• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.1
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• pp.95-112
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• 2020

This paper is to investigate how cognitive bias of college students and entrepreneurs relates to perceived risks and entrepreneurial opportunities that represent uncertainty, and how various cognitive bias and entrepreneurial efficacy In the same way. The purpose of this study is to find improvement points of entrepreneurship education for college students and to suggest problems and improvement possibilities in the decision making process of current entrepreneurs. This empirical study is a necessary to improve the decision-making of individuals who want to start a business at the time when various attempts are made to activate the start-up business and increase the sustainability of the existing SME management. And understanding of the difference in opportunity evaluation, and suggests that it is necessary to provide good opportunities together with the upbringing of entrepreneurs. In order to achieve the purpose of the study, questionnaires were conducted for college students and entrepreneurs. A total of 363 questionnaire data were obtained and demonstrated through structural equation modeling. This study confirms that there is some relationship between perceived risk and cognitive bias. Overconfidence and control illusions among cognitive bias have a significant relationship between perceived risk and wealth. Especially, it is confirmed that control illusion of college students has a significant relationship with perceived risk. Second, cognitive bias demonstrated some significant relationship with opportunity evaluation. Although we did not find evidence that excess self-confidence is related to opportunity evaluation, we have verified that control illusions and current status bias are related to opportunity evaluation. Control illusions were significant in both college students and entrepreneurs. Third, perceived risk has a negative relationship with opportunity evaluation. All students, regardless of whether they are college students or entrepreneurs, judge opportunities positively if they perceive low risk. Fourth, it can be seen from the college students 'group that entrepreneurial efficacy has a moderating effect between perceived risk and opportunity evaluation, but no significant results were found in the entrepreneurs' group. Fifth, the college students and entrepreneurs have different cognitive bias, and they have proved that there is a different relationship between entrepreneurial opportunity evaluation and perceived risk. On the whole, there are various cognitive biases that are caused by time pressure or stress on college students and entrepreneurs who have to make judgments in uncertain opportunities, and in this respect, they can improve their judgment in the future. At the same time, university students can have a positive view of new opportunities based on high entrepreneurial efficacy, but if they fully understand the intrinsic risks of entrepreneurship through entrepreneurial education and fully understand the cognitive bias present in direct entrepreneurial experience, You will get a better opportunity assessment. This study has limitations in that it is based on the fact that university students and entrepreneurs are integrated, and that the survey respondents are selected by the limited random sampling method. It is necessary to conduct more systematic research based on more faithful data in the absence of the accumulation of entrepreneurial research data. Second, the translation tools used in the previous studies were translated and the meaning of the measurement tools might not be conveyed due to language differences. Therefore, it is necessary to construct a more precise scale for the accuracy of the study. Finally, complementary research should be done to identify what competitive opportunities are and what opportunities are appropriate for entrepreneurs.

• Journal of Agricultural Extension & Community Development
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• v.21 no.1
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• pp.35-82
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• 2014

It was main objectives to find the learners characteristics and educational effects of cyber agricultural technology courses in RDA. For the research, it was followed by literature reviews and internet based survey methods. In internet based survey, two staged stratified sampling method was adopted from cyber training members database in RDA along with some key word as open course or certificate course, and enrollment years. Instrument was composed through literature reviews about cyber education effects and educational effect factors. And learner characteristics items were added in survey documents. It was sent to sampled persons by e-mail and 316 data was returned via google survey systems. Through the data cleaning, 303 data were analysed by chi-square, t-test and F-test. It's significance level was .05. The results of the research were as followed; First, the respondent was composed of mainly man(77.9%), and monthly income group was mainly 2,000,000 or 3,000,000 won(24%), bachelor degree(48%), fifty or forty age group was shared to 75%, and their job was changed after learning(12.2%). So major respondents' job was not changed. Their major was not mainly agriculture. Learners' learning style were composed of two or more types as concrete-sequential, mixing, abstract-random, so e-learning course should be developed for the students' type. Second, it was attended at 3.2 days a week, 53.53 minutes a class, totally 172.63 minutes a week. They were very eager or generally eager to study, and attended two or more subjects. The cyber education motives was for farming knowledge, personal competency development, job performance enlarging. They selected subjects along with their interest. A subject person couldn't choose more subjects for little time, others, non interesting subject, but more subject persons were for job performance benefits and previous subjects effectiveness. Most learner was finished their subject, but a fourth was not finished for busy (26.7%). And their entrying behavior was not enough to learn e-course and computer or internet using ability was middle level as software using. And they thought RDA cyber course was comfort in non time or space limit, knowledge acquisition, and personal competency development. Cyber learning group was composed of open course only (12.5%), certificate only(25.7%), both(36.3%). Third, satisfaction and academic achievement of e-learning learners were good, and educational service offering for doing job in learning application category was good, but effect of cyber education was not good, especially, agricultural income increasing was not good because major learner group was not farmer, so they couldn't apply their knowledge to farming. And content structure and design, content comprehension, content amount were good. The more learning subject group responded to good in effects, and both open course and certificate course group satisfied more than open course only group. Based on the results, recommendation was offered as cyber course specialization before main course in RDA training system, support staff and faculty enlargement, building blended learning system with local RDA office, introducing cyber tutor system.

