• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.1101-1121
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• 2024

Thorough understanding of user demands and formulation of product development strategies are crucial in product design, and can effectively stimulate consumer behavior. Scientific categorization and classification of demands contribute to accurate design development, design efficiency, and success rates. In recent years, e-commerce has become important consumption platforms for smart wearable products. However, there are few studies on product design and development among those related to promoting platform product services and sales. Meanwhile, design strategies focusing on real consumer needs are scarce among smart wearable product design studies. Therefore, an empirical consumer demand analysis method is proposed and design development strategies are formulated based on a categorized interpretation of demands. Using representative smart bracelets from wearable smart products as a case, this paper classifies consumer demands with three methods: big data semantic analysis, KANO model analysis, and satisfaction analysis. The results reveal that analysis methods proposed herein can effectively classify consumer demands and confirm that differences in consumer demand categories have varying impacts on consumer behavior. On this basis, corresponding design strategies are proposed based on four categories of consumer demands, aiming to make product design the leading factor and promote consumer behavior on e-commerce platforms. This research further enriches demand research on smart wearable products on e-commerce platforms, and optimizes products from a design perspective, thereby promoting consumption. In future research, different data analysis methods will be tried to compare and analyze changes in consumer demands and influencing factors, thus improving research on impact factors of product design in e-commerce.

• 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.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.8
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• pp.181-190
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• 2019

The purpose of this study is to verify the effects of information system quality on business performance (net benefit) in the Korean public informatization business and to verify whether the user has mediating role in the relationship between information system quality and net benefit. In order to prepare empirical tests, factor analyses was conducted and the validity and reliability of the variables were confirmed to be acceptable. The research hypotheses were tested by regression analyses. The mediating effects as well as the main effects were analyzed by SPSS statistical program. The results show that the higher the quality of the information system becomes, the higher the usability by the users of the system increases. Also, it was found that the users had more positive effect on the net benefit of the public informatization project. In addition, it was confirmed that there was a partial mediating effect by the users on the relationship between information system quality and the benefits. The results of the research suggests that we establish a management system to improve the business performance efficiently according to the priorities of detailed success factors from the viewpoint of the public information business, and the users who use the information system quality and system.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.71-90
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• 2020

Recently, the global insurance industry is rapidly developing digital transformation through the use of artificial intelligence technologies such as machine learning, natural language processing, and deep learning. As a result, more and more foreign insurers have achieved the success of artificial intelligence technology-based InsurTech and platform business, and Ping An Insurance Group Ltd., China's largest private company, is leading China's global fourth industrial revolution with remarkable achievements in InsurTech and Digital Platform as a result of its constant innovation, using 'finance and technology' and 'finance and ecosystem' as keywords for companies. In response, this study analyzed the InsurTech and platform business activities of Ping An Insurance Group Ltd. through the ser-M analysis model to provide strategic implications for revitalizing AI technology-based businesses of domestic insurers. The ser-M analysis model has been studied so that the vision and leadership of the CEO, the historical environment of the enterprise, the utilization of various resources, and the unique mechanism relationships can be interpreted in an integrated manner as a frame that can be interpreted in terms of the subject, environment, resource and mechanism. As a result of the case analysis, Ping An Insurance Group Ltd. has achieved cost reduction and customer service development by digitally innovating its entire business area such as sales, underwriting, claims, and loan service by utilizing core artificial intelligence technologies such as facial, voice, and facial expression recognition. In addition, "online data in China" and "the vast offline data and insights accumulated by the company" were combined with new technologies such as artificial intelligence and big data analysis to build a digital platform that integrates financial services and digital service businesses. Ping An Insurance Group Ltd. challenged constant innovation, and as of 2019, sales reached $155 billion, ranking seventh among all companies in the Global 2000 rankings selected by Forbes Magazine. Analyzing the background of the success of Ping An Insurance Group Ltd. from the perspective of ser-M, founder Mammingz quickly captured the development of digital technology, market competition and changes in population structure in the era of the fourth industrial revolution, and established a new vision and displayed an agile leadership of digital technology-focused. Based on the strong leadership led by the founder in response to environmental changes, the company has successfully led InsurTech and Platform Business through innovation of internal resources such as investment in artificial intelligence technology, securing excellent professionals, and strengthening big data capabilities, combining external absorption capabilities, and strategic alliances among various industries. Through this success story analysis of Ping An Insurance Group Ltd., the following implications can be given to domestic insurance companies that are preparing for digital transformation. First, CEOs of domestic companies also need to recognize the paradigm shift in industry due to the change in digital technology and quickly arm themselves with digital technology-oriented leadership to spearhead the digital transformation of enterprises. Second, the Korean government should urgently overhaul related laws and systems to further promote the use of data between different industries and provide drastic support such as deregulation, tax benefits and platform provision to help the domestic insurance industry secure global competitiveness. Third, Korean companies also need to make bolder investments in the development of artificial intelligence technology so that systematic securing of internal and external data, training of technical personnel, and patent applications can be expanded, and digital platforms should be quickly established so that diverse customer experiences can be integrated through learned artificial intelligence technology. Finally, since there may be limitations to generalization through a single case of an overseas insurance company, I hope that in the future, more extensive research will be conducted on various management strategies related to artificial intelligence technology by analyzing cases of multiple industries or multiple companies or conducting empirical research.

