• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.93-110
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• 2015

The recommender system is a system which recommends products to the customers who are likely to be interested in. Based on automated information filtering technology, various recommender systems have been developed. Collaborative filtering (CF), one of the most successful recommendation algorithms, has been applied in a number of different domains such as recommending Web pages, books, movies, music and products. But, it has been known that CF has a critical shortcoming. CF finds neighbors whose preferences are like those of the target customer and recommends products those customers have most liked. Thus, CF works properly only when there's a sufficient number of ratings on common product from customers. When there's a shortage of customer ratings, CF makes the formation of a neighborhood inaccurate, thereby resulting in poor recommendations. To improve the performance of CF based recommender systems, most of the related studies have been focused on the development of novel algorithms under the assumption of using a single profile, which is created from user's rating information for items, purchase transactions, or Web access logs. With the advent of big data, companies got to collect more data and to use a variety of information with big size. So, many companies recognize it very importantly to utilize big data because it makes companies to improve their competitiveness and to create new value. In particular, on the rise is the issue of utilizing personal big data in the recommender system. It is why personal big data facilitate more accurate identification of the preferences or behaviors of users. The proposed recommendation methodology is as follows: First, multimodal user profiles are created from personal big data in order to grasp the preferences and behavior of users from various viewpoints. We derive five user profiles based on the personal information such as rating, site preference, demographic, Internet usage, and topic in text. Next, the similarity between users is calculated based on the profiles and then neighbors of users are found from the results. One of three ensemble approaches is applied to calculate the similarity. Each ensemble approach uses the similarity of combined profile, the average similarity of each profile, and the weighted average similarity of each profile, respectively. Finally, the products that people among the neighborhood prefer most to are recommended to the target users. For the experiments, we used the demographic data and a very large volume of Web log transaction for 5,000 panel users of a company that is specialized to analyzing ranks of Web sites. R and SAS E-miner was used to implement the proposed recommender system and to conduct the topic analysis using the keyword search, respectively. To evaluate the recommendation performance, we used 60% of data for training and 40% of data for test. The 5-fold cross validation was also conducted to enhance the reliability of our experiments. A widely used combination metric called F1 metric that gives equal weight to both recall and precision was employed for our evaluation. As the results of evaluation, the proposed methodology achieved the significant improvement over the single profile based CF algorithm. In particular, the ensemble approach using weighted average similarity shows the highest performance. That is, the rate of improvement in F1 is 16.9 percent for the ensemble approach using weighted average similarity and 8.1 percent for the ensemble approach using average similarity of each profile. From these results, we conclude that the multimodal profile ensemble approach is a viable solution to the problems encountered when there's a shortage of customer ratings. This study has significance in suggesting what kind of information could we use to create profile in the environment of big data and how could we combine and utilize them effectively. However, our methodology should be further studied to consider for its real-world application. We need to compare the differences in recommendation accuracy by applying the proposed method to different recommendation algorithms and then to identify which combination of them would show the best performance.

• The Journal of Information Systems
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• v.27 no.1
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• pp.153-169
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• 2018

