• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.4
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• pp.277-301
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• 2020

This study surveyed 503 adults to examine the effect of gaming disorder classification, recently announced by World Health Organization(WHO), when it is applied to Korean game market. Considering the difference in respondents' background knowledge on gaming disorder, half of the respondents were randomly assigned to read an informative news article describing WHO's decision and its expected effect on domestic game industry. Based on previous literature of gaming disorder, we categorized respondents into a normal-use group and a potentially problematic-use group. As a result of analyses, it was found that the gaming disorder classification would yield overall reduction of game-related consumption in terms of gaming time(24%), game cost(28%), the number of games(22%), etc. The potentially problematic group showed higher willingness to pay for gaming than the normal group did, even if the game cost presumably increases due to the gaming disorder classification. A similar outcome was observed in those with high stress levels. This implies that the policy to solve game addiction problems may ironically lead to unexpected cost increases to the target group of the policy. Hence, problematic groups, especially, highly stressful people and the people with the lack of self-control, need to be considered when the gaming disorder classification policy is established. Furthermore, the informative news article had the preventive effect on the attitude and the intention of the people with moderate or high self-control capacity, but not to the people with gaming-additive tendencies, Again, this finding confirms the necessity of the tweezers policy to refine target groups by their characteristics and prepare for differentiated policies. When the gaming disorder classification is simply adopted with no consideration of domestic circumstances, irreversible loss could affect Korean game users, game industries, and related companies. This calls for urgent cooperation between academia, government, and industry to set up appropriate measures to deal with the gaming disorder classification.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.1-23
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• 2019

Big data refers to data that is difficult to store, manage, and analyze by existing software. As the lifestyle changes of consumers increase the size and types of needs that consumers desire, they are investing a lot of time and money to understand the needs of consumers. Companies in various industries utilize Big Data to improve their products and services to meet their needs, analyze unstructured data, and respond to real-time responses to products and services. The financial industry operates a decision support system that uses financial data to develop financial products and manage customer risks. The use of big data by financial institutions can effectively create added value of the value chain, and it is possible to develop a more advanced customer relationship management strategy. Financial institutions can utilize the purchase data and unstructured data generated by the credit card, and it becomes possible to confirm and satisfy the customer's desire. CRM has a granular process that can be measured in real time as it grows with information knowledge systems. With the development of information service and CRM, the platform has change and it has become possible to meet consumer needs in various environments. Recently, as the needs of consumers have diversified, more companies are providing systematic marketing services using data mining and advanced CRM (Customer Relationship Management) techniques. KB Kookmin Card, which started as a credit card business in 1980, introduced early stabilization of processes and computer systems, and actively participated in introducing new technologies and systems. In 2011, the bank and credit card companies separated, leading the 'Hye-dam Card' and 'One Card' markets, which were deviated from the existing concept. In 2017, the total use of domestic credit cards and check cards grew by 5.6% year-on-year to 886 trillion won. In 2018, we received a long-term rating of AA + as a result of our credit card evaluation. We confirmed that our credit rating was at the top of the list through effective marketing strategies and services. At present, Kookmin Card emphasizes strategies to meet the individual needs of customers and to maximize the lifetime value of consumers by utilizing payment data of customers. KB Kookmin Card combines internal and external big data and conducts marketing in real time or builds a system for monitoring. KB Kookmin Card has built a marketing system that detects realtime behavior using big data such as visiting the homepage and purchasing history by using the customer card information. It is designed to enable customers to capture action events in real time and execute marketing by utilizing the stores, locations, amounts, usage pattern, etc. of the card transactions. We have created more than 280 different scenarios based on the customer's life cycle and are conducting marketing plans to accommodate various customer groups in real time. We operate a smart offering system, which is a highly efficient marketing management system that detects customers' card usage, customer behavior, and location information in real time, and provides further refinement services by combining with various apps. This study aims to identify the traditional CRM to the current CRM strategy through the process of changing the CRM strategy. Finally, I will confirm the current CRM strategy through KB Kookmin card's big data utilization strategy and marketing activities and propose a marketing plan for KB Kookmin card's future CRM strategy. KB Kookmin Card should invest in securing ICT technology and human resources, which are becoming more sophisticated for the success and continuous growth of smart offering system. It is necessary to establish a strategy for securing profit from a long-term perspective and systematically proceed. Especially, in the current situation where privacy violation and personal information leakage issues are being addressed, efforts should be made to induce customers' recognition of marketing using customer information and to form corporate image emphasizing security.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.191-206
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• 2022

Recently, it is a de-facto approach to utilize a pre-trained language model(PLM) to achieve the state-of-the-art performance for various natural language tasks(called downstream tasks) such as sentiment analysis and question answering. However, similar to any other machine learning method, PLM tends to depend on the data distribution seen during the training phase and shows worse performance on the unseen (Out-of-Distribution) domain. Due to the aforementioned reason, there have been many efforts to develop domain-specified PLM for various fields such as medical and legal industries. In this paper, we discuss the training of a finance domain-specified PLM for the Korean language and its applications. Our finance domain-specified PLM, KB-BERT, is trained on a carefully curated financial corpus that includes domain-specific documents such as financial reports. We provide extensive performance evaluation results on three natural language tasks, topic classification, sentiment analysis, and question answering. Compared to the state-of-the-art Korean PLM models such as KoELECTRA and KLUE-RoBERTa, KB-BERT shows comparable performance on general datasets based on common corpora like Wikipedia and news articles. Moreover, KB-BERT outperforms compared models on finance domain datasets that require finance-specific knowledge to solve given problems.

