• Journal of Korean Home Economics Education Association
• /
• v.35 no.1
• /
• pp.35-52
• /
• 2023

This study investigated the utilization and satisfaction of lunchboxes according to food-related lifestyle. A sample of 819 adults who regularly purchased lunchboxes were studied. This study can provide basic data for effective menu development. The participants of the study were classified into 4 groups: a 'taste-seeking group', an 'economy-seeking group', 'a convenience-seeking group', and a 'health-seeking group'. The purchase price of lunchboxes was in the range of 3,500 to 4,000 won. The 'health-seeking group' was shown to spend the highest amount on lunchboxes, over 5,100 won. Information about lunchboxes was obtained primarily through convenience stores followed by Internet SNS (p<0.05). Most participants considered nutritional value when purchasing a lunchbox (p<0.001), of which protein, caloric, and sodium content were perceived as important. Moreover, lunchboxes with clean and hygienic aesthetics were preferred amongst the 'health-seeking group' (p<0.01). The 'economy-seeking group' had a higher satisfaction linked with taste (3.66) and quantity (3.60, p<0.001). Furthermore, in terms of the satisfaction with a menu variety the 'health-seeking group' showed the highest satisfaction with a score of 3.76, while the 'convenience-seeking group' ranked the lowest satisfaction with a score of 3.46 (p<0.05). All groups were satisfied with the convenience for purchasing lunchbox (p<0.001). Additionally, most participants preferred white rice (p<0.001) and meat (p<0.01) with cooked by fried and grilled. Lastly, in the content of the lunchbox use in the future, most participants indicated the intent for continuous use (p<0.01) and recommendation to others with the reason for the low price (19.2%) in the 'economy-seeking group', fresh ingredients (16.2%) in the 'convenience-seeking group', and nutritive (17.3%) in the 'health-seeking group', as well as for the convenience of purchase in the overall groups. Taken together, 'taste' and 'convenience' were the most important factors for all groups, while 'nutrition of food' and 'addition of condiments' scored relatively low on the satisfaction in all groups. Therefore, we recommend for the growth of the convenience store lunchbox market, that it is necessary to improve the quality of the lunchbox by developing various menus based on lifestyle group and fortifying nutrition.

• Geophysics and Geophysical Exploration
• /
• v.26 no.2
• /
• pp.52-61
• /
• 2023

The use of ultra-high-resolution (UHR) seismic surveys to preceisly characterize coastal and shallow structures have increased recently. UHR surveys derive a spatial resolution of 3.125 m using a high-frequency source (80 Hz to 1 kHz). A digital streamer system is an essential module for acquiring high-quality UHR seismic data. Localization studies have focused on reducing purchase costs and decreasing maintenance periods. Basic performance verification and application tests of the developed streamer have been successfully carried out; however, a comparative analysis with the existing benchmark model was not conducted. In this study, we characterized data obtained by using a developed streamer and a benchmark model simultaneously. Tamhae 2 and auxiliary equipment of the Korea Institute of Geoscience and Mineral Resources were used to acquire 2D seismic data, which were analyzed from different perspectives. The data obtained using the developed streamer differed in sensitivity from that obtained using benchmark model by frequency band.However, both type of data had a very high level of similarity in the range corresponding to the central frequency band of the seismic source. However, in the low frequency band below 60 Hz, data obtained using the developed streamer showed a lower signal-to-noise ratio than that obtained using the benchmark model.This lower ratio can hinder the quality in data acquisition using low-frequency sound sources such as cluster air guns. Three causes for this difference were, and streamers developed in future will attempt to reflect on these improvements.

