• Asia pacific journal of information systems
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• v.31 no.4
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• pp.491-526
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• 2021

Airlines can employ a variety of quality monitoring procedures. In this study, we employ a content analysis of 8 years of online reviews for Korean airlines in contrast to other international airlines. Online airline reviews are infrequent, relative to the total number of passengers - the number of reviews is multiple orders of magnitude lower than passenger volumes - and online airline reviews are, therefore, not representative of passenger attitudes overall. Nevertheless, online reviews may be indicative of specific service issues, and draw attention to aspects that require further study by airline operators. Furthermore, significant words and phrases used in these airline reviews may help airline operators to rapidly automate filtering, partitioning, and analysis of incoming passenger comments via other channels, including email, social media posts, and call center transcripts. The current study provides insights into the contents of online reviews of Korean vs Other-International airlines, and opportunities for service enhancement. Further, we provide a set of marker words and phrases that may be helpful for management dashboards that require automated partitioning of passenger comments.

• Journal of Distribution Science
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• v.22 no.3
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• pp.113-124
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• 2024

Purpose: This study is a quantitative study and analyzes the effect of evaluating the extreme and usefulness of product reviews on sales performance by using text mining techniques based on product review big data. We investigate whether the perceived helpfulness of product reviews serves as a mediating factor in the impact of product review extremity on sales performance. Research design, data and methodology: The analysis emphasizes customer interaction factors associated with both product review helpfulness and sales performance. Out of the 8.26 million Amazon product reviews in the book category collected by He & McAuley (2016), text mining using natural language processing methodology was performed on 300,000 product reviews, and the hypothesis was verified through hierarchical regression analysis. Results: The extremity of product reviews exhibited a negative impact on the evaluation of helpfulness. And the helpfulness played a mediating role between the extremity of product reviews and sales performance. Conclusion: Increased inclusion of extreme content in the product review's text correlates with a diminished evaluation of helpfulness. The evaluation of helpfulness exerts a negative mediating effect on sales performance. This study offers empirical insights for digital market distributors and sellers, contributing to the research field related to product reviews based on review ratings.

• The Journal of Information Systems
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• v.33 no.1
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• pp.115-131
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• 2024

Purpose This study aims to analyse the impact of the development of fintech and the emergence of internet primary banks due to the increasing use of smartphones on the performance of traditional local banks from both financial and non-financial perspectives. Return on equity (ROE) and return on assets (ROA) are used to assess the performance differences between the two types of banks and how these differences are affected by their financial characteristics. Design/methodology/approach Using return on equity (ROE) and return on assets (ROA) as indicators, we identified the differences in operating performance between the two types of banks. In addition, this study analysed the impact of financial characteristics on profitability through regression analysis with various control variables. We further studied the impact of non-financial characteristics (customer reviews, social media reactions, etc.) on operating performance. Findings The net interest margin ratio of local banks had a positive impact, while the marketable securities ratio of Internet primary banks had a negative impact. The non-financial analysis shows that the number of customer reviews and social media reactions have a significant impact on the performance of Internet primary banks, suggesting that customer satisfaction and positive market perception are important factors in the performance of Internet primary banks.

• Management & Information Systems Review
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• v.4
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• pp.285-308
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• 2000

Marketing is a topic of growing interest to nonprofit organizational managers(for example, hospitals, museums, charities and, churches, universities and colleges) as their organizations confront new, complex marketplace problems. These organization heads(specially in university and college) are laking their first, tentative steps toward marketing, often confusing it with its advertising and selling subfunctions. A genuine marketing response has been undertaken by a relatively small number of college in america. Their approach is best described as market-oriented institutional planning. In these approach, marketing is recognized as much more then mere promotion, and indeed, the issue of promotion cannot be settled in principles until more fundamental issues are resolved. According to market-oriented institutional planning customer satisfaction is one of the most important concept in university(college) marketing. Therefore, this study reviews literatures about university(college) marketing and general customer satisfaction. The literature study suggests some research hypotheses about customer satisfaction in college. Next, these hypotheses are tested empirically using ${\chi}^2-test$, multiple linear regression analysis and t-test. The research results worth explorative study on the customer satisfaction in the college.

• Information Systems Review
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• v.24 no.3
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• pp.1-21
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• 2022

This research investigates how a service provider's response(s) to negative customer reviews influences the success of accommodation services in the context of online accommodation reservation platforms. Specifically, we attempt to comprehend the important role of attentive and instant responses to users' negative review comments in fostering future success by analyzing panel data on 856 motels registered in the largest accommodation reservation platform in Korea. The results present that response volume (Attentiveness) and faster responses (Timeliness) are positively associated with success. We further find that the two review-response strategies have a positive interaction effect on success. Moreover, we show that the effect of review responses is strengthened when the reputation of motels drops. The key findings of this research offer a set of practical guidelines for accommodation owners to achieve business success by effectively managing customer reviews and claims

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.41-63
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• 2023

As the online e-commerce market growing, the need for a recommender system that can provide suitable products or services to customer is emerging. Recently, many studies using the sentiment score of online review have been proposed to improve the limitations of study on recommender systems that utilize only quantitative information. However, this methodology has limitation in extracting specific preference information related to customer within online reviews, making it difficult to improve recommendation performance. To address the limitation of previous studies, this study proposes a novel recommendation methodology that applies deep learning technique and uses various linguistic factors within online reviews to elaborately learn customer preferences. First, the interaction was learned nonlinearly using deep learning technique for the purpose to extract complex interactions between customer and product. And to effectively utilize online review, cognitive contents, affective contents, and linguistic style matching that have an important influence on customer's purchasing decisions among linguistic factors were used. To verify the proposed methodology, an experiment was conducted using online review data in Amazon.com, and the experimental results confirmed the superiority of the proposed model. This study contributed to the theoretical and methodological aspects of recommender system study by proposing a methodology that effectively utilizes characteristics of customer's preferences in online reviews.

