• The Journal of Bigdata
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• v.8 no.1
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• pp.131-156
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• 2023

The traditional retail industry significantly changed over the past decade due to the mobile and online technologies. This change has been accompanied by a shift in consumer behavior regarding purchasing patterns. Despite the rise of online shopping, there are still specific categories of products, such as "Processed food" in Mongolia, for which traditional shopping remains the preferred purchase method. To prepare for the inevitable future of retail businesses, firms need to closely analyze the performance of their offline stores to plan their further actions in a new multi-channel environment. Retailers must integrate diverse channels into their operations to stay relevant and adjust to the shifting market. In this research, we have analyzed the performance data such as sales, profit, and amount of sales of offline stores by using clustering approach. From the clustering, we have found the several distinct insights by comparing the circumstances and performance of retail stores. For the certain retail stores, we have proposed three different strategies: a fulfillment hub store between online and offline channels, an experience store to elongate customers' time on the premises, and a merge between two non-related channels that could complement each other to increase traffic based on the store characteristics. With the proposed strategies, it may enhance the user experience and profit at the same time.

• Information Systems Review
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• v.20 no.2
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• pp.111-137
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• 2018

Considering the increase in health insurance benefits and the elderly population of the baby boomer generation, the amount consumed by health care in 2020 is expected to account for 20% of US GDP. As the healthcare industry develops, competition among the medical services of hospitals intensifies, and the need of hospitals to manage the quality of medical services increases. In addition, interest in online reviews of hospitals has increased as online reviews have become a tool to predict hospital quality. Consumers tend to refer to online reviews even when choosing healthcare service providers and after evaluating service quality online. This study aims to analyze the effect of sentiment score of healthcare service quality on hospital rating with Yelp hospital reviews. This study classifies large amount of text data collected online primarily into five service quality measurement indexes of SERVQUAL theory. The sentiment scores of reviews are then derived by SERVQUAL dimensions, and an econometric analysis is conducted to determine the sentiment score effects of the five service quality dimensions on hospital reviews. Results shed light on the means of managing online hospital reputation to benefit managers in the healthcare and medical industry.

• Journal of Internet Computing and Services
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• v.20 no.5
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• pp.87-94
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• 2019

In this paper, the method of analyzing the unformatted data of consumers accumulated on social networks in the era of the Fourth Industrial Revolution by utilizing data from the service design and social psychology aspects was proposed. First, the fandom phenomenon, which shows subjective and collective behavior in a space on a social network rather than physical space, was defined from a data service perspective. The fandom model has been transformed into a collective level of customer Persona that has been analyzed at a personal level in traditional service design, and social network analysis that analyzes consumers' big data has been presented as an efficient way to pattern and visually analyze it. Consumer data collected through social leasing were pre-processed by column based on correlation, stability, missing, and ID-ness. Based on the above data, the company's brand strategy was divided into active and passive interventions and the effect of this strategic attitude on the growth direction of the consumer's fandom community was analyzed. To this end, the fandom model of consumers was proposed by dividing it into four strategies that the brand strategy had: stand-alone, decentralized, integrated and centralized, and the fandom shape of consumers was proposed as a growth model analysis technique that analyzes changes over time.

• The Journal of Bigdata
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• v.8 no.2
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• pp.149-163
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• 2023

Due to the continuous growth of the E-commerce market, the volume of orders that fulfillment centers have to process has increased, and various customer requirements have increased the complexity of order processing. Along with this trend, the operational efficiency of fulfillment centers due to increased labor costs is becoming more important from a corporate management perspective. Using historical performance data as training data, this study focused on real-time box recommendations applicable to packaging areas during fulfillment center shipping. Four types of data, such as product information, order information, packaging information, and delivery information, were applied to the machine learning model through pre-processing and feature-engineering processes. As an input vector, three characteristics were used as product specification information: width, length, and height, the characteristics of the input vector were extracted through a feature engineering process that converts product information from real numbers to an integer system for each section. As a result of comparing the performance of each model, it was confirmed that when the Gradient Boosting model was applied, the prediction was performed with the highest accuracy at 95.2% when the product specification information was converted into integers in 21 sections. This study proposes a machine learning model as a way to reduce the increase in costs and inefficiency of box packaging time caused by incorrect box selection in the fulfillment center, and also proposes a feature engineering method to effectively extract the characteristics of product specification information.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.6
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• pp.223-234
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• 2021

