• The Journal of the Convergence on Culture Technology
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• v.6 no.1
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• pp.147-153
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• 2020

The purpose of this study is to suggest the value and type of online and offline linked advertisement, which is a new advertisement type that emerged with the advent of the 4th industrial revolution. In this study, based on literature research, we understood the marketing method that evolves from "4P (product, price, place, promotion)" to "4C (co-creation, community, conversation, currency)" and extracted derive 4C elements. Based on this, we analyzed what elements of 4C showed online and offline connectivity through the investigation of on and offline linked advertisements. Through the analysis results, online and offline linked advertisements were classified into 4 types and 14 detailed types according to how they were connected to the number of 4C elements. In this paper, as a final result, we verified that 4C elements of marketing that appeared in the era of the 4th Industrial Revolution are represented in advertisements and suggested the typology of advertisements accordingly. This study is expected to contribute to providing new insights to advertisers and researchers who produce and study online and offline advertisements.

• Clinics in Shoulder and Elbow
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• v.26 no.1
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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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• v.11 no.1
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• pp.19-30
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• 2000

As the number of online documents increases enormously with the expansion of information technology, the importance of automatic document classification is greatly enlarged. In this paper, an automatic document classification method is investigated and applied to UseNet 20 newsgroup articles to test its efficacy. The classification system uses Naive Bayes classification algorithm and the experimental result shows that a randomly selected newsgroup arcicle can be classified into its own category over 77% accuracy.

• Journal of Digital Contents Society
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• v.8 no.4
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• pp.419-424
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• 2007

Recently, additional value of games has been started to increase by revitalization of the game market. Especially, Because of the creation of the new trend that is item trade especially in online game, Item trade is as a easy and essential way as item trade can be created. However, compared to other planning factors, occupied weight of item is not as much as expected in exploiting a game. For this reason, we emphasize the importance of game item by giving the opportunity that increases the additional value with raising satisfaction of game through item-based study, plus suggesting new classification.

• Journal of Information Technology Services
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• v.14 no.4
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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.

• Management & Information Systems Review
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• v.26
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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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• v.9 no.4
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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.

• Journal of Internet Computing and Services
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• v.5 no.5
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• pp.39-48
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• 2004

For transporting real-time multimedia traffic, RTP is considered as one of the most promising protocols operating at application layer. In order to manage and control the real-time multimedia traffic within networks, network managers need to monitor and analyze the traffic delivering through their networks. However, conventional traffic analyzing tools can not exactly classify and analyze the real-time multimedia traffic using RTP on the basis of real-time as well as non-real-time operations. In this paper, we propose an efficient online classification method of RTP packets, which can be used on high-speed network links. The accuracy and efficiency of the proposed methodhave been tested using captured data from a KIX node with 100 Mbps links, which interconnects between domestic and overseas Internet networks and is operated by NCA.

• Journal of Korean Society for Quality Management
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• v.48 no.3
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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.

• Journal of the Korean Society of Clothing and Textiles
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• v.36 no.3
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• pp.282-296
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• 2012

This study identified the types of motives for reading consumer reviews of apparel products for online stores and classified shoppers into the groups based on motives. Data were collected from eleven Korean women by a focus group interview and from 313 females by an online survey. Respondents were in their 20s' and 30s' with significant experience reading consumer reviews of apparel products for online stores. The seven motives found by interviews were reduced to four types of motives by factor analysis: Right product choice and judgment of product value, risk reduction, saving time and money, and fun/killing time. The motive for the right product choice and judgment of product value was the highest and the motive for fun/killing time was the lowest. Consumers were classified into four groups based on motives: Utilitarian shoppers (25.8%), shopping-task oriented shoppers (36.8%), multiple-motive shoppers (19.7%), and moderate-motive shoppers (17.7%). There were significant differences among age groups and the amount of reading reviews posted on a product and the duration of reading reviews for online stores. In addition, managerial implications were developed.