• Journal of the Korean Society for Library and Information Science
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• v.46 no.2
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• pp.115-132
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• 2012

The purpose of this study is to examine the current status of user-contributed reviews for the Next Generation Library Catalogs, and the potential impact of user reviews available from the external sources, including Amazon.com and GoodReads.com. During the period of February 16th through April 4th, 2012, the number of holding libraries and user-contributed reviews, tags and reading lists of ten selected books were examined from the WorldCat. Also the user-contributed reviews for the same books available from Amazon.com and GoodReads.com were examined, and a case of reviews for one book was analyzed. The result shows that only a few users participated in the WorldCat, and user-contributed reviews were rarely used, when compared with tags or reading lists. Several hundred to thousand user-contributed reviews for the same books were available from Amazon.com and GoodReads.com directly linked with bibliographic records. A case of one book from Amazon.com reveals the possibility of distorting the function of user-contribution. With the introduction of the function of user-contribution, it is expected that its growth rate should be carefully observed and its potential impact on users should be thoroughly and systematically analyzed in the near future.

• Asia pacific journal of information systems
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• v.17 no.2
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• pp.83-95
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• 2007

We are motivated by how offline and online firms compete. The Internet made many conventional offline firms build a dynamic online business as another sales channel using their advantages such as brand equity, an existing customer base with comprehensive purchasing data, integrated marketing, economies of scale, and longtime experience with the logistics of order fulfillment and customer service. Even though the hybrid selling using both offline and online channel seems to have advantages over a pure online retailer, all the conventional offline firms are not seen to create an online business. Many conventional offline firms began to launch online business since the Internet era, however, just being online business is not likely to guarantee success. According to Bizate.com's report whether the hybrid channel strategy is successful is still under investigation. For example, consider the classic case of Barnes and Noble versus Amazon.com, Barnes and Noble was already the largest chain of bookstores in the U,S., when Amazon.com was established in 1995, BarnesandNoble.com followed suit in 1997, After suffering losses in its initial years, Amazon finally turned profitable in 2003. In 2004, Amazon's net income was $588 million on revenues of $6.92 billion, while Barnes and Noble earned $143 million on revenues of $4.87 billion, which included BarnesandNoble.com's loss of $21 million on revenues of $420 million. While these examples serve to motivate our thinking, it does not explain when offline firms should venture online. It also does not provide an analytical framework that can generalized to other competitive online-offline situations. We attempt to do this in this paper and analyze a hybrid channel model where a conventional offline firm competes against online firms using its own direct online channels. We are particularly interested in an optimal channel strategy when a conventional offline firm sells its products through its own direct online channel to compete with other rival online firms. We consider two situations where its direct online channel and other online firms are symmetric and asymmetric in the brand effect. The analysis of this paper presents several findings. In the symmetric model where a hybrid firm's online channel is not differentiated from a pure online firm, (i) a conventional offline firm will not launch its online business. In the asymmetric model where a hybrid firm's online channel is differentiated from a pure online firm, (ii) a conventional offline firm can launch its online business if its brand effect is greater than a certain threshold. (iii) there is a positive relationship between its brand effect and online customer costs showing that a conventional offline firm needs more brand effect in order to launch online business as online customer costs decrease. (iv) there is a negative relationship between its brand effect and the number of customers with access to the Internet showing that a conventional offline firm tends to launch its online business when customers with access to the Internet increases.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.43-61
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• 2016

