• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.2
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• pp.187-200
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• 2023

The purpose of this study is to examine how the start-up education, start-up support system, and start-up mentoring directly and indirectly conducted in universities affects entrepreneurial Intention, and to present programs and directions that can increase the possibility of start-up for college students. This study analyzed the effects of college students' satisfaction with university entrepreneurship education, awareness of the entrepreneurship support system, and entrepreneurship mentoring on entrepreneurial will, and then analyzed how entrepreneurship efficacy affects entrepreneurial will as a moderating effect. The recognition of the start-up support system was divided into the government start-up support policy and the university start-up support project, and start-up mentoring was divided into mentoring function (problem-solving function, networking function, communication function, motivation function) and mentor trust (cognitive trust, emotional trust). Regression analysis was performed on satisfaction with college entrepreneurship education, perception of entrepreneurship support system, entrepreneurship mentoring as independent variables, and willingness to entrepreneurship as dependent variables. In addition, entrepreneurship efficacy was designated as a moderating variable and analyzed. As a result of the study, first, it was found that satisfaction with start-up education had a positive (+) effect on college students' willingness to start a business. Second, among the start-up support systems, university start-up support projects were found to have a positive (+) effect on college students' willingness to start a business. Third, as a result of verifying whether entrepreneurial self-efficacy has a moderating effect on the relationship between college start-up support projects and college students' willingness to start a business while recognizing the start-up support system, it was found to have a moderating effect. The following implications can be derived based on the analysis results of this study. First, efforts to improve start-up education will be needed to increase the satisfaction of start-up education. Second, Through the operation of the start-up counseling center in the university, it will be possible to recognize the education system supported by the university, the university start-up support project that can carry out the indirect experience of start-up, and the government start-up support policy that supports funds. Third, It will be necessary to open a program that can provide start-up mentoring through connection with mentors in the start-up field, such as professors, employees, and senior start-ups in universities. Fourth, It is necessary to develop thorough education on the preparation process of start-ups, start-up special lectures where senior start-ups can indirectly experience the failure of start-ups, and programs for customized start-up education according to college students' major and individual tendencies.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.149-171
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• 2020

The period at university is to make decision about getting an actual job. As our society develops rapidly and highly, jobs are diversified, subdivided, and specialized, and students' job preparation period is also getting longer and longer. This study analyzed the log data of college students to see how the various activities that college students experience inside and outside of school might have influences on employment. For this experiment, students' various activities were systematically classified, recorded as an activity data and were divided into six core competencies (Job reinforcement competency, Leadership & teamwork competency, Globalization competency, Organizational commitment competency, Job exploration competency, and Autonomous implementation competency). The effect of the six competency levels on the employment status (employed group, unemployed group) was analyzed. As a result of the analysis, it was confirmed that the difference in level between the employed group and the unemployed group was significant for all of the six competencies, so it was possible to infer that the activities at the school are significant for employment. Next, in order to analyze the impact of the six competencies on the qualitative performance of employment, we had ANOVA analysis after dividing the each competency level into 2 groups (low and high group), and creating 6 groups by the range of first annual salary. Students with high levels of globalization capability, job search capability, and autonomous implementation capability were also found to belong to a higher annual salary group. The theoretical contributions of this study are as follows. First, it connects the competencies that can be extracted from the school experience with the competencies in the Human Resource Management field and adds job search competencies and autonomous implementation competencies which are required for university students to have their own successful career & life. Second, we have conducted this analysis with the competency data measured form actual activity and result data collected from the interview and research. Third, it analyzed not only quantitative performance (employment rate) but also qualitative performance (annual salary level). The practical use of this study is as follows. First, it can be a guide when establishing career development plans for college students. It is necessary to prepare for a job that can express one's strengths based on an analysis of the world of work and job, rather than having a no-strategy, unbalanced, or accumulating excessive specifications competition. Second, the person in charge of experience design for college students, at an organizations such as schools, businesses, local governments, and governments, can refer to the six competencies suggested in this study to for the user-useful experiences design that may motivate more participation. By doing so, one event may bring mutual benefits for both event designers and students. Third, in the era of digital transformation, the government's policy manager who envisions the balanced development of the country can make a policy in the direction of achieving the curiosity and energy of college students together with the balanced development of the country. A lot of manpower is required to start up novel platform services that have not existed before or to digitize existing analog products, services and corporate culture. The activities of current digital-generation-college-students are not only catalysts in all industries, but also for very benefit and necessary for college students by themselves for their own successful career development.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.157-173
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• 2021

With the development of information technology, the amount of available information increases daily. However, having access to so much information makes it difficult for users to easily find the information they seek. Users want a visualized system that reduces information retrieval and learning time, saving them from personally reading and judging all available information. As a result, recommendation systems are an increasingly important technologies that are essential to the business. Collaborative filtering is used in various fields with excellent performance because recommendations are made based on similar user interests and preferences. However, limitations do exist. Sparsity occurs when user-item preference information is insufficient, and is the main limitation of collaborative filtering. The evaluation value of the user item matrix may be distorted by the data depending on the popularity of the product, or there may be new users who have not yet evaluated the value. The lack of historical data to identify consumer preferences is referred to as data sparsity, and various methods have been studied to address these problems. However, most attempts to solve the sparsity problem are not optimal because they can only be applied when additional data such as users' personal information, social networks, or characteristics of items are included. Another problem is that real-world score data are mostly biased to high scores, resulting in severe imbalances. One cause of this imbalance distribution is the purchasing bias, in which only users with high product ratings purchase products, so those with low ratings are less likely to purchase products and thus do not leave negative product reviews. Due to these characteristics, unlike most users' actual preferences, reviews by users who purchase products are more likely to be positive. Therefore, the actual rating data is over-learned in many classes with high incidence due to its biased characteristics, distorting the market. Applying collaborative filtering to these imbalanced data leads to poor recommendation performance due to excessive learning of biased classes. Traditional oversampling techniques to address this problem are likely to cause overfitting because they repeat the same data, which acts as noise in learning, reducing recommendation performance. In addition, pre-processing methods for most existing data imbalance problems are designed and used for binary classes. Binary class imbalance techniques are difficult to apply to multi-class problems because they cannot model multi-class problems, such as objects at cross-class boundaries or objects overlapping multiple classes. To solve this problem, research has been conducted to convert and apply multi-class problems to binary class problems. However, simplification of multi-class problems can cause potential classification errors when combined with the results of classifiers learned from other sub-problems, resulting in loss of important information about relationships beyond the selected items. Therefore, it is necessary to develop more effective methods to address multi-class imbalance problems. We propose a collaborative filtering model using CGAN to generate realistic virtual data to populate the empty user-item matrix. Conditional vector y identify distributions for minority classes and generate data reflecting their characteristics. Collaborative filtering then maximizes the performance of the recommendation system via hyperparameter tuning. This process should improve the accuracy of the model by addressing the sparsity problem of collaborative filtering implementations while mitigating data imbalances arising from real data. Our model has superior recommendation performance over existing oversampling techniques and existing real-world data with data sparsity. SMOTE, Borderline SMOTE, SVM-SMOTE, ADASYN, and GAN were used as comparative models and we demonstrate the highest prediction accuracy on the RMSE and MAE evaluation scales. Through this study, oversampling based on deep learning will be able to further refine the performance of recommendation systems using actual data and be used to build business recommendation systems.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.23-46
