• Journal of Engineering Education Research
• /
• v.21 no.6
• /
• pp.35-43
• /
• 2018

In many universities, the Capstone Design course aims to educate creativity, teamwork and leadership, and ultimately aims to cultivate practitioners with practical ability required in the industry. Since the introduction of capstone design as a regular course, it has spread not only to engineering but also to the humanities and social sciences. A typical capstone design is usually carried out within a limited range of schedules and budgets within the scope of a major and a subject. In the case of a special-purpose capstone design, it is necessary to find out excellent items aiming at start-up and commercialization at an early stage, It contributes to the achievement of international convention participation, start-up and commercialization. The teaching styles of capstone design such as multidisciplinary capstone design, fusion capstone design, and global capstone design are developed and operated in various ways. Depending on each type, objectives, curriculum, scope of participation, operation method, performance and so on. In the case of capstone design, it is contributing to increase the achievements such as participation in international conventions, establishment of business and commercialization by early detection of excellent items aiming at start-up and commercialization, development and establishment of support process. Technological and Humanities Convergence Capstone Design Moel is named as the process of designing a four-level idea called "Idea Factory-based Technology-Humannities Fusion Capstone Design Process", and it is used to generate ideas, elaborate ideas, advanced ideas, and commercialization.

• International Journal of Computer Science & Network Security
• /
• v.22 no.2
• /
• pp.406-412
• /
• 2022

Storing large amounts of data has always been a big problem from the beginning of computing history. Big Data has made huge advancements in improving business processes by finding the customers' needs using prediction models based on web and social media search. The main purpose of big data stream processing frameworks is to allow programmers to directly query the continuous stream without dealing with the lower-level mechanisms. In other words, programmers write the code to process streams using these runtime libraries (also called Stream Processing Engines). This is achieved by taking large volumes of data and analyzing them using Big Data frameworks. Streaming platforms are an emerging technology that deals with continuous streams of data. There are several streaming platforms of Big Data freely available on the Internet. However, selecting the most appropriate one is not easy for programmers. In this paper, we present a detailed description of two of the state-of-the-art and most popular streaming frameworks: Apache Ignite and Hazelcast. In addition, the performance of these frameworks is compared using selected attributes. Different types of databases are used in common to store the data. To process the data in real-time continuously, data streaming technologies are developed. With the development of today's large-scale distributed applications handling tons of data, these databases are not viable. Consequently, Big Data is introduced to store, process, and analyze data at a fast speed and also to deal with big users and data growth day by day.

• Journal of Korean Society of Archives and Records Management
• /
• v.21 no.4
• /
• pp.81-99
• /
• 2021

Job and organization analysis of producing institutes is a task that identifies the history of transition and major business functions for various record-producing institutions and must be performed in common within the archives, and many workers must jointly refer to the relevant knowledge. However, in the field, a limited number of people in charge are individually performing by manual work, and the results are not shared. Therefore, this study aims to reduce the work burden of workers through the automation of the job and organization analysis process and build basic resources that can be commonly used by the archives. This study subdivided the task of job and organization analysis into manual, semi-automation, and automation parts by performing FGI with the practitioner of the archive and suggested ways to realize it. In addition, we derive the basic analysis data that can be commonly referenced in the electronic records management process, and by verifying the results through practitioners, efficient use of knowledge resources is suggested. Furthermore, by establishing a standardized work process, we intend to lay the foundation to support consistent and systematic work performance.

• Korean Journal of Construction Engineering and Management
• /
• v.6 no.4 s.26
• /
• pp.101-112
• /
• 2005

Recently, a curtain wall construction is a exterior finishing components which is most used for shortening time in high-rise building as well as the class of key management factors in cost and schedule control. Also, it is recognized that an effective management for curtain wall process is a major subject to accomplish the project successfully. However, as the current management for curtain wall construction is focused on the construction stage, it makes problems such as errors in business performance, rework by mistakes and duplications, errors and omissions by ineffective information management and there has never been any efficient management from a view of the entire Curtain Wall Life-cycle process. Therefore, the aim of this study is to suggest a stage check point for process improvement in the curtain wall Life-cycle process through current curtain wall process analysis, and then to investigate the cause of waste factors using the Muda method from the Toyota Production System and extract the weighted effects of the waste factors using the analytical hierarchy process method. According to the result, Most of the inefficient factors happened in architectural design stage of the entire curtain wall Life-cycle process and my research identified that detail factors of them are a delay of decision making and an approval in changes, a deficit of engineering capacity and a delay of approval in architectural design drawings by owner, etc.

