• Asia pacific journal of information systems
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• v.18 no.1
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• pp.1-23
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• 2008

As managing customer relationship gets more important, companies are strengthening information service using multi-channels to their customers as a part of their customer relationship management (CRM) initiatives. It means companies are now accepting such information services not as simple information -delivering tools, but as strategic initiatives for acquiring and maintaining customer loyalty. In this paper, we attempt to validate whether or not such various information services would impact on organizational performance in terms of CRM strategy. More specifically, our research objective is to answer the next three questions: first, how to construct the instruments to measure not information quality but information service quality?; second, which attributes of information service quality can influence corporate image and customer loyalty?; finally, does each information service type have unique characteristics compared with others in terms of influencing corporate image and customer loyalty? With respect to providing answers to those questions, the previous studies had been limited in that those studies failed to consider the variety of types of information service or restricted the quality of information service to information quality. An appropriate research model answering the above questions should consider the fact that most companies are utilizing multi channels for their information services, and include the recent strategic information service such as customer online community. Moreover, since corporate information service could be regarded as a type of products or services delivered to customer, it is necessary to adopt the criteria for assessing customer's perceived value when to measure the quality of information service. Therefore, considering both multi-channels and multi-traits may enable us to tell the detailed causal routes showing which quality attributes of which information service would affect corporate image and customer loyalty. As information service channels, we include not only homepage and DM (direct mail), which are the most frequently applied information service channels, but also online community, which is getting more strategic importance in recent years. With respect to information service quality, we abstract information quality, convenience of information service, and timeliness of information service through a wide range of relevant literature reviews. As our dependant variables, we consider corporate image and customer loyalty that both of them are the critical determinants of organizational performance, and also attempt to grasp the relationship between the two constructs. We conducted a huge online survey at the homepage of one of representative dairy companies in Korea, and gathered 367 valid samples from 407 customers. The reliability and validity of our measurements were tested by using Cronbach's alpha coefficient and principal factor analysis respectively, and seven hypotheses were tested through performing correlation test and multiple regression analysis. The results from data analysis demonstrated that timeliness and convenience of homepage have positive effects on both corporate image and customer loyalty. In terms of DM, its' information quality was represented to influence both corporate image and customer loyalty, but we found its' convenience have a positive effect only on corporate image. With respect to online community, we found its timeliness contribute significantly both to corporate image and customer loyalty. Finally, as we expected, corporate image was revealed to provide a great influence to customer loyalty. This paper provides several academic and practical implications. Firstly, we think our research reinforces CRM literatures by developing the instruments for measuring information service quality. The previous relevant studies have mainly depended on the measurements of information quality or service quality which were developed independently. Secondly, the fact that we conducted our research in a real situation may enable academics and practitioners to understand the effects of information services more clearly. Finally, since our study involved three different types of information service which are most frequently applied in recent years, the results from our study might provide operational guidelines to the companies that are delivering their customers information by multi-channel. In other words, since we found that, in terms of customer loyalty, the key areas would be different from each other according to the types of information services, our analysis would help to make decisions such as selecting strengthening points or allocating resources by information service channels.

• Fashion & Textile Research Journal
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• v.26 no.1
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• pp.25-34
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• 2024

This study investigates consumer awareness and concerns regarding apparel sizing in the realm of online shopping. A survey was conducted with 450 women aged 18-59 who had engaged in online clothing purchases within the past year. It was observed that consumers shop for clothes online an average of 1.6 times per month, with those under 50 shopping more frequently. The importance of size is higher when buying pants than jackets, especially in online shopping compared to offline purchases. Key references guiding online shopping decisions encompassed product sizing codes, customer reviews, and garment dimensions, which were notably favored by consumers with significant concerns. Respondents opted for Korean-style sizing codes for jackets but chose inch-sizing codes for pants. While awareness of height and weight remains high, knowledge of specific body measurements crucial for clothing size design is lacking, suggesting inadequate communication of size information. Respondents prioritized specific areas for jacket and pants fit, yet the lack of comprehensive self-measurements beyond height and weight might present challenges in determining fit based solely on product dimensions. To address this issue, online retailers should display essential garment dimensions and visually suggest clothing sizes according to various body types. These findings provide valuable insights for online retailers to effectively present size information and lay a foundational framework for consumer size education.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.3
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• pp.116-130
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• 2019