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• 2021

Collaborative filtering, which is often used in personalization recommendations, is recognized as a very useful technique to find similar customers and recommend products to them based on their purchase history. However, the traditional collaborative filtering technique has raised the question of having difficulty calculating the similarity for new customers or products due to the method of calculating similaritiesbased on direct connections and common features among customers. For this reason, a hybrid technique was designed to use content-based filtering techniques together. On the one hand, efforts have been made to solve these problems by applying the structural characteristics of social networks. This applies a method of indirectly calculating similarities through their similar customers placed between them. This means creating a customer's network based on purchasing data and calculating the similarity between the two based on the features of the network that indirectly connects the two customers within this network. Such similarity can be used as a measure to predict whether the target customer accepts recommendations. The centrality metrics of networks can be utilized for the calculation of these similarities. Different centrality metrics have important implications in that they may have different effects on recommended performance. In this study, furthermore, the effect of these centrality metrics on the performance of recommendation may vary depending on recommender algorithms. In addition, recommendation techniques using network analysis can be expected to contribute to increasing recommendation performance even if they apply not only to new customers or products but also to entire customers or products. By considering a customer's purchase of an item as a link generated between the customer and the item on the network, the prediction of user acceptance of recommendation is solved as a prediction of whether a new link will be created between them. As the classification models fit the purpose of solving the binary problem of whether the link is engaged or not, decision tree, k-nearest neighbors (KNN), logistic regression, artificial neural network, and support vector machine (SVM) are selected in the research. The data for performance evaluation used order data collected from an online shopping mall over four years and two months. Among them, the previous three years and eight months constitute social networks composed of and the experiment was conducted by organizing the data collected into the social network. The next four months' records were used to train and evaluate recommender models. Experiments with the centrality metrics applied to each model show that the recommendation acceptance rates of the centrality metrics are different for each algorithm at a meaningful level. In this work, we analyzed only four commonly used centrality metrics: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Eigenvector centrality records the lowest performance in all models except support vector machines. Closeness centrality and betweenness centrality show similar performance across all models. Degree centrality ranking moderate across overall models while betweenness centrality always ranking higher than degree centrality. Finally, closeness centrality is characterized by distinct differences in performance according to the model. It ranks first in logistic regression, artificial neural network, and decision tree withnumerically high performance. However, it only records very low rankings in support vector machine and K-neighborhood with low-performance levels. As the experiment results reveal, in a classification model, network centrality metrics over a subnetwork that connects the two nodes can effectively predict the connectivity between two nodes in a social network. Furthermore, each metric has a different performance depending on the classification model type. This result implies that choosing appropriate metrics for each algorithm can lead to achieving higher recommendation performance. In general, betweenness centrality can guarantee a high level of performance in any model. It would be possible to consider the introduction of proximity centrality to obtain higher performance for certain models.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.1-42
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• 2009