• Journal of Marine Life Science
• /
• v.1 no.2
• /
• pp.95-108
• /
• 2016

This study was performed to investigate the community structure of macrobenthic assemblages in the Environmental Conservation area, Korea. Benthic animals were collected by van Veen grab sampler at spring (May) and summer (August) 2009. The total species number and mean density were 195 species 5.6 m^-2 and 667 individuals m^-2, respectively. Polychaetes were the most dominant faunal group in species (96 species) and abundance (431 individuals m^-2). The major dominant species were the polychaetes Lumbrineris longifolia (76±224 individuals m^-2), Mediomastus californiensis (42±117 individuals m^-2), Tharyx sp.3 (26±110 individuals m^-2), the bivalvia Theora fragilis (54±78 individuals m^-2) and the amphipod Eriopisella schellensis (70±146 individuals m^-2). Based on the cluster and nMDS ordination analysis, macrobenthic communities were divided into three faunal groups. The first group was characterized by high abundance of the polychaeta Sternaspis scutata and the amphipod Ampelisca cyclops iyoensis, which is located by most stations of Hampyeong Bay and St. 4 of Deungnyang Bay. The second group was numerically dominated by the polychaeta Capitella capitata at St. 4 and St. 5 in Gamak Bay where was most pollutant area. Finally, the third group was dominated by the polychaetes Heteromastus filiformis, Tharyx sp.3 and the amphipod Sinocorophium sinensis. Therefore, geochemical characteristics such as the bay shape and pollution gradient may be important factors controlling of the macrobenthic community structure in Environment Conservation Area.

• Korean Journal of Environment and Ecology
• /
• v.37 no.2
• /
• pp.151-162
• /
• 2023

This study investigated the characteristics of fish communities and inhabiting status of the endangered species, Rhodeus pseudosericeus, in the Geumdang Stream in Korea from March to October 2021. A total of 1,698 fish in 5 families and 25 species were collected from 7 survey stations during the survey period. The dominant species was Zacco platypus (relative abundance, 46.5%), and the subdominant species was Squalidus gracilis majimae (16.7%), followed by Rhynchocypris oxycephalus (12.0%), Z. koreanus (5.7%), Pungtungia herzi (3.2%), R. pseudosericeus (2.0%), R. notatus (1.9%), and Acheilognathus rhombeus (1.8%). Nine Korean endemic species (36.0%) were collected, including R. pseudosericeus, R. uyekii, Sarcocheilichthys variegatus wakiyae, Microphysogobio yaluensis, S. gracilis majimae, Z. koreanus, Cobitis nalbanti, Iksookimia koreensis, and Odontobutis interrupta. An exotic species, Micropterus salmoides, designated as an invasive alien species (IAS), was collected downstream. The investigation of the habitat patterns of the endangered species (class II), Rhodeus pseudosericeus, showed a habitat range of about 6 to 7 km in the middle of Geumdang Stream (RP-1 to RP-4), and this species inhabited the edge with water depths of 0.3 through 1.0 m with slow water flow and many aquatic plants. According to the community analysis results, the overall dominance and evenness indexes were low, while diversity and richness indexes were high, and the cluster structure was largely divided into upstream and middle-downstream areas. The river health (fish assessment index) evaluated using fish was assessed as good (3 stations), normal (3 stations), and bad (1 station), and water quality was evaluated as good both upstream and downstream. Compared to previous studies, the number of species was relatively similar, and among the species that appeared in the past, 13 species did not appear in this survey, while 6 species appeared for the first time in this survey. Disturbance factors included river construction, many weirs, and the appearance of the ecosystem-disturbing species, M. salmoides. Since Geumdang Strem has high conservation value because it is home to many species in the Acheilognathinae subfamily, including the endangered species R. pseudosericeus, continuous attention and systematic conservation measures are required.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.17 no.5
• /
• pp.67-80
• /
• 2022

