• Korea journal of population studies
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• v.19 no.2
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• pp.139-173
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• 1996

This study is an attempt to identify factors affecting korean immigrants concentration in small business enterprises in the middleman minority sector including the priphery and core sectors, with the private wage and self-employed worker examined in each sector, employing the 5 percent public use sample from the 1980 United States census. One out of five koreans aged 25∼64 years is engaged in self-employed small businesses, while the majority of koreans (4 out of 5) are in the private wage sector. In contrast to expectations, English language difficulties and inferior education are not the prime factors affecting self-employment small businesses. The korean self-employed small business owners both in the periphery sector and in the core sector showed the 'middle' strata of their position in the social structure in terms of their industry, occupation, earnings, etc.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.179-200
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• 2019

Word-of-mouth (WoM) is defined by consumer activities that share information concerning consumption. WoM activities have long been recognized as important in corporate marketing processes and have received much attention, especially in the marketing field. Recently, according to the development of the Internet, the way in which people exchange information in online news and online communities has been expanded, and WoM is diversified in terms of word of mouth, score, rating, and liking. Social media makes online users easy access to information and online WoM is considered a key source of information. Although various studies on WoM have been preceded by this phenomenon, there is no meta-analysis study that comprehensively analyzes them. This study proposed a method to extract major researches by applying text mining techniques and to grasp the main issues of researches in order to find the trend of WoM research using scholarly big data. To this end, a total of 4389 documents were collected by the keyword 'Word-of-mouth' from 1941 to 2018 in Scopus (www.scopus.com), a citation database, and the data were refined through preprocessing such as English morphological analysis, stopwords removal, and noun extraction. To carry out this study, we adopted main path analysis (MPA) and word co-occurrence network analysis. MPA detects key researches and is used to track the development trajectory of academic field, and presents the research trend from a macro perspective. For this, we constructed a citation network based on the collected data. The node means a document and the link means a citation relation in citation network. We then detected the key-route main path by applying SPC (Search Path Count) weights. As a result, the main path composed of 30 documents extracted from a citation network. The main path was able to confirm the change of the academic area which was developing along with the change of the times reflecting the industrial change such as various industrial groups. The results of MPA revealed that WoM research was distinguished by five periods: (1) establishment of aspects and critical elements of WoM, (2) relationship analysis between WoM variables, (3) beginning of researches of online WoM, (4) relationship analysis between WoM and purchase, and (5) broadening of topics. It was found that changes within the industry was reflected in the results such as online development and social media. Very recent studies showed that the topics and approaches related WoM were being diversified to circumstantial changes. However, the results showed that even though WoM was used in diverse fields, the main stream of the researches of WoM from the start to the end, was related to marketing and figuring out the influential factors that proliferate WoM. By applying word co-occurrence network analysis, the research trend is presented from a microscopic point of view. Word co-occurrence network was constructed to analyze the relationship between keywords and social network analysis (SNA) was utilized. We divided the data into three periods to investigate the periodic changes and trends in discussion of WoM. SNA showed that Period 1 (1941~2008) consisted of clusters regarding relationship, source, and consumers. Period 2 (2009~2013) contained clusters of satisfaction, community, social networks, review, and internet. Clusters of period 3 (2014~2018) involved satisfaction, medium, review, and interview. The periodic changes of clusters showed transition from offline to online WoM. Media of WoM have become an important factor in spreading the words. This study conducted a quantitative meta-analysis based on scholarly big data regarding WoM. The main contribution of this study is that it provides a micro perspective on the research trend of WoM as well as the macro perspective. The limitation of this study is that the citation network constructed in this study is a network based on the direct citation relation of the collected documents for MPA.

• Korean Journal of Heritage: History & Science
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• v.55 no.1
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• pp.199-222
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• 2022

It has been 100 years since the excavation of Geumgwanchong (Gold Crown Tomb), a tomb that was accidentally discovered in Noseo-ri, Gyeongju at the end of September 1921 during Japanese colonial rule. Although it is known for its discovery, not only in the Korean Peninsula but also in Asia and beyond, the excavation report was published in Japanese and English by the Government-General of Korea in 1924 and 1928, three years after the excavation. TOMB "KINKANTSUKA" or THE GOLD CROWN TOMB at KEISHU, AND ITS TREASURES) was published as a series of books and picture books. The excavation report was prepared by Kosaku Hamada (濱田耕作), who was a member of the Ruins Investigation Committee of the Japanese Government-General of Korea (later became the President of Kyoto Imperial University, Kyoto, Japan), and Sueji Umehara (梅原末治), who was commissioned to investigate the remains of the Japanese Government-General of Korea. In this paper, the preface was written in July 1922, about half a year after the excavation of tombs, which was much earlier than the official reports, in the 'Korean and Chinese reading book (稿本 高等朝鮮語及漢文讀本 巻五)' by Hamada Kosaku (濱田耕作) for high school students in Korea, which was titled 'New Excavated Artifacts in Gyeongju (慶州의 新發掘品)' with a subtitle '絶大의 發見', a slightly awkward expression in Korean, but it means 'a very big discovery'. The meaning has been introduced as a single unit, emphasizing its significance in terms of the achievements of the excavation of Geumgwanchong, academic and archaeological discoveries, and cultural history in Korean language rather than Japanese language. Since the manuscript was written immediately after the excavation, the excitement as an archaeological researcher at the time of the excavation and expectations for future research can be read as it is. In this paper, I would like to introduce the voice of the excited field leader of the Japanese Government-General of Korea after the excavation of Geumgwanchong in 2022, the 100th anniversary of the writing. In addition, the process from the discovery of the tomb to the preparation of the report was summarized in one chronological table to make it easier to understand the series of flows.

