• Journal of Digital Convergence
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• v.19 no.1
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• pp.107-115
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• 2021

This study empirically analyzed the effect of SME representative competency on account receivable management and management performance using questionnaire data. The research model was confirmed through EFA, reliability analysis, CFA, and model fit, and the hypothesis was verified with a SEM. As a result, representative's manager competency had a positive(+) effect on account receivable management, and entrepreneurial competency had a negative(-) effect on credit control management. Account receivable management had a positive(+) effect on management performance. In the mediating test, credit sales management had a positive(+) effect but credit control management had a negative(-) effect on the effect between entrepreneurial competence and business performance. The result suggests that representative competency is an important factor and it is necessary to cultivate management competencies such as finance, utilization of management resources, and account receivables knowledge to improve management performance, and to manage account receivable based on insurance and customer credit for stable account receivable management. In the future, research on the impact of external factor such as consulting and government support and the account receivable management is required.

• Journal of Digital Convergence
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• v.19 no.11
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• pp.287-300
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• 2021

The purpose of this study is to verify the influence relationship of digitally converted services on consumers' intention to use since traditional services are being converted to digital services due to technological development and increase in non-face-to-face services. The study consisted of a program development procedure and a program effectiveness verification procedure, and bootstrapping was performed to verify the mediating effect adjusted along with multiple regression analysis. The subjects of this study were 323 university (graduate) students and the general public residing in Korea. Results. First, it was found that the three perceived interaction factors (perceived communication, perceived control, and perceived reactivity) of digital transformed services had a positive effect on perceived usefulness and perceived ease of use, respectively. Second, the relationship of influence of technology acceptance intention was verified. Third, it was confirmed that the effect of the three perceived interaction factors of digital transformed services on intention to use was mediated by perceived usefulness and perceived ease of use. Fourth, the mediating effect mediated by digital disparity was confirmed. As a result, it was confirmed that the three perceived interaction factors of the digitally converted service are important factors in the intention to use the digitally converted service. This suggests that efforts are needed to minimize the digital divide.

• Journal of Industrial Convergence
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• v.20 no.10
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• pp.137-146
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• 2022

The use of RPA (Robotic Process Automation) has been recently reviewed in various industries, but it seems that it is not being applied to companies faster than ever expected. In this study, three perceived risk factors affecting the acceptance conflict and acceptance intention of RPA technology were proposed and the effects of RPA on acceptance conflict and acceptance intention were investigated using RPA experienced people, gender and ICT industries as control variables. For the research, online survey was conducted targeting office workers and analyzed the results by using SPSS 22.0 and AMOS 22.0. As a result, it was found that among the three perceived risk factors, concern about introduction failure, employment insecurity, and execution errors, employment insecurity and execution errors did not affect the acceptance conflict and acceptance intention of RPA. This research shows that concerns over the introduction failure affected the acceptance conflict and acceptance intention. In addition, the acceptance conflict was judged as a factor of the mediation effect of the acceptance intention. From the perspective of companies that want to apply RPA, the theoretical and practical implications of business management are meaningful in that they can identify and respond to particularly important factors among perceived risks.

• Korean Journal of Construction Engineering and Management
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• v.23 no.6
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• pp.101-110
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• 2022

Recently, the global construction market is expected to grow at an annual average of 4.8% by 2025 and the risk of overseas construction is also expected to increase accordingly. In particular, domestic construction companies intensively participated in the EPC(Engineering, Procurement, Construction)/Turnkey project, but as a result of failing to respond to contractual risks, they have suffered losses of trillions of won in overseas business since 2013. Nevertheless, there have been not many studies on the derivation of EPC/Turnkey's contractual key risk sub-clauses. Therefore, in this study, the key risk sub-clauses were studied for the conditions of the 2017 Silver Book contract issued by the International Consulting Engineering Federation(FIDIC). To this end, 30 experts with more than 10 years of experience in international construction contracts were formed as a panel to conduct a Delphi survey on 170 sub-clauses of 21 clauses of FIDIC Silver Book to derive 62 main risk sub-clauses. In addition, the RPN(Risk Priority Number) was finally calculated using the FMEA(Failure Mode and Effect Analysis) technique, and 25 key risk sub-clauses within the Critical Risk range were derived. Through the results of this study, the practical point of view is able to refer to the contract provisions to be carefully reviewed at the bidding and contract signing stage in overseas construction projects. From an academic point of view, it provides direction and basic knowledge of how to study the contract fields used in overseas construction EPC/Turnkey projects.