• The Journal of Society for e-Business Studies
• /
• v.27 no.2
• /
• pp.37-64
• /
• 2022

As the social acceptance of artificial intelligence increases, the number of cases of applying machine learning methods to companies is also increasing. Technical factors such as accuracy and interpretability have been the main criteria for selecting machine learning methods. However, the success of implementing machine learning also affects management factors such as IT departments, operation departments, leadership, and organizational culture. Unfortunately, there are few integrated studies that understand the success factors of machine learning selection in which technical and management factors are considered together. Therefore, the purpose of this paper is to propose and empirically analyze a technology-management integrated model that combines task-tech fit, IS Success Model theory, and John Rice's algorithm selection process model to understand machine learning selection within the company. As a result of a survey of 240 companies that implemented machine learning, it was found that the higher the algorithm quality and data quality, the higher the algorithm-problem fit was perceived. It was also verified that algorithm-problem fit had a significant impact on the organization's innovation and productivity. In addition, it was confirmed that outsourcing and management support had a positive impact on the quality of the machine learning system and organizational cultural factors such as data-driven management and motivation. Data-driven management and motivation were highly perceived in companies' performance.

• Asia Marketing Journal
• /
• v.8 no.1
• /
• pp.49-76
• /
• 2006

This study investigates the effect of market orientation of knowledge-based service suppliers on the sourcing process of service recipients. Focusing on a dyadic relationship between a supplier and a buyer, this study proposed a conceptual model of market orientation incorporating the antecedents and consequences of market orientation. This study empirically tested research hypotheses delineated from the conceptual framework. The present study revealed that the impact on the buyer's performance of the supplier's customer and competitor orientation turned out to be more influential than that of inter-departmental cooperation. Also these two dimensions of customer and competitor orientation played a positive role in reducing buyer's perceived risk and uncertainty related to the evaluation of services out-sourced. Interestingly enough, the supplier's perceived importance on the distance between the buyer and supplier remains important especially when the degree of buyer's market orientation is high. This finding is somewhat contrary to the fact that the geographic location of the buyer becomes less important for the internet-based B2B service providers. Based on the findings, this study suggested managerial implications and broadened the scope of academic research in the field of business services. Future research directions and the limitations of this study are also discussed.

• Information Systems Review
• /
• v.21 no.1
• /
• pp.1-22
• /
• 2019

Mindfulness is the process of actively noticing new things. Today, companies have introduced and run mindfulness programs because the mindfulness has possible applications of productivity and innovation in corporation. However, role of mindfulness has not been clearly investigated in behavior research of Information System. The purpose of this study is to confirm the effects of mindfulness on technology acceptance process. Based on UTAUT Model, we examined how mindfulness in technology acceptance process moderate antecedent factors of acceptance intentions and use behavior. For empirical research, we conducted a survey on acceptance of smart watch of internet of things for employees of companies applying the mindfulness programs. then, we analyzed survey sample in empirical methodologies. Based on the empirical analysis, cognizance of alternative technologies in mindfulness factors increased the impact of performance expectancy on acceptance intention. Novelty seeking in mindfulness factors increased the impact of effort expectancy on acceptance intention. Awareness of local context in mindfulness factors decreased the impact of social influence on acceptance intention. engagement with technology in mindfulness factors increased the impact of facilitating conditions on use behavior. This study suggests academic implications and practical implications based on the results of the research. The implications will help to support and extend the theory of technology acceptance model while providing practical insights for IT acceptance by suggesting ways to utilize mindfulness in corporation.