This study was conducted to derive policy measures such as fostering and supporting by examining the actual conditions of domestic startups. The subject of this study was the start-ups located in Seongnam-si, where Pangyo Techno Valley, which is the highest-level innovation cluster in Korea and is evaluated as a start-up mecca. Startups were defined as startups under 7 years old based on new technologies such as IT, BT, and CT, and the subjects of the study were selected. This can be seen as a step forward from previous research in that it embodies the concept of a startup that was previously abstract in a quantitatively measurable way. As a result of the analysis, about 94% of startups are distributed in the so-called "Death Valley" growth stage, and startups above scale-up, which means full-scale growth beyond BEP, account for about 6%. appeared to be occupied. He cited the problem of start-up funds as the biggest difficulty in the early stages of startups, and cited the loan evaluation method that prioritizes sales or collateral in raising funds as the biggest problem. In addition, start-ups rated the access to private investment capital such as VC, AC, and angel investors at a low level compared to policy funds, which are public funds. Most startups showed a lot of interest in overseas expansion, and they chose matching overseas investors such as overseas VCs as the biggest support for overseas expansion. The overall competitiveness in the overseas market was 49.6 points, which is less than 50 points out of 100, indicating that the overall competitiveness was somewhat inferior. It was analyzed that public support and investment in overseas sales channels (sales channels, distribution networks, etc.) should be prioritized along with enhancement of technological competitiveness in order for domestic startups to increase their competitiveness in overseas markets as well as in the domestic market.

• Korean Journal of Environment and Ecology
• /
• v.38 no.1
• /
• pp.55-66
• /
• 2024

This study conducted a survey to investigate the characteristics of fish communities and the inhabiting status of endangered species in the Byekgye Stream, Korea from April to September 2020. A total of 3,415 fish of 9 families and 31 species were collected from 7 survey stations during the survey period. The dominant species was Zacco koreanus (relative abundance of 31.2%), and the subdominant species was Z. platypus (15.0%), followed by Pungtungia herzi (11.7%), Acheilognathus yamatsutae (5.4%), A. lanceolata intermedia (4.8%), Rhinogobius brunneus (4.4%), and Pseudopungtungia tenuicorpa (4.3%). Among the fish species collected, 19 (61.3%) were identified as Korean endemic species, and two cold-water fish species sensitive to climate change (Rhynchocypris kumgangensis and Cottus koreanus) were collected. Four species were designated as class II endangered wildlife by the Ministry of Environment: A. signifer, P. tenuicorpa, Rhodeus pseudosericeus, and C. koreanus. A. signifer and P. tenuicorpa mainly inhabited the mid to lower streams, R. pseudosericeus in the midstream, and R. pseudosericeus in the upstream. P. tenuicorpa inhabited in large numbers, and estimating the age by total length-frequency distribution in July, the total length of the 26-35 mm group was estimated as 0 years old, the 54-75 mm group as 1 year old, 82-97 mm group as 2 years old, 104-109 mm group as 3 years or older. The cluster analysis showed that the dominance index decreased from upstream to downstream, but the diversity, evenness, and richness index increased. The water quality of Byekgye Stream was evaluated as good overall since the river health (fish assessment index, FAI) using fish was evaluated as excellent (5 stations) and good (2 stations). Byekgye Stream has relatively well-preserved habitats, but conservation measures are required as habitats are disturbed by river repair work in some parts of the midstream and downstream areas where many endangered species inhabit.

• Asia pacific journal of information systems
• /
• v.18 no.3
• /
• pp.123-145
• /
• 2008

