• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.23
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• pp.155-219
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• 2004

This paper focuses on two recent legislative developments in electronic commerce: the "Uniform Computer Information Transactions Act" ("UCITA") of USA and the "preliminary draft convention on the use of data message in [international trade] [the context of international contracts]" ("preliminary draft Convention") of UNCITRAL. UCITA provides rules contracts for computer information transactions. UCITA supplies modified contract formation rules adapted to permit and to facilitate electronic contracting. UCITA also adjusts commonly recognized warranties as appropriate for computer information transactions; for example, to recognize the international context in connection with protection against infringement and misappropriation, and First Amendment considerations involved with informational content. Furthermore, UCITA adapts traditional rules as to what is acceptable performance to the context of computer information transactions, including providing rules for the protection of the parties concerning the electronic regulation of performance to clarify that the appropriate general rule is one of material breach with respect to cancellation (rather than so-called perfect tender). UCITA also supplies guidance in the case of certain specialized types of contracts, e.g., access contracts and for termination of contracts. While for the most part carrying over the familiar rules of Article 2 concerning breach when appropriate in the context of the tangible medium on which the information is fixed, but also adapting common law rules and rules from Article 2 on waiver, cure, assurance and anticipatory breach to the context of computer information transactions, UCITA provides a remedy structure somewhat modeled on that of Article 2 but adapted in significant respects to the different context of a computer information transaction. For example, UCITA contains very important limitations on the generally recognized common law right of self-help as applicable in the electronic context. The UNCITRAL's preliminary draft Convention applies to the use of data messages in connection with an existing or contemplated contract between parties whose places of business are in different States. Nothing in the Convention affects the application of any rule of law that may require the parties to disclose their identities, places of business or other information, or relieves a party from the legal consequences of making inaccurate or false statements in that regard. Likewise, nothing in the Convention requires a contract or any other communication, declaration, demand, notice or request that the parties are required to make or choose to make in connection with an existing or contemplated contract to be made or evidenced in any particular form. Under the Convention, a communication, declaration, demand, notice or request that the parties are required to make or choose to make in connection with an existing or contemplated contract, including an offer and the acceptance of an offer, is conveyed by means of data messages. Also, the Convention provides for use of automated information systems for contract formation: a contract formed by the interaction of an automated information system and a person, or by the interaction of automated information systems, shall not be denied on the sole ground that no person reviewed each of the individual actions carried out by such systems or the resulting agreement. Further, the Convention provides that, unless otherwise agreed by the parties, a contract concluded by a person that accesses an automated information system of another party has no legal effect and is not enforceable if the person made an error in a data message and (a) the automated information system did not provide the person with an opportunity to prevent or correct the error; (b) the person notifies the other party of the error as soon as practicable when the person making the error learns of it and indicates that he or she made an error in the data message; (c) The person takes reasonable steps, including steps that conform to the other party's instructions, to return the goods or services received, if any, as a result of the error or, if instructed to do so, to destroy such goods or services.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.187-201
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• 2011

