• Journal of Distribution Research
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• v.4 no.2
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• pp.41-58
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• 1999

The relationship-orientation is an important concept for understanding both of marketing theory and practice. However, not many research have focused on how to develop the relationship-orientation of end users. Therefore, the objective of this research is to confirm the key factors relevant to the relationship-orientation. This research studies(1) the transaction style of individuals, and (2) social influences on the relationship-orientation. Customers want to reduce the number of choice sets because of transaction style, including (1) efficiency of decision making, (2) simplification of information processing, (3) avoidance of future perceived risk, and (4) pursuit of cognitive consistency. Customers are influenced by social factors such as family members, reference groups, and opinion leaders. The following conclusions were drawn based on results of research analysis: (1) efficiency of decision making and avoidance of future perceived risk affect the relationship-orientation, and (2) influences of family members and opinion leaders to the focal relationship affect the relationship-orientation of individuals.

• The Journal of Information Systems
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• v.22 no.1
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• pp.65-87
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• 2013

The development and expansion of communication technology in the field of information technology (IT) have changed the method and culture of communication, mediating communication among people. In particular, since social network service (SNS) has the attributes of information delivery and processing, it has a more powerful dissipating effect and influence than other existing communication methods. The role of users in SNS is important because it has the communication structure of producer-consumer, which consists of sharing, connection and participation of users. In this line, the purpose of this study is to investigate the intention for continued use of SNS by user ability. In order to explain the motivation and behavior for continued use of SNS by users, this study employed the motivation theory and post-adaptation model. The study applied 'media literacy' to the characteristics of SNS media and environment and expanded it into the concept of 'SNS literacy' to identify the moderating effect by user ability. Referred to as 'user's ability that is required to use SNS,' the SNS literacy was verified for its moderating effect with the three sub-dimensions: 'technical accessing ability,' 'understanding ability' and 'creative ability.' The major findings of this study are as follows. First, the perceived usefulness and playfulness were found to have a significant effect on the intention for continued use of SNS, showing the same result with previous studies on technology acceptance. In other words, usefulness and playfulness are variables with an explanatory power in the SNS environment as well. Second, the conceptualization of SNS literacy with accessing ability, understanding ability and creative ability was found to be valid. Third, it was verified that there was a significant difference in the SNS literacy between perceived usefulness and continued use, indicating that users with higher ability respond sensitively to usefulness and affect continued behavior. The moderating effect of SNS literacy was also verified in the relationship between perceived playfulness and intention for continued use. The results above confirm the difference in post-adaptation behavior of individuals, and are expected to provide several implications.

• The KIPS Transactions:PartB
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• v.19B no.4
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• pp.231-236
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• 2012

According to consumer behavior theory, human propensity can be divided into two regulatory focus types: promotion and prevention. These two types have much influence on the consumer's decision in many diverse areas. In this research, we apply regulatory focus theory to personalized recommendation to minimize the cold start problem and to improve the performance of recommendation algorithms. To achieve this goal, we extract the consumer behavior variables and information exploration activity index from web shopping logs. We then use them for classifying regulatory focus of the consumer. This research has the contribution to show the possibility of systematization of consumer behavior theory as an interdisciplinary research tool of social science and information technology. Based on this attempt, we will extend the research to IT services adapting theories on other areas.

• The Journal of Society for e-Business Studies
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• v.27 no.1
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• pp.95-109
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• 2022

A blockchain based platform can ensure data integrity, reliability, and security by applying distributed processing and encryption technology for transaction records. In the existing knowledge sharing platform, the created knowledge could not be shared or utilized sufficiently due to information asymmetry and centralization. However little research has been done so far on this area. In this study, we will examine case studies and development potentials for blockchain based knowledge sharing platforms based on previous studies of blockchain technology, token economy, knowledge sharing, motivation theory, and social exchange theory. Blockchain based platforms can contribute to the activation of knowledge sharing, by resolving information asymmetry, simplifying unnecessary work procedures through unified knowledge sharing flow and excluded centralization of authority by decentralization, and strengthening access and utilization of the knowledge produced by the platform.

• Korean Journal of Cognitive Science
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• v.34 no.1
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• pp.39-66
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• 2023

New media moved the place of social exchange to the Internet, allowing large groups to communicate in one place beyond the limits of time and space. Recent studies have also reported cases in which human social abilities do not keep up with the expansion of group scale through social media. In this context, current study investigated how human perception of social information is affected by the expansion of the group scale in the context of majority bias. Using Internet-based task, the psychological processes that group ratio and group size are perceived and affect majority-biased social information use were investigated, and whether group scale moderates those processes was examined. The group ratio has a positive effect on the majority bias, and the relationship was partially mediated by ratio perception. Group scale did not moderate the relationship between group ratio and ratio perception. On the other hand, the correlation between group size and majority-biased social information use was not significant. Group scale moderates group size perception. The group size and size perception showed positive correlation under the smaller group scale condition. However under the extended group scale condition, the perceived group size became significantly lower and lost its correlation with group size. These results provide evidence that the psychological mechanism related to group size perception was not properly responding to the expansion of the group scale. Furthermore, the possibility of a specific psychological mechanism for processing group size information and the form of information input specifically accepted by majority bias were discussed from perspective of evolutionary psychology.

