• 문화기술의 융합
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• 제6권1호
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• pp.147-153
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• 2020

본 연구는 4차 산업혁명 시대의 도래에 따라 출현하고 있는 새로운 광고 형식인 온·오프라인 연계 광고의 가치와 유형을 제시하는데 목적이 있다. 본 연구에서 문헌연구를 기반으로 '4P(제품, 가격, 유통, 촉진)'에서 '4C(공동창조, 공동체, 대화, 통화)'로 진화하는 마케팅 방식을 이해하고 4C 요소를 도출하였다. 이를 기반으로 온·오프라인 연계광고 사례들의 조사를 통해 4C의 어떠한 요소가 온·오프라인 연결성을 나타내고 있는지 분석하였다. 분석 결과를 통해 온·오프라인 연계 광고는 최종적으로 4C의 요소가 연결된 방식에 따라 총 4가지의 대 유형과 14가지의 세부 유형으로 분류되었다. 본 논문에서는 최종 결과물로 4차 산업혁명 시대에 따라 출현한 마케팅의 4C 요소가 광고에서 표현되고 있음을 검증하였고 이에 따른 광고의 유형화를 제시하였다. 본 연구는 온·오프라인 연계 광고를 제작하고 연구하는 광고인과 연구자에게 새로운 통찰력을 공급하는데 공헌할 것으로 기대된다.

• Clinics in Shoulder and Elbow
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• 제26권1호
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• pp.55-63
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• 2023

Background: Common questions about shoulder arthroplasty (SA) searched online by patients and the quality of this content are unknown. The purpose of this study is to uncover questions SA patients search online and determine types and quality of webpages encountered. Methods: The "People also ask" section of Google Search was queried to return 900 questions and associated webpages for general, anatomic, and reverse SA. Questions and webpages were categorized using the Rothwell classification of questions and assessed for quality using the Journal of the American Medical Association (JAMA) benchmark criteria. Results: According to Rothwell classification, the composition of questions was fact (54.0%), value (24.7%), and policy (21.3%). The most common webpage categories were medical practice (24.6%), academic (23.2%), and medical information sites (14.4%). Journal articles represented 8.9% of results. The average JAMA score for all webpages was 1.69. Journals had the highest average JAMA score (3.91), while medical practice sites had the lowest (0.89). The most common question was, "How long does it take to recover from shoulder replacement?" Conclusions: The most common questions SA patients ask online involve specific postoperative activities and the timeline of recovery. Most information is from low-quality, non-peer-reviewed websites, highlighting the need for improvement in online resources. By understanding the questions patients are asking online, surgeons can tailor preoperative education to common patient concerns and improve postoperative outcomes. Level of evidence: IV.

• Journal of the Korean Data and Information Science Society
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• 제11권1호
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• pp.19-30
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• 2000

정보통신기술의 비약적인 발전은 온라인으로 생성되는 전자문서의 양을 폭발적으로 증가시키고 있다. 따라서 수동으로 문서를 분류하던 종래의 방법 대신 문서의 자동분유 기술 개발이 특별히 요구되고 있다. 본 논문에서는 베이지안 학습 기법을 이용하여 문서를 자동으로 분류하는 방법을 연구하고, 20개의 유즈넷 뉴스그룹 문서들을 분류하도록 시험하였다. 사용한 알고리즘은 Naive Bayes Classifier이며, 구현한 시스템을 이용해 유즈넷 문서를 대상으로 자동분류를 실험한 결과 분류의 정확률이 약 77%로 나타났다.

• 디지털콘텐츠학회 논문지
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• 제8권4호
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• pp.419-424
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• 2007

최근, 많은 게임시장의 활성화로 게임의 부가가치가 상승하기 시작했다. 특히 온라인 게임에 아이템 거래라는 새로운 트렌드가 창조됨으로 아이템거래의 유통시장이 형성될 정도로 용이한 재료가 되고 있다. 그러나 게임 개발 면에서 다른 기획요소에 비해 아이템이 차지하는 비중을 생각보다 많이 두고 있지 않는다. 그러므로 아이템을 기반으로 하는 분류를 통해 새로운 게임의 만족도를 높여 부가가치를 올리는 계기를 마련하고, 이와 더불어 새로운 분류법을 제시함으로서 게임 아이템의 중요성을 강조하고자 한다.

• 한국IT서비스학회지
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• 제14권4호
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• pp.159-169
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• 2015

Customer reviews are one of the important sources for purchase decision makings in online stores. Online stores have tried to provide useful reviews in product pages to customers. To assess the usefulness of customer reviews before other users have voted enough on the reviews, diverse aspects of reviews were utilized in prevous studies. Style and semantic information were utilized in many studies. This study aims to test diverse alogrithms and datasets for identifying a proper classification method and threshold to classify useful reviews. In particular, most researches utilized ratio type helpfulness index as Amazon.com used. However, there is another type of usefulness index utilized in TripAdviser.com or Yelp.com, count type helpfulness index. There was no proper threshold to classify useful reviews yet for count type helpfulness index. This study used reivews and their usefulness votes on restaurnats from Yelp.com to devise diverse datasets and applied text mining approaches to classify useful reviews. Random Forest, SVM, and GLMNET showed the greater values of accuracy than other approaches.