KOSPI200 index is the Korean stock price index consisting of actively traded 200 stocks in the Korean stock market. Its base value of 100 was set on January 3, 1990. The Korea Exchange (KRX) developed derivatives markets on the KOSPI200 index. KOSPI200 index futures market, introduced in 1996, has become one of the most actively traded indexes markets in the world. Traders can make profit by entering a long position on the KOSPI200 index futures contract if the KOSPI200 index will rise in the future. Likewise, they can make profit by entering a short position if the KOSPI200 index will decline in the future. Basically, KOSPI200 index futures trading is a short-term zero-sum game and therefore most futures traders are using technical indicators. Advanced traders make stable profits by using system trading technique, also known as algorithm trading. Algorithm trading uses computer programs for receiving real-time stock market data, analyzing stock price movements with various technical indicators and automatically entering trading orders such as timing, price or quantity of the order without any human intervention. Recent studies have shown the usefulness of artificial intelligent systems in forecasting stock prices or investment risk. KOSPI200 index data is numerical time-series data which is a sequence of data points measured at successive uniform time intervals such as minute, day, week or month. KOSPI200 index futures traders use technical analysis to find out some patterns on the time-series chart. Although there are many technical indicators, their results indicate the market states among bull, bear and flat. Most strategies based on technical analysis are divided into trend following strategy and non-trend following strategy. Both strategies decide the market states based on the patterns of the KOSPI200 index time-series data. This goes well with Markov model (MM). Everybody knows that the next price is upper or lower than the last price or similar to the last price, and knows that the next price is influenced by the last price. However, nobody knows the exact status of the next price whether it goes up or down or flat. So, hidden Markov model (HMM) is better fitted than MM. HMM is divided into discrete HMM (DHMM) and continuous HMM (CHMM). The only difference between DHMM and CHMM is in their representation of state probabilities. DHMM uses discrete probability density function and CHMM uses continuous probability density function such as Gaussian Mixture Model. KOSPI200 index values are real number and these follow a continuous probability density function, so CHMM is proper than DHMM for the KOSPI200 index. In this paper, we present an artificial intelligent trading system based on CHMM for the KOSPI200 index futures system traders. Traders have experienced on technical trading for the KOSPI200 index futures market ever since the introduction of the KOSPI200 index futures market. They have applied many strategies to make profit in trading the KOSPI200 index futures. Some strategies are based on technical indicators such as moving averages or stochastics, and others are based on candlestick patterns such as three outside up, three outside down, harami or doji star. We show a trading system of moving average cross strategy based on CHMM, and we compare it to a traditional algorithmic trading system. We set the parameter values of moving averages at common values used by market practitioners. Empirical results are presented to compare the simulation performance with the traditional algorithmic trading system using long-term daily KOSPI200 index data of more than 20 years. Our suggested trading system shows higher trading performance than naive system trading.

• Korean Security Journal
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• no.18
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• pp.119-141
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• 2009

Administrative execution by proxy is one of forced executions of administration and is also called as "enforced execution by proxy" in which administration institutions or the third party executes by proxy on behalf of parties who did not execute obligations under administration law and files claims to compensate expenses required in the proxy execution. Despite the actual site of administrative execution by law, social problems are generated because various violence and behaviors of infringement of human rights between executer and obligator are rampant and thus causing human damages since forced execution by physical force is carried out and cases of police indictments and petition to human rights committee are gradually increasing. Majority of people mobilized in this actual site of violence are supplied by private security companies which provide service contract and mobilization of people without qualification of guards or security service and irrational execution by proxy and violent actions by so-called service hooligans connected to violence organizations are now becoming social issues. In these actual sites of violence, structurally very complicated problems such as economic rights, right of residence, struggle for living, and intervention by outsiders are contained. This thesis has analyzed causes of outbreaks of violence and discussed about improvement countermeasure by paying attention to mobilization of people by private security companies. As the result, through revision and improvement of laws and systems, execution institution and policemen must be present at actual sites of execution by proxy to control physical execution of private security companies to be carried out legally and when violent collisions are occurring, it shall be stipulated that police should immediately intervene. Practices of execution by proxy of execution administration institutions shall be avoided and causes of occurrences of violence shall be eliminated by discrete decisions of execution by proxy, elimination of service contract conditions focused on accomplishments, and stipulation of responsibility of execution institutions when problems occur. Practices of solving petitions through collective actions of obligators shall be eliminated and strict enforcement of laws such as disturbance of official execution or compensation claims for expenses of execution by proxy must be carried out and intervention by the third parties must be intercepted. Mobilization of manpower by security companies shall be limited to people with prior registration who have acquired and finished qualification and education by security business law and before putting them on actual sites, it shall be obliged that execution plan with clear written records of working location, mission, and work rules must be submitted in advance to police station in charge and also they must be controlled to follow laws and statutes such as uniform and equipments. In addition, personal criminal responsibility for violent actions must be clearly stipulated and advanced securing soundness of security companies such as limits of service contracts with records of accidents is required. Order placement behaviors of special organizations under the pretext of rehabilitation business must be eradicated and companies with capability and strong intention of observation of laws must be able to receive orders by intercepting chains of contracts and sub-contracts. Issues of improvement countermeasure of social problem, living, and compensation including rights of residence and environment are excluded from the discussion.

