• Management & Information Systems Review
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• v.28 no.1
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• pp.71-97
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• 2009

In this study, I compare the ability of alternative accounting method for employee stock option to reflect firm value using the Ohlson's(1995) valuation model for 200 firms. The each methods, I compare are employee stock option expense recognition based on the K-GAAP disclosures, and asset recognition at the grant date based on the SFAS No. 123 Exposure Draft: Accounting for stock-based compensation. The model include: (1) a model that uses reported earnings, equity book value, and compensation expense based on the K-GAAP disclosures; (2) a model that uses pro-forma earnings, equity book value and adds a measure of the unrecognized asset arising form granting of employee stock options. Finding form estimating equations that the K-GAAP method for calculating compensation has no explanatory power, and the SFAS No.123 Draft Exposure method for arising asset and fair value compensation better captures than market's perception of the economic impact of stock options on firm values. However, the correlation of employee stock option compensation expense is positive. These results suggest that incentive benefits derived from employee stock option plans outweigh the cost associated with plan. In addition, I couldn't find evidence that company in KOSDAQ that have high growth potential benefit more from employee stock option plan compared to lager, more mature firm in SEC.

• The Korean Society of Law and Medicine
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• v.17 no.2
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• pp.85-124
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• 2016

The Human gene testing act (GenDG) in Germany starts from the characteristic features of gene testing, i.e. dualisting structure consisted of anlaysis on the one side and the interpretation on the other side. The linguistic distincion of 'testing', 'anlaysis' and 'judgment' in the act is a fine example. Another important basis of the regulation is the ideological purpose of the law, that is information autonomy. The normative texts as such and the founding principle are the basis of the classification of testing types. Especially in the case of gene testing for medical purpose is classified into testing for diagnostic purpose and predictive purpose. However, those two types are not always clearly differentiated because the predictive value of testing is common in both types. In the legal regulation of gene testing it is therefore important to manage the uncertainty and subjectivity which are inherent in the gene-analysis and the judgment. In GenDG the system ensuring the quality of analysis is set up and GEKO(Commity for gene tisting) based on the section 23 of GenDG concretes the criterium of validity through guidelines. It is also very important in the case of gene testing for medical purpose to set up the system for ensurement of procedural rationality of the interpretation. The interpretation of the results of analysis has a wide spectrum because of the consistent development of technology on the one side and different understandings of different subjects who performs gene testings. Therefore the process should include the communication process for patients in oder that he or she could understand the meaning of gene testing and make plans of life. In GenDG the process of genetic counselling and GEKO concretes the regulation very precisely. The regulation as such in GenDG seems to be very suggestive to Korean legal polic concerning the gene testing.

• Korea Science and Art Forum
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• v.23
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• pp.111-122
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• 2016

The purpose of this multi-disciplinary convergent study is to establish Image Copyright Archive Model for Museums to protect image copyright and vitalize the use of images out of necessity of research and development on copyright services over the life cycle of art contents created by the museums and out of the necessity to vitalize distribution market of image copyright contents in creative industry and to formulate management system of copyright services. This study made various suggestions for enhancement of transparency and efficiency of art contents ecosystem through vitalization of use and recycling of image copyright materials by proposing standard system for calculation, distribution, settlement and monitoring of copyright royalty of 1,000 domestic museums, galleries and exhibit halls. First, this study proposed contents and structure design of image copyright archive model and, by proposing art contents distribution service platform for prototype simulation, execution simulation and model operation simulation, established art contents copyright royalty process model. As billing system and technological development for image contents are still in incipient stage, this study used the existing contents billing framework as basic model for the development of billing technology for distribution of museum collections and artworks and automatic division and calculation engine for copyright royalty. Ultimately, study suggested image copyright archive model which can be used by artists, curators and distributors. In business strategy, study suggested niche market penetration of museum image copyright archive model. In sales expansion strategy, study established a business model in which effective process of image transaction can be conducted in the form of B2B, B2G, B2C and C2B through flexible connection of museum archive system and controllable management of image copyright materials can be possible. This study is expected to minimize disputes between copyright holder of artwork images and their owners and enhance manageability of copyrighted artworks through prevention of such disputes and provision of information on distribution and utilization of art contents (of collections and new creations) owned by the museums. In addition, by providing a guideline for archives of collections of museums and new creations, this study is expected to increase registration of image copyright and to make various convergent businesses possible such as billing, division and settlement of copyright royalty for image copyright distribution service.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.85-109
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• 2018

Recommender system recommends the items expected to be purchased by a customer in the future according to his or her previous purchase behaviors. It has been served as a tool for realizing one-to-one personalization for an e-commerce service company. Traditional recommender systems, especially the recommender systems based on collaborative filtering (CF), which is the most popular recommendation algorithm in both academy and industry, are designed to generate the items list for recommendation by using 'overall rating' - a single criterion. However, it has critical limitations in understanding the customers' preferences in detail. Recently, to mitigate these limitations, some leading e-commerce companies have begun to get feedback from their customers in a form of 'multicritera ratings'. Multicriteria ratings enable the companies to understand their customers' preferences from the multidimensional viewpoints. Moreover, it is easy to handle and analyze the multidimensional ratings because they are quantitative. But, the recommendation using multicritera ratings also has limitation that it may omit detail information on a user's preference because it only considers three-to-five predetermined criteria in most cases. Under this background, this study proposes a novel hybrid recommendation system, which selectively uses the results from 'traditional CF' and 'CF using multicriteria ratings'. Our proposed system is based on the premise that some people have holistic preference scheme, whereas others have composite preference scheme. Thus, our system is designed to use traditional CF using overall rating for the users with holistic preference, and to use CF using multicriteria ratings for the users with composite preference. To validate the usefulness of the proposed system, we applied it to a real-world dataset regarding the recommendation for POI (point-of-interests). Providing personalized POI recommendation is getting more attentions as the popularity of the location-based services such as Yelp and Foursquare increases. The dataset was collected from university students via a Web-based online survey system. Using the survey system, we collected the overall ratings as well as the ratings for each criterion for 48 POIs that are located near K university in Seoul, South Korea. The criteria include 'food or taste', 'price' and 'service or mood'. As a result, we obtain 2,878 valid ratings from 112 users. Among 48 items, 38 items (80%) are used as training dataset, and the remaining 10 items (20%) are used as validation dataset. To examine the effectiveness of the proposed system (i.e. hybrid selective model), we compared its performance to the performances of two comparison models - the traditional CF and the CF with multicriteria ratings. The performances of recommender systems were evaluated by using two metrics - average MAE(mean absolute error) and precision-in-top-N. Precision-in-top-N represents the percentage of truly high overall ratings among those that the model predicted would be the N most relevant items for each user. The experimental system was developed using Microsoft Visual Basic for Applications (VBA). The experimental results showed that our proposed system (avg. MAE = 0.584) outperformed traditional CF (avg. MAE = 0.591) as well as multicriteria CF (avg. AVE = 0.608). We also found that multicriteria CF showed worse performance compared to traditional CF in our data set, which is contradictory to the results in the most previous studies. This result supports the premise of our study that people have two different types of preference schemes - holistic and composite. Besides MAE, the proposed system outperformed all the comparison models in precision-in-top-3, precision-in-top-5, and precision-in-top-7. The results from the paired samples t-test presented that our proposed system outperformed traditional CF with 10% statistical significance level, and multicriteria CF with 1% statistical significance level from the perspective of average MAE. The proposed system sheds light on how to understand and utilize user's preference schemes in recommender systems domain.