• Journal of Multimedia Information System
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• v.7 no.3
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• pp.215-220
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• 2020

Many state agencies and companies collect personal data for the purpose of providing public services and marketing activities and use it for the benefit and results of the organization. In order to prevent the spread of COVID-19 recently, personal data is being collected to understand the movements of individuals. However, due to the lack of technical and administrative measures and internal controls on collected personal information, errors and leakage of personal data have become a major social issue, and the government is aware of the importance of personal data and is promoting the protection of personal information. However, theory-based training and document-based intrusion prevention training are not effective in improving the capabilities of the privacy officer. This study analyzes the processing steps and types of accidents of personal data managed by the organization and describes measures against personal data leakage and misuse in advance. In particular, using Capture the Flag (CTF) scenarios, an evaluation platform design is proposed to respond to personal data breaches. This design was proposed as a troubleshooting method to apply ISMS-P and ISO29151 indicators to reflect the factors and solutions to personal data operational defects and to make objective measurements.

• Journal of Information Science Theory and Practice
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• v.10 no.spc
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• pp.143-153
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• 2022

With the development of networks and the increase in the number of network devices, the number of cyber attacks targeting them is also increasing. Since these cyber-attacks aim to steal important information and destroy systems, it is necessary to minimize social and economic damage through early detection and rapid response. Many studies using machine learning (ML) and artificial intelligence (AI) have been conducted, among which payload learning is one of the most intuitive and effective methods to detect malicious behavior. In this study, we propose a preprocessing method to maximize the performance of the model when learning the payload in term units. The proposed method constructs a high-quality learning data set by eliminating unnecessary noise (stopwords) and preserving important features in consideration of the machine language and natural language characteristics of the packet payload. Our method consists of three steps: Preserving significant special characters, Generating a stopword list, and Class label refinement. By processing packets of various and complex structures based on these three processes, it is possible to make high-quality training data that can be helpful to build high-performance ML/AI models for security monitoring. We prove the effectiveness of the proposed method by comparing the performance of the AI model to which the proposed method is applied and not. Forthermore, by evaluating the performance of the AI model applied proposed method in the real-world Security Operating Center (SOC) environment with live network traffic, we demonstrate the applicability of the our method to the real environment.

• Journal of Distribution Research
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• v.4 no.1
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• pp.123-140
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• 1999

For more than a decade, trust and commitment have received much attention in marketing channels literature. This study investigates the effects of these two core relational dimensions on the performance of EDI-supported marketing channels. A survey of the 92 suppliers of large department stores in Korea indicates that commitment to EDI increases significantly the organizational and economic performance of EDI-supported marketing channels. Trust was found to increase EDI performance indirectly by enhancing commitment to EDI. Based on the Social Information Processing(SIP) theory the present study also explored the effects of time on trust and commitment to EDI and their impacts on EDI performance. Analyses supported the hypothesized time effects that trust and commitment to EDI develop positively as EDI moves from initial stage to application stage. However, the impacts of these two dimensions on EDI performance did not change significantly over time.

• Management & Information Systems Review
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• v.38 no.3
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• pp.137-150
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• 2019

As job market squeezes and more institutions have been requiring NCS(National Competency Standards) based recruitment, the importance of NCS has been growing. Among the 10 domains of NCS, the most relevant one with task processing and organizational performance filed is 'task processing capability group', which is becoming more important with the advent of the fourth industrial revolution era. The purpose of this study is to investigate the effect of college students' task processing capability group on their career outcome expectation and career preparation behavior. In this study, we set up a process model to comprehend the effect of college students' task processing capability group on career outcome expectation and career preparation behavior based on social cognitive career theory. Empirical analysis showed that task processing capability group(problem-solving capability, information capability, resource management capability, organizational capability) positively influenced college students' career outcome expectation and career preparation behavior for employment. However, the impact of technical capability on career outcome expectation and career preparation behavior was not explained. In order to strengthen the task processing capability group of college students, not only university-level efforts, but also college and faculty's efforts should be accompanied. Other academic and practical implications are discussed.

• Journal of Information Science Theory and Practice
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• v.10 no.spc
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• pp.135-142
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• 2022

Recently, with the development of data processing technology and the increase of computational power, methods to solving social problems using Artificial Intelligence (AI) are in the spotlight, and AI technologies are replacing and supplementing existing traditional methods in various fields. Meanwhile in Korea, heavy rain is one of the representative factors of natural disasters that cause enormous economic damage and casualties every year. Accurate prediction of heavy rainfall over the Korean peninsula is very difficult due to its geographical features, located between the Eurasian continent and the Pacific Ocean at mid-latitude, and the influence of the summer monsoon. In order to deal with such problems, the Korea Meteorological Administration operates various state-of-the-art observation equipment and a newly developed global atmospheric model system. Nevertheless, for precipitation nowcasting, the use of a separate system based on the extrapolation method is required due to the intrinsic characteristics associated with the operation of numerical weather prediction models. The predictability of existing precipitation nowcasting is reliable in the early stage of forecasting but decreases sharply as forecast lead time increases. At this point, AI technologies to deal with spatio-temporal features of data are expected to greatly contribute to overcoming the limitations of existing precipitation nowcasting systems. Thus, in this project the dataset required to develop, train, and verify deep learning-based precipitation nowcasting models has been constructed in a regularized form. The dataset not only provides various variables obtained from multiple sources, but also coincides with each other in spatio-temporal specifications.