In a knowledge-based society, a firm's intellectual capital represents the wealth of ideas and ability to innovate, which are indispensable elements for the future growth. Therefore, the intellectual capital is evidently recognized as the most valuable asset in the organization. Considered as intangible asset, intellectual capital is the basis based on which firms can foster their sustainable competitive advantage. One of the essential components of the intellectual capital is a social capital, indicating the firm's individual members' ability to build a firm's social networks. As such, social capital is a powerful concept necessary for understanding the emergence, growth, and functioning of network linkages. The more social capital a firm is equipped with, the more successfully it can establish new social networks. By providing a shared context for social interactions, social capital facilitates the creation of new linkages in the organizational setting. This concept of "person-environment fit" has long been prevalent in the management literature. The fit is grounded in the interaction theory of behavior. The interaction perspective has a fairly long theoretical tradition, beginning with proposition that behavior is a function of the person and environment. This view asserts that neither personal characteristics nor the situation alone adequately explains the variance in behavioral and attitudinal variables. Instead, the interaction of personal and situational variables accounts for the greatest variance. Accordingly, the person-environment fit is defined as the degree of congruence or match between personal and situational variables in producing significant selected outcomes. In addition, information systems acceptance factors enable organizations to build large electronic communities with huge knowledge resources. For example, the Intranet helps to build knowledge-based communities, which in turn increases employee communication and collaboration. It is vital since through active communication and collaborative efforts can employees build common basis for shared understandings that evolve into stronger relationships embedded with trust. To this aim, the electronic communication network allows the formation of social network to be more viable to rapid mobilization and assimilation of knowledge assets in the organizations. The purpose of this study is to investigate: (1) the impact of person-environment fit(person-job fit, person-person fit, person-group fit, person-organization fit) on social capital(network ties, trust, norm, shared language); (2) the impact of information systems acceptance factors(availability, perceived usefulness, perceived ease of use) on social capital; (3) the impact of social capital on personal performance(work performance, work satisfaction); and (4) the mediating role of social capital between person-environment fit and personal performance. In general, social capital is defined as the aggregated actual or collective potential resources which lead to the possession of a durable network. The concept of social capital was originally developed by sociologists for their analysis in social context. Recently, it has become an increasingly popular jargon used in the management literature in describing organizational phenomena outside the realm of transaction costs. Since both environmental factors and information systems acceptance factors affect the network of employee's relationships, this study proposes that these two factors have significant influence on the social capital of employees. The person-environment fit basically refers to the alignment between characteristics of people and their environments, thereby resulting in positive outcomes for both individuals and organizations. In addition, the information systems acceptance factors have rather direct influences on the social network of employees. Based on such theoretical framework, namely person-environment fit and social capital theory, we develop our research model and hypotheses. The results of data analysis, based on 458 employee cases are as follow: Firstly, both person-environment fit(person-job fit, person-person fit, person-group fit, person-organization fit) and information systems acceptance factors(availability perceived usefulness, perceived ease of use) significantly influence social capital(network ties, norm, shared language). In addition, person-environment fit is a stronger factor influencing social capital than information systems acceptance factors. Secondly, social capital is a significant factor in both work satisfaction and work performance. Finally, social capital partly plays a mediating role between person-environment fit and personal performance. Our findings suggest that it is vital for firms to understand the importance of environmental factors affecting social capital of employees and accordingly identify the importance of information systems acceptance factors in building formal and informal relationships of employees. Firms also need to reflect their recognition of the importance of social capital's mediating role in boosting personal performance. Some limitations arisen in the course of the research and suggestions for future research directions are also discussed.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.179-196
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• 2019

Recently, the "Smart Consumer" has been emerging. He or she is increasingly inclined to search for and purchase products by taking into account personal judgment or expert reviews rather than by relying on information delivered through manufacturers' advertising. This is especially true when purchasing cosmetics. Because cosmetics act directly on the skin, consumers respond seriously to dangerous chemical elements they contain or to skin problems they may cause. Above all, cosmetics should fit well with the purchaser's skin type. In addition, changes in global cosmetics consumer trends make it necessary to study this field. The desire to find one's own individualized cosmetics is being revealed to consumers around the world and is known as "Finding the Holy Grail." Many consumers show a deep interest in customized cosmetics with the cultural boom known as "K-Beauty" (an aspect of "Han-Ryu"), the growth of personal