• Journal of Bio-Environment Control
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• v.29 no.2
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• pp.141-152
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• 2020

The rapid on-site measurement of hydroponic nutrients allows for the more efficient use of crop fertilizers. This paper reports on the development of an embedded on-site system consisting of multiple ion-selective electrodes (ISEs) for the real-time measurement of the concentrations of macronutrients in hydroponic solutions. The system included a combination of PVC ISEs for the detection of NO₃, K, and Ca ions, a cobalt-electrode for the detection of H₂PO₄, a double-junction reference electrode, a solution container, and a sampling system consisting of pumps and valves. An Arduino Due board was used to collect data and to control the volume of the sample. Prior to the measurement of each sample, a two-point normalization method was employed to adjust the sensitivity followed by an offset to minimize potential drift that might occur during continuous measurement. The predictive capabilities of the NO₃ and K ISEs based on PVC membranes were satisfactory, producing results that were in close agreement with the results of standard analyzers (R² = 0.99). Though the Ca ISE fabricated with Ca ionophore II underestimated the Ca concentration by an average of 55%, the strong linear relationship (R² > 0.84) makes it possible for the embedded system to be used in hydroponic NO₃, K, and Ca sensing. The cobalt-rod-based phosphate electrodes exhibited a relatively high error of 24.7±9.26% in the phosphate concentration range of 45 to 155 mg/L compared to standard methods due to inconsistent signal readings between replicates, illustrating the need for further research on the signal conditioning of cobalt electrodes to improve their predictive ability in hydroponic P sensing.

• 대한공업교육학회지
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• v.44 no.2
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• pp.104-122
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• 2019

In the era of the fourth industrial revolution, maker education is drawing attention as a method of student-led education. At a time when interest in maker education is also growing in technology education, figuring out what stage of concern(SoC) a middle school technology teacher is critical to effective implementation. This study analyzed SoC in maker education by layer sampling among 400 middle school technology teachers using Concerns-based adoption model. SoC was then obtained by measuring the origin using the SoCQ and then presenting it as a SOCQ profile. Gender, training experience with two lower variables were analyzed using t verification, working cities, teaching experience with more than three lower variables were analyzed using one-way ANOVA. Studies showed that SoC in maker education of middle school technology teachers showed the most similar characteristics to that of non-users. The difference in concern depending on gender was that male teachers were more concerned in maker education than female teachers. The difference in concern depending on the working city was that teachers working in the township were more concerned in the maker education than teachers working in the large city, and the difference in concern depending on the teaching career was higher among teachers with middle experience than those with low and high experience. There was also a higher stage of concern in maker education than in teachers without training experience. Therefore, it is necessary to provide middle school technology teachers with an introduction to the maker education and various information, teaching, learning and evaluation data to enhance overall concern and to support the use and evaluation of the maker education in the classroom by providing various teacher training and consulting on the maker education in the future. Further, through further study, we should conduct study that analyzes both Stage of Concern, Level of Use and Innovation Configuration, to put in the effort for effective settlement of maker education.