Customer reviews help potential customers make purchasing decisions. However, the prevalence of reviews on websites push the customer to sift through them and change the focus from a mere search to identifying which of the available reviews are valuable and useful for the purchasing decision at hand. To identify useful reviews, websites have developed different mechanisms to give customers options when evaluating existing reviews. Websites allow users to rate the usefulness of a customer review as helpful or not. Amazon.com uses a ratio-type helpfulness, while Yelp.com uses a count-type usefulness index. This usefulness index provides helpful reviews to future potential purchasers. This study investigated the effects of sentiment and readability on useful votes for customer reviews. Similar studies on the relationship between sentiment and readability have focused on the ratio-type usefulness index utilized by websites such as Amazon.com. In this study, Yelp.com's count-type usefulness index for restaurant reviews was used to investigate the relationship between sentiment/readability and usefulness votes. Yelp.com's online customer reviews for stores in the beverage and food categories were used for the analysis. In total, 170,294 reviews containing information on a store's reputation and popularity were used. The control variables were the review length, store reputation, and popularity; the independent variables were the sentiment and readability, while the dependent variable was the number of helpful votes. The review rating is the moderating variable for the review sentiment and readability. The length is the number of characters in a review. The popularity is the number of reviews for a store, and the reputation is the general average rating of all reviews for a store. The readability of a review was calculated with the Coleman-Liau index. The sentiment is a positivity score for the review as calculated by SentiWordNet. The review rating is a preference score selected from 1 to 5 (stars) by the review author. The dependent variable (i.e., usefulness votes) used in this study is a count variable. Therefore, the Poisson regression model, which is commonly used to account for the discrete and nonnegative nature of count data, was applied in the analyses. The increase in helpful votes was assumed to follow a Poisson distribution. Because the Poisson model assumes an equal mean and variance and the data were over-dispersed, a negative binomial distribution model that allows for over-dispersion of the count variable was used for the estimation. Zero-inflated negative binomial regression was used to model count variables with excessive zeros and over-dispersed count outcome variables. With this model, the excess zeros were assumed to be generated through a separate process from the count values and therefore should be modeled as independently as possible. The results showed that positive sentiment had a negative effect on gaining useful votes for positive reviews but no significant effect on negative reviews. Poor readability had a negative effect on gaining useful votes and was not moderated by the review star ratings. These findings yield considerable managerial implications. The results are helpful for online websites when analyzing their review guidelines and identifying useful reviews for their business. Based on this study, positive reviews are not necessarily helpful; therefore, restaurants should consider which type of positive review is helpful for their business. Second, this study is beneficial for businesses and website designers in creating review mechanisms to know which type of reviews to highlight on their websites and which type of reviews can be beneficial to the business. Moreover, this study highlights the review systems employed by websites to allow their customers to post rating reviews.

• Asia pacific journal of information systems
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• v.13 no.4
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• pp.29-47
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• 2003

This research explores early mover advantages and performance in the cyber market based on an empirical test. It also examines whether early mover strategic capabilities are able to adopt mutually cumulative relationship in the cyber market. Early movers such as eBay.com and Amazon.com seem to have been able to defy exclusive relationship between strategic capabilities. Compared with their followers such as uBid.com and buy.com, they have been able to adopt strong focus, differentiation, and cost leadership strategies. The purpose of this paper is to investigate the differences in strategic choices based on the strategic capabilities and performance of online firms between early movers and followers. The study reviews early mover advantages and disadvantages, and a strategic typology based on Porter's model, as well as strategic capabilities based on the sand cone model.

• 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.

• 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.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.10a
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• pp.472-484
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• 2004

인터넷 쇼핑이 성장하면서 비교쇼핑몰의 중요성도 그만큼 증가하고 있다. 그러나 현재의 비교쇼핑몰들은 웹으로부터 제품가격정보와 같은 단순한 XML 데이터만을 수집하여 비교할 뿐, 배송비와 같이 규칙을 기반으로 한 정확한 비교는 제공하지 못하고 있다. 각 쇼핑몰은 저마다 배송비에 관한 정책을 다양하게 제시하고 있으나 비교쇼핑몰에서는 이를 반영할 구조를 가지고 있지 못하기 때문이다. 따라서, 본 연구에서는 규칙 기반의 추론을 이용해 상품가격에 배송비를 포함하여 비교를 수행할 수 있는 비교쇼핑몰을 구현하고자 한다. 이를 위해 eXtensible Rule Markup Language (XRML)를 이용하여 각 쇼핑몰의 웹페이지의 문장과 표로부터 규칙을 습득하는 방안을 제시하였다. 이 구조를 이용하면 웹페이지에서 완전에 가까운 규칙을 자동생성할 수 있을 뿐만 아니라, 각 사이트에서 변화가 발생하면 이를 반영하여 규칙을 일관성있게 수정하도록 지원할 수 있다. 본 연구에서는 인터넷 상의 대표적 서점인 Amazon.com, BarnesandNoble.com, Powells.com에 대해 XRML을 기반으로 설계한 비교쇼핑몰의 프로토타입 ConsiderD를 개발하였다. 이 과정에서 웹으로 부터 규칙을 자동생성 할 수 있는 잠재력을 검정하고, 배송비 효과의 중요성을 실험을 통해 예시하였다.