Recent advances in the 4th Industrial Revolution have accelerated the change of the shopping behavior from offline to online. Search queries show customers' information needs most intensively in online shopping. However, there are not many search query research in the field of search, and most of the prior research in the field of search query research has been studied on a limited topic and data-based basis based on researchers' qualitative judgment. To this end, this study defines the type of search query with data-based quantitative methodology by applying machine learning to search research query field to define the 15 topics of search query by conducting topic modeling based on search query and clicked document information. Furthermore, we present a new classification system of new search query types representing searching behavior characteristics by extracting key variables through principal component analysis and analyzing. The results of this study are expected to contribute to the establishment of effective search services and the development of search systems.

• The Journal of Society for e-Business Studies
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• v.23 no.4
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• pp.153-169
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• 2018

Previous e-financial anomalies analysis and detection technology collects large amounts of electronic financial transaction logs generated from electronic financial business systems into big-data-based storage space. And it detects abnormal transactions in real time using detection rules that analyze transaction pattern profiling of existing customers and various accident transactions. However, deep analysis such as attempts to access e-finance by insiders of financial institutions with large scale of damages and social ripple effects and stealing important information from e-financial users through bypass of internal control environments is not conducted. This paper analyzes the management status of e-financial security programs of financial companies and draws the possibility that they are allies in security control of insiders who exploit vulnerability in management. In order to efficiently respond to this problem, it will present a comprehensive e-financial security management environment linked to insider threat monitoring as well as the existing e-financial transaction detection system.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.653-658
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• 2021

Convergence of ICT technology and various industry have been proliferated. It makes human life affluent pulling existing industry paradigm down while they do not realize. Also, it impacts on personal life and becomes a driving force which strengthen the state level and its competitiveness. Complexity is increasing, knowledge is expanding and ICT technology is evolving in the present industrial society. Thus, new business model is being created, established business without competitiveness is eliminated and new ecosystem is constructed for industry change or creation. The new business and its competitive order driving in current convergence environment is apt to be exposed to uncertainty risk. These changes promote the collapse of existing industry or create new business models. There are digital transformation and ICT convergence in the center of the changes. The change of society and industry caused by this phenomenon will be analyzed. Also, development direction, strategy and execution method for securing industry competitiveness of ICT convergence will be researched.

• Journal of Software Assessment and Valuation
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• v.16 no.2
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• pp.163-172
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• 2020

Recently, concepts of the Fourth Industrial Revolution technologies such as artificial intelligence, big data, and cloud computing have been introduced and the limits of individual or team development policies are being reviewed. Also, a lot of latest technology source codes have been opened to the public, and related studies are being conducted based on them. Meanwhile, the company is applying the strengths of the open source software development methodology to proprietary software development, and publicly announcing support for open source development methodology. In this paper, we introduced several software development methodology such as open source model, inner source model, and the similar DevOps model, which have been actively discussed recently, and compared their characteristics and components. Rather than claiming the excellence of a specific model, we argue that if the software development policy of an individual or affiliated organization is established according to each benefit, they will be able to achieve software quality improvement while satisfying customer requirements.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.357-359
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• 2021

Railway system as a mass transportation is under progress to smart railway system beyond high speed and automation era. Communication network technology including 5G-R(5th Generation - Railway) mobile communication technology and information convergence technology of Big Data, Deep Learnig, AI(Artificial Intelliegnce) and Block Chain have to be used for implementation and operation of this smart railway system. In this paper, a communication network structure is suggested for this smart railway system. This suggested smart railway commnuication network structure is composed with layered structure of plane unit for safety operation of high speed railway, railway system management and customer services, and also have some complexed function of each plane. Results of this study can be used for smart railway communication network implementation, operation and managements, development of railway communication standards.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.6
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• pp.125-138
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• 2018

Recently, most firms have difficulties in predicting business context due to uncontrollable factors such as natural disasters, terrorism, social and political interests, as well as market factors such as rapid technological change, diversification of customer needs, and intensification of competition with competitors, thereby increasing the importance of risk management. The purpose of this study is to analyze trends of the risk management field concentrating on SCM, which is increasingly interested, and to identify key researches in this field and provide useful academic information. This study collected the information of the articles published in journals using the Scopus database, and analyzed both the network generated by keywords proposed in the articles and the network generated by the information for citations and co-authorship.