• Journal of the Korean Society of Clothing and Textiles
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• v.31 no.6 s.165
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• pp.974-984
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• 2007

The purpose of this study was to find differences in evaluation criteria and to find differences in preferred images based on benefits segmented groups of jeans products consumers. Male and female Korean university students participated in the study. Quota sampling method was used to collect the data based on gender and a residential area of the respondents. Data from 492 questionnaires were used in the analysis. Factor analysis, Cronbach's alpha coefficient, cluster analysis, one-way ANOVA, and post-hoc test were conducted. As a result, respondents who seek multi-benefits considered aesthetic criteria(e.g., color, style, design, fit) and quality performance criteria(e.g., durability, ease of care, contractibility, flexibility) more importantly when evaluating and purchasing jeans products. Respondents who seek brand name considered extrinsic criteria(e.g., brand reputation, status symbol, country of origin, fashionability) more importantly than respondents who seek economic efciency. Respondents who seek multi-benefits such as attractiveness, fashion, individuality, and utility tend to prefer all the images: individual image, active image, sexual image, sophisticated image, and simple image when wearing jeans products. Respondents who seek fashion are likely to prefer individual image, and respondents who seek brand name more prefer both individual image and polished image. Mean while, respondents who seek economical efficiency less prefer sexual image and polished image.

• Korean Journal of Ecology and Environment
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• v.54 no.4
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• pp.303-314
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• 2021

To understand how to efficiently observe the biomass and community of phytoplankton, phytoplankton sampling was carried out from June to October 2019 at the Yeongju dam sediment control reservoir(YJ) and Bohyeonsan dam reservoir(BH1 and BH2). The results derived from microscopic observation, such as the conventional phytoplankton qualitative/quantitative analysis, and from the CHEMTAX method based on the pigments, were compared. The relative contribution of phytoplankton, calculated by the microscopy and CHEMTAX methods, showed a significant difference in all four classes: cryptophyta, chlorophyta, cyanobacteria, and diatoms. In addition, the correlation between the two observation methods was poor. This might be caused by methodological differences in microscopy that do not consider the varying cell sizes among phytoplankton species. In this study, by converting the cells into carbon, the slope between both carbon biomasses based on microscopy and CHEMTAX was improved close to the 1 : 1 line, and the y-intercept was closer to 0 for cryptophyta and diatoms. For cyanobacteria, the slope increased, the y-intercept decreased, and the plot approached 1 : 1 although the correlation coefficients were not improved in all classes. The present study suggests that application of CHEMTAX based on pigment analysis could be a possible approach to efficiently determine the relative carbon proportions of individual classes of phytoplankton community composition.

• Korean Journal of Environmental Biology
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• v.41 no.2
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• pp.109-125
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• 2023

This study was conducted to select target fish species as baseline research for accumulation analysis of major hazardous chemicals entering the aquatic ecosystem in Korea and to analyze the impact on fish community. The test bed was selected from a sewage treatment plant, which could directly confirm the impact of the inflow of harmful chemicals, and the Geum River estuary where harmful chemicals introduced into the water system were concentrated. A multivariable metric model was developed to select target candidate fish species for hazardous chemical analysis. Details consisted of seven metrics: (1) commercially useful metric, (2) top-carnivorous species metric, (3) pollution fish indicator metric, (4) tolerance fish metric, (5) common abundant metric, (6) sampling availability (collectability) metric, and (7) widely distributed fish metric. Based on seven metric models for candidate fish species, eight species were selected as target candidates. The co-occurring dominant fish with target candidates was tolerant (50%), indicating that the highest abundance of tolerant species could be used as a water pollution indicator. A multi-metric fish-based model analysis for aquatic ecosystem health evaluation showed that the ecosystem health was diagnosed as "bad conditions". Physicochemical water quality variables also influenced fish feeding and tolerance guild in the testbed. Eight water quality parameters appeared high at the T1 site, indicating a large impact of discharging water from the sewage treatment plant. T2 site showed massive algal bloom, with chlorophyll concentration about 15 times higher compared to the reference site.

• Journal of Venture Innovation
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• v.3 no.2
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• pp.41-76
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• 2020