grooming, and the emergence of "self-culture" that includes "self-beauty" and "self-interior." These trends have led to the explosive popularity of cosmetics made in Korea in the Chinese and Southeast Asian markets. In order to meet the customized cosmetics needs of consumers, cosmetics manufacturers and related companies are responding by concentrating on delivering premium services through the convergence of ICT(Information, Communication and Technology). Despite the evolution of companies' responses regarding market trends toward customized cosmetics, there is no "Intelligent Data Platform" that deals holistically with consumers' skin condition experience and thus attaches emotions to products and services. To find the Holy Grail of customized cosmetics, it is important to acquire and analyze consumer data on what they want in order to address their experiences and emotions. The emotions consumers are addressing when purchasing cosmetics varies by their age, sex, skin type, and specific skin issues and influences what price is considered reasonable. Therefore, it is necessary to classify emotions regarding cosmetics by individual consumer. Because of its importance, consumer emotion analysis has been used for both services and products. Given the trends identified above, we judge that consumer emotion analysis can be used in our study. Therefore, we collected and indexed data on consumers' emotions regarding their cosmetics experiences focusing on consumers' language. We crawled the cosmetics emotion data from SNS (blog and Twitter) according to sales ranking ($1^{st}$ to $99^{th}$), focusing on the ample/serum category. A total of 357 emotional adjectives were collected, and we combined and abstracted similar or duplicate emotional adjectives. We conducted a "Consumer Sentiment Journey" workshop to build a "Consumer Sentiment Dictionary," and this resulted in a total of 76 emotional adjectives regarding cosmetics consumer experience. Using these 76 emotional adjectives, we performed clustering with the Self-Organizing Map (SOM) method. As a result of the analysis, we derived eight final clusters of cosmetics consumer sentiments. Using the vector values of each node for each cluster, the characteristics of each cluster were derived based on the top ten most frequently appearing consumer sentiments. Different characteristics were found in consumer sentiments in each cluster. We also developed a cosmetics experience pattern map. The study results confirmed that recommendation and classification systems that consider consumer emotions and sentiments are needed because each consumer differs in what he or she pursues and prefers. Furthermore, this study reaffirms that the application of emotion and sentiment analysis can be extended to various fields other than cosmetics, and it implies that consumer insights can be derived using these methods. They can be used not only to build a specialized sentiment dictionary using scientific processes and "Design Thinking Methodology," but we also expect that these methods can help us to understand consumers' psychological reactions and cognitive behaviors. If this study is further developed, we believe that it will be able to provide solutions based on consumer experience, and therefore that it can be developed as an aspect of marketing intelligence.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.55-78
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• 2021

Tosolve complex and diverse social problems and ensure the quality of life of individuals, social robots that can interact with humans are attracting attention. In the past, robots were recognized as beings that provide labor force as they put into industrial sites on behalf of humans. However, the concept of today's robot has been extended to social robots that coexist with humans and enable social interaction with the advent of Smart technology, which is considered an important driver in most industries. Specifically, there are service robots that respond to customers, the robots that have the purpose of edutainment, and the emotionalrobots that can interact with humans intimately. However, popularization of robots is not felt despite the current information environment in the modern ICT service environment and the 4th industrial revolution. Considering social interaction with users which is an important function of social robots, not only the technology of the robots but also other factors should be considered. The design elements of the robot are more important than other factors tomake consumers purchase essentially a social robot. In fact, existing studies on social robots are at the level of proposing "robot development methodology" or testing the effects provided by social robots to users in pieces. On the other hand, consumer emotions felt from the robot's appearance has an important influence in the process of forming user's perception, reasoning, evaluation and expectation. Furthermore, it can affect attitude toward robots and good feeling and performance reasoning, etc. Therefore, this study aims to verify the effect of appearance of social robot and consumer emotions on consumer's attitude toward social robot. At this time, a social robot design evaluation model is constructed by combining heterogeneous data from different sources. Specifically, the three quantitative indicator data for the appearance of social robots from the ABOT Database is included in the model. The consumer emotions of social robot design has been