• Information Systems Review
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• v.17 no.1
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• pp.49-64
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• 2015

Customer reviews which include subjective opinions for the product or service in online store have been generated rapidly and their influence on customers has become immense due to the widespread usage of SNS. In addition, a number of studies have focused on opinion mining to analyze the positive and negative opinions and get a better solution for customer support and sales. It is very important to select the key terms which reflected the customers' sentiment on the reviews for opinion mining. We proposed a document-level terms-based sentiment classification model by select in the optimal terms with part of speech tag. SVMs (Support vector machines) are utilized to build a predictor for opinion mining and we used the combination of POS tag and four terms extraction methods for the feature selection of SVM. To validate the proposed opinion mining model, we applied it to the customer reviews on Amazon. We eliminated the unmeaning terms known as the stopwords and extracted the useful terms by using part of speech tagging approach after crawling 80,000 reviews. The extracted terms gained from document frequency, TF-IDF, information gain, chi-squared statistic were ranked and 20 ranked terms were used to the feature of SVM model. Our experimental results show that the performance of SVM model with four POS tags is superior to the benchmarked model, which are built by extracting only adjective terms. In addition, the SVM model based on Chi-squared statistic for opinion mining shows the most superior performance among SVM models with 4 different kinds of terms extraction method. Our proposed opinion mining model is expected to improve customer service and gain competitive advantage in online store.

• Fashion & Textile Research Journal
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• v.15 no.1
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• pp.95-102
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• 2013

In order to understand meaningful customer experience in social media, this study profiles customer engagement by exposing the essential brand experience rooms in hyper-reality contexts. This study selects Snuggie as a target brand as it uses multiple contact points, including social media, to provide meaningful experience to customers. With their unique marketing strategy, Snuggie became a popular brand among the U. S. customers beyond just a wearable blanket. Upon analyzing a total of 364 customer reviews about Snuggie in Amazon.com, five experience rooms were exposed; "Physical artifacts" and "customer involvement" are influential experience rooms which signify interactions between products and customers, while "intangible artifacts", "technology" and "customer placement" reflect a lower degree of experiential engagement. This approach suggests a theoretical foundation in understanding the customer engagement concepts by the means of brand experience dimensions in social media. The ability to create compelling engagement in social media depends on the successful facilitation of relationships and information, which lead to a creative, communicative and interactive experience.

• Asia Marketing Journal
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• v.16 no.1
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• pp.195-214
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• 2014

Many oil refinery companies have been proactively carried out marketing activities to survive in highly intensified service station industry. In 2012, SK Energy ranked number one in three customer satisfaction surveys (NCSI, KCSI, and KS-SQI). SK Energy's success is in its differentiated customer satisfaction business strategy. SK Energy has been implementing various marketing activities. As a part of the activities, it issued an affinity credit card with on spot discount privilege for the first time in the oil refinery industry. SK Energy also issued EnClean bonus card for OK Cashbag points that can be used for discounts at the service stations. On top of all, SK Energy generated point value-up with '3K Exclusive Privilege' program. In addition, team 'CS 119' was formed to noticeably improve the service. 'CS 119' visited each service station, diagnosed its CS service level then provided customized field training for improvement. Long-termimplementation of 'ACE Program', a field-base CS monitoring system, regularly checked customer satisfaction level. 'ACE Program' has significantly contributed improving SK Service Station's service quality and customer satisfaction. This case reviews customer satisfaction marketing activities SK Energy carried out. The focus is on distinctive factors that distinguish SK Energy's customer satisfaction marketing activities from competitors.

• Journal of KIISE:Software and Applications
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• v.35 no.6
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• pp.392-403
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• 2008

User reviews are valuable information that can be used for various purposes. In particular, the product reviews on online shopping sites are important information which can directly affect the purchasing decision of the customers. In this paper, we present our design and implementation of a system for summarizing the customer's opinion and the features of each product by analyzing reviews on a commercial shopping site. During the analysis process, several natural language processing(NLP) techniques and the semantic dictionary were used. The semantic dictionary contains vocabularies that are used to express product features and customer's opinions. And it was constructed in semi-automatic way with the help of the tool we implemented. Furthermore, we discuss how to handle the vocabularies that have different meanings according to the context. We analyzed 1796 reviews about 20 products of 2 categories collected from an actual shopping site and implemented a novel ranking system. We obtained 88.94% for precision and 47.92% for recall on extracting opinion expression, which means our system can be applicable for real use.