• Journal of the Korean Society for Library and Information Science
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• v.32 no.3
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• pp.231-250
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• 1998

The purpose of this study was to investigate factors affecting work performance among systems librarians in Korean academic libraries. The factors were divided in three categories: Job characteristics, organizational characteristics, and individual characteristics. Data from questionnaire were analyzed using the SPSS for windows. Multiple regression analyses were performed on three sets of variables related to the hypotheses of the study. The result of the analysis Identified the folowing factors : Job characteristics which affect work performance among systems librarians in academic library are autonomy, task significance, skill variety, and feedback from supervision. Organizational characteristics which affect work performance are job security, capability of automation systems, coworkers. Individual characteristics which affect work performance are growth need strength, knowledge, and systems librarian experience.

• Korea Journal of Hospital Management
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• v.21 no.4
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• pp.1-12
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• 2016

The purpose of this study are suggest to performance measurement model of Electronic Medical Record(EMR) and Key Performance Index(KPI). For data collection, 665 questionnaires were distributed to medical record administrators and insurance reviewers at 31 hospitals, and 580 questionnaires were collected(collection rate: 87.2%). Regarding methodology, Critical Success Factor(CSF) and index of the information system were derived based on previous studies, and these were set as performance measurement factors of EMR system. The performance measurement factors were constructed by perspective using BSC, and analysis on causal relationship between factors was conducted. A model of causal relationship was established, and performance measurement model of EMR system was proposed through model validation. Analysis on causal relationship between performance management factors revealed that utility cognition of the learning & growth perspective factor had causal relationship with job efficiency(${\beta}=0.20$) and decision support(${\beta}=0.66$) of the internal process perspective factors, and security had causal relationship with system satisfaction(${\beta}=0.31$) of the customer perspective factor. System quality had causal relationship with job efficiency(${\beta}=0.66$) and decision support(${\beta}=0.76$) of the internal process perspective factors, all of which were statistically significant(P<0.01). Job efficiency of the internal process perspective had causal relationship with system satisfaction(${\beta}=0.43$), and decision support had causal relationship with decision support satisfaction(${\beta}=0.91$) and job satisfaction (${\beta}=0.74$), all of which were statistically significant(P<0.01). System satisfaction of the customer perspective had causal relationship with job satisfaction(${\beta}=0.12$), job satisfaction had causal relationship with cost reduction(${\beta}=0.53$) of the financial perspective, and decision support satisfaction had causal relationship with productivity improvement(${\beta}=0.40$)of the financial perspective(P<0.01). Also, cost reduction of the financial perspective had causal relationship with productivity improvement(${\beta}=0.37$), all which were statistically significant(P<0.05). Suitability index verification of the performance measurement model whose causal relationship was found to be statistically significant revealed that $X^2/df=2.875$, RMR=0.036, GFI=0.831, AGFI=0.810, CFI=0.887, NFI=0.838, IFI=0.888, RMSEA=0.057, PNFI=0.781, and PCFI=0.827, all of which were in suitable levels. In conclusion, the performance measurement indices of EMR system include utility cognition, security, and system quality of the learning & growth perspective, decision support and job efficiency of the internal process perspective, system satisfaction, decision support satisfaction, and job satisfaction of the customer perspective, and productivity improvement and cost reduction of the financial perspective. In this study, it is expected that the performance measurement indices and model of EMR system which are suggested by the author, will be a measurement tool available for system performance measurement of EMR system in medical institutions.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.9
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• pp.1802-1807
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• 2015

The cloud computing service provides physical IT resources to VM instances to users using virtual technique and the users pay cost of VM instances to service provider. The auction model based on cloud computing provides available resources of service provider to users through auction mechanism. The users bid spot instances to process their a job until its deadline time. If the bidding price of users is higher than the spot price, the user will be provided the spot instances by service provider. In this paper, we propose a new bidding strategy to minimize the total cost for job completion. Typically, the users propose bidding price as high as possible to get the spot instances and the spot price get high. we lower the spot price using proposed strategy and minimize the total cost for job completion. To evaluate the performance of our strategy, we compare the spot price and the total cost for job completion with real workload data.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.137-142
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• 2021

Machine-learning systems have proven their worth in various industries, including healthcare and banking, by assisting in the extraction of valuable inferences. Information in these crucial sectors is traditionally stored in databases distributed across multiple environments, making accessing and extracting data from them a tough job. To this issue, we must add that these data sources contain sensitive information, implying that the data cannot be shared outside of the head. Using cryptographic techniques, Privacy-Preserving Machine Learning (PPML) helps solve this challenge, enabling information discovery while maintaining data privacy. In this paper, we talk about how to keep your data mining private. Because Data mining has a wide variety of uses, including business intelligence, medical diagnostic systems, image processing, web search, and scientific discoveries, and we discuss privacy-preserving in deep learning because deep learning (DL) exhibits exceptional exactitude in picture detection, Speech recognition, and natural language processing recognition as when compared to other fields of machine learning so that it detects the existence of any error that may occur to the data or access to systems and add data by unauthorized persons.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.2
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• pp.343-361
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• 2015

The financial industry in South Korea has witnessed a paradigm shift from selling traditional loan/deposit products to diversified consumption channels and financial products. Consequently, personification of financial services has accelerated and the value of finance-related personal information has risen rapidly. As seen in the 2014 card company information leakage incident, most of major finance-related information leakage incidents are caused by personnel with authorized access to certain data. Therefore, it is strongly required to confirm whether there are problems in the existing access control policy for personnel who can access a great deal of data, and to complement access control policy by considering risk factors of information security. In this paper, based on information of IT personnel with access to sensitive finance-related data such as job, position, sensitivity of accessible data and on a survey result, we will analyze influence factors for personnel risk measurement and apply data access control policy reflecting the analysis result to an actual case so as to introduce measures to minimize IT personnel risk in financial companies.