• The Journal of Small Business Innovation
• /
• v.19 no.2
• /
• pp.53-68
• /
• 2016

Motivation and activities for technological learning, entrepreneurship, innovation, and creativity are driving forces of economic development in Asian countries. In the early stages of technological development, technological learning and entrepreneurship are efficient ways in which to catch up with advanced countries because firms can accumulate skills and knowledge quickly at relatively low risk. In the later stages of technological development, however, innovation and creativity become more important. This study aims to identify a) the factors (learning capabilities) that influence technological learning performance and b) barriers to enhancing innovation capabilities for the creative economy and organizations. The major part of this study is related to learning capabilities in the post-catch-up era. Based on a literature review and observations from Korean experiences, this study proposes a technological learning model composed of various influencing factors on technological learning. Three hypotheses are derived, and data are collected from Korean machine tool manufacturers. Intense interviews with CEOs and R&D directors are conducted using structured questionnaires. Statistical analysis, such as correlation and ANOVA are then carried out. Furthermore, this study addresses how to enhance innovation capabilities to move forward. Innovation enablers and barriers are identified by case studies and policy analysis. The results of the empirical study identify several levels of firms' learning capabilities and activities such as a) stock of technology, b) potential of technical labor, c) explicit technological efforts, d) readiness to learn, e) top management support, f) a formal technological learning system, g) high learning motivation, h) appropriate technology choice, and i) specific goal setting. These learning capabilities determine firms' learning performance, especially in the early stages of development. Furthermore, it is found that the critical factors for successful technological learning vary along the stages of technology development. Throughout the statistical and policy analyses, this study confirms that technological learning can be understood as an intrinsic principle of the technology development process. Firms perform proactive and creative learning in the late stages, while reactive and imitative learning prevails in the early stages. In addition, this study identifies the driving forces or facilitating factors enhancing innovation performance in the post catch-up era. The results of the preliminary case studies and policy analysis show some facilitating factors such as a) the strategic intent of the CEO and corporate culture, b) leadership and change agents, c) design principles and routines, d) ecosystem and collaboration with partners, and e) intensive R&D investment.

• Journal of Practical Engineering Education
• /
• v.14 no.1
• /
• pp.37-48
• /
• 2022

This study analyzed the effect of internal audit on the performance of the IATF 16949 automotive quality management system to understand the internal audit of companies and propose measures to activate the company's internal audit process. It was identified with the empirical analysis that 'The internal auditor competence, internal audit planning, internal audit implementation, infrastructure, culture/environment, and CEO support' to characterize IATF 16949 internal audit of automotive quality management system affects the internal performance and business performance of the company. In addition, I checked the size of the company and the period of certification period as moderating variables according to the sales based on the presented as factors that can improve the performance of the company, and how the moderating effects are seen in the relationship with the performance of the organization. I did analysis of technical statistics, exploratory factors, reliability, and multi-regression analysis with SPSS program. I summarized the results of the study, as a result of that, it was found that the internal audit planning, internal audit implementation, culture/ environment, and CEO support of independent variables affected the parameter and dependent variables (the internal performance and management performance of companies).

• Journal of Convergence for Information Technology
• /
• v.8 no.1
• /
• pp.201-206
• /
• 2018

Recently, as technologies for realizing artificial intelligence have become more common, machine learning is widely used. Machine learning provides insight into collecting large amounts of data, batch processing, and taking final action, but the effects of the work are not immediately integrated into the learning process. In this paper proposed an adaptive learning model to improve the performance of real-time stream analysis as a big business issue. Adaptive learning generates the ensemble by adapting to the complexity of the data set, and the algorithm uses the data needed to determine the optimal data point to sample. In an experiment for six standard data sets, the adaptive learning model outperformed the simple machine learning model for classification at the learning time and accuracy. In particular, the support vector machine showed excellent performance at the end of all ensembles. Adaptive learning is expected to be applicable to a wide range of problems that need to be adaptively updated in the inference of changes in various parameters over time.