• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.1
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• pp.91-107
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• 2023

With the recent expansion of the commerce ecosystem, a large number of user evaluations have been produced. Accordingly, attempts to create business insights using user evaluation data have been actively made. However, since user evaluation can change after the user experiences the product, it is difficult to say that the analysis based only on reviews immediately after purchase fully reflects the user's evaluation of the product. Moreover, studies conducted so far on user evaluation have overlooked the fact that the length of time a user has used a product can affect the user's product evaluation. Therefore, in this study, we build a model that predicts the direction of change in the user's rating after use from the user's rating and reviews immediately after purchase. In particular, the proposed model reflects the product's period of use in predicting the change direction of the star rating. However, since the posterior information on the duration of product use cannot be used as input in the inference process, we propose a structure that utilizes information about the product's period of use using an auxiliary classifier. As a result of an experiment using 599,889 user evaluation data collected from the shopping platform 'N' company, we confirmed that the proposed model performed better than the existing model in terms of accuracy.

• Journal of the Korea Institute of Building Construction
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• v.4 no.3
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• pp.117-124
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• 2004

This study aims to evaluate the feasibility of remodeling business by predicting the future price of apartment house after remodeling using Hedonic Price Model. The data concerning such 8 independent variables as location, unit size, unit plan, landscape, parking, the number of elapsed years after completion, number of units, brand per apartment unit from 25 regions in Seoul metropolitan city were collected and evaluated by established evaluation criteria. The coefficients affecting the price of apartment unit were made by way of linear multi-regression and put into Hedonic Price Model. The feasibility evaluation model for apartment was made and verified by data of remodelled apartment. The predicted results using suggested evaluation model coincide with actual apartment market situations.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.2
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• pp.83-91
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• 2006

Internet shopping mall has become a huge distribution channel with dramatic growth in recent years. The number of consumers has exponentially increased as the scale of shopping mall has been large so that shopping malls with thousands or millions of consumers become a general case. However, it is essential to evaluate whether current assortment of consumers is proper or not in the strategic aspect in order to operate Internet shopping mall effectively and gain profits. That is, it is important to evaluate whether consumer strategy of corporation is proper or not from the corporation. Despite this business importance, consumer assortment has not been evaluated well and related study is not sufficient. This study supposes a framework for consumer assortment evaluation, which evaluates whether consumer assortment of Internet shopping mall is proper or not. In the framework for consumer assortment evaluation, analysis data based on order data and consumer data in database is made. Then, four factors, consumer maintenance rate, consumer profitability, consumer securing rate and consumer conversion are setup, and 22 measurement indexes are drawn. Finally, a consumer assortment evaluation score card is made by integrating them. This study has applied a supposed framework to a domestic typical community based shopping mall, and it is expected that the evaluation result will be used as informant strategic information to operate the shopping mall effectively.

• Journal of the Korean Operations Research and Management Science Society
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• v.40 no.2
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• pp.1-17
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• 2015

In this paper, we propose a portfolio selection model utilizing a Markov chain for investing in the foreign exchange market based on market forecasts and exchange rate movement predictions. The proposed model is utilized to compute optimum investment portfolio weights for investing in margin-based markets such as the FX margin market. We further present an objective investment algorithm for applying the proposed model in real-life investments. Empirical performance of the proposed model and investment algorithm is evaluated by conducting an experiment in the FX market consisting of the 7 most traded currency pairs, for a period of 9 years, from the beginning of 2005 to the end of 2013. We compare performance with 1) the Dollar Index, 2) a 1/N Portfolio that invests the equal amount in the N target assets, and 3) the Barclay BTOP FX Index. Performance is compared in terms of cumulated returns and Sharpe ratios. The results suggest that the proposed model outperforms all benchmarks during the period of our experiment, for both performance measures. Even when compared in terms of pre- and post-financial crisis, the proposed model outperformed all other benchmarks, showing that the model based on objective data and mathematical optimization achieves superior performance empirically.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2993-3002
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• 2018

This case study introduces the process of developing the technology rating evaluation model for investment. The technology evaluation rating model for investment is a project that the Financial Services Commission and the Ministry of Commerce, Industry and Energy collaborated to expand the scope of technology finance from loan to investment. The technology evaluation model for investment was developed with the aim of predicting high growth companies. The model consists of a statistical model and an expert model. Here, statistical models were modeled by using logistic regression analysis. Expert models gathered opinions of experts and identified the weight of each evaluation item and set the model. The rating system of the model is composed of 10 grades. The distribution of the model was consistent with KTRS grade distribution. Interestingly, the emphasis is on technology and marketability. In the technology valuation grade model for the goddess, there is a considerable difference from the emphasis on managerial competence or business performance.