• Asia Marketing Journal
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• v.15 no.4
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• pp.61-101
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• 2014

This study investigates when and how disagreements in online customer ratings prompt more favorable product evaluations. Among the three metrics of volume, valence, and variance that feature in the research on online customer ratings, volume and valence have exhibited consistently positive patterns in their effects on product sales or evaluations (e.g., Dellarocas, Zhang, and Awad 2007; Liu 2006). Ratings variance, or the degree of disagreement among reviewers, however, has shown rather mixed results, with some studies reporting positive effects on product sales (e.g., Clement, Proppe, and Rott 2007) while others finding negative effects on product evaluations (e.g., Zhu and Zhang 2010). This study aims to resolve these contradictory findings by introducing preference heterogeneity as a possible moderator and causal attribution as a mediator to account for the moderating effect. The main proposition of this study is that when preference heterogeneity is perceived as high, a disagreement in ratings is attributed more to reviewers' different preferences than to unreliable product quality, which in turn prompts better quality evaluations of a product. Because disagreements mostly result from differences in reviewers' tastes or the low reliability of a product's quality (Mizerski 1982; Sen and Lerman 2007), a greater level of attribution to reviewer tastes can mitigate the negative effect of disagreement on product evaluations. Specifically, if consumers infer that reviewers' heterogeneous preferences result in subjectively different experiences and thereby highly diverse ratings, they would not disregard the overall quality of a product. However, if consumers infer that reviewers' preferences are quite homogeneous and thus the low reliability of the product quality contributes to such disagreements, they would discount the overall product quality. Therefore, consumers would respond more favorably to disagreements in ratings when preference heterogeneity is perceived as high rather than low. This study furthermore extends this prediction to the various levels of average ratings. The heuristicsystematic processing model so far indicates that the engagement in effortful systematic processing occurs only when sufficient motivation is present (Hann et al. 2007; Maheswaran and Chaiken 1991; Martin and Davies 1998). One of the key factors affecting this motivation is the aspiration level of the decision maker. Only under conditions that meet or exceed his aspiration level does he tend to engage in systematic processing (Patzelt and Shepherd 2008; Stephanous and Sage 1987). Therefore, systematic causal attribution processing regarding ratings variance is likely more activated when the average rating is high enough to meet the aspiration level than when it is too low to meet it. Considering that the interaction between ratings variance and preference heterogeneity occurs through the mediation of causal attribution, this greater activation of causal attribution in high versus low average ratings would lead to more pronounced interaction between ratings variance and preference heterogeneity in high versus low average ratings. Overall, this study proposes that the interaction between ratings variance and preference heterogeneity is more pronounced when the average rating is high as compared to when it is low. Two laboratory studies lend support to these predictions. Study 1 reveals that participants exposed to a high-preference heterogeneity book title (i.e., a novel) attributed disagreement in ratings more to reviewers' tastes, and thereby more favorably evaluated books with such ratings, compared to those exposed to a low-preference heterogeneity title (i.e., an English listening practice book). Study 2 then extended these findings to the various levels of average ratings and found that this greater preference for disagreement options under high preference heterogeneity is more pronounced when the average rating is high compared to when it is low. This study makes an important theoretical contribution to the online customer ratings literature by showing that preference heterogeneity serves as a key moderator of the effect of ratings variance on product evaluations and that causal attribution acts as a mediator of this moderation effect. A more comprehensive picture of the interplay among ratings variance, preference heterogeneity, and average ratings is also provided by revealing that the interaction between ratings variance and preference heterogeneity varies as a function of the average rating. In addition, this work provides some significant managerial implications for marketers in terms of how they manage word of mouth. Because a lack of consensus creates some uncertainty and anxiety over the given information, consumers experience a psychological burden regarding their choice of a product when ratings show disagreement. The results of this study offer a way to address this problem. By explicitly clarifying that there are many more differences in tastes among reviewers than expected, marketers can allow consumers to speculate that differing tastes of reviewers rather than an uncertain or poor product quality contribute to such conflicts in ratings. Thus, when fierce disagreements are observed in the WOM arena, marketers are advised to communicate to consumers that diverse, rather than uniform, tastes govern reviews and evaluations of products.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.3
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• pp.17-32
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• 2023

