• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.12
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• pp.5594-5600
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• 2011

Even though many studies have showed that competence is positively related to organizational performance, few studies have attempted to find out the process of competency - performance. This study focuses on the organizational factors to explore their effect on the competence of customer relationship. Based on the data collected by KRIVET and the Ministry of employment and labor, strategic role of HR department and information systems are examined. As well human resource competency is investigated as a mediating variable. This study used surveys targeting department managers and executive members in firms and sample size was 1086 after cleaning the dataset by deleting all the cases with many missing values. The findings show that strategic role of HR department and information system has an influence on human resource competency, significantly. In addition, the human resource competency affect customer relationship competency, positively. Implications and directions for future research are discussed.

• Journal of Internet Computing and Services
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• v.10 no.6
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• pp.205-217
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• 2009

Recently, many organizations such as companies or institutions have demanded induction of very large-scale workflow management system in order to process a large number of business-instances. Workflow vendors have focused on physical extension of workflow engines based on device-level clustering, so as to provide very large-scale workflow services. Performance improvement of workflow engine by simple physical-connection among computer systems which don't consider logical-level software architecture leads to wastes of time or cost for construction of very large-scale workflow service environment. In this paper, we propose workcase-based workflow architecture and implement a very large-scale workflow management system based on the architecture. We prove that software architectures to be applied on a workflow engine have an effect on scalability and performance through workcase response-time evaluation of our proposed system.

• The Journal of Industrial Distribution & Business
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• v.5 no.4
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• pp.31-42
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• 2014

Purpose - According to the applied studies knowledge, management implementation can improve organizational performance. The main objective of this study is to develop an understanding of critical success factors that enhance the successful implementation of knowledge management. Research design, data, and methodology - This study used Analytical Hierarchy Procedure (AHP), which is a multi-criteria decision making model that works on fuzzy logic. Using this method, researchers can find the proportion of success due to the contribution of the critical success factors (CSFs). Results - The results show that more than 70% of respondents indicate the possibility of success in knowledge management implementation. Further, the results show that top management support has the greatest relationship with the success of knowledge management implementation. This was followed by information technology, performance measurement, and culture, which had a high relation with knowledge management success. Process and activities have a moderate positive relation, while education and training has a low relation with success. Because of an inappropriate p-value, knowledge management strategies show no relation to the success of knowledge management in the Iranian health Industry. Conclusions - This study was conducted because of a critical issue in the Iranian health industry that indicated that a significant portion of the workforce would retire in 5 to 10 years. Most highly experienced and knowledge oriented employees would become eligible for retirement. Therefore, knowledge management is presented as a complete solution in the Iranian health sector.

• Journal of Convergence for Information Technology
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• v.11 no.11
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• pp.10-16
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• 2021

As the implementation of smart factories spreads widely, the need for research to improve data efficiency is raised by prioritizing massive amounts of big data using IoT devices in terms of relevance and quality. The purpose of this study is to investigate whether prioritizing big data in management accounting decisions such as cost volatility estimation and recipe optimization can improve smart solution performance and decision-making effectiveness. Based on the survey answers of 84 decision makers at domestic small and medium-sized manufacturers who operate smart solutions such as ERP and MES that link manufacturing data in real time, empirical research was conducted. As a result, it was analyzed that setting prioritization of big data has a positive effect on decision-making in management accounting. became In addition, it was found that big data prioritization has a mediating effect that indirectly affects smart solution performance by using big data in management accounting decision making. Through the research results, it will be possible to contribute as a prior research to develop a scale to evaluate the correlation between big data in the process of business decision making.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.186-197
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• 2023

In this study, we focus on the improvement of data quality transmitted from a weather buoy that guides a route of ships. The buoy has an Internet-of-Thing (IoT) including sensors to collect meteorological data and the buoy's status, and it also has a wireless communication device to send them to the central database in a ground control center and ships nearby. The time interval of data collected by the sensor is irregular, and fault data is often detected. Therefore, this study provides a framework to improve data quality using machine learning models. The normal data pattern is trained by machine learning models, and the trained models detect the fault data from the collected data set of the sensor and adjust them. For determining fault data, interquartile range (IQR) removes the value outside the outlier, and an NGBoost algorithm removes the data above the upper bound and below the lower bound. The removed data is interpolated using NGBoost or long-short term memory (LSTM) algorithm. The performance of the suggested process is evaluated by actual weather buoy data from Korea to improve the quality of 'AIR_TEMPERATURE' data by using other data from the same buoy. The performance of our proposed framework has been validated through computational experiments based on real-world data, confirming its suitability for practical applications in real-world scenarios.

• Journal of Convergence for Information Technology
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• v.10 no.10
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• pp.1-8
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• 2020

The need for storage, management, and retrieval techniques for alternative data has emerged as technologies based on data generated from business activities conducted by enterprises have emerged as the key to business success in recent years. Existing big data platform systems must load a large amount of data generated in real time without delay to process unstructured data, which is an alternative data, and efficiently manage storage space by utilizing a deduplication system of different storages when redundant data occurs. In this paper, we propose a multi-layer distributed data deduplication process system using the similarity of the Cuckoo hashing filter technique considering the characteristics of big data. Similarity between virtual machines is applied as Cuckoo hash, individual storage nodes can improve performance with deduplication efficiency, and multi-layer Cuckoo filter is applied to reduce processing time. Experimental results show that the proposed method shortens the processing time by 8.9% and increases the deduplication rate by 10.3%.