collected through (1) the existing design evaluation literature and (2) online buzzsuch as product reviews and blogs, (3) qualitative interviews for social robot design. Later, we collected the score of consumer emotions and attitudes toward various social robots through a large-scale consumer survey. First, we have derived the six major dimensions of consumer emotions for 23 pieces of detailed emotions through dimension reduction methodology. Then, statistical analysis was performed to verify the effect of derived consumer emotionson attitude toward social robots. Finally, the moderated regression analysis was performed to verify the effect of quantitatively collected indicators of social robot appearance on the relationship between consumer emotions and attitudes toward social robots. Interestingly, several significant moderation effects were identified, these effects are visualized with two-way interaction effect to interpret them from multidisciplinary perspectives. This study has theoretical contributions from the perspective of empirically verifying all stages from technical properties to consumer's emotion and attitudes toward social robots by linking the data from heterogeneous sources. It has practical significance that the result helps to develop the design guidelines based on consumer emotions in the design stage of social robot development.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.17-35
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• 2015

Demand forecasting is the activity of estimating the quantity of a product or service that consumers will purchase for a certain period of time. Developing precise forecasting models are considered important since corporates can make strategic decisions on new markets based on future demand estimated by the models. Many studies have developed market growth curve models, such as Bass, Logistic, Gompertz models, which estimate future demand when a market is in its early stage. Among the models, Bass model, which explains the demand from two types of adopters, innovators and imitators, has been widely used in forecasting. Such models require sufficient demand observations to ensure qualified results. In the beginning of a new market, however, observations are not sufficient for the models to precisely estimate the market's future demand. For this reason, as an alternative, demands guessed from those of most adjacent markets are often used as references in such cases. Reference markets can be those whose products are developed with the same categorical technologies. A market's demand may be expected to have the similar pattern with that of a reference market in case the adoption pattern of a product in the market is determined mainly by the technology related to the product. However, such processes may not always ensure pleasing results because the similarity between markets depends on intuition and/or experience. There are two major drawbacks that human experts cannot effectively handle in this approach. One is the abundance of candidate reference markets to consider, and the other is the difficulty in calculating the similarity between markets. First, there can be too many markets to consider in selecting reference markets. Mostly, markets in the same category in an industrial hierarchy can be reference markets because they are usually based on the similar technologies. However, markets can be classified into different categories even if they are based on the same generic technologies. Therefore, markets in other categories also need to be considered as potential candidates. Next, even domain experts cannot consistently calculate the similarity between markets with their own qualitative standards. The inconsistency implies missing adjacent reference markets, which may lead to the imprecise estimation of future demand. Even though there are no missing reference markets, the new market's parameters can be hardly estimated from the reference markets without quantitative standards. For this reason, this study proposes a case-based expert system that helps experts overcome the drawbacks in discovering referential markets. First, this study proposes the use of Euclidean distance measure to calculate the similarity between markets. Based on their similarities, markets are grouped into clusters. Then, missing markets with the characteristics of the cluster are searched for. Potential candidate reference markets are extracted and recommended to users. After the iteration of these steps, definite reference markets are determined according to the user's selection among those candidates. Then, finally, the new market's parameters are estimated from the reference markets. For this procedure, two techniques are used in the model. One is clustering data mining technique, and the other content-based filtering of recommender systems. The proposed system implemented with those techniques can determine the most adjacent markets based on whether a user accepts candidate markets. Experiments were conducted to validate the usefulness of the system with five ICT experts involved. In the experiments, the experts were given the list of 16 ICT markets whose parameters to be estimated. For each of the markets, the experts estimated its parameters of growth curve models with intuition at first, and then with the system. The comparison of the experiments results show that the estimated parameters are closer when they use the system in comparison with the results when they guessed them without the system.