In recent years, the need for social ventures that aim to grow while solving social problems through the efficiency and effectiveness of commercial organizations in the market has increased, while there is a limit to how much the government and the public can do to solve social problems. Against this background, the number of social venture startups is increasing in the domestic startup ecosystem, and interest in impact investors, which are investors in social ventures, is also increasing. Therefore, this research utilized judgment analysis technology to objectively analyze the validity and weight of judgment information based on the cognitive process and decision-making environment in the investment decision-making of impact investors. We proceeded with the research by constructing three classifications; first, investment priorities at the initial investment stage for financial benefit and return on investment as an investor, second, the political skills of the entrepreneurs (teams) for the social impact and ripple power, and social venture coexistence and solidarity, third, the social mission of a social venture that meets the purpose of an impact investment fund. As a result of this research, first of all, the investment decision-making priorities of impact investors are the expertise of the entrepreneur (team), the potential rate of return when the entrepreneur (team) succeeds, and the social mission of the entrepreneur (team). Second, impact investors do not have a uniform understanding of the investment decision-making factors, and the factors that determine investment decisions are different, and there are differences in the degree of the weighting. Third, among the various investment decision-making factors of impact investment, "entrepreneur's (team's) networking ability", "entrepreneur's (team's) social insight", "entrepreneur's (team's) interpersonal influence" was relatively lower than the other four factors. The practical contribution through this research is to help social ventures understand the investment determinant factors of impact investors in the process of financing, and impact investors can be expected to improve the quality of investment decision-making by referring to the judgment cases and analysis of impact investors. The academic contribution is that it empirically investigated the investment priorities and weighting differences of impact investors.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.79-96
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• 2012

As the pace of competition dramatically accelerates and the complexity of change grows, a variety of research have been conducted to improve firms' short-term performance and to enhance firms' long-term survival. In particular, researchers and practitioners have paid their attention to identify promising technologies that lead competitive advantage to a firm. Discovery of promising technology depends on how a firm evaluates the value of technologies, thus many evaluating methods have been proposed. Experts' opinion based approaches have been widely accepted to predict the value of technologies. Whereas this approach provides in-depth analysis and ensures validity of analysis results, it is usually cost-and time-ineffective and is limited to qualitative evaluation. Considerable studies attempt to forecast the value of technology by using patent information to overcome the limitation of experts' opinion based approach. Patent based technology evaluation has served as a valuable assessment approach of the technological forecasting because it contains a full and practical description of technology with uniform structure. Furthermore, it provides information that is not divulged in any other sources. Although patent information based approach has contributed to our understanding of prediction of promising technologies, it has some limitations because prediction has been made based on the past patent information, and the interpretations of patent analyses are not consistent. In order to fill this gap, this study proposes a technology forecasting methodology by integrating patent information approach and artificial intelligence method. The methodology consists of three modules : evaluation of technologies promising, implementation of technologies value prediction model, and recommendation of promising technologies. In the first module, technologies promising is evaluated from three different and complementary dimensions; impact, fusion, and diffusion perspectives. The impact of technologies refers to their influence on future technologies development and improvement, and is also clearly associated with their monetary value. The fusion of technologies denotes the extent to which a technology fuses different technologies, and represents the breadth of search underlying the technology. The fusion of technologies can be calculated based on technology or patent, thus this study measures two types of fusion index; fusion index per technology and fusion index per patent. Finally, the diffusion of technologies denotes their degree of applicability across scientific and technological fields. In the same vein, diffusion index per technology and diffusion index per patent are considered respectively. In the second module, technologies value prediction model is implemented using artificial intelligence method. This studies use the values of five indexes (i.e., impact index, fusion index per technology, fusion index per patent, diffusion index per technology and diffusion index per patent) at different time (e.g., t-n, t-n-1, t-n-2, ${\cdots}$) as input variables. The out variables are values of five indexes at time t, which is used for learning. The learning method adopted in this study is backpropagation algorithm. In the third module, this study recommends final promising technologies based on analytic hierarchy process. AHP provides relative importance of each index, leading to final promising index for technology. Applicability of the proposed methodology is tested by using U.S. patents in international patent class G06F (i.e., electronic digital data processing) from 2000 to 2008. The results show that mean absolute error value for prediction produced by the proposed methodology is lower than the value produced by multiple regression analysis in cases of fusion indexes. However, mean absolute error value of the proposed methodology is slightly higher than the value of multiple regression analysis. These unexpected results may be explained, in part, by small number of patents. Since this study only uses patent data in class G06F, number of sample patent data is relatively small, leading to incomplete learning to satisfy complex artificial intelligence structure. In addition, fusion index per technology and impact index are found to be important criteria to predict promising technology. This study attempts to extend the existing knowledge by proposing a new methodology for prediction technology value by integrating patent information analysis and artificial intelligence network. It helps managers who want to technology develop planning and policy maker who want to implement technology policy by providing quantitative prediction methodology. In addition, this study could help other researchers by proving a deeper understanding of the complex technological forecasting field.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.3
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• pp.193-211
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• 2021