• Journal of Information Technology Services
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• v.13 no.3
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• pp.93-110
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• 2014

With the explosive proliferation of smart devices that may be connected to the fast Internet, virtual desktop interfaces(VDI) are being implemented in enterprises allowing employees to work anywhere anytime in the same technological environment. As this kind of systems are intended to raise the work performance, a research model is constructed from the review of research literature on service quality and work design. The model consists of VDI system service quality (ubiquity, availability, compatibility, security and ease of use), system satisfaction, task performance and job satisfaction. As VDI is designed as a support system for cooperative work, the task interdependence adopted from the work design theory is postulated here as moderating the effect of user satisfaction on task performance and job satisfaction. 147 data points were collected by a survey of VDI users in a global firm and subjected to PLS analysis. Interestingly, ubiquity and compatibility seem to be not statistically significant for user satisfaction while availability, security and ease of use of VDI system are statistically significant. Task interdependence is found to be a relatively strong mediator between system user satisfaction and task performance, however, interestingly, the coefficient come out as negative. This may indicate that the influence of VDI user satisfaction on task performance would not be high in highly interdependent tasks. Considering that VDI is intended for supporting 'interdependence' in cooperative work, this finding is a bit surprising. In-depth discussions are made in the discussion followed by future research directions.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.113-124
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• 2024

One of the goals of the Saudi Arabia 2030 vision is to ensure full employment of its citizens. Recruitment of graduates depends on the quality of skills that they may have gained during their study. Hence, the quality of education and ensuring that graduates have sufficient knowledge about the in-demand skills of the market are necessary. However, IT graduates are usually not aware of whether they are suitable for recruitment or not. This study builds a prediction model that can be deployed on the web, where users can input variables to generate predictions. Furthermore, it provides data-driven recommendations of the in-demand skills in the Saudi IT labor market to overcome the unemployment problem. Data were collected from two online job portals: LinkedIn and Bayt.com. Three machine learning algorithms, namely, Support Vector Machine, k-Nearest Neighbor, and Naïve Bayes were used to build the model. Furthermore, descriptive and data analysis methods were employed herein to evaluate the existing gap. Results showed that there existed a gap between labor market employers' expectations of Saudi workers and the skills that the workers were equipped with from their educational institutions. Planned collaboration between industry and education providers is required to narrow down this gap.

• Journal of Family Resource Management and Policy Review
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• v.11 no.2
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• pp.1-19
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• 2007

The purpose of this study was to analyze the affecting factors on employed mens' retirement and reemployment decision making. The focus was on the process of employed mens' decision on retirement and their reemployment decision after retirement from present job. The major findings were as follows ; First, the employed men who had a retirement plan were having more household income, more household net asset, more savings and investment for elderly life, and more positive attitude toward retirement. Second, the major factors affecting on having retirement plan or not were employed mens' age, household income, expected income after retirement, savings and investment for elderly life, job, and attitude toward retirement. Third, the major affecting factors on expected retirement age were employed mens' age, health status, job security, and attitude toward retirement. Forth, the employed mens' reemployment decision was affected from their household income, expected income after retirement, pension ownership, and attitude toward retirement. From the findings, it can be concluded that the employed mens' age, economic status, and attitude toward retirement played a important role in the process of retirement and reemployment decision making.

• Journal of Internet Computing and Services
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• v.2 no.4
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• pp.41-53
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• 2001

Multi-distributed web cluster model built for high availability E-Business model exposes internal system nodes on its structural characteristics and has a potential that normal job performance is impossible due to the intentional prevention and attack by an illegal third party. Therefore, the security system which protects the structured system nodes and can correspond to the outflow of information from illegal users and unfair service requirements effectively is needed. Therefore the suggested distributed invasion detection system is the technology which detects the illegal requirement or resource access of system node distributed on open network through organic control between SC-Agents based on the shared memory of SC-Server. Distributed invasion detection system performs the examination of job requirement packet using Detection Agent primarily for detecting illegal invasion, observes the job process through monitoring agent when job is progressed and then judges the invasion through close cooperative works with other system nodes when there is access or demand of resource not permitted.

• Convergence Security Journal
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• v.24 no.3
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• pp.117-129
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• 2024

With the announcement of Transformer, a new type of architecture, in 2017, there have been many changes in language models. In particular, the development of LLM (Large language model) has enabled generative AI services such as search and chatbot to be utilized in various business areas. However, security issues such as personal information leakage and reliability issues such as hallucination, which generates false information, have raised concerns about the effectiveness of these services. In this study, we aimed to analyze the factors that are increasing the frequency of using generative AI in the workplace despite these concerns. To this end, we derived eight factors that affect the quality of LLM-based generative AI response data and empirically analyzed the impact of these factors on job satisfaction using a valid sample of 195 respondents. The results showed that expertise, accessibility, diversity, and convenience had a significant impact on intention to continue using, security, stability, and reliability had a partially significant impact, and completeness had a negative impact. The purpose of this study is to academically investigate how customer perception of response data quality affects business utilization satisfaction and to provide meaningful practical implications for customer-centered services.