• Title/Summary/Keyword: Quality Management Capabilities

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GIS AND WEB-BASED DSS FOR PRELIMINARY TMDL DEVELOPMENT

  • Choi, Jin-Yong;Bernard A. Engel;Yoon, Kwang-Sik
    • Water Engineering Research
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    • v.4 no.1
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    • pp.19-30
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    • 2003
  • TMDL development and implementation have great potential fur use in efforts to improve water quality management, but the TMDL approach still has several difficulties to overcome in terms of cost, time requirements, and suitable methodologies. A well-defined prioritization approach for identifying watersheds of concern among several tar-get locations that would benefit from TMDL development and implementation, based on a simple screening approach, could be a major step in solving some of these difficulties. Therefore, a web-based decision support system (DSS) was developed to help identify areas within watersheds that might be priority areas for TMDL development. The DSS includes a graphical user interface based on the HTML protocol, hydrological models, databases, and geographic information system (GIS) capabilities. The DSS has a hydrological model that can estimate non-point source pollution loading based on over 30 years of daily direct runoff using the curve number method and pollutant event mean concentration data. The DSS provides comprehensive output analysis tools using charts and tables, and also provides probability analysis and best management practice cost estimation. In conclusion, the DSS is a simple, affordable tool for the preliminary study of TMDL development via the Internet, and the DSS web site can also be used as an information web server for education related to TMDL.

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A Study on the Semantic Network Analysis for Exploring the Generative AI ChatGPT Paradigm in Tourism Section (관광분야 생성형 AI ChatGPT 패러다임 탐색을 위한 의미연결망 연구)

  • Han Jangheon
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.19 no.4
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    • pp.87-96
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    • 2023
  • ChatGPT, a leader in generative AI, can use natural expressions like humans based on large-scale language models (LLM). The ability to grasp the context of the language and provide more specific answers by algorithms is excellent. It also has high-quality conversation capabilities that have significantly developed from past Chatbot services to the level of human conversation. In addition, it is expected to change the operation method of the tourism industry and improve the service by utilizing ChatGPT, a generative AI in the tourism sector. This study was conducted to explore ChatGPT trends and paradigms in tourism. The results of the study are as follows. First, keywords such as tourism, utilization, creation, technology, service, travel, holding, education, development, news, digital, future, and chatbot were widespread. Second, unlike other keywords, service, education, and Mokpo City data confirmed the results of a high degree of centrality. Third, due to CONCOR analysis, eight keyword clusters highly relevant to ChatGPT in the tourism sector emerged.

Antecedents of Manufacturer's Private Label Program Engagement : A Focus on Strategic Market Management Perspective (제조업체 Private Labels 도입의 선행요인 : 전략적 시장관리 관점을 중심으로)