Purpose Since human brains catch images faster than texts or numbers, infographics has been widely used in business in the form of "information dashboard" to enhance the efficiency of decision-making. Groupware, however, has neglected the adoption and use of infographics, in particular, in the idea generation process. Given that an overall performance of groupware-based idea generation is no better than that of the (paper-and-pencil-based) Nominal Group Technique, Jung et al. (2010) adopted the notion of infographics in the form of performance feedback to solve the productivity paradox. With the consistent results, which demonstrate beneficial effects of infographics on performance enhancement, an interesting observation that groups with the bar chart treatment performed better than groups with the dot chart treatment was made. The main purpose of this study was to find if there were a performance consistency between the outcomes from the previous study and the outcomes from the current study. Design/methodology/approach In experiment 1, we employed the same system used in the previous study (i.e., Jung et al., 2010). As individuals' contributions accumulated, the mechanism visually displayed individuals' performances two-dimensionally in the form of a bar chart or a dot chart. Then, we compared the performance outcomes from this study to the outcomes from previous study (i.e., Jung et al., 2010). In experiment 2, we modified the performance graph to test the effect of "playfulness" on performance by converting dots to car images. Then, we compared the performance outcome from experiment 2 to the outcomes from experiment 1. Findings Just like our interesting (and unexpected) finding in Jung et al.'s study (2010), the outcome confirmed a consistent superior performance of a bar chart. This implies that a bar chart is a better choice when stimulating performance with a visual aid in the context of groupware-based idea generation. Although a bar chart was criticized in a way that errors of length-area judgments are 40 ~ 250% greater than those of positional judgments along a common scale, such illusion turned out to be facilitating upward performance comparison better. Regarding Experiment 2, the outcome showed that the revised-dot graph is as good as the bar graph in terms of quantity and quality score of ideas. We attribute the performance enhancement of the resized-dot to the interaction between the motivational characteristic and the situational characteristic of playfulness because individuals in the revised-dot graph treatment performed better than individuals in the dot graph treatment. Given the order of performance (Bar >= Revised Dot > Dot) that the revised-dot treatment performed the same as (or lower than) the bar treatment, an additional research is warranted to reach to a consistent outcome.

• The Journal of Industrial Distribution & Business
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• v.9 no.6
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• pp.77-85
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• 2018

Purpose - The purpose of this study is to subdivide trade transaction-centered structure in a logistics/distribution industry system to apply blockchain, to establish and resolve with which types of technology, and to provide policy direction of government institution and technology to apply blockchain in this kind of industry. Research design, data, and methodology - This study was conducted with previous researches centered on cases applied in various industry sectors on the basis of blockchain technology. Results - General fields of blockchain application include digital contents distribution, IoT platform, e-Commerce, real-estate transaction, decentralized app. development(storage), certification service, smart contract, P2P network infrastructure, publication/storage of public documents, smart voting, money exchange, payment/settlement, banking security platform, actual asset storage, stock transaction and crowd funding. Blockchain is being applied in various fields home and abroad and its application cases can be explained in the banking industry, public sector, e-Commerce, medical industry, distribution and supply chain management, copyright protection. As examined in the blockchain application cases, it is expected to establish blockchain that can secure safety through distributed ledger in trade transaction because blockchain is established and applied in various sectors of industries home and abroad. Parties concerned of trade transaction can secure visibility even in interrupted specific section when they provide it as a base for distributed ledger application in trade and establish trade transaction model by applying blockchain. In case of interrupted specific section by using distributed ledger, blockchain model of trade transaction needs to be formed to make it possible for parties concerned involved in trade transaction to secure visibility and real-time tracking. Additionally, management should be possible from the time of contract until payment, freight transfer to buyers through land, air and maritime transportation. Conclusions - In order to boost blockchain-based logistics/distribution industry, the government, institutionally, needs to back up adding legal plan of shipping, logistics and distribution, reviewing standardization of electronic switching system and coming up with blockchain-based industrial road maps. In addition, the government, technologically, has to support R&D for integration with other high technology, standardization of distribution industry's blockchain technology and manpower training to expand technology development.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.4
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• pp.563-568
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• 2007

This paper presents a methodology for predicting nonlinear time series based on the neural network with weighted fuzzy membership functions (NEWFM). The degree of classification intensity is obtained by bounded sum of weighted fuzzy membership functions extracted by NEWFM, then weighted average defuzzification is used for predicting nonlinear time series. The experimental results demonstrate that NEWFM has the classification capability of 92.22% against the target class of GDP. The time series created by NEWFM model has a relatively close approximation to the GDP which is a typical business cycle indicator, and has been proved to be a useful indicator which has the turning point forecasting capability of average 12 months in the peak point and average 6 months in the trough point during 5th to 8th cyclical period. In addition, NEWFM measures the efficiency of the economic indexes by the feature selection and enables the users to forecast with reduced numbers of 7 among 10 leading indexes while improving the classification rate from 90% to 92.22%.