Recently, as the information communication technology develops, the discussion regarding the ubiquitous environment is occurring in diverse perspectives. Ubiquitous environment is an environment that could transfer data through networks regardless of the physical space, virtual space, time or location. In order to realize the ubiquitous environment, the Pervasive Sensing technology that enables the recognition of users' data without the border between physical and virtual space is required. In addition, the latest and diversified technologies such as Context-Awareness technology are necessary to construct the context around the user by sharing the data accessed through the Pervasive Sensing technology and linkage technology that is to prevent information loss through the wired, wireless networking and database. Especially, Pervasive Sensing technology is taken as an essential technology that enables user oriented services by recognizing the needs of the users even before the users inquire. There are lots of characteristics of ubiquitous environment through the technologies mentioned above such as ubiquity, abundance of data, mutuality, high information density, individualization and customization. Among them, information density directs the accessible amount and quality of the information and it is stored in bulk with ensured quality through Pervasive Sensing technology. Using this, in the companies, the personalized contents(or information) providing became possible for a target customer. Most of all, there are an increasing number of researches with respect to recommender systems that provide what customers need even when the customers do not explicitly ask something for their needs. Recommender systems are well renowned for its affirmative effect that enlarges the selling opportunities and reduces the searching cost of customers since it finds and provides information according to the customers' traits and preference in advance, in a commerce environment. Recommender systems have proved its usability through several methodologies and experiments conducted upon many different fields from the mid-1990s. Most of the researches related with the recommender systems until now take the products or information of internet or mobile context as its object, but there is not enough research concerned with recommending adequate store to customers in a ubiquitous environment. It is possible to track customers' behaviors in a ubiquitous environment, the same way it is implemented in an online market space even when customers are purchasing in an offline marketplace. Unlike existing internet space, in ubiquitous environment, the interest toward the stores is increasing that provides information according to the traffic line of the customers. In other words, the same product can be purchased in several different stores and the preferred store can be different from the customers by personal preference such as traffic line between stores, location, atmosphere, quality, and price. Krulwich(1997) has developed Lifestyle Finder which recommends a product and a store by using the demographical information and purchasing information generated in the internet commerce. Also, Fano(1998) has created a Shopper's Eye which is an information proving system. The information regarding the closest store from the customers' present location is shown when the customer has sent a to-buy list, Sadeh(2003) developed MyCampus that recommends appropriate information and a store in accordance with the schedule saved in a customers' mobile. Moreover, Keegan and O'Hare(2004) came up with EasiShop that provides the suitable tore information including price, after service, and accessibility after analyzing the to-buy list and the current location of customers. However, Krulwich(1997) does not indicate the characteristics of physical space based on the online commerce context and Keegan and O'Hare(2004) only provides information about store related to a product, while Fano(1998) does not fully consider the relationship between the preference toward the stores and the store itself. The most recent research by Sedah(2003), experimented on campus by suggesting recommender systems that reflect situation and preference information besides the characteristics of the physical space. Yet, there is a potential problem since the researches are based on location and preference information of customers which is connected to the invasion of privacy. The primary beginning point of controversy is an invasion of privacy and individual information in a ubiquitous environment according to researches conducted by Al-Muhtadi(2002), Beresford and Stajano(2003), and Ren(2006). Additionally, individuals want to be left anonymous to protect their own personal information, mentioned in Srivastava(2000). Therefore, in this paper, we suggest a methodology to recommend stores in U-market on the basis of ubiquitous environment not using personal information in order to protect individual information and privacy. The main idea behind our suggested methodology is based on Feature Matrices model (FM model, Shahabi and Banaei-Kashani, 2003) that uses clusters of customers' similar transaction data, which is similar to the Collaborative Filtering. However unlike Collaborative Filtering, this methodology overcomes the problems of personal information and privacy since it is not aware of the customer, exactly who they are, The methodology is compared with single trait model(vector model) such as visitor logs, while looking at the actual improvements of the recommendation when the context information is used. It is not easy to find real U-market data, so we experimented with factual data from a real department store with context information. The recommendation procedure of U-market proposed in this paper is divided into four major phases. First phase is collecting and preprocessing data for analysis of shopping patterns of customers. The traits of shopping patterns are expressed as feature matrices of N dimension. On second phase, the similar shopping patterns are grouped into clusters and the representative pattern of each cluster is derived. The distance between shopping patterns is calculated by Projected Pure Euclidean Distance (Shahabi and Banaei-Kashani, 2003). Third phase finds a representative pattern that is similar to a target customer, and at the same time, the shopping information of the customer is traced and saved dynamically. Fourth, the next store is recommended based on the physical distance between stores of representative patterns and the present location of target customer. In this research, we have evaluated the accuracy of recommendation method based on a factual data derived from a department store. There are technological difficulties of tracking on a real-time basis so we extracted purchasing related information and we added on context information on each transaction. As a result, recommendation based on FM model that applies purchasing and context information is more stable and accurate compared to that of vector model. Additionally, we could find more precise recommendation result as more shopping information is accumulated. Realistically, because of the limitation of ubiquitous environment realization, we were not able to reflect on all different kinds of context but more explicit analysis is expected to be attainable in the future after practical system is embodied.