This study confirmed factors affecting smart factory technology acceptance through empirical analysis. It is a study on what factors have an important influence on the introduction of the smart factory, which is the core field of the 4th industry. I believe that there is academic and practical significance in the context of insufficient research on technology acceptance in the field of smart factories. This research was conducted based on the Unified Theory of Acceptance and Use of Technology (UTAUT), whose explanatory power has been proven in the study of the acceptance factors of information technology. In addition to the four independent variables of the UTAUT : Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions, Government Assistance Expectancy, which is expected to be an important factor due to the characteristics of the smart factory, was added to the independent variable. And, in order to confirm the technical factors of smart factory technology acceptance, the Task Technology Fit(TTF) was added to empirically analyze the effect on Behavioral Intention. Trust is added as a parameter because the degree of trust in new technologies is expected to have a very important effect on the acceptance of technologies. Finally, empirical verification was conducted by adding Innovation Resistance to a research variable that plays a role as a moderator, based on previous studies that innovation by new information technology can inevitably cause refusal to users. For empirical analysis, an online questionnaire of random sampling method was conducted for incumbents of domestic small and medium-sized enterprises, and 309 copies of effective responses were used for empirical analysis. Amos 23.0 and Process macro 3.4 were used for statistical analysis. For accurate statistical analysis, the validity of Research Model and Measurement Variable were secured through confirmatory factor analysis. Accurate empirical analysis was conducted through appropriate statistical procedures and correct interpretation for causality verification, mediating effect verification, and moderating effect verification. Performance Expectancy, Social Influence, Government Assistance Expectancy, and Task Technology Fit had a positive (+) effect on smart factory technology acceptance. The magnitude of influence was found in the order of Government Assistance Expectancy(β=.487) > Task Technology Fit(β=.218) > Performance Expectancy(β=.205) > Social Influence(β=.204). Both the Task Characteristics and the Technology Characteristics were confirmed to have a positive (+) effect on Task Technology Fit. It was found that Task Characteristics(β=.559) had a greater effect on Task Technology Fit than Technology Characteristics(β=.328). In the mediating effect verification on Trust, a statistically significant mediating role of Trust was not identified between each of the six independent variables and the intention to introduce a smart factory. Through the verification of the moderating effect of Innovation Resistance, it was found that Innovation Resistance plays a positive (+) moderating role between Government Assistance Expectancy, and technology acceptance intention. In other words, the greater the Innovation Resistance, the greater the influence of the Government Assistance Expectancy on the intention to adopt the smart factory than the case where there is less Innovation Resistance. Based on this, academic and practical implications were presented.

• Journal of Korean Home Economics Education Association
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• v.30 no.2
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• pp.39-56
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• 2018

The purpose of this study was to develop Home Economics(HE) teaching and learning process plans applied Problem Based Learning(PBL) focusing on 'food and nutrition' unit for students with intellectual disability and to evaluate the effects of the HE instruction on their food choice·management knowledge and problem-solving skills after implementing the instruction for students with intellectual disability. To develop HE teaching and learning process plans applied PBL focusing on 'food and nutrition' unit for students with intellectual disability, problems that arise in daily life to trigger interest of students were firstly developed. The selected problems and teaching and learning process plans were reviewed for validity by one home economics education professor and three teachers who are experts in special education. This study used the one group pretest and posttest design, sampling 6 students who are in special-education middle school with the intellectual disability. After HE instruction of 6 sessions applied PBL method, this study tested the effects of the instruction. The first three sessions taught how to choose and keep food. The fourth session taught purchasing food ingredients and keeping them for sandwiches. The fifth and sixth sessions let the students make sandwiches and give them to others. The instruments of the study comprised of tools for food choice and management knowledge, tools for problem-solving skills evaluation, self-evaluation sheets, evaluation form of course satisfaction for students, evaluation form of behavior in class for teachers, and daily observation journal and all tools. These instruments were proved to have reliability and validity. The results of this study are as follows. First, all six students who took HE instruction applied PBL method focusing on 'food and nutrition' unit scored 30 points higher out of 100 points after taking the instruction in food choice and management knowledge and scored 5 points higher out of 14 points in problem-solving skills on average. Therefore, it was interpreted that HE instruction applied PBL affected the food choice·management knowledge and the problem solving skills of students with intellectual disability. Secondly, the students with intellectual disability participated actively in HE instruction applied PBL focusing on 'food and nutrition' unit and expressed satisfaction. Three special education experts evaluated HE teaching·learning process plans applied PBL focusing on 'food and nutrition' unit to be well-developed. This study showed that HE instruction applied PBL focusing on 'food and nutrition' unit allowed the students with intellectual disability to acquire comprehensive skills in choosing, keeping, and making safe food and helped them solve problems of their life by themselves. Therefore I suggest that Home Economics should be adopted as a formal subject matter in special school curriculum for students with intellectual disability.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.