• Information Systems Review
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• v.19 no.2
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• pp.71-94
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• 2017

Recommender systems reduce information overload and enhance choice quality. This technology is used in many services and industry. Previous studies did not consider recommendation quantity and the repetitive recommendations of an item. This study is the first to examine recommender systems by considering recommendation quantity and repetitive recommendations. Only a limited number of items are displayed in offline stores because of their physical limitations. Determining the type and number of items that will be displayed is an important consideration. In this study, I suggest the use of a user-based recommender system that can recommend the most appropriate items for each store. This model is evaluated by MAE, Precision, Recall, and F1 measure, and shows higher performance than the baseline model. I also suggest a new performance evaluation measure that includes Quantity Precision, Quantity Recall, and Quantity F1 measure. This measure considers the penalty for short or excess recommendation quantity. Novelty is defined as the proportion of items in a recommendation list that consumers may not experience. I evaluate the new revenue creation effect of the suggested model using this novelty measure. Previous research focused on recommendations for customer online, but I expand the recommender system to cover stores offline.

• Journal of Korean Society of Transportation
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• v.35 no.4
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• pp.360-373
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• 2017

The BTO-a projects is the types, which has a demand risk among the type of PPP projects in Korea. When demand risk is realized, private investor encounters financial difficulties due to lower revenue than its expectation and the government may also have a problem in stable infrastructure operation. In this regards, the government has applied various risk sharing policies in response to demand risk. However, the amount of government's risk sharing is the government's contingent liabilities as a result of demand uncertainty, and it fails to be quantified by the conventional NPV method of expressing in the text of the concession agreement. The purpose of this study is to estimate the value of investment risk sharing by the government considering the demand risk in the profit sharing system (BTO-a) introduced in 2015 as one of the demand risk sharing policy. The investment risk sharing will take the form of options in finance. Private investors have the right to claim subsidies from the government when their revenue declines, while the government has the obligation to pay subsidies under certain conditions. In this study, we have established a methodology for estimating the value of investment risk sharing by using the Black - Scholes option pricing model and examined the appropriateness of the results through case studies. As a result of the analysis, the value of investment risk sharing is estimated to be 12 billion won, which is about 4% of the investment cost of the private investment. In other words, it can be seen that the government will invest 12 billion won in financial support by sharing the investment risk. The option value when assuming the traffic volume risk as a random variable from the case studies is derived as an average of 12.2 billion won and a standard deviation of 3.67 billion won. As a result of the cumulative distribution, the option value of the 90% probability interval will be determined within the range of 6.9 to 18.8 billion won. The method proposed in this study is expected to help government and private investors understand the better risk analysis and economic value of better for investment risk sharing under the uncertainty of future demand.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.157-176
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• 2018

The purpose of this research is to develop a detection model for companies designated as administrative issue in KOSDAQ market using financial data. Administration issue designates the companies with high potential for delisting, which gives them time to overcome the reasons for the delisting under certain restrictions of the Korean stock market. It acts as an alarm to inform investors and market participants of which companies are likely to be delisted and warns them to make safe investments. Despite this importance, there are relatively few studies on administration issues prediction model in comparison with the lots of studies on bankruptcy prediction model. Therefore, this study develops and verifies the detection model of the companies designated as administrative issue using financial data of KOSDAQ companies. In this study, logistic regression and decision tree are proposed as the data mining models for detecting administrative issues. According to the results of the analysis, the logistic regression model predicted the companies designated as administrative issue using three variables - ROE(Earnings before tax), Cash flows/Shareholder's equity, and Asset turnover ratio, and its overall accuracy was 86% for the validation dataset. The decision tree (Classification and Regression Trees, CART) model applied the classification rules using Cash flows/Total assets and ROA(Net income), and the overall accuracy reached 87%. Implications of the financial indictors selected in our logistic regression and decision tree models are as follows. First, ROE(Earnings before tax) in the logistic detection model shows the profit and loss of the business segment that will continue without including the revenue and expenses of the discontinued business. Therefore, the weakening of the variable means that the competitiveness of the core business is weakened. If a large part of the profits is generated from one-off profit, it is very likely that the deterioration of business management is further intensified. As the ROE of a KOSDAQ company decreases significantly, it is highly likely that the company can be delisted. Second, cash flows to shareholder's equity represents that the firm's ability to generate cash flow under the condition that the financial condition of the subsidiary company is excluded. In other words, the weakening of the management capacity of the parent company, excluding the subsidiary's competence, can be a main reason for the increase of the possibility of administrative issue designation. Third, low asset turnover ratio means that current assets and non-current assets are ineffectively used by corporation, or that asset investment by corporation is excessive. If the asset turnover ratio of a KOSDAQ-listed company decreases, it is necessary to examine in detail corporate activities from various perspectives such as weakening sales or increasing or decreasing inventories of company. Cash flow / total assets, a variable selected by the decision tree detection model, is a key indicator of the company's cash condition and its ability to generate cash from operating activities. Cash flow indicates whether a firm can perform its main activities(maintaining its operating ability, repaying debts, paying dividends and making new investments) without relying on external financial resources. Therefore, if the index of the variable is negative(-), it indicates the possibility that a company has serious problems in business activities. If the cash flow from operating activities of a specific company is smaller than the net profit, it means that the net profit has not been cashed, indicating that there is a serious problem in managing the trade receivables and inventory assets of the company. Therefore, it can be understood that as the cash flows / total assets decrease, the probability of administrative issue designation and the probability of delisting are increased. In summary, the logistic regression-based detection model in this study was found to be affected by the company's financial activities including ROE(Earnings before tax). However, decision tree-based detection model predicts the designation based on the cash flows of the company.

