Search results for: Soojin Kim

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Kim</title> <meta name="description" content="Search results for: Soojin Kim"> <meta name="keywords" content="Soojin Kim"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" 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id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="Soojin Kim"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 4</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Soojin Kim</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4</span> Sales Patterns Clustering Analysis on Seasonal Product Sales Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Soojin%20Kim">Soojin Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Jiwon%20Yang"> Jiwon Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Sungzoon%20Cho"> Sungzoon Cho</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As a seasonal product is only in demand for a short time, inventory management is critical to profits. Both markdowns and stockouts decrease the return on perishable products; therefore, researchers have been interested in the distribution of seasonal products with the aim of maximizing profits. In this study, we propose a data-driven seasonal product sales pattern analysis method for individual retail outlets based on observed sales data clustering; the proposed method helps in determining distribution strategies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clustering" title="clustering">clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=distribution" title=" distribution"> distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=sales%20pattern" title=" sales pattern"> sales pattern</a>, <a href="https://publications.waset.org/abstracts/search?q=seasonal%20product" title=" seasonal product"> seasonal product</a> </p> <a href="https://publications.waset.org/abstracts/22411/sales-patterns-clustering-analysis-on-seasonal-product-sales-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22411.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">595</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3</span> The Role of Ethical Orientation in Two Countries: Different Outcomes in Perception of Corporate Authenticity and Pro-Firm Behavior Intention</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kyujin%20Shim">Kyujin Shim</a>, <a href="https://publications.waset.org/abstracts/search?q=Soojin%20Kim"> Soojin Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study identifies and examines the impact of factors on two types of CSR outcomes, consumers’ perceptions of corporate authenticity and their pro-firm behavior intentions. Specifically we investigated the roles of two factors - the consumers’ perceptions of CSR motives of a company (i.e. business-oriented vs. society-oriented) and their ethical orientations (i.e. deontology vs. consequentialism). A web-based survey was conducted in South Korea and the United States respectively to compare the differences of consumer reactions between the two countries. The results show that consumers in two countries behave differently to a firm’s CSR motives. In the United States, when consumers perceive a company’s CSR motive as society-oriented, they are more likely to perceive the company authentic and as a result more likely to engage in pro-firm behavior. However, when consumers’ ethical orientation is considered, only consumers’ consequential orientation led to their pro-firm behavioral intention. In South Korea, interpretation of two different CSR motives affects the valence in consumers’ perceptions of corporate authenticity (i.e. society-oriented CSR motive and positive perception of corporate authenticity vs. business-oriented CSR motive and negative perception of corporate authenticity). Korean consumers also showed same pattern in terms of relationship among society-oriented CSR motive, perception of corporate authenticity, and pro-firm behavior intention. Interestingly, Korean consumers’ consequential orientation affects both their perception of corporate authenticity and their pro-firm behavior intention positively. In addition, there was an interaction effect of business-oriented CSR motive and deontological orientation on perception of corporate authenticity. Theoretical and practical implications will be discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=corporate%20authenticity" title="corporate authenticity">corporate authenticity</a>, <a href="https://publications.waset.org/abstracts/search?q=corporate%20social%20responsibility" title=" corporate social responsibility"> corporate social responsibility</a>, <a href="https://publications.waset.org/abstracts/search?q=consequentialist%20ethics" title=" consequentialist ethics"> consequentialist ethics</a>, <a href="https://publications.waset.org/abstracts/search?q=CSR%20motives" title=" CSR motives"> CSR motives</a>, <a href="https://publications.waset.org/abstracts/search?q=deontological%20ethics" title=" deontological ethics"> deontological ethics</a> </p> <a href="https://publications.waset.org/abstracts/41864/the-role-of-ethical-orientation-in-two-countries-different-outcomes-in-perception-of-corporate-authenticity-and-pro-firm-behavior-intention" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41864.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">279</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2</span> Comparison of Donor Motivations in National Collegiate Athletic Association Division I vs Division II</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Soojin%20Kim">Soojin Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Yongjae%20Kim"> Yongjae Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Continuous economic downturn and ongoing budget cuts poses higher education with profound challenges which has a direct impact on the collegiate athletic programs. In response to the ever-changing landscape of the fiscal environment, universities seek to boost revenues, resorting to alternative sources of funding. In particular, athletic programs have become increasingly dependent on financial support from their alumni and boosters, which is how athletic departments attempt to offset budget shortfalls and make capital improvements. Although there currently exists three major divisions within National Collegiate Athletic Association (NCAA), the majority of the sport management studies on college sport tend to focus