• Asia Marketing Journal
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• v.12 no.3
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• pp.43-62
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• 2010

The notion of customer orientation is now importantly considered in the context of banking industries. Despite customer-oriented organizational cultures, there are few studies addressing the relationship between customer orientation and its outcomes. In particular, this study aims at testing the effect of customer orientation as a key marketing effort designed by a bank. This is because interest rate sensitivity is critical for evaluating banking services after raising the base rate. In so doing, first, this study investigates the relationships among customer orientation, interest rate sensitivity, and customer loyalty. Second, this paper examines how the moderating effects of both deposit interest and loan interest rates influence the linkages of customer orientation-interest rate sensitivity and customer orientation-customer loyalty. To test the proposed model, research data are collected from 304 subjects who use banking services(e.g., Shin-Han, Kookmin, the First Bank, Hana, and Woori banks). Each construct was measured by published items and the psychometric properties of the three constructs, excluding two constructs of the moderators, were evaluated by employing the method of confirmatory factor analysis via the use of AMOS. The model fit was also evaluated using the CFI, TLI, and RMSEA fit indices that are recommended based on their relative stability and insensitivity to sample size. The findings show that the relationship between customer orientation and customer loyalty is significant, whereas the relationships between customer orientation and interest rate sensitivity and between interest rate sensitivity and customer loyalty are not supported. Although customer orientation is highly evaluated, customers' interest rate sensitivity that results in the comparison of interest rates plays an important role in reducing the effect of customer orientation. As a consequence, interest rate sensitivity does not influence customer loyalty. First of all, one of interesting results in this study is that the moderating effect of loan interest rate is quite different from deposit interest rate. In the case of deposit interest rate, the linkages both customer orientation-interest rate sensitivity and customer orientation-customer loyalty are insignificant. In the case of loan interest rate, however, the two proposed linkages are supported. As our proposed relationships are still in its infancy in the context of banking industry, our study contributes to enhance scholars' knowledge of bank services and provides insights for practitioners when their marketing strategies, particularly both deposit and interest rates, have to be established. Finally, this research also illuminates the need for further research that considers the influence of customer orientation on consumer's decision-making and bank profits. More specifically, the results are encouraging and will lead us to further investigate this key outcome of the banking deposit/interest rates.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.93-111
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• 2013

As Internet and information technology (IT) continues to develop and evolve, the issue of big data has emerged at the foreground of scholarly and industrial attention. Big data is generally defined as data that exceed the range that can be collected, stored, managed and analyzed by existing conventional information systems and it also refers to the new technologies designed to effectively extract values from such data. With the widespread dissemination of IT systems, continual efforts have been made in various fields of industry such as R&D, manufacturing, and finance to collect and analyze immense quantities of data in order to extract meaningful information and to use this information to solve various problems. Since IT has converged with various industries in many aspects, digital data are now being generated at a remarkably accelerating rate while developments in state-of-the-art technology have led to continual enhancements in system performance. The types of big data that are currently receiving the most attention include information available within companies, such as information on consumer characteristics, information on purchase records, logistics information and log information indicating the usage of products and services by consumers, as well as information accumulated outside companies, such as information on the web search traffic of online users, social network information, and patent information. Among these various types of big data, web searches performed by online users constitute one of the most effective and important sources of information for marketing purposes because consumers search for information on the internet in order to make efficient and rational choices. Recently, Google has provided public access to its information on the web search traffic of online users through a service named Google Trends. Research that uses this web search traffic information to analyze the information search behavior of online users is now receiving much attention in academia and in fields of industry. Studies using web search traffic information can be broadly classified into two fields. The first field consists of empirical demonstrations that show how web search information can be used to forecast social phenomena, the purchasing power of consumers, the outcomes of political elections, etc. The other field focuses on using web search traffic information to observe consumer behavior, identifying the attributes of a product that consumers regard as important or tracking changes on consumers' expectations, for example, but relatively less research has been completed in this field. In particular, to the extent of our knowledge, hardly any studies related to brands have yet attempted to use web search traffic information to analyze the factors that influence consumers' purchasing activities. This study aims to demonstrate that consumers' web search traffic information can be used to derive the relations among brands and the relations between an individual brand and product attributes. When consumers input their search words on the web, they may use a single keyword for the search, but they also often input multiple keywords to seek related information (this is referred to as simultaneous searching). A consumer performs a simultaneous search either to simultaneously compare two product brands to obtain information on their similarities and differences, or to acquire more in-depth information about a specific attribute in a specific brand. Web search traffic information shows that the quantity of simultaneous searches using certain keywords increases when the relation is closer in the consumer's mind and it will be possible to derive the relations between each of the keywords by collecting this relational data and subjecting it to network analysis. Accordingly, this study proposes a method of analyzing how brands are positioned by consumers and what relationships exist between product attributes and an individual brand, using simultaneous search traffic information. It also presents case studies demonstrating the actual application of this method, with a focus on tablets, belonging to innovative product groups.