• Korean Journal of Construction Engineering and Management
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• v.20 no.4
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• pp.22-33
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• 2019

The government has steadily revised and developed laws and systems for activating remodeling of apartments in response to the problems of aged apartments. However, despite such efforts, remodeling has yet to be activated. For many reasons, this study noted that there were no tools for reasonable profitability judgements and decision making in the preliminary stages of the remodeling project. Thus, the feasibility evaluation model was developed. Generally, the profitability judgements are made after the conceptual design. However, decisions to drive remodeling projects are made at the preliminary stage. So a feasibility evaluation model is required at the preliminary stage. Accordingly, In this study, a feasibility evaluation model was developed for determining preliminary stage profitability. Construction costs, business expenses, financial expenses, and generally sales revenue were calculated using the initial available information and remodeling variables derived through the existing cases. Through this process, we developed an algorithm that can give an overview of the return on investment. In addition, the preliminary stage feasibility evaluation model developed was applied to three cases to verify the applicability of the model. Although applied in three cases, the difference between the model's forecast and actual case values is less than 5%, which is considered highly applicable. If cases are expanded in the future, it will be a useful tool that can be used in actual work. The feasibility evaluation model developed in this study will support decision making by union members, and if the model is applied in different regions, it will be expected to help local governments to understand the size of possible remodeling projects.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.1-31
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• 2017

Modern businesses are adopting new technologies to serve their markets better as well as to improve efficiency and productivity. The advertising industry has continuously experienced disruptions from the traditional channels (radio, television and print media) to new complex ones including internet, social media and mobile-based advertising. This case study focuses on proposing intelligent advertising business model in Seoul's metro network. Seoul has one of the world's busiest metro network and transports a huge number of travelers on a daily basis. The high number of travelers coupled with a well-planned metro network creates a platform where marketers can initiate engagement and interact with both customers and potential customers. In the current advertising model, advertising is on illuminated and framed posters in the stations and in-car, non-illuminated posters, and digital screens that show scheduled arrivals and departures of metros. Some stations have digital screens that show adverts but they do not have location capability. Most of the current advertising media have one key limitation: space. For posters whether illuminated or not, one space can host only one advert at a time. Empirical literatures show that there is room for improving this advertising model and eliminate the space limitation by replacing the poster adverts with digital advertising platform. This new model will not only be digital, but will also provide intelligent advertising platform that is driven by data. The digital platform will incorporate location sensing, e-commerce, and mobile platform to create new value to all stakeholders. Travel cards used in the metro will be registered and the card scanners will have a capability to capture traveler's data when travelers tap their cards. This data once analyzed will make it possible to identify different customer groups. Advertisers and marketers will then be able to target specific customer groups, customize adverts based on the targeted consumer group, and offer a wide variety of advertising formats. Format includes video, cinemagraphs, moving pictures, and animation. Different advert formats create different emotions in the customer's mind and the goal should be to use format or combination of formats that arouse the expected emotion and lead to an engagement. Combination of different formats will be more effective and this can only work in a digital platform. Adverts will be location based, ensuring that adverts will show more frequently when the metro is near the premises of an advertiser. The advertising platform will automatically detect the next station and screens inside the metro will prioritize adverts in the station where the metro will be stopping. In the mobile platform, customers who opt to receive notifications will receive them when they approach the business premises of advertiser. The mobile platform will have indoor navigation for the underground shopping malls that will allow customers to search for facilities within the mall, products they may want to buy as well as deals going on in the underground mall. To create an end-to-end solution, the mobile solution will have a capability to allow customers purchase products through their phones, get coupons for deals, and review products and shops where they have bought a product. The indoor navigation will host intelligent mobile-based advertisement and a recommendation system. The indoor navigation will have adverts such that when a customer is searching for information, the recommendation system shows adverts that are near the place traveler is searching or in the direction that the traveler is moving. These adverts will be linked to the e-commerce platform such that if a customer clicks on an advert, it leads them to the product description page. The whole system will have multi-language as well as text-to-speech capability such that both locals and tourists have no language barrier. The implications of implementing this model are varied including support for small and medium businesses operating in the underground malls, improved customer experience, new job opportunities, additional revenue to business model operator, and flexibility in advertising. The new value created will benefit all the stakeholders.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.33-49
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• 2018

