• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.8
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• pp.3389-3404
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• 2012

Based on the case study methods, the study analyzes emergence and ensuing typology of global ebook platforms such as Google eBook, Amazon Kindle, iBooks Store. Global ebook platforms show adaptation process responding to rapidly changing digital technological envirment and it's fitness landscape. The critical elements in its emerging process are the distinct selection criteria, the degree of resource abundance and the search process based on open innovation. Based on these critical elements, the global platforms show speciation process, so called niche creation and are evolving into a variety of the typology based on the initial condition of key resource which makes the platform emerge and grow. Each global ebook platforms is evolving into open platform, hybrid platform, closed platform. Google eBook has openness and extensibility due to a variety of devices based on Android and a direct involvement of actors. Amazon Kindle has developed from a online bookstore and into the hybrid platform which have not only closed quality but also openness with ebook devices and mobile network. iBooks Store has developed into the closed platform through the agency model based on competitive hardwares and closed quality with iphone and ipad.

• Journal of the Korea Society of Computer and Information
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• v.16 no.4
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• pp.205-214
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• 2011

With the success of Kindle, an electronic book reader developed by Amazon.com, there has been a growing interest in both electronic books and readers in Korea. In this paper, we analyze electronic book user needs through fuzzy analytic hierarchy process (AHP) and conjoint analysis. First, we select the important factors which can affect the intention to purchase electronic book readers by applying the fuzzy AHP with the help of electronic book experts. Next, we perform conjoint analysis to reveal the detailed needs of electronic book users for each of the selected factors. Some useful implications and research limitations are also presented with future research directions.

• Information Systems Review
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• v.23 no.1
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• pp.155-172
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• 2021

With the recent increase in online product purchases, a recommender system that recommends products considering users' preferences has still been studied. The recommender system provides personalized product recommendation services to users. Collaborative Filtering (CF) using user ratings on products is one of the most widely used recommendation algorithms. During CF, the item-based method identifies the user's product by using ratings left on the product purchased by the user and obtains the similarity between the purchased product and the unpurchased product. CF takes a lot of time to calculate the similarity between products. In particular, it takes more time when using text-based big data such as review data of Amazon store. This paper suggests a hybrid recommendation system using a 2-phase methodology and text data mining to calculate the similarity between products easily and quickly. To this end, we collected about 980,000 online consumer ratings and review data from the online commerce store, Amazon Kinder Store. As a result of several experiments, it was confirmed that the suggested hybrid recommendation system reflecting the user's rating and review data has resulted in similar recommendation time, but higher accuracy compared to the CF-based benchmark recommender systems. Therefore, the suggested system is expected to increase the user's satisfaction and increase its sales.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.255-277
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• 2011

Product Service System(PSS), which is an integrated combination of product and service, provides new value to customer and makes companies sustainable as well. The objective of this paper draws Critical Successful Factors(CSF) of PSS through multiple case study. First, we review various concepts and types in PSS and Platform business literature currently available on this topic. Second, after investigating various cases with the characteristics of PSS and platform business, we select four cases of 'iPod of Apple', 'Kindle of Amazon', 'Zune of Microsoft', and 'e-book reader of Sony'. Then, the four cases are categorized as successful and failed cases according to criteria of case selection and PSS classification. We consider two methodologies for the case selection, i.e., 'Strategies for the Selection of Samples and Cases' proposed by Bent(2006) and the seven case selection procedures proposed by Jason and John(2008). For case selection, 'Stratified sample and Paradigmatic cases' is adopted as one of several options for sampling. Then, we use the seven case selection procedures such as 'typical', 'diverse', 'extreme', 'deviant', 'influential', 'most-similar', and 'mostdifferent' and among them only three procedures of 'diverse', 'most?similar', and 'most-different' are applied for the case selection. For PSS classification, the eight PSS types, suggested by Tukker(2004), of 'product related', 'advice and consulancy', 'product lease', 'product renting/sharing', 'product pooling', 'activity management', 'pay per service unit', 'functional result' are utilized. We categorize the four selected cases as a product oriented group because the cases not only sell a product, but also offer service needed during the use phase of the product. Then, we analyze the four cases by using cross-case pattern that Eisenhardt(1991) suggested. Eisenhardt(1991) argued that three processes are required for avoiding reaching premature or even false conclusion. The fist step includes selecting categories of dimensions and finding within-group similarities coupled with intergroup difference. In the second process, pairs of cases are selected and listed. The second step forces researchers to find the subtle similarities and differences between cases. The third process is to divide the data by data source. The result of cross-case pattern indicates that the similarities of iPod and Kindle as successful cases are convenient user interface, successful plarform strategy, and rich contents. The differences between the successful cases are that, wheares iPod has been recognized as the culture code, Kindle has implemented a low price as its main strategy. Meanwhile, the similarities of Zune and PRS series as failed cases are lack of sufficient applications and contents. The differences between the failed cases are that, wheares Zune adopted an undifferentiated strategy, PRS series conducted high-price strategy. From the analysis of the cases, we generate three hypotheses. The first hypothesis assumes that a successful PSS system requires convenient user interface. The second hypothesis assumes that a successful PSS system requires a reciprocal(win/win) business model. The third hypothesis assumes that a successful PSS system requires sufficient quantities of applications and contents. To verify the hypotheses, we uses the cross-matching (or pattern matching) methodology. The methodology matches three key words (user interface, reciprocal business model, contents) of the hypotheses to the previous papers related to PSS, digital contents, and Information System (IS). Finally, this paper suggests the three implications from analyzed results. A successful PSS system needs to provide differentiated value for customers such as convenient user interface, e.g., the simple design of iTunes (iPod) and the provision of connection to Kindle Store without any charge. A successful PSS system also requires a mutually benefitable business model as Apple and Amazon implement a policy that provides a reasonable proft sharing for third party. A successful PSS system requires sufficient quantities of applications and contents.