• Journal of Distribution Science
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• v.14 no.2
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• pp.73-82
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• 2016

Purpose - Counterproductive work behavior(CWB) was typically categorized according to the behavior whether it targets other people(i.e., interpersonal CWB: I-CWB). Employing organizations(i.e., organizational CWB: O-CWB) has emerged as major concerns among researchers, managers, and the general public. An abundance of researches has informed us about the understanding for the antecedents of CWB, whereas little is known about the antecedents of CWB directed distribution service in employee's emotional labor. Therefore, the purpose of this research is to propose a research model in which surface acting enhances emotional exhaustion as an emotional labor strategy, which eventually increases counterproductive work behavior(including I-CWM and O-CWB). Research design, data, and methodology - This empirical research data were gathered from the samples of full time frontline hotel employees(including front office, call center, food/beverage, concierge, and room service) in South Korea. Six hotels were selected ranged from four to five stars, including privately owned and joint-venture properties. A convenience sampling method was used to select hotels. Full time frontline hotel employees from the six hotels were surveyed using a self-administered instrument for data collection. With the strong support of hotel managers, a total of 300 questionnaires were distributed, and 252 responses were collected indicating a response rate of 84.0%. In the process of working with the 252 samples, structural equation modeling is employed to test research hypotheses(H1: The relationship between surface acting and Interpersonal counterproductive work behavior(I-CWB) is mediated by emotional exhaustion, H2: The relationship between surface acting and organizational counterproductive work behavior(O-CWB) is mediated by emotional exhaustion). SPSS 18.0 and M-Plus 7.31 software were used for the data analysis. Descriptive statistics were used to assess the distribution of the employee profiles and correlations between factors. M-Plus 7.31 software was used to test the model fit, validity, and reliability of the factors, significance of the relationship between factors, and the effects of factors in the model. Results - To test our mediation hypotheses, we used an analytical strategy suggested by Preacher & Hayes (2008) and Shrout & Bolger (2002). This mediation approach directly tests the indirect effect between the predictor and the criterion variables through the mediator via a bootstrapping procedure. Thus, it addresses some weaknesses associated with the Sobel test. We found that surface acting was positively related to emotional exhaustion. Furthermore, emotional exhaustion was a significant predictor from the two kinds of counterproductive work behavior. In addition, surface acting was not significantly associated with the two kinds of counterproductive work behavior. These results indicated that the surface acting by frontline hotel employees was associated with higher emotional exhaustion, which is related with higher interpersonal counterproductive work behavior(I-CWB) and organizational counterproductive work behavior(O-CWB). In sum, we confirmed that the positive relationship between surface acting and the two kinds of counterproductive work behavior was fully mediated by emotional exhaustion. Conclusions - The current research broadens the conceptual work and empirical studies in counterproductive work behavior literature by representing a fundamental mechanism that how surface acting affects counterproductive work behavior.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.111-123
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• 2013