• Journal of Service Research and Studies
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• v.13 no.3
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• pp.82-104
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• 2023

This study analyzes the effect of users' experiences using AI chatbot services on relationship quality and behavioral intention. For the study, a survey was conducted on users who experienced AI chatbot services, and the research hypothesis was verified by analyzing the final 299 copies of valid data. As a result of the analysis, it was confirmed that satisfaction and trust, which are the relationship quality dimensions of AI chatbot service, were formed in users through the cognitive experience, emotional experience, and relational experience. In addition, it was confirmed that satisfaction and trust have a positive effect on the intention to continue using and recommending AI chatbot services, which correspond to the level of consumers' behavioral intentions, respectively. In addition, in terms of relationship quality, it was significant in all paths of the road of behavior, but in satisfaction, the path coefficient of the road of continuous use of AI chatbot and recommended road was significantly higher than the path coefficient in trust. This study provided a theoretical foundation that the relationship with relationship quality that affects behavioral intention also affects AI chatbot services in the online environment, and it is significant in that it suggests that relationship quality is an important mediating factor in establishing long-term relationships with consumers.

• 한국체육학회지인문사회과학편
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• v.57 no.6
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• pp.123-134
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• 2018

The purpose of this study is to analyze the economic efficiency of the policy implemented by Ministry of Culture, Sports and Tourism on leading company in sport industry. The leading companies in sport industry are those who have a certain amount of sales in sport industry and the ones with potential to become global companies. Supporting areas include business advancement, overseas market development, and overseas PR marketing integration support. The research is performed by developing the equilibrium model composed of supply as well as demand and applying input-output analysis. The economic efficiency is estimated to in the form of changes in the sales of corporations and the ripple effect of the national economy. The results of the study are as follows. First, it is estimated that the sales growth rate of the company due to the implementation of the policy is from 3.74% to 5.19%. Second, the increase in sales reaches to a maximum of KRW 4,081 billion with a minimum of KRW 1,573 million, depending on the size of the company. Third, it is estimated that the production inducement effect for the national economy is from KRW 36 billion to KRW 93.4 billion. Fourth, the induced value added for the national economy is estimated to be at least KRW 11.3 billion, up to KRW 29.2 billion.

• The Korean Journal of Franchise Management
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• v.2 no.1
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• pp.74-99
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• 2011

This study aims to introduce conversion franchising strategy by utilizing the case of Nadle-Gagae. The case study of Nadle-Gagae shows that conversion franchising to Nadel-Gagae increases sales, the number of customer visits or visiting rates, and the level of satisfaction of store-owner and customer. This implies that conversion franchising benefits conversion franchising company, store-owner, and customer; it can be conducted as a competitive edge or strategy. However, it is limited to conclude that conversion franchising strategy will apply to all general franchising companies by only analysizing the case of Nadle-Gagae, because the business was initiated by government agency or governmental policy. Therefore, the franchising management should consider more conditions or circumstances related to franchising industry.