• Journal of Intelligence and Information Systems
• /
• v.22 no.3
• /
• pp.143-163
• /
• 2016

The demographics of Internet users are the most basic and important sources for target marketing or personalized advertisements on the digital marketing channels which include email, mobile, and social media. However, it gradually has become difficult to collect the demographics of Internet users because their activities are anonymous in many cases. Although the marketing department is able to get the demographics using online or offline surveys, these approaches are very expensive, long processes, and likely to include false statements. Clickstream data is the recording an Internet user leaves behind while visiting websites. As the user clicks anywhere in the webpage, the activity is logged in semi-structured website log files. Such data allows us to see what pages users visited, how long they stayed there, how often they visited, when they usually visited, which site they prefer, what keywords they used to find the site, whether they purchased any, and so forth. For such a reason, some researchers tried to guess the demographics of Internet users by using their clickstream data. They derived various independent variables likely to be correlated to the demographics. The variables include search keyword, frequency and intensity for time, day and month, variety of websites visited, text information for web pages visited, etc. The demographic attributes to predict are also diverse according to the paper, and cover gender, age, job, location, income, education, marital status, presence of children. A variety of data mining methods, such as LSA, SVM, decision tree, neural network, logistic regression, and k-nearest neighbors, were used for prediction model building. However, this research has not yet identified which data mining method is appropriate to predict each demographic variable. Moreover, it is required to review independent variables studied so far and combine them as needed, and evaluate them for building the best prediction model. The objective of this study is to choose clickstream attributes mostly likely to be correlated to the demographics from the results of previous research, and then to identify which data mining method is fitting to predict each demographic attribute. Among the demographic attributes, this paper focus on predicting gender, age, marital status, residence, and job. And from the results of previous research, 64 clickstream attributes are applied to predict the demographic attributes. The overall process of predictive model building is compose of 4 steps. In the first step, we create user profiles which include 64 clickstream attributes and 5 demographic attributes. The second step performs the dimension reduction of clickstream variables to solve the curse of dimensionality and overfitting problem. We utilize three approaches which are based on decision tree, PCA, and cluster analysis. We build alternative predictive models for each demographic variable in the third step. SVM, neural network, and logistic regression are used for modeling. The last step evaluates the alternative models in view of model accuracy and selects the best model. For the experiments, we used clickstream data which represents 5 demographics and 16,962,705 online activities for 5,000 Internet users. IBM SPSS Modeler 17.0 was used for our prediction process, and the 5-fold cross validation was conducted to enhance the reliability of our experiments. As the experimental results, we can verify that there are a specific data mining method well-suited for each demographic variable. For example, age prediction is best performed when using the decision tree based dimension reduction and neural network whereas the prediction of gender and marital status is the most accurate by applying SVM without dimension reduction. We conclude that the online behaviors of the Internet users, captured from the clickstream data analysis, could be well used to predict their demographics, thereby being utilized to the digital marketing.

• Journal of Global Scholars of Marketing Science
• /
• v.20 no.1
• /
• pp.59-69
• /
• 2010