• Journal of Digital Convergence
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• v.10 no.8
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• pp.135-148
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• 2012

This work is intended to investigate how the factors of enterprise mobile SNS affect user satisfaction and continuous use intention through technology acceptance model proposed by Davis. To achieve the purpose, this researcher explored Information Systems Success model proposed by DeLone & McLean, Technology Acceptance Model proposed by Davis, and Model after Acceptance, and on the basis of the investigation, performed a study. For the data of this work, 9 enterprises, each of which has more than 100 employees and is located in Seoul, were chosen, and a questionnaire survey was conducted on their 276 employees who experienced enterprise mobile SNS. As a data collection tool, a structured self-administered questionnaire was used. For data analysis, SPSS 18.0 and AMOS 18.0 were used for applying Structural Equation modelling. According to the results of this work, three factors of enterprise mobile SNS-systematic factor (system quality, information quality, and service quality), user factor (personal innovation and personal familiarity), social factor (social effects and social interaction)-affected user satisfaction and continuous use intention through perceived availability, perceived easiness, and perceived enjoyment. Also, it was found that the direction of effects matched a theoretical prediction. And, it was revealed that the decision variables and mediating variables significantly affected user satisfaction and continuous use intention. Theoretical and practical meanings were discussed for the study result, and some suggestions were made for the issues of this work and future studies.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.1
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• pp.32-39
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• 2015

The voice communications with wireless backbone based networks are evolving into a packet switching VoIP systems. In those networks, a call processing scheme is required for management of subscribers and connection between them. A VoIP service scheme for those systems requires reliable subscriber management and connection establishment schemes, but the conventional call processing schemes based on the centralized server has lack of reliability. Thus, the mutual-backup architecture of SIP-servers is required to ensure efficient subscriber management and reliable VoIP call processing capability, and the synchronization and call processing schemes should be changed as the architecture is changed. In this paper, a mutual-backup architecture of SIP-servers is proposed for wireless backbone based networks. A message format for synchronization and information exchange between SIP servers is also proposed in the paper. This paper also proposes a FSM scheme for the fast call processing in unreliable networks to detect multiple servers at a time. The performance analysis results show that the mutual backup server architecture increases the call processing success rates than conventional centralized server architecture. Also, the FSM scheme provides the smaller call processing times than conventional SIP, and the time is not increased although the number of SIP servers in the networks is increased.

