• Title/Summary/Keyword: 핵심센터

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Text Mining of Successful Casebook of Agricultural Settlement in Graduates of Korea National College of Agriculture and Fisheries - Frequency Analysis and Word Cloud of Key Words - (한국농수산대학 졸업생 영농정착 성공 사례집의 Text Mining - 주요단어의 빈도 분석 및 word cloud -)

  • Joo, J.S.;Kim, J.S.;Park, S.Y.;Song, C.Y.
    • Journal of Practical Agriculture & Fisheries Research
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    • v.20 no.2
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    • pp.57-72
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    • 2018
  • In order to extract meaningful information from the excellent farming settlement cases of young farmers published by KNCAF, we studied the key words with text mining and created a word cloud for visualization. First, in the text mining results for the entire sample, the words 'CEO', 'corporate executive', 'think', 'self', 'start', 'mind', and 'effort' are the words with high frequency among the top 50 core words. Their ability to think, judge and push ahead with themselves is a result of showing that they have ability of to be managers or managers. And it is a expression of how they manages to achieve their dream without giving up their dream. The high frequency of words such as "father" and "parent" is due to the high ratio of parents' cooperation and succession. Also 'KNCAF', 'university', 'graduation' and 'study' are the results of their high educational awareness, and 'organic farming' and 'eco-friendly' are the result of the interest in eco-friendly agriculture. In addition, words related to the 6th industry such as 'sales' and 'experience' represent their efforts to revitalize farming and fishing villages. Meanwhile, 'internet', 'blog', 'online', 'SNS', 'ICT', 'composite' and 'smart' were not included in the top 50. However, the fact that these words were extracted without omission shows that young farmers are increasingly interested in the scientificization and high-tech of agriculture and fisheries Next, as a result of grouping the top 50 key words by crop, the words 'facilities' in livestock, vegetables and aquatic crops, the words 'equipment' and 'machine' in food crops were extracted as main words. 'Eco-friendly' and 'organic' appeared in vegetable crops and food crops, and 'organic' appeared in fruit crops. The 'worm' of eco-friendly farming method appeared in the food crops, and the 'certification', which means excellent agricultural and marine products, appeared only in the fishery crops. 'Production', which is related to '6th industry', appeared in all crops, 'processing' and 'distribution' appeared in the fruit crops, and 'experience' appeared in the vegetable crops, food crops and fruit crops. To visualize the extracted words by text mining, we created a word cloud with the entire samples and each crop sample. As a result, we were able to judge the meaning of excellent practices, which are unstructured text, by character size.

The Hidden Lynchpin of Startup Accelerators : Accelerator Entrepreneur Passion (스타트업 액셀러레이터의 감춰진 린치핀 : 액셀러레이터 창업가 열정)

  • Kim, Sang-cheol;Chung, Byoung-gyu
    • Journal of Venture Innovation
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    • v.5 no.1
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    • pp.1-18
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    • 2022
  • There is growing empirical evidence that passion is an important part of entrepreneurship and influences the intentions, behaviors and performance of entrepreneurs, employees and startups. Passion is especially important in an entrepreneurial context, given the effort and challenge that entrepreneurs starting a startup must overcome. The purpose of this study was to confirm the effect of the passion of startup entrepreneurs participating in the accelerator incubation program and the passion of accelerator entrepreneurs and managers on the entrepreneurial performance of incubator startups. In addition, we tried to confirm whether entrepreneurial self-efficacy plays a mediating role in this influence relationship. The survey was conducted online by startups entrepreneur who completed the accelerator incubation program. A total of 330 questionnaires were used for the analysis. As a result of the empirical analysis, it was confirmed that the passion of startup entrepreneurs and the passion of accelerator entrepreneurs and managers all had a positive (+) effect on the entrepreneurial performance of incubator startups. The influence of passion was found to be high in the order of startup entrepreneurs, accelerator entrepreneurs, and accelerator managers. It was confirmed that entrepreneurial self-efficacy plays a mediating role between the passion of startup entrepreneurs, the passion of accelerator entrepreneurs, and the entrepreneurial performance of incubator startups, respectively. However, no significant mediating role was identified between the passion of accelerator managers and the entrepreneurial performance of incubator startups. This study is significant in empirically confirming for the first time that the passion of accelerator entrepreneurs and managers has a positive effect on the entrepreneurial performance of incubator startups. The passion of accelerator entrepreneurs and managers is playing an important role as a hidden lynchpin in creating the entrepreneurial performance of incubator startups. In particular, since the passion of accelerator entrepreneurs has a great influence on the performance of incubator startups, it is necessary to recognize this fact and carefully examine their passion reputation when startups select accelerators.