on Division I level. Particularly within the donor motivation literature, a plethora of donor motivation studies exist, but mainly on NCAA Division I athletic programs. Since each athletic department functions differently in a number of different dimensions, while institutional difference can also have a huge impact on athletic donor motivations, the current study attempts to fill this gap that exists in the literature. As such, the purpose of this study was to (I) reexamine the factor structure of the Athletic Donor motivation scale; and (II) identify the prominent athletic donor motives in a NCAA Division II athletic program. For the purpose of this study, a total of 232 actual donors were used for analysis. A confirmatory factor analysis (CFA) was employed to test construct validity, and the reliability of the scale was assessed using Composite Reliability. To identify the prominent motivational factors, the means and standard deviations were examined. Results of this study indicated that Vicarious Achievement, Philanthropy, and Commitment are the three primary motivational factors, while Tangible Benefits, was consistently found as an important motive in prior studies was found low. Such findings highlight the key difference and suggest different salient motivations exist that are specific to the context. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=college%20athletics" title="college athletics">college athletics</a>, <a href="https://publications.waset.org/abstracts/search?q=donor" title=" donor"> donor</a>, <a href="https://publications.waset.org/abstracts/search?q=motivation" title=" motivation"> motivation</a>, <a href="https://publications.waset.org/abstracts/search?q=NCAA" title=" NCAA"> NCAA</a> </p> <a href="https://publications.waset.org/abstracts/90330/comparison-of-donor-motivations-in-national-collegiate-athletic-association-division-i-vs-division-ii" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/90330.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">147</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1</span> Enhancing Large Language Models' Data Analysis Capability with Planning-and-Execution and Code Generation Agents: A Use Case for Southeast Asia Real Estate Market Analytics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kien%20Vu">Kien Vu</a>, <a href="https://publications.waset.org/abstracts/search?q=Jien%20Min%20Soh"> Jien Min Soh</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Jahangir%20Abubacker"> Mohamed Jahangir Abubacker</a>, <a href="https://publications.waset.org/abstracts/search?q=Piyawut%20Pattamanon"> Piyawut Pattamanon</a>, <a href="https://publications.waset.org/abstracts/search?q=Soojin%20Lee"> Soojin Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Suvro%20Banerjee"> Suvro Banerjee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recent advances in Generative Artificial Intelligence (GenAI), in particular Large Language Models (LLMs) have shown promise to disrupt multiple industries at scale. However, LLMs also present unique challenges, notably, these so-called "hallucination" which is the generation of outputs that are not grounded in the input data that hinders its adoption into production. Common practice to mitigate hallucination problem is utilizing Retrieval Agmented Generation (RAG) system to ground LLMs'response to ground truth. RAG converts the grounding documents into embeddings, retrieve the relevant parts with vector similarity between user's query and documents, then generates a response that is not only based on its pre-trained knowledge but also on the specific information from the retrieved documents. However, the RAG system is not suitable for tabular data and subsequent data analysis tasks due to multiple reasons such as information loss, data format, and retrieval mechanism. In this study, we have explored a novel methodology that combines planning-and-execution and code generation agents to enhance LLMs' data analysis capabilities. The approach enables LLMs to autonomously dissect a complex analytical task into simpler sub-tasks and requirements, then convert them into executable segments of code. In the final step, it generates the complete response from output of the executed code. When deployed beta version on DataSense, the property insight tool of PropertyGuru, the approach yielded promising results, as it was able to provide market insights and data visualization needs with high accuracy and extensive coverage by abstracting the complexities for real-estate agents and developers from non-programming background. In essence, the methodology not only refines the analytical process but also serves as a strategic tool for real estate professionals, aiding in market understanding and enhancement without the need for programming skills. The implication extends beyond immediate analytics, paving the way for a new era in the real estate industry characterized by efficiency and advanced data utilization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=large%20language%20model" title="large language model">large language model</a>, <a href="https://publications.waset.org/abstracts/search?q=reasoning" title=" reasoning"> reasoning</a>, <a href="https://publications.waset.org/abstracts/search?q=planning%20and%20execution" title=" planning and execution"> planning and execution</a>, <a href="https://publications.waset.org/abstracts/search?q=code%20generation" title=" code generation"> code generation</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=prompt%20engineering" title=" prompt engineering"> prompt engineering</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20analysis" title=" data analysis"> data analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=real%20estate" title=" real estate"> real estate</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20sense" title=" data sense"> data sense</a>, <a href="https://publications.waset.org/abstracts/search?q=PropertyGuru" title=" PropertyGuru"> PropertyGuru</a> </p> <a href="https://publications.waset.org/abstracts/176529/enhancing-large-language-models-data-analysis-capability-with-planning-and-execution-and-code-generation-agents-a-use-case-for-southeast-asia-real-estate-market-analytics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/176529.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge 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Search results for: Soojin Kim