  • Lim, Chae-Un;Yi, Ho-Taek
    • Journal of Distribution Research
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    • v.17 no.1
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    • pp.65-86
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    • 2012
  • The $20^{th}$ century was the era of manufacturer brands which built higher brand equity for consumers. Consumers moved from generic products of inconsistent quality produced by local factories in the $19^{th}$ century to branded products from global manufacturers and manufacturer brands reached consumers through distributors and retailers. Retailers were relatively small compared to their largest suppliers. However, sometime in the 1970s, things began to slowly change as retailers started to develop their own national chains and began international expansion, and consolidation of the retail industry from mom-and-pop stores to global players was well under way (Kumar and Steenkamp 2007, p.2) In South Korea, since the middle of the 1990s, the bulking up of retailers that started then has changed the balance of power between manufacturers and retailers. Retailer private labels, generally referred to as own labels, store brands, distributors own private-label, home brand or own label brand have also been performing strongly in every single local market (Bushman 1993; De Wulf et al. 2005). Private labels now account for one out of every five items sold every day in U.S. supermarkets, drug chains, and mass merchandisers (Kumar and Steenkamp 2007), and the market share in Western Europe is even larger (Euromonitor 2007). In the UK, grocery market share of private labels grew from 39% of sales in 2008 to 41% in 2010 (Marian 2010). Planet Retail (2007, p.1) recently concluded that "[PLs] are set for accelerated growth, with the majority of the world's leading grocers increasing their own label penetration." Private labels have gained wide attention both in the academic literature and popular business press and there is a glowing academic research to the perspective of manufacturers and retailers. Empirical research on private labels has mainly studies the factors explaining private labels market shares across product categories and/or retail chains (Dahr and Hoch 1997; Hoch and Banerji, 1993), factors influencing the private labels proneness of consumers (Baltas and Doyle 1998; Burton et al. 1998; Richardson et al. 1996) and factors how to react brand manufacturers towards PLs (Dunne and Narasimhan 1999; Hoch 1996; Quelch and Harding 1996; Verhoef et al. 2000). Nevertheless, empirical research on factors influencing the production in terms of a manufacturer-retailer is rather anecdotal than theory-based. The objective of this paper is to bridge the gap in these two types of research and explore the factors which influence on manufacturer's private label production based on two competing theories: S-C-P (Structure - Conduct - Performance) paradigm and resource-based theory. In order to do so, the authors used in-depth interview with marketing managers, reviewed retail press and research and presents the conceptual framework that integrates the major determinants of private labels production. From a manufacturer's perspective, supplying private labels often starts on a strategic basis. When a manufacturer engages in private labels, the manufacturer does not have to spend on advertising, retailer promotions or maintain a dedicated sales force. Moreover, if a manufacturer has weak marketing capabilities, the manufacturer can make use of retailer's marketing capability to produce private labels and lessen its marketing cost and increases its profit margin. Figure 1. is the theoretical framework based on a strategic market management perspective, integrated concept of both S-C-P paradigm and resource-based theory. The model includes one mediate variable, marketing capabilities, and the other moderate variable, competitive intensity. Manufacturer's national brand reputation, firm's marketing investment, and product portfolio, which are hypothesized to positively affected manufacturer's marketing capabilities. Then, marketing capabilities has negatively effected on private label production. Moderating effects of competitive intensity are hypothesized on the relationship between marketing capabilities and private label production. To verify the proposed research model and hypotheses, data were collected from 192 manufacturers (212 responses) who are producing private labels in South Korea. Cronbach's alpha test, explanatory / comfirmatory factor analysis, and correlation analysis were employed to validate hypotheses. The following results were drawing using structural equation modeling and all hypotheses are supported. Findings indicate that manufacturer's private label production is strongly related to its marketing capabilities. Consumer marketing capabilities, in turn, is directly connected with the 3 strategic factors (e.g., marketing investment, manufacturer's national brand reputation, and product portfolio). It is moderated by competitive intensity between marketing capabilities and private label production. In conclusion, this research may be the first study to investigate the reasons manufacturers engage in private labels based on two competing theoretic views, S-C-P paradigm and resource-based theory. The private label phenomenon has received growing attention by marketing scholars. In many industries, private labels represent formidable competition to manufacturer brands and manufacturers have a dilemma with selling to as well as competing with their retailers. The current study suggests key factors when manufacturers consider engaging in private label production.

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The Effect of the Components of Hotel Food & Beverage Service Quality on Customer Satisfaction and Brand Loyalty (호텔 식음료 서비스품질 요인이 고객만족과 브랜드 애호도에 미치는 영향)

  • Han, Jong-Hun;Seo, Jung-Woon
    • Culinary science and hospitality research
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    • v.22 no.5
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    • pp.277-294
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    • 2016
  • This study was to empirically determine the relationship between hotel food & beverage service quality, and customer satisfaction and brand loyalty. A total of 260 survey responses were collected from food & beverage' customers of seven five star hotels in Seoul. The results of hypothesis testing can be summarized as follows. The results showed that facility and reliability factors, and employee service and excellence factors had significant effects on customer satisfaction, and then reliability factors and excellence factor had significant effects on brand loyalty. Customer satisfaction also had a significant effect on brand loyalty. It can be concluded that the service quality components of hotel food & beverages are perceived responsiveness, service capabilities, food and beverages products, and physical environments. A strategy should be developed to create internal communication programs based on the empirical analysis results of this study.

A Study on AI-Based Real Estate Rate of Return Decision Models of 5 Sectors for 5 Global Cities: Seoul, New York, London, Paris and Tokyo (인공지능 (AI) 기반 섹터별 부동산 수익률 결정 모델 연구- 글로벌 5개 도시를 중심으로 (서울, 뉴욕, 런던, 파리, 도쿄) -)

  • Wonboo Lee;Jisoo Lee;Minsang Kim
    • Journal of Korean Society for Quality Management
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    • v.52 no.3
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    • pp.429-457
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    • 2024
  • Purpose: This study aims to provide useful information to real estate investors by developing a profit determination model using artificial intelligence. The model analyzes the real estate markets of six selected cities from multiple perspectives, incorporating characteristics of the real estate market, economic indicators, and policies to determine potential profits. Methods: Data on real estate markets, economic indicators, and policies for five cities were collected and cleaned. The data was then normalized and split into training and testing sets. An AI model was developed using machine learning algorithms and trained with this data. The model was applied to the six cities, and its accuracy was evaluated using metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared by comparing predicted profits to actual outcomes. Results: The profit determination model was successfully applied to the real estate markets of six cities, showing high accuracy and predictability in profit forecasts. The study provided valuable insights for real estate investors, demonstrating the model's utility for informed investment decisions. Conclusion: The study identified areas for future improvement, suggesting the integration of diverse data sources and advanced machine learning techniques to enhance predictive capabilities.