This study investigates passenger-authored online reviews of airline services using social network analysis to compare the differences in customer perceptions between full service carriers (FSCs) and low cost carriers (LCCs). While deriving words with high frequency and weight matrix based on the text analysis for FSCs and LCCs respectively, we analyze the semantic network (betweenness centrality, eigenvector centrality, degree centrality) to compare the degree of connection between words in online reviews of each airline types using the social network analysis. Then we compare the words with high frequency and the connection degree to gauge their influences in the network. Moreover, we group eight clusters for FSCs and LCCs using the convergence of iterated correlations (CONCOR) analysis. Using the resultant clusters, we match the clusters to dimensions of two types of service quality models ($Gr{\ddot{o}}nroos$, Brady & Cronin (B&C)) to compare the airline service quality and determine which model fits better. From the semantic network analysis, FSCs are mainly related to inflight service words and LCCs are primarily related to the ground service words. The CONCOR analysis reveals that FSCs are mainly related to the dimension of outcome quality in $Gr{\ddot{o}}nroos$ model, but evenly distributed to the dimensions in B&C model. On the other hand, LCCs are primarily related to the dimensions of process quality in both $Gr{\ddot{o}}nroos$ and B&C models. From the CONCOR analysis, we also observe that B&C model fits better than $Gr{\ddot{o}}nroos$ model for the airline service because the former model can capture passenger perceptions more specifically than the latter model can.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.1
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• pp.49-62
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• 2022

Online customer review data can be easily collected on the Internet and also they describe sentimental evaluation of a product in different aspects. Previous sentiment analysis studies evaluate the degree of sentiment with review data, which may have multiple sentences describing different product aspects. Since different aspects of a product can be described in a sentence, the proposed method suggested analyzing a sentence to build a pair of a product aspect terms and sentimental terms. Bidirectional LSTM and CRF algorithms were used in this paper. A pair of aspect terms and sentimental terms are evaluated by pre-defined evaluation rules. The paper suggested using the result of evaulation as inputs of QFD, so that the quantified customer voices effect on the requirements of a new product. Online reviews for a hair dryer were used as an example showing that the proposed approach can derive reasonable sentiment analysis results.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.325-345
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• 2023

In the rapidly evolving domain of e-commerce, our study presents a cohesive approach to enhance customer satisfaction prediction from online reviews, aligning methodological innovation with practical insights. We integrate the RFE-SHAP feature selection with LDA topic modeling to streamline predictive analytics in e-commerce. This integration facilitates the identification of key features-specifically, narrowing down from an initial set of 28 to an optimal subset of 14 features for the Random Forest algorithm. Our approach strategically mitigates the common issue of overfitting in models with an excess of features, leading to an improved accuracy rate of 84% in our Random Forest model. Central to our analysis is the understanding that certain aspects in review content, such as quality, fit, and durability, play a pivotal role in influencing customer satisfaction, especially in the clothing sector. We delve into explaining how each of these selected features impacts customer satisfaction, providing a comprehensive view of the elements most appreciated by customers. Our research makes significant contributions in two key areas. First, it enhances predictive modeling within the realm of e-commerce analytics by introducing a streamlined, feature-centric approach. This refinement in methodology not only bolsters the accuracy of customer satisfaction predictions but also sets a new standard for handling feature selection in predictive models. Second, the study provides actionable insights for e-commerce platforms, especially those in the clothing sector. By highlighting which aspects of customer reviews-like quality, fit, and durability-most influence satisfaction, we offer a strategic direction for businesses to tailor their products and services.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.2
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• pp.213-219
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• 2020

Studies related to Electronic Word-of-Mouth (eWOM) show that the acceptance of eWOM information is an important factor in customer purchase decisions. When consumers accept eWOM information, they tend to use that information in considering before making purchase decisions. In Viet Nam, there are few studies about eWOM information, especially on the acceptance of eWOM information. Research is conducted to test the influence of consumers on the perception of the senders' identity to the acceptance of online reviews (a kind of eWOM) in Viet Nam - a case study in Ho Chi Minh City. Using adjustment techniques, inspecting the scales and a theoretical model represent the relationship among the influential factors. The research is based on a sample of 522 consumers who use the Internet to search for product reviews before buying and used Structural Equation Modeling (SEM) to test the relationships among the variables. The research results show that the scales of the variables: Message Quality, Source Credibility, Perceived Message Usefulness, Perceived Senders' Identity, Perceived Message Credibility, Message Acceptance attain the validity and reliability in the research. The research contributes to the understanding of the determinants that influence the acceptance of eWOM information, which are informational factors, and factors related to consumer skepticism.