A Study on the Social Venture Startup Phenomenon Using the Grounded Theory Approach (근거이론 접근법을 이용한 소셜벤처 창업 현상에 관한 고찰)

  • Seol, Byung Moon;Kim, Young Lag
    • Asia-Pacific Journal of Business Venturing and Entrepreneurship
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    • v.18 no.1
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    • pp.67-83
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    • 2023
  • The social venture start-up phenomenon is found from the perspectives of social enterprise and for-profit enterprise. This study aims to fundamentally explore the start-up phenomenon of social ventures from these two perspectives. Considering the lack of prior research that researched both social and commercial perspectives at the same time, this paper analyzed using grounded theory approach of Strauss & Corbin(1998), an inductive research method that analyzes based on prior research and interview data. In order to collect data for this study, eight corporate representatives currently operating social ventures were interviewed and data and phenomena were analyzed. This progressed to a theoretical saturation where no additional information was derived. The analysis results of this study using the grounded theory approach are as follows. As a result of open coding and axial coding, 147 concepts and 70 subcategories were derived, and 18 categories were derived through the final abstraction process. In the selective coding, 'expansion of social venture entry in the social domain' and 'expansion of social function of for-profit companies' were selected as key categories, and a story line was formed around this. In this study, we saw that it is necessary to conduct academic research and analysis on the competitive factors required for companies that pursue the values of two conflicting relationships, such as social ventures, to survive with competitiveness. In practice, concepts such as collaboration with for-profit companies, value combination, entrepreneurship competency and performance improvement, social value execution competency reinforcement, communication strategy, for-profit enterprise value investment, and entrepreneur management competency were derived. This study explains the social venture phenomenon for social enterprises, commercial enterprises, and entrepreneurs who want to enter the social venture field. It is expected to provide the implications necessary for successful social venture startups.

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A Study on the Impact of Artificial Intelligence on Decision Making : Focusing on Human-AI Collaboration and Decision-Maker's Personality Trait (인공지능이 의사결정에 미치는 영향에 관한 연구 : 인간과 인공지능의 협업 및 의사결정자의 성격 특성을 중심으로)

  • Lee, JeongSeon;Suh, Bomil;Kwon, YoungOk
    • Journal of Intelligence and Information Systems
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    • v.27 no.3
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    • pp.231-252
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    • 2021
  • Artificial intelligence (AI) is a key technology that will change the future the most. It affects the industry as a whole and daily life in various ways. As data availability increases, artificial intelligence finds an optimal solution and infers/predicts through self-learning. Research and investment related to automation that discovers and solves problems on its own are ongoing continuously. Automation of artificial intelligence has benefits such as cost reduction, minimization of human intervention and the difference of human capability. However, there are side effects, such as limiting the artificial intelligence's autonomy and erroneous results due to algorithmic bias. In the labor market, it raises the fear of job replacement. Prior studies on the utilization of artificial intelligence have shown that individuals do not necessarily use the information (or advice) it provides. Algorithm error is more sensitive than human error; so, people avoid algorithms after seeing errors, which is called "algorithm aversion." Recently, artificial intelligence has begun to be understood from the perspective of the augmentation of human intelligence. We have started to be interested in Human-AI collaboration rather than AI alone without human. A study of 1500 companies in various industries found that human-AI collaboration outperformed AI alone. In the medicine area, pathologist-deep learning collaboration dropped the pathologist cancer diagnosis error rate by 85%. Leading AI companies, such as IBM and Microsoft, are starting to adopt the direction of AI as augmented intelligence. Human-AI collaboration is emphasized in the decision-making process, because artificial intelligence is superior in analysis ability based on information. Intuition is a unique human capability so that human-AI collaboration can make optimal decisions. In an environment where change is getting faster and uncertainty increases, the need for artificial intelligence in decision-making will increase. In addition, active discussions are expected on approaches that utilize artificial intelligence for rational decision-making. This study investigates the impact of artificial intelligence on decision-making focuses on human-AI collaboration and the interaction between the decision maker personal traits and advisor type. The advisors were classified into three types: human, artificial intelligence, and human-AI collaboration. We investigated perceived usefulness of advice and the utilization of advice in decision making and whether the decision-maker's personal traits are influencing factors. Three hundred and eleven adult male and female experimenters conducted a task that predicts the age of faces in photos and the results showed that the advisor type does not directly affect the utilization of advice. The decision-maker utilizes it only when they believed advice can improve prediction performance. In the case of human-AI collaboration, decision-makers higher evaluated the perceived usefulness of advice, regardless of the decision maker's personal traits and the advice was more actively utilized. If the type of advisor was artificial intelligence alone, decision-makers who scored high in conscientiousness, high in extroversion, or low in neuroticism, high evaluated the perceived usefulness of the advice so they utilized advice actively. This study has academic significance in that it focuses on human-AI collaboration that the recent growing interest in artificial intelligence roles. It has expanded the relevant research area by considering the role of artificial intelligence as an advisor of decision-making and judgment research, and in aspects of practical significance, suggested views that companies should consider in order to enhance AI capability. To improve the effectiveness of AI-based systems, companies not only must introduce high-performance systems, but also need employees who properly understand digital information presented by AI, and can add non-digital information to make decisions. Moreover, to increase utilization in AI-based systems, task-oriented competencies, such as analytical skills and information technology capabilities, are important. in addition, it is expected that greater performance will be achieved if employee's personal traits are considered.