The Impact of SMEs' Smart Factory Systems Implementation on Management Accounting (중소제조기업 스마트공장시스템 도입이 관리회계에 미치는 영향)

  • Kim, Kyung-Ihl
    • Journal of Convergence for Information Technology
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    • v.10 no.9
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    • pp.8-14
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    • 2020
  • The objective of this research is to investigate how implementation of smart factory systems(SFS) effects management accounting(MA). The results based on data collected from 108 Korea small and medium enterprises(SME) confirmed that SFS implementation caused significant MA changes. Estimated regression models revealed that the most important SFS characteristic were the analytical capabilities since it positively influenced MA changes in four dimensions: internal reporting, budgeting, application of modern accounting techniques and MA employee's job. In the segment of budgeting, the quality of implementation of specialized bedgeting software had significant and positive influence. The only negative correlation founded was the one between the uncertainty of business environment and adoption of modern accounting techniques. Results from this study provide that SME should put special focus on implementation of business analytics modules in order to achieve comprehensive benefits in MA prctices.

Comparative Analysis of On-site Construction Management in Korea and Japan (건설현장 관리기술에 대한 한·일간 비교분석 예비연구)

  • Song, Sang-Hoon;Sohn, Jeon-Grak
    • Journal of the Korea Institute of Building Construction
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    • v.10 no.6
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    • pp.27-38
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    • 2010
  • Considering the similarity in legislation and long-term industrial trend of Korea and Japan, continuous attention to the Japanese situation is useful for the development of a domestic construction strategy. In order to compare the current on-site construction management practice of the two countries and analyze the reasons for discrepancy, a literature review, expert interview, and site investigation were executed. A detailed survey of experts with experience in both countries revealed that overall, Korea's level of competitiveness is gaining on that of Japan. However, the Japanese construction industry was still evaluated as maintaining higher competitiveness, due mainly to comprehensive planning for operation, well-established design documents, and effective total quality safety environment management, all of which are combined with institutional support and a cooperative attitude by labor. Adjustments should be made for advanced technologies considering the attributes of a typical Japanese construction prior to establishing and implementing application strategies according to the suggestion of experts. Based on the results of this study, a benchmarking of Japanese strength should be conducted, and this is eventually expected to contribute to establishing green construction, enhancing safety, improving culture, and reinforcing the capabilities of participants.

A Study on the Methods for the Robust Job Stress Management for Nuclear Power Plant Workers using Response Surface Data Mining (반응표면 데이터마이닝 기법을 이용한 원전 종사자의 강건 직무 스트레스 관리 방법에 관한 연구)

  • Lee, Yonghee;Jang, Tong Il;Lee, Yong Hee
    • Journal of the Korean Society of Safety
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    • v.28 no.1
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    • pp.158-163
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    • 2013
  • While job stress evaluations are reported in the recent surveys upon the nuclear power plants(NPPs), any significant advance in the types of questionnaires is not currently found. There are limitations to their usefulness as analytic tools for the management of safety resources in NPPs. Data mining(DM) has emerged as one of the key features for data computing and analysis to conduct a survey analysis. There are still limitations to its capability such as dimensionality associated with many survey questions and quality of information. Even though some survey methods may have significant advantages, often these methods do not provide enough evidence of causal relationships and the statistical inferences among a large number of input factors and responses. In order to address these limitations on the data computing and analysis capabilities, we propose an advanced procedure of survey analysis incorporating the DM method into a statistical analysis. The DM method can reduce dimensionality of risk factors, but DM method may not discuss the robustness of solutions, either by considering data preprocesses for outliers and missing values, or by considering uncontrollable noise factors. We propose three steps to address these limitations. The first step shows data mining with response surface method(RSM), to deal with specific situations by creating a new method called response surface data mining(RSDM). The second step follows the RSDM with detailed statistical relationships between the risk factors and the response of interest, and shows the demonstration the proposed RSDM can effectively find significant physical, psycho-social, and environmental risk factors by reducing the dimensionality with the process providing detailed statistical inferences. The final step suggest a robust stress management system which effectively manage job stress of the workers in NPPs as a part of a safety resource management using the surrogate variable concept.