• Journal of Technology Innovation
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• v.31 no.3
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• pp.39-73
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• 2023

The field of Management of Technology(MOT) emerged in response to the need for research management within U.S. public research institutions during the 1960s. Since its inception, it has proliferated significantly, being practiced in more than 809 institutions globally and over 19 institutions in Korea, encompassing both research and educational endeavors. Particularly noteworthy is the substantial investment of government resources, primarily channeled through the Ministry of Industry since 2007, which has expeditiously established a comprehensive framework for cultivating graduate-level MOT expertise, marked by both quantitative and qualitative advancements. The educational curriculum in the realm of Korean MOT deviates from foreign counterparts through distinctive pathways, exemplified by its emphasis on industry practice-oriented educational programs, standardization and isomorphism across different schools, as well as its interconnectedness with proximate academic disciplines. This research systematically undertakes an analysis of the curriculum in Korean MOT graduate schools, thereby ascertaining its intrinsic identity and distinct attributes. In this endeavor, a comprehensive examination of eleven principal MOT textbooks(three in Korean and eight in English) is conducted to delineate the primary content of the curriculum across seven thematic domains. Moreover, the study deliberates on its differentiation from neighboring academic disciplines and the definitional attributes of MOT. Subsequently, this analysis also encompasses nine Korean MOT graduate programs, projecting the seven thematic domains onto their respective curricula. The findings illuminate that within the context of Korean graduate programs, a substantial proportion of the curriculum, amounting to 62.5%, is dedicated to facets encompassing the operational aspects of technology management within corporate contexts, technology management specific to varying industries and technologies, and collaborative endeavors between academia and industry in the form of projects and seminars. Evidently, the Korean approach to technology management education is notably geared towards the cultivation of adept practitioners capable of executing technology management functions at a mid-tier managerial level, aligned with the exigencies of regional industries. Grounded in the analysis of technology management curricula, this study extrapolates implications for the future trajectory of MOT education in Korea, encompassing a consideration of the stages of industrial development. It underscores the necessity to augment the educational curricula pertaining conceptual foundation of technology and innovation, strategic perspectives of technology and innovation, and the socio-economic context of technology management.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.1-25
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• 2020

Sentiment Analysis (SA) is a Natural Language Processing (NLP) task that analyzes the sentiments consumers or the public feel about an arbitrary object from written texts. Furthermore, Aspect-Based Sentiment Analysis (ABSA) is a fine-grained analysis of the sentiments towards each aspect of an object. Since having a more practical value in terms of business, ABSA is drawing attention from both academic and industrial organizations. When there is a review that says "The restaurant is expensive but the food is really fantastic", for example, the general SA evaluates the overall sentiment towards the 'restaurant' as 'positive', while ABSA identifies the restaurant's aspect 'price' as 'negative' and 'food' aspect as 'positive'. Thus, ABSA enables a more specific and effective marketing strategy. In order to perform ABSA, it is necessary to identify what are the aspect terms or aspect categories included in the text, and judge the sentiments towards them. Accordingly, there exist four main areas in ABSA; aspect term extraction, aspect category detection, Aspect Term Sentiment Classification (ATSC), and Aspect Category Sentiment Classification (ACSC). It is usually conducted by extracting aspect terms and then performing ATSC to analyze sentiments for the given aspect terms, or by extracting aspect categories and then performing ACSC to analyze sentiments for the given aspect category. Here, an aspect category is expressed in one or more aspect terms, or indirectly inferred by other words. In the preceding example sentence, 'price' and 'food' are both aspect categories, and the aspect category 'food' is expressed by the aspect term 'food' included in the review. If the review sentence includes 'pasta', 'steak', or 'grilled chicken special', these can all be aspect terms for the aspect category 'food'. As such, an aspect category referred to by one or more specific aspect terms is called an explicit aspect. On the other hand, the aspect category like 'price', which does not have any specific aspect terms but can be indirectly guessed with an emotional word 'expensive,' is called an implicit aspect. So far, the 'aspect category' has been used to avoid confusion about 'aspect term'. From now on, we will consider 'aspect category' and 'aspect' as the same concept and use the word 'aspect' more for convenience. And one thing to note is that ATSC analyzes the sentiment towards given aspect terms, so it deals only with explicit aspects, and ACSC treats not only explicit aspects but also implicit aspects. This study seeks to find answers to the following issues ignored in the previous studies when applying the BERT pre-trained language model to ACSC and derives superior ACSC models. First, is it more effective to reflect the output vector of tokens for aspect categories than to use only the final output vector of [CLS] token as a classification vector? Second, is there any performance difference between QA (Question Answering) and NLI (Natural Language Inference) types in the sentence-pair configuration of input data? Third, is there any performance difference according to the order of sentence including aspect category in the QA or NLI type sentence-pair configuration of input data? To achieve these research objectives, we implemented 12 ACSC models and conducted experiments on 4 English benchmark datasets. As a result, ACSC models that provide performance beyond the existing studies without expanding the training dataset were derived. In addition, it was found that it is more effective to reflect the output vector of the aspect category token than to use only the output vector for the [CLS] token as a classification vector. It was also found that QA type input generally provides better performance than NLI, and the order of the sentence with the aspect category in QA type is irrelevant with performance. There may be some differences depending on the characteristics of the dataset, but when using NLI type sentence-pair input, placing the sentence containing the aspect category second seems to provide better performance. The new methodology for designing the ACSC model used in this study could be similarly applied to other studies such as ATSC.