Green or an environmental consciousness has been a major issue for businesses and government offices, as well as consumers, worldwide. In response to this movement, the Korean government announced, in the early 2000s, the era of "Green Growth" as a way to encourage green-related business activities. The Korean fashion industry, in various levels of involvement, presents diverse eco-friendly products as a part of the green movement. These apparel products include organic products and recycled clothing. For these companies to be successful, they need information about who are the consumers who consider green issues (e.g., environmental sustainability) as part of their personal values when making a decision for product purchase, use, and disposal. These consumers can be considered as eco-sumers. Previous studies have examined consumers' purchase intention for or with eco-friendly products. In addition, studies have examined influential factors used to identify the eco-sumers or green consumers. However, limited attention was paid to eco-sumers' disposal or recycling behavior of clothes in comparison with their green product purchases. Clothing disposal behaviors are ways that consumer can get rid of unused clothing and in clue temporarily lending the item or permanently eliminating the item by "handing down" (e.g., giving it to a younger sibling), donating, exchanging, selling, or simply throwing it away. Accordingly, examining purchasing behaviors of eco-friendly fashion items in conjunction with clothing disposal behaviors should improve understanding of a consumer's clothing consumption behavior from the environmental perspective. The purpose of this exploratory study is to provide descriptive information about Korean eco-sumers who have ecologically-favorable lifestyles and behaviors when buying and disposing of clothes. The objectives of this study are to (a) categorize Koreans on the basis of clothing disposal behaviors; (b) investigate the differences in demographics, lifestyles, and clothing consumption values among segments; and (c) compare the purchase intention of eco-friendly fashion items and influential factors among segments. A self-administered questionnaire was developed based on previous studies. The questionnaire included 10 items of clothing disposal behavior, 22 items of LOHAS (Lifestyles of Health and Sustainability) characteristics, and 19 items of consumption values, measured by five-point Likert-type scales. In addition, the purchase intention of two eco-friendly fashion items and 11 attributes of each item were measured by seven-point Likert type scales. Two polyester fleece pullovers, made from fabric created from recycled bottles with the PET identification code, were selected from one Korean brand and one US imported brand among outdoor sportswear brands. A brief description of each product with a color picture was provided in the survey. Demographic variables (i.e., gender, age, marital status, education level, income, occupation) were also included. The data were collected through a professional web survey agency during May 2009. A total of 600 final usable questionnaires were analyzed. The age of respondents ranged from 20 to 49 years old with a mean age of 34 years. Fifty percent of the respondents were males and about 58% were married, and 62% reported having earned university degrees. Principal components factor analysis with varimax rotation was used to identify the underlying dimensions of the clothing disposal behavior scale, and three factors were generated (i.e., reselling behavior, donating behavior, non-recycling behavior). To categorize the respondents on the basis of clothing disposal behaviors, k-mean cluster analysis was used, and three segments were obtained. These consumer segments were labeled as 'Resale Group', 'Donation Group', and 'Non-Recycling Group.' The classification results indicated approximately 98 percent of the original cases were correctly classified. With respect to demographic characteristics among the three segments, significant differences were found in gender, marital status, occupation, and age. LOHAS characteristics were reduced into the following five factors: self-satisfaction, family orientation, health concern, environmental concern, and voluntary service. Significant differences were found in the LOHAS factors among the three clusters. Resale Group and Donation Group showed a similar predisposition to LOHAS issues while the Non-Recycling Group presented the lowest mean scores on the LOHAS factors compared to the other segments. The Resale and Donation Groups described themselves as enjoying or being satisfied with their lives and spending spare-time with family. In addition, these two groups cared about health and organic foods, and tried to conserve energy and resources. Principal components factor analysis generated clothing consumption values into the following three factors: personal values, social value, and practical value. The ANOVA test with the factors showed differences primarily between the Resale Group and the other two groups. The Resale Group was more concerned about personal value and social value than the other segments. In contrast, the Non-Recycling Group presented the higher level of social value than did Donation Group. In a comparison of the intention to purchase eco-friendly products, the Resale Group showed the highest mean score on intent to purchase Product A. On the other hand, the Donation Group presented the highest intention to purchase for Product B among segments. In addition, the mean scores indicated that the Korean product (Product B) was more preferable for purchase than the U.S. product (Product A). Stepwise regression analysis was used to identify the influence of product attributes on the purchase intention of eco product. With respect to Product A, design, price and contribution to environmental preservation were significant to predict purchase intention for the Resale Group, while price and compatibility with my image factors were significant for the Donation Group. For the Non-Recycling Group, design, price compatibility with the factors of my image, participation to eco campaign, and contribution to environmental preservation were significant. Price appropriateness was significant for each of the three clusters. With respect to Product B, design, price and compatibility with my image factors were important, but different attributes were associated significantly with purchase intention for each of the three groups. The influence of LOHAS characteristics and clothing consumption values on intention to purchase Products A and B were also examined. The LOHAS factor of health concern and the personal value factor were significant in the relationships with the purchase intention; however, the explanatory powers were low in the three segments. Findings showed that each group as classified by clothing disposal behaviors showed differences in the attributes of a product, personal values, and the LOHAS characteristics that influenced their purchase intention of eco-friendly products. Findings would enable organizations to understand eco-friendly behavior and to design appropriate strategic decisions to appeal eco-sumers.