• Journal of Information Technology Applications and Management
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• v.22 no.2
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• pp.219-230
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• 2015

Recently, electronic-book (e-book) market is growing rapidly due to the evolution of information technology and e-book standard EPUB. Users can search e-book on-line and download easily to their e-book readers such as amazon's kindle or smartphones. On the other hand, there is lack of e-book contents for the reading disabled people because of high cost of making e-book accessibility for the reading disabled people. If we can translate EPUB specified e-book contents to the alternative e-book contents suitable for the reading disabled people, then there are many advantages to acquire various types and large volumes of e-book contents for the reading disabled people. This paper suggests a new electronic publishing ecosystem for the reading disabled people using e-book translation method. It also suggests a promotion strategy of e-book market for the reading disabled people.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.6-9
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• 2010

2007년 Amazon.com이 미국에서 e-Book 전용 단말기 'Kindle'을 출시한 이래, Sony와 대형 서점 Barnes&Noble등 메이저 업체는 물론 다수의 중소업체들이 e-Book 시장에 진출하고 있다. 최근에는 Apple이 iPad를 출시하고 e-Book 시장에 진출한 가운데, Google 역시 6월 이후 e-Book 시장에 진출할 것을 발표함으로써 e-Book 시장의 경쟁이 더욱 치열해지고 있다. e-Book의 급속한 보급증가와 함께 이런 방대한 도서를 관리하는 곳에서 자동 도서 분류의 필요성도 증가하고 있다. 기존의 문서분류 방법들은 대게 수작업, 텍스트(단어)의 집합으로 간주하여 기계 학습방법을 그대로 적용하거나 약간의 변형을 가한 방법들이 대부분 이었다. 본 제안서에서는 데이터 마이닝 분야에서 사용되는 DRFP-Tree 구조를 이용하여 e-Book 내의 문장들의 패턴을 저장하고 이를 사용하여 e-Book을 분류하는 방법을 제안한다.

• Journal of the Korean Society for information Management
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• v.28 no.3
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• pp.313-333
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• 2011

Many e-book readers have become available as e-books are in popular use these days. We first developed the evaluation criteria and elements for testing the usability of e-book readers and then examined the usability of three e-book readers. The usability testing was conducted using three e-book readers that were most popular with 15 participants that consisted of middle school students, university students, and office workers. The study found that iPad showed the best usability. In case of Amazon Kindle, participants indicated that the lack of the touch function imposed a great deal of negative impact on the usability of the interface. Also, the hardware- and software-oriented usability provided by an e-book reader was considered to be a very important element for the users, and the portability in connection with the hardware-oriented aspect of an e-book reader was shown to be of great effect to the overall usability of an e-book reader.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.105-122
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• 2019