Semantic similarity/relatedness measure between two concepts plays an important role in research on system integration and database integration. Moreover, current research on keyword recommendation or tag clustering strongly depends on this kind of semantic measure. For this reason, many researchers in various fields including computer science and computational linguistics have tried to improve methods to calculating semantic similarity/relatedness measure. This study of similarity between concepts is meant to discover how a computational process can model the action of a human to determine the relationship between two concepts. Most research on calculating semantic similarity usually uses ready-made reference knowledge such as semantic network and dictionary to measure concept similarity. The topological method is used to calculated relatedness or similarity between concepts based on various forms of a semantic network including a hierarchical taxonomy. This approach assumes that the semantic network reflects the human knowledge well. The nodes in a network represent concepts, and way to measure the conceptual similarity between two nodes are also regarded as ways to determine the conceptual similarity of two words(i.e,. two nodes in a network). Topological method can be categorized as node-based or edge-based, which are also called the information content approach and the conceptual distance approach, respectively. The node-based approach is used to calculate similarity between concepts based on how much information the two concepts share in terms of a semantic network or taxonomy while edge-based approach estimates the distance between the nodes that correspond to the concepts being compared. Both of two approaches have assumed that the semantic network is static. That means topological approach has not considered the change of semantic relation between concepts in semantic network. However, as information communication technologies make advantage in sharing knowledge among people, semantic relation between concepts in semantic network may change. To explain the change in semantic relation, we adopt the cognitive semantics. The basic assumption of cognitive semantics is that humans judge the semantic relation based on their cognition and understanding of concepts. This cognition and understanding is called 'World Knowledge.' World knowledge can be categorized as personal knowledge and cultural knowledge. Personal knowledge means the knowledge from personal experience. Everyone can have different Personal Knowledge of same concept. Cultural Knowledge is the knowledge shared by people who are living in the same culture or using the same language. People in the same culture have common understanding of specific concepts. Cultural knowledge can be the starting point of discussion about the change of semantic relation. If the culture shared by people changes for some reasons, the human's cultural knowledge may also change. Today's society and culture are changing at a past face, and the change of cultural knowledge is not negligible issues in the research on semantic relationship between concepts. In this paper, we propose the future directions of research on semantic similarity. In other words, we discuss that how the research on semantic similarity can reflect the change of semantic relation caused by the change of cultural knowledge. We suggest three direction of future research on semantic similarity. First, the research should include the versioning and update methodology for semantic network. Second, semantic network which is dynamically generated can be used for the calculation of semantic similarity between concepts. If the researcher can develop the methodology to extract the semantic network from given knowledge base in real time, this approach can solve many problems related to the change of semantic relation. Third, the statistical approach based on corpus analysis can be an alternative for the method using semantic network. We believe that these proposed research direction can be the milestone of the research on semantic relation.

• Asia pacific journal of information systems
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• v.18 no.1
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• pp.145-162
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• 2008

One of the big concerns in e-society is privacy issue. In special, in developing robust ubiquitous smart space and corresponding services, user profile and preference are collected by the service providers. Privacy issue would be more critical in context-aware services simply because most of the context data themselves are private information: user's current location, current schedule, friends nearby and even her/his health data. To realize the potential of ubiquitous smart space, the systems embedded in the space should corporate personal privacy preferences. When the users invoke a set of services, they are asked to allow the service providers or smart space to make use of personal information which is related to privacy concerns. For this reason, the users unhappily provide the personal information or even deny to get served. On the other side, service provider needs personal information as rich as possible with minimal personal information to discern royal and trustworthy customers and those who are not. It would be desirable to enlarge the allowable personal information complying with the service provider's request, whereas minimizing service provider's requiring personal information which is not allowed to be submitted and user's submitting information which is of no value to the service provider. In special, if any personal information required by the service provider is not allowed, service will not be provided to the user. P3P (Platform for Privacy Preferences) has been regarded as one of the promising alternatives to preserve the personal information in the course of electronic transactions. However, P3P mainly focuses on preserving the buyers' personal information. From time to time, the service provider's business data should be protected from the unintended usage from the buyers. Moreover, even though the user's privacy preference could depend on the context happened to the user, legacy P3P does not handle the contextual change of privacy preferences. Hence, the purpose of this paper is to propose a mutual P3P-based negotiation mechanism. To do so, service provider's privacy concern is considered as well as the users'. User's privacy policy on the service provider's information also should be informed to the service providers before the service begins. Second, privacy policy is contextually designed according to the user's current context because the nomadic user's privacy concern structure may be altered contextually. Hence, the methodology includes mutual privacy policy and personalization. Overall framework of the mechanism and new code of ethics is described in section 2. Pervasive platform for mutual P3P considers user type and context field, which involves current activity, location, social context, objects nearby and physical environments. Our mutual P3P includes the privacy preference not only for the buyers but also the sellers, that is, service providers. Negotiation methodology for mutual P3P is proposed in section 3. Based on the fact that privacy concern occurs when there are needs for information access and at the same time those for information hiding. Our mechanism was implemented based on an actual shopping mall to increase the feasibility of the idea proposed in this paper. A shopping service is assumed as a context-aware service, and data groups for the service are enumerated. The privacy policy for each data group is represented as APPEL format. To examine the performance of the example service, in section 4, simulation approach is adopted in this paper. For the simulation, five data elements are considered: $\cdot$ UserID $\cdot$ User preference $\cdot$ Phone number $\cdot$ Home address $\cdot$ Product information $\cdot$ Service profile. For the negotiation, reputation is selected as a strategic value. Then the following cases are compared: $\cdot$ Legacy P3P is considered $\cdot$ Mutual P3P is considered without strategic value $\cdot$ Mutual P3P is considered with strategic value. The simulation results show that mutual P3P outperforms legacy P3P. Moreover, we could conclude that when mutual P3P is considered with strategic value, performance was better than that of mutual P3P is considered without strategic value in terms of service safety.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• Asia pacific journal of information systems
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• v.19 no.1
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• pp.33-62
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• 2009