• Journal of Intelligence and Information Systems
• /
• v.25 no.4
• /
• pp.1-33
• /
• 2019

The small and medium sized enterprises (hereinafter SMEs) are already at a competitive disadvantaged when compared to large companies with more abundant resources. Manufacturing SMEs not only need a lot of information needed for new product development for sustainable growth and survival, but also seek networking to overcome the limitations of resources, but they are faced with limitations due to their size limitations. In a new era in which connectivity increases the complexity and uncertainty of the business environment, SMEs are increasingly urged to find information and solve networking problems. In order to solve these problems, the government funded research institutes plays an important role and duty to solve the information asymmetry problem of SMEs. The purpose of this study is to identify the differentiating characteristics of SMEs that utilize the public information support infrastructure provided by SMEs to enhance the innovation capacity of SMEs, and how they contribute to corporate performance. We argue that we need an infrastructure for providing information support to SMEs as part of this effort to strengthen of the role of government funded institutions; in this study, we specifically identify the target of such a policy and furthermore empirically demonstrate the effects of such policy-based efforts. Our goal is to help establish the strategies for building the information supporting infrastructure. To achieve this purpose, we first classified the characteristics of SMEs that have been found to utilize the information supporting infrastructure provided by government funded institutions. This allows us to verify whether selection bias appears in the analyzed group, which helps us clarify the interpretative limits of our study results. Next, we performed mediator and moderator effect analysis for multiple variables to analyze the process through which the use of information supporting infrastructure led to an improvement in external networking capabilities and resulted in enhancing product competitiveness. This analysis helps identify the key factors we should focus on when offering indirect support to SMEs through the information supporting infrastructure, which in turn helps us more efficiently manage research related to SME supporting policies implemented by government funded institutions. The results of this study showed the following. First, SMEs that used the information supporting infrastructure were found to have a significant difference in size in comparison to domestic R&D SMEs, but on the other hand, there was no significant difference in the cluster analysis that considered various variables. Based on these findings, we confirmed that SMEs that use the information supporting infrastructure are superior in size, and had a relatively higher distribution of companies that transact to a greater degree with large companies, when compared to the SMEs composing the general group of SMEs. Also, we found that companies that already receive support from the information infrastructure have a high concentration of companies that need collaboration with government funded institution. Secondly, among the SMEs that use the information supporting infrastructure, we found that increasing external networking capabilities contributed to enhancing product competitiveness, and while this was no the effect of direct assistance, we also found that indirect contributions were made by increasing the open marketing capabilities: in other words, this was the result of an indirect-only mediator effect. Also, the number of times the company received additional support in this process through mentoring related to information utilization was found to have a mediated moderator effect on improving external networking capabilities and in turn strengthening product competitiveness. The results of this study provide several insights that will help establish policies. KISTI's information support infrastructure may lead to the conclusion that marketing is already well underway, but it intentionally supports groups that enable to achieve good performance. As a result, the government should provide clear priorities whether to support the companies in the underdevelopment or to aid better performance. Through our research, we have identified how public information infrastructure contributes to product competitiveness. Here, we can draw some policy implications. First, the public information support infrastructure should have the capability to enhance the ability to interact with or to find the expert that provides required information. Second, if the utilization of public information support (online) infrastructure is effective, it is not necessary to continuously provide informational mentoring, which is a parallel offline support. Rather, offline support such as mentoring should be used as an appropriate device for abnormal symptom monitoring. Third, it is required that SMEs should improve their ability to utilize, because the effect of enhancing networking capacity through public information support infrastructure and enhancing product competitiveness through such infrastructure appears in most types of companies rather than in specific SMEs.