The impact of TV Drama success on TV Rating and the channel promotion effectiveness is very high. The cultural and business impact has been also demonstrated through the Korean Wave. Therefore, the early prediction of the blockbuster success of TV Drama is very important from the strategic perspective of the media industry. Previous studies have tried to predict the audience ratings and success of drama based on various methods. However, most of the studies have made simple predictions using intuitive methods such as the main actor and time zone. These studies have limitations in predicting. In this study, we propose a model for predicting the popularity of drama by analyzing the customer's viewing pattern based on various theories. This is not only a theoretical contribution but also has a contribution from the practical point of view that can be used in actual broadcasting companies. In this study, we collected data of 280 TV mini-series dramas, broadcasted over the terrestrial channels for 10 years from 2003 to 2012. From the data, we selected the most highly ranked and the least highly ranked 45 TV drama and analyzed the viewing patterns of them by 11-step. The various assumptions and conditions for modeling are based on existing studies, or by the opinions of actual broadcasters and by data mining techniques. Then, we developed a prediction model by measuring the viewing-time distance (difference) using Euclidean and Correlation method, which is termed in our study similarity (the sum of distance). Through the similarity measure, we predicted the success of dramas from the viewer's initial viewing-time pattern distribution using 1~5 episodes. In order to confirm that the model is shaken according to the measurement method, various distance measurement methods were applied and the model was checked for its dryness. And when the model was established, we could make a more predictive model using a grid search. Furthermore, we classified the viewers who had watched TV drama more than 70% of the total airtime as the "passionate viewer" when a new drama is broadcasted. Then we compared the drama's passionate viewer percentage the most highly ranked and the least highly ranked dramas. So that we can determine the possibility of blockbuster TV mini-series. We find that the initial viewing-time pattern is the key factor for the prediction of blockbuster dramas. From our model, block-buster dramas were correctly classified with the 75.47% accuracy with the initial viewing-time pattern analysis. This paper shows high prediction rate while suggesting audience rating method different from existing ones. Currently, broadcasters rely heavily on some famous actors called so-called star systems, so they are in more severe competition than ever due to rising production costs of broadcasting programs, long-term recession, aggressive investment in comprehensive programming channels and large corporations. Everyone is in a financially difficult situation. The basic revenue model of these broadcasters is advertising, and the execution of advertising is based on audience rating as a basic index. In the drama, there is uncertainty in the drama market that it is difficult to forecast the demand due to the nature of the commodity, while the drama market has a high financial contribution in the success of various contents of the broadcasting company. Therefore, to minimize the risk of failure. Thus, by analyzing the distribution of the first-time viewing time, it can be a practical help to establish a response strategy (organization/ marketing/story change, etc.) of the related company. Also, in this paper, we found that the behavior of the audience is crucial to the success of the program. In this paper, we define TV viewing as a measure of how enthusiastically watching TV is watched. We can predict the success of the program successfully by calculating the loyalty of the customer with the hot blood. This way of calculating loyalty can also be used to calculate loyalty to various platforms. It can also be used for marketing programs such as highlights, script previews, making movies, characters, games, and other marketing projects.