• Fashion & Textile Research Journal
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• v.22 no.5
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• pp.617-627
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• 2020

This study investigates the apparel sizing communication presented in online retail websites focusing on women's ready-to-wear tailored jackets and to analyze the meaning of these information as the actual product size guide factor. A total of 34 retail websites were selected based on the highest growth fashion companies list and the best fashion brands list. We collected size information in two types: size specifications including sizing code, body measurements, garment measurements, and size references including customized size guide tools, size information in customer reviews, model size information, and others. Most websites prefer to present garment measurements rather than body measurements that are recommended notations under Korean standards and related regulations. In addition, there was the absence of consistency in presenting measurements list and terms that can confuse consumers in size communication. This study found that the stature measurement was a key factor in size reference despite that it did not represent a proper garment size. The obsolete Korean numbering sizing code such as '55 and '66 was still used in many ways such as idiomatic expressions for body shape. It also implied that we can take advantage of the old sizing code for accessible size information. The finding of this study gives an in-depth diagnosis of current online sizing information problems and suggests useful basic data for developing online apparel size standards and marketing strategies.

• The KIPS Transactions:PartD
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• v.18D no.4
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• pp.237-244
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• 2011

Opinion Mining summarizes with classifying sensitive opinions of customers in huge online customer reviews for the attributes of products or services by positive and negative opinions. Because the customers represent their interests through subjective opinions as well as objective facts, the existing opinion mining techniques, which can analyze just the sensitive opinions, need to be expanded.. In this paper, We propose the novel entity association network model which expands the existing opinion mining techniques. The entity association model can not only represent positive and negative degree of the sensitive opinions, but also can represent the degree of the associations and relative importances between entities. We designed and implemented the customer reviews analysis system based on the entity association network model. We recognized that the system can represent more abundant information than the existing opinion mining techniques.

• The Journal of Industrial Distribution & Business
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• v.13 no.8
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• pp.9-18
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• 2022

Purpose: K-beauty products industry trends, estimates and dynamics are examined in this study to discover a potential possibility for growth. There is a thorough examination of the elements that drive and impede the expansion of the K-beauty industry. This study aims to investigate marketing strategy of K beauty product to enhance economic growth in South Korea. Research design, data and methodology: This study used one of the most famous approach for analyzing the current literature which is a PRISMA (Process and Systematic Reviews and Meta-Analyses) method. This method maps out the number of records identified, the included and the excluded ones with the reasons for the exclusion. The technique clearly states the research problem and the appropriate scope. Results: The theoretical findings of prior literature indicates K-beauty companies should retain physical locations despite the trend toward online commerce, in order to guarantee that they meet the demands of different customers and enhance customer experiences to develop trust and loyalty. Conclusions: The findings of this research are of academic importance since they provide light on customer preferences for new K-beauty products. While past research has often ignored certain kinds of influencers, this study emphasized the need of considering influencers and certain product exposure strategies together, which has major academic consequences.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.259-266
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• 2023

In this paper, we propose an algorithm that can improve the accuracy performance of collaborative filtering using attribute-based opinion mining (ABOM). For the experiment, a total of 1,227 online consumer review data about smartphone apps from domestic smartphone users were used for analysis. After morpheme analysis using the KKMA (Kkokkoma) analyzer and emotional word analysis using KOSAC, attribute extraction is performed using LDA topic modeling, and the topic modeling results for each weighted review are used to add up the ratings of collaborative filtering and the sentiment score. MAE, MAPE, and RMSE, which are statistical model performance evaluations that calculate the average accuracy error, were used. Through experiments, we predicted the accuracy of online customers' app ratings (APP_Score) by combining traditional collaborative filtering among the recommendation algorithms and the attribute-based opinion mining (ABOM) technique, which combines LDA attribute extraction and sentiment analysis. As a result of the analysis, it was found that the prediction accuracy of ratings using attribute-based opinion mining CF was better than that of ratings implementing traditional collaborative filtering.