Recently, interest in Korean Wave craze and K-beauty, led by K-pop, is increasing. In addition, the popularity and influence of the domestic beauty service industry has increased, and the economic and cultural ripple effects have been continuously expanding. The need to professional manpower training in response to the demand for manpower due to the growing development of domestic beauty services is emphasized, and the number of trainees who are actual consumers of beauty academy is increasing. Therefore, the purpose of our study is to examine the importance of quality factors of educational services to achieve educational purposes in the educational services provided by the Beauty Academy and the relationship between relationship commitment and educational service performance. Furthermore, it is to draw the importance of administrative support services, educational programs as well as educational service provision activities. However, the research for professional manpower training according to the provision of beauty services is insufficient compared to the development speed of the beauty industry. Therefore, at the present time when beauty service education is emphasized, our study will examine the relationship between relationship commitment and educational service performance based on the quality of education service by the students of domestic beauty academy. The measurement variables set for our study are program, instructor quality, tuition, external service, service fairness, relationship commitment, trust level, and educational service performance. The variables were analyzed and derived through the survey, and the following contents were derived from the empirical analysis. First, the quality of education service provided by the beauty academy, such as program, external service, service fairness, relationship commitment and trust level, had a significant effect on relationship commitment. Educational services provided by the institute, such as the systematicity and diversity of educational programs, enabled students to have a uniform relationship commitment. The quality of education service itself is to learn the expertise necessary for providing beauty service from the standpoint of the students and play an organic role in the relationship with the institute. Second, the moderating effect of trust level between academies and students was significant in the quality of education service and the relationship commitment. This means that students will feel higher level of service quality through the practical trust relationship of the students about the educational services provided by the institute. Based on the results of the empirical analysis, the implications of our study are to find ways to improve the students' ability and satisfaction represented by the results of educational services. This is because the quality of education services provided by the institute called Beauty Academy will have a great impact on the career choice of educational facilities and students. The characteristics of consistency, convenience, and knowledge orientation of education itself should be considered comprehensively, and a strong market position should be established through image formation through external service factors, which are external environments of academies.Furthermore, in terms of presenting differentiated strategies with competitors, the educational service quality factors play a significant role in the commitment to the relationship with the students, so the role of relationship marketing will be important for the psychological stability experienced by the students by grasping the demand accompanying the behavior of the students in advance.

• Asia pacific journal of information systems
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• v.21 no.2
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• pp.89-116
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• 2011