• Asia pacific journal of information systems
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• 제34권2호
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• pp.372-399
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• 2024

Online reviews play a significant role in consumer purchase decisions on e-commerce platforms. To address information overload in the context of online reviews, factors that drive review helpfulness have received considerable attention from scholars and practitioners. The purpose of this study is to explore the differential effects of discrete emotions (anger, disgust, fear, joy, sadness, and surprise) on perceived review helpfulness, drawing on cognitive appraisal theory of emotion and expectation-confirmation theory. Emotions embedded in 56,157 hotel reviews collected from TripAdvisor.com were extracted based on a transfer learning model to measure emotion variables as an alternative to dictionary-based methods adopted in previous research. We found that anger and fear have positive impacts on review helpfulness, while disgust and joy exert negative impacts. Moreover, hotel star-classification significantly moderates the relationships between several emotions (disgust, fear, and joy) and perceived review helpfulness. Our results extend the understanding of review assessment and have managerial implications for hotel managers and e-commerce vendors.

• 경영과정보연구
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• 제26권
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• pp.153-180
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• 2008

Most previous electronic commerce studies have focused on a single product or similar products. The effects of different product types have been relatively neglected. and so previous studies have limited the generalization. The purpose of this study was to explore the effects of different product types. The Internet product and service classification grid proposed by Peterson et al.(1997). A survey-based approach was employed to investigate the research questions. Regression analysis demonstrated that the determinants of online shopping acceptance differ among product or service types. As a result of analysis, personal innovativeness of information technology, perceived Web security, personal privacy concerns, and product involvement can influence consumer acceptance of online shopping, but their influence varies according to product or service types.

• Journal of Information Technology Applications and Management
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• 제9권4호
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• pp.127-149
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• 2002

Online consumers were classified four groups by online buying frequency and shopping mall loyalty in this study; high frequency-high loyalty, high frequency-low loyalty, low frequency-high loyalty, and low frequency-low loyalty groups. Four groups were compared by Internet usage, flow experience, innovativeness, perceived risks of Internet shopping, Internet shopping behaviors, and demographics. Through an online survey of 396 Internet shoppers, there found significant differences of those variables among four groups. The implications for customer relationship management of Internet shopping mall are discussed and further researches are suggested.

• 인터넷정보학회논문지
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• 제5권5호
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• pp.39-48
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• 2004

RTP (real-time transport protocol)는 인터넷상에서 실시간 멀티미디어 트래픽을 전송하기 위한 유력한 프로토콜로서 간주되고 있다. 망내에서 실시간 멀티미디어 트래픽을 제어하고 관리하기 위하여는 망 관리자가 망을 통하여 전달되는 실시간 멀티미디어 트래픽들을 감시하고 분석해내는 것이 필요하지만, 기존의 트래픽 분석 도구들은 RTP 패킷들을 비실시간 뿐만 아니라 실시간으로도 정확히 분류, 분석해 내지 못하고 있다. 본 논문에서는 인터넷에서 RTP를 사용하는 실시간 멀티미디어 트래픽을 실시간으로 분류해 내기 위한 방법을 제안한다. 한국전산원의 국제망 연동을 위한 게이트웨이 라우터에서 직접 수집한 데이터를 사용하여, 제안 방법의 정확성과 신속성을 보였다.

• 품질경영학회지
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• 제48권3호
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• pp.421-431
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• 2020

Purpose: The purpose of this study is to compare and analyze the tokenizer in natural language processing for customer satisfaction in sentiment analysis. Methods: In this study, a supervised learning-based tokenizer Mecab-Ko and an unsupervised learning-based tokenizer SentencePiece were used for comparison. Three algorithms: Naïve Bayes, k-Nearest Neighbor, and Decision Tree were selected to compare the performance of each tokenizer. For performance comparison, three metrics: accuracy, precision, and recall were used in the study. Results: The results of this study are as follows; Through performance evaluation and verification, it was confirmed that SentencePiece shows better classification performance than Mecab-Ko. In order to confirm the robustness of the derived results, independent t-tests were conducted on the evaluation results for the two types of the tokenizer. As a result of the study, it was confirmed that the classification performance of the SentencePiece tokenizer was high in the k-Nearest Neighbor and Decision Tree algorithms. In addition, the Decision Tree showed slightly higher accuracy among the three classification algorithms. Conclusion: The SentencePiece tokenizer can be used to classify and interpret customer sentiment based on online reviews in Korean more accurately. In addition, it seems that it is possible to give a specific meaning to a short word or a jargon, which is often used by users when evaluating products but is not defined in advance.