• Information Systems Review
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• v.7 no.2
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• pp.41-59
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• 2005

Mobile games have emerged as the most innovative entertainment technology adding new revenue streams, taking advantage of the potential of wireless consumer applications and service offerings. Mobile games, like any other types of computer game, offer a unique value for users in providing an exciting digital experience in virtual worlds. Players can become empowered through the development of new characters and strategies within games to achieve rewarding successes against the computers and other players. In this paper, we attempt to investigate the factors influencing the usage and acceptance of the mobile games in Korea, based on the extended version of the Technology Acceptance Model(TAM). Based on data collected from survey, we show that perceived usefulness is the major determinant for users to play mobile games. Two factors, including perceived enjoyment and self-expressiveness, are empirically shown to determine perceived usefulness. In addition, perceived ease of use, rewards, operational quality of device, and design/story have been showed to significantly and directly affect perceived enjoyment. It was also confirmed that self-efficacy and operational quality of device are the antecedents of perceived ease of use. Based upon the statistical results, some useful guidelines for game development and market penetration strategies are also provided.

• Journal of Korea Port Economic Association
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• v.24 no.2
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• pp.209-239
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• 2008

The purpose of this paper is to investigate the effectiveness of Korean port investment by using the newly developed slack-based multi-year panel congestion model of DEA(Data Envelopment Analysis). Inputs[port investment amount, cargo handling capacity, and berthing capacity], and outputs[cargo handling amount, number of ship calls, revenue, and score of customer service satisfaction] are used during 1994-2004 for 20 Korean seaports. Empirical analysis identified congestion especially in port investment as input at the ports of Gunsan, and Busan in the all 3 models, and the ports of Pyungtag, Mogpo, Yeosu, and leju in over 2 models. Port investment induced the rapid increase of port efficiency from the ports of Masan, Incheon, Donghae, and Samcheok. Therefore other ports except these ports should examine the reason about the inefficiency of port investment by searching out the situation of each ports directly. The main policy implication based on the findings of this study is that The Ministry of Land, Transport and Maritime Affairs in Korea should introduce the new measurement way after reviewing the multi-year slack-based congestion approach when the amount of port investment for each port is decided.

• Journal of KIISE
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• v.44 no.8
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• pp.803-812
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• 2017

T-commerce is technology-fusion service on which the user can purchase using data broadcasting technology based on bi-directional digital TVs. To achieve the best revenue under a limited environment in regard to the channel number and the variety of sales goods, organizing broadcast programs to maximize the expected sales considering the selling power of each product at each time slot. For this, this paper proposes a method to predict the sales of goods when it is assigned to each time slot. The proposed method predicts the sales of product at a time slot given the week-in-year and weather of the target day. Additionally, it combines a statistical predict model applying SVD (Singular Value Decomposition) to mitigate the sparsity problem caused by the bias in sales record. In experiments on the sales data of W-shopping, a T-commerce company, the proposed method showed NMAE (Normalized Mean Absolute Error) of 0.12 between the prediction and the actual sales, which confirms the effectiveness of the proposed method. The proposed method is practically applied to the T-commerce system of W-shopping and used for broadcasting organization.

• Journal of Korea Port Economic Association
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• v.26 no.3
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• pp.18-41
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• 2010

The purpose of this paper is to show the empirical measurement way for predicting the seaport efficiency by using Super SBM(Slack-based Measure) with Wilcoxson signed-rank test under CRS(constant returns to scale) condition for 20 Korean ports during 11 years(1997-2007) for 3 inputs(port investment amount, birthing capacity, and cargo handling capacity) and 5 outputs(Export and Import Quantity, Number of Ship Calls, Port Revenue, Customer Satisfaction Point for Port Service and Container Cargo Throughput). The main empirical results of this paper are as follows. First, Super SBM model has well reflected the real data according to the Wilcoxon signed rank test, because p values have exceeded the significance level. Second,Super-SBM has shown about 87% of predicting ratio for the ports efficiency and the optimal size of investment in domestic seaport. The policy implication to the Korean seaports and planner is that Korean seaports should introduce the new methods like Super-SBM method with Wilcoxon signed rank test for predicting the efficiency of port performance and the optimal size of investment as indicated by Panayides et al.(2009, pp.203-204).