• Journal of Korean Society of Archives and Records Management
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• v.2 no.2
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• pp.1-38
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• 2002

This study examines the status quo of Korean archives and records management from the Governmental as well as professional activities for the development of the field in relation to the new legislation on records management. Among many concerns, this study primarily explores the following four perspectives: 1) the Government Archives and Records Services; 2) the Korean Association of Archives; 3) the Korean Society of Archives and Records Management; 4) the Journal of Korean Society of Archives and Records Management. One of the primary tasks of the is to build the special depository within which the Presidential Library should be located. As a result, the position of the GARS can be elevated and directed by an official at the level of vice-minister right under a president as a governmental representative of managing the public records. In this manner, GARS can sustain its independency and take custody of public records across government agencies. made efforts in regard to the preservation of paper records, the preservation of digital resources in new media formats, facilities and equipments, education of archivists and continuing, training of practitioners, and policy-making of records preservation. For further development, academia and corporate should cooperate continuously to face with the current problems. has held three international conferences to date. The topics of conferences include respectively: 1) records management and archival education of Korea, Japan, and China; 2) knowledge management and metadata for the fulfillment of archives and information science; and 3) electronic records management and preservation with the understanding of ongoing archival research in the States, Europe, and Asia. The Society continues to play a leading role in both of theory and practice for the development of archival science in Korea. It should also suggest an educational model of archival curricula that fits into the Korean context. The Journals of Records Management & Archives Society of Korea have been published on the six major topics to date. Findings suggest that "Special Archives" on regional or topical collections are desirable because it can house subject holdings on specialty or particular figures in that region. In addition, archival education at the undergraduate level is more desirable for Korean situations where practitioners are strongly needed and professionals with master degrees go to manager positions. Departments of Library and Information Science in universities, therefore, are needed to open archival science major or track at the undergraduate level in order to meet current market demands. The qualification of professional archivists should be moderate as well.

• Asia pacific journal of information systems
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• v.18 no.3
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• pp.21-43
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• 2008

The task network which is formed of different individuals can be recognized as a social network. Therefore, the way to communicate with people inside or outside the network has considerable influence on their outcome. Moreover, the position on which a member stands in a network shows the different effects of the information systems supporting communication with others. In this paper, it is to be studied how personal CMC (computer-mediated communication) tools affect the mission that those who work for a network perform through diverse task networks. Especially, we focused on synchronicity of CMC. On this score, the perspective of Media Synchronicity Theory was taken that had been suggested by criticizing Media Richness Theory. It is the objective, from this perspective, to find which characteristics of networks make the value of IT supporting synchronicity high. In the research trends of social networks, there have been two traditional perspectives to explain the effect of network: embeddedness and diversity ones. These differ from the aspect which type of social network can provide much more economic benefits. As similar studies have been reported by various researchers, these are also divided into the bonding and bridging views which are based on internal and external tie, respectively, Size, density, and centrality were measured as the characteristics of personal task networks. Size means the level of relationship between members. It is the total number of other colleagues who work with a specific member for a certain project. It means, the larger the size of task network, the more the number of coworkers who interact each other through the job. Density is the ratio of the number of relationships arranged actually to the total number of available ones. In an ego-centered network, it is defined as the ratio of the number of relationship made really to the total number of possible ones between members who are actually involved each other. The higher the level of density, the larger the number of projects on which the members collaborate. Centrality means that his/her position is on the exact center of whole network. There are several methods to measure it. In this research, betweenness centrality was adopted among them. It is measured by the position on which one member stands between others in a network. The determinant to raise its level is the shortest geodesic that represents the shortest distance between members. Centrality also indicates the level of role as a broker among others. To verify the hypotheses, we interviewed and surveyed a group of employees of a nationwide financial organization in which a groupware system is used. They were questioned about two CMC applications: MSN with a higher level of synchronicity and email with a lower one. As a result, the larger the size of his/her own task network, the smaller its density and the higher the level of his/her centrality, the higher the level of the effect using the task network with CMC tools. Above all, this positive effect is verified to be much more produced while using CMC applications with higher-level synchronicity. Among the a variety of situations under which the use of CMC gives more benefits, this research is considered as one of rare cases regarding the characteristics of task network as moderators by focusing ITs for the operation of his/her own task network. It is another contribution of this research to prove empirically that the values of information system depend on the social, or comparative, characteristic of time. Though the same amount of time is shared, the social characteristics of users change its value. In addition, it is significant to examine empirically that the ITs with higher-level synchronicity have the positive effect on productivity. Many businesses are worried about the negative effect of synchronous ITs, for their employees are likely to use them for personal social activities. However. this research can help to dismiss the concern against CMC tools.