Internet computing is a disruptive IT innovation. Semantic Web can be considered as an IT innovation because the Semantic Web technology possesses the potential to reduce information overload and enable semantic integration, using capabilities such as semantics and machine-processability. How should organizations adopt the Semantic Web? What factors affect the adoption and diffusion of Semantic Web innovation? Most studies on adoption and diffusion of innovation use empirical analysis as a quantitative research methodology in the post-implementation stage. There is criticism that the positivist requiring theoretical rigor can sacrifice relevance to practice. Rapid advances in technology require studies relevant to practice. In particular, it is realistically impossible to conduct quantitative approach for factors affecting adoption of the Semantic Web because the Semantic Web is in its infancy. However, in an early stage of introduction of the Semantic Web, it is necessary to give a model and some guidelines and for adoption and diffusion of the technology innovation to practitioners and researchers. Thus, the purpose of this study is to present a model of adoption and diffusion of the Semantic Web and to offer propositions as guidelines for successful adoption through a qualitative research method including multiple case studies and in-depth interviews. The researcher conducted interviews with 15 people based on face-to face and 2 interviews by telephone and e-mail to collect data to saturate the categories. Nine interviews including 2 telephone interviews were from nine user organizations adopting the technology innovation and the others were from three supply organizations. Semi-structured interviews were used to collect data. The interviews were recorded on digital voice recorder memory and subsequently transcribed verbatim. 196 pages of transcripts were obtained from about 12 hours interviews. Triangulation of evidence was achieved by examining each organization website and various documents, such as brochures and white papers. The researcher read the transcripts several times and underlined core words, phrases, or sentences. Then, data analysis used the procedure of open coding, in which the researcher forms initial categories of information about the phenomenon being studied by segmenting information. QSR NVivo version 8.0 was used to categorize sentences including similar concepts. 47 categories derived from interview data were grouped into 21 categories from which six factors were named. Five factors affecting adoption of the Semantic Web were identified. The first factor is demand pull including requirements for improving search and integration services of the existing systems and for creating new services. Second, environmental conduciveness, reference models, uncertainty, technology maturity, potential business value, government sponsorship programs, promising prospects for technology demand, complexity and trialability affect the adoption of the Semantic Web from the perspective of technology push. Third, absorptive capacity is an important role of the adoption. Fourth, suppler's competence includes communication with and training for users, and absorptive capacity of supply organization. Fifth, over-expectance which results in the gap between user's expectation level and perceived benefits has a negative impact on the adoption of the Semantic Web. Finally, the factor including critical mass of ontology, budget. visible effects is identified as a determinant affecting routinization and infusion. The researcher suggested a model of adoption and diffusion of the Semantic Web, representing relationships between six factors and adoption/diffusion as dependent variables. Six propositions are derived from the adoption/diffusion model to offer some guidelines to practitioners and a research model to further studies. Proposition 1 : Demand pull has an influence on the adoption of the Semantic Web. Proposition 1-1 : The stronger the degree of requirements for improving existing services, the more successfully the Semantic Web is adopted. Proposition 1-2 : The stronger the degree of requirements for new services, the more successfully the Semantic Web is adopted. Proposition 2 : Technology push has an influence on the adoption of the Semantic Web. Proposition 2-1 : From the perceptive of user organizations, the technology push forces such as environmental conduciveness, reference models, potential business value, and government sponsorship programs have a positive impact on the adoption of the Semantic Web while uncertainty and lower technology maturity have a negative impact on its adoption. Proposition 2-2 : From the perceptive of suppliers, the technology push forces such as environmental conduciveness, reference models, potential business value, government sponsorship programs, and promising prospects for technology demand have a positive impact on the adoption of the Semantic Web while uncertainty, lower technology maturity, complexity and lower trialability have a negative impact on its adoption. Proposition 3 : The absorptive capacities such as organizational formal support systems, officer's or manager's competency analyzing technology characteristics, their passion or willingness, and top management support are positively associated with successful adoption of the Semantic Web innovation from the perceptive of user organizations. Proposition 4 : Supplier's competence has a positive impact on the absorptive capacities of user organizations and technology push forces. Proposition 5 : The greater the gap of expectation between users and suppliers, the later the Semantic Web is adopted. Proposition 6 : The post-adoption activities such as budget allocation, reaching critical mass, and sharing ontology to offer sustainable services are positively associated with successful routinization and infusion of the Semantic Web innovation from the perceptive of user organizations.