Analysis of the Relationship between Teaching Presence, Academic Achievement and Learning Satisfaction in a University Online Tutoring Learning Environment (대학 온라인 튜터링 학습환경에서 교수실재감, 학업성취도 및 학습만족도 간의 관계 분석)

  • Byeon, So-Yeon;Chu, Sung-Kyung;Yoon, Hae-Gyung
    • The Journal of the Korea Contents Association
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    • v.21 no.11
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    • pp.814-825
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    • 2021
  • The purpose of this study is to analyze the effect of teaching presence on academic achievement and learning satisfaction in the university online tutoring learning environment, and to find teaching methods for students' adaption of college life and their reinforcement of learning capabilities. In the study, the relationship between teaching presence, academic achievement and learning satisfaction were analyzed for those who participants in the tutoring program of the Busan D School OO Research Center during the first semester of 2021. As a result of the study, the relationship between teaching presence, academic achievement and learning satisfaction was indicated high correlation in the order of learning management, participation management and content structure of learning activities; the effect of teaching presence on academic achievement and learning satisfaction was found a significant effect in learning management, which is a sub-area of the tutees' learning activities. These results therefore suggest the direction of the operation process and method reflecting teaching presence, and provide an in-depth discussion on the learning management method that can improve the quality of the learner's learning experience in the learning environment.

Synthetic Data Generation with Unity 3D and Unreal Engine for Construction Hazard Scenarios: A Comparative Analysis

  • Aqsa Sabir;Rahat Hussain;Akeem Pedro;Mehrtash Soltani;Dongmin Lee;Chansik Park;Jae- Ho Pyeon
    • International conference on construction engineering and project management
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    • 2024.07a
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    • pp.1286-1288
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    • 2024
  • The construction industry, known for its inherent risks and multiple hazards, necessitates effective solutions for hazard identification and mitigation [1]. To address this need, the implementation of machine learning models specializing in object detection has become increasingly important because this technological approach plays a crucial role in augmenting worker safety by proactively recognizing potential dangers on construction sites [2], [3]. However, the challenge in training these models lies in obtaining accurately labeled datasets, as conventional methods require labor-intensive labeling or costly measurements [4]. To circumvent these challenges, synthetic data generation (SDG) has emerged as a key method for creating realistic and diverse training scenarios [5], [6]. The paper reviews the evolution of synthetic data generation tools, highlighting the shift from earlier solutions like Synthpop and Data Synthesizer to advanced game engines[7]. Among the various gaming platforms, Unity 3D and Unreal Engine stand out due to their advanced capabilities in replicating realistic construction hazard environments [8], [9]. Comparing Unity 3D and Unreal Engine is crucial for evaluating their effectiveness in SDG, aiding developers in selecting the appropriate platform for their needs. For this purpose, this paper conducts a comparative analysis of both engines assessing their ability to create high-fidelity interactive environments. To thoroughly evaluate the suitability of these engines for generating synthetic data in construction site simulations, the focus relies on graphical realism, developer-friendliness, and user interaction capabilities. This evaluation considers these key aspects as they are essential for replicating realistic construction sites, ensuring both high visual fidelity and ease of use for developers. Firstly, graphical realism is crucial for training ML models to recognize the nuanced nature of construction environments. In this aspect, Unreal Engine stands out with its superior graphics quality compared to Unity 3D which typically considered to have less graphical prowess [10]. Secondly, developer-friendliness is vital for those generating synthetic data. Research indicates that Unity 3D is praised for its user-friendly interface and the use of C# scripting, which is widely used in educational settings, making it a popular choice for those new to game development or synthetic data generation. Whereas Unreal Engine, while offering powerful capabilities in terms of realistic graphics, is often viewed as more complex due to its use of C++ scripting and the blueprint system. While the blueprint system is a visual scripting tool that does not require traditional coding, it can be intricate and may present a steeper learning curve, especially for those without prior experience in game development [11]. Lastly, regarding user interaction capabilities, Unity 3D is known for its intuitive interface and versatility, particularly in VR/AR development for various skill levels. In contrast, Unreal Engine, with its advanced graphics and blueprint scripting, is better suited for creating high-end, immersive experiences [12]. Based on current insights, this comparative analysis underscores the user-friendly interface and adaptability of Unity 3D, featuring a built-in perception package that facilitates automatic labeling for SDG [13]. This functionality enhances accessibility and simplifies the SDG process for users. Conversely, Unreal Engine is distinguished by its advanced graphics and realistic rendering capabilities. It offers plugins like EasySynth (which does not provide automatic labeling) and NDDS for SDG [14], [15]. The development complexity associated with Unreal Engine presents challenges for novice users, whereas the more approachable platform of Unity 3D is advantageous for beginners. This research provides an in-depth review of the latest advancements in SDG, shedding light on potential future research and development directions. The study concludes that the integration of such game engines in ML model training markedly enhances hazard recognition and decision-making skills among construction professionals, thereby significantly advancing data acquisition for machine learning in construction safety monitoring.