• Journal of Korean Society for Quality Management
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• v.45 no.4
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• pp.811-828
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• 2017

Purpose: In the meantime, studies on the measurement methods of service quality have mainly been conducted in views of the service recipients. In this study, we introduce a co-orientation model that compares the perceptions of service provider and recipient and examine the applicability in service quality field. Methods: In this study, we conducted a case study on the specific service called 'National R&D Project Plan Evaluation Service' to examine the applicability of the co-orientation model in the service quality field. Results: We could identify the phenomenon of how service providers and recipients perceive differently about specific services introduced in the case study. This study confirms that it can be used to identify problems in mutually oriented service quality activities and to take practical measures to improve them. As we have seen in this case study, the co-orientation model is expected to be of great help in exploring opportunities for quality improvement in the area of service quality. Conclusion: The service quality co-orientation model allows the service provider to distinguish between what they think of differently with the service recipient and what they have in common, so the service provider will be able to find the agenda of service quality improvement.

• International Journal of Advanced Culture Technology
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• v.10 no.4
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• pp.499-510
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• 2022

This study examines the reporting factors of crime against business in Korea and proposes a corresponding predictive model using machine learning. While many previous studies focused on the individual factors of theft victims, there is a lack of evidence on the reporting factors of crime against a business that serves the public good as opposed to those that protect private property. Therefore, we proposed a crime prevention model for the willingness factor of theft reporting in businesses. This study used data collected through the 2015 Commercial Crime Damage Survey conducted by the Korea Institute for Criminal Policy. It analyzed data from 834 businesses that had experienced theft during a 2016 crime investigation. The data showed a problem with unbalanced classes. To solve this problem, we jointly applied the Synthetic Minority Over Sampling Technique and the Tomek link techniques to the training data. Two prediction models were implemented. One was a statistical model using logistic regression and elastic net. The other involved a support vector machine model, tree-based machine learning models (e.g., random forest, extreme gradient boosting), and a stacking model. As a result, the features of theft price, invasion, and remedy, which are known to have significant effects on reporting theft offences, can be predicted as determinants of such offences in companies. Finally, we verified and compared the proposed predictive models using several popular metrics. Based on our evaluation of the importance of the features used in each model, we suggest a more accurate criterion for predicting var.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.1005-1015
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• 2020

This paper aims to propose a Comprehensive Decision Support Model to evaluate retail companies' financial performance traded on the Vietnam Stock Exchange Market. The financial performance has been examined in terms of the valuations ratios, profitability ratios, growth rates, liquidity ratios, efficiency ratios, and leverage ratios. The data of twelve companies from the first quarter to the fourth quarter of 2019 and the first quarter of 2020 were employed. The weights of 18 chosen financial ratios are calculated by using the Standard Deviation method (SD). Grey Relational Analysis technique was applied to obtain the final ranking of each company in each quarter. The results showed that leverage ratios have the most significant impact on the retail companies' financial performance and gives some long-term investment recommendations for stakeholders and indicated that the Taseco Air Services Joint Stock Company (AST), Mobile World Investment Corporation (MWG), and Cam Ranh International Airport Services Joint Stock Company (CIA) are three of the top efficient companies. The three of the worst companies are Viglacera Corporation (VGC), Saigon General Service Corporation (SVC), and HocMon Trade Joint Stock Company (HTC). Furthermore, this study suggests that the GRA model could be implemented effectively to ranking companies of other industries in the future research.

• Journal of Korea Game Society
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• v.6 no.4
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• pp.17-23
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• 2006

Most companies operate their business unit by products which occupy mostly their sales or can increase their sales. The evaluation of business units is very important part to check the market position of its business, the value of investment and business diversification. This paper applies 5 game flat forms to Korean game industry at base on GE model which can evaluate business unit with market merit and competitive advantage in market. the result of study indicates that main flat form and the other flat form distribute diversely, and can determine the time for the new product development, the flat form of main investment and marketing strategy.

• The Journal of Society for e-Business Studies
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• v.22 no.4
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• pp.159-173
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• 2017

In order to provide technical financial support to small and medium-sized venture companies based on technology, the government implemented the TCB evaluation, which is a kind of technology rating evaluation, from the Kibo and a qualified private TCB. In this paper, we briefly review the current state of TCB evaluation and available indicators related to technology evaluation accumulated in the Korea Credit Information Services (TDB), and then use indicators that have a significant effect on the technology rating score. Multiple regression techniques will be explored. And the relative importance and classification accuracy of the indicators were calculated by applying the key indicators as independent features applied to the generalized boosting model, which is a representative machine learning classifier, as the class influence and the fitness of each model. As a result of the analysis, it was analyzed that the relative importance between the two models was not significantly different. However, GBM model had more weight on the InnoBiz certification, R&D department, patent registration and venture confirmation indicators than regression model.