• The Journal of Information Systems
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• v.5
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• pp.115-147
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• 1996

1. Introduction The new computing era started with the various computer technologies and services having been used in communication and automation area since 1980's. We call that era information technology(IT) era. In such era, especially communication plays very important roles in every aspect. So Schoderbek named that era the ege of c2. Therefore, communition became widely used in organizations. Now the majority of organizations have computer-aided communication capabilities that facilitate access to people and information, both within and outside organization. So one objective of this study is to assess the effects of these changes in data communication on decision making. Decision making is the essence of management and is too important to organizational success. This dissertation has three basic objectives: 1)to clarify the concept of data communication, who influences on decision making, and the concept of decision types, managerial and operational, may be affected differently by data communication 2)to investigate whether the effects of data communication upon decision making may be organizational variables. 3)to verify that business and decision types may affect different impact on decision making.2. Hypotheses Four attributes are selected to make hypotheses from the information attributes presented by famous scholars. They are as follows. ①effectiveness ②routinization ③communication easiness ④timeliness Hypotheses are developed according to these attributes, which are chosen from the literature study and theory H1 : Data communication is positively related to the effectiveness of DM H2 : Data communication is positively related to the routinization of DM H3 : Data communication is positively related to the communication easiness of DM H4 : Data communication is positively related to the timeliness of information for DM3. Methodology After pilot study, data are collected from the decision makers in 200 companies located at Seoul and the metropolitan area. A random sample of 174 employees sent back their questionnaires(response rate of 87%). Among them, 151 questionnaires was useful to the analysis of this study(useful rate of 75.5%).4. Conclusion and Discussion Among four proposed hypotheses, all hypotheses are fully supported. They are as follows. 1)effectiveness 2)routinization 3)communication easiness 4)timeliness. So, first objective of this study is proved. Namely, to clarify that the effects of data communication upon DM is fully supported. But they are different from the decision types. Second one is not apparently verfied. i.e. the effect of data communication on the decision variables is not moderated by organizational variables. Third is inspected. The effects of data communication differs from the industry and decision types evidently. This study has many limitations to generalize the statistical results. Since the definition of data communication has broad meanings in reality. So allare not contained in this research. Another restrict in this study is like this. Decision types are usually divided into three types-operational, managerial, strategic DM. But in this study, strategic DM is left out.