Dimensionality reduction is one of the methods to handle big data in text mining. For dimensionality reduction, we should consider the density of data, which has a significant influence on the performance of sentence classification. It requires lots of computations for data of higher dimensions. Eventually, it can cause lots of computational cost and overfitting in the model. Thus, the dimension reduction process is necessary to improve the performance of the model. Diverse methods have been proposed from only lessening the noise of data like misspelling or informal text to including semantic and syntactic information. On top of it, the expression and selection of the text features have impacts on the performance of the classifier for sentence classification, which is one of the fields of Natural Language Processing. The common goal of dimension reduction is to find latent space that is representative of raw data from observation space. Existing methods utilize various algorithms for dimensionality reduction, such as feature extraction and feature selection. In addition to these algorithms, word embeddings, learning low-dimensional vector space representations of words, that can capture semantic and syntactic information from data are also utilized. For improving performance, recent studies have suggested methods that the word dictionary is modified according to the positive and negative score of pre-defined words. The basic idea of this study is that similar words have similar vector representations. Once the feature selection algorithm selects the words that are not important, we thought the words that are similar to the selected words also have no impacts on sentence classification. This study proposes two ways to achieve more accurate classification that conduct selective word elimination under specific regulations and construct word embedding based on Word2Vec embedding. To select words having low importance from the text, we use information gain algorithm to measure the importance and cosine similarity to search for similar words. First, we eliminate words that have comparatively low information gain values from the raw text and form word embedding. Second, we select words additionally that are similar to the words that have a low level of information gain values and make word embedding. In the end, these filtered text and word embedding apply to the deep learning models; Convolutional Neural Network and Attention-Based Bidirectional LSTM. This study uses customer reviews on Kindle in Amazon.com, IMDB, and Yelp as datasets, and classify each data using the deep learning models. The reviews got more than five helpful votes, and the ratio of helpful votes was over 70% classified as helpful reviews. Also, Yelp only shows the number of helpful votes. We extracted 100,000 reviews which got more than five helpful votes using a random sampling method among 750,000 reviews. The minimal preprocessing was executed to each dataset, such as removing numbers and special characters from text data. To evaluate the proposed methods, we compared the performances of Word2Vec and GloVe word embeddings, which used all the words. We showed that one of the proposed methods is better than the embeddings with all the words. By removing unimportant words, we can get better performance. However, if we removed too many words, it showed that the performance was lowered. For future research, it is required to consider diverse ways of preprocessing and the in-depth analysis for the co-occurrence of words to measure similarity values among words. Also, we only applied the proposed method with Word2Vec. Other embedding methods such as GloVe, fastText, ELMo can be applied with the proposed methods, and it is possible to identify the possible combinations between word embedding methods and elimination methods.

• STI Policy Review
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• v.3 no.2
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• pp.111-129
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• 2012

The integration of products and services is being expanded in both manufacturing and service companies such as in Apple's iPod & iTunes, Amazon's Kindle, and Hyundai Motor Company's Mozen. This phenomenon has recently accelerated due to multiple factors including market change, lessening of differences in quality of products or services, the paradigm of participation and sharing, and deindustrialization and evolution toward becoming a service economy. The objective of this paper is to investigate and analyze the status and characteristics of integration of products and services in Korean firms and to suggest policy directions promoting this integration. Towards this purpose, income statements from the Korea Listed Companies Association (KLCA) database of companies listed on the Korea Stock Exchange are analyzed regarding the servitization of manufacturing firms as well as the productization of service firms. In addition, this research investigates the Korean Innovation Survey 2011 database for the service sector and 2010 database for the manufacturing sector in order to evaluate R&D activity in each. In the manufacturing sector, the average ratio of service sales (servitization) was low at 0.208, with bias in the level and distribution of ratios associated with the manufacturing sector. 18 out of a total of 23 sectors (78%) have low servitization, showing there's a long way to go for servitization in the Korean manufacturing sector. In the service sector, the average ratio of product sales (productization) was 9.53%, which is relatively high compared to that of the manufacturing sector. However, the distribution of ratios is also biased, as with the manufacturing sector. Based on this analysis, policy directions are proposed in terms of 1) R&D, 2) concept boost, 3) R&D result spread, 4) statistics, 5) infrastructure and 6) green growth.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.6
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• pp.235-245
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• 2011

EBooks refer to electronic versions of books which are accessible via internet with forms of digital texts. In recent years, Amazon's Kindle digital eBooks has revealed the possibilities of success of the e-book market, which leads other companies to launch eBook service such as Google's eBook stores and Apple's iPad and eBook service. These reveal that the eBook market is finally showing a substantial amount of growth. Although the issue of technical support of eBook copyright protection emerges from the fast growing eBook marketplace, current technic of commercial DRM for protecting eBook copyright protection still has problems of non-comparability. Therefore, with the current technical status, DRM comparability problems, which have already occurred in music DRM environment, would also happen in eBook environment. This study suggests a standard framework to support eBook DRM comparability. When development of the standard reference software for eBook DRM comparability is completed, the sources will be registered as shareware to be open to public.