• Journal of Service Research and Studies
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• v.7 no.4
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• pp.51-68
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• 2017

Financial technology (FinTech) is an emerging financial service sector include innovations in financial literacy and investment, retail banking, education, and crypto-currencies like bitcoin. One of the crucial branch of financial technology-third-party payment (TPP) is undergoing rapid growth, with online/mobile systems replacing offline financial systems. System quality and user attitudes are key perceptions driving third-party payment usage, the importance of these perceptions, however, may be different with countries as users' thinking varies from country to country. Thus, the purpose of this study is to elaborate how factors differ from China to Korea by drawing on the unified theory of acceptance and use of technology (UTAUT2). Additionally, this study also aims to propose a multi-attribute evaluation of the third-party online payment system based on analytic hierarchy process (AHP), fuzzy sets and technique for order performance by similarity to ideal solution (TOPSIS), to examine the relative importance of the perceptions influencing new technology adoption intention. The results showed that the price value has the most significant influence on Chinese perceptions, while the perceived credibility has the most significant effect on Korean perceptions. Sub-criteria also performs different results to Chinese and Korean third-party online payment system.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.165-184
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• 2023

Recently, Deep learning analysis of unstructured text data using language models, such as Google's BERT and OpenAI's GPT has shown remarkable results in various applications. Most language models are used to learn generalized linguistic information from pre-training data and then update their weights for downstream tasks through a fine-tuning process. However, some concerns have been raised that privacy may be violated in the process of using these language models, i.e., data privacy may be violated when data owner provides large amounts of data to the model owner to perform fine-tuning of the language model. Conversely, when the model owner discloses the entire model to the data owner, the structure and weights of the model are disclosed, which may violate the privacy of the model. The concept of offsite tuning has been recently proposed to perform fine-tuning of language models while protecting privacy in such situations. But the study has a limitation that it does not provide a concrete way to apply the proposed methodology to text classification models. In this study, we propose a concrete method to apply offsite tuning with an additional classifier to protect the privacy of the model and data when performing multi-classification fine-tuning on Korean documents. To evaluate the performance of the proposed methodology, we conducted experiments on about 200,000 Korean documents from five major fields, ICT, electrical, electronic, mechanical, and medical, provided by AIHub, and found that the proposed plug-in model outperforms the zero-shot model and the offsite model in terms of classification accuracy.

• Information Systems Review
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• v.16 no.2
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• pp.97-124
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• 2014

This study applies the concepts of sociotechnical theory and role theory in order to verify the effects of stress (that is, technostress) created from information and computer technology (ICT) concerning role stress and performance. We suggest diverse perspectives concerning the elements leading to technostress along with the stress-creating process of ICT among organizational members. The following four major hypotheses are proposed: (1) Technostress is positively related to role stress (e.g., conflict and overload of roles), adverse psychological outcomes, and adverse IS use related outcomes, (2) Role conflict is positively related to adverse psychological outcomes and adverse IS use related outcomes, (3) Role overload is positively related to adverse psychological outcomes and adverse IS use related outcomes, and (4) Innovation support will play as a moderator between technostress, roles tress, adverse psychological outcomes and adverse IS use related outcomes. A partial least square (PLS) with data gathered data from ICT users of organizations was used to verify the proposed hypotheses. The results supported our claims excluding the hypothesis associated to the relationship between technostress and role stress. We suggest a theoretical implication based on the verification of technostress related to the current concept of stress experienced by individuals within organizations. We also propose that a practical implication concerning the opposite effect of technostress, can be utilized as a method of reducing role stress, and suggests a diagnostic tool for assessing the degree of technostress within organizations.

• The Journal of Society for e-Business Studies
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• v.11 no.1
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• pp.1-23
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• 2006

As data on the Web is more and more in XML due to proliferation of Web applications such as e-Commerce, it is strongly required to rapidly process XML queries. One of such techniques is XML query caching. For frequently submitted queries, their results could be cached in order to guarantee fast response for the same queries. In this paper, we propose techniques for XML query performance improvement whereby the set of node identifiers(NIS) for an XML query is cached. NIS is most commonly employed as a format of XML query result,, consisting of the identifiers of the XML elements that comprise the query result. With NIS, it is suitable to meet the Web applications data retrieval requirements because reconstruction and/or modification of query results and integration of multiple query results can be efficiently done. Incremental refresh of NIS against its source updates can also be efficiently done. When the query result is requested in XML, however, materialization of NIS is needed by retrieving the source XML elements through their identifiers. In this paper, we consider three different types of NISs. proposing the algorithms of their creation, materialization, and incremental refresh. All of them were implemented using an RDBMS. Through a detailed set of performance experiments, we showed the efficiency of the proposed XML query caching techniques.