• Knowledge Management Research
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• v.20 no.2
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• pp.25-42
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• 2019

AI speakers which are wireless speakers with smart features have released from many manufacturers and adopted by many customers. Though smart features including voice recognition, controlling connected devices and providing information are embedded in many mobile phones, AI speakers are sitting in home and has a role of the central en-tertainment and information provider. Many surveys have investigated the important factors to adopt AI speakers and influ-encing factors on satisfaction. Though most surveys on AI speakers are cross sectional, we can track customer attitude toward AI speakers longitudinally by analyzing customer reviews on AI speakers. However, there is not much research on the change of customer attitude toward AI speaker. Therefore, in this study, we try to grasp how the attitude of AI speaker changes with time by applying text mining-based analysis. We collected the customer reviews on Amazon Echo which has the highest share of AI speakers in the global market from Amazon.com. Since Amazon Echo already have two generations, we can analyze the characteristics of reviews and compare the attitude ac-cording to the adoption time. We identified all sub topics of customer reviews and specified the topics for smart features. And we analyzed how the share of topics varied with time and analyzed diverse meta data for comparisons. The proportions of the topics for general satisfaction and satisfaction on music were increasing while the proportions of the topics for music quality, speakers and wireless speakers were decreasing over time. Though the proportions of topics for smart fea-tures were similar according to time, the share of the topics in positive reviews and importance metrics were reduced in the 2nd generation of Amazon Echo. Even though smart features were mentioned similarly in the reviews, the influential effect on satisfac-tion were reduced over time and especially in the 2nd generation of Amazon Echo.

• Journal of Intelligence and Information Systems
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• v.11 no.1
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• pp.123-151
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• 2005

In the world of Web pages, there are oceans of documents in natural language texts and tables. To extract rules from Web pages and maintain consistency between them, we have developed the framework of XRML(extensible Rule Markup Language). XRML allows the identification of rules on Web pages and generates the identified rules automatically. For this purpose, we have designed the Rule Identification Markup Language (RIML) that is similar to the formal Rule Structure Markup Language (RSML), both as pares of XRML. RIML is designed to identify rules not only from texts, but also from tables on Web pages, and to transform to the formal rules in RSは syntax automatically. While designing RIML, we considered the features of sharing variables and values, omitted terms, and synonyms. Using these features, rules can be identified or changed once, automatically generating their corresponding RSML rules. We have conducted an experiment to evaluate the effect of the RIML approach with real world Web pages of Amazon.com, BamesandNoble.com, and Powells.com We found that $97.7\%$ of the rules can be detected on the Web pages, and the completeness of generated rule components is $88.5\%$. This is good proof that XRML can facilitate the extraction and maintenance of rules from Web pages while building expert systems in the Semantic Web environment.

• The Journal of Society for e-Business Studies
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• v.26 no.4
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• pp.95-118
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• 2021

As the AI speaker business has risen significantly in recent years, the potential for numerous uses of AI speakers has gotten a lot of attention. Consumers have created an environment in which they can express and share their experiences with products through various channels, resulting in a large number of reviews that leave consumers with a variety of candid opinions about their experiences, which can be said to be very useful in analyzing consumers' thoughts. Using this review data, this study aimed to examine the factors driving the continued use of AI speakers. Above all, it was determined whether the seven characteristics associated with the intention to adopt AI identified in prior studies appear in consumer reviews. Based on customer review data on Amazon.com, text mining and social network analysis were utilized to examine Amazon eco-products. CONCOR analysis was used to classify words with similar connectivity locations, and Connection centrality analysis was used to classify the factors influencing the continuous use of AI speakers, focusing on the connectivity between words derived by classifying review data into positive and negative reviews. Consumers regarded personality and closeness as the most essential characteristics impacting the continued usage of AI speakers as a result of the favorable review survey. These two parameters had a strong correlation with other variables, and connectedness, in addition to the components established from prior studies, was a significant factor. Furthermore, additional negative review research revealed that recognition failures and compatibility are important problems that deter consumers from utilizing AI speakers. This study will give specific solutions for consumers to continue to utilize Amazon eco products based on the findings of the research.