• Information Systems Review
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• v.15 no.1
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• pp.91-104
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• 2013

As coming smart computing era, the government and leading telecommunication companies have actively supported the development of mobile applications. However, since it is processed apart from the characteristics of small businesses, policy problems and support ways have continuously been pointed out. In this study, we selected priority of support project for the activation of the mobile application development, then proposed support ways for the success of small development business. Firstly, we researched the mobile business literatures, support projects and policy trends of the Korean government, academia and private sector. Next, we developed the activation model for small development business by synthesizing of support projects, literatures and expert group interviews. Based on this model, we selected priority of support projects according to AHP (Analytic Hierarchy Process) analysis results. In conclusion, we categorize the five support ways for the activation of mobile application development. We are expecting that these support ways will be useful to the development of effective policies and selection of detail tasks for the support of small business or developers.

• Information Systems Review
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• v.6 no.1
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• pp.67-84
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• 2004

In spite of the widely held belief that e-Marketplace will be expanded continuously, scholars are still reluctant to accept the positive expectation. Many anecdotal evidence and case studies indicate that there are several obstacles that hinder e-business firms from building the e-Marketplaces. Actually, this obstacles has led many e-marketplaces' failures. The purpose of this study is to verify the possibility of the e-Marketplace expansion and to explore how to deal the obstacles in the e-Marketplace operation. To achieve such objectives, a successful e-Marketplace case was investigated. The conclusion of this research is as follows : (1) e-Marketplaces can be built even in the high asset specificity and high complexity of product description market. Therefore it is acceptable that e-marketplace will be expanded continuously as argumented by Malone et al.(1989). (2) But the driving factor is not solely information technologies but the capability of e-business firms that consider technical factors, social factors, and service related factors all together. It was also noted that the capability of e-business firms to handle the inherent obstacles is important for the expansion of e-Marketplaces.

• Information Systems Review
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• v.9 no.2
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• pp.41-66
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• 2007

The e-marketplace is a virtual marketplace where buyers and sellers meet in order to conduct transactions through the intermediation of market-makers. For the success of an e-marketplace, it is crucial for the market-maker to induce both buyers and sellers to make active transactions in it. However, their participation is frequently deterred by potential risk factors caused mainly by the inherent, structural complexity of the e-marketplace. Therefore, it is a critical challenge for the market-maker to identify and manage the transactional risk perceived by both the buyers and sellers. In this paper, we investigate the sources of buyers' and sellers' perceived risks and their mitigating factors in the e-marketplace. Specifically, we derive an analysis framework based on the economic theory of agency relationship. The framework includes four sources of the risks(perceived information asymmetry, fears of seller opportunism, fears of buyer opportunism, and concerns about market-maker's role incompleteness) and five mitigators of the risks(website informativeness, trust in market-maker, trust in seller, product diagnosticity, and social presence). Then, we empirically verify the framework through a case study on four successful e-marketplaces, and provide implications and strategies for the market-maker to effectively manage the transactional risks.

• The KIPS Transactions:PartD
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• v.16D no.3
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• pp.451-462
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• 2009

Collaborative Filtering (CF), one of the most successful technologies among recommender systems, is a system assisting users in easily finding the useful information. However, despite its success and popularity, CF encounters a serious limitation with quality evaluation, called cold start problems. To alleviate this limitation, in this paper, we propose a unique method of building models derived from explicit ratings and applying the models to CF recommender systems. The proposed method is divided into two phases, an offline phase and an online phase. First, the offline phase is a building pre-computed model phase in which most of tasks can be conducted. Second, the online phase is either a prediction or recommendation phase in which the models are used. In a model building phase, we first determine a priori predicted rating and subsequently identify prediction errors for each user. From this error information, an error-reflected model is constructed. The error-reflected model, which is reflected average prior prediction errors of user neighbors and item neighbors, can make accurate predictions in the situation where users or items have few opinions; this is known as the cold start problems. In addition, in order to reduce the re-building tasks, the error-reflected model is designed such that the model is updated effectively and users'new opinions are reflected incrementally, even when users present a new rating feedback.