• Journal of Fashion Business
• /
• v.12 no.1
• /
• pp.45-62
• /
• 2008

The purpose of this study is to identify how the components of a fashion show can influence the perception of the spectators, and what the spectators most like in a fashion show. Through the analysis on the literature reviews and in-depth interviews with professional's organizing fashion shows we established a list of the main components, which allowed us to set up a questionnaire. The results regarding the viewing satisfaction of the spectators at a fashion show were investigated and came out as follows: First, the components of fashion show are generally viewed to be four factors: the program, the directing, the model, and the sets. These four factors are influencing the perception of the spectators and are very important to the success of a fashion show and the enjoyment of a fashion show by spectators. Second, it is shown to us that the most influential factor for the perception is 'the directing'(stage scenery, background music, lighting, effects etc.), and the next is 'the sets'(convenience seat, display, service facilities etc.). So, in order to raise the level of satisfaction for the spectators, it is advisable to concentrate on these two main factors. Finally, we would suggest that the organizer of a fashion show shall carefully analyze how spectators understand and perceive the components of a fashion show. This study provides information about how the components of a fashion show can influence the spectator's perception and presents suggestions on how to improve a fashion show by reorienting it towards the satisfaction of the spectator. In addition, there needs to be a strategy by customer satisfaction experience to reinforce a customer-oriented fashion show and heighten a viewing satisfaction for spectators.

• Journal of Korea Society of Industrial Information Systems
• /
• v.13 no.2
• /
• pp.35-46
• /
• 2008

This research developed a strategy for mobile banking services based on the important factors deduced from quality assessment of mobile banking service that is experiencing rapid growth of user. SERVQUAL model and importance-performance tool were used. First, SERVQUAL model uses the important factors of mobile banking service selected from literature reviews. As a result, four dimensions that affect user satisfaction are found; assurance, customer orientation, tangibility, and reliability. Second, importance-performance analysis is to develop the strategy using the four factors. The final results revealed that assurance dimension had the strongest influence on user satisfaction. Assurance dimension of high expectation and yet low real performance needs immediate improvement. Customer orientation dimension of low expectation and performance should be reconsidered definition of satisfaction. On the other hand, tangibility dimension of higher performance than expectation is simply to maintain current level. Reliability dimension of high expectation and performance is recommended consistent management.

• Journal of Intelligence and Information Systems
• /
• v.25 no.1
• /
• pp.21-41
• /
• 2019

Thanks to the rapid development of information technologies, the data available on Internet have grown rapidly. In this era of big data, many studies have attempted to offer insights and express the effects of data analysis. In the tourism and hospitality industry, many firms and studies in the era of big data have paid attention to online reviews on social media because of their large influence over customers. As tourism is an information-intensive industry, the effect of these information networks on social media platforms is more remarkable compared to any other types of media. However, there are some limitations to the improvements in service quality that can be made based on opinions on social media platforms. Users on social media platforms represent their opinions as text, images, and so on. Raw data sets from these reviews are unstructured. Moreover, these data sets are too big to extract new information and hidden knowledge by human competences. To use them for business intelligence and analytics applications, proper big data techniques like Natural Language Processing and data mining techniques are needed. This study suggests an analytical approach to directly yield insights from these reviews to improve the service quality of hotels. Our proposed approach consists of topic mining to extract topics contained in the reviews and the decision tree modeling to explain the relationship between topics and ratings. Topic mining refers to a method for finding a group of words from a collection of documents that represents a document. Among several topic mining methods, we adopted the Latent Dirichlet Allocation algorithm, which is considered as the most universal algorithm. However, LDA is not enough to find insights that can improve service quality because it cannot find the relationship between topics and ratings. To overcome this limitation, we also use the Classification and Regression Tree method, which is a kind of decision tree technique. Through the CART method, we can find what topics are related to positive or negative ratings of a hotel and visualize the results. Therefore, this study aims to investigate the representation of an analytical approach for the improvement of hotel service quality from unstructured review data sets. Through experiments for four hotels in Hong Kong, we can find the strengths and weaknesses of services for each hotel and suggest improvements to aid in customer satisfaction. Especially from positive reviews, we find what these hotels should maintain for service quality. For example, compared with the other hotels, a hotel has a good location and room condition which are extracted from positive reviews for it. In contrast, we also find what they should modify in their services from negative reviews. For example, a hotel should improve room condition related to soundproof. These results mean that our approach is useful in finding some insights for the service quality of hotels. That is, from the enormous size of review data, our approach can provide practical suggestions for hotel managers to improve their service quality. In the past, studies for improving service quality relied on surveys or interviews of customers. However, these methods are often costly and time consuming and the results may be biased by biased sampling or untrustworthy answers. The proposed approach directly obtains honest feedback from customers' online reviews and draws some insights through a type of big data analysis. So it will be a more useful tool to overcome the limitations of surveys or interviews. Moreover, our approach easily obtains the service quality information of other hotels or services in the tourism industry because it needs only open online reviews and ratings as input data. Furthermore, the performance of our approach will be better if other structured and unstructured data sources are added.

