DPRK trade statistics. The main findings of the paper are as follows. First, Japan's sanction can have two different effects on DPRK trade: 'Sanction Country Effect' and "Third Country Effect.' The former means that the sanction diminishes DPRK trade with Japan while the latter refers to the effects on DPRK trade with other countries as well. The third country effect can arise not simply because the DPRK changes its trade routes to circumvent the sanction, but because the sanction forces the DPRK to readjust its major trade items and patterns. Second, currently no official DPRK trade statistics are available. Thus, the so-called mirror data referring to DPRK trading partners' statistics should be employed for the analysis of the sanction effects. However, all currently available mirror data suffer from three fundamental problems: 1) they may omit the real trade partners of the DPRK; 2) they may confuse ROK trade with DPRK trade; 3) they cannot distinguish non-commercial trade from commercial trade, whereas only the latter concerns Japan's sanction. Considering those problems, we have to adopt the following method in order to reach a reasonable conclusion about the sanction effect. That is, we should repeat the same analysis using all different mirror data currently available, which include KOTRA, IMF and UN Commodity Trade Statistics, and then discuss only the common results from them. Third, currently available mirror data make the following points. 1) DPRK trade is well explained by the gravity model. 2) Japan's sanction has not only the sanction country effect but also the third country effect on DPRK trade. 3) The third country effect occurs differently on DPRK export and import. In case of export, the mirror statistics reveal positive (+) third country effects on all of the major trade partners of the DPRK, including South Korea, China and Thailand. However, on DPRK import, such third country effects are not statistically significant even for South Korea and China. 4) This suggests that Japan's sanction has greater effects on DPRK import rather than its export. Fourth, as far as DPRK export is concerned, it is possible to resolve the abovementioned fundamental problems of mirror data and thus reconstruct more accurate statistics on DPRK trade. Those reconstructed statistics lead us to following conclusions. 1) Japan's economic sanction diminished DPRK's export to Japan from 2004 to 2006 by 103 million dollars on annual average (Sanction Country Effect). It comprises around 60 percent of DPRK's export to Japan in 2003. 2) However, for the same period, the DPRK diverted its exports to other countries to cope up with Japan's sanction, and as a result its export to other countries increased by 85 million dollars on annual average (Third Country Effect). 3) This means that more than 80 per cent of the sanction country effect was made up for by the third country effect. And the actual size of impact that Japan's sanction made on DPRK export in total was merely 30 million dollars on annual average. 4) The third country effect occurred mostly in inter-Korean trade. In fact, Japan's sanction increased DPRK export to the ROK by 72 million dollars on annual average. In contrast, there was no statistically significant increase in DPRK export to China caused by Japan's sanction. 5) It means that the DPRK confronted Japan's sanction and mitigated its impact primarily by using inter-Korean trade and thus the ROK. Fifth, two things should be noted concerning the fourth results above. 1) The results capture the third country effect caused only by trade transfer. Facing Japan's sanction, the DPRK could transfer its existing trade with Japan to other countries. Also it could change its main export items and increase the export of those new items to other countries as mentioned in the first result. However, the fourth results above reflect only the former, not the latter. 2) Although Japan's sanction did not make a huge impact on DPRK export, it might not be necessarily true for DPRK import. Indeed the currently available mirror statistics suggest that Japan's sanction has greater effects on DPRK import. Hence it would not be wise to argue that Japan's sanction did not have much impact on DPRK trade in general, simply using the fourth result above.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.149-169
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• 2020

"The Urban Renewal New Deal project", one of the government's major national projects, is about developing underdeveloped areas by investing 50 trillion won in 100 locations on the first year and 500 over the next four years. This project is drawing keen attention from the media and local governments. However, the project model which fails to reflect the original characteristics of the area as it divides project area into five categories: "Our Neighborhood Restoration, Housing Maintenance Support Type, General Neighborhood Type, Central Urban Type, and Economic Base Type," According to keywords for successful urban regeneration in Korea, "resident participation," "regional specialization," "ministerial cooperation" and "public-private cooperation", when local governments propose urban regeneration projects to the government, they can see that it is most important to accurately understand the characteristics of the city and push ahead with the projects in a way that suits the characteristics of the city with the help of local residents and private companies. In addition, considering the gentrification problem, which is one of the side effects of urban regeneration projects, it is important to select and implement urban regeneration types suitable for the characteristics of the area. In order to supplement the limitations of the 'Urban Regeneration New Deal Project' methodology, this study aims to propose a system that recommends urban regeneration types suitable for urban regeneration sites by utilizing various machine learning algorithms, referring to the urban regeneration types of the '2025 Seoul Metropolitan Government Urban Regeneration Strategy Plan' promoted based on regional characteristics. There are four types of urban regeneration in Seoul: "Low-use Low-Level Development, Abandonment, Deteriorated Housing, and Specialization of Historical and Cultural Resources" (Shon and Park, 2017). In order to identify regional characteristics, approximately 100,000 text data were collected for 22 regions where the project was carried out for a total of four types of urban regeneration. Using the collected data, we drew key keywords for each region according to the type of urban regeneration and conducted topic modeling to explore whether there were differences between types. As a result, it was confirmed that a number of topics related to real estate and economy appeared in old residential areas, and in the case of declining and underdeveloped areas, topics reflecting the characteristics of areas where industrial activities were active in the past appeared. In the case of the historical and cultural resource area, since it is an area that contains traces of the past, many keywords related to the government appeared. Therefore, it was possible to confirm political topics and cultural topics resulting from various events. Finally, in the case of low-use and under-developed areas, many topics on real estate and accessibility are emerging, so accessibility is good. It mainly had the characteristics of a region where development is planned or is likely to be developed. Furthermore, a model was implemented that proposes urban regeneration types tailored to regional characteristics for regions other than Seoul. Machine learning technology was used to implement the model, and training data and test data were randomly extracted at an 8:2 ratio and used. In order to compare the performance between various models, the input variables are set in two ways: Count Vector and TF-IDF Vector, and as Classifier, there are 5 types of SVM (Support Vector Machine), Decision Tree, Random Forest, Logistic Regression, and Gradient Boosting. By applying it, performance comparison for a total of 10 models was conducted. The model with the highest performance was the Gradient Boosting method using TF-IDF Vector input data, and the accuracy was 97%. Therefore, the recommendation system proposed in this study is expected to recommend urban regeneration types based on the regional characteristics of new business sites in the process of carrying out urban regeneration projects."