• Cross-Cultural Studies
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• v.50
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• pp.103-130
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• 2018

This research aims to explore the associated linguistic features and functions of Chinese as used for business trading purposes, and which is based on a discourse analysis through a case in which a Korean buyer and a Chinese supplier have exchanged Internet based e-mails. The research questions include first, the linguistic functions and characteristics of Chinese shown as identified in this trade case through e-mails, second, the use of Chinese trade specific terms, and third, the apparent and dynamic negotiation strategies that are identified as followed by the cultural value systems which are used for resolving interest conflicts and issues between the buyer and supplier in the course of negotiating business contracts between two parties. The participants of this research pertain to a Korean buyer, James and a Chinese supplier, Sonya. The associated data consists of 74 e-mails exchanged between the two parties, initiated in an effort to begin and complete a trade item, in this case namely the product of napkin holders. The research for the study is based on the discourse analysis and empirically analyses models of Chinese linguistic functions and features. The findings are the following. First, as identified, the specific Chinese functions used and sequenced in this trade case are of a procedure, request, informing, negotiation and persuasion. Second, the essential trade terms used in this business interaction involve the relevant issues of 1) ordering and price negotiating, 2) marking the origin of the products, 3) the arrangement of the product examination and customs declaration for the anticipated import items, 4) preparation of the necessary legal documents, and 5) the package and transport of the product in the final instance. Third, the impact of the similarities and differences in the cultural value systems between Korea and China on the negotiations and conflict resolution during a negotiated contract between two parties are speculated in terms of the use of culturally based techniques such as face-saving and the utilization of uncertainty-avoiding strategies as meant to prevent misunderstandings from developing between the parties. The concluding part of the study discusses the implications for a practical Chinese language education utilizing the linguistic functions and features of the Chinese culture and language strategies as useful in business associations for trading purposes, and the importance of intercultural communication styles based on similar of different identified cultural values as noted between two parties.

• Korean Journal of School Psychology
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• v.15 no.3
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• pp.459-488
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• 2018

The self-report assessment has been most commonly used to estimate bullying/victimization (B/V) rates in most domestic and international prevalence studies. However, the presence of many potential biasing factors in such an assessment method, including specific operationalization/measurement strategies and testing conditions, has become an issue due to a considerable variability in reported involvement rates across studies. This study analyzed self-reported B/V involvement rates on Olweus Bullying Questionnaire (OBQ) among 690 Korean middle school students by gender and two different cut-offs (generous vs. strict cut-offs) and examined if the involvement rates were significantly varied by testing conditions such as presentation vs. omission of a precise definition of B/V, anonymous vs. non-anonymous/confidential administration, and the use of global vs. specific questions. Chi-square analyses revealed that boys displayed higher involvement rates on global measures of B/V and on items related to direct forms of B/V, with no significant gender differences on specific measures of relational B/V rates. It was also found that a global rate of bullying and specific rates of verbal B/V were 111% to 157% higher when no definition was provided. However, anonymous vs. non-anonymous administration had no significant impacts on rates of involvement, except for one item; there were also no significant differences in reported degrees of frankness and perceived confidentiality of their responses across two adminstration conditions. Finally, when involvement rates were assessed by using specific vs. global items, they were 68% to 148% higher with binominal correlations in low to moderate ranges. Findings also indicated that global items had a high specificity but a relatively low sensitivity. Implications of these findings were fully discussed for researchers and practitioners in the field of B/V assessment.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.11
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• pp.471-480
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• 2023

The Korean construction order volume in South Korea grew significantly from 91.3 trillion won in public orders in 2013 to a total of 212 trillion won in 2021, particularly in the private sector. As the size of the domestic and overseas markets grew, the scale and complexity of EPC (Engineering, Procurement, Construction) projects increased, and risk management of project management and ITB (Invitation to Bid) documents became a critical issue. The time granted to actual construction companies in the bidding process following the EPC project award is not only limited, but also extremely challenging to review all the risk terms in the ITB document due to manpower and cost issues. Previous research attempted to categorize the risk terms in EPC contract documents and detect them based on AI, but there were limitations to practical use due to problems related to data, such as the limit of labeled data utilization and class imbalance. Therefore, this study aims to develop an AI model that can categorize the contract terms based on the FIDIC Yellow 2017(Federation Internationale Des Ingenieurs-Conseils Contract terms) standard in detail, rather than defining and classifying risk terms like previous research. A multi-text classification function is necessary because the contract terms that need to be reviewed in detail may vary depending on the scale and type of the project. To enhance the performance of the multi-text classification model, we developed the ELECTRA PLM (Pre-trained Language Model) capable of efficiently learning the context of text data from the pre-training stage, and conducted a four-step experiment to validate the performance of the model. As a result, the ensemble version of the self-developed ITB-ELECTRA model and Legal-BERT achieved the best performance with a weighted average F1-Score of 76% in the classification of 57 contract terms.