In collaborative tagging systems such as Delicious.com and Flickr.com, users assign keywords or tags to their uploaded resources, such as bookmarks and pictures, for their future use or sharing purposes. The collection of resources and tags generated by a user is called a personomy, and the collection of all personomies constitutes the folksonomy. The most significant need of the folksonomy users Is to efficiently find useful resources or experts on specific topics. An excellent ranking algorithm would assign higher ranking to more useful resources or experts. What resources are considered useful In a folksonomic system? Does a standard superior to frequency or freshness exist? The resource recommended by more users with mere expertise should be worthy of attention. This ranking paradigm can be implemented through a graph-based ranking algorithm. Two well-known representatives of such a paradigm are Page Rank by Google and HITS(Hypertext Induced Topic Selection) by Kleinberg. Both Page Rank and HITS assign a higher evaluation score to pages linked to more higher-scored pages. HITS differs from PageRank in that it utilizes two kinds of scores: authority and hub scores. The ranking objects of these pages are limited to Web pages, whereas the ranking objects of a folksonomic system are somewhat heterogeneous(i.e., users, resources, and tags). Therefore, uniform application of the voting notion of PageRank and HITS based on the links to a folksonomy would be unreasonable, In a folksonomic system, each link corresponding to a property can have an opposite direction, depending on whether the property is an active or a passive voice. The current research stems from the Idea that a graph-based ranking algorithm could be applied to the folksonomic system using the concept of mutual Interactions between entitles, rather than the voting notion of PageRank or HITS. The concept of mutual interactions, proposed for ranking the Semantic Web resources, enables the calculation of importance scores of various resources unaffected by link directions. The weights of a property representing the mutual interaction between classes are assigned depending on the relative significance of the property to the resource importance of each class. This class-oriented approach is based on the fact that, in the Semantic Web, there are many heterogeneous classes; thus, applying a different appraisal standard for each class is more reasonable. This is similar to the evaluation method of humans, where different items are assigned specific weights, which are then summed up to determine the weighted average. We can check for missing properties more easily with this approach than with other predicate-oriented approaches. A user of a tagging system usually assigns more than one tags to the same resource, and there can be more than one tags with the same subjectivity and objectivity. In the case that many users assign similar tags to the same resource, grading the users differently depending on the assignment order becomes necessary. This idea comes from the studies in psychology wherein expertise involves the ability to select the most relevant information for achieving a goal. An expert should be someone who not only has a large collection of documents annotated with a particular tag, but also tends to add documents of high quality to his/her collections. Such documents are identified by the number, as well as the expertise, of users who have the same documents in their collections. In other words, there is a relationship of mutual reinforcement between the expertise of a user and the quality of a document. In addition, there is a need to rank entities related more closely to a certain entity. Considering the property of social media that ensures the popularity of a topic is temporary, recent data should have more weight than old data. We propose a comprehensive folksonomy ranking framework in which all these considerations are dealt with and that can be easily customized to each folksonomy site for ranking purposes. To examine the validity of our ranking algorithm and show the mechanism of adjusting property, time, and expertise weights, we first use a dataset designed for analyzing the effect of each ranking factor independently. We then show the ranking results of a real folksonomy site, with the ranking factors combined. Because the ground truth of a given dataset is not known when it comes to ranking, we inject simulated data whose ranking results can be predicted into the real dataset and compare the ranking results of our algorithm with that of a previous HITS-based algorithm. Our semantic ranking algorithm based on the concept of mutual interaction seems to be preferable to the HITS-based algorithm as a flexible folksonomy ranking framework. Some concrete points of difference are as follows. First, with the time concept applied to the property weights, our algorithm shows superior performance in lowering the scores of older data and raising the scores of newer data. Second, applying the time concept to the expertise weights, as well as to the property weights, our algorithm controls the conflicting influence of expertise weights and enhances overall consistency of time-valued ranking. The expertise weights of the previous study can act as an obstacle to the time-valued ranking because the number of followers increases as time goes on. Third, many new properties and classes can be included in our framework. The previous HITS-based algorithm, based on the voting notion, loses ground in the situation where the domain consists of more than two classes, or where other important properties, such as "sent through twitter" or "registered as a friend," are added to the domain. Forth, there is a big difference in the calculation time and memory use between the two kinds of algorithms. While the matrix multiplication of two matrices, has to be executed twice for the previous HITS-based algorithm, this is unnecessary with our algorithm. In our ranking framework, various folksonomy ranking policies can be expressed with the ranking factors combined and our approach can work, even if the folksonomy site is not implemented with Semantic Web languages. Above all, the time weight proposed in this paper will be applicable to various domains, including social media, where time value is considered important.