• The Journal of Society for e-Business Studies
• /
• v.27 no.1
• /
• pp.15-28
• /
• 2022

Due to recent expansion of online market such as clothing, utilizing customer review has become a major marketing measure. User review has been used as a tool of analyzing sentiment of customers. Sentiment analysis can be largely classified with machine learning-based and lexicon-based method. Machine learning-based method is a learning classification model referring review and labels. As research of sentiment analysis has been developed, multi-modal models learned by images and video data in reviews has been studied. Characteristics of words in reviews are differentiated depending on products' and customers' categories. In this paper, sentiment is analyzed via considering review data and metadata of products and users. Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Self Attention-based Multi-head Attention models and Bidirectional Encoder Representation from Transformer (BERT) are used in this study. Same Multi-Layer Perceptron (MLP) model is used upon every products information. This paper suggests a multi-modal sentiment analysis model that simultaneously considers user reviews and product meta-information.

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

• Journal of Intelligence and Information Systems
• /
• v.25 no.1
• /
• pp.139-161
• /
• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.

• Journal of Information Technology Services
• /
• v.14 no.2
• /
• pp.177-193
• /
• 2015

A great number of customers, who want to watch movies usually check out online reviews before choosing what to watch a movie. The most representative online media that customers consult are portal sites and SNS (Social Network Service). Although there have been numerous studies on online eWOM (e-Word of Mouth) and the effects of online media in businesses, it remains a question that which media is best for WOM (Word of Mouth) when selecting movies. This research examines customer's intention for consulting eWOM and for watching movies according to the number and tendency of online replies. We have compared portal sites and SNS about information of movie. The study shows that a large number of positive replies can affect the intention for WOM and choosing movies. Facebook has more influence than portal sites when choosing what to watch when replies consist of large and positive comments. However, there is no difference between the two types of media when they consist of negative comments.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.8 no.12
• /
• pp.1811-1818
• /
• 2013

As the broadband network are being deployed recently, the IPTV (Internet Protocol TV) has appeared and become a most representative service that incorporates audio, video, etc. In spite of the long-period efforts, however, no standardized method exists to measure and evaluate the quality of video that is experienced through TV in the customer's premises. Therefore, before developing a method to measure and evaluate the quality of experience (QoE) for video that is the ultimate goal, this paper reviews the outcomes from the past standardization activities and drive a few important implications that would be reflected to the method to measure and evaluate QoE for video.

• Korean Journal of Organic Agriculture
• /
• v.26 no.3
• /
• pp.327-349
• /
• 2018

The purpose of the study focuses on the agriculture education services in the changing rural areas conditions such as population decline, aging society, and returning farmers. The study reviews the effects of agricultural education services on returning farmers and local residents for satisfaction, intention for recommendation, and intention to continue participation. Further, the study aims to investigate any difference in the level of satisfaction for two groups. The results suggested that there is a meaningful difference between return-farmers and local residents. Among the demographic variables, age and income showed a notable difference. However, sex, level of education and type of household did not suggest noticeable differences. In addition, the study accessed agricultural education from a service perspective and analyzed its service quality and customer satisfaction, loyalty and relationship using a service profit chain model. Like the result of most other studies, the analysis showed that these had positive relationships. While the study focused on the efficiency of agriculture education training program in agriculture technology centers, the study carries a meaningful value in that it discovered a meaningful difference in the satisfaction level between returning farmers and locals despite the fact that agriculture education was applied as a part of service. In practical terms, the study pointed out the need for consumer-centered education that reflects the characteristics of the groups rather than standardized education.

• IE interfaces
• /
• v.17 no.4
• /
• pp.482-489
• /
• 2004

As the functional characteristics of passenger cars have reached to a satisfactory level, customers place more concerns with the aesthetic aspects of interior designs. The present study developed satisfaction models of passenger car interior materials for six parts including crash pad, steering wheel, transmission gearshift knob, audio panel, metal grain, and wooden grain. Eight to fifteen material design variables such as color, embossing, and smoothness were defined for the six interior parts based on literature survey, customer reviews, and expert opinions. A satisfaction survey was conducted for 30 vehicles with 30 participants ($mean{\pm}SD$ of age = $28.7{\pm}6.6$) by using a modified magnitude estimation scale. Based on the survey results, the material design variables were screened from statistical, technical, and practical aspects. With the screened variables, satisfaction models were developed by using the quantification I method for the six interior parts, indicating the importance of material design variables and preferred material properties.