• Asia Marketing Journal
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• v.10 no.2
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• pp.31-69
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• 2008

The purpose of this study is to identify the organizational characteristics that enhance CRM performances of a company. Based on a review of diverse definitions of CRM performance, this study examines the relationships among CRM performance measures and organizational characteristics. A questionnaire survey of 123 CRM managers of Korean companies was conducted to test the proposed research model, and a series of structural equation modeling identified the strong effects of organizational characteristics on CRM performance. It was found that top management commitment to CRM and a firm's strategic readiness lead to high levels of CRM investment, which, in turn, enhance directly task-related performance and indirectly customer-related performance. This study also confirmed that customer orientation is significantly related to task-related CRM performance and that the variables of CRM commitment and organizational culture may enhance customerrelated performance indirectly through their effects on the task-related performance. However, organizational members' resistance to change was found to have no effects on CRM performance. Overall our research broadly supports the role of organizational characteristics revealed in the CRM literature.

• 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.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.93-108
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• 2014

To support business decision making, interests and efforts to analyze and use transaction data in different perspectives are increasing. Such efforts are not only limited to customer management or marketing, but also used for monitoring and detecting fraud transactions. Fraud transactions are evolving into various patterns by taking advantage of information technology. To reflect the evolution of fraud transactions, there are many efforts on fraud detection methods and advanced application systems in order to improve the accuracy and ease of fraud detection. As a case of fraud detection, this study aims to provide effective fraud detection methods for auction exception agricultural products in the largest Korean agricultural wholesale market. Auction exception products policy exists to complement auction-based trades in agricultural wholesale market. That is, most trades on agricultural products are performed by auction; however, specific products are assigned as auction exception products when total volumes of products are relatively small, the number of wholesalers is small, or there are difficulties for wholesalers to purchase the products. However, auction exception products policy makes several problems on fairness and transparency of transaction, which requires help of fraud detection. In this study, to generate fraud detection rules, real huge agricultural products trade transaction data from 2008 to 2010 in the market are analyzed, which increase more than 1 million transactions and 1 billion US dollar in transaction volume. Agricultural transaction data has unique characteristics such as frequent changes in supply volumes and turbulent time-dependent changes in price. Since this was the first trial to identify fraud transactions in this domain, there was no training data set for supervised learning. So, fraud detection rules are generated using outlier detection approach. We assume that outlier transactions have more possibility of fraud transactions than normal transactions. The outlier transactions are identified to compare daily average unit price, weekly average unit price, and quarterly average unit price of product items. Also quarterly averages unit price of product items of the specific wholesalers are used to identify outlier transactions. The reliability of generated fraud detection rules are confirmed by domain experts. To determine whether a transaction is fraudulent or not, normal distribution and normalized Z-value concept are applied. That is, a unit price of a transaction is transformed to Z-value to calculate the occurrence probability when we approximate the distribution of unit prices to normal distribution. The modified Z-value of the unit price in the transaction is used rather than using the original Z-value of it. The reason is that in the case of auction exception agricultural products, Z-values are influenced by outlier fraud transactions themselves because the number of wholesalers is small. The modified Z-values are called Self-Eliminated Z-scores because they are calculated excluding the unit price of the specific transaction which is subject to check whether it is fraud transaction or not. To show the usefulness of the proposed approach, a prototype of fraud transaction detection system is developed using Delphi. The system consists of five main menus and related submenus. First functionalities of the system is to import transaction databases. Next important functions are to set up fraud detection parameters. By changing fraud detection parameters, system users can control the number of potential fraud transactions. Execution functions provide fraud detection results which are found based on fraud detection parameters. The potential fraud transactions can be viewed on screen or exported as files. The study is an initial trial to identify fraud transactions in Auction Exception Agricultural Products. There are still many remained research topics of the issue. First, the scope of analysis data was limited due to the availability of data. It is necessary to include more data on transactions, wholesalers, and producers to detect fraud transactions more accurately. Next, we need to extend the scope of fraud transaction detection to fishery products. Also there are many possibilities to apply different data mining techniques for fraud detection. For example, time series approach is a potential technique to apply the problem. Even though outlier transactions are detected based on unit prices of transactions, however it is possible to derive fraud detection rules based on transaction volumes.